Electronics Cooling, Vol. 10, No. 2, pp. 22-28, May 2004
Peter Rodgers
CALCE EPSC
University of Maryland
College Park, MD 20742
Valerie Eveloy
Electronics Thermal Management Ltd.
Westport, Mayo, Ireland
Introduction:
The thermal design of today's electronic equipment relies significantly on the use of Computational Fluid Dynamics (CFD) software for the prediction of electronics operational temperature. In the early to intermediate product design phase, CFD analysis is used to select a cooling strategy and refine a thermal design by parametric analysis. In the final design phase, detailed analysis of product thermal performance is performed to provide boundary conditions for performance and reliability prediction. However, it is recognized that progress in reliability prediction is currently hampered by the lack of methods to accurately predict electronics operational temperature, in terms of either absolute temperature, or spatial or temporal temperature gradients.
While over the last decade the pre- and post processing capabilities of CFD software dedicated to the thermal analysis of electronics have evolved considerably to improve the productivity of design analysis, their turbulent flow modeling capabilities have remained confined to zero-equation mixing length or standard two-equation high-Reynolds number k-e eddy viscosity turbulence models. These models meet the criteria of robustness, in terms of promoting stable convergence, and to some extent, universality, which make them popular for practical engineering calculations. They are by far the most widely-used and validated, and are considered as computationally viable in a design environment. Unfortunately, this approach is not entirely satisfactory for modeling the thermal and kinematic complexity of thermofluid problems in forced air-cooled electronic systems. Based on experimental benchmarks, this article provides an overview of the potential shortcomings of the fluid flow modeling currently employed for populated Printed Circuit Board (PCB) thermal analysis, and illustrates the potential of alternative low-Reynolds number eddy viscosity turbulent flow modeling strategies to offer improved predictive accuracy.
Complete article is available to CALCE Consortium Members.