1,249 research outputs found

    Many-Objective Hybrid Optimization Under Uncertainty With Applications

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    A novel method for solving many-objective optimization problems under uncertainty was developed. It is well known that no single optimization algorithm performs best for all problems. Therefore, the developed method, a many-objective hybrid optimizer (MOHO), uses five constitutive algorithms and actively switches between them throughout the optimization process allowing for robust optimization. MOHO monitors the progress made by each of the five algorithms and allows the best performing algorithm more attempts at finding the optimum. This removes the need for user input for selecting algorithm as the best performing algorithm is automatically selected thereby increasing the probability of converging to the optimum. An uncertainty quantification framework, based on sparse polynomial chaos expansion, to propagate the uncertainties in the input parameter to the objective functions was also developed and validated. Where the samples and analysis runs needed for standard polynomial chaos expansion increases exponentially with the dimensionality, the presented sparse polynomial chaos approach efficiently propagates the uncertainty with only a few samples, thereby greatly reducing the computational cost. The performance of MOHO was investigated on a total of 65 analytical test problems from the DTLZ and WFG test suite, for which the analytical solution is known. MOHO is also applied to two additional real-life cases of aerodynamic shape design of subsonic and hypersonic bodies. Aerodynamic shape optimization is often computationally expensive and is, therefore, a good test case to investigate MOHO`s ability to reduce the computational time through robust optimization and accelerated convergence. The subsonic design optimization had three objectives: maximize lift and minimize drag and moment. The hypersonic design optimization had two objectives: maximize volume and minimize drag. Two accelerated solvers based on fast multipole method and Newton impact theory are developed for simulating subsonic and hypersonic flows. The results show that MOHO performed, on average, better than all five remaining algorithms in 52% of the DTLZ+WFG problems. The results of robust optimization of a subsonic body and hypersonic bodies were in good agreement with theory. The MOHO developed is capable of solving many-objective, multi-objective and single objective, constrained and unconstrained optimization problems with and without uncertainty with little user input

    On the effect of model uncertainty on the Hopf bifurcation of aeroelastic systems

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    This paper investigates the effect of model uncertainty on the nonlinear dynamics of a generic aeroelastic system. Among the most dangerous phenomena to which these systems are prone, Limit Cycle Oscillations are periodic isolated responses triggered by the nonlinear interactions among elastic deformations, inertial forces, and aerodynamic actions. In a dynamical systems setting, these responses typically emanate from Hopf bifurcation points, and thus a recently proposed framework, which address the problem of robustness from a nonlinear dynamics viewpoint, is employed. Briefly, the notion of robust bifurcation margin extends the concept of mu analysis technique from the robust control theory. The main contribution of this article is a systematic investigation of the numerous scenarios arising in the study of nonlinear flutter when uncertainties in the model are accounted for in the analyses. The advantages of adopting this framework include the possibility to: quantify relevant information for the determination of the nonlinear stability envelope; gain a more in-depth understanding of the physical mechanisms triggering subcritical and supercritical Hopf bifurcations; and reveal properties of the nominal system by identifying isolated branches not straightforward to detect with conventional numerical approaches.Open Access funding provided by Swiss Federal Institute of Technology Zurich This work has received funding from the Horizon 2020 research and innovation programme under grant agreement No. 636307, project FLEXOP

    Efficient Prediction and Uncertainty Propagation of Correlated Loads

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    2008 and 2009 Research and Engineering Annual Report

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    Selected research and technology activities at NASA Dryden Flight Research Center are summarized. These activities exemplify the Center's varied and productive research efforts

    Surrogate - Assisted Optimisation -Based Verification & Validation

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    This thesis deals with the application of optimisation based Validation and Verification (V&V) analysis on aerospace vehicles in order to determine their worst case performance metrics. To this end, three aerospace models relating to satellite and launcher vehicles provided by European Space Agency (ESA) on various projects are utilised. As a means to quicken the process of optimisation based V&V analysis, surrogate models are developed using polynomial chaos method. Surro- gate models provide a quick way to ascertain the worst case directions as computation time required for evaluating them is very small. A sin- gle evaluation of a surrogate model takes less than a second. Another contribution of this thesis is the evaluation of operational safety margin metric with the help of surrogate models. Operational safety margin is a metric defined in the uncertain parameter space and is related to the distance between the nominal parameter value and the first instance of performance criteria violation. This metric can help to gauge the robustness of the controller but requires the evaluation of the model in the constraint function and hence could be computationally intensive. As surrogate models are computationally very cheap, they are utilised to rapidly compute the operational safety margin metric. But this metric focuses only on finding a safe region around the nominal parameter value and the possibility of other disjoint safe regions are not explored. In order to find other safe or failure regions in the param- eter space, the method of Bernstein expansion method is utilised on surrogate polynomial models to help characterise the uncertain param- eter space into safe and failure regions. Furthermore, Binomial failure analysis is used to assign failure probabilities to failure regions which might help the designer to determine if a re-design of the controller is required or not. The methodologies of optimisation based V&V, surrogate modelling, operational safety margin, Bernstein expansion method and risk assessment have been combined together to form the WCAT-II MATLAB toolbox

    Challenges, Ideas, and Innovations of Joined-Wing Configurations: A Concept from the Past, an Opportunity for the Future

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    Diamond Wings, Strut- and Truss-Braced Wings, Box Wings, and PrandtlPlane, the so-called “Join-edWings”, represent a dramatic departure from traditional configurations. Joined Wings are characterized by a structurally overconstrained layout which significantly increases the design space with multiple load paths and numerous solutions not available in classical wing systems. A tight link between the different disciplines (aerodynamics, flight mechanics, aeroelasticity, etc.) makes a Multidisciplinary Design and Optimization approach a necessity from the early design stages. Researchers showed potential in terms of aerodynamic efficiency, reduction of emissions and superior performances, strongly supporting the technical advantages of Joined Wings. This review will present these studies, with particular focus on the United States joined-wing SensorCraft, Strut- and Truss- Braced Wings, Box Wings and PrandtlPlane

    不確実性下での設計に対するMulti-Fidelity不確定性定量化とSurrogate-Based Memeticアルゴリズム

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    学位の種別: 課程博士審査委員会委員 : (主査)東京大学教授 土屋 武司, 東京大学教授 鈴木 真二, 東京大学教授 李家 賢一, 東京大学准教授 大山 聖, 東北大学准教授 下山 幸治University of Tokyo(東京大学
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