The high-lift performance of a multi-element airfoil was optimized by using neural-net predictions that were trained using a computational data set. The numerical data was generated using a two-dimensional, incompressible, Navier-Stokes algorithm with the Spalart-Allmaras turbulence model. Because it is difficult to predict maximum lift for high-lift systems, an empirically-based maximum lift criteria was used in this study to determine both the maximum lift and the angle at which it occurs. The 'pressure difference rule, ' which states that the maximum lift condition corresponds to a certain pressure difference between the peak suction pressure and the pressure at the trailing edge of the element, was applied and verified with experimental observations for this configuration. Multiple input, single output networks were trained using the NASA Ames variation of the Levenberg-Marquardt algorithm for each of the aerodynamic coefficients (lift, drag and moment). The artificial neural networks were integrated with a gradient-based optimizer. Using independent numerical simulations and experimental data for this high-lift configuration, it was shown that this design process successfully optimized flap deflection, gap, overlap, and angle of attack to maximize lift. Once the neural nets were trained and integrated with the optimizer, minimal additional computer resources were required to perform optimization runs with different initial conditions and parameters. Applying the neural networks within the high-lift rigging optimization process reduced the amount of computational time and resources by 44% compared with traditional gradient-based optimization procedures for multiple optimization runs. Greenman, Roxana M. Ames Research Center NEURAL NETS; ANGLE OF ATTACK; NAVIER-STOKES EQUATION; LIFT; INCOMPRESSIBLE FLOW; COMPUTERS; AIRFOILS; AERODYNAMIC CONFIGURATIONS; AERODYNAMIC COEFFICIENTS; TURBULENCE MODELS; TRAILING EDGES; SUCTION; GRADIENTS; DRAG; FLAPPING; DEFLECTION; ALGO..

Computer simulations is a fundamental tool of the design process in many engineering disciplines including aerospace engineering. However, although high-fidelity numerical models are accurate, they can be computationally expensive with evaluation time for a single design as long as hours, days or even weeks. Simulation-driven design using conventional optimization techniques may be therefore prohibitive.This book explores the alternative: performing computationally efficient design using surrogate-based optimization, where the high-fidelity model is replaced by its computationally cheap but still reasonably accurate representation: a surrogate. The emphasis is on physics-based surrogates. Application-wise, the focus is on aerodynamics and the methods and techniques described in the book are demonstrated using aerodynamic shape optimization cases. Applications in other engineering fields are also demonstrated.State-of-the-art techniques and a depth of coverage never published before make this a unique and essential book for all researchers working in aerospace and other engineering areas and dealing with optimization, computationally expensive design problems, and simulation-driven design.

This book reports on the latest numerical and experimental findings in the field of high-lift technologies. It covers interdisciplinary research subjects relating to scientific computing, aerodynamics, aeroacoustics, material sciences, aircraft structures, and flight mechanics. The respective chapters are based on papers presented at the Final Symposium of the Collaborative Research Center (CRC) 880, which was held on December 17-18, 2019 in Braunschweig, Germany. The conference and the research presented here were partly supported by the CRC 880 on “Fundamentals of High Lift for Future Civil Aircraft,” funded by the DFG (German Research Foundation). The papers offer timely insights into high-lift technologies for short take-off and landing aircraft, with a special focus on aeroacoustics, efficient high-lift, flight dynamics, and aircraft design.