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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

基本信息

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

项目摘要

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.

由于英国近25%的排放量是由交通运输产生的，如果英国要达到具有法律约束力的目标，到2050年大幅减少温室气体排放，提高航空和公路车辆的能源效率至关重要。实现这一目标的一个有希望的方法是使用主动流动控制来改善地面和空中交通工具的空气动力学性能。在主动流量控制中，对流量进行实时测量，并根据这些测量结果采取积极的措施来调整流量。例如，飞机机翼或道路车辆后部的可移动表面可以动态调整，以减少空气动力阻力，从而减少燃料消耗。为了实施主动控制策略，通常需要一个近似的流量模型，该模型允许从给定的流量测量中计算出有益的控制动作。然而，一个重大的挑战是，实际感兴趣的流动具有高度复杂和错综复杂的动力学，而计算和理论限制意味着近似模型必须是简单的或低维的。弥合这一差距的一种方法是识别流中的连贯结构，即对其演变和表现具有动态重要性的空间特征。如果能够识别出一小组具有代表性的连贯结构，则可以将它们用作流模型的构建块，从而促进成功的流控制策略。本项目的目的是发展最优模态分解（OMD）算法，一种系统地从实验或数值流体流动数据集合中提取动态重要相干结构的方法。数据提取的系统性质很重要，因为它提供了一种易于处理的方法来分析现在可用的越来越多的大规模流体流动数据集。拟议的研究将彻底测试OMD算法在一系列基本和具有挑战性的流体流动中的性能，评估提取的结构用于流动建模和估计的适用性，最后进行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量控制的应用，以提高能源效率，从而解决当前和根本重要的环境和经济问题。