Thermal management of inductors and transformers is critical to the design of power converters. Thermal network models can provide quick and accurate temperature predictions, making them ideal for circuit thermal design and optimization. Although there are several thermal models for magnetic components in still air, various factors difficult their application. Challenges include limited experimental validation for a single component orientation, inappropriate heat transfer correlations, arbitrary selection of thermal parameters, and the necessity for heat transfer coefficient calibration. This study developed thermal network models capable of predicting maximum temperature rise for horizontally and vertically oriented magnetic components based on toroidal and E-shaped cores in still air (natural convection) without introducing any empirical or numerical adjustment coefficients. Heat transfer coefficients were calculated for each surface of the magnetic components, based on existing correlations. The models were validated with CFD data and experimentally in a purpose-built facility to acquire the component temperature distribution and measure and control the power losses. When compared to experimental data, the thermal network results presented averaged deviations below the experimental uncertainty of 2.0 ∘ C for the temperature rise. Therefore, the model has proven to be a suitable tool for predicting temperature and enabling the optimization of magnetic components.     «

Thermal management of inductors and transformers is critical to the design of power converters. Thermal network models can provide quick and accurate temperature predictions, making them ideal for circuit thermal design and optimization. Although there are several thermal models for magnetic components in still air, various factors difficult their application. Challenges include limited experimental validation for a single component orientation, inappropriate heat transfer correlations, arbitrar...     »