1. A method for predicting the surface and three-dimensional precipitation and water delivery rate spatial distributions for landscapes irrigated by sprinklers comprising: identifying at least one sprinkler head; determining a pressure at said at least one sprinkler head; determining a nozzle on said at least one sprinkler head; determining a range of arc operation of said at least one sprinkler head; determining a head tilt of said at least one sprinkler head; determining a head tilt direction of said at least one sprinkler head; determining a throw adjustment of said at least one sprinkler head; determining a run time of said at least one sprinkler head; determining a sprinkler location of said at least one sprinkler head; determining at least one environmental variable of said at least one sprinkler head, and predicting a local distribution uniformity measure as represented by a following equation 5 DUi=MinimumwithinlocalareaforithsprinklerAverageoverIrrigatedArea.
2. The method according to claim 1 wherein said environmental variable is selected from a group comprising a landscape elevation as a function of horizontal position and a wind profile for an irrigated site.
3. The method according to claim 1 that further includes prediction of said local distribution uniformity for each individual sprinkler given each sprinklers operational variables.
4. The method according to claim 3 that further includes prediction of a total spatial distribution uniformity for an Irrigated system using the principle of superposition.
5. The method according to claim 1 wherein results of the method are used to predict the distribution uniformity (DU) for any irrigated region of a landscape and an overall distribution uniformity for an entire irrigated site.
6. The method according to claim 5 wherein said results use a standard definition of a lower quarter distribution uniformity.
7. The method according to claim 6 wherein said standard definition of said lower quarter distribution uniformity is used to determine either a precipitation rate or a water delivery spatial distribution.
8. The method according to claim 1 that is used to accurately interpolate spatial distributions for any arbitrary pressure, range of arc for partial arc sprinkler, and nozzle wear as defined by a following equations (6) to (18) Φ(r)=P(r)∫0T2πrP(r)r(Equation6)q→=r→T(Equation7)P(r)=96.25(4π)GPMT2φ(Tq→)(Equation8)GPM=KGPSIx(Equation9)T=KTPSIx1+∑k=1nakPSIk(Equation10)ri=iNr(i=1,2,3,…Nr)(Equation11)φ(PSI,i)=∑k=0Np-1AikPSIk(Equation12)ΦAR=ΦE-ΦS(Equation13)P(φAR,r→)=360φARP(r→)AF(r→)(Equation14)AF(r→)={1withintherangeofarc0otherwise(Equation15)P(φAR,r→)=φAR360P(r→)AF(r→).(Equation16)AF(r→)={≤1withintherangeofarc0otherwise(Equation17)q→=r→T(r→).
9. The method according to claim 1 that further includes a formula for predicting the throw as function of distance as defined by a following equation (10). T=KTPSIx1+∑k=1nakPSIk.
10. The method according to claim 1 that further includes a two dimensional transform method for wind as defined by a following equations (19) to (24), r=x2+y2(Equation19)c=xr(Equation20)s=yr(Equation21)T→=T0(1+Ac+Bc2+Cs2)(Equation22)r→′=r→+(r2T0)(Ac+Bc2+Cs2)u→w(Equation23)PT(r→′)=P(r→)g(r→)(Equation24) and said two dimensional transform method for wind, further predicts a spatial distributions using measurements of a throw distance as a function direction from said at least one sprinkler head.
11. The method according to claim 1 wherein a spatial distribution is predicted with given a wind profile, and said special distribution method further predicts a surface distribution for an arbitrary terrain topology under known wind conditions.
12. A method for calculating a two-dimensional transform for irrigated slopes comprising: identifying at least one sprinkler head; determining a pressure at said at least one sprinkler head; determining a nozzle on said at least one sprinkler head; determining a head tilt of said at least one sprinkler head; determining a head tilt direction of said at least one sprinkler head; determining a throw adjustment of said at least one sprinkler head; determining at least one environmental variable of said at least one sprinkler head; applying said components to a following equations (25) to (29), Rxy(θ)=(cos(θ)-sin(θ)0sin(θ)cos(θ)0001)(Equation25)Rzx(θ)=(cos(θ)0-sin(θ)01sin(θ)0cos(θ))(Equation26)u→′=Rxy(θHdt)Rzx(-θHt)Rxy(θHr)Rzx(θNz)ux(Equation27)r→′=(r+r2T(Ac+Bc2+Cs2))υ→xy(Equation28)r→′=(r+f1(r)f2(c,s))υ→xy(Equation29) whereby said equations predict a spatial distribution using measurements of a throw distance as a function of direction from said sprinkler head.
13. The method according to claim 12 that further includes an aerodynamic model for predicting the water drops trajectories by applying a following equations (35) to (42), a→=-gz→-Kυυ→(Equation35)K=(34)(ρairρwater)(CDDw)(Equation36)CD=24Re+1(Equation37)Re=ρairυDwμair(Equation38)a→=-gz→+(a0+a1t+a2t2)υ→υ→(Equation39)υI=12.19PSI(Equation40)υ→→υ→-υ→wind(Equation41)υwind=υfln(zz0+1)(Equation42) and further coupling solutions of a result from said method to a steam model for water emerging from said sprinkler head.
14. The method according to claim 13 wherein a spatial distribution is predicted with given a wind profile, and said special distribution method further predicts a surface distribution for an arbitrary terrain topology under known wind conditions.
15. A method for determining three dimensional flow line comprising: identifying at least one sprinkler head; determining a pressure at said at least one sprinkler head; determining a nozzle on said at least one sprinkler head; determining a range of arc operation of said at least one sprinkler head; determining a head tilt of said at least one sprinkler head; determining a head tilt direction of said at least one sprinkler head; determining a throw adjustment of said at least one sprinkler head; determining a sprinkler location of said at least one sprinkler head; predicting a three dimensional flow line by applying said components to a following equations (30) to (34); r=x2+y2(Equation30)s=zr(Equation31)q=rT(s).(Equation32)q′=q+(1-q)(A1q+A2s+A3q2+A4qs+A5s2+…)(Equation33)PT(q′,s)=P(q,0)g(s,q)(Equation34) to extrapolate a spatial distributions on flat ground, obtaining said three-dimensional spatial distributions, and when projecting said three-distributional spatial distribution onto a terrain surface, said equations predict a surface distribution for an arbitrary terrain topology.
This application claims the benefit of Provisional 61/102,277 filed Oct. 2, 2008 the entire contents of which is hereby expressly incorporated by reference herein.
This present invention relates generally to the predicting the distribution uniformity of landscape irrigation sprinkler systems under conditions of actual use, and more particularly, to modeling Irrigation distribution under all possible sprinkler operational conditions on three-dimensional terrain In the presence of wind. From the irrigation distribution modeling results, the distribution and efficiency are predicted.
Modem irrigation systems are designed and operated with the objective of conserving water. The efficiency is the ratio of the amount of the water need by the plants to the amount of water delivered to a landscape. An effective Irrigation design and site operation maximizes the Irrigation efficiency.
Ideally, when an Irrigation system is designed, its efficiency would also be calculated. The design could then be adjusted to maximize tile Irrigation efficiency, thus minimizing the water consumption. In practice, it is very difficult to predict the efficiency of an Irrigation system. Instead, the design of irrigation system is often attempted using general rules based on experience. For example, for a rectangular or triangular pattern, the design rule is to use head to head spacing. In this case, distance to the nearest head is the same as the throw distance. It is not always possible to arrange the sprinkler heads Into a regular pattern In this case the recommend spacing must be used along with ones best Judgment. The Irrigation designer usually does not make use of irrigation efficiency calculation.
As modifications to an irrigation system occur, it would be advantageous for the effect on irrigation efficiency to be considered first, but in practice, technicians working in the field have little capability to do this. Technicians usually do not make use of an irrigation efficiency calculation.
In principle, the method for computing irrigation uniformity and efficiency is rather simple. If the distribution for each sprinkler nozzle is known, then the total distribution is obtained by superposition. From the total distribution, the uniformity and efficiency is then calculated. To use this method, one must first obtain measured distribution data. Available test data, such the data provided by the Center for Irrigation Technology at California State University, Fresno, provides distribution data for sprinkler nozzles irrigating flat ground with no wind present. Given this type of data, the irrigation distribution and efficiency can be calculated given the sprinkler positions and primary operational variables. For each sprinkler, these are the range of arc and pressure. This method, however, is very limited in its applicability to real landscapes.
Although the principles behind the computation for irrigation uniformity efficiency are well known, these principles have received only limited application. At the present time, only two products make any use of these principles, and both of these are very limited in their application. One is SPACE PRO™, which is available through the Center for Irrigation Technology at California State University. The other is the irrigation analysis component of LANDLOGIC by Landloglc in Scattsdale, Az. LANDLOGIC is mostly a facility management software tool. Both SPACE PRO™ and LANDLOGIC can only be used on flat terrain and only at a pressure corresponding to the test data.
In reality, to calculate the irrigation efficiency accurately, many other variables must be accounted for. The most important of these is the effect of terrain topology. Sprinklers are often used to irrigate slopes and rolling hill sides. For sprinkler located on slopes, the sprinkler is tilted in the direction of the slope. Typically the tilt angle is chosen to be about one half of the angle corresponding to the slope. The head tilt causes distribution to become asymmetric. The water then falls on an irregular three-dimensional surface, producing additional asymmetry. For this case, a flat ground calculation is not appropriate because the result would be inaccurate. For a slope, not only does the water not fall on a level surface, the sprinkler is not in its vertical upright position as it would be for a flat ground test. Flat ground test data cannot be used to calculate the distribution on slopes, rolling hills, or any other no-flat terrain topology.
Another very signification factor is the effect of wind. Unless the average wind is lower than around 5 miles per hours, using the test data obtained under windless laboratory conditions is also not appropriate because again the predicted results would be inaccurate. The data obtained under windless conditions cannot be used to calculate the distribution when the wind speed exceeds around 5 miles per hour.
