Bayesian grey-box identification of nonlinear convection effects in heat transfer dynamics
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866917716162510848 |
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| author | Kouw, Wouter M. Gruijthuijsen, Caspar Blanken, Lennart Evers, Enzo Rogers, Timothy |
| author_facet | Kouw, Wouter M. Gruijthuijsen, Caspar Blanken, Lennart Evers, Enzo Rogers, Timothy |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_01226 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Bayesian grey-box identification of nonlinear convection effects in heat transfer dynamics Kouw, Wouter M. Gruijthuijsen, Caspar Blanken, Lennart Evers, Enzo Rogers, Timothy Systems and Control Computational Engineering, Finance, and Science Machine Learning 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. |
| title | Bayesian grey-box identification of nonlinear convection effects in heat transfer dynamics |
| topic | Systems and Control Computational Engineering, Finance, and Science Machine Learning |
| url | https://arxiv.org/abs/2407.01226 |