Numerical optimization has been applied to wingdesign problems for over 40 years. Over the decades, the scope and detail of optimization problems have advanced considerably. At the present time, the state-of-the-art ...
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Numerical optimization has been applied to wingdesign problems for over 40 years. Over the decades, the scope and detail of optimization problems have advanced considerably. At the present time, the state-of-the-art in wing design optimization incorporates high-fidelity modeling of the steady-state aeroelastic response of the wing at both on-design and off-design operating conditions. Reynolds-averaged solutions of the Navier–Stokes equations coupled with linear finite element anal- ysis offer the highest fidelity modeling currently tenable in an optimization con- text. However, the complexity of implementing and cost of executing high-fidelity aerostructural optimization have limited the extent of research on the topic. The goal of this dissertation is to examine the general application of these tools to wingdesign problems and highlight several factors pertaining to their usefulness and versatility. Two types of wingdesign problems are considered in this dissertation: refin- ing and exploratory. Refining problems are more common in practice, especially for high-fidelity optimization, because they start from a good design and make small changes to improve it. Exploratory problems are intended to have liberal parametrizations predisposed to have significant differences between the original and final designs. The investigation of exploratory problems yields novel findings regarding multimodality in the design space and robustness of the framework. Multimodality in the design space can impact the usefulness and versatility of gradient-based optimization in wingdesign. Both aerodynamic and aerostructural wingdesign problems are shown to be amenable to gradient-based optimization despite the existence of multimodality in some cases. For example, a rectangular wing with constant cross-section is successfully converted, through gradient-based optimization, into a swept-back wing with transonic airfoils and a minimum-mass structure. These studies introduce new insight
A lightweight amphibious aircraft hybrid composite wing was designed and optimized in this study. The Ansys Composite PrepPost and Ansys Mechanical Module use finite element modeling to simulate and assess the static ...
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A lightweight amphibious aircraft hybrid composite wing was designed and optimized in this study. The Ansys Composite PrepPost and Ansys Mechanical Module use finite element modeling to simulate and assess the static structural test. It is possible to build a lightweight and cost-effective composite wing by balancing the amount and orientation of carbon fiber and glass fiber ply patterns. The BII2 wingdesign case (spar/rib/skin : (+/- 45)C,(0/90)C,....20/(+/- 45)C,(0/90)G,(+/- 45)C,F)S/(+/- 45)C,(0/90)G,F) is the best option of 72 case studies, with a total weight of 45.46 kg and a manufacturing cost of 1,288 USD. The optimal design composite wing mock-up was built and tested on a universal test rig. The test demonstrated that the optimal wingdesign could withstand the maximum load (+6G and -3G) without structural collapse. The experimental structural deformation and elastic strain were consistent with the FEM model, within an acceptable error range.
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 trajecto...
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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 designoptimization (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, wingdesign 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.
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