使用基于混合遗传算法的支持矢量回归智能方法对磁性制冷应用的基于异过渡金属的金属间合金进行磁热效应建模,使用基于混合遗传算法的支持矢量回归智能方法
1Department of Mechanical Engineering, College of Engineering, University of Hafr Al Batin, Hafr Al Batin, Saudi Arabia.
PloS one
|February 6, 2024
概括
稀土二氧化金属间合金具有非常适合绿色制冷的磁性. 一个新的计算模型准确地预测了磁热效应,为环保冷却技术铺平了道路.
科学领域:
- 材料科学 材料科学 材料科学
- 热力学是一种热力学.
- 计算化学的计算化学
背景情况:
- 含有稀土二离子的金属间合金具有独特的磁性.
- 这些特性对于开发先进的绿色制冷技术至关重要.
- 基于磁热效应的磁性制冷,为传统冷却提供了一个环保的替代方案.
研究的目的:
- 为了建模和预测Dy-T-X金属间合金中的最大磁变化.
- 研究应用磁场,离子度和离子半径对磁热效应的影响.
- 开发一种精确的计算工具,用于指导这些材料用于制冷应用的探索.
主要方法:
- 利用基于混合遗传算法的支持向量回归 (GSVR) 计算智能方法.
- 使用应用磁场,离子度和离子半径作为建模的描述符.
- 将GSVR模型的性能与高斯函数和多项式内核函数进行比较.
主要成果:
- 使用高斯内核函数 (GSVR-G) 的GSVR模型显著超过了多项式内核函数模型 (GSVR-P).
- 在测试样本中,相关系数 (CC),平均绝对误差 (MAE) 和根平均平方误差 (RMSE) 的改进分别达到85.23%,78.82%和78.67%.
- 该模型成功地研究了外部磁场对DyCuAl.Al.磁热效应的影响.
结论:
- 开发的GSVR模型准确地预测了磁热效应,规避了实验挑战.
- 该模型的高精度有助于发现用于高效和环保冷却的新型金属间化合物.
- 这项研究支持绿色制冷技术的发展,以解决环境问题.
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