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Article

Statistical Mechanics-Based Surrogates for Scalar Transport in Channel Flow

Department of Mechanical and Nuclear Engineering, Kansas State University, Manhattan, KS 66506, USA
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Author to whom correspondence should be addressed.
Academic Editor: Thomas Höhne
Fluids 2021, 6(2), 79; https://doi.org/10.3390/fluids6020079
Received: 11 January 2021 / Revised: 2 February 2021 / Accepted: 2 February 2021 / Published: 10 February 2021
Thermal hydraulics, in certain components of nuclear reactor systems, involve complex flow scenarios, such as flows assisted by free jets and stratified flows leading to turbulent mixing and thermal fluctuations. These complex flow patterns and thermal fluctuations can be extremely critical from a reactor safety standpoint. The component-level lumped approximations (0D) or one-dimensional approximations (1D) models for such components and subsystems in safety analysis codes cannot capture the physics accurately, and may introduce a large degree of modeling uncertainty. On the other hand, high-fidelity computational fluid dynamics codes, which provide numerical solutions to the Navier–Stokes equations, are accurate but computationally intensive, and thus cannot be used for system-wide analysis. An alternate way to improve reactor safety analysis is by building reduced-order emulators from computational fluid dynamics (CFD) codes to improve system scale models. One of the key challenges in developing a reduced-order emulator is to preserve turbulent mixing and thermal fluctuations across different-length scales or time-scales. This paper presents the development of a reduced-order, non-linear, “Markovian” statistical surrogate for turbulent mixing and scalar transport. The method and its implementation are demonstrated on a canonical problem of differentially heated channel flow, and high-resolution direct numerical simulations (DNS) data are used for emulator or surrogate development. This statistical surrogate model relies on Kramers–Moyal expansion and emulates the turbulent velocity signal with a high degree of accuracy. View Full-Text
Keywords: data driven; multi-scale; turbulence statistics data driven; multi-scale; turbulence statistics
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MDPI and ACS Style

Ross, M.; Bindra, H. Statistical Mechanics-Based Surrogates for Scalar Transport in Channel Flow. Fluids 2021, 6, 79. https://doi.org/10.3390/fluids6020079

AMA Style

Ross M, Bindra H. Statistical Mechanics-Based Surrogates for Scalar Transport in Channel Flow. Fluids. 2021; 6(2):79. https://doi.org/10.3390/fluids6020079

Chicago/Turabian Style

Ross, Molly, and Hitesh Bindra. 2021. "Statistical Mechanics-Based Surrogates for Scalar Transport in Channel Flow" Fluids 6, no. 2: 79. https://doi.org/10.3390/fluids6020079

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