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.

数值模拟在气动设计中起着重要的作用，因为实验测量通常是有限的，很难在所有区域的流动进行测量。模拟可以提供比实验更多的信息，但建模假设是必要的，因为模拟现实的流动条件在计算上是不可处理的。例如，在许多工业应用中，模拟使用湍流模型求解时间平均方程。然后，使用有限的实验数据，可用的模拟进行验证。该项目通过将实验数据用作模拟的输入来研究实验数据的更积极作用。实验测量，这是不完整的和不确定的，被送入一个低保真度的模拟，以产生一个混合流场，模仿实验中的大规模功能。这一过程被称为数据同化，旨在解决模拟中实验数据和建模模糊性的不足，以产生更好的流量预测。数据同化对流体力学特别感兴趣，因为高分辨率实验或全保真度模拟所需的资源通常非常昂贵。因此，数据同化是以可承受的成本预测复杂流动的唯一现实选择。这些预测不仅对设计至关重要，而且对理解如何通过控制来操纵流动以减少阻力和增加气动效率也至关重要。一个严格的框架，它可以容纳三维流动和控制设备，是需要预测和操纵工业流程。该项目最终将开发出一种工具，可以将复杂几何形状的有限实验数据转换为完全解析的速度场，以改进空气动力学设计。这项工作将数据同化不完整的实验测量模型车辆周围的三维速度场，以提高模拟为基础的预测平均流量。试验数据将由模拟组成，该模拟已被故意破坏以类似于实验数据。换句话说，输入数据将是脱节的，嘈杂的和零星的。此外，将系统地减少这些数据，以确定成功进行数据同化所需的最低数量的测量。一旦实现了这一点，该框架将应用于风洞实验数据。改进后的平均流场将用于设计一种控制策略，该策略使用合成射流在飞行器上减少阻力。最后，控制流将被数据同化，以量化阻力的减少。