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DOE OSTI · 1417305

Data Driven Smart Proxy for CFD: Application of Big Data Analytics & Machine Learning in Computational Fluid Dynamics (Part 1)

Ansari, A.·Mohaghegh, S.·Shahnam, M.·Dietiker, J. F.·Takbiri Borujeni, A.·Fathi, E.
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Abstract

Simulation technologies can reduce the time and cost of the development and deployment of advanced technologies and allow rapid scale-up of these technologies for fossil fuel based energy systems. However, to ensure their usefulness in practice, the credibility of the simulations needs to be established with Uncertainty Quantification (UQ) methods. National Energy Technology Laboratory (NETL) has been applying non-intrusive UQ methodologies to categorize and quantify uncertainties in CFD simulations of gas-solid multiphase flows. To reduce the computational cost associated with gas-solid flow simulations required for UQ analysis, techniques commonly used in the area of Artificial Intelligence (AI) and Data Mining (DM) are used to construct smart proxy models, which can reduce the computational cost of conducting large number of multiphase CFD simulations.

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97 MATHEMATICS AND COMPUTINGCFDMachine learningMultiphase flowNeural network

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Ansari, A., Mohaghegh, S., Shahnam, M., Dietiker, J. F., Takbiri Borujeni, A., Fathi, E.. 2017-11-17. Data Driven Smart Proxy for CFD: Application of Big Data Analytics & Machine Learning in Computational Fluid Dynamics (Part 1). https://doi.org/10.2172/1417305

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