White Paper: From Material Composition to Effective Thermal Conductivity: A Physics-Based Method for Improving Thermal Simulation Accuracy

Accurate PCBA thermal simulation is critical for high-power, high-density AI and accelerator systems. Modern liquid- and hybrid-cooled servers integrate densely arranged power components that generate significant heat. During early-stage development, however, detailed vendor thermal models for board-level components may be unavailable, while only limited material and structural information may be accessible. Initial simulations may therefore rely on generic modeling assumptions, potentially resulting in discrepancies between simulated and measured component temperatures.

This whitepaper presents a physics-based method for deriving effective thermal conductivity from the limited material and structural information available during early-stage development. The method enables improved PCBA-level thermal modeling in the absence of detailed vendor thermal models. Validation on an ORv3-based server system demonstrates close agreement between simulated and measured component temperatures. The proposed method improves the accuracy of early-stage thermal predictions and enables earlier identification of thermal risks.

Download the whitepaper to explore our validated physics-based methodology for early-stage PCBA thermal modeling, bridge data gaps before vendor models are available, and de-risk your next-generation ORv3 high-density AI architectures.