Author ORCID Identifier
https://orcid.org/0000-0003-4647-3455
https://orcid.org/0000-0002-4148-4051
N/A
N/A
Document Type
Article
Publication Date
2019
College/Unit
Statler College of Engineering and Mining Resources
Department/Program/Center
Petroleum and Natural Gas Engineering
Abstract
Simulations can reduce the time and cost to develop and deploy advanced technologies and enable their rapid scale-up 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. The National Energy Technology Laboratory (NETL) has been applying non-intrusive UQ methodologies to categorize and quantify uncertainties in computational fluid dynamics (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 are used to construct smart proxy models, which can reduce the computational cost of conducting large numbers 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 non-reacting rectangular fluidized bed. The NETL’s in house multiphase solver, Multiphase Flow with Interphase eXchanges (MFiX), was used to generate simulation data for the rectangular fluidized bed. The artificial neural network (ANN) was used to construct a CFD smart proxy, which is able to reproduce the CFD results with reasonable error (about 10%). Several blind cases were used to validate this technology. The results show a good agreement with CFD runs while the approach is less computationally expensive. The developed model can be used to generate the time averaged results of any given fluidized bed with the same geometry with different inlet velocity in couple of minutes.
Digital Commons Citation
Ansari, Amir; Mohaghegh, Shahab D.; Shahnam, Mehrdad; and Dietiker, Jean-François, "Modeling Average Pressure and Volume Fraction of a Fluidized Bed Using Data-Driven Smart Proxy" (2019). Faculty & Staff Scholarship. 1801.
https://researchrepository.wvu.edu/faculty_publications/1801
Source Citation
Ansari, Mohaghegh, Shahnam, & Dietiker. (2019). Modeling Average Pressure and Volume Fraction of a Fluidized Bed Using Data-Driven Smart Proxy. Fluids, 4(3), 123. https://doi.org/10.3390/fluids4030123
Comments
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).