In addition to the effect of terrain topology and wind, there are other operational variables that are also important. For modern sprinklers the flow and throw distance can often be adjusted via a screw setting on the sprinkler head. Typically, the reason for using this adjustment is to keep water spray inside the intended irrigation area. For example, one does not wish to irrigate concrete areas such as sidewalks or to irrigate beyond the property line. While flow/throw adjustments are common and have a significant effect on irrigation distribution, data corresponding to the various sprinkler settings is seldom available.
Not only is each of the above described variables important, the combined effect of the various variables is not additive. For example, if one has test data for a 10 mph wind on flat ground, and additionally, one has test data for no wind for a 1:2 slope. One cannot then calculate the distribution for a 10 mph wind going easterly on a west facing 1:2 slopes.
The combined effect of pressure variation, flow/throw setting, head tilt, and wind, produces a very rich variety of Irrigation distributions. To obtain data for all these variables, and all combinations of variables would be unfeasible. It would not be cost effective to conduct all the necessary tests.
At present, irrigated systems for landscapes are designed and operated without the benefit of irrigation efficiency because the current available methods are too limited in their applicability. These are applicable to only the simple case of flat ground with no wind at selected pressures and with the flow/throw adjustment set to the standard position.
There is need for a versatile method to accurately predict landscape irrigation efficiency. Such a method should account for all operational and environmental variables. These are the pressure variation, range of arc operation, flow/throw adjustment, head tilt, terrain topology, and wind. A calculation method with this versatility would be usable to anyone with a need to calculate Irrigation efficiency. The benefit of this kind of calculation would be clearly evident to anyone skilled in the art of irrigation design and operation.
The starting point for any distribution calculation data is measured data. To account for the effects of terrain topology and Wind, three dimensional test data is required. For flat ground testing, it is common to measure the distribution every two feet. For three dimensional testing, it would seem obvious to simply set up water buckets on a two foot grid that is both horizontal and vertical. However, that approach would be impractical. Too many points would be required, and many of these would be difficult to reach from the test floor.
The pressure delivered to each sprinkler nozzle is usually not uniform throughout an irrigated landscape. The pressure variation is due to elevation variation and pressure loss to due to flow through the Irrigation system pipes and equipment. While it is possible to make the pressure more uniform by installing pressure regulating valves, it is very common for an Irrigated site to have unregulated valves. A hydraulic calculation can predict the pressure at each sprinkler. Test data is usually obtained for a few selected pressures with the flow/throw adjustment set to the standard position.
Typically, these pressures are multiples of 10 PSI. What is need is a reliable method to predict irrigation distribution for intermediate pressures and throw settings.
What is a needed is method whereby the three dimensional distribution may be calculated from the minimal number of test points. In principle, the three dimensional distribution with wind present could be measured outdoors. However, the wind direction and speed cannot be controlled, and thus, the results would be unrepeatable. What is need for this case is an alternative method to determine the distribution for a specified wind condition.
The present application embodies ways of predicting the precipitation rate and water delivery spatial distribution for landscapes irrigated by sprinkler devices. From these results, the spatial distribution uniformity and efficiency is calculated. Additionally, this invention introduces a definition of a local irrigation uniformity and efficiency. This quantity is a measure of the spatial distribution uniformity surrounding each irrigation sprinkler.
The use of this invention requires an operational and environmental description of the Irrigation system. The operation description consists of a full description of each irrigation sprinkler. For each irrigation sprinkler, this includes the head model, installed nozzle, range of are, flow/throw setting, head tilt, spatial position, and delivery pressure. The last quantity is the pressure delivered to the sprinkler at the base of each sprinkler head. Multiple irrigation sprinklers are often controlled from a common valve. The association between valves and sprinkler is part of the operational description. The environmental description consists of elevation information for the landscape and a wind profile. Since wind conditions typically vary, a wind profile consists of the average, and optionally, its variation.
Given the description for each sprinkler, this invention predicts the spatial distribution for each sprinkler. The total spatial distribution is then computed using the principle of superposition.
Each irrigated area is defined by a closed path. Within each irrigated area, there may be sub-interior regions that are not irrigated. The sub-interior regions are also described by a close path. For each irrigation area, the Irrigation uniformity and efficiency is calculated. The overall distribution uniformity and efficiency for the landscape is obtained using an area weight average over all the irrigated areas.
For each sprinkler, a local distribution uniformity and efficiency is also computed. Any location will be closest to one sprinkler. The points closest to one particular sprinkler define a cell area. A distribution uniformity and efficiency are calculated for each cell. In this sense, each sprinkler has a local uniformity and efficiency.
The local distribution uniformity and efficiency calculation pinpoints problems with the Irrigation system. Typically, a site will be required to have a local efficiency exceeding a specified requirement. The local distribution uniformity and efficiency prediction is more useful than the overall average. This is because, for a large area, small problem areas may not have much effect on the overall numerical average. Yet Irrigation schedules for irrigation controllers are set to account for the worst points to ensure that all plants are sufficiently watered. When every sprinkler has local distribution uniformity and efficiency exceeding the specified requirement, the overall distribution uniformity and efficiency for the site will also meet the same specified requirement.
The embodiments of the present invention use interpolation in a transformed coordinated system. Using this method, the precipitation rate for a specific sprinkler can be predicted for conditions where test data is not available. This greatly reduces the amount of testing that must be obtained for each sprinkler nozzle.
To predict the spatial distribution as a function of pressure, test data is required at one or more pressures. Any arbitrary number of test pressures can be used; however, it is best to use at least three different pressures spanning the operation limits of for the nozzle. A good choice is the minimum and maximum pressures limits recommended for the sprinkler by its manufacture, and a pressure midway between the minimum and maximum. Although obtaining test data for these three pressures is recommend, if spatial data from only one pressure is available, but the flow and throw are available for other pressures, then results are predictable for small variations in pressure. The same type of approach is also used to predict effect of a nozzle wear.
The prediction of the spatial distribution as a function of other the parameters such as head tilt, and flow/throw adjustment makes use of a testing for a few specific choices of these parameters. The spatial distribution for arbitrary choices for these parameters is then obtained using interpolation in a transformed coordinate system.
In a preferred embodiment, the present invention uses two dimensional coordinate transformations and function-specific interpolation methods. In contrast to general interpolation where no Information about the characteristic of a function is provided, our function-specific interpolation makes uses of the known operational properties of sprinkler devices. Given data at one or more pressures, the transformation methods are used to predict the spatial distributions at intermediate pressures. Given this data for one more slopes with different inclinations, the transformation methods are used to predict the spatial distribution for intermediate Inclination. Given data for one or more flow/throw adjustments, the transformation methods are used to predict the spatial distribution for intermediate flow/throw adjustments. Given data with no wind and one or more wind condition(s), the transformation methods are used to predict the spatial distribution for intermediate wind conditions. By applying the transforms in succession, the combined effect of variation of multiple parameters is predicted.
In another preferred embodiment, the present invention uses these calculation of flow lines to predict the three dimensional distribution. The flow lines are obtained using a method of best fit given test data on a reference surface and points above and/or below the reference surface. The reference surface should be well sampled with a reasonably fine spacing between test points. The other test points can be relatively sparse and do not need to be arranged into any particular pattern provided they reasonably sample the flow lines.
The surface distribution is obtained by following each flow line from the sprinkler through the air to the ground. The spread in the flow lines are used to predict the three dimensional distribution given two dimensional data on a reference surface. Examples of good choices for a reference surface are flat ground and slopes with a constant tilt.
It should be the noted that although distributions for surface precipitation and water delivery rate are two dimensional quantities, we use a three dimensional flow line method because compared with general interpolation methods, It is more accurate and requires far fewer test points to sample the three dimensional distribution. In general, we find that the number of test points required is only three to five times greater than for a two dimensional surface.
In an alternative embodiment, the flow lines are obtained from analysis of photographs of the sprinklers spray pattern. This method is especially applicable to rotary sprinklers. In this case, the photo is taken when the stream from the sprinkler nozzle is in the image plane of the camera. The shutter speed is set so that the motion of the water drops is observable as a blur. The flow lines are easily seen by eye and good estimate can be obtained by drawing in the flows lines on the photograph by hand. More accurate results are obtained using image processing methods to deduce the flow lines.
In additional embodiment, the trajectories of the water drops are predicted using Newton's equations. These trajectories correspond directly with the flow lines. The trajectories are computed assuming an initial condition and a drag coefficient. For any point on a now line, the velocity direction is the same as the direction of the flow line. This leaves only two unknown parameters, the velocity magnitude and the drag coefficient. These parameters are obtained by the choice of values such that the predicted trajectories reproduce the previous calculated flow lines.
The initial conditions for each flow line are chosen to be at point where the stream breaks up into water drops. Newton's equations now reproduce the flow lines previously determined from windless data. By adding terms to Newton's equations for wind force, the trajectories are recomputed. This predicts the flows lines given a wind profile. The spatial distribution is recomputed using the new flow lines. In this way, data obtained under windless conditions can be used to predict the spatial distribution given a wind profile.
As a matter of convention, the term distribution means two a two dimension distribution unless stated otherwise. Hence, the term precipitation rate spatial distribution refers to the distribution along the ground level of the landscape. On the other hand, the term three dimensional distribution refers to the distribution over all of space.
For all the three dimension methods, the three dimension distribution is calculated first. Then given elevation information, the surface distribution is calculated. In the case where wind is present, the wind profile is affected by the terrain topology. In this case, the predicted results for the three dimensional distribution will be a function of the terrain topology.
These and other embodiments and benefits of the present invention will be clear to those skilled in the art of irrigation and computer modeling after reading the best mode of operation.
