Chapter

Computational Optimization and Applications in Engineering and Industry

Volume 359 of the series Studies in Computational Intelligence pp 205-220

Parameter Estimation from Laser Flash Experiment Data

  • Louise WrightAffiliated withNational Physical Laboratory, Mathematics and Scientific Computing
  • , Xin-She YangAffiliated withNational Physical Laboratory, Mathematics and Scientific Computing
  • , Clare MatthewsAffiliated withNational Physical Laboratory, Mathematics and Scientific Computing
  • , Lindsay ChapmanAffiliated withNational Physical Laboratory, Mathematics and Scientific Computing
  • , Simon RobertsAffiliated withNational Physical Laboratory, Mathematics and Scientific Computing

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Abstract

Optimisation techniques are commonly used for parameter estimation in a wide variety of applications. The application described here is a laser flash thermal diffusivity experiment on a layered sample where the thermal properties of some of the layers are unknown. The aim is to estimate the unknown properties by minimising, in a least squares sense, the difference between model predictions and measured data. Two optimisation techniques have been applied to the problem. Results suggest that the classical nonlinear least-squares optimiser is more efficient than particle swarm optimisation (PSO) for this type of problem. Results have also highlighted the importance of defining a suitable objective function and choosing appropriate model parameters.