© 2021, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.Uncertainty-based Design Optimization techniques present a powerful toolkit to achieve robust and reliable optimal designs in the presence of uncertainty, having probability theory as its backbone. The central limit theorem is a key concept in probability theory due to its notorious usefulness in a wide number of statistical problems. However, it seems that the theorem applicability to problems involving non-normal probability distributions has led to a mistaken belief that stochastic responses resulting from the propagation of input uncertainties naturally tend to be normally distributed. The purpose of this paper is to briefly discuss why this is not necessarily true and why this misconception can be detrimental. Three reasons are identified and illustrated through simple test cases using Monte Carlo simulations and Anderson-Darling test.

Copyright © 2018 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.The strut-braced wing aircraft configuration promises to reduce fuel burn by enabling higher spans that reduce lift-induced drag. A successful design for this configuration depends on a careful trade-off between the various sources of drag and structural weight. When using computational fluid dynamic tools for aerodynamic shape optimization, generating high-quality structured meshes for the strut-braced wing configuration becomes challenging, especially near junctions. Furthermore, mesh deformation procedures frequently generate negative volume cells when applied to these structured meshes during optimization. This paper addresses this issue by using overset meshes and a component-based parametrization technique to achieve a flexible design optimization cycle capable of handling changing junctions. This study uses this approach to minimize drag of the PADRI 2017 strut-braced wing benchmark for a fixed lift constraint at transonic flight conditions. The drag of the optimized configuration is 15% lower than the baseline due to the reduction of shocks and separation in the wing-strut junction region. This result represents an example in which high-fidelity modeling is required to quantify the benefits of a new aircraft configuration and address potential issues during the conceptual design.

© 2018, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.The strut-braced wing aircraft configuration promises to reduce fuel burn by enabling higher spans that reduce the lift-induced drag. A successful design for this configuration depends on a careful trade-off between induced drag, interference drag, and structural weight. Previous work used highfidelity tools integrated in adjoint-based optimization frameworks to address the compromise among these design drivers in terms of an aerodynamic shape optimization problem, where drag coefficient was minimized subject to thickness constraints. When performing aerodynamic shape optimization based on structured CFD meshes, the generation of high-quality multiblock meshes for this configuration and is challenging, especially near junctions. Furthermore, mesh deformation procedures frequently generated negative volume cells when applied to these multiblock meshes. We address this issue by developing overset meshes and a component-based parametrization technique to achieve a robust design optimization cycle capable of handling changing junctions. We use this approach to minimize drag of a transonic strut-braced wing aircraft for a fixed lift constraint. The drag of the optimized configuration is 15% lower than the baseline due to improvements in the overall lift-induced drag of the wing and strut system, and also due to the reduction of shocks and separation in the wingstrut junction region. We also conduct the aerodynamic shape optimization of the junction region alone, and we achieved a drag reduction of 6% for this case. These results are an example where high-fidelity modeling is required to quantify the benefit of a new aircraft configuration.

Copyright © 2018 by the American Institute of Aeronautics and Astronautics, Inc.Mesh generation for high-fidelity computational fluid dynamics simulations and aerodynamic shape optimization is a time-consuming task. Complex geometries can be accurately modeled using overset meshes, whereby multiple high-quality structured meshes corresponding to different aircraft components overlap to model the full aircraft configuration. However, from the standpoint of geometry manipulation, most methods operate on the entire geometry rather than on separate components, which diminishes the advantages of overset meshes. To address this issue, a geometry module is introduced that operates on individual components and automatically computes their intersections to update overset meshes during optimization. Reverse-mode automatic differentiation is applied to compute partial derivatives across this geometry module so that it fits into an optimization framework that uses a hybrid adjoint method (known as ADjoint) to efficiently compute gradients for a large number of design variables. By using these automatically updated meshes and the corresponding derivatives, the aerodynamic shape of the DLR-F6 geometry is optimized while allowing changes in the wing-fuselage intersection. Sixteen design variables control the fuselage shape, and 128 design variables determine the wing surface. Under transonic flight conditions, the optimization reduces drag by 15 counts (5%) as compared with the baseline design.

© 2017, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.Structured mesh computational fluid dynamic solvers are inherently faster than unstructured solvers, which is particularly advantageous for aerodynamic design optimization, where hundreds of flow solutions are required. However, generating body-fitted multiblock meshes for complex geometries is challenging and is a time consuming task. The overset mesh technique greatly reduces the manual effort required to generate meshes over complex geometries by overlapping a series of simpler meshes. However, generating the necessary connectivity information between meshes in a robust and computationally efficient manner remains a challenge. We address this challenge by developing an efficient parallel overset grid assembly technique based on implicit hole cutting that is fully automatic. The method is fully parallel and scales to hundreds of processors. Several optimizations of the Common Research Model wing-body-tail configuration are performed using the meshes generated by our technique. We compare the best drag reduction obtained from multiblock and overset meshes using two different artificial dissipation schemes. The smooth, highly orthogonal overset meshes produce better results than the multiblock meshes, by up to 3 drag counts. An application to rotorcraft design is also presented. The demonstrated meshing flexibility and accurate transonic solutions make the overset mesh technique ideally suited for aerodynamic shape optimization.

