Aerodynamic Inverse Design Framework Using Discrete Adjoint Method

Brézillon, Joël; Abu-Zurayk, Mohammad

doi:10.1007/978-3-642-35680-3_58

Cited by 12 publications

(10 citation statements)

References 9 publications

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“…To bring the coupled adjoint approach into application, a drag reduction optimization was performed under the constraint of constant lift. The previous test case was taken for this optimization, where the python-based optimization environment used; Pyranha, [4] employed a conjugate gradient optimization algorithm. Figure 3 shows the convergence of the cost function throughout the optimization.…”

Section: Resultsmentioning

confidence: 99%

Development of the Adjoint Approach for Aeroelastic Wing Optimization

Abu-Zurayk¹,

Brézillon²

2013

Notes on Numerical Fluid Mechanics and Multidisciplinary Design

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In the context of gradient-based optimization techniques for multidisciplinary problems an efficient approach was sought to evaluate the gradient of the cost function with respect to the design variables; also called the sensitivities. The traditional approach to calculate the sensitivities, the finite differences, can become prohibitively expensive in high-fidelity optimizations. For this reason an existing adjoint approach was suggested to be further developed in order to suit coupled aero-structural systems. Then the developed approach was evaluated and tested. The results showed that the approach can provide accurate sensitivities in a very efficient way.

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Section: Resultsmentioning

confidence: 99%

Development of the Adjoint Approach for Aeroelastic Wing Optimization

Abu-Zurayk¹,

Brézillon²

2013

Notes on Numerical Fluid Mechanics and Multidisciplinary Design

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“…The optimizer uses the Sequential Quadratic Programming (SQP) algorithm from the optimization framework Pyranha (Python based framework for optimizations relying on high-fidelity approach). 13 The optimizer suggests the outer shape design variables, and the CFD and the computational structural mechanics (CSM) models are updated accordingly. On the CFD side, the update is applied directly onto the grid using mesh deformation, whereas on the CSM side, a new structural model is built into the updated geometry.…”

Section: Gradient-based Aero-structural Optimization Architecturementioning

confidence: 99%

Aero-Structural Optimization of the NASA Common Research Model

Keye

Klimmek

Abu-Zurayk

et al. 2017

18th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference

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A combined aerodynamic and structural, gradient-based optimization has been performed on the NASA/Boeing Common Research Model civil transport aircraft configuration. The computation of aerodynamic performance parameters includes a Reynoldsaveraged Navier-Stokes CFD solver, coupling to a linear static structural analysis using the finite element method to take into account aero-elastic effects. Aerodynamic performance gradients are computed using the adjoint approach. Within each optimization iteration, the wing's structure is sized via a gradient-based algorithm and an updated structure model is forwarded for the performance analysis. In this pilot study wing profile shape is optimized in order to study engine installation effects. This setting was able to improve the aerodynamic performance by 4% I. IntroductionT he aircraft industry is continuously in the search for advanced designs that consume less fuel and produce less emissions. The Flightpath 2050 a provides a vision for Europe's aviation systems and industries by the year 2050. Concerning the environmental aspects, the vision expects a high reduction in CO 2 and NOx emission per passenger kilometer, in addition to a 65% reduction in noise emission of air vehicles -by 2050relative to those of the year 2000. On the other hand, the worldwide air traffic is predicted to grow by 4-5% per year, which makes the fulfillment of these expectations a highly challenging task.Aircraft optimization, which can be performed using numerical techniques, is an indispensable choice to face the Flightpath 2050 challenges and to increase the aircraft's efficiency. An optimization that incorporates more than one discipline is called a multidisciplinary design optimization (MDO). Employing MDO in aircraft design, yields realistic designs that fulfill the constraints of the engaged disciplines and reduces the development risks. Moreover, MDO reduces the design cycle of an aircraft. However, the complexity of the problem in MDO significantly increases when compared to single-disciplinary optimizations. Therefore, optimization algorithms that drive high-fidelity MDO need to be particularly efficient.There are mainly two types of optimization algorithms, depending on whether the gradients information is required throughout the optimization; gradient-free algorithms and gradient-based algorithms. In the former, only the objectives and constraints values are required by the end of a design (or optimization) iteration. In the latter, on the other hand, the values and the gradients of the objectives and the constraints are required throughout the search for the optimum. The extra expense of these algorithms pays off as efficiency; gradient-based algorithms can reach an optimum more efficiently than gradient-free algorithms, at least the nearest (potentially local) optimum.These characteristics of gradient-based algorithms make them good candidates for optimization problems that start from a good design and require some fine tuning. In this paper the wing of the Common Research

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“…The optimisation algorithm used for evaluating the objective function is called subplex (27) . It is implemented in the in-house optimisation toolbox Pyranha (28) . A subplex can be understood as a number of Nelder-Mead simplex algorithms carried out on their own subspaces.…”

Section: Optimisation Proceduresmentioning

confidence: 99%

DLR natural and hybrid transonic laminar wing design incorporating new methodologies

Streit¹,

Wedler²,

Kruse³

2015

Aeronaut.j.

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In the present work natural laminar flow (NLF) and hybrid laminar flow (HLF) wing designs are presented which were obtained by combining new methodologies with experience and knowledge obtained with traditional laminar wing design methods. The NLF wing design is performed for wing-body configurations with backward swept wing (BSW) and forward swept wing (FSW). Initial aerofoil sections were obtained by using a new sectional conical wing method which allows the design of transonic wing sections, taking into account the effects of sweep and taper for the computational cost of a 2D method. Except for flow regions with strong 3D influence, wings constructed with these aerofoils showed an acceptably large region with laminar boundary layer and small shocks at design and specified off-design conditions. For regions close to the body and the tip a 3D inverse design method was further required. For the BSW case, due to cross flow a premature transition occurred. Therefore, a HLF panel was required to obtain a larger laminar region. A suction distribution was obtained using the suction distribution module of the automated target pressure generator (ATPG). This generator optimises the pressure distributions in terms of minimising drag while keeping certain boundary conditions constant, e.g. lift and momentum. Using the ATPG, the laminar extent of the BSW NLF design could be further improved for the inboard wing. With the new methodologies design work was reduced. They lead to a design with reserves that allow for acceptable off-design performance qualities by keeping the wing laminar over a wide range of flight conditions.

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Aerodynamic Inverse Design Framework Using Discrete Adjoint Method

Cited by 12 publications

References 9 publications

Development of the Adjoint Approach for Aeroelastic Wing Optimization

Development of the Adjoint Approach for Aeroelastic Wing Optimization

Aero-Structural Optimization of the NASA Common Research Model

DLR natural and hybrid transonic laminar wing design incorporating new methodologies

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