higher cooling efficiency
A leading German enterprise needed to improve its motor efficiency and performance. They also wanted to improve the manufacturing process by reducing costly and time-consuming testing, improving motor efficiency with better insulation, and making the impregnation process cleaner and more efficient.
The challenge was the 30K temperature difference between the inlet and surface temperature. The cooling performance was evaluated by calculating the heat transfer of coolant flowing across the coil and component surfaces. They started a workflow in the Dive CFD software to define the geometry from their CAD files.
First, they set the fluid properties, inlet velocities, and boundary conditions (like the surface temperatures). Then the simulation could start. As a result, they got a flow prediction with detailed insights into the local wetting, temperature distribution, and flow velocities.
Step 1: Set up the e-motor from existing CAD files. The colored coils need to be cooled. The individual cooling on each of the 12 coils will be assessed. All boundaries are assumed to be adiabatic except the coils, with a temperature of 380K.
Step 2: Run the simulation to obtain the local temperature and flow rates.
Finally, they could asses the cooling effect on each coil, in terms of the heat transfer rate. Using this workflow for various inlet designs and flow rates allows the engineers to identify the optimal design for their motor. Because Dive runs fully in the cloud, all the tests can be computed simultaneously, with results ready in a few hours.
Thanks to our cloud-native software, you can run multiple tests simultaneously and minimize the risk of product failure
The design engineers found that by using direct liquid cooling (DLC) instead of e.g. water-jacket cooling, the company could design motors with less installation space, higher torques, and power densities. In less time than with other CAE tools, the engine manufacturer was able to not only optimize its motor’s cooling system but also improve its overall efficiency and sustainability. The overall turn-around time was reduced, thanks to powerful simulations and a highly accurate SPH model.
Step 3: Get the heat transfer rate per coil.