High-Fidelity Aerodynamic Shape Optimization of a Full Configuration Regional Jet

Bons, Nicolas; Mader, Charles A.; Martins, Joaquim R. R. A.; Cuco, Ana Paula C.

doi:10.2514/6.2018-0106

Cited by 13 publications

(6 citation statements)

References 29 publications

(30 reference statements)

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“…To tackle the objectives proposed for this work we use a RANS-based CFD tool coupled with an adjoint solver to efficiently capture flow characteristics and its sensitivities at supersonic and subsonic speed, a robust geometry and mesh perturbation routine and an efficient gradient-based optimizer. These tools are part of MACH, a framework for MDO that has been proven capable of performing coupled aerodynamic and structural optimization taking into account aeroelastic deformations [57,58] and improve the aerodynamic performance of both conventional [10,59] and unconventional aircraft configurations [60,61,62]. For the ASO study hereby presented we use MACH's geometric manipulation (pyGeo) and mesh deformation modules (IDwarp), flow solver (ADflow) and numerical optimization algorithm (SNOPT).…”

Section: Methodsmentioning

confidence: 99%

Multipoint Aerodynamic Shape Optimization for Subsonic and Supersonic Regimes

Mangano

Martins

2019

AIAA Scitech 2019 Forum

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The second-generation of supersonic civil transport has to match ambitious targets in terms of noise reduction and efficiency to become economically and environmentally viable. High-fidelity numerical optimization offers a powerful approach to address the complex trade-offs intrinsic to this novel configuration. Past and current research has proven the potential of supersonic aircraft shape optimization but lacks deeper insight on final layouts. This work partially fills this gap by investigating RANS-based aerodynamic optimization for both supersonic, transonic and subsonic conditions. We perform single and multi-point optimization to minimize the drag over an ideal supersonic aircraft flight envelope and assess the influence of physical and numerical parameters on optimization accuracy and robustness. Leading and trailing edge morphing capabilities are introduced to improve the efficiency at transonic and subsonic flight speed by relaxing the trade-offs on clean shape optimization. Benefits in terms of drag reduction are quantified and benchmarked with fixed-edges results. We observe how the optimized airfoils outperform baseline reference shapes from a minimum of 4% up to 86% for different design cases and flight conditions. The study is then extended to the optimization of a planar, low-aspect-ratio, and low-sweep wing, using the same schematic approach of 2D analysis. We investigate the influence of wing twist alone and twist and shape on cruise performance, obtaining a drag reduction of 6% and 25% respectively as the optimizer copes with both viscosity and compressibility effects over the wing. Preliminary results for 3D multi-point optimization suggest that the proposed strategy enables a fast and effective design of highly-efficient wings, with drag reduction ranging from a minimum of 24% up to 74% for cruise at different speeds and altitudes. The benefits of RANS-based aerodynamic shape optimization to capture non-intuitive design trade-offs and offer deeper physical insight are ultimately discussed and quantified.

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Section: Methodsmentioning

confidence: 99%

Multipoint Aerodynamic Shape Optimization for Subsonic and Supersonic Regimes

Mangano

Martins

2019

AIAA Scitech 2019 Forum

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“…45, Kenway and Martins [424] developed a buffet onset constraint formulation based on a separation sensor function with an upper bound of 4% determined by experiment-based comparison. Although the bound of 4% was defined using a limited number of observations, the formulated constraint is effective and has been used in aircraft aerodynamic and aerostructural design optimization [434][435][436][437]. In addition to being implemented in ADflow [79], the Kenway-Martins buffet-onset sensor has been implemented in the open-source CFD solver SU2 [438].…”

Section: Practical Constraint Formulationmentioning

confidence: 99%

Machine Learning in Aerodynamic Shape Optimization

Li¹,

Martins²

2022

Preprint

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Large volumes of experimental and simulation aerodynamic data have been rapidly advancing aerodynamic shape optimization (ASO) via machine learning (ML), whose effectiveness has been growing thanks to continued developments in deep learning. In this review, we first introduce the state of the art and the unsolved challenges in ASO. Next, we present a description of ML fundamentals and detail the ML algorithms that have succeeded in ASO. Then we review ML applications contributing to ASO from three fundamental perspectives: compact geometric design space, fast aerodynamic analysis, and efficient optimization architecture. In addition to providing a comprehensive summary of the research, we comment on the practicality and effectiveness of the developed methods. We show how cutting-edge ML approaches can benefit ASO and address challenging demands like interactive design optimization. However, practical large-scale design optimizations remain a challenge due to the costly ML training expense. A deep coupling of ML model construction with ASO prior experience and knowledge, such as taking physics into account, is recommended to train ML models effectively.

