An aerothermodynamic design optimization framework for hypersonic vehicles

In the aviation field great interest is growing in passengers transportation at hypersonic speed. This requires, however, careful study of the enabling technologies necessary for the optimal design of hypersonic vehicles. In this framework, the present work reports on a highly integrated design environment that has been developed in order to provide an optimization loop for vehicle aerothermodynamic design. It includes modules for geometrical parametrization, automated data transfer between tools, automated execution of computational analysis codes, and design optimization methods. This optimization environment is exploited for the aerodynamic design of an unmanned hypersonic cruiser flying at M∞=8 and 30 km altitude. The original contribution of this work is mainly found in the capability of the developed optimization environment of working simultaneously on shape and topology of the aircraft. The results reported and discussed highlight interesting design capabilities, and promise extension to more challenging and realistic integrated aerothermodynamic design problems

Exploring the fitness landscape of a realistic turbofan rotor blade optimization

Aerodynamic shape optimization has established itself as a valuable tool in the engineering design process to achieve highly efficient results. A central aspect for such approaches is the mapping from the design parameters which encode the geometry of the shape to be improved to the quality criteria which describe its performance. The choices to be made in the setup of the optimization process strongly influence this mapping and thus are expected to have a profound influence on the achievable result. In this work we explore the influence of such choices on the effects on the shape optimization of a turbofan rotor blade as it can be realized within an aircraft engine design process. The blade quality is assessed by realistic three dimensional computational fluid dynamics (CFD) simulations. We investigate the outcomes of several optimization runs which differ in various configuration options, such as optimization algorithm, initialization, number of degrees of freedom for the parametrization. For all such variations, we generally find that the achievable improvement of the blade quality is comparable for most settings and thus rather insensitive to the details of the setup. On the other hand, even supposedly minor changes in the settings, such as using a different random seed for the initialization of the optimizer algorithm, lead to very different shapes. Optimized shapes which show comparable performance usually differ quite strongly in their geometries over the complete blade. Our analyses indicate that the fitness landscape for such a realistic turbofan rotor blade optimization is highly multi-modal with many local optima, where very different shapes show similar performance

Discovering Representations for Black-box Optimization

The encoding of solutions in black-box optimization is a delicate, handcrafted balance between expressiveness and domain knowledge -- between exploring a wide variety of solutions, and ensuring that those solutions are useful. Our main insight is that this process can be automated by generating a dataset of high-performing solutions with a quality diversity algorithm (here, MAP-Elites), then learning a representation with a generative model (here, a Variational Autoencoder) from that dataset. Our second insight is that this representation can be used to scale quality diversity optimization to higher dimensions -- but only if we carefully mix solutions generated with the learned representation and those generated with traditional variation operators. We demonstrate these capabilities by learning an low-dimensional encoding for the inverse kinematics of a thousand joint planar arm. The results show that learned representations make it possible to solve high-dimensional problems with orders of magnitude fewer evaluations than the standard MAP-Elites, and that, once solved, the produced encoding can be used for rapid optimization of novel, but similar, tasks. The presented techniques not only scale up quality diversity algorithms to high dimensions, but show that black-box optimization encodings can be automatically learned, rather than hand designed.Comment: Presented at GECCO 2020 -- v2 (Previous title 'Automating Representation Discovery with MAP-Elites'

Optimal Energy-Driven Aircraft Design Under Uncertainty

Aerodynamic shape design robust optimization is gaining popularity in the aeronautical industry as it provides optimal solutions that do not deteriorate excessively in the presence of uncertainties. Several approaches exist to quantify uncertainty and, the dissertation deals with the use of risk measures, particularly the Value at Risk (VaR) and the Conditional Value at Risk (CVaR). The calculation of these measures relies on the Empirical Cumulative Distribution Function (ECDF) construction. Estimating the ECDF with a Monte Carlo sampling can require many samples, especially if good accuracy is needed on the probability distribution tails. Furthermore, suppose the quantity of interest (QoI) requires a significant computational effort, as in this dissertation, where has to resort to Computational Fluid Dynamics (CFD) methods. In that case, it becomes imperative to introduce techniques that reduce the number of samples needed or speed up the QoI evaluations while maintaining the same accuracy. Therefore, this dissertation focuses on investigating methods for reducing the computational cost required to perform optimization under uncertainty. Here, two cooperating approaches are introduced: speeding up the CFD evaluations and approximating the statistical measures. Specifically, the CFD evaluation is sped up by employing a far-field approach, capable of providing better estimations of aerodynamic forces on coarse grids with respect to a classical near-field approach. The advantages and critical points of the implementation of this method are explored in viscous and inviscid test cases. On the other hand, the approximation of the statistical measure is performed by using the gradient-based method or a surrogate-based approach. Notably, the gradient-based method uses adjoint field solutions to reduce the time required to evaluate them through CFD drastically. Both methods are used to solve the shape optimization of the central section of a Blended Wing Body under uncertainty. Moreover, a multi-fidelity surrogate-based optimization is used for the robust design of a propeller blade. Finally, additional research work documented in this dissertation focuses on utilizing an optimization algorithm that mixes integer and continuous variables for the robust optimization of High Lift Devices

Machine Learning for Fluid Mechanics

The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from field measurements, experiments and large-scale simulations at multiple spatiotemporal scales. Machine learning offers a wealth of techniques to extract information from data that could be translated into knowledge about the underlying fluid mechanics. Moreover, machine learning algorithms can augment domain knowledge and automate tasks related to flow control and optimization. This article presents an overview of past history, current developments, and emerging opportunities of machine learning for fluid mechanics. It outlines fundamental machine learning methodologies and discusses their uses for understanding, modeling, optimizing, and controlling fluid flows. The strengths and limitations of these methods are addressed from the perspective of scientific inquiry that considers data as an inherent part of modeling, experimentation, and simulation. Machine learning provides a powerful information processing framework that can enrich, and possibly even transform, current lines of fluid mechanics research and industrial applications.Comment: To appear in the Annual Reviews of Fluid Mechanics, 202

Multifidelity Aerodynamic Optimization of a Helicopter Rotor Blade

International audienceAmultifidelity optimization technique is applied to the design of a helicopter rotor blade to improve its performance in forward flight. This optimization technique is based on a surrogate model that replaces the high-fidelity computational fluid dynamics (CFD)/computational structural dynamics (CSD) simulations necessary to capture the three-dimensional unsteady effects generated in the flowfield of a complex blade geometry. The single low-fidelity model based on kriging methodology and generated by lifting-line simulations leads to a power benefit of 2.5%, which is not reproducible by an a posteriori high-fidelity CSD/CFD computation. The optimization procedure using cokriging surrogate models based on two levels of fidelity (lifting-line and CSD/CFD simulations) leads to a realistic blade planform, for which the power benefit is estimated at 2.2%. This optimized solution, obtained after a factor-ofsix reduction in CPU time, shows the advantages of using a cokriging surrogate model (rather than a single-fidelity kriging model) for aerodynamic optimizations

Euromech Colloquium 509: Vehicle Aerodynamics. External Aerodynamics of Railway Vehicles, Trucks, Buses and Cars - Proceedings

During the 509th Colloquium of the Euromech society, held from March 24th & 25th at TU Berlin, fifty leading researchers from all over europe discussed various topics affecting both road vehicle as well as railway vehicle aerodynamics, especially drag reduction (with road vehicles), cross wind stability (with trains) and wake analysis (with both). With the increasing service speed of modern high-speed railway traffic, aerodynamic aspects are gaining importance. The aerodynamic research topics comprise both pure performance improvements, such as the continuous lowering of aerodynamic drag for energy efficiency, as well as safety relevant topics, such as cross-wind stability. The latter topic was most recently brought to attention when a swiss narrow-gauge train overturned during the severe storm Kyrill in january 2007. The shape of the train head usually has largest influence on cross wind stability. Slipstream effects of passing trains cause aerodynamic loads on objects and passengers waiting at platforms. The strength of the slipstream is determined by both the boundary layer development along the length of the train and the wake developing behind the tail of the train. Since high-speed trains can be considered to be as smooth as technically possible, attention is drawn to the wake region. The wake of the train again is also one important factor for the total drag of a train. Due to the fact that trains are bidirectional, optimisation of the leading car of a train with respect to drag and cross wind performance while simultaneously minimising the wake of the train for drag and slipstream performance is a great challenge. Modern optimisation tools are used to aid this multi-parameter multi-constraint design optimisation in conjunction with both CFD and wind tunnel investigations. Since many of the aerodynamic effects in the railway sector are of similar importance to road vehicles, the aim of the colloquium is to bridge the application of shape optimisation principles between rail- and road vehicles. Particular topics to be addressed in the colloquium are: Drag, Energy consumption and emissions: Due to increase in energy cost, drag reduction has gained focus in the past years and attention will grow in the future. Pressure induced drag is of common importance for both rail- and road vehicles. The optimisation of head- and tail shape for road vehicles as well as for bi-directional vehicles (trains) is in the focus. Interference drag between adjacent components shall also be treated. Slipstream Effects: Are a safety issue for high-train operation (Prams sucked into track due to train-induced draught flows) when trains passing platforms at high speeds. For Road vehicles, the ride stability of overtaking cars is influenced by the wake of the leading trucks and busses. Common interest is the minimisation of wake effects for both rail and road vehicles. Cross-Wind Safety, Ride stability under strong winds: Both are safety issues for rail- and road vehicles. Aerodynamic forces shall be minimised (roll moment for trains and also yaw moment for road vehicles). Strategies for Vehicle shape optimisation (head, tail and roof shape) in order to minimise aerodynamic moments. Possibilities of Flow control. Optimisation strategies: Parametrisation, analyses (CFD), Optimisation tools and methods, Application to Drag, Cross-Wind, Ride stability and Snow issue