Aircraft Air Inlet Design Optimization via Surrogate-Assisted Evolutionary Computation

Lombardi, A. M.; Ferrari, Denise; Santos, Luis

doi:10.1007/978-3-319-15892-1_21

Cited by 13 publications

(11 citation statements)

References 6 publications

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“…An optimal inlet design ensures sufficient thrust by providing the requisite airflow pressure to the compressor. As a consequence, several researchers (1)(2)(3) have explored different inlet designs and identified the critical parameters to improve the airflow pressure and the overall aerodynamic features of the aircraft. Amongst these designs, the submerged inlet (also called the flush inlet) has attracted significant interest from researchers due to its unique architecture (4)(5)(6)(7)(8)(9) , especially for aircraft operating in the subsonic flow regime.…”

Section: Introductionmentioning

confidence: 99%

Design space optimisation of an unmanned aerial vehicle submerged inlet through the formulation of a data-fusion-based hybrid model

et al. 2021

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The research focuses on the design space optimisation of National Advisory Committee for Aeronautics (NACA) submerged inlets through the formulation of a hybrid data fusion methodology. Submerged inlets have drawn considerable attention owing to their potential for good on-design performance, for example during cruise flight conditions. However, complexities due to the geometrical topology and interactions among various design variables remain a challenge. This research enhances the current design knowledge of submerged inlets through the utilisation of data mining and Computational Fluid Dynamics (CFD) methodologies, focusing on design space optimisation. A two-pronged approach is employed where the first step encompasses a low-fidelity model through data mining and surrogate modelling to predict and optimise the design parameters, while the second step uses the Design of Experiments (DOE) approach based on the CFD results for the candidate design geometry to construct a surrogate model with high fidelity for design refinement. The feasibility of the proposed methodology is demonstrated for the optimisation of the total pressure recovery of a NACA submerged inlet for the subsonic flight regime. The proposed methodology is found to provide good agreement between the surrogate and CFD-based model and reduce the optimisation processing time by half in comparison with conventional (global-based) CFD optimisation approaches.

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Section: Introductionmentioning

confidence: 99%

Design space optimisation of an unmanned aerial vehicle submerged inlet through the formulation of a data-fusion-based hybrid model

et al. 2021

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“…However, the inlet position and structural parameters are different. Currently, most design of NACA air inlets follows the instruction of ESDU 86002 document [2], in which a two-dimensional surface design instruction is presented. It cannot be extended to all of the NACA shape design especially to three-dimensional shape of different aircraft.…”

Section: Introductionmentioning

confidence: 99%

Structural Optimization of the Aircraft NACA Inlet Based on BP Neural Networks and Genetic Algorithms

Chen

Pei

et al. 2020

International Journal of Aerospace Engineering

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With the development of the increasing demand for cooling air in cabin and electronic components on aircraft, it urges to present an energy-efficient optimum method for the ram air inlet system. A ram air performance evaluation method is proposed, and the main structural parameters can be extended to a certain type of aircraft. The influence of structural parameters on the ram air performance is studied, and a database for the performance is generated. A new method of integrating the BP neural networks and genetic algorithm is used for structure optimization and is proven effective. Moreover, the optimum result of the structure of the NACA ram air inlet system is deduced. Results show that (1) the optimization algorithm is efficient with less prediction error of the mass flow rate and fuel penalty. The average relative error of the mass flow rate is 1.37%, and the average relative error of the fuel penalty is 1.41% in the full samples. (2) Predicted deviation analysis shows very little difference between optimized and unoptimized design. The relative error of the mass flow rate is 0.080% while that of the fuel penalty is 0.083%. The accuracy of the proposed optimization method is proven. (3) The mass flow rate after optimization is increased to 2.506 kg/s, and the fuel penalty is decreased by 74.595 Et kg. The BP neural networks and genetic algorithms are studied to optimize the design of the ram air inlet system. It is proven to be a novel approach, and the efficiency can be highly improved.

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“…Gradient-based optimization algorithms utilizing the adjoint method 2 are relatively cost-e↵ective, but are stymied by even low degrees of multimodality, and any confidence that the obtained solution is the global optimum only extends so far as the confidence that the design space is unimodal. On the other hand, gradient-free approaches like genetic algorithms 3 are quite robust with regard to even highly multimodal problems, but require a greater investment of computational resources than comparable gradient-based methods, 4 even if one considers potential cost savings from the use of hierarchical genetic algorithms, 5 surrogate methods, [6][7][8] or hybrid algorithms. 9,10 It has been demonstrated that multimodality can exist in aerodynamic shape optimization problems, 11 and the importance of understanding multimodality in this context is underscored by the recent inclusion of a multimodal problem in the Aerodynamic Design and Optimization Discussion Group (ADODG) test suite at the AIAA Aviation 2017 conference.…”

Section: Introductionmentioning

confidence: 99%

Correction: A Parametric Study of Multimodality in Aerodynamic Shape Optimization of Wings

Streuber

Zingg

2018

2018 Multidisciplinary Analysis and Optimization Conference

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A parametric study of multimodality in wing optimization is undertaken utilizing a gradient-based multistart method. The lift-constrained drag minimization of a wing is performed using between 17 and 33 initial geometries in subsonic and transonic viscous flows with varying degrees of freedom and under a variety of constraints. In nearly every examined case, including the ADODG CRM case, multimodality of some degree is found. The cross-sectional optimization of a wing is found to be unimodal to somewhat multimodal in both subsonic and transonic flows, depending on which degrees of freedom are permitted. Permitting large-scale planform deformations and non-planar geometries is shown to produce clear and significant multimodality in all examined cases. Requiring straight leading and trailing edges can significantly reduce, but not fully eliminate, multimodality. Other examined constraints, such as linear taper, linear twist, minimum thickness, and minimum pitching moment are shown to have lesser e↵ects on multimodality. Multimodality is ultimately found to be an inherent characteristic of most wing optimization design spaces, and it is concluded that gradient-based optimization based on a single initial geometry may not be su cient to ensure global optimality even in tightly constrained practical problems.

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Aircraft Air Inlet Design Optimization via Surrogate-Assisted Evolutionary Computation

Cited by 13 publications

References 6 publications

Design space optimisation of an unmanned aerial vehicle submerged inlet through the formulation of a data-fusion-based hybrid model

Design space optimisation of an unmanned aerial vehicle submerged inlet through the formulation of a data-fusion-based hybrid model

Structural Optimization of the Aircraft NACA Inlet Based on BP Neural Networks and Genetic Algorithms

Correction: A Parametric Study of Multimodality in Aerodynamic Shape Optimization of Wings

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