Artificial Neural Network for predicting thermal cool-down behavior of a School Bus Cabin

Passenger vehicles, such as buses, exhibit significant heat absorption when parked under direct sunlight, leading to elevated interior temperatures during daytime and a consequent reduction in thermal comfort. In our previous study, we developed and validated a transient numerical method to predict the thermal behavior of the cool-down process in a school bus cabin. Despite its accuracy, modeling such complex simulations using Computational Fluid Dynamics (CFD) remains time-intensive, with pre-processing taking up to a week and simulations requiring an additional 8-10 hours. While physics-based simulations are known for their accuracy, they are inherently slower. Conversely, data-based meta models offer faster results, but their outputs must be carefully correlated with test data or physics-based model data. This study aims to leverage existing CFD data to train and develop an Artificial Neural Network (ANN) based meta model, which is then benchmarked against test results for validation. To develop the model, a combination of environmental conditions (ambient temperature, sun azimuth angle, and initial temperature inside the bus) and HVAC settings for eight vents (flowrate and temperature profiles) were used as input features. A comprehensive training dataset encompassing the entire range of boundary conditions was generated using the available validated CFD model of the bus configuration. Feedforward artificial neural networks (ANN) were applied to the simulation data to predict temperatures at three sensor locations, consistent with the cool-down test and CFD. The prediction performance of the trained ANN model was evaluated using cool-down test data, achieving a mean absolute error (MAE) below 5% or less at the sensor locations. The developed machine learning model can predict temperatures at the sensor locations in real-time, given various boundary conditions, without relying on computationally expensive CFD simulations.