The details of the preferred embodiments for carrying out the idea of the Invention will be now described. It should be understood that the details are not intended to limit the invention to those details. On the contrary, the details are merely intended to describe the best mode of operation for carrying out the Ideas of the invention. Numerous alternatives, modifications, and equivalents of the embodiments described herein will be apparent to persons skilled in the art of irrigation and computer modeling.
The preferred embodiments of the invention are described for a calculation method to predict the spatial distribution and distribution uniformity of landscapes irrigated by sprinklers. The key idea of the invention is the use of coordinate transforms and physical modeling to predict the spatial distribution of any given sprinkler under all various conditions it may be operated.
Section 1. Calculation Objectives
The variables for this section are listed and defined in the below table.
{right arrow over (r)} Spatial Position
P({right arrow over (r)}) Precipitation Rate Function
W({right arrow over (r)}) Water Delivery Distribution
{right arrow over (r)}i Spatial Position of ith Sprinkler
Pi({right arrow over (r)}) Local Precipition Rate Distribution Function for ith Sprinkler
ti Run time for ith Sprinkler
DULQ Lower Quarter Distribution Uniformity
DUSite Site Distribution Distribution Uniformtiy
The equations for this section as represented in following equation 1.
P(r→)=∑iPi(r→-r→i)
The objective of this invention is to calculate the precipitation rate and water delivery spatial distributions for Irrigation systems, and the corresponding distribution uniformity ratios.
At any spatial position {right arrow over (r)} we seek to calculate the precipitation rate P({right arrow over (r)}) and water delivery rate w({right arrow over (r)}) for a system of irrigation sprinklers. The precipitation at any given point will usually be produced by more than one sprinkler. The spatial position of ith sprinkler is represented by {right arrow over (r)}i. The local precipitation rate distribution function for ith sprinkler is represented by Pi({right arrow over (r)}). The run time for ith sprinkler is represented by ti.
Given the precipitation rate for each rate for each sprinkler, the total precipitation rate spatial distribution for a collection of sprinklers is calculated using the principle of superposition as shown in equation 1.
When an irrigation area contains different kinds of sprinklers with different average precipitation rates, each kind of sprinkler have a different run time. In this case, it is more relevant to calculate the amount of water delivered. The water delivered by each sprinkler is its precipitation rate times its run time. The water delivery spatial distribution is again calculated using the principle of superposition as represented in following equation 2.
W(r→)=∑itiPi(r→-r→i)
It should be noted that both equations (1) and (2) require as inputs the precipitation rate spatial distribution for each sprinkler. In this invention, we present a general method of predicting the precipitation rate spatial distribution for any sprinkler under any operational or environmental conditions. This prediction method is the core of our Invention.
After the spatial distributions have been calculated, the lower quarter uniformity distribution uniformity ratio is calculated as represented in equation (3) where DULQ is the lower quarter distribution uniformity. This ratio is the average over the lowest quarter precipitation values to the overall average within an irrigated area.
DULQ=LQAverageAvearge
The overall site distribution uniformity (DU) is obtained by computing the area weight average over all irrigated areas as represented in equation (4) where DUSite is the site distribution uniformity.
SiteDU=∑iAiDUi∑iAi
Another useful measure of DUi is the local distribution uniformity as represented in equation (5).
DUi=MinimumwithinlocalareaforithspinklerAverageoverIrrigatedArea
In this case, local area is defined to be those points that are closest to a given sprinkler. The local DUi is the ratio minimum value within this area to the average over an Irrigated area. This local DUi is useful for identifying the specific areas within an irrigated landscape that have poor distribution uniformity.
Equations (1) to (4) are standard definitions, but they require as inputs that the local precipitation rate spatial distribution function be known for every sprinkler within the Irrigated landscape. In particular, this requires the spatial distribution be obtained for all types of sprinklers and nozzles, and under all operational conditions. In general, this kind of data is not available, thus the calculation of the spatial distributions and distribution uniformities usually not feasible.
This Invention solves this problem by providing a calculation method to predict all of the necessary local precipitation rate spatial distribution functions. Once the local precipitation rate spatial distribution function is calculated for each sprinkler, then equations (1) to (2) are used to calculate the composite spatial distributions for the system of irrigation sprinklers. The corresponding distribution uniformity ratios are then calculated using equations (3) to (5).
Section 2. Calculation Inputs
The inputs and variables for this section are listed and defined in the below table or in the table from the previous section.
PSI Pressure at Base of Sprinkler Head
Head Head Model
Nozzle Nozzle Choice
θS Start Angle for Range of Arc
θE End Angle for Range of Arc
TADJ Throw Adjustment
θHD Head Tilt
t Run Time
z(x, y) Elevation Function
{right arrow over (v)}({right arrow over (r)}) Wind Profile Function
The operational variables for each sprinkler are:
Spatial position ({right arrow over (r)}) that are horizontal coordinates, such as would be obtained from a map.
The pressure (PSI) at the base of the sprinkler head. This is the pressure which will drive water through the nozzle and produce the spray pattern. The head model (Head) and the nozzle choice (Nozzle).
If the spray pattern for a sprinkler is less than 360 degrees, the spray pattern will be confined to an arc. The arc is specified by the start angle (θS) and end (θE) angle. The angle defined to be measured counter clockwise from east. This corresponds to the angle as measured with a protractor on a map with the north direction pointing up the page.
For many sprinklers the throw adjust (TADJ) can be adjusted by turning a screw on the sprinkler head. A throw adjustment may also potentially change the flow rate. A throw adjustment is quantified by measuring the new throw distance.
When sprinkler are used to irrigate a slope, the sprinkler is head is tilted (θTD) in a direction facing down the slope. The tilt angle is typically one half the angle of the slope. Tilting the head will change the spatial distribution produced by the sprinkler. The tilt angle is defined to be the angle between the axis of the sprinkler head and the vertical direction.
The run time (t) is the total time a sprinkler is operated in a given interval of time. This interval may be per cycle or day or any other interval of interest
The environmental information consists of the following inputs:
The terrain topology (z(x,y)) includes an elevation function. This is a description of the landscape elevation as function of horizontal position. The elevation profile does not need have absolute accuracy, since only relative height information is need. In particular, the relative difference in elevation within the throw area for each sprinkler should be specified with an accuracy of about one foot.
The wind profile (v({right arrow over (r)})) is a description of the wind velocity as a function of both horizontal position and height. This information may be obtained a number of practical ways:
i) The wind profile can be obtained using the standard logarithmic wind formulas for flow over a surface.
ii) If the terrain topology is complex, the wind profile may be the result for an air flow calculation over the landscape. There exist commercially available software programs that can perform this calculation.
Section 3. Transformation to Dimensionless Variables.
The inputs and variables for this section are listed and defined in the below table or in the tables from the previous sections.
P({right arrow over (r)}) Precipitation Rate Function for Sprinkler
{right arrow over (r)} Displacement Vector from Sprinkler
Φ Dimensionaless Precipitation Rate
{right arrow over (q)} Dimensionaless Distance from Sprinkler
Φ({right arrow over (q)}) Dimensionaless Preciptional Rate Function
T Throw Distance from Sprinkler
GPM Flow Rate
Where P({right arrow over (r)}) is the precipitation rate function for sprinkler in inches of precipitation per hour (in/hr). Where ({right arrow over (r)}) is the displacement vector from sprinkler based on the local spatial position. Where φ is a dimensionless precipitation rate. Where q is a dimensionless distance from the sprinkler. Where φ(q) is a dimensionless precipitation rate function. Where T is the throw distance from the sprinkler in feet (ft). Where GPM is the flow rate in gallons per minute.
The key idea of this method is to map the spatial distribution to the unity circle with a coordinate transform. The interpolation is then performed within the transformed coordinate system. Since most sprinkler produce irrigate within a circle, we illustrate this method for the case of circular sprinklers.
For a circular sprinkler, the precipitation rate is a function of only the radial distance from the sprinkler. There is no angular dependence. The precipitation rate is normalized as represented in equation (6) and the distance from the sprinkler head is normalized as represented in equation (7). This produces the dimensionless precipitation rate and dimensionless radial distance respectively.
Φ(r)=P(r)∫0T2πrP(r)r(Equation6)q→=r→T
The transformation back to physical variables is done as represented in equation (8).
P(r)=96.25(4π)GPMT2φ(Tq→)
The advantage of the transformation to dimensionless variables will become apparent in the next section.
Section 4. Spatial Distribution Interpolation for Intermediate Pressures
PSI Pressure
KD Nozzle Coefficent
KT, ak Throw Equation Fitting Coefficents
Nr Number of Radial Interpolation Steps
Φ(PSI, i) Interpolated Dimensionless Spatial Distribution
Aik Fitting Coefficents of Φ function
We have observed that the most of the variation in precipitation rate spatial distribution results from the variation of flow rate (GPM) and throw distance (T) with pressure. The variation in the dimensionless precipitation rate with changes in pressure is much slower. This result that can be seen from equation (8).
As the pressure is varied, the ratio (GPM/T2) varies rapidly, but the shape of the spatial distribution varies slowly. The shape of the spatial distribution is defined by the dimensionless precipitation rate spatial distribution.
We calculate the GPM and throw as a function of pressure using formulas, whereas the dimensionless precipitation spatial distribution for intermediate pressure is calculated by interpolation. In this approach, precipitation rate spatial distribution data is required at one or more pressures. The best approach is to use three pressures. These correspond to the maximum and minimum operation pressures recommended by the manufacture, and the midpoint pressure. Since the dimensionless precipitation rate spatial distribution varies slowly with pressure, data at just these three pressures is sufficient to accurately interpolate the dimensionless precipitation rate spatial distribution at any arbitrary pressure within the recommend operational pressure range.
