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一个不可逆转的磁动力循环的四个目标优化
Qingkun Wu1,2,3, Lingen Chen1,2,3, Yanlin Ge1,2,3
1Institute of Thermal Science and Power Engineering, Wuhan Institute of Technology, Wuhan 430205, China.
Entropy (Basel, Switzerland)
|July 8, 2023
概括
这项研究利用有限时间热力学和遗传算法优化了不可逆转的磁动力学循环. 多目标优化比单个目标的功率输出和效率优化方法产生更好的结果.
科学领域:
- 热力学是一种热力学.
- 磁动力学是一种磁动力学.
- 计算工程 计算工程 计算工程
背景情况:
- 现有的不可逆磁动力学 (MHD) 循环模型为热力学分析提供了基础.
- 有限时间热力学为优化在有限时间约束中的过程提供了一个框架.
- 多目标优化对于在复杂系统中平衡竞争性性能指标至关重要.
研究的目的:
- 执行一个不可逆转的磁动力循环的多目标优化.
- 评估不同目标功能组合和决策方法的性能.
- 将多目标优化结果与单目标优化结果进行比较.
主要方法:
- 利用有限时间热力学理论和非主导排序遗传算法II (NSGA-II).
- 引入了热交换器的热导电分布和工作流体的异热温度比作为优化变量.
- 定义了输出功率,效率,生态功能和功率密度作为优化目标函数.
主要成果:
- 使用LINMAP和TOPSIS决策方法进行多目标优化,与Shannon Entropy (0.1940,0.1950) 相比,产生较低的偏差指数 (0.1764在恒定的气体速度下,0.1767在恒定的马赫数下).
- 这些多目标结果优于任何单一目标优化功率输出,效率,生态功能或功率密度的结果.
- 多目标优化的偏差指数明显低于各个性能指标的单目标优化.
结论:
- 与单一目标策略相比,多目标优化提供了一种更有效的方法来提高磁动力循环性能.
- 有限时间热力学和NSGA-II的结合有效地平衡了多个性能标准.
- 在复杂的热力学系统中,LINMAP和TOPSIS决策方法适用于选择最佳参数.
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