Abstract
The accurate knowledge of Heat Transfer Coefficients is essential for the design of precise heat transfer operations. The determination of these values requires Inverse Heat Transfer Calculations, which are usually based on heuristic optimisation techniques, like Genetic Algorithms or Particle Swarm Optimisation. The main bottleneck of these heuristics is the high computational demand of the cost function calculation, which is usually based on heat transfer simulations producing the thermal history of the workpiece at given locations. This Direct Heat Transfer Calculation is a well parallelisable process, making it feasible to implement an efficient GPU kernel for this purpose. This paper presents a novel step forward: based on the special requirements of the heuristics solving the inverse problem (executing hundreds of simulations in a parallel fashion at the end of each iteration), it is possible to gain a higher level of parallelism using multiple graphics accelerators. The results show that this implementation (running on 4 GPUs) is about 120 times faster than a traditional CPU implementation using 20 cores. The latest developments of the GPU-based High Power Computations area were also analysed, like the new NVLink connection between the host and the devices, which tries to solve the long time existing data transfer handicap of GPU programming.
Highlights
As a fundamental experience of modern materials science, material properties are influenced by the microstructure; these can be altered to improve the mechanical attributes (Oksman et al, 2014)
It is necessary to know the attributes of the given material and the environment, to achieve the best results, especially the Heat Transfer Coefficient (HTC) which shows the amount of heat exchanged between the object and the surrounding cooling medium
In the case of Genetic Algorithms (GAs), which hardware configuration is preferred for a given population size? The expectation is that it is worth using the CPU for small populations and the GPU for larger populations
Summary
As a fundamental experience of modern materials science, material properties are influenced by the microstructure; these can be altered to improve the mechanical attributes (Oksman et al, 2014). One of the most widely used methods for this purpose is heat treatment which usually consists of two consecutive steps: heating up the work object to a given high temperature and cooling it down in a precisely controlled environment. It is necessary to know the attributes of the given material and the environment, to achieve the best results, especially the Heat Transfer Coefficient (HTC) which shows the amount of heat exchanged between the object and the surrounding cooling medium. The aim of these methods is to find the HTC function giving the minimal deviation of the measured and predicted temperature data
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