Efficient meta-modelling of complex process simulations with time-space-dependent outputs
Chen, T, Hadinoto, K, Yan, W and Ma, Y (2011) Efficient meta-modelling of complex process simulations with time-space-dependent outputs Computers and Chemical Engineering, 35 (3). pp. 502-509.
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Process simulations can become computationally too complex to be useful for model-based analysis and design purposes. Meta-modelling is an efficient technique to develop a surrogate model using “computer data”, which are collected from a small number of simulation runs. This paper considers meta-modelling with time–space-dependent outputs in order to investigate the dynamic/distributed behaviour of the process. The conventional method of treating temporal/spatial coordinates as model inputs results in dramatic increase of modelling data and is computationally inefficient. This paper applies principal component analysis to reduce the dimension of time–space-dependent output variables whilst retaining the essential information, prior to developing meta-models. Gaussian process regression (also termed kriging model) is adopted for meta-modelling, for its superior prediction accuracy when compared with more traditional neural networks. The proposed methodology is successfully validated on a computational fluid dynamic simulation of an aerosol dispersion process, which is potentially applicable to industrial and environmental safety assessment.
|Divisions :||Faculty of Engineering and Physical Sciences > Chemical and Process Engineering|
|Date :||8 March 2011|
|Identification Number :||https://doi.org/10.1016/j.compchemeng.2010.05.013|
|Depositing User :||Symplectic Elements|
|Date Deposited :||28 Sep 2011 12:04|
|Last Modified :||23 Sep 2013 18:44|
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