UQ4CFD: An Uncertainty Quantification Platform for CFD Simulation

The credibility of Computational Fluid Dynamics (CFD) has been a topic of debate due to the significant uncertainties inherent in its modeling processes and numerical implementations. Uncertainty Quantification (UQ) offers a scientific framework for quantitatively assessing and mitigating uncertainties in CFD simulations. However, this procedure typically requires numerous CFD simulations and considerable manual effort for both simulation management and data analysis. To overcome these challenges, this work develops a platform called UQ4CFD, a browser–server software that provides automated and customized uncertainty quantification capabilities for CFD studies. The UQ4CFD platform integrates different kinds of methodologies to perform comprehensive uncertainty analysis, including uncertainty propagation, sensitivity analysis, surrogate modeling, numerical discretization uncertainty analysis, model validation, model calibration, etc. A tightly coupled CFD-UQ workflow is built to automate the complete analytical process, encompassing parameter sampling, simulation execution, and results analysis, which significantly improves computational efficiency while reducing risks associated with data processing errors. Comprehensive validation employing both analytical benchmark functions and practical CFD cases has been conducted to demonstrate the platform’s effectiveness and adaptability in diverse UQ scenarios.

Authors

Institutions

Publication Details

Journal
Aerospace
Published
2025-09-30
DOI
https://doi.org/10.3390/aerospace12100886
Citations
1
Primary Topic
Nuclear Engineering Thermal-Hydraulics
Type
article
Field-Weighted Citation Impact
4.99

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

UQ4CFD: An Uncertainty Quantification Platform for CFD Simulation

Jiangtao Chen, Xiao‐Jun Wu, Wei Xiao, Peihong Zhang et al.
1 citations
Aerospace
Nuclear Engineering Thermal-Hydraulics
4.99
article

UQ4CFD: An Uncertainty Quantification Platform for CFD Simulation

Jiangtao Chen, Xiao‐Jun Wu, Wei Xiao, Peihong Zhang, Jiao Zhao, Luogeng Lv
article en
1 citations

Abstract

The credibility of Computational Fluid Dynamics (CFD) has been a topic of debate due to the significant uncertainties inherent in its modeling processes and numerical implementations. Uncertainty Quantification (UQ) offers a scientific framework for quantitatively assessing and mitigating uncertainties in CFD simulations. However, this procedure typically requires numerous CFD simulations and considerable manual effort for both simulation management and data analysis. To overcome these challenges, this work develops a platform called UQ4CFD, a browser–server software that provides automated and customized uncertainty quantification capabilities for CFD studies. The UQ4CFD platform integrates different kinds of methodologies to perform comprehensive uncertainty analysis, including uncertainty propagation, sensitivity analysis, surrogate modeling, numerical discretization uncertainty analysis, model validation, model calibration, etc. A tightly coupled CFD-UQ workflow is built to automate the complete analytical process, encompassing parameter sampling, simulation execution, and results analysis, which significantly improves computational efficiency while reducing risks associated with data processing errors. Comprehensive validation employing both analytical benchmark functions and practical CFD cases has been conducted to demonstrate the platform’s effectiveness and adaptability in diverse UQ scenarios.

AerospaceVol. 12(10)
China Aerodynamics Research and Development Center (CN)
National Natural Science Foundation of China, National Safety Academic Fund
Openalex Percentile: Top 4%
Nuclear Engineering Thermal-Hydraulics
4.99
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.