以物理为基础的神经网络作为水力动力学模拟器的替代模型
James Donnelly1, Alireza Daneshkhah2, Soroush Abolfathi3
1Centre for Computational Science & Mathematical Modelling, Coventry University, UK; School of Engineering, University of Warwick, UK.
The Science of the total environment
|November 28, 2023
概括
这项研究引入了一种新的基于物理的神经网络,用于更快地预测洪水,在数据稀缺的情况下提高准确性. 这种新的方法增强了气候变化适应的水力动力学模拟.
科学领域:
- 环境科学 环境科学
- 计算流体动力学的流体动力学.
- 机器学习 机器学习
背景情况:
- 气候变化正在增加洪水风险,需要准确和快速的洪水预测模型.
- 目前的高分辨率洪水模拟是计算密集的,限制了它们的实际应用.
- 许多科学问题,包括洪水建模,面临着稀疏数据的挑战,需要"小数据"解决方案.
研究的目的:
- 为水力动力学模拟器开发一个高效的机器学习替代模型.
- 在气候变化的背景下,解决对快速准确洪水预测的需求.
- 创建一个在"小数据"场景中有效执行的模型.
主要方法:
- 为浅水方程开发了一种基于物理信息的神经网络 (PINN) 的新型替代模型.
- 基于物理的先前信息,特别是质量的保存,被整合到神经网络架构中.
- 该模型在高分辨率的内陆洪水和大型区域潮模拟中进行了演示.
主要成果:
- 提出的基于PINN的替代模型与现有的数据驱动方法相比,表现优越.
- 该模型在最先进的数据驱动方法中实现了高达25%的改进.
- 该方法有效地结合了基于物理的约束,而不需要在损失函数中连续推导计算.
结论:
- 基于物理学的方法为洪水和水气候研究中的替代模型提供了显著的好处和稳定性.
- 开发的模型为洪水预测提供了一个计算效率高,准确的替代方案.
- 这项研究突出了PINNs在通过改进洪水风险评估来推进气候变化适应战略的潜力.
相关概念视频
01:30Typical Model Studies
361
Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
361
01:12Modeling and Similitude
268
Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
268
01:21Design Example: Creating a Hydraulic Model of a Dam Spillway
186
Scaled hydraulic models of dam spillways provide a practical way to replicate and study the intricate flow dynamics of these structures. Often built to a 1:15 ratio, these models allow for observing critical water behavior, such as velocity distribution, flow patterns, and energy dissipation.
186
01:23Plane Potential Flows
394
Plane potential flows simplify fluid motion by assuming the fluid to be irrotational and incompressible. These characteristics allow these flows to be described by a velocity potential function, ϕ, representing the flow speed in a given direction, and a stream function, ψ, that visualizes the flow path, both governed by Laplace's equation. These parameters help in estimating flow patterns, velocity distributions, and pressure fields around various hydraulic structures.
Uniform...
Uniform...
394
01:28Navier–Stokes Equations
518
For incompressible Newtonian fluids, where density remains constant, stresses show a linear relationship with the deformation rate, defined by normal and shear stresses. Normal stresses depend on the pressure exerted on the fluid and the rate of deformation in specific directions, which determines how fluid flows under varying pressures. Shear stresses, on the other hand, act tangentially across fluid layers. They explain how adjacent fluid layers slide relative to one another, connecting...
518
01:18Newtonian Fluid: Problem Solving
230
Newtonian fluids exhibit a constant viscosity, meaning their shear stress and shear strain rate are directly proportional. This property ensures a predictable and stable response to applied forces, maintaining a linear relationship between force and flow. Examples include water, air, and light oils, consistently demonstrating this proportional behavior regardless of external conditions.
A velocity gradient forms within the fluid when a Newtonian fluid is placed between two parallel plates, with...
A velocity gradient forms within the fluid when a Newtonian fluid is placed between two parallel plates, with...
230