AWWA Water Science Author Spotlight: Jonathan B. Burkhardt

Abstract

Having recently published an article in AWWA Water Science, Jonathan B. Burkhardt answered questions from the publication's editor-in-chief, Kenneth L. Mercer, about the research.

Pressure-Dependent Analysis in Premise Plumbing System Modeling

Jonathan B. Burkhardt, John Minor, Feng Shang, and William E. Platten III

Jonathan Burkhardt is an environmental engineer in the US Environmental Protection Agency's Office of Research and Development.

I earned PhD, MS, and BS degrees in chemical engineering from the University of Cincinnati. My undergraduate education included a cooperative learning experience, where I worked in a chemical manufacturing facility. Following graduate school, I participated in an ORISE (Oak Ridge Institute for Science and Education) post-doctoral fellowship with the EPA, conducting research into event detection software and multispecies water quality modeling. I also routinely participate in AWWA and ASCE (American Society of Civil Engineers) conferences to keep up to date with current research.

For models to be effective, they typically require a theoretical foundation (or the mathematical formulas on which they are built) and usually require information about the parameters that make those formulas relevant for appropriate conditions or scenarios. Much of my work focuses on developing appropriate parameters that make the models work.

I am also trying to understand whether the parameters or models are valid for what we are studying. For our recent paper, we focused on trying to determine parameters for predicting how the pressure at faucets affected the rate at which water was supplied by those faucets. This information is important because it helps us improve those predictions in the EPANET (a public domain software application for modeling water distribution systems) network modeling and more accurately predict flow rates as pressures change in those premise plumbing (home and building) systems.

Jon enjoys a hike through Turkey Run State Park in Indiana.

We did not really use any new techniques in this work but rather relied on available pressure and flow sensors attached to a data logger to help capture the data we reported. The results of this work do provide valuable information to the larger research project about premise plumbing modeling and are being incorporated in our model development.

As research goes, this research went fairly smoothly. Using automated data collection required a little work up front to make sure we could read that data into our scripts correctly, but this was generally an easy and expected step. The differences in pressure/flow relationships for various fixtures was an expected outcome. The only real challenge was isolating cold- or hot-only sides of the shower mixing valve; luckily, this was easily overcome with adjustment screws that are present on the mixing valve.

This work was part of a larger effort to improve modeling of premise plumbing systems, and the results will be used to inform future modeling in our research. We plan to use the developed parameters from this work to conduct additional model scenarios for simulating various conditions in home or building plumbing. Since flow through these systems is dictated by the pressure/flow responses we explored, our parameters can help improve future predictions on how much actually comes out during use.

Our goal is to leverage these results to improve predictions around the fate and transport of water and associated water quality concerns in premise plumbing systems. We have associated research looking at dispersion modeling, which requires accurate predictions of flow rates, and these parameters will directly inform that modeling effort.

I enjoy hiking and have been trying to visit many state and national parks. I also enjoy photography—specifically nature photography. I have also built some furniture and enjoy other woodworking and DIY projects.

I think modeling is a valuable tool in a water engineer's toolbox. Most current computers can support a variety of models, even computationally intensive ones, and we can get access to online cloud resources in many cases to handle more complex jobs. Models need data, and we are at a stage where data acquisition is getting more accessible, and we can leverage the data to improve overall decision-making with respect to the water sector. The areas of digital twins and machine learning or artificial intelligence and associated models will hopefully help water utilities to optimize their operations.

Water is complex, but continuing to build understanding about how various decisions affect water delivery and its quality will only benefit the water industry and its customers. Availability of quality data will serve to help with that, and quality data will help improve modeling and associated results or decision-making. More data can also be a challenge because we need to make sure it gets processed efficiently, but I think that leads to more opportunities for engineers or others to create the next generation of solutions in this area.

To learn more about Jonathan's research, visit the article, available online at https://doi.org/10.1002/aws2.1344.