Augmenting flow simulations with experimental data to improve aerodynamic efficiency

利用实验数据增强流动模拟以提高空气动力效率

• 批准号: EP/W009935/1
• 负责人: Sean Symon
• 金额: $ 35.26万
• 依托单位: University of Southampton
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FW009935%2F1
• 关键词: Augmenting; flow; simulations; experimental; data

Numerical simulations play an important role in aerodynamic design since experimental measurements are typically limited and difficult to measure in all regions of the flow. Simulations can provide significantly more information than experiments, but modelling assumptions are necessary since it is not computationally tractable to simulate realistic flow conditions. In many industrial applications, for example, simulations solve the time-averaged equations using a turbulence model. The resulting simulations are then validated using the limited experimental data that are available. This project investigates a more active role for experimental data by using it as an input to simulations. Experimental measurements, which are incomplete and uncertain, are fed into a low-fidelity simulation to produce a hybrid flow field that mimics large-scale features in the experiment. This procedure, known as data-assimilation, seeks to address the deficiencies of experimental data and modelling ambiguities in simulations to produce better flow predictions. Data assimilation is of particular interest to fluid mechanics since the resources that are required for a high resolution experiment or a full-fidelity simulation are often prohibitively expensive. As such, data assimilation is the only realistic option to predict complicated flows at an affordable cost. These predictions are essential not only for design, but also for understanding how flows can be manipulated by control to reduce drag and increase aerodynamic efficiency. A rigorous framework, which can accommodate three-dimensional flows and control devices, is needed for predicting and manipulating industrial flows. The project will culminate in a tool that can convert limited experimental data around complex geometries into a fully resolved velocity field in order to improve aerodynamic design. This work will data-assimilate incomplete experimental measurements of three-dimensional velocity fields around a model vehicle to improve simulation-based predictions of mean flow quantities. The trial data will consist of a simulation that has been intentionally corrupted to resemble experimental data. In other words, the input data will be disjointed, noisy and sporadic. Moreover, these data will be systematically reduced in order to identify the minimum number of measurements that are required for successful data assimilation. Once this has been achieved, the framework will be applied to experimental data from a wind tunnel. The improved mean flow field will be used to design a control strategy that reduces drag using synthetic jets on the vehicle. Finally, the controlled flow will be data-assimilated to quantify the reduction in drag.

数值模拟在空气动力学设计中起重要作用，因为实验测量通常受到限制，并且在流动的所有区域中都难以测量。模拟比实验可以提供更多的信息，但是建模假设是必需的，因为它在模拟现实流动条件上是不可计算的。例如，在许多工业应用中，模拟使用湍流模型求解了时间平均的方程。然后使用有限的实验数据验证所得的模拟。该项目通过将其用作模拟的输入来研究实验数据的更积极的作用。实验测量不完整且不确定，被馈入低保真模拟，以产生混合流场，该混合流场模仿实验中的大规模特征。该程序被称为数据复合，旨在解决实验数据的缺陷和模拟模拟的模拟，以产生更好的流动预测。数据同化对流体力学特别感兴趣，因为高分辨率实验或全因此模拟所需的资源通常非常昂贵。因此，数据同化是以负担得起的成本预测复杂流动的唯一现实选择。这些预测不仅对于设计至关重要，而且对于了解如何通过控制可以操纵流量以减少阻力和提高空气动力学效率。一个严格的框架可以容纳三维流量和控制设备，以预测和操纵工业流。该项目将在一个工具中达到高潮，该工具可以将围绕复杂几何形状的有限实验数据转换为完全分辨的速度场，以改善空气动力学设计。这项工作将对模型车辆周围的三维速度场进行数据测量，以改善基于模拟的平均流量预测。试验数据将包括一个有意损坏的模拟，以类似于实验数据。换句话说，输入数据将脱节，嘈杂且零星。此外，这些数据将被系统地减少，以确定成功数据同化所需的最小测量数量。一旦实现了这一目标，该框架将应用于风洞的实验数据。改进的平均流场将用于设计控制策略，该控制策略可使用车辆上的合成喷气机减少阻力。最后，将对受控流量进行数据概括以量化阻力的减少。