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AIAA Aviation Forum

作者： Abu-Zurayk, Mohammad Schulze, Matthias German Aerosp Ctr Inst Aerodynam & Flow Technol Braunschweig Germany German Aerosp Ctr Inst Aeroelast Gottingen Germany

ISBN: (数字)9781624107047

ISBN: (纸本)9781624107047

By virtue of their efficiency, gradient-based algorithms can play a profound role in assessing wing designers overcome the challenges of decarbonizing the aviation industry. This role becomes more valuable when non-linearities increase in the corresponding physical discipline. The coupled aero-elastic adjoint approach, is used to efficiently compute the gradients of aerodynamic cost functions with respect to shape design parameters. This approach helps aerodynamic designers engage the effects of wing structure flexibility, earlier in the design process. To compute the gradients via the coupled aero-elastic adjoint approach, a fixed-point iteration between the aerodynamic adjoint equation and the structure adjoint equation is carried out. The cost of computing the coupled aero-elastic adjoint for a specific cost function is, hence, higher than that of computing the aerodynamic adjoint. In practice it costs three to ten times more, based on the structure flexibility. The aircraft industry tends currently to build wings with higher structure flexibility. This is the result of using composite materials instead of metal, in order to save structural mass, or advancing towards higher aspect ratio wings in order to improve the aerodynamic performance. These trends, which result in increased wing flexibility, motivate investigating the benefit of the coupled aeroelastic adjoint approach, over the aerodynamic adjoint approach. In the literature, some studies show the importance of employing the coupled adjoint, while others show barely any benefit for it. Hence, the aim of this study is to investigate, for the test case at hand, the relation between the wing flexibility and the necessity to employ the relatively costly, but accurate, coupled aero-elastic adjoint approach. Five structure models were generated with different elasticities and were employed in two sets of optimizations;the first uses the aerodynamic adjoint to compute the gradients, and the other uses the coupled

关键词： multidisciplinary design and optimization Young's Modulus Computing Wing Root Aerodynamic Performance Aircraft Industry High Aspect Ratio Composite Materials Decarbonizing Aviation Wing Span

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MDO Formulations for Simultaneous design and Trajectory optimization

MDO Formulations for Simultaneous Design and Trajectory Opti...

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AIAA SciTech Forum

作者： Kaneko, Shugo Martins, Joaquim R. R. A. Univ Michigan Dept Aerosp Engn Ann Arbor MI 48109 USA

ISBN: (数字)9781624107115

ISBN: (纸本)9781624107115

Simultaneous design and trajectory optimization aims to find the best possible design of a dynamic engineering system, such as an aircraft, by considering the coupling between a physical system design and its trajectory. multidisciplinary design optimization (MDO) fully considers this coupling and corresponding design trade-offs. This article discusses the computational efficiency of MDO formulations for design-trajectory optimization. We perform numerical studies to compare two MDO architectures and two design-trajectory coupling strategies on aircraft design test problems. The test problems concurrently optimize a climb trajectory, wing design based on a low-fidelity aerostructural analysis, and aircraft sizing variables. The results indicate that surrogate-based coupling is more efficient than direct coupling when there are only a few coupling variables from a trajectory to a disciplinary model, whereas direct coupling is preferable otherwise. The simultaneous analysis and design (SAND) architecture outperforms the multidisciplinary feasible (MDF) architecture when using direct coupling, whereas the costs of SAND and MDF are comparable with surrogate-based coupling. The results and discussion in this paper provide general guidelines for selecting a computationally efficient MDO formulation for simultaneous design and trajectory optimization.

关键词： Optimal Control Problem multidisciplinary design and optimization Wing design optimization Aerostructural Analysis Aircraft Conceptual design Aerostructural Model Richardson Extrapolation High Altitude Long Endurance Energy Consumption Linear Quadratic Regulator

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Topology optimization design of Resonant Structures Based on Antiresonance Eigenfrequency Matching Informed by Harmonic Analysis

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JOURNAL OF MECHANICAL design 2023年第10期145卷 101704-101704页

作者： Guzman, Daniel Giraldo Lissenden, Clifford Shokouhi, Parisa Frecker, Mary Penn State Univ Dept Mech Engn University Pk PA 16802 USA Penn State Univ Dept Engn Sci & Mech University Pk PA 16802 USA

