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Response Surface Methodology-based prediction model for working fluid temperature during stand-alone operation of vertical cylindrical thermal energy storage tank

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  • Khurana, Hitesh
  • Majumdar, Rudrodip
  • Saha, Sandip K.

Abstract

Domestic hot water applications rely on the stand-alone operation of thermal energy storage (TES), in which natural convection significantly affects the efficiency of a storage system. The present study develops a two-dimensional axisymmetric numerical model for detailed simulation. Subsequently, an effective prediction model for a vertical stand-alone cylindrical storage tank is formed. The results of the detailed model are found to have good agreement with the findings of the in-house experiments. The experimental and numerical results show the formation of temperature gradients with time, known as thermal stratification, which affects the storage efficiency and thermodynamic quality of the stored heat. A seven parameter three-level Box-Behnken Design (BBD) Response Surface Methodology (RSM) is used to develop a prediction model for the working fluid temperature inside the tank. Analysis of variance (ANOVA) is performed to evaluate the effects and interactions of the process parameters for selecting the statistically significant terms. Based on the ANOVA, a reduced prediction equation is generated. Various plots such as normal probability, predicted versus actual response, versus fits, versus order, Pareto charts, main effects and interaction effects are created to analyze the model. Comparing the results of the detailed model and prediction equation, it is concluded that the newly formulated scaling-based prediction equation can predict and characterize the performance of the TES under test with adequate accuracy.

Suggested Citation

  • Khurana, Hitesh & Majumdar, Rudrodip & Saha, Sandip K., 2022. "Response Surface Methodology-based prediction model for working fluid temperature during stand-alone operation of vertical cylindrical thermal energy storage tank," Renewable Energy, Elsevier, vol. 188(C), pages 619-636.
    • Handle: RePEc:eee:renene:v:188:y:2022:i:c:p:619-636
    DOI: 10.1016/j.renene.2022.02.040
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