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.

我们提出了一个计算程序，用于识别热传递动力学中的对流现象。该程序基于高斯过程潜在力模型，包括白盒子组件（即已知的物理学）用于传导和线性对流效应，以及作为非线性对流效应的黑盒子组件的高斯过程。通过贝叶斯平滑来推断状态，并使用拉普拉斯方法获得核协方差函数的超参数的近似后验分布。通过贝叶斯回归模型，从高斯过程状态中恢复非线性对流函数。我们通过使用识别的非线性对流函数进行模拟误差验证程序，对来自模拟系统的数据和物理装配的测量数据进行验证。

基于贝叶斯灰箱的热传递动力学非线性对流效应识别