Physics-Based Regression vs. CFD for Hagen-Poiseuille and Womersley Flows and Uncertainty Quantification

dc.contributor.authorLi, H.
dc.contributor.authorIslam, M.
dc.contributor.authorYu, H.
dc.contributor.authorDu, X.
dc.contributor.departmentMechanical and Energy Engineering, School of Engineering and Technology
dc.date.accessioned2023-11-17T21:56:31Z
dc.date.available2023-11-17T21:56:31Z
dc.date.issued2022-07-01
dc.description.abstractComputational fluid dynamics (CFD) and its uncertainty quantification are computationally expensive. We use Gaussian Process (GP) methods to demonstrate that machine learning can build efficient and accurate surrogate models to replace CFD simulations with significantly reduced computational cost without compromising the physical accuracy. We also demonstrate that both epistemic uncertainty (machine learning model uncertainty) and aleatory uncertainty (randomness in the inputs of CFD) can be accommodated when the machine learning model is used to reveal fluid dynamics. The demonstration is performed by applying simulation of Hagen-Poiseuille and Womersley flows that involve spatial and spatial-tempo responses, respectively. Training points are generated by using the analytical solutions with evenly discretized spatial or spatial-temporal variables. Then GP surrogate models are built using supervised machine learning regression. The error of the GP model is quantified by the estimated epistemic uncertainty. The results are compared with those from GPU-accelerated volumetric lattice Boltzmann simulations. The results indicate that surrogate models can produce accurate fluid dynamics (without CFD simulations) with quantified uncertainty when both epistemic and aleatory uncertainties exist.
dc.eprint.versionFinal published version
dc.identifier.citationLi, H., Islam, M., Yu, H., & Du, X. Physics-Based Regression vs. CFD for Hagen-Poiseuille and Womersley Flows and Uncertainty Quantification. Eleventh International Conference on Computational Fluid Dynamics (ICCFD11). https://par.nsf.gov/biblio/10381940-physics-based-regression-vs-cfd-hagen-poiseuille-womersley-flows-uncertainty-quantification
dc.identifier.urihttps://hdl.handle.net/1805/37117
dc.language.isoen_US
dc.publisherNSF
dc.relation.journalEleventh International Conference on Computational Fluid Dynamics (ICCFD11)
dc.rightsPublisher Policy
dc.sourcePublisher
dc.subjectSupervised Machine Learning
dc.subjectComputational Fluid Dynamics
dc.subjectVolumetric lattice Boltzmann method
dc.subjectSurrogate Model
dc.subjectUncertainty Quantification
dc.titlePhysics-Based Regression vs. CFD for Hagen-Poiseuille and Womersley Flows and Uncertainty Quantification
dc.typeConference proceedings
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