The emerging multi-mode cooling tower can cool down the circulating water by flexibly switching the operating modes according to varying weather conditions. Herein, a computational framework for addressing a large-sca...
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The emerging multi-mode cooling tower can cool down the circulating water by flexibly switching the operating modes according to varying weather conditions. Herein, a computational framework for addressing a large-scale stochastic simulation-optimization task is developed to obtain the optimal thermodynamic performance of the multi-mode cooling system. First, the numerical model is constructed using a well-validated evaporative cooler in the wet and wet-heating modes, as well as an air cooler in the dry mode. A well-suited experimental design is performed for generating an optimal set of samples by approximating the multivariate probability distributions of uncertain data. To reduce the computational burden, a customized parallel computing strategy is presented via parallelization of the task using the message-passing interface. Finally, an example illustrates that the time reduction is up to 93.5%, while the optimal exergy efficiency ratios are expected to be 37.0%, 17.3%, and 22.6% for the wet, dry, and wet-heating modes, respectively.
In a stochasticsimulation context, iterative methods of optimization, which perform at each step of their optimization procedure a comparison between two different values of the objective function, need the use of st...
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In a stochasticsimulation context, iterative methods of optimization, which perform at each step of their optimization procedure a comparison between two different values of the objective function, need the use of statistical tests in order to properly evaluate and compare the simulation results. However, when the objective function to be optimized is a multicriteria function involving several performance measures, classical statistical procedures, which do not take into account the correlation between the performance measures, could reject acceptable solutions. To avoid this, we propose an efficient and rigorous statistical procedure already used in a multicriteria context, Hotelling's T-2 procedure. This paper shows that this procedure is very well adapted when the problem is to compare simultaneously several criteria in a stochastic simulation-optimization context. (C) 2000 Elsevier Science B.V. All rights reserved.
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