Taguchi and Grey Relational Analysis for Simultaneous Optimization of Efficiency and Thermal Management
DOI:
https://doi.org/10.63682/jns.v13i1.9936Keywords:
Taguchi method, efficiency optimization, heat dispersion factor, signal-to-noise ratio, ANOVA, design of experimentsAbstract
The Taguchi design of experiments method is used in this work to systematically optimize efficiency and heat dispersion factor (HDF). An L27 orthogonal array was used to examine six control parameters, each at three levels, in order to assess their impact on system performance. The larger-is-better signal-to-noise (S/N) ratio was used to study efficiency, while the smaller-is-better criterion was used to assess HDF. To find statistically significant factors and measure their contributions, analysis of variance (ANOVA) was used. With statistical significance at the 95% confidence level, the findings demonstrate that factor C is the most important parameter affecting efficiency. While the other factors had relatively little effect, factors B and E showed moderate effects. A₂B₂C₁D₃E₃F₃ was found to be the best combination of parameters for optimizing efficiency. None of the characteristics were statistically significant for the heat dispersion factor, although factor F had the greatest relative impact on thermal performance. At the ideal setting A₂B₂C₃D₃E₂F₁, the lowest HDF was attained. A trade-off between efficiency and heat dispersion was found in a comparison analysis, especially with factor C. This suggests that multi-objective optimization is necessary in real-world applications. All things considered, the study shows that the Taguchi method is a useful and efficient technique for maximizing several performance attributes while reducing experimental effort...
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