Design optimization of high-performance regenerator for caloric cycles using numerical and experimental methods

Kang, Minwoong

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https://hdl.handle.net/2142/122233 Copy

Description

Title

Design optimization of high-performance regenerator for caloric cycles using numerical and experimental methods

Author(s)

Kang, Minwoong

Issue Date

2023-11-27

Director of Research (if dissertation) or Advisor (if thesis)

Elbel, Stefan

Doctoral Committee Chair(s)

Miljkovic, Nenad

Committee Member(s)

• Wang, Sophie
• Zhang, Yuanhui

Department of Study

Mechanical Sci & Engineering

Discipline

Mechanical Engineering

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

magnetocaloric cooling, caloric regenerator, heat transfer and pressure drop, heat exchanger design, optimization

Abstract

The regenerator is a key component to determine the performance of the active regenerative caloric cycle. Although the geometry of the regenerator is an important factor, very limited geometries have been used in prototypes due to the limitations of conventional manufacturing. Therefore, this study proposed a new type of regenerator with a high heat transfer rate for the caloric cycle, which is a packed rod bed, using additive manufacturing. In order to ensure a systematic approach for the new design of the regenerator, a new design optimization using an artificial neural network – genetic algorithm with the help of computational fluid dynamics was introduced. Artificial neural network models were used to predict the j and f factors of the packed rod bed and showed a mean relative error of less than 2.0 % for the j factor, and a mean relative error of less than 7.5 % for the f factor. The accurate results of artificial neural networks contribute to improving the optimization process. The regenerator optimized through the artificial neural network – genetic algorithm method increased the system efficiency by 4.7 % and the cooling capacity by 13.0 % compared to the baseline caloric cycle using a parallel plate matrix. Moreover, comprehensive research on artificial neural networks for heat exchangers and a comparative study between artificial neural networks and regression correlations were investigated. While correlations provide an intuitive understanding of the effect of the input parameters on output parameters through formulas, artificial neural networks displayed much greater accuracy in predicting the performance of tube banks. A new testing device utilizing electric heating was introduced to investigate thermal performance of the regenerator and the influence of regenerator geometry on the caloric cycle. The performance of the new design of regenerator, packed rod bed produced by additive manufacturing, was evaluated using a new testing device. The packed rod regenerator with elliptical rods demonstrated relatively good balance between heat transfer performance and pressure drop compared to the other geometries of regenerator. When using additive manufacturing technology, the regenerators tend to have significantly higher surface roughness compared to those made through traditional manufacturing. Therefore, regenerators were fabricated using both traditional and additive manufacturing methods. The results indicated that as the surface roughness increased by approximately 8-9 times, the temperature span increased by about 4.1 – 7.6 %, and the pressure drop increased by approximately 6.1 – 11.2 %. Furthermore, the performance of a caloric cycle depends on the heat transfer and pressure drop between the heat transfer fluid and the caloric material in the regenerator. However, research on heat transfer fluids for solid-state caloric cycles is limited, with most studies focusing solely on single phase flow. To address this gap, this study utilized a heat transfer fluid consisting of a 50/50% blend of ethane and isobutane with a temperature glide of approximately 40 K. This choice enabled the introduction of two-phase flow into the solid-state caloric cycle, allowing for the generation of a temperature gradient within the regenerator and leveraging the environmentally friendly aspects of hydrocarbon refrigeration. The system with a 50/50% blend of R170/R600a achieved an approximate specific cooling capacity of 395.4 W kg-1 with a system efficiency of 5.1 when the temperature span was 20 K. This is 3.2 times higher specific cooling capacity and 4.3 times higher system efficiency compared to the liquid water system.

Graduation Semester

2023-12

Type of Resource

Thesis

Copyright and License Information

© 2023 MINWOONG KANG

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