Any general interpolation method can be used, but the best method is to resample the data for each pressure into the same number of evenly spaced dimensionless distances as represented in equation (11). Where Nr is the number of radial interpolation steps.
ri=iNr(i=1,2,3,...Nr)
A good choice for the number of re-sample points is 100. A robust method of re-sampling is cubic interpolation. Next, the dimensionless precipitation rate values for each re-sampled point is fitted to a polynomial function of pressure as represented in equation (12). Where φ(PSI, i) is an interpolated dimensionless spatial distribution. Where Aik is the fitting coefficients of φ function.
φ(PSI,i)=∑k=0Np-1AikPSIk
The fitting parameters only need to be determined once. Thereafter, equation (12) provides an efficient method of computing the dimensionless precipitation rate for any arbitrary pressure. This method produces values at each of the regularly spaced dimensionless radial positions. The values for arbitrary radial distances are then calculated using linear interpolation.
The GPM for arbitrary pressure is calculated as represented in equation (9). This is a standard equation, well known in the irrigation industry.
GPM=KGPSIX
The throw distance for an arbitrary pressure is calculated as represented in equation (10). We have noticed that this equation provides a good fit to pressure-throw data where KT, ak is the throw equation fitting coefficients.
T=KTPSIx1+∑k=1nakPSIk
The fitting coefficients in equations (9) and (10) are obtained by fitting to data at a number of different pressures. Again, it best to use three pressures, the maximum and minimum recommended operating pressures and the mid-point pressure.
Note that the flow rate (GPM) and throw distance (T) for arbitrary pressure are calculated using a formula with fitting parameters, whereas the dimensionless precipitation rate spatial distribution φ({right arrow over (r)}) is interpolated. Each of the three quantities, GPM, T, and φ({right arrow over (r)}), are calculated as a function for pressure. These three Quantities are then used in equation (8) to predict the precipitation rate spatial distribution as a function of any arbitrary pressure.
Section 5. Spatial Distribution Interpolation for Nozzle Wear
The interpolation procedure for nozzle wear is similar to procedure in the last section for interpolation for intermediate pressures.
Most of the change in spatial distribution due to nozzle wear is caused by change in GPM and throw distance as the nozzle orifices wears with time. The change in the dimensionless precipitation rate spatial distribution is a secondary factor. The nozzle wear depends on the total run time. For example, if a sprinkler is run 30 minutes a day for 100 days, the total wear time is 50 hours.
The wear rate can be measure by operating a nozzle continuously for a few weeks, and measuring the flow and throw distance every few days. In this way, the fitting coefficients for equations (9) and (10) are determined as function of time. At the end of the test, the spatial distribution is measured, and the dimensionless spatial distribution is calculated. The dimensional spatial distribution for any arbitrary time is then calculated using interpolation.
Using this method the flow rate, throw distance, and dimensionless precipitation rate spatial distribution is predicted as function of time. The corresponding precipitation rate spatial distribution is then calculated using equation (8).
Section 6, Partial Circle Sprinklers
ΦAR Arc Range Angle
AR({right arrow over (r)}) Arc Factor Function
P(r) Precipitation Rate for 360 degree operation
P(ΦAR, {right arrow over (r)}) Precipitation Rate for Partial Circle Operation
{right arrow over (q)} Dimensionless Throw Distance Vector
T({right arrow over (r)}) Throw Distance in direction of the vector {right arrow over (r)}
The interpolation method for partial circle sprinklers is different, depending on whether it is a rotary or spray sprinkler. The equation for determining the arc range angle is represented in equation 13 where φAR is the arc range angle in degrees.
φAR=φE−φS
Rotary sprinklers put out a stream of water that rotates as the sprinkler head turns. The turn rate is usually less than 1 rpm. For a partial circle operation, the rotation reverses direction at the limits of the range of arc. Since the reverse is quick, usually in about a second, the distribution of water within the range of arc is independent of direction. The results of section 5 are applied to partial circle sprinklers by scaling the precipitation rate as represented in equations (14) and (15) where P(φAR, {right arrow over (r)}) is the precipitation rate for partial circle operation.
P(φAR,r→)=360φARP(r→)AF(r→)(Equation14)AF(r→)={1withintherangeofarc0otherwise
For spray sprinklers, the water emerges through slit about the circumference of the nozzle. As the arc is reduced from 360 degrees, the now rate is reduced.
In this case, the relation between precipitation rate and range of arc as represented in equations (16) and (17).
P(φAR,r→)=φAR360P(r→)AF(r→).(Equation16)AF(r→)={≤1withintherangeofarc0otherwise
The water (low at ends of the range of arc is not the same as in the interior of the arc. For this reason the GPM for partial arc sprays does not scale exactly with range of arc. For this case, equation (17) is defined so that values of AF({right arrow over (r)}) tapper off near the two ends for the range of arc. This is done in such a way as to produce the measured GPM at the selected range of arc. For example, if quarter arc nozzle has a GPM that is 80% of one fourth of the GPM for full circle operation, then the average of AR({right arrow over (r)}) should be 0.8. The rate of tapering is determined by testing.
Section 7. Multi-Stream Circular Sprinklers
Multi-stream rotary sprinklers produce multiple streams. Each stream is equivalent except for the direction of the stream. The orifices are arranged evenly about the nozzle, and the nozzle rotates. These sprinkles can also be used in either full or partial circle operation. For both cases, they can be treated as spray sprinklers using the procedure in section 6. However for partial circle operation, there are no end effects.
Section 8. Asymmetric Sprinklers
For side strip sprinklers, the spray pattern is asymmetric. These the range of arc for these sprinklers is usually 90 or 180 degrees.
This case is treated the same as in the previous sections except that now the throw distance is direction dependent. Let the throw in the direction be denoted as T({right arrow over (r)}). Then the normalized position vector as represented in by equation (18).
q→=r→T(r→)
The fitting parameters for the throw using equation (10) are obtained for each direction. As the pressure is varied, the throw equation is used to predict throw distances. Since the distribution will vary with direction, the normalized precipitation rate is interpolated with respect to both radial distance and angle. The spatial distribution is predicted using equation (8) with the term T2 replaced by (A/π) where A is the area within the throw. The area A is also interpolated with respect to pressure.
Section 9. Throw Adjustment
The throw adjustment may or may not change the flow. This depends on how the head nozzle device is constructed. The throw adjustment is quantified by measuring the throw as a function of throw adjustment. This adjustment is usually produced by screw setting on the sprinkler head. For a given screw setting, and measurements of the throw and flow at that setting, to first approximation, the spatial distribution is predicted using equation (8) assuming the normalized precipitation rate function does not change.
More precise results are obtained for measuring the distribution for one or two different throw adjustments and using the same interpolation methods as for pressure variation. In this case, the interpolation is with respect to normalized throw adjustment instead of pressure. The normalized throw adjustment is the throw divided the maximum throw.
For Intermediate pressures with a throw adjustment, the spatial distribution is predicted for each test pressure for selected throw adjustment. Then the results are interpolated to the selected pressure.
Section 10. Two Dimensional Transform Method for Wind
T0 Throw Distance from Sprinkler without Wind
{right arrow over (T)} Throw Distance Vector with Wind
r Radial Distance from Sprinkler in xy Plane
c Cosine in xy Plane
s Sine in xy Plane
{right arrow over (u)}w Unit Vector for Wind Direction
{right arrow over (r)} Radial Displacement from Sprinkler in xy Plane
{right arrow over (r)}′ Transformed Radial Displacement Spinkler in xy Plane
P(r) Precipitation Rate for No Wind
PT({right arrow over (r)}′) Transformed Precipitation Rate with Wind
g({right arrow over (r)}) Geometric Factor
The spatial distributions for sprinklers operated with a wind blowing can be extrapolated for the case where sprinklers are operated under windless conditions using the following transformation method. The procedure is illustrated for circular sprinklers.
When wind is present the trajectories from the sprinkler head, through the air, and to the ground is altered. This causes a distortion in the spatial distribution of the precipitation rate. The distance the water is thrown from the sprinkler head is also altered.
We have found that end of throw distance fits equation (22) where c and s are the cosine and sine of the angle between the throw direction and the wind as represented by in equations (19) to (21) where r is the radial distance from a sprinkler in the x and y plane and c is the cosine in the x and y plane, s is the sine in the x and y plane. The coefficients as represented by equation (22) are fitted by measuring the throw at three points. A good choice is directly up and down wind from the sprinkler head and at right angles to wind directly outwards from the sprinkler head where T bar is the throw distance vector with wind and T0 is the throw distance from a sprinkler without wind.
r=x2+y2(Equation19)c=xr(Equation20)s=yr(Equation21)T→=T0(1+Ac+Bc2+Cs2)
The lowest order terms of the transformation consistent with equation (22) leads to equation (23). In equation (23), {right arrow over (u)}w is the unit vector in the direction of the wind. The predicted spatial distribution is given by equation (24). In equation (24), the term g(r) is a geometric factor.
r→′=r→+(r2T0)(Ac+Bc2+Cs2)u→w(Equation23)PT(r→′)=P(r→)g(r→)
The geometric factor is defined in the following way. Let a small area centered at point {right arrow over (r)} be transformed using equation (23). The geometric ratio g({right arrow over (r)}) is the ratio.
In principle more terms could be added to equation (23) and the extra coefficients could be determined by fitting the prediction of equation (24) to test data. In practice, however, data collected under conditions of wind is rather unrepeatable. This is due larger to the fact that the wind conditions will not usually remain constant long enough to complete a test. Nevertheless, equations (23) and (24) provide a reasonably accurate method of prediction.
The coefficients in equations (23) and (24) will depend on the wind speed. The coefficients can be determined for a number of wind speed and the values interpolated for intermediate wind speeds.
