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Using fuzzy clustering and weighted cumulative probability distribution techniques for optimal design of phase change material thermal energy storage

Author:
Lin, Wenye, Ren, Haoshan, Ma, Zhenjun, Yang, Luwei
Source:
Journal of cleaner production 2019 v.233 pp. 1259-1268
ISSN:
0959-6526
Subject:
air, air flow, algorithms, bricks, heat transfer, multi-criteria decision making, phase transition, probability distribution, thermal energy
Abstract:
This paper presents the development and application of a bi-objective optimisation strategy to optimise the design of thermal energy storage (TES) systems using phase change materials (PCMs). The overall objective is to maximise the average heat transfer effectiveness of the PCM TES system, while minimising the effective PCM charging time. The proposed strategy featured with the utilisation of a fuzzy clustering algorithm and a weighted cumulative probability distribution (FC-WCPD) technique to identify optimal designs of the PCM TES systems. The fuzzy clustering algorithm was first time introduced in a bi-objective optimisation algorithm and was used to evaluate and select the potential optimal solutions in each generation by taking into account the trade-off between the two conflicting optimisation objectives. The weighted cumulative probability distribution technique was used to transfer the optimal characteristics through iterations and facilitate the convergence of the optimisation process. The optimal design identified for the case studied was the inlet air temperature of 42 °C, the air flow rate of 74.91 l/s, the number of the PCM bricks in the air flow direction of 5, and the number of air channels of 4 when the weight factors were 0.5 for both optimisation objectives. By using this optimal design, the average heat transfer effectiveness of the PCM TES system and the effective PCM charging time were 61.87% and 6.61 h, respectively. Further comparison showed that the optimal design was consistent with the optimal solutions identified using a controlled elitist non-dominated sorting genetic algorithm (NSGA) and a multi-criteria decision-making (MCDM) process.
Agid:
6475563