© 2017 American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.Mesh generation for high-fidelity CFD simulation and aerodynamic shape optimization is a timeconsuming task. We can model complex geometries accurately using overset meshes where multiple high-quality structured meshes corresponding to different aircraft components overlap to model the full aircraft configuration. Nevertheless, from the geometry manipulation standpoint, most methods operate on the entire geometry rather than on each component, which diminishes the advantages of overset meshes. To address this issue, we introduce a geometry module that operates on individual components and automatically computes their intersections to automate the overset mesh updates during optimization. This method is also differentiated to compute derivatives with respect to component-based design variables and is integrated within an optimization framework. Using these automatically updated meshes and the corresponding derivatives, we perform aerodynamic shape optimization including the wing-body intersection for the DLR-F6 geometry and achieve a reduction of 15 drag counts (5%) compared to the baseline design.

© 2017 Emerald Publishing Limited.Purpose - Multidisciplinary design frameworks elaborated for aeronautical applications require considerable computational power that grows enormously with the utilization of higher fidelity tools to model aeronautical disciplines like aerodynamics, loads, flight dynamics, performance, structural analysis and others. Surrogate models are a good alternative to address properly and elegantly this issue. With regard to this issue, the purpose of this paper is the design and application of an artificial neural network to predict aerodynamic coefficients of transport airplanes. The neural network must be fed with calculations from computational fluid dynamic codes. The artificial neural network system that was then developed can predict lift and drag coefficients for wing-fuselage configurations with high accuracy. The input parameters for the neural network are the wing planform, airfoil geometry and flight condition. An aerodynamic database consisting of approximately 100,000 cases calculated with a full-potential code with computation of viscous effects was used for the neural network training, which is carried out with the back-propagation algorithm, the scaled gradient algorithm and the Nguyen-Wridow weight initialization. Networks with different numbers of neurons were evaluated to minimize the regression error. The neural network featuring the lowest regression error is able to reduce the computation time of the aerodynamic coefficients 4,000 times when compared with the computing time required by the full potential code. Regarding the drag coefficient, the average error of the neural network is of five drag counts only. The computation of the gradients of the neural network outputs in a scalable manner is possible by an adaptation of back-propagation algorithm. This enabled its use in an adjoint method, elaborated by the authors and used for an airplane optimization task. The results from that optimization were compared with similar tasks performed by calling the full potential code in another optimization application. The resulting geometry obtained with the aerodynamic coefficient predicted by the neural network is practically the same of that designed directly by the call of the full potential code. Design/methodology/approach - The aerodynamic database required for the neural network training was generated with a full-potential multiblock-structured code. The training process used the back-propagation algorithm, the scaled-conjugate gradient algorithm and the Nguyen-Wridow weight initialization. Networks with different numbers of neurons were evaluated to minimize the regression error. Findings - A suitable and efficient methodology to model aerodynamic coefficients based on artificial neural networks was obtained. This work also suggests appropriate sizes of artificial neural networks for this specific application. We demonstrated that these metamodels for airplane optimization tasks can be used without loss of fidelity and with great accuracy, as their local minima might be relatively close to the minima of the original design space defined by the call of computational fluid dynamics codes. Research limitations/implications - The present work demonstrated the ability of a metamodel with artificial neural networks to capture the physics of transonic and subsonic flow over a wing-fuselage combination. The formulation that was used was the full potential equation. However, the present methodology can be extended to model more complex formulations such as the Euler and Navier-Stokes ones. Practical implications - Optimum networks reduced the computation time for aerodynamic coefficient calculations by 4,000 times when compared with the full-potential code. The average absolute errors obtained were of 0.004 and 0.0005 for lift and drag coefficient prediction, respectively. Airplane configurations can be evaluated more quickly. Social implications - If multidisciplinary optimization tasks for airplane design become more efficient, this means that more efficient airplanes (for instance less polluting airplanes) can be designed. This leads to a more sustainable aviation. Originality/value - This research started in 2005 with a master thesis. It was steadily improved with more efficient artificial neural networks able to handle more complex airplane geometries. There is a single work using similar techniques found in a conference paper published in 2007. However, that paper focused on the application, i.e. providing very few details of the methodology to model aerodynamic coefficients.

