Multipoint optimization on fuel efficiency in conceptual design of wide-body aircraft

Chai, Xiao; Yu, Xiaohui; Wang, Yu

doi:10.1016/j.cja.2017.10.006

Cited by 22 publications

(13 citation statements)

References 11 publications

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“…In order to evaluate the competitiveness of the design candidates, the values of attributes in Figure 3 are needed to be computed or assigned. In this work a multidisciplinary analysis code 21,22 for the commercial aircraft conceptual design is applied to compute the values of DOC, takeoff and landing performance, payload-range capacity, noise, emission, etc. The multidisciplinary analysis code consists of geometric, propulsion, weight, aerodynamics, performance, DOC and noise, emission modules.…”

Section: Optimization Formulation and Resultsmentioning

confidence: 99%

“…Considering that this article focuses on conducting the competitiveness model of the commercial aircraft and the detailed multidisciplinary analysis and optimization model have been published in literature. 21,22 So, the methods used in each analysis module will be briefly introduced as follows.…”

Section: Optimization Formulation and Resultsmentioning

confidence: 99%

“…35 More details about the above modules are presented in literature. 21,22 All discipline modules are integrated into the multidisciplinary analysis model via a database, which archives all information on the commercial aircraft being designed, including aircraft geometry, propulsion, weight, aerodynamics, mission performance, field performance, DOC, noise, emission, etc.…”

Section: Framework Of the Conceptual Design Optimizationmentioning

confidence: 99%

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Conceptual design optimization of a wide-body commercial aircraft using a competitiveness model

Chai

2019

Proceedings of the Institution of Mechanical Engineers, Part G:

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The conceptual design optimization of wide-body commercial aircraft is very challenging today. This paper deals with the conceptual design optimization problem of a next-cycle wide-body commercial aircraft, which has many similarities to the one being co-developed by China and Russia. The most difficult weaknesses are that the design requirements cannot be traded off during the conceptual design optimization process. To overcome this issue, a competitiveness model is built up for the wide-body commercial aircraft. The analytic hierarchy process is applied to formulate the competitiveness model, which is a multi-level evaluation model. The competiveness model includes three levels, i.e. target level, criteria level, and attributes level. The target level has an output, i.e. the competitiveness of the investigated aircraft, which serves as the single objective function in the conceptual design optimization problem. The criteria level includes four elements of economics, comfort, environmental impact, and adaptability. In addition, each criterion is further divided into more attributes, which are parameters obtained from the commercial aircraft conceptual design. By using the competitiveness model, the conceptual design optimization problem is converted into an unconstrained one that can be solved easily. Two optimizations with different judgment matrices for criteria level were performed. Compared with the baseline design candidate, the overall competitiveness of the optimal design for the optimization case 1 and case 2 increased by 9.28% and 11.51%, respectively, which benefits the designers and decision-makers.

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Section: Optimization Formulation and Resultsmentioning

confidence: 99%

Section: Optimization Formulation and Resultsmentioning

confidence: 99%

Section: Framework Of the Conceptual Design Optimizationmentioning

confidence: 99%

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Conceptual design optimization of a wide-body commercial aircraft using a competitiveness model

Chai

2019

Proceedings of the Institution of Mechanical Engineers, Part G:

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“…To improve the robustness of objective metrics and reliability on design constraints, ASO should be performed with reasonable consideration of the uncertainties. This can be done by significantly increasing the number of design points [99,100] or using stochastic metrics [101], both of which lead to a significant increase in the computational cost. Therefore, most robust design [102] or reliability-based design [103] applications focus on two-dimensional airfoil shape design [104,105] or three-dimensional configuration design considering the uncertainty of several operating parameters such as the Mach number and lift coefficient [106][107][108][109][110], which is still far from satisfying the industrial demand.…”

Section: Existing Challengesmentioning

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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“…Moreover, MDO was first applied to the coupled aero-structural optimization problem for aircraft wings [4]. The merits of MDO have been widely proven in the design practices of engineering systems, such as aircraft [5][6][7][8][9][10], automobiles [11][12][13], ships [14,15], and electric devices [16,17]. In the past decade, MDO has also been applied to aerospace systems, including satellites [18][19][20][21], constellations [20,22,23], and launch rockets [24][25][26][27], to improve the overall system performance.…”

Section: Introductionmentioning

confidence: 99%

Metamodel-based multidisciplinary design optimization methods for aerospace system

Shi

et al. 2021

Astrodyn

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The design of complex aerospace systems is a multidisciplinary design optimization (MDO) problem involving the interaction of multiple disciplines. However, because of the necessity of evaluating expensive black-box simulations, the enormous computational cost of solving MDO problems in aerospace systems has also become a problem in practice. To resolve this, metamodel-based design optimization techniques have been applied to MDO. With these methods, system models can be rapidly predicted using approximate metamodels to improve the optimization efficiency. This paper presents an overall survey of metamodel-based MDO for aerospace systems. From the perspective of aerospace system design, this paper introduces the fundamental methodology and technology of metamodel-based MDO, including aerospace system MDO problem formulation, metamodeling techniques, state-of-the-art metamodel-based multidisciplinary optimization strategies, and expensive black-box constraint-handling mechanisms. Moreover, various aerospace system examples are presented to illustrate the application of metamodel-based MDOs to practical engineering. The conclusions derived from this work are summarized in the final section of the paper. The survey results are expected to serve as guide and reference for designers involved in metamodel-based MDO in the field of aerospace engineering.

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Multipoint optimization on fuel efficiency in conceptual design of wide-body aircraft

Cited by 22 publications

References 11 publications

Conceptual design optimization of a wide-body commercial aircraft using a competitiveness model

Conceptual design optimization of a wide-body commercial aircraft using a competitiveness model

Machine Learning in Aerodynamic Shape Optimization

Metamodel-based multidisciplinary design optimization methods for aerospace system

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