Author(s): Haochen Li; John J. Sansalone

Linked Author(s): John J. Sansalone

Keywords: No Keywords

Abstract: DeepXtorm combines computational fluid dynamics (CFD) and machine learning (ML) as a modeling platform (CFD-ML) developed from physical model data and CFD simulations over a wide range of urban drainage clarifier configurations, loadings, hydrodynamics and particulate matter (PM) granulometry. A novel augmentation of CFD with ML models is developed and trained to create surrogate clarification models. For a clarifier, this CFD-ML platform facilitates (1) analysis, (2) design optimization, and (3) optimization of clarifier retrofits to minimize cost for a required level of clarification. Results with CFD-ML benchmarking indicate that: (a) historical models based on residence time (RT) are not accurate or generalizable for clarifier PM separation, (b) RT models are agnostic to geometrics, hydrodynamics and PM granulometry; and do not reproduce PM separation, (c) trained ML models provide high predictive capability (± 15%) for PM separation. Dynamic similitude analysis indicates that clarification is primarily a function of the Hazen number and clarifier horizontal to vertical aspect ratio. With a common presumptive guidance of 80% for PM separation, a Pareto frontier analysis with the CFD-ML model generates significant economic benefit for planning/design/retrofits. CFD-ML demonstrate that enlarging clarifier dimensions (increasing RT) to address impaired behavior can result in exponential cost increases, irrespective of infrastructure adjacency conflicts.

DOI:

Year: 2022