The efficient global optimization approach was often used to reduce the computational cost in the optimization of complex engineering systems. This algorithm can, however, remain expensive for large-scale problems bec...
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The efficient global optimization approach was often used to reduce the computational cost in the optimization of complex engineering systems. This algorithm can, however, remain expensive for large-scale problems because each simulation uses the full numerical model. A novel optimization approach for such problems is proposed in this paper, in which the numerical model solves partial differential equations involving the resolution of a large system of equations, such as by finite element. This method is based on the combination of the efficient global optimization approach and reduced-basis modeling. The novel idea is to use inexpensive, sufficiently accurate reduced-basis solutions to significantly reduce the number of full system resolutions. Two applications of the proposed surrogate-based optimization approach are presented: an application to the problem of stiffness maximization of laminated plates and an application to the problem of identification of orthotropic elastic constants from full-field displacement measurements based on a tensile test on a plate with a hole. Compared with the crude efficient global optimization algorithm, a significant reduction in computational cost was achieved using the proposed efficient reduced-basis global optimization.
The tightly constrained guidance problem of impact on a stationary target is considered, subject to look-angle and lateral-acceleration limits, as well as final impact-angle and look-angle constraints. Proportional-na...
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The tightly constrained guidance problem of impact on a stationary target is considered, subject to look-angle and lateral-acceleration limits, as well as final impact-angle and look-angle constraints. Proportional-navigation guidance with a time-varying gain is proposed to address this problem. The problem has been formulated as an optimal-control problem, where the time-varying PN gain is regarded to be the control, subject to nonlinear engagement kinematics and all the inequality and terminal constraints. A successive solution approach is proposed to solve this nonlinear optimal-control problem as a sequence of convex optimal-control problems that can be numerically solved as second-order cone programs. Compared with standard direct-trajectory-optimization approaches based on general nonlinear-programming methods, the proposed method can solve the problem two orders of magnitude faster in terms of computational time. Compared with existing PN guidance approaches, the proposed method provides a more rigorous and systematic guidance approach, and assures the satisfaction of all guidance objectives in highly constrained but feasible scenarios.
Traditional testing methodologies often result in spacecraft equipment undergoing vibration testing that is harsher than the launch environment it must survive. This leads to over designing as items must be designed t...
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In literature, a lot of research works have been presented on crashworthiness in order to develop crash performance of vehicles and thin-wall structures. In this research, a new hybrid optimization algorithm based on ...
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In literature, a lot of research works have been presented on crashworthiness in order to develop crash performance of vehicles and thin-wall structures. In this research, a new hybrid optimization algorithm based on gravitational search algorithm and Nelder-Mead algorithm is introduced to improve crash performance of vehicles during frontal impact. The results show that the hybrid approach is very effective to develop crash performance of the vehicle components and thin-wall structures.
In order to efficiently design power conditioning systems, operating conditions have to be considered. For instance, power converter design should take into account the intermittency of the renewable energy sources an...
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In order to efficiently design power conditioning systems, operating conditions have to be considered. For instance, power converter design should take into account the intermittency of the renewable energy sources and the relatively low efficiency of some of their subsystems such as photovoltaic modules. This paper deals with dc-dc power converter design and aims to improve both reliability and efficiency. The approach adopted is based on a boost-based topology equipped with three MOSFETs in parallel. It uses a novel biobjective algorithm based on a biobjective function that optimizes the power converter efficiency and reliability. The proposed algorithm defines the number of MOSFETs to commutate in order to guarantee the best compromise between the converter efficiency and its reliability, depending on the available input power. The converter efficiency is determined by the power converter losses, calculated using the power device parameters and their junction temperature measurement as inputs. The converter reliability is calculated using its mean time between failures as assessed in the military handbook for reliability prediction of electronic equipment (MIL-HDBK-217F). Experimental results carried out on a 2.5 kW prototype demonstrate the effectiveness of the proposed system and the details from one day's photovoltaic power production are presented and discussed.
The nationwide air traffic flow management problem often encounters computational difficulty because it is generally modeled as an integer programming problem that requires computationally expensive optimization algor...