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“…As thinner wings will generally have lower induced drag for a given shape and wetted area, shape optimisation of wing sections needs to be constrained in thickness and/or volume to prevent unrealistically thin sections from being produced [24]. High-fidelity estimates are often restricted to purely aerostructural considerations [25]. These two disciplines are certainly the most tightly-coupled and important for wing optimisation and offer a clear benefit over the traditional approaches, but the two disciplines involved are not exhaustive.…”

Section: Shortcomings Of Modern Mdomentioning

confidence: 99%

“…Load-bearing structural weight and cruise aerodynamics are tightly-coupled and extremely important for wing design, but the whole-wing weight estimation is still needed to make an accurate estimate of the structural contribution to the induced drag of the aircraft (and therefore design objectives such as reducing mission fuel burn). Although the load-bearing structural weight can be estimated to a high degree of precision within a coupled aerostructural optimisation, total wing weight does not enjoy the same level of fidelity [25]. Within a conceptual aerostructural optimisation of a cruise wing, total wing weight estimation is usually performed using one of several empirical correlations that either extrapolate total wing weight from the FEM wing weight and the final wing area [33], or from the geometric parameters of the selected wing design [34].…”

Section: Wing Weight and Aeroelastic Considerationsmentioning

confidence: 99%

“…Furthermore, there is distinct lack of consideration for the high-lift design at the concept phase and with the MDO community. As an example, Bons et al [25] produced a high-fidelity aerodynamic shape optimization of a full configuration regional jet which included "consider[ing] the impact of adding constraints such as climb rate, take-off field length, and buffet onset margin". In order to assess the TOFL and LFL, some prediction of the aerodynamic performance of the aircraft in a high-lift configuration was required.…”

Section: Part I: a Priori High-lift System Sizing Using Industrial Datamentioning

confidence: 99%

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High-Lift Actuation Weight Estimation Using Low-Cost Methods

Moss

Ronch

Tyler

2020

AIAA Scitech 2020 Forum

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Faculty of Engineering and Physical Sciences Aeronautical and Astronautical Engineering Doctor of PhilosophyHigh-lift actuation weight estimation using low-cost methods by Benjamin Moss Civil passenger aircraft use high-power actuation systems to deploy high-lift surfaces on the leading and trailing edges of their wings. The mass and size of these systems scales with the expected load the high-lift surfaces are deploy. The problem is that estimated maximum panel loads are not known to a high degree of confidence in the conceptual and preliminary phases of design. The concequence of this in the early phases of the design cycle is that actuation system mass cannot be incorporated to any optimisation loop and traded off against design variables as part of an aerostructural optimisation. This increases uncertainty in the wing weight and the corresponding structural design margins. Ultimately, this uncertainty degrades performance and provides pessimistic estimates of dynamic aeroelastic response.This thesis presents a solution in three parts. First it presents a method for estimating the high-lift actuation system using the aircraft's high-lift geometry. Second, it introduces a novel approach to estimating high-lift panel loads using low cost computational methods suitable for conceptual and preliminary aircraft design. Third, it quantifies the impact of the high-lift actuation system on the dynamic aeroelastic response. vii

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High-Fidelity Aerodynamic Shape Optimization of a Full Configuration Regional Jet

Cited by 13 publications

References 29 publications

Multipoint Aerodynamic Shape Optimization for Subsonic and Supersonic Regimes

Multipoint Aerodynamic Shape Optimization for Subsonic and Supersonic Regimes

Machine Learning in Aerodynamic Shape Optimization

High-Lift Actuation Weight Estimation Using Low-Cost Methods

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