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20152020
most citedA Physics Informed Machine Learning Approach for Reconstructing Reynolds Stress Modeling Discrepancies Based on DNS Data

632 citations · 2.8k across the 17 of their papers we have counts for

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Showing 2015Show all

3 papers · 1 filter

physics.flu-dyn2015★ 10 cited

Incorporating Prior Knowledge for Quantifying and Reducing Model-Form Uncertainty in RANS Simulations

Jian-Xun Wang, Jin-Long Wu, Heng Xiao

Simulations based on Reynolds-Averaged Navier--Stokes (RANS) models have been used to support high-consequence decisions related to turbulent flows. Apart from the deterministic mo…

physics.flu-dyn2015★ 58 cited

A Bayesian Calibration-Prediction Method for Reducing Model-Form Uncertainties with Application in RANS Simulations

J. -L. Wu, J. -X. Wang, H. Xiao

Model-form uncertainties in complex mechanics systems are a major obstacle for predictive simulations. Reducing these uncertainties is critical for stake-holders to make risk-infor…

physics.comp-ph2015★ 316 cited

Quantifying and Reducing Model-Form Uncertainties in Reynolds-Averaged Navier-Stokes Simulations: A Data-Driven, Physics-Based Bayesian Approach

H. Xiao, J. -L. Wu, J. -X. Wang +2

Despite their well-known limitations, Reynolds-Averaged Navier-Stokes (RANS) models are still the workhorse tools for turbulent flow simulations in today's engineering application.…