位置参数优化通过人工神经网络对表面穿孔螺旋进行优化
Masoud Zarezadeh1, Nowrouz Mohammad Nouri2, Reza Madoliat1
1School of Mechanical Engineering, Iran University of Science and Technology, Tehran, 16846-13114, Iran.
Scientific reports
|January 27, 2024
概括
这项研究优化了使用人工神经网络和非主导排序遗传算法II的表面穿孔螺旋性能. 这些方法准确地预测了螺旋的性能,为类似的海洋工程挑战提供了具有成本效益的解决方案.
科学领域:
- 海军建筑和海洋工程
- 计算流体动力学的流体动力学.
- 优化算法 优化算法
背景情况:
- 穿透表面的螺旋对于海洋推进来说至关重要.
- 优化它们的性能需要理解复杂的水力动力学相互作用.
- 现有的方法可能耗时或缺乏准确性来预测性能参数.
研究的目的:
- 调查影响表面穿孔螺旋性能的关键因素.
- 为螺旋性能开发准确的预测模型.
- 优化螺旋位置参数以提高效率.
主要方法:
- 利用人工神经网络 (ANN) 进行非线性模型识别.
- 使用非主导排序基因算法II (NSGA-II) 进行优化.
- 从IUST的HYDROTECH培训和验证中心收集的实验数据.
主要成果:
- 在训练数据中,ANN的平均误差为7.5e-5,在验证/测试数据中为1e-4.
- 优化的结果是,推力系数的相对误差为9.7%,扭矩系数的相对误差为7.5%.
- 验证了ANN和NSGA-II在预测和优化螺旋性能方面的有效性.
结论:
- 联合ANN和NSGA-II方法提供了准确的性能预测.
- 这种方法为螺旋优化提供了具有成本效益和节省时间的解决方案.
- 这项研究表明了一种可行的方法来提高穿透表面的螺旋效率.
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