Improving Contaminant Mixing Models For Water Distribution Pipe Networks

1. Source: http://toxics.usgs.gov/highlights/gw_cessation.html 8 8 6 6 3 3 7 5 7 5 Sensor Locations Sensor Locations 4 4 2 1 2 1 8 8 6 6 3 7 5 3 7 5 Sensor Locations Sensor Locations 4 4 2 1 2 1 Improving Contaminant Mixing Models For Water Distribution Pipe Networks Clifford K. Ho Sandia National Laboratories Albuquerque, NM ckho@sandia.gov Siri Sahib S. Khalsa University of Virginia Charlottesville, VA khalsa@virginia.edu Ongoing Research Development of New Mixing Model Problem Understanding and predicting solute transport through water distribution pipe networks are important to mitigate potential contamination events Flow Simulation • Analysis of mixing behavior in other pipe-junction configurations 3-D simulations with computational fluid dynamics software reveal incomplete mixing within junctions, contrary to the assumption of the current mixing model • Bulk fluid momentum is retained in the mixing process • Our new mixing model honors bulk advective transport as a lower bound to the amount of mixing in a junction • A mixing parameter is used to scale the results between the complete-mixing and bulk-mixing model predictions Much of the water distribution industry relies on the Environmental Protection Agency’s water quality simulation software (EPANET) Clean Inlet Clean Inlet Tracer Inlet Tracer Inlet U Junction Double-T Junction • Inverse modeling to calibrate combined mixing model to pipe-network data However, EPANET incorrectly assumes contaminants mix instantaneously and completely in junctions, leading to potentially inaccurate transport predictions • Development of mixing parameter regressions as functions of different flow conditions Equal Flow Rates Unequal Flow Rates Implementation and Data Testing • Validation of combined mixing model using both large-scale and laboratory pipe-network data The new mixing model was implemented into EPANET and used to predict contaminant concentrations at various sensor locations in laboratory pipe networks Objective and Approach Clean Water 3x3 Network: Tracer Inlet Flow Rate > Clean Inlet Flow Rate Sensor Concentrations in 3x3 Network • Working with the Environmental Protection Agency to implement and distribute our new models Objective: Predict concentrations resulting from two pipe flows intersecting at a junction Tracer Inlet Flow Rate > Clean Inlet Flow Rate New Mixing Model Solution Original EPANET Solution Normalized Concentration ContaminatedWater Conclusions Employ computational fluid dynamics software to study contaminant mixing in cross junctions, develop a new mixing model, and implement it into EPANET Sensor Sensor Concentrations in 3x3 Network 3x3 Network: Clean Inlet Flow Rate > Tracer Inlet Flow Rate • Mixing in a cross junction is incomplete and physically bounded by predictions of the complete-mixing and bulk-mixing models • Our new models have been implemented into EPANET, and results show significant improvement in contaminant transport predictions Clean Inlet Flow Rate > Tracer Inlet Flow Rate New Mixing Model Solution Original EPANET Solution Normalized Concentration Use laboratory data to test and improve our new mixing model Sensor Predictions by EPANET implemented with the new mixing model agree with laboratory measurements when appropriate mixing parameters are used Sandia is a multiprogram laboratory operated by Sandia Corporation, a Lockheed Martin Company, for the United States Department of Energy’s National Nuclear Security Administration under Contract DE-AC04-94AL85000.