Numerical predictions of heat-transfer applied to electrical machines
Abstract: In order to meet the need for increased electrification, and at the same time reduce the total demand for electric energy, behavior change and technological innovation is needed. Over the decades power density of electric motors have increased, leading to increased demands on the cooling system design and performance. The need for reduced energy demand, increased efficiency, and continued increase in performance require continuous development effort regarding cooling systems, understanding of temperature distributions and heat transfer, and thermal simulation tools applicable in the motor manufacturing industry. A study on how simulation assumptions affect the resolved temperature field in a traction motor prototype is presented. Here different assumptions regarding loss distributions and air flow distributions are considered. The study illustrates how different simulation assumptions affect the temperature field, and how the results compare to measurements. Application of numerical methods for resolving heat transfer, and how the heat transfer is linked to features in the fluid flow, is presented. An air jet impinging on a heated surface is investigated through the application of Large Eddy Simulations (LES) and obtained data processed using the Extended Proper Orthogonal Decomposition (EPOD) method. The study shows the link between structures in the flow and the associated structures in heat transfer. Thermal analysis is an integral part of the motor design and dimensioning process. The method employed in theses studies is often the Lumped Parameter Thermal Network (LPTN). In this work a prototype method for automatic calibration of an LPTN, based on external temperature data, is presented. Application of Computational Fluid Dynamics (CFD) in computing input data needed for LTPNs is presented, where an extension to existing heat transfer correlations related to the end-winding of a form-wound machine is suggested. The studies are aiming at enabling advancing the prediction capability of heat transfer and temperature simulation methods applied in analysis of electrical machines.
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