Development of an Artificial Neural Network Based Thermal Model for Heat Sinks in Power Electronics Applications

Abstract

Heat sinks are a fundamental component of power electronics converters, so it is important to have a reliable method to study and optimize their size. Thermal analysis of heat sinks can be a complex problem as it involves different heat transfer mechanisms, and it is often necessary to use finite element simulations to obtain accurate results. However, these simulations, being very slow, are relegated to the validation process. This paper proposes a thermal model of heat sinks based on artificial neural networks. The model, unlike previous state-of-the-art models that only obtain the average temperature of the heat sink, is able to obtain a thermal map of the heat sink surface, as if it were an image, by using convolutional layers. The main advantage of this approach is that using these convolutional layers, the model is able to efficiently process how the elements are distributed on the heat sink. This model, valid for heat sinks of very different sizes in both laminar and turbulent flow, has an error of less than 1.5% and is 1500 times faster than finite element simulations, so it can be easily used in brute-force optimization processes, where many different designs need to be analyzed.