It should be noted that once the transformation is determined, it can be applied to any of the predicted results from the previous sections. Using this method, the spatial distribution obtained under windless conditions is transformed for a wind with a specified velocity.
Section 11. Two Dimensional Transform Method for Slopes
T0 Throw Distance from Sprinkler on Level Ground
{right arrow over (T)} Throw Distance Vector on Slope
θNz Nozzle Pitch Angle
θHt Head Tilt Angle
θHtd Head Tilt Direction Angle
θHr Head Rotation Angle
Rxy(θ) Rotation Matrix for Rotation θ in xy Plane
Rzx(θ) Rotation Matrix for Rotation θ in zx Plane
{right arrow over (u)}x Unit vector in x direction
{right arrow over (u)}′ Nozzle Direction Unit Vector
{right arrow over (v)}xy Projection of {right arrow over (u)}′ onto xy Plane
{right arrow over (r)} Horizontal Radial Displacement from Sprinkler
{right arrow over (r)}′ Transformed Horizontal Radial Displacement Spinkler
The spatial distributions for sprinklers operated on level ground can be extrapolated for the case where sprinklers are used to irrigate slopes using the following transformation method. The procedure is illustrated for circular sprinklers.
When a slope is irrigated with sprinklers its head is tilted in the direction of the slope. The tilt angle is typically set to about one half of the angle of Inclination of the slope.
We have found that for this case, the end of throw distance also fits equation (22). The coefficients are fitted by measuring the throw at three points. A good choice is to directly up and down the slope from the head and at right angles directly outward from the head. In practice the throw distances are measure along the ground with a tape measure. These distances are converted to horizontal distance by dividing the throws by the cotangent of the slope inclination angle.
When the head is tilted the direction of water velocity exiting the nozzle is also rotated. This rotation depends on the nozzle pitch, head rotation angle, head tilt angle, and the head tilt direction. The matrix equations for the rotational transformation produced by this rotation are represented in equations (25) to (27).
Rxy(θ)=(cos(θ)-sin(θ)0sin(θ)cos(θ)0001)(Equation25)Rzx(θ)=(cos(θ)0-sin(θ)01sin(θ)0cos(θ))(Equation26)u→′=Rxy(θHdt)Rzx(-θHt)Rxy(θHr)Rzx(θNz)ux
The resulting transformation as represented in equation (28).
r→′=(r+r2T(Ac+Bc2+Cs2))υ→xy
This transformation can be made more accurate by additional terms and determining the extra coefficients solving for the values that provide the best match to the measured test data. Since the amount of water within any differential arc must is conserved, the expansion function term would be a product of a function of r and function of c and s. This leads to what is represented in equation (29).
{right arrow over (r)}′=(r+ƒ1(r)ƒ2(c,s)){right arrow over (v)}xy
This method has an advantage that is not obvious. The transformation method will conserve the amount of water within each differential arc. This will correct for Inaccuracies of the test data.
The transformation coefficients are determined for a number of different slopes. The values for other slopes are found by interpolation. In way, the transform for an arbitrary slope is predicted.
It should be noted that once the transformation is determined, it can be applied to any of the predicted results of the previous sections. Using this method, the spatial distribution for a flat ground test is transformed to a slope with specified inclination.
Section 12. Three-dimensional Flow Line Method
{right arrow over (T)}(s) Horizontal Throw Distance Vector for Inclination s
q Dimensionaless Horizonal Radial Distance from Sprinkler
q′ Transformed Dimensionaless Horizontal Radial Distance
P(q, 0) Precipitation Rate on Level Ground
PT(q′, s] Tranformed Precipitaion Rate in Three Dimenional Space
In this section, the three-dimensional precipitation rate distribution is calculated from the water flow lines. As the water travels form the sprinkler head, through the air, and to the ground, its paths define flow lines. As the flow lines spread apart, the precipitation rate correspondingly decreases.
To carry out this calculation, we introduce a new co-ordinate system as represented in equations (30)-(32).
r=x2+y2(Equation30)s=zr(Equation31)q=rT(s)
For the case of circular symmetry, a point (q, 0) on the level plane passing through the sprinkler head is transformed to points (q′, s) along the flow line. The transformation from the point (q, 0) to the points (q′, s) as represented in equation (33).
q′=q+(1−q)(A1q+A2s+A3q2+A4qs+A5s2+ . . . )
The corresponding precipitation rate is calculated as represented using equation (34).
PT(q′,s)=P(q,0)g(s,q)
In equation (34), the term q(s, q) is the geometric factor at the point (s, q). To obtain this factor, a small area is transformed. The geometric factor is the ratio of the transformed area to the original area.
To determine the fitting coefficients in equation (33), a number of different approaches may be used. One method is to collect precipitation data along lines in different direction moving outward from the sprinkler head. The precipitation rate is integrated along these lines. In this integration, the precipitation rate is weighted by the distance from the sprinkler head. Points that integrate to the same value correspond to the same flow line.
Another more general method is to collect precipitation rate data over points distributed in three dimensions. The level plane distribution is transformed using equation (33) to each of the other collection points, and the corresponding precipitation rate is predicted using equation (34). The transformation coefficients determined by solving for the best least square fit between the predicted and observed precipitation rates.
Section 13. Surface Precipitation Rate Spatial Distribution.
Given an evaluation model for the landscape, the three-dimensional precipitation rate distribution is evaluated at the landscape surface. This produces the surface precipitation rate spatial distribution. Thus, using the three-dimensional flow line method, the precipitation rate spatial distribution on the plane level with the sprinkler head is transformed to any arbitrary landscape surface topology.
Section 14. Photographic Trajectory Tracing Method
For rotary sprinklers, the flow lines are readily apparent from a photograph of the spray pattern. The photo is taken when the stream is in the Image plane of the camera. The shutter speed is set so that the velocity direction is observable as a blur.
The trajectories of the water drops are easily observable in photographs, and a reasonable sketch of the trajectories could be drawn by hand. A more precise method is as follows: Using the method of section 12, the flow direction vector for any point is calculated. For each point the flow direction is also determined from the photograph. The error measure is the dot product between the direction vector in the photograph and the transformation prediction. The coefficients of the transform are determined by the method of best fit.
Section 15. Aerodynamic Water Drop Model
{right arrow over (v)} Velocity Vector
g Gravitational Acceleration
α Acceleration Vector
ρair Density of Air
ρwater Density of Water
Dw Effective Diameter of Water Drop
CD Aerodynamic Drag Coefficent
K Coefficent of Drag
uair Viscosity of Air
α0, α1, α2 Stream Trajectory Fitting Parameters
vI Exit Velocity from nozzle
{right arrow over (v)}wind Wind Velocity Vector
{right arrow over (v)}f Friction Velocity Vector
z0 Surface Roughness
The Newton's equations of motion for a water drop subject the drag force due motion through air as represented by equation (35) where the coefficient of drag, K is calculated as represented using equations (36) to (38).
a→=-gz→-Kυυ→(Equation35)K=(34)(ρairρwater)(CDDw)(Equation36)CD=24Re+1(Equation37)Re=ρairυDwμair
Equation (35) is easily solved using the fourth order Runga-Kutta method. The Initial condition for each trajectory is chosen for each flow line is chosen at a point just after the stream has broken up into water drops.
The flow lines obtained in section 12 are the trajectory lines predicted by equation (35). For any point on a flow line, the velocity direction is along the direction of the flow line. In principle, this leaves only two unknowns, the velocity magnitude and the drag coefficient. In practice, we find that there are families of parameters that produce nearly the same trajectories. Instead, the velocity can be measured using the photographic method described in section 13. The blur distance vector divided by the shutter speed gives the velocity of the water drops. Once, the velocity and drag coefficient are determined, then the equation (35) predicts the trajectory.
The trajectory from the nozzle to the initial condition for the water drop calculation is obtained as represented by using equation (39).
a→=-gz→+(a0+a1t+a2t2)υ→υ→
The initial velocity direction is determined by the pitch of the nozzle. The initial velocity magnitude in feet per sec is calculated as represented in equation (40).
vI=12.19√{square root over (PSI)}
The fitting coefficients for equation (39) are determined by matching to the initial condition for the water drop trajectories.
Using this method Newton's equations reproduce the flow lines obtained using the methods in section 12.
When the head is tilted, the initial velocity vector is rotated uses equations (25) to (27). Then equation (35) is solved again using the new Initial condition. This produces a new set of flow lines, and the resulting spatial distribution is calculated using the procedure in section 12. In this way the spatial distribution for the standard head tilt can be transformed for any arbitrary head tilt.
Section 16. Wind Modeling
When wind is present, Newton's equations are modified by adding replacing the velocity term in equation (35) with the velocity relative to the wind as represented by equation (41).
{right arrow over (v)}→{right arrow over (v)}−{right arrow over (v)}wind
Given a wind profile equation (35) is solved again using the fourth order Runga-Kutta method. One good choice for a wind profile is the standard logarithmic wind profile as represented in equation (42).
υwind=υfln(zz0+1)
For a given type of plant covering, the surface roughness is available in tables. The friction velocity is obtained by measuring the wind speed at a height, and solving using equation (42). For complex terrains, it may be necessary to use fluid dynamic program to model the air flow of the terrain surface. However the wind profile is obtained, given a model for the wind, the results of this section transform the spatial distribution under windless conditions to conditions where wind is present.
Among the many various potential applications of this invention, there two applications that we are aware of for which our methods will have an immediate application.
First, during the planning and design of an irrigation system, an irrigation designer can use our invention to obtain a numerical measure of the irrigation distribution uniformity. This provides an objective means of determining whether or not a proposed design meets planning and/or regulatory requirements. Often, one of the irrigation system requirements is that the distribution uniformity will exceed a minimum requirement. The alternative to using this invention is the current practice of using design rules without performing a distribution uniformity calculation. The deficiency of that method is that the irrigation does not really know for sure whether his or her design meets the required distribution uniformity requirement.
