A multidisciplinary design optimization for conceptual design of hybrid-electric aircraft

Silva, Higor Luis; Resende, Gustavo Jorge; Neto, Roberto M. C.; Carvalho, André; Gil, Alexandre Acerra; Cruz, Mateus Alves Araújo; Guimarães, Thiago A.

doi:10.1007/s00158-021-03033-8

Cited by 18 publications

(7 citation statements)

References 25 publications

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“…However, a mechanical system is a large engineering machine with complex structure and close interactions between mechanisms, and its subsystems interact with each other. The parameters changed by the optimization of one mechanism will affect not only the mechanism under study but also other mechanisms [14]. In other words, the design parameters adjusted for the optimization of a subsystem, although making the performance of the studied subsystem better, may cause the performance of other subsystems to decline.…”

Section: Research Status Of Multidisciplinary Designmentioning

confidence: 99%

Multidisciplinary Reliability Design Considering Hybrid Uncertainty Incorporating Deep Learning

Chen

Pan

2022

Wireless Communications and Mobile Computing

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Multidisciplinary reliability design optimization is considered an effective method for solving complex product design optimization problems under the influence of uncertainty factors; however, the high computational cost seriously affects its application in practice. As an important part of multidisciplinary reliability design optimization, multidisciplinary reliability analysis plays a direct leading role in its computational efficiency. At present, multidisciplinary reliability analysis under mixed uncertainty is still nested or sequential execution mode, which leads to the problem of poor disciplinary autonomy and inefficiency in the reliability analysis of complex products. To this end, a multidisciplinary reliability assessment method integrating deep neural networks and probabilistic computational models under mixed uncertainty is proposed for the problem of multidisciplinary reliability analysis under mixed uncertainty. The method considers the stochastic-interval-fuzzy uncertainty, decouples the nested multidisciplinary probability analysis, multidisciplinary likelihood analysis, and multidisciplinary interval analysis, uses deep neural networks to extract subdisciplinary high-dimensional features, and fuses them with probabilistic computational models. Moreover, the whole system is divided into several independent subsystems, then the collected reliability data are classified, and the fault data are attributed to each subsystem. Meanwhile, the environmental conditions of the system are considered, and the corresponding environmental factors are added as input neurons along with each subsystem. In this paper, the effectiveness of the proposed method is verified on numerical calculations and real inverter power failure data.

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Section: Research Status Of Multidisciplinary Designmentioning

confidence: 99%

Multidisciplinary Reliability Design Considering Hybrid Uncertainty Incorporating Deep Learning

Chen

Pan

2022

Wireless Communications and Mobile Computing

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“…12 Later, this approach was extended to multi-disciplinary optimization, which considers systems that involve several disciplines and subsystems. 13,14 Consideration of the assembly process during design optimization attracts significantly less attention compared to performance-based optimization, and hardly any widely used software for assembly process analysis exists. Assembly process consideration and related research, initiated in the 1960s, resulted in design for assembly (DFA) guidelines, which were manually used by companies and designers when necessary.…”

Section: Introductionmentioning

confidence: 99%

“…12 Later, this approach was extended to multi-disciplinary optimization, which considers systems that involve several disciplines and subsystems. 13,14…”

Section: Introductionmentioning

confidence: 99%

Framework for metamodel-based design optimization considering product performance and assembly process complexity

Eremeev,

Cock,

Devriendt

et al. 2024

SIMULATION

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This paper proposes a method for simultaneous evaluation of the assembly process complexity together with the performance of the future product. It allows for product design optimization, considering different aspects of the future design at the early stage of the development process. The proposed method, embodied in a fully automated framework, substitutes the traditional sequential development process with a more efficient and rapid combined procedure, which addresses multiple design aspects simultaneously. Design for assembly (DFA) rules, used as quantitative metrics of the ease-of-assembly of the whole product and individual assembly operations, are automatically evaluated together with performance metrics, estimated based on finite element (FE) simulations. The direct solution to this optimization problem might be inefficient or impossible since it requires the recurrent evaluation of computationally expensive discrete and continuous functions with unknown behavior that represent the optimization objectives and constraints. For that reason, the proposed framework employs regression models based on the Gaussian process and artificial neural networks, thus achieving the optimal design of a product as a result of metamodel-based design optimization (MBDO). The suggested approach is demonstrated in the optimization of a gearbox assembly, considering its mechanical performance and assembly process. Comparing the results of the metamodel-based and direct design optimization shows that MBDO allows finding a better solution using a three times smaller computational budget. In addition, analysis of the results obtained using stationary sampling data sets of different sizes highlighted the limitations of the employed sampling procedure.

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“…are not yet benefited, since long-range missions require a lot of stored energy, which would imply tons of batteries [20]. Thus, the technological advancement of specific energy of batteries is still crucial [21].…”

Section: Introductionmentioning

confidence: 99%

Design and aeroelastic analysis of truss-based modular wing structures

Silva

Guimarães

Castro

2023

AIAA SCITECH 2023 Forum

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Modular structures come with the promise of efficient manufacturing and reduced maintenance costs. The amount of modularization is usually limited by weight efficiency, meaning that the minimum weight always lies on the most customized design, where all the structural elements are allowed to be different across the structural domain. This work studies a new design approach and structural and aeroelastic optimization of wings using truss-based modular structures. Furthermore, the work proposes a new approach to structural topology, eliminating traditional elements, such as spars and ribs, and replacing them with modular truss-based structures, which are connected by spherical joints at their ends. The topological mesh of the structures are created from the Delaunay triangulation and tessellation. The structural model is based on two types of finite elements: beam and quadrilateral elements. The beam elements are defined from consistent Timoshenko elements and the quadrilaterals are based on Mindlin-Reissner kinematics using bi-linear interpolation and reduced integration to prevent shear locking. The Doublet-Lattice Method is used to predict the unsteady subsonic aerodynamics, and the P-K method is used to compute the aeroelastic system solution. For the examples and case studies, a reference wing geometry from the FLEXOP project is used as a baseline. Two optimizations are proposed, where in all the objective functions are to minimize the structural weight of the wing and to maximize the flutter speed. The first optimization has as design variables the number of control points, or nodes, in each airfoil and the number of sections along the span. In the second optimization, the external diameters and thicknesses of each of the modular structures are individually optimized, even eliminating unnecessary ones. The results show that it is possible to obtain relatively light wings that meet the structural and aeroelastic requirements; however, the definition of the optimization parameters directly influence the mesh generation and computational cost of the optimization. Above all, modular structures have proved to be a good strategy in the design of structures for new wing concepts.

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A multidisciplinary design optimization for conceptual design of hybrid-electric aircraft

Cited by 18 publications

References 25 publications

Multidisciplinary Reliability Design Considering Hybrid Uncertainty Incorporating Deep Learning

Multidisciplinary Reliability Design Considering Hybrid Uncertainty Incorporating Deep Learning

Framework for metamodel-based design optimization considering product performance and assembly process complexity

Design and aeroelastic analysis of truss-based modular wing structures

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