Magnetic Component Design & Analysis for High-Voltage, High-Power Converters

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Magnetic Component Design & Analysis for High-Voltage, High-Power Converters
Feb.07,2025

Background

       The client is a domestic manufacturer designing high-voltage, high-power power converters, and needs to consider the thermal loss and temperature rise of the inductive components during design. In the past, to verify whether the designed inductor components met the requirements, they had to spend time and manufacturing cost to build physical prototypes already in the early design stage, and then conduct potentially dangerous experimental tests. Therefore, the client hoped to use software-based analysis to assist design and reduce the number of experiments.

 

Results

  • Completed 3D inductor component modeling and parameterization using the Flux sketch and modeling tools.

  • Through Flux 3D simulation, obtained the electromagnetic characteristics of the inductor, including vector distribution of magnetic flux density, harmonic analysis of current components, calculation of copper loss, iron loss and other losses, and confirmed whether fringing flux occurs.

  • Based on the loss results obtained from the magnetic analysis, performed thermal analysis in Flux 3D to evaluate the steady-state temperature distribution, thereby supporting the structural design of the inductor.

 
 

Flux3D電感器磁通密度向量圖

Magnetic component design and analysis for high-voltage, high-power power converters


 

Technical Highlights

  • Flux provides powerful sketching and parameterization capabilities. After parameterizing the geometry, design specifications can be quickly adjusted for new simulations, making it possible to understand how different parameters affect the magnetic and thermal behavior.

  • Loss results obtained from magnetic field analysis in Flux can be used directly as heat sources in thermal analysis. By inputting parameters such as material emissivity, radiation, and environmental convection coefficients, the iterative analysis will converge to predict the steady-state temperature distribution under the corresponding loss conditions.


 

Source: 2024 RTC Technical Highlights

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