Real World Variables University Wind Studies Part 1. Randy Montgomery

Transcription

1 Real World Variables University Wind Studies Part 1 Randy Montgomery

2 Research Purpose Current and proposed legislation is looking at all kinds of irrigation efficiency numbers EPA WaterSense Single Family New Homes Specification requires a minimum 65% DU LQ as measured by the largest spray-irrigated area. In California, AB 1881 (California Model Efficient Landscape Ordinance) requires a system level irrigation efficiency of Some contend precipitation rate alone equates efficiency It seems every other day a group or committee is posting recommendations for how to qualify a product resulting in more real-use water savings/efficiencies This is all well intended but what is glaringly missing? The REAL WORLD! How does the real world effect these magical numbers? Do they hold up when you take product outside? Isn t it about time we stop fearing complexity!?

3 Why Do This? Rain Bird is committed to the intelligent use of water Rain Bird desires to continue to lead in the area of efficiency and efficient products If Rain Bird hangs their hat on any metric, Rain Bird wants to know it is technically accurate not just the latest marketing gimmick Rain Bird wants to find and point out limitations in their product (before someone else does)

4 What Is Rain Bird s Intention? Begin a series of real world testing to determine how our products stand-up to efficiency metrics Historically, technology limitations made out-door testing difficult Repeatability is certainly questionable, however The first variable we desire to understand is the effects of real wind on typical efficiency metrics DU numbers are determined in a zero-wind building. What happens to distribution uniformity outside? We also wanted to look at how badly the pattern gets shifted due to wind. You have to get water in the target (root) zone! Rain Bird is partnering with Universities to run independent studies and analyze the results to ensure scientific and rationale conclusions Multiple Universities have been chosen in different locations to obtain differing wind speeds and to increase statistical relevance of results

5 Round 1 (Summer Season) Independent testing is being conducted at the University of Arizona to determine the effect of wind on nozzle efficiency Summer season results complete; Winter season in progress Average wind differs in Arizona between these two seasons The primary objective is to begin to paint a picture and determine how wind effects efficiency metrics Distribution Uniformity versus wind speed Application efficiency (AE) = how much of the water thrown actually made it into the target zone versus wind speed Secondary Objective is to see how Rain Bird nozzles compared to each other as well as to a leading competitor Comparing Rain Bird to Rain Bird helps us to understand and validate design advantages, such as our value nozzle versus our high efficiency nozzle

6 Analysis Definitions Application Efficiency The amount of water in the catch-cans divided by the total amount of water applied, as provided by the plot supply meter INTERPRETATION: Ideally, we want as much of the water supplied to end up in the target zone. Any amount that does not end up in the target zone was most likely lost through misting, evaporation or water pushed out of the zone by wind. As wind increases, it is expected that most of the water loss is due to pattern being pushed out of the target zone. If water doesn t make it into the target zone, does distribution uniformity really matter? Distribution Uniformity How evenly water is applied to the zone

7 The Setup Three head-to-head trials will be executed during each season Trial 1: Rain Bird HEVAN versus Toro Precision Nozzle Trial 2: Rain Bird MPR versus Toro Precision Nozzle Trial 3: Rain Bird U-Series versus Toro Precision Nozzle Each nozzle model is given qty 4 12x12 plots next to each other. Each plot has its own water meter and is calibrated to provide ½ inch of water per irrigation cycle. Each nozzle was installed on a PRS stem to ensure optimal performance. Each plot starts irrigation at the same time each day, at least 3 times per week. Irrigation cycles set in the morning, per widely held belief this is the best time to do so for multiple rationales. During each irrigation event, a weather station records temperature, wind direction, humidity, and wind speed every 15 seconds. Standard catch-cans are installed at grade level and measurements are recorded immediately after every irrigation event. Data was analyzed after every run to track and determine if more runs are necessary to ensure statistical relevance.

