An adaptive knowledge-based data-driven approach for turbulence modeling using ensemble learning technique under complex flow configuration: 3D PWR sub-channel with DNS data

Journal Article · 16 May 2022 · Nuclear Engineering and Design

DOI: https://doi.org/10.1016/j.nucengdes.2022.111814 · OSTI ID:1977511

Zhu, Yangmo ^[1]; Dinh, Nam T. ^[2]; Saini, Nadish ^[2]; Bolotnov, Igor A. ^[2]

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1. North Carolina State University, Raleigh, NC (United States); OSTI
2. North Carolina State University, Raleigh, NC (United States)

This work describes a new approach to increase the accuracy of Reynolds-averaged Navier–Stokes (RANS) in modeling turbulence flow leveraging the machine learning technique. Traditionally, different turbulence models for Reynolds stress are developed for different flow patterns based on human knowledge. Each turbulence model has a certain application domain and prediction uncertainty. In recent years, with the rapid improvements of machine learning techniques, researchers start to develop an approach to compensate for the prediction discrepancy of traditional turbulence models with statistical models and data. However, the approach has deficiencies in several aspects. For example, the amount of human knowledge introduced to the statistical model couldn’t be controlled, which makes the statistical model learn from a very naïve stage and limits its application. In this work, a new approach is developed to address those deficiencies. Here, the new approach uses the “ensemble learning” technique to control the amount of human knowledge introduced into the statistical model. Therefore, the new approach could be adaptive to the multiple application domains. In conclusion, according to the results of case study, the new approach shows higher accuracy than both traditional turbulence models and the previous machine learning approach.

• Research Organization: North Carolina State University, Raleigh, NC (United States)
• Sponsoring Organization: USDOE; USDOE Office of Nuclear Energy (NE), Nuclear Energy University Program (NEUP)
• Grant/Contract Number: NE0008530
• OSTI ID: 1977511
• Journal Information: Nuclear Engineering and Design, Journal Name: Nuclear Engineering and Design Vol. 393; ISSN 0029-5493
• Publisher: ElsevierCopyright Statement
• Country of Publication: United States
• Language: English