In this article, we present a design methodology for resonant structures exhibiting particular dynamic responses by combining an eigenfrequency matching approach and a harmonic analysis-informed eigenmode identification strategy. This systematic design methodology, based on topology optimization, introduces a novel computationally efficient approach for 3D dynamic problems requiring antiresonances at specific target frequencies subject to specific harmonic loads. The optimization's objective function minimizes the error between target antiresonance frequencies and the actual structure's antiresonance eigenfrequencies, while the harmonic analysis-informed identification strategy compares harmonic displacement responses against eigenvectors using a modal assurance criterion, therefore ensuring an accurate recognition and selection of appropriate antiresonance eigenmodes used during the optimization process. At the same time, this method effectively prevents well-known problems in topology optimization of eigenfrequencies such as localized eigenmodes in low-density regions, eigenmodes switching order, and repeated eigenfrequencies. Additionally, our proposed localized eigenmode identification approach completely removes the spurious eigenmodes from the optimization problem by analyzing the eigenvectors' response in low-density regions compared to high-density regions. The topology optimization problem is formulated with a density-based parametrization and solved with a gradient-based sequential linear programming method, including material interpolation models and topological filters. Two case studies demonstrate that the proposed design methodology successfully generates antiresonances at the desired target frequency subject to different harmonic loads, design domain dimensions, mesh discretization, or material properties.

关键词： topology optimization eigenfrequency matching frequency response analysis antiresonances design methodology design optimization generative design multidisciplinary design and optimization simulation-based design smart materials and structures structural optimization

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Optimizing Architecture Transitions Using Decision Networks

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JOURNAL OF MECHANICAL design 2020年第12期142卷 1-14页

作者： Siddiqi, Afreen Rebentisch, Eric Dorchuck, Samuel Imanishi, Yuto Tanimichi, Taisetsu MIT Dept Aeronaut & Astronaut Cambridge MA 02139 USA MIT Sociotech Syst Res Ctr Cambridge MA 02139 USA MIT Dept Elect Engn & Comp Sci Cambridge MA 02139 USA Hitachi Amer Automot Prod Res Lab Res & Dev Div 34500 Grand River Ave Farmington Hills MI 48335 USA Hitachi Automot Syst Amer Inc Adv Dev Ctr 34500 Grand River Ave Farmington Hills MI 48335 USA

Architecture selection for systems undergoing rapid technological and market change is challenging. It is desirable to select architectures that can provide cost-effective possibilities for future changes and avoid architecture lock-in. However, optimal architectures for prevailing conditions may not be changeable for future adaptation. This tension between objectives for system (product) development for both short-term and long-term competitiveness has been an enduring challenge for system architects. Here, we use time-expanded decision networks (TDNs) with time-varying costs and demands to systematically explore future architecture transition pathways and strategically identify useful designs. We demonstrate a new application for autonomous driving (AD) systems, a nascent technology, where the design and capabilities of constituent components (such as sensors, processors, and data communication links) are still evolving and significant market and regulatory uncertainties persist. In this case, we model technology costs with time-based factors to explicitly include future trends. The results show that as cost differences between architectures increase and demand for new functionality changes with time, the approach is able to identify potential transition points between architecture choices that optimize the net present value (NPV) of the system. For some of the specific scenarios analyzed in this study, the NPV with optimal architecture transitions is at least 10-20% larger as compared with fixed cases. Overall, this work presents a case for planning and partly constructing architecture transition roadmaps for new systems wherein dominant architectures have not emerged.

关键词： system architecture systems engineering autonomous driving systems autonomous vehicles decision networks technology planning architecture roadmap design evaluation multidisciplinary design and optimization simulation-based design systems design uncertainty analysis

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A Parametric Level Set Method for Topology optimization Based on Deep Neural Network

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JOURNAL OF MECHANICAL design 2021年第9期143卷 091702-091702页

作者： Deng, Hao To, Albert C. Univ Pittsburgh Dept Mech Engn & Mat Sci Pittsburgh PA 15261 USA

This paper proposes a new parametric level set method for topology optimization based on deep neural network (DNN). In this method, the fully connected DNN is incorporated into the conventional level set methods to construct an effective approach for structural topology optimization. The implicit function of level set is described by fully connected DNNs. A DNN-based level set optimization method is proposed, where the Hamilton-Jacobi partial differential equations (PDEs) are transformed into parametrized ordinary differential equations (ODEs). The zero-level set of implicit function is updated through updating the weights and biases of networks. The parametrized reinitialization is applied periodically to prevent the implicit function from being too steep or too flat in the vicinity of its zero-level set. The proposed method is implemented in the framework of minimum compliance, which is a well-known benchmark for topology optimization. In practice, designers desire to have multiple design options, where they can choose a better conceptual design base on their design experience. One of the major advantages of the DNN-based level set method is capable to generate diverse and competitive designs with different network architectures. Several numerical examples are presented to verify the effectiveness of the proposed DNN-based level set method.

