基于物理和机器学习的模型用于准确的扫描深度预测
Ajay Jatoliya1, Debayan Bhattacharya1, Bappaditya Manna1
1Department of Civil Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India.
概括
这项研究比较了基于物理的数值建模和机器学习 (ML) 来估计海上结构的扫描深度. 机器学习模型,特别是人工神经网络,显示出高效率,补充了用于准确和及时评估扫描的数值分析.
科学领域:
- 地质技术工程 地质技术工程
- 海洋工程 海洋工程
- 计算流体动力学的流体动力学.
背景情况:
- 冲浪现象对离岸结构的稳定性构成重大风险.
- 准确的扫地深度估计对于结构完整性和安全性至关重要.
- 在复杂的海洋环境中,现有的方法可能缺乏效率或准确性.
研究的目的:
- 使用基于物理的数值建模和机器学习 (ML) 算法估计扫描深度.
- 为了比较不同ML模型的有效性,并与实验数据对数值结果进行验证.
- 突出ML和数值建模的联合潜力,以实现高效和准确的扫描评估.
主要方法:
- 机器学习 (ML) 算法,包括人工神经网络和自适应神经模糊接口系统,在现有数据集上进行训练.
- 使用REEF3D计算流体动力学 (CFD) 平台进行基于物理的数值建模.
- 对于单电流和合波电流条件进行了数值模拟.
- 模型结果与统计措施,报告结果和实验研究相对验证.
主要成果:
- 机器学习模型,特别是人工神经网络和自适应的神经模糊接口系统,在预测扫描深度方面表现出高效.
- 数字分析结果与报告的实验值有很好的一致性.
- 仅在当前条件下,正常化的扫地深度 (S/D) 为0.65 (前) 和0.81 (后).
- 在波流条件下,正常化的扫地深度 (S/D) 为0.26.
结论:
- 基于机器学习和基于物理的数值建模都是评估海上结构的扫地深度的有价值工具.
- 机器学习算法提供了一种有效和高效的方法,补充了传统的数值方法.
- 该研究证实了数值模拟的可靠性和ML的潜力,用于及时和准确的扫描分析.
相关概念视频
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01:12Modeling and Similitude
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01:27Uniform Depth Channel Flow
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01:24Rapidly Varying Flow
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01:10Design Example: Measuring Distance Between Two Points with Obstructions
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