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.
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Wright, L., Yang, XS., Matthews, C., Chapman, L., Roberts, S. (2011). Parameter Estimation from Laser Flash Experiment Data. In: Yang, XS., Koziel, S. (eds) Computational Optimization and Applications in Engineering and Industry. Studies in Computational Intelligence, vol 359. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20986-4_8
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DOI: https://doi.org/10.1007/978-3-642-20986-4_8
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