8 Spray Heads The Setup 4-12 x12 plots per product per trial Inner 16 catch cans Outer 32 edge catch cans Weather Station Each plot has it s own valve and water meter

9 The Results MPR vs. TPN

10 Application Efficiency A measure of how much of the actual water thrown made it into the target zone Application Efficiency vs. Wind Speed MPR 12Q vs. TPN O-12-Q (All 32 Cups) 100% 90% 80% MPR 12Q TPN O-12-Q Wind Speed Application Efficiency (%) 70% 60% 50% 40% 30% 20% Wind Speed (MPH) 10% 0% 29 Jun 2 Jul 3 Jul 5 Jul 6 Jul 9 Jul 10 Jul 11 Jul 12 Jul 13 Jul Irrigation Dates 0

11 Distribution Uniformity (Lower Quarter) A measure of how even the water is applied to the target zone Distribution Uniformity, Low Quarter (DUlq) vs. Wind Speed MPR 12Q vs. TPN O-12-Q (Middle 16 Cups) 100% 2.5 MPR 12Q Distribution Uniformity (%) 90% 80% 70% 60% 50% 40% 30% Wind Speed (MPH) TPN O-12-Q Wind Speed 20% % 0% 29 Jun 2 Jul 3 Jul 5 Jul 6 Jul 9 Jul 10 Jul 11 Jul 12 Jul 13 Jul Irrigation Date 0

12 Data combined and Sorted by Increasing Wind Speed 100% 90% Efficiency Metrics vs. Wind Speed MPR 12Q vs. TPN O-12-Q (All 32 Cups for AE) 90% 80% Note Potential Reversal of DU Performance as wind increases 80% 70% Application Efficiency (%) 70% 60% 50% 40% 30% 60% 50% 40% 30% Distribution Uniformity (LQ) AE - MPR 12Q AE - TPN O-12-Q DU - MPR 12Q DU - TPN O-12-Q 20% 20% 10% 10% 0% Wind Speed (MPH) 0%

13 Conclusions? MPR vs. TPN This trial experienced primarily calm winds Not enough information to really draw any conclusions. One data point of interest (in light of data yet to be revealed) Final data points show MPR close the gap of performance and outperform TPN in DU at 2 MPH. At higher winds, could the MPR consistently outperform the TPN in both metrics? Does a tipping point of performance exist? 70% 60% 50% 40% 30% ribution Uniformity (LQ)

14 The Results U-Series vs. TPN

15 Application Efficiency A measure of how much of the actual water thrown made it into the target zone Application Efficiency vs. Wind Speed U-12Q vs. TPN O-12-Q (All 32 Cups) 100% 90% 80% U-12Q' TPN O-12-Q Wind Speed Application Efficiency (%) 70% 60% 50% 40% 30% 20% 10% Wind Speed (MPH) 0% 31 Jul 1 Aug 2 Aug 3 Aug 6 Aug 7 Aug 8 Aug 9 Aug 9 Aug 10 Aug Irrigation Dates 0.0

16 Distribution Uniformity (Lower Quarter) A measure of how even the water is applied to the target zone Distribution Uniformity, Low Quarter (DUlq) vs. Wind Speed U-12Q vs. TPN O-12-Q (Middle 16 Cups) 100% 90% 80% U-12Q TPN O-12-Q Wind Speed Distribution Uniformity (%) 70% 60% 50% 40% 30% 20% 10% Wind Speed (MPH) 0% 31 Jul 1 Aug 2 Aug 3 Aug 6 Aug 7 Aug 8 Aug 9 Aug 9 Aug 10 Aug Irrigation Date 0

17 Data combined and Sorted by Increasing Wind Speed Efficiency Metrics vs. Wind Speed U-12Q vs. TPN O-12-Q (All 32 Cups for AE) 100% 90% 80% 90% 80% 70% AE - U-12Q AE - TPN O-12-Q DU - U-12Q DU - TPN O-12-Q Application Efficiency (%) 70% 60% 50% 40% 30% 20% 60% 50% 40% 30% 20% Distribution Uniformity (LQ) 10% 10% 0% Wind Speed (MPH) 0%

18 Conclusions? U-Series vs. TPN This trial experienced even more calm winds than the other two trials Not enough information to really draw any conclusions. Really need stronger winds to dig deeper into this head-to-head

19 The Results HE-VAN vs. TPN

20 The Results HEVAN vs. TPN This trial experienced higher winds than normal for this time of year This trial experienced higher winds than the other two trials Other two trials maxed out at 2 MPH This trial provided the most interesting data!

