Augmenting flow simulations with experimental data to improve aerodynamic efficiency

数值模拟在气动设计中扮演着重要的角色，因为实验测量通常是有限的，很难在流动的所有区域进行测量。模拟可以提供比实验多得多的信息，但建模假设是必要的，因为模拟真实的流动条件在计算上不容易处理。例如，在许多工业应用中，模拟使用湍流模型来求解时间平均方程。然后使用有限的可用实验数据来验证所得到的模拟结果。这个项目通过将实验数据作为模拟的输入来研究它的更积极的作用。将不完整和不确定的实验测量结果输入到低保真模拟中，以产生模拟实验中大尺度特征的混合流场。这一过程被称为数据同化，旨在解决实验数据的不足和模拟中的模棱两可，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保真模拟所需的资源往往非常昂贵。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。这些预测不仅对设计至关重要，而且对于理解如何通过控制来操纵流动以减少阻力和提高空气动力学效率也是至关重要的。为了预测和操纵工业流动，需要一个能够容纳三维流动和控制设备的严格框架。该项目最终将产生一种工具，该工具可以将复杂几何形状的有限实验数据转换为完全可分辨的速度场，以改进气动设计。这项工作将对模型飞行器周围三维速度场的不完全实验测量进行数据同化，以改进基于模拟的平均流量预测。试验数据将由一个被故意破坏的模拟组成，以便与实验数据相似。换句话说，输入的数据将是脱节的、有噪声的和零星的。此外，将系统地减少这些数据，以确定成功的数据同化所需的最低测量次数。一旦实现了这一点，该框架将应用于风洞的实验数据。改进后的平均流场将被用来设计一种控制策略，通过在车辆上使用合成射流来减少阻力。最后，受控流动将被数据同化，以量化阻力的减少。

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.