Ansari, A., Mohaghegh, S., Shahnam, M., Dietiker, J. F., Takbiri Borujeni, A., & Fathi, E. Data Driven Smart Proxy for CFD: Application of Big Data Analytics & Machine Learning in Computational Fluid Dynamics, Part One: Proof of Concept; NETL-PUB-21574; NETL Technical Report Series; U.S. Department of Energy, National Energy Technology Laboratory: Morgantown, WV, 2017.. United States. doi:10.2172/1417305.
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. The feasibility of using AI and machine learning to construct a smart proxy for a gas-solid multiphase flow has been investigated by looking at the flow and particle behavior in a nonreacting rectangular fluidized bed. NETL’s in house multiphase solver, MFiX, has been used to generate simulation data for the rectangular fluidized bed. The CFD data is then used to train a smart proxy that can reproduce the CFD results with reasonable error (about 10%). MATLAB neural network toolbox has been used for the current development effort.
Keywords: Artificial Intelligence, Computational Fluid Dynamics, Multiphase Flow with Interphase eXchanges