Peak Water Demand Study: Development of Metrics and Method for Estimating Design Flows in Buildings ACEEE 2016 HOT WATER FORUM: PORTLAND, OR TORITSEJU OMAGHOMI, STEVEN BUCHBERGER, TIMOTHY WOLFE, JASON HEWITT, AND DANIEL COLE FEBRUARY 22 ND, 2016 1
Outline Background Task Group Water Use Database Key Parameters and Considerations Application Conclusion 2
BACKGROUND 3
GPM Hunter s Curve High efficiency fixtures = lower q 1940 Today, Hunter s curve is often faulted for giving overly conservative design.why? 2016 Uncongested use = lower p Fixture Units 4
Changing Times 5
Modified Hunter s Curve(s) Different curve for different end users http://www.armstronginternational.com/files/products/wheaters/pdf/sizing%20chart.pdf 6
TASK GROUP 7
IAPMO Task Group Orders.will work singularly to develop the probability model to predict peak demands based on the number of plumbing fixtures of different kinds installed in one system. (Bring Hunter into 21 st Century) 8
Project Activities Acquire Data Analyze Data Develop Model Peer Review & Publication of Report 9
WATER USE DATABASE 10
Database: Location of Homes Over 1000 households Surveyed between 1996-2011 2,800 residents 11,350 monitoring days 11
Percentage Database: Summary of Measured Data 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Water Use Event Duration Volume Bathtub Clothes washer Dishwasher Faucet Shower Toilet Others Leak Six unique fixtures 862,900 water use events (excluding leaks) 1,591,700 gallons 889,700 minutes -Evaporative Cooler 12
Database: Details of Water Use at Fixture FREQUENCY OF FIXTURE USE PER CAPITA PER DAY VOLUME OF WATER USE PER CAPITA Bathtub 0.5% Clothes washer 3.2% Toilet 25.8% Bathtub 5.2% Toilet 18.8% Dishwasher 1.5% Clothes washer 25.0% Dishwasher 1.9% Shower 2.5% Faucet 73.5% Shower 21.9% Faucet 20.2% 13
Database: Residential Fixture Classification Fixture* Ultra-Efficient Efficient Inefficient Clothes washer (gallons /load) Dishwasher (gallons/cycle) Shower (gallon/minute) Toilet (gallon/flush) *Bathtubs and faucets were excluded < 20 20 30 > 30 < 1.8 1.8 3.0 > 3.0 < 2.2 2.2 2.5 > 2.5 < 1.8 1.8 2.2 > 2.2 14
KEY PARAMETERS & CONSIDERATIONS 15
Key Fixture Characteristics 1-2 - n: q: Fixture Count Fixture flow rate 1 2 3... x.... n 3 - p: Fixture Probability of use p T t i 16
Computed Fixture Flow Rate; q q Volumeof each pulse ( gallons) Duration of each pulse minutes ( ) 17
Flow rate (gpm) Clothes Washer Flow Rate 7 6 5 4 3 2 1 Mean Outliers Recommended 0 Ultra C. (16.9%) Efficient C. (17.2%) Inefficient C. (65.9%) Fixture classification (Percentage of sampled homes) Clothes washer efficiency measured in gallons/load 18
Flow rate (gpm) Dishwasher Flow Rate 2.0 1.8 1.6 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0 Ultra D. (24.6%) Efficient D. (48.5%) Inefficient D. (26.9%) Mean Outliers Recommended Fixture classification (Percentage of sampled homes) Dishwasher efficiency measured in gallons/cycle 19
Flow rate (gpm) Shower Flow Rate 5.0 4.0 3.0 2.0 1.0 Mean Outliers Recommended 0.0 Ultra S. (67.5%) Efficient S. (15.0%) Inefficient S. (17.5%) Fixture classification (Percentage of sampled homes) Shower efficiency measured in gallons/minute 20
Flow rate (gpm) Toilet (Water Closet) Flow Rate 6.0 5.0 4.0 3.0 2.0 1.0 Mean Outliers Recommended 0.0 Ultra T. (21.5%) Efficient T. (24.9%) Inefficient T. (53.6%) Fixture classification (Percentage of sampled homes) Toilet efficiency measured in gallons/flush 21
Flow rate (gpm) Bathtub Flow Rate 10 9 8 7 6 5 4 3 2 1 0 Bathtub (100%) Mean Outliers Recommended Fixture (Percentage of sampled homes) 22
Flow rate (gpm) Faucet Flow Rate 2.5 2.0 1.5 1.0 0.5 Kitchen and Laundry sink Bathroom sink Mean Outliers Recommended 0.0 Faucet (100%) Fixture (Percentage of sampled homes) 23
Flow rate (gpm) Summary of Efficient Fixture Flow Rate 10 9 8 7 6 5 4 3 2 1 0 Mean Outliers Recommended Bathtub Clothes Washer Toilet Shower Faucet Dishwasher Efficient = Ultra-efficient and Efficient Fixtures 24