Second, after a site has been constructed, and possibility modified after initial design, a survey of the irrigation system may produce Irrigation as-built. The as built provides description of the sprinkler heads and installed nozzle choice and the corresponding spatial positions. This information, together with a specification of the operational variables such as pressure, throw adjustments, head tilt, etc., together with the elevation and wind profile for the site may form a complete specification of the operational and environmental variables for the irrigation system. Given this information, the invention can perform an electronic water audit. The alternative to this is the common practice of placing a course grid of water buckets within selected small areas of the site. Each of these areas covers usually just a few serving areas, corresponding to running just a few irrigation valves. The deficiency of the approach is that is known to be inaccurate, and in particular, produces results that may have a poor repeatability.
The advantage of our invention, applied to either a proposed design or an existing as built design, is that the calculation is highly accurate and these results can be obtained nearly real-time. Changes to a proposed or as-built design can be tested first on the computer. That is after, each change is introduced to a design, the resulting Irrigation spatial distribution is predicted and the result distribution uniformity is calculated thus, different solutions for Irrigation problems that may exist can be first tested on the computer prior to Implementation. This will significantly reduce the time and expense for Irrigation design verification and irrigation system operational management.
Thus specific embodiments of an accurate method for modeling the spatial distribution for irrigation systems for landscapes have been disclosed. It should be apparent, however, to those skilled in the art that many more modifications besides those described are possible without departing from the inventive concepts herein. The inventive subject matter, therefore, is not to be restricted except in the spirit of the appended claims.


The Early Arrival of Dreams:...
A Likely Story: One Summer...
Whoredom in Kimmage Whoredom...
After readiing this, I'm committed to reading all of Rosemary Mahoney's books. This non-fiction adventure is written in a fluid and sensitive way. I just gave a copy as a graduation present to a ...


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Choose one of the 8 Standard Street, U.S.A. SlowpokeHolland. Whether you cheer the ice-drawn carriage, the double-decker trolley, or the old-fashioned muppngland in the 1860's!


cross Latin America a “public health emergency of global concern.” Zika is a mosquito-borne virus related to the dengue and yellow fever viruses. In most cases, infection by Zika has no symptoms, which means the disease is usually not diagnosed. Only 20 ...


A couple of months back, I wrote about Foursquare. It is, as far as I can tell, the location-based social network with the most legs. It…Read article
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Though I also have the sad suspicion that MTGO and their reporting tools somehow can't handle a difference in policy between formats. I can't imagine how this would be the case but I also can't imagine how it would take a month to implement B&R changes online either...
Isn't it just constantly changing?Stale?Stale like how?
Do we even know if Wizards has the ability to data mine their own results?
@garbageaggro Yes, this was my question exactly. I don't play Standard; is it true that in Standard, there is always a "best deck," and that "solving" the format means determining the decklist of this best deck?Isn't that more an indictment of Wizards R&D, than of data miners?
However it's not clear to me 1) that this kind of analysis is actually useful in the wild, or 2) that having access to this kind of analysis is harmful or makes Magic "less interesting."
If I see that no one played Dredge in the last P9 event, I might be inclined to skimp a little on my Dredge hate. Another player might look at the data and determine that players are likely to skimp on Dredge hate and therefore Dredge is a good plan. Then you get the "next level" where I know that you know that I know that Dredge is underplayed!
A few recent B&R changes (or lack thereof) across multiple formats indicate that this data-drive decision making is not the case. Rather, WotC, through their control of the DCI (a whole other thing I'm not a fan of) is attempting to sculpt formats to their vision of the formats, rather than what is "healthy" or "diverse."


Queen Nation to string together a ‘Bohemian Rhapsody’ at the Egyptian
8 p.m., Wednesday Dec. 26, through Friday, Dec. 28
Gregory Finsley said this is the year to sing in a Queen tribute band because of the buzz surrounding Rami Malek's portrayal of singer Freddie Mercury in the biopic, "Bohemian Rhapsody.""It's giant, and we're very busy," said Finsley, Queen Nation's lead singer.
Songs include"Stone Cold Crazy" and, of course, "Bohemian Rhapsody," according to the singer.
It was Dec. 10, 1977, at the Tarrant County Convention Center, and I will never forget it...” Gregory Finsley, Queen Nation singer
"We're trying to pay homage to Queen and the iconic songs that have been out for so many decades," Finsley said. "We play to audiences from 48 to 9,000 people, and we make sure we play what they want to hear."
Queen Nation has spent the past few years touring the Western Hemisphere – both on land and at sea."We have been able to perform in Canada, Jamaica and have been blessed with performing on cruises," said Finsley, who has been with the band for 11 of its 14 years. "The audiences have been great. It seems everybody is singing more of the words with us, now. And the all-ages shows are probably some of our favorites to do because parents and their kids are there."
Finsley takes his role seriously because Mercury, who redefined the meaning of "rock star" before his passing in 1991, was such an icon.
"Before we go on stage, I have to have to 'clock' in as Freddie Mercury and remember his mannerisms, the quirks and the twists," he said. "I have get the inflections and tonalities. Freddie didn't use a lot of falsetto, so I have to bring what I have."
Every few weeks, Finsley gives himself a refresher course by listening to an album or reviewing footage of Queen's live performances.
"It pays to go over some scenes you like or even don't like just so I can hear how he enunciates and pronounces things here and there," he said. "We have to also make sure we have a good, fitted costume wardrobe, which we continually try to do."
The more challenging songs in the sets for Finsley include uptempo compositions such as "Stone Cold Crazy."
"The trick is to make sure you don't go faster than you have to, especially if you're already amped up for the show," he said. "The more you pull back the adrenaline, the song gets fatter and bigger."
As for hitting the notes written for Mercury's wide vocal range, Finsley said the songs "The Show Must Go On" and "Somebody to Love" push his voice.
"You want to get those high glissando notes at the end," he said. "The more you bring in that authenticity, you start to hear people tell you that the show takes them back to their 11th grade, or that they got married to this song or that song."
Even as he pays tribute to a discography as deep and varied as Queen's, Finsley is hard-pressed to name his favorite track."'If I had to name three," he said, "I would have to say 'The Prophet Song' and 'Take My Breath Away' and some of the stuff off of their 'Sheer Heart Attack' album."
Finsley said his performances wouldn't be where it is without his bandmates — guitarist Mike McManus, bassist Parker Combs and drummer Peter Burke.
"These are the finest gentlemen I've ever worked with," Finsley said. "They are the consummate musicians. They have great talents aside from their musicality."
Finsley's older cousin introduced the future superfan to Queen in the mid-1970s, the band's prime.
"d.
"," Finsley said. "The band rose out of the smoke and I saw Freddie in the whole diamond outfit, and it was an incredible experience."


Why wasn't the year given an 'important events of the year' listing like it would have, had it been included in Sixth World Almanac?
It just seems like there would have been the perfect place to include one, as SWA includes a major event from the year and then a summary of all major events, the SOTA one would have been Hestaby's address.
I mainly bring this up as there are a few events that occur in 2073 that I would like to have an exact date for (one example being when exactly Sirrurg started attacking Aztlan).
Thanks for any reply that you have.
Because SWA was "published" near the end of 2072.
In other words, the other stories weren't written yet.
If I understand correctly, what chinlamp was suggesting was to have a timeline of year 2073 next to the Hestaby speech in SOTA:2073, simply following the format used in Sixth World Almanac.
That wasn't what I was asking, what I asked was why wasn't 2073 summed up in SOTA 2073 ala Sixth World Almanac.
Probably didn't word it right to make it clear.
Wow, just as I was typing this Nath said exactly this.
No, according to this thread, SotA:2073 was written at the "end of 2073" - which means we are now into 2074 in the game-world.
The SR game takes place, officially, 62 years from today.
Ah... Sorry for the misunderstanding.
Null perspiration, chummer.
So, was there a reason for this that anyone is aware of?
Or, failing that, is there a timeline of major events for 2073 somewhere else?
I am sure someone will answer that faster than I can. I am still in the midst of reading through some of the latest books and adventures and working out timeline clues. More as I know it, however.


Cardiff Library – Cardiff, CA – 07/06/16 Written by Peter Sprague on July 6, 2016 · 6, 2016 7:00pm - Free!
Hi Peter! It’s Susannah & Patrick from Sedona. We’re planning to be there at the Cardiff Lbrary concert. We’ll be in San Diego July 1-8 and look forward to seeing and hearing you! We’ll have Patrick’s dad with us too, from Oahu.
We love what you and Leonard do together. Very cool music. Really enjoyed your Stevie Wonder tribute concert. Good Times!! And oh so amazing that you’re playing with one of my all-time favorite vocalists, Dianne Reeves. I got to see her at Yoshi’s a long time ago with Russell Malone. I would so love to see your show with her. Someday…: )
Please let us know if you have any other performances or jams going on during that week we can catch you at : )
How nice Susannah! Love that you and Patrick are coming into town. Yes on the Cardiff Library concert and also, June 2, I’ll be at Dizzy’s playing an All Beatles show. This isn’t on the gig calendar yet but I’ll get to that tomorrow. Busy day today, recording and a private gig. Love the life and it’ll be even better having you all in SD for the good summer vibe. It’s special! stay well and see you really soon, love, Peter


I too would like to learn more.
From the small amount I have learned from Merijn and forums like this I recognize the problems that I hear from what I read. I would like to learn more though.
Then you can see for yourself (all are welcome).


I’m running up on the end of my day, and I don’t think I’ll have enough time to really give this topic the amount of time it deserves, so I’ll bottom line it up front, and then go into more depth until I absolutely have to leave the office and head home: I like Twitter.
