Work

High-Fidelity Simulation of Turbulence in the Piscataqua River Using a Novel Neural Network Surrogate

Year: 2026

Type: article

Abstract: Accurate three-dimensional characterization of turbulent flows in natural waterways is essential for the effective design of tidal farms and other critical infrastructure situated along or across rivers. High-fidelity predictions based on the large-eddy simulation (LES) method capture the necessary physics but incur computational costs that hinder rapid scenario testing. Statistically, a relatively long history of instantaneous flow fields is required to generate reliable turbulence statistics, e.g., mean velocity and Reynolds stresses, of river flow. Such a requirement often incurs high simulation runtime and data storage costs. This study seeks to develop a neural network surrogate model that learns from a limited number of instantaneous flow realizations and approximates the outputs of the corresponding time-averaged fields with LES-level accuracy. Such a surrogate would eliminate the need to accumulate extensive ensembles, enabling faster hydrodynamic assessment and making LES-informed analyses more accessible for practical engineering decisions.

Source: Water

DOI: 10.3390/w18121500

Authors: Samin Shapour Miandouab, Mustafa Meriç Aksen, Mehrshad Gholami Anjiraki, Fotis Sotiropoulos, SeokKoo Kang, Ali Khosronejad

Institutions: Stony Brook University, Pennsylvania State University, Hanyang University

Language: English

Topic: Model Reduction and Neural Networks

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

SDG: Clean water and sanitation

Open Access status: gold

Estimated APC paid: $3,137