Data-Driven Calibration of Rough Heat Transfer Prediction Using Bayesian Inversion and Genetic Algorithm

Kevin Ignatowicz, Elie Solaï, François Morency and Héloïse Beaugendre
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Kevin Ignatowicz: Mechanical Engineering Department, École de Technologie Supérieure, Montréal, QC H3C1K3, Canada
Elie Solaï: Institut National de Recherche en Informatique et en Automatique (INRIA), F-33405 Talence, France
François Morency: Mechanical Engineering Department, École de Technologie Supérieure, Montréal, QC H3C1K3, Canada
Héloïse Beaugendre: University Bordeaux, INRIA, CNRS, Bordeaux INP, IMB, UMR 5251, F-33400 Talence, France

Energies, 2022, vol. 15, issue 10, 1-20

Abstract: The prediction of heat transfers in Reynolds-Averaged Navier–Stokes (RANS) simulations requires corrections for rough surfaces. The turbulence models are adapted to cope with surface roughness impacting the near-wall behaviour compared to a smooth surface. These adjustments in the models correctly predict the skin friction but create a tendency to overpredict the heat transfers compared to experiments. These overpredictions require the use of an additional thermal correction model to lower the heat transfers. Finding the correct numerical parameters to best fit the experimental results is non-trivial, since roughness patterns are often irregular. The objective of this paper is to develop a methodology to calibrate the roughness parameters for a thermal correction model for a rough curved channel test case. First, the design of the experiments allows the generation of metamodels for the prediction of the heat transfer coefficients. The polynomial chaos expansion approach is used to create the metamodels. The metamodels are then successively used with a Bayesian inversion and a genetic algorithm method to estimate the best set of roughness parameters to fit the available experimental results. Both calibrations are compared to assess their strengths and weaknesses. Starting with unknown roughness parameters, this methodology allows calibrating them and obtaining between 4.7% and 10% of average discrepancy between the calibrated RANS heat transfer prediction and the experimental results. The methodology is promising, showing the ability to finely select the roughness parameters to input in the numerical model to fit the experimental heat transfer, without an a priori knowledge of the actual roughness pattern.

Keywords: Bayesian inversion; genetic algorithm; data-driven analysis; calibration; rough heat transfers; computational fluid dynamics (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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