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re-sparked my curiosity , so I immediately started research into what the fixture unit truly was . It was during this research that I came across by complete chance the name of Dr . Steven Buchberger . As I was googling Hunter ’ s Curve , I discovered an ACEEE presentation that Dr . Buchberger had given . In his slides , he had content discussing flow rates , probability , and plumbing codes . I decided I had to see if I could speak with the professor , so I found and then sent him an email . Fortunately , Dr . Buchberger is as kind and generous as he is knowledgeable . He wrote me back immediately and we had a call in which my years of questions about plumbing fixture units and Hunter ’ s Curve were finally answered .
A flow to a faucet ( or any other plumbing fixture ) can either be “ on ” or “ off ” — and is thus binary . When plumbing systems are operated , flows surge at various rates and stop suddenly as people use toilets , sinks , showers , etc ., at various times and places inside a building . Additionally , when water comes out of a faucet , if implemented correctly , it will never go back into the plumbing water system – rather , it goes down the drain to the sewer system . Therefore , it can be said that plumbing water systems operate intermittently and are open – as opposed to heating and cooling systems that have constant flow that never leaves the system ( making plumbing systems more complex ). The challenge then becomes , how does one size a piece of cold water pipe to be able to handle the variations of flow ?
Dr . Hunter realised that each plumbing fixture in a building could be “ on ” or “ off .” For example , if someone is washing their hands the plumbing fixture is on , but then when they turned off the faucet to dry their hands the fixture is off . But more importantly , Dr . Hunter realised there was some measure of probability of the fixture being “ on ” or “ off ” against other fixtures . In a building , the likelihood of every faucet or shower being on is very low , but at a given instance there is a possibility that a certain percentage of plumbing fixtures could be on at the same time . Additionally , Dr . Hunter realised that every plumbing fixture had a different flow rate — a toilet has a higher flow rate than a shower , which in turn has a higher flow rate than a faucet . So , the two variables that Dr . Hunter had to balance against each other were ( 1 ) probability of simultaneous usage and ( 2 ) the flow rate of the fixture . that was very conservative : “ Person A ” steps up to the plumbing fixture , turns it on and / or uses it , turns it off , steps away from the fixture , and is immediately replaced by “ Person B ,” who steps up to the plumbing fixture , turns it on and / or uses it , turns it off , steps away from the fixture , and is immediately replaced by “ Person C ,” and so on . In essence , the situation to determine probability Dr . Hunter had in mind was probably an opera house or theater at intermission ( In current times , we would probably imagine a sports stadium at halftime ). In essence it was a lot of people wanting to use a small number of bathrooms , all at the same time . What this type of usage equates , in a mathematical sense , is a 99.9 % probability of simultaneous use . Going with a simultaneous usage less than 100 % allowed for drastic reduction in plumbing domestic water pipe sizing : for example , assuming 25 gallons per minute of flow per water closet , for a bank of 10 water closets the breakdown in flow and pipe sizing would be as follows :
A 100 % simultaneous usage would equate to 250 gpm , which equals a 4-inch water supply to the 10 water closets . A 99 % simultaneous usage ( Dr . Hunter ’ s Curve ) would equate to 75 gpm , which equals a 2-inch water supply to the 10 water closets .
By reducing the estimated simultaneous usage by this fraction , the pipe size in this instance was able to be reduced to half the original size . The result was significant cost savings due to lower pipe material costs . This was one of the main driving factors , if not the driving factor for the U . S . Commerce Department in hiring Dr . Hunter : to make building construction less expensive . PA
Dr . Hunter ’ s brilliant inspiration in 1940 was to create a benchmark unit that consolidated first the probability of a fixture being on and then the flow rate . To create a new unit of measurement , he needed to create a baseline for all the plumbing fixtures against which to be compared . His choice was well thought out : the toilet ( or professionally known as a “ water closet ”). The water closet has the highest peak flow rate of any plumbing fixture . This meant that if Dr . Hunter could determine the worst-case scenario accurately , he could confidently base all other fixtures off of this one unit , saving him immense time while also giving him confidence that he had enough safety factor to mitigate failure based on flow .
So having his base unit of flow identified , Dr . Hunter moved on to trying to identify the correct probability of two or more plumbing fixtures being on at the same time . He didn ’ t want to underestimate the occurrence of multiple fixtures being on at the same time , as he realised this would cause massive pressure drops inside buildings . Again , going back to his base fixture , the toilet , he ended up using a measure of probability
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