Complex Wake Flows

现代商用运输机使用开缝襟翼系统以在低速飞行期间获得高升力系数。在这些布局中，缝翼和主翼的尾流受到逆压梯度和流向流曲率的影响，这两者都是由相应的尾翼元件引起的。逆压梯度产生了强烈的横向尾流宽度增加，有时甚至嵌入区域的反向流动。这降低了升力系数并限制了最大升力。由于缺乏可靠的数据库，目前不可能精确预测这些影响，因此，不可能详细验证飞机设计中使用的电流模型。本研究项目的目的是建立一个综合的数据库，尾流在高雷诺数与强大的逆压梯度。虽然在先前的DFG-RBRF项目“逆压梯度中的尾流”中彻底研究了有和无逆压梯度的对称尾流的典型情况，但拟议的后续项目将为逆压梯度下弯曲尾流的更一般的典型情况创建一个可靠的数据库。通过光学手段进行的湍流解析模拟和详细的流动测量将产生沿弯曲尾流路径沿着的雷诺应力方程的详细平衡。湍流数据将用于推导应力方程和控制湍流长度尺度的传输方程的基于物理的扩展。两个湍流的Boltzold-stress模型将以这种方式扩展，并且具有最低模型误差的模型扩展的识别及其校准将通过基于贝叶斯推断的不确定性量化方法来完成。该项目将验证用于预测开缝机翼剖面的增升性能的湍流的Elderolds-Stress模型的改进。因此，本项目将建立一个三元翼型MD 30 P-30 N的连续性分辨数据库，其尾流的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担。圣彼得堡理工大学的团队将进行混合湍流解析模拟，而布伦瑞克工业大学将进行详细的风洞实验。RANS建模及其验证将由布伦瑞克工业大学和位于哥廷根的德国航天中心团队共享。风洞数据、混合RANS/LES模拟和扩展RANS模型的综合比较将在现代空气动力学的一个非常具有挑战性的应用领域中推进最先进的技术。

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.