Our mission

Nexgen water infrastructure

To advance multiphysics and multiphase flow prediction in natural and built water infrastructures, and to enable robust and efficient NexGen water infrastructure design, optimization, and retrofit.

Our research

(1) leverages the high-performance computing (HPC) and high-order numerical method to elucidate the underlying turbulence physics and physicochemical fate dynamics of particulate matter, pathogens, and chemicals in fluid systems; (2) develops and benchmarks a wide range of open-source computational fluid dynamics (CFD) tool for water infrastructure simulation, optimization, uncertainty quantification and vulnerability assessment; (3) integrates machine learning (ML) and CFD for robust and efficient water infrastructure optimization and retrofit, and create CFD-ML augmented framework for engineering practices and environmental regulations.

  • State-of-the-art physical modeling facility for multiphase turbulent flow

    A fully self-sustaining, energy-efficient hydraulic laboratory equipped with volumetric Particle Image Velocimetry (PIV) systems.

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  • Higher-fidelity computational fluid dynamics (CFD) simulation of multiphase and multiphysics flow

    Turbulence resolving simulation based on high-order spectral element method(SEM).

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  • Physics-informed machine learning (ML) tool for robust and efficient water infrastructure design, optimization, and retrofit

    Hybridize first-principle scientific computing and data-driven machine learning

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Free-surface turbulent flows over pile

VOF+DS-LES, FVM 2nd order, 40 million DOF

Turbulence structure in a hydrodynamic separator

HRT-LES, SEM 15th order, 60 million DOF

Elution dynamics in stormwater clarification basins

DA-LES, FVM 3rd order, 4 million DOF

Treatment dyanmics of clarification-filtration system under storm event


Particulate matter filtration in porous media

Pore-scale LPT, FVM 2nd order, 10 million DOF

Phosphate transport and fate in a volumetric-adsorptive reactor

interAdsFoam, VOF+URANS, FVM 2nd order, Non-equilibrium adsorption

Recent Publications

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., & 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. (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., 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

Our Team