A high-fidelity multi-physics CFD study was conducted to ensure PCB thermal reliability, preventing overheating and localized hotspots through detailed electro-thermal simulations. Detailed ECAD data was imported and cleaned to accurately represent the PCB geometry, and orthotropic thermal conductivity maps were generated for each layer based on trace and via distributions. Resistive heating in traces and vias was modeled using DCIR electrical analysis results, and a coupled electro-thermal workflow between Siwave and Icepak was implemented for iterative convergence to capture temperature-dependent electrical behavior accurately. Full conjugate heat transfer models were built including the PCB, mounted components, and surrounding airflow environment. The methodology enabled precise prediction of temperature distribution, identification and mitigation of thermal hotspots, and provided a robust framework for future PCB thermal reliability analysis.

A Python and SpaceClaim-driven workflow was developed to accelerate external condenser placement analysis for data centers, cutting setup time from one week to minutes. The system automates PyFluent script generation, including on-the-fly C++ UDF creation and compilation, allowing users to perform reliable simulations without deep CFD expertise. This framework streamlines repetitive tasks, reduces human error, and enables rapid evaluation of multiple design iterations.

A CFD and electro-thermal study was conducted to optimize battery pack thermal management. Using a detailed 3D model of modules and internal cells, coupled simulations under realistic drive cycles identified hotspots and ensured uniform temperature distribution. Python automation and Reduced Order Modeling accelerated the workflow, enabling rapid design optimization and support for Digital Twin applications. The framework improves thermal uniformity, extends battery life, and reduces computational time.