Hybrid Parallel Collaborative Simulation Framework Integrating Device Physics with Circuit Dynamics for PDAE-Modeled Power Electronic Equipment
Qingyuan Shi, Chijie Zhuang, Jiapeng Liu, Bo Lin, Xiyu, Peng, Dan Wu, Zhicheng Liu, Rong Zeng

TL;DR
This paper introduces a hybrid parallel simulation framework that integrates device physics with circuit dynamics to enable fast, accurate PDAE-based power electronic equipment analysis, significantly improving computational efficiency for complex multiscale simulations.
Contribution
It presents a novel hybrid-parallel collaborative framework that efficiently solves coupled PDAEs in power electronics, enabling high-speed, physics-based simulations of multiple devices simultaneously.
Findings
Achieves up to 60x faster simulation speed compared to commercial software.
Maintains carrier-level accuracy in complex device and circuit simulations.
Effectively handles multiscale, nonlinear PDAE systems in power electronics.
Abstract
Optimizing high-performance power electronic equipment, such as power converters, requires multiscale simulations that incorporate the physics of power semiconductor devices and the dynamics of other circuit components, especially in conducting Design of Experiments (DoEs), defining the safe operating area of devices, and analyzing failures related to semiconductor devices. However, current methodologies either overlook the intricacies of device physics or do not achieve satisfactory computational speeds. To bridge this gap, this paper proposes a Hybrid-Parallel Collaborative (HPC) framework specifically designed to analyze the Partial Differential Algebraic Equation (PDAE) modeled power electronic equipment, integrating the device physics and circuit dynamics. The HPC framework employs a dynamic iteration to tackle the challenges inherent in solving the coupled nonlinear PDAE system,…
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Taxonomy
TopicsReal-time simulation and control systems · Simulation Techniques and Applications · Silicon Carbide Semiconductor Technologies
MethodsSPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings
