Aerodynamic improvement methods for a medium-altitude long-endurance UAV wing

Aerodynamic studies are critical in the development of aircraft and aircraft technology. To this end, a study of three means for improving the aerodynamic performance using range and endurance metrics is presented in this thesis to guide future design iterations of a mediumaltitude long-endurance tactical unmanned aerial vehicle, the Hydra Technologies S45 Bàalam. The results presented are obtained using computational fluid dynamics simulations and are therefore of high fidelity. Surrogate-based modeling using Gaussian processes is used to reduce the number of computationally-intensive simulations required in the optimizations performed. A Bayesian efficient global optimization algorithm using expected improvement is used in the two optimization series. The first set of results establishes the baseline performance of the wing and assesses the impact of an optionally-installed upswept blended winglet on the development of forces on the wing. Results show that the winglet consistently improves the wing aerodynamics. The spanwise distribution of forces shows that the presence of the winglet introduces a component of force in the direction of thrust owing to the curved shape and flow field, thus reducing drag at the wing tip. The second set of results presents an optimization study on global wing parameters. Three planform parameters, the aspect ratio, taper ratio, and sweep angle, as well as the out-of-plane geometric twist angle, are the design variables. Results show that possible improvements are modest at best unless the aspect ratio is increased because there are no significant design levers to increase the lift without causing a greater increase in the drag. Wing twist is identified to be a parameter useful in manipulating the angle of attack at which the maximum lift-to-drag ratio occurs. The third set of results focuses on the aerodynamic enhancements achievable through active morphing of the flexible upper surface of the wing in flight using actuated rods. Three amplitudes of displacement of the deformable surface are used to represent the morphing process simulated at a range of angles of attack and flow speeds over the full flight envelope of the vehicle. Up to 4 % improvement is obtained on the range and endurance metrics. Improvements are not obtained at all flight conditions tested. It is observed that the morphing process gains influence as the Reynolds number becomes higher because of the associated increase in turbulent flow on the wing which can be delayed to obtain improved aerodynamic coefficients

Multidisciplinary design optimization under uncertainty of a blended-wing-body aircraft for passenger transport

