Estimation of spatially varying heat transfer coefficient from a flat plate with flush mounted heat sources using Bayesian inference

7th European Thermal-Sciences Conference

Jakkareddy, Pradeep S.^1 ; Balaji, C.^1

Heat Transfer and Thermal Power Laboratory, Department of Mechanical Engineering, Indian Institute of Technology Madras, Chennai, Tamil Nadu

600 036, India^1

关键词: Bayesian approaches;    Conjugate conduction;    Conjugate convection;    Discrete heat sources;    Inverse heat transfer problem;    Markov chain monte carlo algorithms;    Maximum a posteriori;    Measured temperatures;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/745/3/032094/pdf
DOI  :  10.1088/1742-6596/745/3/032094

来源: IOP

【 摘 要 】

This paper employs the Bayesian based Metropolis Hasting - Markov Chain Monte Carlo algorithm to solve inverse heat transfer problem of determining the spatially varying heat transfer coefficient from a flat plate with flush mounted discrete heat sources with measured temperatures at the bottom of the plate. The Nusselt number is assumed to be of the form Nu = aReb(x/l)c. To input reasonable values of 'a' and 'b' into the inverse problem, first limited two dimensional conjugate convection simulations were done with Comsol. Based on the guidance from this different values of 'a' and 'b' are input to a computationally less complex problem of conjugate conduction in the flat plate (15mm thickness) and temperature distributions at the bottom of the plate which is a more convenient location for measuring the temperatures without disturbing the flow were obtained. Since the goal of this work is to demonstrate the eficiacy of the Bayesian approach to accurately retrieve 'a' and 'b', numerically generated temperatures with known values of 'a' and 'b' are treated as 'surrogate' experimental data. The inverse problem is then solved by repeatedly using the forward solutions together with the MH-MCMC aprroach. To speed up the estimation, the forward model is replaced by an artificial neural network. The mean, maximum-a-posteriori and standard deviation of the estimated parameters 'a' and 'b' are reported. The robustness of the proposed method is examined, by synthetically adding noise to the temperatures.