Bayesian grey-box identification of nonlinear convection effects in heat transfer dynamics

Autor:	Kouw, Wouter M., Gruijthuijsen, Caspar, Blanken, Lennart, Evers, Enzo, Rogers, Timothy
Rok vydání:	2024
Předmět:	Electrical Engineering and Systems Science - Systems and Control Computer Science - Computational Engineering Finance and Science Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	We propose a computational procedure for identifying convection in heat transfer dynamics. The procedure is based on a Gaussian process latent force model, consisting of a white-box component (i.e., known physics) for the conduction and linear convection effects and a Gaussian process that acts as a black-box component for the nonlinear convection effects. States are inferred through Bayesian smoothing and we obtain approximate posterior distributions for the kernel covariance function's hyperparameters using Laplace's method. The nonlinear convection function is recovered from the Gaussian process states using a Bayesian regression model. We validate the procedure by simulation error using the identified nonlinear convection function, on both data from a simulated system and measurements from a physical assembly. Comment: 6 pages, 4 figures. Published in the proceedings of the IEEE Conference on Control Technology and Applications 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2407.01226 Zobrazit plný text záznamu View this record from Arxiv