Monday, March 22, 2010

Optimization of Heat Sink Design


Economic Optimization of Heat Sink Design
INTRODUCTION
This paper describes the analysis and derivation of an optimum heat
sink design for maximizing the thermoelectric cooling performance
of a laboratory liquid chiller. The methods employed consisted of
certain key changes in the design of the heat sink in order to improve
its thermal performance. Parametric studies were performed in order
to determine the optimized cooling system design per dollar."
"The objective of this project was to analyze the thermal performance
of an initial simple heat sink design and improve cooling
performance while reducing the cost and overall size of the cooling
system. Several changes were examined in an effort to improve the
thermal performance and/or to reduce overall cost. The result
obtained has provided some guidelines for the selection/design of the
most effective and economical heat sink configuration. These results
were somewhat surprising since they are contrary to what one might
instinctively expect without the benefit of the detailed analysis
presented in this paper.


Optimization of Heat Sink Design and Fan Selection in Portable Electronics Environment
Abstract
Modern portable electronics have seen component heat loads
increasing, while the space available for heat dissipation has
decreased, both factors working against the thermal designer.
This requires that the thermal management system be optimized
to attain the highest performance in the given space. While
adding fins to the heat sink increases surface area, it also
increases the pressure drop. This reduces the volumetric airflow,
which also reduces the heat transfer coefficient. There exists a
point at which the number of fins in a given area can be optimized
to obtain the highest performance for a given fan. The primary
goal of this paper is to find the optimization points for several
different fan-heat sink designs. The secondary goal is to find a
theoretical methodology that will accurately predict the
optimization point and the expected performance.