L’un des plus grands défis dans la conception amont d’aéronefs non conventionnels est l’incertitude découlant des modèles numériques qui peuvent être inadaptés à plusieurs niveaux en raison d’un manque de connaissances de la future configuration finale. Les hypothèses sous-jacentes retenues pour les aéronefs conventionnels peuvent ne plus être valides pour une configuration significativement différente. Dans la phase conceptuelle d’un aéronef ou plus généralement d’un système mécanique, une configuration initiale est habituellement obtenue en résolvant un problème d’optimisation déterministe contraint, pour lequel le critère de performance peut être la traînée, la consommation de carburant ou le coût d'exploitation, et les contraintes peuvent être les distances de décollage et d’atterrissage, ou des critères de maniabilité. Tout au long du processus de développement aval, comme dans les phases détaillées de conception et de fabrication, la conception initiale peut faire l’objet de modifications relativement mineures en raison de contraintes supplémentaires qui n’ont pas été prises en compte à la phase conceptuelle. Ces modifications peuvent entraîner une dégradation significative des performances et/ou des violations de contraintes. Il vaut donc la peine de trouver, dans le voisinage de la conception initiale, une nouvelle conception réalisable qui est la moins sensible aux modifications à venir. Dans le cadre de cette thèse, un nouveau problème de conception optimale robuste est formulé visant à trouver le paramétrage garantissant des performances optimales et la satisfaction des contraintes avec des niveaux de risques acceptables donnés. Comme la solution optimale robuste est recherchée dans le voisinage d’une conception optimale déterministe initiale, les développements de Taylor de deuxième ordre sont utilisés pour obtenir des approximations du critère de performance et des fonctions de contrainte. Sous cette forme, le problème de conception optimale robuste est généralement insoluble numériquement en raison de calculs d’intégration multidimensionnelle des distributions statistiques des incertitudes du processus en aval. Pour surmonter cet inconvénient, une nouvelle méthode pour convertir ce problème sous une forme quadratique à contraintes quadratiques (QCQP) déterministe est proposée. Cette transformation permet l’utilisation de tous les algorithmes disponibles et efficaces dans la résolution des QCQP. La méthodologie proposée est ensuite appliquée pour l’optimisation robuste d’une aile volante pour le transport de passagers. Les interactions entre les principales disciplines sont traitées efficacement à l’aide de modèles disciplinaires particulièrement adaptés à l’aile volante. L’objectif final est de trouver une configuration d’aile volante qui offre les meilleures performances tout en validant les contraintes les plus importantes en situation réelle avec des niveaux de confiance prédéfinis. Par rapport aux recherches publiées existantes, les travaux présentés sont basés sur des modèles aussi modernes et adaptés que possible aux ailes volantes et l’approche d’optimisation tient compte du manque actuel de connaissances sur les configurations d’aéronefs non conventionnels.One of the biggest challenges in the design of unconventional aircraft configurations is the uncertainty stemming from the numerical models which may be flawed on several levels owing to a lack of knowledge. Underlying assumptions which held for conventional aircraft may no longer hold for a drastically different configuration. At the conceptual design phase of an aircraft or more generally of a mechanical system, an initial design is ordinarily obtained by solving a constrained deterministic optimization problem, where the performance criterion can be the drag, block fuel, cash operating cost, or direct operating cost, and the constraints can be takeoff and landing distances, or manoeuvrability criteria, for example. Throughout the downstream development process, such as in the detailed design and manufacturing phases, the initial design may be subject to relatively small modifications owing to additional constraints which have not been taken into account at the conceptual phase. These modifications can lead to significant performance degradation and/or constraint violations. It is therefore worthwhile to find, in the neighbourhood of the initial design, a new feasible design that is less sensitive to those coming modifications. In this thesis, a new robust optimal design problem is formulated which aims to find the design guaranteeing optimal performance and constraint satisfaction with given acceptable risks on either of them. As the robust optimal solution is sought in the neighbourhood of an initial design, second-order Taylor expansions are used to approximate the performance criterion and constraint functions. In this form, this robust optimal design problem is generally numerically intractable due to multidimensional integration computations with respect to underlying statistical distributions of uncertainties of the downstream process. To overcome this drawback, a new method to convert this problem into a deterministic quadratically-constrained quadratic program (QCQP) is proposed. This transformation allows the use of all the available and efficient algorithms for solving QCQPs. This proposed methodology is then applied for the robust design optimization of a blended-wing-body (BWB) aircraft for passenger transport. Interactions among the main disciplines are handled efficiently using disciplinary models particularly adapted to the BWB case. The end goal is to find a BWB design that offers the best performance while satisfying the most important real-life constraints. Compared to existing published research, the work presented is based on models which are as modern and tailored to BWB aircraft as possible and the optimization approach takes into consideration the lack of knowledge on unconventional aircraft configurations

Surrogate model development for optimized blended-wing-body aerodynamics

International audienceIn the conceptual design phase of conventional-configuration aircraft, calibrated low-fidelity methods provide sufficiently accurate estimates of aerodynamic coefficients. It has been observed, however, that for blended-wing-body aircraft, important flow effects are not captured adequately with low-fidelity aerodynamic tools. Consequently, high-fidelity methods become necessary to study blended-wing-body aerodynamics. Since repeated function calls are needed in an optimization loop, high-fidelity analysis is prohibitively expensive in the conceptual design phase, where several optimization scenarios are considered. In this paper, the integration of high-fidelity data for blended-wing-body aircraft for a mission calculation module is presented. A surrogate model based on Gaussian processes (GPs) with acceptably low prediction error is sought as an alternative to RANS CFD. Three adaptations are considered: sparse GPs, mixtures of GP approximators, and need-based filtering for GP. The results provide benchmark values for this case and show that the combination of subsonic and transonic behaviors in the training set is problematic and that, for the considered datasets, sparse GP models suffer from oversmoothing while mixtures of GPs models suffer from overfitting. From the error levels, it is observed that a GP with an infinitely-differentiable squared exponential kernel based on reduced data pertinent to mission analysis is the most effective option