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.

翻译：本文提出了一种用于辨识传热动力学中对流效应的计算流程。该流程基于高斯过程隐力模型，该模型包含用于描述传导和线性对流效应的白箱组件（即已知物理规律），以及一个作为黑箱组件用于描述非线性对流效应的高斯过程。状态通过贝叶斯平滑进行推断，并利用拉普拉斯方法获得核协方差函数超参数的近似后验分布。非线性对流函数通过贝叶斯回归模型从高斯过程状态中恢复得到。我们通过在仿真系统数据和物理组件实测数据上，使用所辨识的非线性对流函数进行仿真误差分析，验证了该流程的有效性。