Adaptive-surrogate-based robust optimization of transonic natural laminar flow nacelle

Yao, Yuan; Ma, Dongli; Yang, Muqing; Zhang, Liang; Guo, Yang

doi:10.1016/j.cja.2021.01.007

Cited by 15 publications

(5 citation statements)

References 31 publications

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“…All these investigations were based on computationally expensive numerical simulations that are not feasible within a preliminary design stage. There is very limited information in the open literature that considers the nacelle design process with low order models [18][19][20][21][22][23]. This is caused by the large non-linearity associated to compact aero-engine nacelles at transonic conditions, and the difficulties to build accurate and reliable metamodels.…”

Section: Nomenclaturementioning

confidence: 99%

“…The overall method had a standard error for the prediction of mid-cruise drag of 3.6%. Other studies have also considered low order models for nacelle optimisation purposes [19][20][21]. For example, Yao et al [21] developed an adaptive-surrogate-based optimization method for aero-engine nacelles.…”

Section: Nomenclaturementioning

confidence: 99%

“…Other studies have also considered low order models for nacelle optimisation purposes [19][20][21]. For example, Yao et al [21] developed an adaptive-surrogate-based optimization method for aero-engine nacelles. It was used for nacelle shape optimization at mid-cruise conditions with M = 0.85.…”

Section: Nomenclaturementioning

confidence: 99%

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Deep-Learning for Flow-Field Prediction of 3D Non-Axisymmetric Aero-Engine Nacelles

Tejero

MacManus

García

et al. 2023

AIAA AVIATION 2023 Forum

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Computational fluid dynamics (CFD) methods have been widely used for the design and optimisation of complex non-linear systems. Within this context, the overall process can typically have a large computational overhead. For preliminary design studies, it is important to establish design capabilities that meet the usually conflicting requirements of rapid evaluations and accuracy. Of particular interest is the aerodynamic design of components or subsystems within the transonic range. This can pose notable challenges due to the non-linearity of this flow regime. There is a need to develop low order models for future civil aero-engine nacelle applications. The aerodynamics of compact nacelles can be sensitive to changes in geometry and operating conditions. For example within the cruise segment different flow-field characteristics may be encountered such as shock-wave boundary layer interaction or shock induced separation. As such, an important step in the successful design of these new architectures is to develop methods for fast and accurate flow-field prediction. This work studies two different metamodelling approaches for flow-field prediction of 3D non-axisymmetric nacelles. Firstly, a reduced order model based on an artificial neural network (ANN) is considered. Secondly, a low order model that combines singular value decomposition and an artificial neural network (SVD+ANN) is investigated. Across a wide geometric design space, the ANN and SVD+ANN methods have an overall uncertainty in the isentropic Mach number prediction of about 0.02. However, the ANN approach has better capabilities to predict pre-shock Mach numbers and shock-wave locations.

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Section: Nomenclaturementioning

confidence: 99%

Section: Nomenclaturementioning

confidence: 99%

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Deep-Learning for Flow-Field Prediction of 3D Non-Axisymmetric Aero-Engine Nacelles

Tejero

MacManus

García

et al. 2023

AIAA AVIATION 2023 Forum

View full text Add to dashboard Cite

show abstract

“…The aerodynamic design of civil aircraft is becoming increasingly critical with respect to fuel consumption, pollution emissions, and other performance indicators. As an essential component of an aero-engine, the aerodynamic performance of its nacelle has a direct and sensitive impact on the aircraft [1,2] Therefore, research on efficient aerodynamic design of nacelles has received much attention, and numerous outstanding aerodynamic specialists have made significant contributions to nacelle design in recent years [3][4][5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, deep learning techniques, such as variational encoders (VAE) [19] and generative adversarial networks (GANs) [20], have been tried for their ability to learn low-dimensional representations from complex and highly distributed data. In previous nacelle designs, deep learning techniques have been employed in the construction of surrogate models for optimization processes [6][7][8][9] and can provide the inspiration for geometric dimensionality reduction of a 3D nacelle. Compared to traditional dimension reduction methods, deep learning methods can employ a compact set of variables and the prior experience to model the highly complex variability of the design [21].…”

Section: Introductionmentioning

confidence: 99%

Aerodynamic Optimization Framework for a Three-Dimensional Nacelle Based on Deep Manifold Learning-Assisted Geometric Multiple Dimensionality Reduction

et al. 2023

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As a core component of an aero-engine, the aerodynamic performance of the nacelle is essential for the overall performance of an aircraft. However, the direct design of a three-dimensional (3D) nacelle is limited by the complex design space consisting of different cross-section profiles and irregular circumferential curves. The deep manifold learning-assisted geometric multiple dimensionality reduction method combines autoencoders (AE) with strong capabilities for non-linear data dimensionality reduction and class function/shape function transformation (CST). A novel geometric dimensionality reduction method is developed to address the typical constraints of nacelle parameterization. Low-dimensional latent variables are extracted from the high-dimensional design space to achieve a parametric representation of 3D nacelle manifolds. Compared with traditional parametric methods, the proposed geometric dimensionality reduction method improves the accuracy and efficiency of geometric reconstruction and aerodynamic evaluation. A multi-objective optimization framework is proposed based on deep manifold learning to increase the efficiency of 3D nacelle design. The Pareto front curves under drag divergence constraints reveal the correlation between the geometry distribution and the surface isentropic Mach number distribution of 3D nacelles. This paper demonstrates the feasibility of the proposed geometric dimensionality reduction method for direct multi-objective optimization of 3D nacelles.

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Framework of nacelle inverse design method based on improved generative adversarial networks

Wang

et al. 2022

Aerospace Science and Technology

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Adaptive-surrogate-based robust optimization of transonic natural laminar flow nacelle

Cited by 15 publications

References 31 publications

Deep-Learning for Flow-Field Prediction of 3D Non-Axisymmetric Aero-Engine Nacelles

Deep-Learning for Flow-Field Prediction of 3D Non-Axisymmetric Aero-Engine Nacelles

Aerodynamic Optimization Framework for a Three-Dimensional Nacelle Based on Deep Manifold Learning-Assisted Geometric Multiple Dimensionality Reduction

Framework of nacelle inverse design method based on improved generative adversarial networks

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