Haochen Li

Assistant Professor

Biography

His lab, Water Infrastructure Laboratory (Ψ Lab), leverages and integrates physical modeling (e.g., particle image velocimetry, PIV), first-principles simulations (e.g., computational fluid dynamics, CFD), and artificial intelligence (AI) techniques (e.g., machine learning, ML) to address challenging issues in the urban water cycle and sustainable development. The missions of the Ψ Lab are to advance multiphysics and multiphase flow prediction in natural and built water infrastructures and to enable robust and efficient next-generation water infrastructure planning, design, optimization, and management.

Research

• Urban Hydrology
• Water Treatment
• Environmental Flow
• Computational Fluid Dynamics
• Physics-informed Machine Learning

Education

• PhD in Environmental Engineering, University of Florida, 2019
• MS in Mechanical Engineering, University of Florida, 2019
• MS in Civil Engineering, University of Florida, 2015
• BS in Coastal Engineering, Hohai University, 2013

Professional Service

• Member, ASCE/EWRI Computational Fluid Dynamics Committee
• Reviewer for Water Research, International Journal of Multiphase Flow, Powder Technology, Journal of Environmental Engineering, Environmental Science and Pollution Research, and npj Clean Water, and Micromachines.

Awards and Recognitions

• Rudolph Hering Medal, ASCE, 2023
• Editor choice, Journal of Environmental Engineering ASCE, 2021
• Editor choice, Journal of Environmental Engineering ASCE, 2020
• Graduate School Fellowship, University of Florida, 2015
• Academic Achievement Award, University of Florida, 2013

Publications

Recent publications are listed below. A full list of publications is available on Google Scholar.

Li, H., & Sansalone, J. (2022). Implementing machine learning to optimize the cost-benefit of urban water clarifier geometrics. Water Research, 220, 118685. https://doi.org/10.1016/J.WATRES.2022.118685

Li, H., & Sansalone, J. (2022). A CFD-ML augmented alternative to residence time for clarification basin scaling and design. Water Research, 209, 117965. https://doi.org/10.1016/J.WATRES.2021.117965

Li, H., & Sansalone, J. (2022). Interrogating common clarification models for unit operation systems with dynamic similitude. Water Research, 215, 118265. https://doi.org/10.1016/J.WATRES.2022.118265

Li, H., & Sansalone, J. (2022). InterAdsFoam: An Open-Source CFD Model for Granular Media–Adsorption Systems with Dynamic Reaction Zones Subject to Uncontrolled Urban Water Fluxes. Journal of Environmental Engineering, 148(9), 04022049. https://doi.org/10.1061/(ASCE)EE.1943-7870.0002027

Li, H., Balachandar, S., & Sansalone, J. (2021). Discordance of Tracer Transport and Particulate Matter Fate in a Baffled Clarification System. Journal of Fluids Engineering, 143(5), 051202. https://doi.org/10.1115/1.4049690

Li, H., Balachandar, S., & Sansalone, J. (2021). Large-eddy simulation of flow turbulence in clarification systems. Acta Mechanica, 232(4), 1389–1412. https://doi.org/10.1007/s00707-020-02914-1

Li, H., Spelman, D., & Sansalone, J. (2021). Baffled clarification basin hydrodynamics and elution in a continuous time domain. Journal of Hydrology, 595, 125958. https://doi.org/10.1016/j.jhydrol.2021.125958

Li, H., & Sansalone, J. (2021). Benchmarking Reynolds-Averaged Navier–Stokes Turbulence Models for Water Clarification Systems. Journal of Environmental Engineering, 147(9), 04021031. https://doi.org/10.1061/(ASCE)EE.1943-7870.0001889

Li, H., & Sansalone, J. (2021). CFD with evolutionary optimization for stormwater basin retrofits. Journal of Environmental Engineering, 147(7), 04021017. https://doi.org/10.1061/(ASCE)EE.1943-7870.0001881

Li, H., & Sansalone, J. J. (2021). Representation of near-wall particle fate in a Eulerian-Lagrangian approach for clarifier unit operations. Journal of Environmental Engineering, 147(7), 04021019. https://doi.org/10.1061/(ASCE)EE.1943-7870.0001887