关键词： topology optimization deep neural networks level set method diverse and competitive design artificial intelligence design optimization machine learning multidisciplinary design and optimization

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A Reliability-Based Formulation for Simulation-Based Control Co-design Using Generalized Polynomial Chaos Expansion

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JOURNAL OF MECHANICAL design 2022年第5期144卷 051705-051705页

作者： Behtash, Mohammad Alexander-Ramos, Michael J. Univ Cincinnati Dept Mech Engn Cincinnati OH 45221 USA

Combined plant and control design (control co-design (CCD)) methods are often used during product development to address the synergistic coupling between the plant and control parts of a dynamic system. Recently, a few studies have started applying CCD to stochastic dynamic systems. In their most rigorous approach, reliability-based design optimization (RBDO) principles have been used to ensure solution feasibility under uncertainty. However, since existing reliability-based CCD (RBCCD) algorithms use all-at-once formulations, only most-probable-point (MPP) methods can be used as reliability analysis techniques. Though effective for linear/quadratic RBCCD problems, the use of such methods for highly nonlinear RBCCD problems introduces solution error that could lead to system failure. A multidisciplinary feasible (MDF) formulation for RBCCD problems would eliminate this issue by removing the dynamic equality constraints and instead enforcing them through forward simulation. Since the RBCCD problem structure would be similar to traditional RBDO problems, any of the well-established reliability analysis methods could be used. Therefore, in this work, a novel reliability-based MDF formulation of multidisciplinary dynamic system design optimization has been proposed for RBCCD. To quantify the uncertainty propagated by the random decision variables, Monte Carlo simulation has been applied to the generalized polynomial chaos expansion of the probabilistic constraints. The proposed formulation is applied to two engineering test problems, with the results indicating the effectiveness of both the overall formulation as well as the reliability analysis technique for RBCCD.

关键词： design optimization multidisciplinary design and optimization reliability in design simulation-based design systems design uncertainty analysis

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design for Crashworthiness of Categorical Multimaterial Structures Using Cluster Analysis and Bayesian optimization

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JOURNAL OF MECHANICAL design 2019年第12期141卷 1-15页

作者： Liu, Kai Wu, Tong Detwiler, Duane Panchal, Jitesh H. Tovar, Andres Purdue Univ Sch Mech Engn W Lafayette IN 47907 USA Indiana Univ Purdue Univ Dept Mech & Energy Engn Indianapolis IN 46202 USA Honda R&D Amer Inc Raymond OH 43067 USA

This work introduces a cluster-based structural optimization (CBSO) method for the design of categorical multimaterial structures subjected to crushing, dynamic loading. The proposed method consists of three steps: conceptual design generation, design clustering, and Bayesian optimization. In the first step, a conceptual design is generated using the hybrid cellular automaton (HCA) algorithm. In the second step, threshold-based cluster analysis yields a lower-dimensional design. Here, a cluster validity index for structural optimization is introduced in order to qualitatively evaluate the clustered design. In the third step, the optimal design is obtained through Bayesian optimization, minimizing a constrained expected improvement function. This function allows to impose soft constraints by properly redefining the expected improvement based on the maximum constraint violation. The Bayesian optimization algorithm implemented in this work has the ability to search over (i) a real design space for sizing optimization, (ii) a categorical design space for material selection, or (iii) a mixed design space for concurrent sizing optimization and material selection. With the proposed method, materials are optimally selected based on multiple attributes and multiple objectives without the need for material ranking. The effectiveness of this approach is demonstrated with the design for crashworthiness of multimaterial plates and thin-walled structures.

关键词： structural optimization finite element-based optimization Bayesian optimization metamodel-based design unsupervised machine learning clustering multimaterial structures design optimization metamodeling multidisciplinary design and optimization

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Towards Integrated design and Operation of Complex Engineering Systems With Predictive Modeling: State-of-the-Art and Challenges

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JOURNAL OF MECHANICAL design 2022年第9期144卷 090801-090801页

作者： Liu, Xinyang Ghosh, Sayan Liu, Yongming Wang, Pingfeng Univ Illinois Dept Ind & Enterprise Syst Engn Urbana IL 61801 USA GE Res Niskayuna NY 12309 USA Arizona State Univ Dept Mech & Aerosp Engn Tempe AZ 85281 USA

