Extracting coherent structures and low-order modelling using Optimal Mode Decomposition

使用最佳模式分解提取相干结构和低阶建模

• 批准号: EP/N015398/1
• 负责人: Andrew Wynn
• 金额: $ 12.35万
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2016
• 资助国家: 英国
• 起止时间: 2016 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FN015398%2F1
• 关键词: Extracting; coherent; structures; low; order; Extracting; coherent; structures; low; order

Since nearly 25% of UK emissions are generated by transport, improving the energy efficiency of aviation and road vehicles is vital if the UK is to meet legally binding targets to significantly reduce greenhouse gas emissions by 2050. One promising approach to achieving this aim is to improve the aerodynamic performance of ground and air vehicles using active flow control. In active flow control, measurements are taken from the flow in real time and, in response to those measurements, action is taken to positively modify the flow. For example, movable surfaces on an aircraft's wing or on the rear of a road vehicle may be dynamically adjusted to reduce aerodynamic drag and hence decrease fuel consumption. To implement an active control strategy typically requires an approximating flow model, which allows a beneficial control action to be computed from a given flow measurement. However, a significant challenge is that flows of practical interest have highly complicated and intricate dynamics, while computational and theoretical restrictions mean that the approximating models must be simple, or low-dimensional. One method of bridging this gap is to identify coherent structures in a flow, that is, spatial features which are of dynamical importance to its evolution and performance. If a small set of representative coherent structures can be identified, they can be used as the building blocks for a flow model and therefore facilitate a successful flow control strategy. The aim of this project is to develop the Optimal Mode Decomposition (OMD) algorithm, a method which systematically extracts dynamically important coherent structures from ensembles of experimental or numerical fluid flow data. The systematic nature of the data extraction is important since it provides a tractable method of analysing the increasing large-scale sets of fluid flow data that are now available. The proposed research will thoroughly benchmark the performance of the OMD algorithm across a range of fundamental and challenging fluid flows, assess the suitability of the extracted structures to be used for flow modelling and estimation and, finally, undertake a theoretical study to explain any observed behaviour. This research will therefore develop a methodology to underpin the application of flow control for improved energy efficiency and consequently addresses a current and fundamentally important environmental and economic issue.

由于将近25％的英国排放是通过运输产生的，因此，如果英国要达到具有法律约束力的目标，以在2050年达到合法约束的目标，从而提高航空和公路车辆的能源效率至关重要。到2050年，实现这一目标的一种有希望的方法是提高地面和空中汽车的空气动力性能，使用主动流动控制。在主动流量控制中，测量值是实时从流量中进行的，并且，为了响应这些测量，采取行动以积极修改流量。例如，可以动态调整飞机机翼或公路车辆后部的可移动表面，以减少空气动力的阻力，从而减少燃油消耗。要实现主动控制策略，通常需要一个近似流量模型，这允许从给定的流量测量中计算出有益的控制动作。但是，一个重大的挑战是，实践感兴趣的流具有高度复杂和复杂的动态，而计算和理论限制意味着近似模型必须简单或低维。弥合此差距的一种方法是识别流中的相干结构，即对其演变和性能具有动态重要性的空间特征。如果可以识别一小部分代表性的连贯结构，则可以将它们用作流程模型的构建块，从而促进成功的流控制策略。该项目的目的是开发最佳模式分解（OMD）算法，该方法从实验或数值流体流量数据的集合中系统地提取动态重要的相干结构。数据提取的系统性质很重要，因为它提供了一种可拖动的方法来分析现在可用的大规模流体流量数据集。拟议的研究将彻底基准基准在一系列基本和具有挑战性的流体流中，OMD算法的性能，评估用于流程建模和估计的提取结构的适用性，最后，进行了一项理论研究来解释任何观察到的行为。因此，这项研究将开发一种方法来支持流动控制以提高能源效率的应用，并因此解决了当前且根本上重要的环境和经济问题。