I won’t go into the whole history of how this stupid little service became popular. You can find a hundred other blog posts that talk about the birth, death, and re-birth (in terms of popularity) of this little Web 2.0 app. What I will say is that this simple little script is useful for a number of varied reasons, and has an added benefit of stirring a bit of nostalgia for the ol’ Mr. Rizzn here.
Twitter is defined as status microblogging, in case your wondering. Essentially, if you’re familiar with MySpace… imagine the bulletins system hooked into your Instant Message client or your SMS system for your phone, with RSS capabilities.
I was talking to my friend Levontaun about it (one of my old D*land friends). He said:
Myspace has changed everything once again. You can browse local, but chances are most people you find are not computer nerdy-type people like us. Most of the people on there are your average computer illiterate assholes or wannabe hood rats.
I miss the old days. Does this make me old?
If there were a way to bottle and repackage that culture, well, it probably would still be un-profitable, but I’d enjoy the heck out of it.
Still, and keep in mind I have no stake in Twitter or anything, I think you should check it out. It’s like a low committment/involvement community thing that lacks the MySpace clunkiness. If I was able to populate my Twitter circle with the old crew, I think it might feel a bit like home. 🙂


Home > Community > Families > Relationships > Think I’m losing it:
Think I’m losing it:
I am confused as to whether I really do miss him, or just the attention he gave me, or it’s just the pregnancy hormones. I think I am losing it because I have never been so happy to be out of a relationship, but I feel like I miss him. Idk what to dooooo. Sorry I’m an emotional wreck typing this..
Loveandfaith28
The hormones are horrible!!!! Wait until after the baby is here. If you still feel the same way, act on it then. Right now, it’s more than likely the hormones and longing for love. That’s normal. I can’t stop crying over anything. Don’t settle for any abusive jerk.
It doesn't matter whether or not you miss him or it's the hormones. He was abusive. Period. Abuse typically happens in cycles. He might be very loving and sweet for a while and then the abuse happens and it will increase.
Don't have sex with him. You don't know where he has been and you could put you and your baby at risk with STIs. Try and remember all the horrible things he has donebaby doesn't deserve to be raised in an abusive household.
Why does he get to treat you crap, abuse you and still have access to you? That’s exactly what I just would ask my friend if she was in your shoes. Why are you settling for him ? Yes you miss him, totally normal.. but I certainly hope you love yourself more than miss him...
Contracting std’s during pregnancy is dangerous.
chinaandback
@chinaandback
@abbieraeh
You're welcome!! You are obviously a strong person by breaking up with him in the first place, knowing you don't deserve to be disrespected and manipulated. Maybe he will mature and change for the better, maybe not. You're a strong mama. You and your baby will be great! <3
beautifulblue1989
I'm starting to lose hope...
Created by lillianaduke1 Last post 2 years ago
I think I’m out..
Created by Alexiss1771 Last post 12 months ago
I'm losing my mind... I think??
Created by Pfam3to4 Last post 11 months ago


There just seems to be discussions about thermal effects not magnetism on this board and on the internet.
Anyone knows if heatsinks in magnetic materials make any difference? Does it affect any components if so in what way? Any links would be appreciated.
Most heatsinks are made of aluminium. Aluminium is not magnetic. That's why it's not discussed on the internet.
Anders - Check out DNM's site. Morecroft has definite opinions on the subject.
Interesting site. Apparently they aren't concerned at all about RF Interference, as they offer no shielding what so ever. This is just a casual comment, after a casual browse of their site. Looks like lots of little antennas on the circuit boards.
I live very near a radio transmitter tower, so this is something I am concerned with a great deal.
I am no expert but at a minimum I am suspicious of such claims.
Well, I've solved the issue about what to believe in such matters once and for all.
To me it's quite simple, we know that eddy currents is for real, and we know that RF noise is for real. I have failed to hear any influence on the music by eddy currents, but have frequently had RF niose problems due to cell phones,switch mode PSU's and motors.
Amen. I like big metal chassis.
So one shouldn’t worry about magnetism from the heatsinks?
I personally couldn't care less about the magnetism of the heatsinks......if you take a look at the factors that we know influences the sound to an extent that we all can hear, the by far most important factor according to solid state devices and heatsinks is to make the best possible heat transfer from the device to the heatsink and to make the temperature steady and even for all the devices.
This is the reason I advocate sturdy heatsinks.
If you take a look at the average heatsink, the back-bone is too skimpy to actually utilize the heatsink. This is done in order to save material.
Like with engines "there is no replacement for displacement".
If you take a look at the heatsinks I use www.briangt.com/gallery/magura , you will see that they differ quite a bit from the heatsinks you see most people here use.
The heatsinks in the gallery are based on the same heatsinks as the ones I used for the AX-100 chassis "kits" I made for Steenoe and Seve.
I'm planning to use heatssinks made out of galvanized steel.
I'm building an Aleph-X. If I'm building my own heatsink out of steel I should be able to get about 20m2 conducing area for under 100 UDS. Hopefully it's enough för an Aleph-X.
To shield my transformers, I have been putting them in steel boxes inside the amp. Here's an oversized electrical box (116mm square, 55mm deep). It'll hold up to (I think) a 330VA Avel torroid.
And I started playing with shielded wood for my cases. This is going to be the side wall of my stereo BrianGT 4780 chip amp. It's 6/4 walnut with a thin sheet of aluminum (flashing actually) laminated to it.
The steel box contains much of the EMI from the transformer, and the aluminum provides decent RFI from the outside world.
Aluminium is non magnetic if it’s pure! This is rare to find.... and very expensive!
In the market we find combinations of aluminium with and copper and others materials.
What I like in Al is the combination of low weight and thermal conductivity.
To isolate the transformer, caps and cables, I use a combination of kitchen aluminium sheets and plastic sheets.
You can find this in a Hypermarket.
Isn't that the basis of a capacitor?
Magnetism in aluminium - floating coils !!
I know that aluminium is not magnetic, as stated before in this thread, but isnt it inductive, i mean - if a magnetic field is imposed on an aluminium surface, it generates a opposite magnetic field. But it does not "remember" this magnetic field, ie it is only magnetic if influenced by a magnetic field ? Once we made an experiment with a large coil (severel kilos) that was placed on an aluminium plate (about 10 mm thick) and then connected it to a variac, and thus the large donut coil hovered about 1 cm above the aluiminium plate. Wouldnt the power field from a large toroid create the same effect, and thus induce some magnetism (and thereby voltage??) in a thick aluminium heat sink ??
You are perfectly right Hans, but the problem is that you have to choose between eddy currents and RF shielding. Eddy currents are usually not a problem if the grounding is made with a little common sense (star ground point). On the other hand shielding is a major issue, as noise is picked up from anything and everything if you have not done something to shield your amp. This is especially true for preamps, though I'd be hard pressed to explain why.
SMALl signals (low voltages) are more suseptable to RF interfereence, because with lower voltages, an RF induced voltage will be a larger percentage of the pverall signal.
For example, say you have a signal cable with a 2v p-p singnal and a speaker cable with 30v p-p signal exposed to the same RF interference. Let say the RF induced .01 V of noise. In the 2V singal, thats .5% of the overall signal, whereas in the speaker level signal, it's .03% (much less significant).
In addition, the low level signal is then going to be amplified, so that tiny RF induced signal is now quite large.


- Control technology [91]
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- See more See less. sensors and actuators to meet the requirements of house occupants by specifying their customised user policies. However, the introduction of features and policies may result in undesired behaviours, and this effect is known as feature interactions. In this study, the authors proposed a Semantic W to model smart home services and policies with the aids of ontological analysis in the smart home domain, so as to construct a semantic context for inferring the interaction of policies. The authors focus their work on user policies interaction, which are detected by using the Semantic Web rule language in semantic context. The approach is successfully applied to the smart home system and is able to detect 90 interactions among 32 user policies by automated reasoning with tools support as Protégé and Jess..)
Improving energy efficiency in intelligent buildings is an important issue in reducing global warming. Real smart energy efficiency is a combination of energy saving and providing for the satisfaction of occupants. However, the comfort of occupants has an uncertainty in prediction and can conflict with preferences of other occupants. So the "Conflict Resolution Architecture" (CRA) is introduced in this paper to solve the conflict among occupants using the integration of RFID systems and sensor network systems. A CRA consists of two control systems that are applied to two separate types of locations. One is the central rule- based control system in a public zone and the other is the individual utility-based control system in a private zone. Two methods that consist of a priority approach and a privilege-average approach are suggested for the CRA control. Then a CRA is able to make conflict resolution among occupants in an intelligent office. (8 pages)
Data convergence is the overall connecting of building control devices by allowing formerly non-interactive agents to share and use common information. Convergence can be best achieved through open control networks; as integration is the key to information exchange. Integration at the building level means access to information. Let's begin by explain before describing networking convergence between LON, LAN and WAN.
Building automation has been hamstrung by the lack of practical and affordable communications technologies, but ZigBee makes it feasible to embed wireless communications into virtually any commercial building automation product. ZigBee-based building automation applications are usually built on resource-constrained 8-bit or 16-bit microcontrollers, with the networking stack and application often sharing the same processor. Many ZigBee-based applications already exist in other forms, often using wired networks or no networking. Consider, for example, a ZigBee wireless thermostat. Its debugging challenges are similar to those posed by standard microcontroller unit (MCU)-based thermostats. But the debugging challenges are compounded when the MCU has to be shared by an application and a network stack, not to mention the non-determinism of wireless networking. As ZigBee systems grow in size, from a single thermostat to a complete building HVAC system, for example, the applications become more distributed with many processors and communications links involved. A new approach to debugging is needed to enable this type of application development.- about developing the wireless technologies and protocols needed (and thinking up innovative names for the options) it is clear that those at the cliff face of product and applications marketing face a major challenge in customer education. The battleground is set between the leading players in the home automation - chief among these are ZigBee and Z-Wave.
