Data Driven Smart Proxy for CFD: Application of Big Data Analytics & Machine Learning in Computational Fluid Dynamics (Part 1)
- West Virginia Univ., Morgantown, WV (United States)
- West Virginia Univ., Morgantown, WV (United States); Oak Ridge Institute for Science and Education (ORISE), Oak Ridge, TN (United States)
- National Energy Technology Laboratory (NETL), Pittsburgh, PA, Morgantown, WV, and Albany, OR (United States). Research and Innovation Center
- National Energy Technology Laboratory (NETL), Pittsburgh, PA, Morgantown, WV, and Albany, OR (United States). Research and Innovation Center; West Virginia Univ., Morgantown, WV (United States)
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.
- Research Organization:
- National Energy Technology Laboratory (NETL), Pittsburgh, PA, Morgantown, WV, and Albany, OR (United States)
- Sponsoring Organization:
- USDOE Office of Fossil Energy (FE)
- OSTI ID:
- 1417305
- Report Number(s):
- NETL-PUB-21574
- Country of Publication:
- United States
- Language:
- English
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