Daily Volume (gallons) Peak Hour of Fixture Use (by Volume) 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 Shower 07 Toilet 07 Clothes washer 10 Faucet 18 Bathtub 19 Dishwasher 20 00 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Hour of Day Average hourly volume of water use at a fixture per home per day 25
Volume (gallons) Single Home 12 Day Water Use Profile (10S761) 400 350 300 250 200 150 100 50 0 Peak Hour Clothes washer Dishwasher Faucet Shower Toilet Total Volume 00 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Hour of Day 26
Average Volume daily (gallons) volume (gallons) Multiple Fixture peak Homes hour Water of water Use Profile use (by (by Volume) 30 25 20 15 10 5 0 Home #1; Average hourly volume Home of water #2; consumed at each fixture Home #3; 10S761 10S764 Cal_Fed-15151 50 Clothes washer; 10 45 Shower; 07 Dishwasher; 20 40 35 Faucet; 18 Toilet; 07 Bathtub; 19 30 25 20 15 10 5 0 00 01 00 02 01 03 02 04 03 05 04 06 05 07 06 08 07 09 08 09 10 10 11 11 12 12 13 13 14 14 15 15 16 16 17 17 18 1819192020212122222323 Hour of Day Hour of Day 27
Percentage (%) Distribution of Peak Hour of Water Use 15 151 homes (N = 1,038 Homes) 10 57 homes 5 2 homes 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Hour of Day 28
Peak Hour Probability of Fixture Use Focused on only efficient fixtures t i i p n D T th ti Duration of i water use event n Number of fixtures D Number of observation days T 60 minutes ( 1hour observation window) 29
Probability of Fixture Use at Individual Homes p t i i n D T p 10 minutes 1 fixture 1day 60minutes 0.17 Considered only efficient fixtures 30
Probability of Fixture Use Group of Homes Number of residents Positive Correlation Number of bathrooms Number of bedrooms 31
Probability of Shower Use N = 100 N = 365 N = 142 N = 141 N = 58 N = 18 N = 13 Recommended Value = 0.025 I Group weighted average 32
Frequency Distribution of Shower p values for Homes with 2 residents 160 140 120 100 80 60 40 20 0 147 Recommended value (N = 365) 76 69 35 14 4 5 7 2 1 1 3 1 0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1 0.11 0.12 Probability 33
Summary Probability of Fixture of Use Each fixture has a unique water use profile. Probability of fixture use does not dependent on how frequent a fixture used. Probability of fixture use increased as the number of residents increased. 34
Design Fixture Probability Values p = 0.050 p = 0.010 p = 0.025 p = 0.005 35
APPLICATION 36
Binomial Model exactly xbusy n x Pr p (1 p ) out of nfixtures x n x *n in a big buildings Hunter did this 37
Frequency Factor Approach x Mean z Standard Deviation 0.99 99 th Percentile 38
Normal Approximation of Peak Flow Q z 0.99 q 0.99 q K 1 2 Q n p q z n p p q 0.99 k k k 0.99 k k k k k 1 k 1 K - Wistort (1995) 39
Binomial Distribution *n in a small buildings 40
Zero Truncated Binomial Distribution (ZTBD) 41
Modified Wistort s Model 2 1 K K K 2 Q0.99 nk pkqk z0.99 1 P0 nk pk 1 pk qk P0 nk pkqk 1 P 0 k 1 k 1 k 1 Note: k P0 1 p k 1 n k K 1 2 Q n p q z n p p q 0.99 k k k 0.99 k k k k k 1 k 1 is probability of stagnation in a home (i.e. no water use) Addresses water demand in single family homes with high P 0 Transitions back to Wistort s model as P 0 approaches 0 K 42
Hypothetical Residential Building Pipe Layout 43
Peak Flow Calculations Example: 2.5 bath single family home Section UPC FU UPC Method (gpm) MW Method (gpm) 1 25.5 18.0 11.8 2 10.5 8.5 6.9 gpm - gallons per minute 44
Sizing Pipes Example: 2.5 bath single family home Section UPC Method Pipe Size * MW Method Pipe Size * 1 18.0 gpm 1 11.8 gpm 3/4" 2 8.5 gpm 3/4" 6.9 gpm 3/4" * Maximum flow rate at 8 f/s 45
Demand Sensitivity to p and q values 46
CONCLUSION 47
Conclusion Introduced a computational model to estimate peak water demand in single and multi-family dwellings. Recommended fixture p and q values. Model applicable to a wide spectrum of buildings. Single Family Homes 48
Questions? omaghotu@mail.uc.edu University of Cincinnati 49
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