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Title: Data Driven Smart Proxy for CFD Application of Big Data Analytics & Machine Learning in Computational Fluid Dynamics, Report Two: Model Building at the Cell Level

Abstract

To ensure the usefulness of simulation technologies in practice, their credibility needs to be established with Uncertainty Quantification (UQ) methods. In this project, smart proxy is introduced to significantly reduce the computational cost of conducting large number of multiphase CFD simulations, which is typically required for non-intrusive UQ analysis. Smart proxy for CFD models are developed using pattern recognition capabilities of Artificial Intelligence (AI) and Data Mining (DM) technologies. Several CFD simulation runs with different inlet air velocities for a rectangular fluidized bed are used to create a smart CFD proxy that is capable of replicating the CFD results for the entire geometry and inlet velocity range. The smart CFD proxy is validated with blind CFD runs (CFD runs that have not played any role during the development of the smart CFD proxy). The developed and validated smart CFD proxy generates its results in seconds with reasonable error (less than 10%). Upon completion of this project, UQ studies that rely on hundreds or thousands of smart CFD proxy runs can be accomplished in minutes. Following figure demonstrates a validation example (blind CFD run) showing the results from the MFiX simulation and the smart CFD proxy for pressure distribution across amore » fluidized bed at a given time-step (the layer number corresponds to the vertical location in the bed).« less

Authors:
 [1];  [2];  [3];  [4];  [5]
  1. West Virginia Univ., Morgantown, WV (United States). Petroleum and Natural Gas Engineering Dept.
  2. West Virginia Univ., Morgantown, WV (United States). Petroleum and Natural Gas Engineering Dept.; Oak Ridge Inst. for Science and Education (ORISE), Oak Ridge, TN (United States)
  3. National Energy Technology Lab. (NETL), Morgantown, WV (United States). Research and Innovation Center (R&IC) and Energy Conversion Engineering Directorate
  4. National Energy Technology Lab. (NETL), Morgantown, WV (United States). Research and Innovation Center (R&IC) and Energy Conversion Engineering Directorate; West Virginia Univ., Morgantown, WV (United States). Research Corp.
  5. National Energy Technology Lab. (NETL), Morgantown, WV (United States). Research and Innovation Center (R&IC) and Energy Conversion Engineering Directorate; AECOM, Morgantown, WV (United States)
Publication Date:
Research Org.:
National Energy Technology Lab. (NETL), Pittsburgh, PA, and Morgantown, WV (United States); Oak Ridge Inst. for Science and Education (ORISE), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Fossil Energy (FE)
OSTI Identifier:
1431303
Report Number(s):
NETL-PUB-21634
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; Machine learning; Neural network; CFD; Multiphase flow

Citation Formats

Ansari, A., Mohaghegh, S., Shahnam, M., Dietiker, J. F., and Li, T.. Data Driven Smart Proxy for CFD Application of Big Data Analytics & Machine Learning in Computational Fluid Dynamics, Report Two: Model Building at the Cell Level. United States: N. p., 2018. Web. doi:10.2172/1431303.
Ansari, A., Mohaghegh, S., Shahnam, M., Dietiker, J. F., & Li, T.. Data Driven Smart Proxy for CFD Application of Big Data Analytics & Machine Learning in Computational Fluid Dynamics, Report Two: Model Building at the Cell Level. United States. doi:10.2172/1431303.
Ansari, A., Mohaghegh, S., Shahnam, M., Dietiker, J. F., and Li, T.. Mon . "Data Driven Smart Proxy for CFD Application of Big Data Analytics & Machine Learning in Computational Fluid Dynamics, Report Two: Model Building at the Cell Level". United States. doi:10.2172/1431303. https://www.osti.gov/servlets/purl/1431303.
@article{osti_1431303,
title = {Data Driven Smart Proxy for CFD Application of Big Data Analytics & Machine Learning in Computational Fluid Dynamics, Report Two: Model Building at the Cell Level},
author = {Ansari, A. and Mohaghegh, S. and Shahnam, M. and Dietiker, J. F. and Li, T.},
abstractNote = {To ensure the usefulness of simulation technologies in practice, their credibility needs to be established with Uncertainty Quantification (UQ) methods. In this project, smart proxy is introduced to significantly reduce the computational cost of conducting large number of multiphase CFD simulations, which is typically required for non-intrusive UQ analysis. Smart proxy for CFD models are developed using pattern recognition capabilities of Artificial Intelligence (AI) and Data Mining (DM) technologies. Several CFD simulation runs with different inlet air velocities for a rectangular fluidized bed are used to create a smart CFD proxy that is capable of replicating the CFD results for the entire geometry and inlet velocity range. The smart CFD proxy is validated with blind CFD runs (CFD runs that have not played any role during the development of the smart CFD proxy). The developed and validated smart CFD proxy generates its results in seconds with reasonable error (less than 10%). Upon completion of this project, UQ studies that rely on hundreds or thousands of smart CFD proxy runs can be accomplished in minutes. Following figure demonstrates a validation example (blind CFD run) showing the results from the MFiX simulation and the smart CFD proxy for pressure distribution across a fluidized bed at a given time-step (the layer number corresponds to the vertical location in the bed).},
doi = {10.2172/1431303},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Mon Apr 02 00:00:00 EDT 2018},
month = {Mon Apr 02 00:00:00 EDT 2018}
}

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