21 Application Efficiency A measure of how much of the actual water thrown made it into the target zone Application Efficiency vs. Wind Speed HE-VAN-12 vs. TPN O-12-Q (All 32 Cups) 100% 10.0 HEVAN 12' 90% 9.0 TPN O-12-Q Wind Speed 80% 8.0 Application Efficiency (%) 70% 60% 50% 40% Wind Speed (MPH) 30% % % 1.0 0% 16 May 17 May 18 May 23 May 23 May 24 May 25 May 29 May 30 May 31 May 11 Jun 12 Jun 13 Jun Irrigation Dates 0.0

22 Distribution Uniformity (Lower Quarter) A measure of how even the water is applied to the target zone Distribution Uniformity, Low Quarter (DUlq) vs. Wind Speed HE-VAN-12 vs. TPN O-12-Q (Middle 16 Cups) 100% 90% 80% HEVAN 12' TPN O-12-Q Wind Speed Distribution Uniformity (%) 70% 60% 50% 40% Wind Speed (MPH) 30% % % 1.0 0% 16 May 17 May 18 May 23 May 23 May 24 May 25 May 29 May 30 May 31 May 11 Jun 12 Jun 13 Jun Irrigation Date 0.0

23 Let s compare this information to some major cities *Average MPH Per Month Region City Years Averaged JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC Southwest Phoenix, AZ Southwest Los Angeles, CA Southwest San Diego, CA Pacific Northwest Seattle, WA Rocky Mountains Denver, CO South Houston, TX South Dallas, TX Midwest St. Louis, MO Great Lakes Chicago, IL Great Lakes Indianapolis, IN Southeast Atlanta, GA Southeast Orlando, FL Northeast Norfolk, VA *Wind speed from On average, most major cities experience greater than 4 MPH wind year-round Digging deeper into the data reveals, on average, most major cities have greater than 3 MPH wind 24-hours a day This testing at the University of Arizona was performed in the region with the lowest average wind speed per month

24 In the Real World the HEVAN Maintains Higher Efficiency versus TPN Efficiency Factors vs. Wind Speed HE-VAN-12 vs. TPN O-12-Q 100% 90% "Real World" 80% 70% 80% 70% 60% Application Efficiency (%) 60% 50% 40% 50% 40% 30% Distribution Uniformity (LQ) AE - HEVAN 12' AE - TPN O-12-Q DU - HEVAN 12' DU - TPN O-12-Q 30% 20% 20% 10% 10% 0% Wind Speed (MPH) 0%

25 How did it get concluded it was pattern drift? Product LQDU LHDU AE16 AE32 HE-VAN-12 52% 72% 81% 78% Toro O-T-12Q 40% 64% 62% 60% Water at Perimiter vs. Wind Speed HE-VAN-12 vs. Toro O-T-12Q Inches Wind Speed (MPH)

26 Drawing Conclusions Only the HEVAN versus the TPN trial had enough wind variation to draw solid conclusions we are hoping for higher winds during the winter season trials At just a 5 MPH wind (low edge of real-world ) The Toro TPN gets 55% of the water thrown into the target zone versus HEVAN s 78% application efficiency The Toro TPN is knocked down 13 more distribution uniformity percentage points than the HEVAN In the long run, you will have to run the Toro TPN longer to get the needed amount of water in the driest part of the target zone which will do more than just cancel out any savings estimates A study performed by Inland Empire Utilities Agency and the Chino Basin Water Conservation District found during a one-year study that the Toro TPN s used 15 more acre-feet per year of water after switching from a standard precipitation rate nozzle to the Toro TPN. The study cited numerous possible explanations.

27 Take Away(s) from this Independent Scientific Research? Manufacturer efficiency numbers are typically determined in a zero-wind building Although product s efficiencies appear to be the same, there can be drastic differences in the real world Nozzle design accounts for a large amount of real-world performance differences Most nozzles will have a tipping point in which wind will cause a drastic decline in performance Design effects average water droplet size which determines amount of pattern drift due to wind, evaporative rates, misting, etc. Trade-offs exist to satisfy market demands (such as lower precipitation rates); however, those trade-offs may come at the expense of other factors such as average water droplet size (see previous sub-bullet) Any claimed savings can quickly be wiped away in the realworld

28 What s Next? The University of Arizona is currently running the winter season trials. Once data is available, it will be compiled with these results to determine further statistical relevance. The University of Florida is currently repeating this study to validate The University of Arizona s findings and to hopefully provide higher wind profiles

29 QUESTIONS? This is just the start I hope you join me for the next webinar (stay tuned) where I will consolidate all of the final University wind study information I also hope you will also join me for the studies we plan on running after the University wind studies; where we will look at other Real World Variables If you would like a copy of the full report on The University of Arizona s summer season testing, please contact me. Randy Montgomery rmontgomery@rainbird.com Direct Line: