Computational design optimization of PCM-based attenuator of fluid temperature fluctuations

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A fluid-PCM heat exchanger (attenuator) of a circular design with water as the fluid was investigated both numerically and experimentally. A computational model of the PCM-based attenuator was developed with the use of the control volume method and the effective heat capacity. Square wave fluctuations of the water temperature at the inlet of the attenuator were considered in the study. The model and its functionality was validated by means of experimental data. The experimental investigation was carried out in a lab environment and two tanks containing water of different temperatures with the computer-controlled mixing valve were used to simulate square wave temperature fluctuations. The validated model was then coupled with metaheuristic optimization methods. The bee algorithm, the genetic algorithm, and the particle swarm optimization algorithm were applied in the study. Design optimization of the attenuator was performed with the aim to maximize the attenuation capability of the attenuator, but considering a cost factor as well. Results indicated that the metaheuristic approach represents a viable way for the solution of this kind of problems. All three metaheuristics provided comparable results in terms of the value of objective function as well as of the computational efficiency.

A fluid-PCM heat exchanger (attenuator) of a circular design with water as the fluid was investigated both numerically and experimentally. A computational model of the PCM-based attenuator was developed with the use of the control volume method and the effective heat capacity. Square wave fluctuations of the water temperature at the inlet of the attenuator were considered in the study. The model and its functionality was validated by means of experimental data. The experimental investigation was carried out in a lab environment and two tanks containing water of different temperatures with the computer-controlled mixing valve were used to simulate square wave temperature fluctuations. The validated model was then coupled with metaheuristic optimization methods. The bee algorithm, the genetic algorithm, and the particle swarm optimization algorithm were applied in the study. Design optimization of the attenuator was performed with the aim to maximize the attenuation capability of the attenuator, but considering a cost factor as well. Results indicated that the metaheuristic approach represents a viable way for the solution of this kind of problems. All three metaheuristics provided comparable results in terms of the value of objective function as well as of the computational efficiency.

temperature fluctuations; attenuator; phase change materials

08.11.2019

ASME

978-0-7918-5945-2

Proceedings of ASME IMECE 2019

1–8

8