Growing trends towards increased complexity and prolonged useful lives of engineering systems present challenges for system designers in accounting for the impacts of post-design activities (e.g., manufacturing, condition monitoring, remaining life prediction, maintenance, service logistics, end-of-life options, etc.) on system performance (e.g., costs, reliability, customer satisfaction, environmental impacts, etc.). It is very difficult to develop accredited lifecycle system performance models because these activities only occur after the system is built and operated. Thus, system design and post-design decision-making have traditionally been addressed separately, leading to suboptimal performance over the systems lifecycle. With significant advances in computational modeling, simulation, sensing & condition monitoring, and machine learning & artificial intelligence, the capability of predictive modeling has grown prominently over the past decade, leading to demonstrated benefits such as improved system availability and reduced operation and maintenance costs. Predictive modeling can bridge system design and post-design stages and provide an optimal pathway for system designers to effectively account for future system operations at the design stage. In order to achieve optimal performance over the system's lifecycle, post-design decisions and system operating performance can be incorporated into the initial design with the aid of state-of-the-art predictive modeling approaches. Therefore, optimized design and operation decisions can be explored jointly in an enlarged system design space. This article conducted a literature review for the integrated design and operation of engineering systems with predictive modeling, where not only the predictive modeling approaches but also the strategies of integrating predictive models into the system design processes are categorized. Although predictive modeling has been handled from data-driven, statistical, analytical, and e

关键词： system design operations management optimization predictive modeling design integration lifecycle analysis and design multidisciplinary design and optimization

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A GPU-Based Parallel Region Classification Method for Continuous Constraint Satisfaction Problems

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JOURNAL OF MECHANICAL design 2024年第4期146卷 041705-041705页

作者： Zhang, Guanglu Feng, Wangchuan Cagan, Jonathan Carnegie Mellon Univ Dept Mech Engn Pittsburgh PA 15213 USA

Continuous constraint satisfaction is prevalent in many science and engineering fields. When solving continuous constraint satisfaction problems, it is more advantageous for practitioners to derive all feasible regions (i.e., the solution space) rather than a limited number of solution points, since these feasible regions facilitate design concept generation and design tradeoff evaluation. Several central processing unit (CPU)-based branch-and-prune methods and geometric approximation methods have been proposed in prior research to derive feasible regions for continuous constraint satisfaction problems. However, these methods have not been extensively adopted in practice, mainly because of their high computational expense. To overcome the computational bottleneck of extant CPU-based methods, this paper introduces a GPU-based parallel region classification method to derive feasible regions for continuous constraint satisfaction problems in a reasonable computational time. Using interval arithmetic, coupled with the computational power of GPU, this method iteratively partitions the design space into many subregions and classifies these subregions as feasible, infeasible, and indeterminate regions. To visualize these classified regions in the design space, a planar visualization approach that projects all classified regions into one figure is also proposed. The GPU-based parallel region classification method and the planar visualization approach are validated through two case studies about the bird function and the welded beam design. These case studies show that the method and the approach can solve the continuous constraint satisfaction problems and visualize the results effectively and efficiently. A four-step procedure for implementing the method and the approach in practice is also outlined.

关键词： constraint satisfaction design optimization engineering design GPU programming interval arithmetic parallel computing design automation design evaluation design visualization multidisciplinary design and optimization multi-objective optimization

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Dual-Objective Mechanobiological Growth optimization for Heterogenous Lattice Structures

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JOURNAL OF MECHANICAL design 2024年第7期146卷 072001-072001页

作者： Arefin, Amit M. E. Egan, Paul F. Texas Tech Univ Dept Mech Engn Lubbock TX 79409 USA

Computational design is growing in necessity for advancing biomedical technologies, particularly for complex systems with numerous trade-offs. For instance, in tissue scaffolds constructed from repeating unit cells, the structure's porosity and topology affect biological tissue and vasculature growth. Here, we adapt curvature-based tissue growth and agent-based vasculature models for predicting scaffold mechanobiological growth. A non-dominated sorting genetic algorithm (NSGA-II) is used for dual-objective optimization of scaffold tissue and blood vessel growth with heterogeneous unit cell placement. design inputs consist of unit cells of two different topologies, void unit cells, and beam diameters from 64 to 313 mu m. Findings demonstrate a design heuristic for optimizing scaffolds by placing two selected unit cells, one that favors high tissue growth density and one that favors blood vessel growth, throughout the scaffold. The pareto front of solutions demonstrates that scaffolds with large porous areas termed channel voids or small voids improve vasculature growth while lattices with no larger void areas result in higher tissue growth. Results demonstrate the merit in computational investigations for characterizing tissue scaffold design trade-offs and provide a foundation for future design multi-objective optimization for complex biomedical systems.

关键词： design for manufacturing medical and bio-design of mechanisms and robotics multidisciplinary design and optimization multi-objective optimization

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