Complex Wake Flows

现代商用运输机使用缝隙襟翼系统在低速飞行时获得高升力系数。在这些构型中，板条和主翼的尾迹受到不利的压力梯度和流向流曲率的影响，这两者都是由各自的尾翼元件引起的。逆压梯度使尾迹宽度横向增大，有时甚至形成逆流嵌入区域。这降低了升力系数并限制了最大升力。目前还不可能准确预测这些影响，因为缺乏可靠的数据库，因此不可能对飞机设计中使用的当前流动模型进行详细验证。本研究项目的目的是建立一个高雷诺数、强逆压梯度的尾流综合数据库。之前的DFG-RBRF项目“逆压梯度中的尾迹流动”对对称尾迹有或没有逆压梯度的典型情况进行了深入的研究，而拟议的后续项目将为逆压梯度下弯曲尾迹的更一般的正则流动情况建立一个可靠的数据基础。湍流解析模拟和光学方法的详细流动测量将产生沿弯曲尾迹流动路径的雷诺应力方程的详细平衡。湍流数据将被用来推导基于物理的应力方程和控制湍流长度尺度的传输方程的扩展。对两种雷诺应力湍流模型进行推广，利用基于贝叶斯推理的不确定度量化方法，对模型误差最小的模型扩展进行识别和标定。该项目将验证雷诺-应力湍流模型的改进，以预测缝隙机翼部分的高升力性能。因此，该项目将建立一个三段翼型MD 30P-30N的湍流分辨数据库，该数据库的尾迹流场测量可从文献中获得。该项目的研究工作将由三个著名的研究团队分享。圣彼得堡理工大学的团队将进行混合湍流分解模拟，而TU Braunschweig将进行详细的风洞实验。RANS建模及其验证将由TU Braunschweig和德国航天中心在Göttingen的团队共享。风洞数据的综合比较、混合RAN/LES模拟和扩展的RANS模型将推动现代空气动力学这一非常具有挑战性的应用领域的最新技术。

Modern commercial transport aircraft use slotted flap systems for obtaining high lift coefficients during low-speed flight. The wakes of slats and main wings present in these configurations are subjected to the adverse pressure gradient and streamwise flow curvature, both induced by the respective trailing wing element. The adverse pressure gradient generates a strong lateral increase of wake width and sometimes even embedded regions of reverse flow. This reduces the lift coefficient and limits maximum lift. Precise predictions of these effects are presently not possible as there is a lack of a trustworthy data base and hence, detailed validation of current flow models used in aircraft design is impossible. The objective of the present research project is to establish a comprehensive data base of wake flows at high Reynolds numbers with strong adverse pressure gradients. While the canonical case of a symmetrical wake with and without adverse pressure gradient was thoroughly investigated in the preceding DFG-RBRF Project “Wake Flows in Adverse Pressure Gradient”, the proposed follow-on project will create a sound data base for the more general canonical flow case of the curved wake under adverse pressure gradient. Turbulence resolving simulations and detailed flow measurements by optical means will generate the detailed balance of the Reynolds stress equations along the curved wake flow path. The turbulence data will be used to derive physics-based extensions to the stress equations and the transport equation governing the turbulence length scale. Two Reynolds-stress models of turbulence will be extended in that way, and identification of the model extensions with the lowest model error and their calibration will be accomplished by methods of Uncertainty Quantification based on Bayesian Inference. The project will validate the improvement of the Reynolds-Stress models of turbulence for predicting the high-lift performance of a slotted wing section. Therefore, the project will establish a turbulence-resolving data base for the three-element airfoil MD 30P-30N, for which flow field measurements of the wake are available from literature. The research work of the project will be shared between three renowned research teams. The team of St Petersburg Polytechnic University will undertake hybrid turbulence resolving simulations, whereas TU Braunschweig will perform detailed wind tunnel experiments. RANS modelling and its validation will be shared by TU Braunschweig and the DLR team in Göttingen. Comprehensive comparisons of wind tunnel data, hybrid RANS/LES simulations, and the extended RANS models will advance the state of the art in a very challenging application field of modern aerodynamics.