While the idea of integrating enabling technology into the fabric of a house at the construction stage is still in its infancy, it is an area where innovations in communications and control are encouraging builders to consider the possibility for new homes of all sizes. One of the companies that have shown interest in this particular field is Siemens Automation & Drives. Siemens is making sure that houses built now will be able to handle anything that future owners might want to install. Siemens offers a range of packages so buyers get what they can afford, but with the potential to upgrade easily. These packages can range from a basic system that turns lights on and off as a security measure to onwater temperature in a fish tank.)
Although electronic devices permeate the home and offer unsurpassed power and features, many impediments still exist to realizing the concept of the smart-home: configuration complexity, minimal device interoperability, difficulty of use, lack of personalization, and no integration with sensors for awareness and adaptation. We define four tenets of smart-home environment: zero-configuration, universality, multi-user optimality, and adaptability (ZUMA), and describe a platform based on a set of clean abstractions for users, content, and devices. The platform enables configuration and organization of content and networked heterogeneous devices in a smart-home environment. We validate the platform with a prototype implementation and analyze its flexibility and infrastructure requirements. )
e MIMOSA project's first user evaluation activities, the scenario evaluations carried out in summer 2004. The scenarios covered five different application fields: everyday, sports, fitness, health care and housing. In this paper the author concentrates on housing scenarios which were evaluated by VTT in Finland and by Legrand in France. According to the evaluation results the main challenge with housing systems is co-operation with existing house information systems. The users are not carrying their mobile phones with them at home, so the mobile phone centric system has to be based on the mobile phone connecting to the existing house network. Preventing problems (water leaks, unwanted visitors etc.) is one of the main positive effects that the potential users of a new technology valued. Extensive automation of home system raised fears but also advantages were noticed particularly for certain user groups. (10)
With this research the author prove that the involvement of other disciplines with architecture, and in this specific project, the daylighting and the material science, can lead to new design solutions and new typologies of leaving. This research is a starting point for the involvement of disciplines with architecture and can be expanded to other sciences as sound engineering, psychology and ergonomics in order to create more intelligent habitable environments. )
This paper proposes home network, which is extension of broad band network to home and an important composing part of NGN, and home gateway, which is the core of home network. The paper analyzes architecture of universal home gateway from hardware and software architecture. Functions of universal home gateway are analyzed and researched deeply from five sides, which include access function, interconnect function, transmission function, core function and service function. .
There has been increased interest in the ZigBee standard, in particular forZigBee Alliance has identified six application spaces for ZigBee: consumer electronics, PC and peripherals, residential/light commercial control, industrial control, building automation and personal healthcare. This article deals mainly with industrial control and building automation. Increasingly, companies developing monitoring environments are looking to wireless technologies like ZigBee to save the cost of wiring and installation and also to allow more flexible deployment of systems.). (7 pages)
A risk based approach offers potential safety benefits but requires active risk management and new competences to achieve the safety goal. The concept of functional safety must be embraced if the potential safety benefits to be derived from emerging electronic technologies are to be realised. IEC 61508/EN 61508 is the key international and European standard for the safe application of complex electronic technologies and adopts a risk based approach. (34 pages)
This presentation will look at the evolution of building automation network infrastructure, the potential for integration into existing IT network infrastructure, the issues to be considered, potential benefits with reference to "Open Standards", reliability and security. (12 pages).ustries, has formulated a proposal for a generic development platform for future-proof CE appliances designed expressly to satisfy these convergence criteria. The company has called its proposal the Unified Digital Platform (UDP). The UDP is designed to support a minimum set of common hardware components, comprising a RISC-based CPU, an MPEG -2 video decoder, an audio decoder (MPEG, Dolby), a graphics engine, a video interface, an audio interface and a network interface. Additional hardware components can be introduced to meet specific application requirements. Central to the UDP design is a data bus, the MM-bus (multimedia bus), designed to support data transfers between the system hardware components at rates consistent with the requirements of real-time A/V applications.
The BluetoothTMexploits the wireless interconnectivity that is possible with radio. Bluetooth chips are already available and early applications include cordless connections from mobile phones to laptop computers and wireless headsets. Even if only 10% of mobile phones and computers were to incorporate Bluetooth chips, then the technology would achieve the critical mass necessary for chip prices to fall below $5 and for the standard to become firmly established in the market-place. It is expected that Bluetooth products will be present in the home, where applications will include: conversion of the humble cellphone to a short-range cordless handset; providing a link from a PC to the Internet; home automation and cable replacement; toys and white goods. This paper introduces the Bluetooth wireless technology and its applications and provides a concise description of its air interface and protocol stack..
Batch and online recursive (ARMAX) models are developed for a three-zoned building management system test facility. Validation of these models is carried out and the practical aspects of implementing the online identification scheme are discussed. The use of the models in industrial context is discussed.
Discusses the role that organisational modelling will have in developing controls for intelligent buildings. The paper suggests a simple organisation model and derives consequential control requirements from this. It is argued that this approach should prove to be of significant benefit for intelligent buildings as their success is dependent upon understanding and pre-empting the demands of the occupants. Organisational classification has a clear role to play in this process having already proven itself to be of benefit in the fields of space planning and service design.
New technology could bring about a revolution in the way care services are delivered. Intelligent building systems take inputs from sensing devices located around a building and then use this information to control devices connected to a network. Our proposed solution is based upon the fact that the physical and logical unit of an intelligent building is a single room. Each room may contain many sensors and output devices which will be controlled and monitored locally by a node. All the nodes are connected together via a network, thus forming a decentralised architecture that enables building-wide collaboration. We utilise AI techniques to produce a system that is capable of learning how to modify itsoccupant's preferences, with only a minimum amount of explicit input from the user or carer. This differs from existing building automation systems, which tend to be pre-programmed by an expert and impose a rigid set of conditions on all of the building's occupants. A behaviour-based approach is used for the internal architecture of the nodes. In our system, each node could be thought of as an agent, with its own set of both fixed and dynamic behaviours. (3 pages)
More efficient and cost-effective use of the built environment is increasingly being driven by economic and environmental pressures requiring reduction of the cost of ownership and emerging solution to these pressures is the creation of innovative IT-enabled solutions.


Ex-soap star 'was in murder gang'
Former Brookside star Brian Regan has been accused of driving an alleged gunman to the scene of a fatal shooting
A former soap exsoap-star-was-in-murder-gang-286774.ece/07f39/AUTOCROP/h342/UK%20News%208-1.jpg
Brian Regan, 53, who played Terry Sullivan in Channel 4's Brookside for 14 years, drove alleged gunman Edward Heffey to and from the scene of the fatal shooting of Bahman Faraji a 44-year-old nightclub doorman, in Liverpool, the city's Crown Court was told.
Mr Faraji was gunned down at close range as he stood outside the Belgrave public house in the Aigburth suburb of the city on the evening of February 24 this year.
Regan, of St Mary's Road, Liverpool, denies murder alongside Heffey, 40, of Beloe Street; Lee Dodson, 42, of Logfield Drive, and Simon Smart, 32, of Kylemore Way, also LiverpoolC . The shooting was orchestrated "at a distance" by Smart acting on the orders of another man, Mr Cummings said.
"Edward Heffey was the gunman who actually shot the victim, and Brian Regan, by arrangement with Lee Dodson, drove Heffey to and from the scene of the shooting, fully aware of the purpose of Heffey's trip," Mr Cummings told the jury. "All the indications were that this was a dirty phone obtained for the purpose of luring Mr Faraji to his death and used in a disciplined way that was designed to leave no clue as to the identity of its user," the barrister said. A short while later a man approached and said to Mr Faraji, 'Are you Ben?', Mr Cummings said.
He added: "Mr Faraji nodded and the man raised what appeared to be a shortened double barrelled shotgun and fired it directly into Mr Faraji's face. There was a cloud of smoke and Mr Faraji fell to the ground.


Another beautiful Mississippi Sunday afternoon, though it's morning. Finally got a shower last night, first one since Monday. Went into Brookhaven, about 40 minutes from here, with Carrie and Jessica. A dry county.. Apparently, that means they can sell at stores, but there are no bars and you can't drink at restaurants. I'm trying very hard not to … judge this state, but it seems messed up! There are hints of Mississippi Burning corruption in the local law enforcement, too. Especially when we called the sheriff about the Deliverance Klan cutting thru fences and stealing the pit-bulls. It was a "boys-will-be-boys" response, after waiting all day for them to show up. Guarding/walking the perimeter at night, I’ve run a few off the cowards off myself. What’s funny is that so have some of the 50-year old women! Brookhaven was full of the stereotypical rednecks. Big trucks, squealing tires etc. Fuck Mississippi.
I was amazed at what the people at the Winn-Dixie parking lot were coping with. They were doing the same things for the animals we were, with far less. Their tent was falling down from the wind. They had ropes tied to the concrete parking barriers because they are in a parking lot and couldn't tie their tents down. Sometimes, out of nowhere, you'd see people actually chasing their tents! I have tons of respect for all of them. I sensed nothing but "get it done."
Rick was blindsided by a large pit bull here on Thursday. That dog was screwed up. When I walked by his cage feeding them; one second he'd seem happy in the next he was trying to tear through the fence to get me. Rick never saw him coming, and was drug 4 or 5 feet by his forearm. He wound up in the hospital all day and now has a brace on his arm. "Ringo" (he had ringworm) was put down that afternoon and tested for rabies. Everyone here seems to be pro-pitbull, but after seeing that dog I understand the laws. Some of the pits are very gentle dogs, but several seemed quite aggressive. Ringo had bitten another handler that same day.