© 2015, American Institute of Aeronautics and Astronautics Inc, AIAA. All rigths reserved.The truss-braced wing is an aircraft configuration that has the potential to be more efficient than conventional configurations. The coupling between aerodynamics, structures, and propulsion that is present in this configuration significantly increases the complexity of the design, but it offers the potential for a large improvement in performance over the cantilever wing, which we aim to achieve through numerical optimization in this study. Previous studies have primarily used low-fidelity tools that rely on empirical equations or low-order models. Here, we perform high-fidelity aerodynamic shape optimization using the RANS equations with 750 shape and twist design variables. Through optimization, we are able to reduce the drag by more than 28% compared to the baseline geometry, obtaining a final L/D ratio of 25.3, which is a lower than expected value due to high interference drag caused by limited shape design flexibility in the junctions. Despite the limitations, we believe that these results can provide a useful benchmark for future studies involving the truss-braced wing configuration, in addition to revealing insights regarding the complex aerodynamic phenomena associated with this configuration.

© 2015 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.Multi-disciplinary frameworks for airplane optimal design require a lot of computational power, which grows enormeously if higher fidelity tools are used to model aeronautical disciplines like aerodynamics, loads, flight dynamics, performance, and structural analysis. In order to address properly and elegantly this issue, surrogate models are employed. In this highlight, the main goal of the present work is the design and application of an artificial neural network to predict aerodynamic coefficients of wing-body configurations of transport airplanes in order to replace a computational fluid dynamic code for future optimizations. The artificial neural network system that was developed is able to predict lift and drag coefficients for wing-fuselage configurations of transport airplanes. The input parameters for the neural network are the wing planform, airfoil geometry, and flight condition. An aerodynamic database consisting of 100,000 cases evaluated with the BLWF V2.81 full-potential code is used for the neural network training. The neural network training is carried out with the back-propagation algorithm, the scaled gradient algorithm, and the Nguyen-Wridow weight initialization. Networks with different numbers of neurons are evaluated in order to minimize the regression error. The optimum networks reduce the computation time of the aerodynamic coefficients in 4000 times when compared with BLWF V2.81, and with an average error of only five counts for the drag coefficient. We present an adaptation of the back-propagation algorithm that allows the computation of the gradients of the neural network outputs in a scalable manner, and then we use it for an airplane optimization task. The results are then compared with similar tasks that were performed by calling the full potential code. An adjoint-method is employed to use ANN in constraint functions as well. The resulting geometry obtained with the ANN methodology is compared to one designed directly with BLWF. The optimal geometry is practically the same, and the drag coefficients predicted by each method differ in four drag counts.

The present work is concerned with the accurate modeling of transport airplanes. This is of primary importance to reduce aircraft development risks and because multi-disciplinary design and optimization (MDO) frameworks require an accurate airplane modeling to carry out realistic optimization tasks. However, most of them still make use of tail volume coefficients approach for sizing horizontal and vertical tail areas. The tail-volume coefficient method is based on historical aircraft data and it does not consider configuration particularities like wing sweepback angle and tail topology. A methodology based on static stability and controllability criteria was elaborated and integrated into a MATLAB application for airplane design. Immediate advantages with the present methodology are the design of realistic tail surfaces and properly sized airplanes. Its validation was performed against data of five airliners ranging from the regional jet CRJ-100 to the Boeing 747-100 intercontinental airplane. An existing airplane calculator application incorporated the present tail-sizing methodology. In order to validate the updated application, the Fokker 100 airliner was fully conceptually designed using it.

This paper describes a multi-disciplinary design and optimization framework tailored for the conceptual development of high-altitude solar-powered unmanned aerial vehicles. The aircraft baseline configuration that the framework is able to handle is very similar to that of Zephyr, which is developed by the UK based company QinetiQ. The disciplines of aerodynamics, structures, stability, weight, and systems were considered and integrated into a modeFrontier® workflow, capable of providing a relatively simple sizing, but highly realistic airplane.

The conceptual design of an aircraft involves several multidisciplinary analisys in order to reach the optimum configuration according to market and certification requirements. An application is being developed in the Technological Institute of Aeronautics aiming to group such analisys in a single interface. Previously, aerodynamic drag was calculated through semi-analytical methods and empirical data interpolations, demanding strong computational resources, which became critical in optimizations processes. In this work, an artifical neural network was developed to replace the old aerodynamic module. This neural network was trained with a database containing more than one hundred thousand configurations, which were previously analysed with the BLWF 28. The metamodel gave acurrate results with low computational costs, thus attaining the required performance for multidisciplinary optimizations. Copyright © 2012 by the American Institute of Aeronautics and Astronautics, Inc.