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The nationwide air traffic flow management problem often encounters computational difficulty because it is generally modeled as an integer programming problem that requires computationally expensive optimization algorithms. This paper introduces a customized Spark-based optimization architecture for such large-scale integer programming problems to further speed up the modeling and optimization process, where Spark is a big data cluster-computing platform. First, a novel layered aggregate model is developed for handling flexible rerouting problem, which is not well handled in a previous link transmission model. As an aggregate linear model, the layered aggregate model has the nice features of computational efficiency and scalability, which make it suitable for Apache Spark. By applying a dual decomposition method, the original large-scale problem is decomposed into a number of small subproblems. The optimization proceeds by iteratively solving subproblems and updating Lagrange multipliers. This paper encapsulated the process into the Spark-based data processing model such that the optimization is automatically scheduled to run in parallel. Spark gains efficiency by means of in-memory computing and dynamic schedule allocation. This is demonstrated in the experimental results that are compared to an earlier Hadoop MapReduce-based model, where Hadoop MapReduce is a basic cloud computing framework;the Spark-based model is solved twice as fast as the Hadoop MapReduce-based model.
The design of high-lift systems represents a challenging task within the aerospace community, being a multidisciplinary, multi-objective, and multipoint problem. The Design, Simulation and Flight Reynolds Number Testi...
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The design of high-lift systems represents a challenging task within the aerospace community, being a multidisciplinary, multi-objective, and multipoint problem. The Design, Simulation and Flight Reynolds Number Testing for Advanced High-Lift Solution project, funded by the European Commission under the Seventh Framework Programme, aimed at improving the aerodynamics of high-lift systems by developing, in a coordinated approach, both efficient numerical design strategies and measurement techniques for cryogenic conditions. Within the Design, Simulation and Flight Reynolds Number Testing for Advanced High-Lift Solution project, different partners used several numerical automatic optimization strategies for high-lift system design purposes. A realistic multi-objective and multipoint optimization problem was defined and solved by adopting different flow model dimensionality, meshing techniques, geometry parameterization, and optimization strategies. Special attention was devoted to perform a fair comparison of the results, and useful information was obtained about trends, pros, and cons of the approaches used. The outcome of these activities is that an efficient design process can be set up through decoupling of the original multi-objective problem into several, sequential suboptimization processes. Nevertheless, several decoupling possibilities exist, and the most efficient one can be identified only on the bases of preanalysis or preknowledge of the specific problem. Second, the exercise carried out demonstrated the maturity and feasibility of a full three-dimensional automatic high-lift design.
In this article, A novel nature-inspired optimization algorithm known as Lightning Attachment Procedure optimization (LAPO) is proposed. The proposed approach mimics the lightning attachment procedure including the do...
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In this article, A novel nature-inspired optimization algorithm known as Lightning Attachment Procedure optimization (LAPO) is proposed. The proposed approach mimics the lightning attachment procedure including the downward leader movement, the upward leader propagation, the unpredictable trajectory of lightning downward leader, and the branch fading feature of lightning. Final optimum result would be the lightning striking point. The proposed method is free from any parameter tuning and it is rarely stuck in the local optimum points. To evaluate the proposed algorithm, 29 mathematical benchmark functions are employed and the results are compared to those of 9 high quality well-known optimization methods The results of the proposed method are compared from different points of views, including quality of the results, convergence behavior, robustness, and CPU time consumption. Superiority and high quality performance of the proposed method are demonstrated through comparing the results. Moreover, the proposed method is also tested by five classical engineering design problems including tension/compression spring, welded beam, pressure vessel designs, Gear train design, and Cantilever beam design and a high constraint optimization problem known as Optimal Power Flow (OPF) which is a high constraint electrical engineering problem. The excellence performance of the proposed method in solving the problems with large number of constraints and also discrete optimization problems are also concluded from the results of the six engineering problem. (C) 2017 Elsevier B.V. All rights reserved.
A tool selection process and optimization method based on big data was proposed during the stage of tool selection in order to effectively utilize the production-process-data(PPD) to drive the selection of tools, and ...
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A tool selection process and optimization method based on big data was proposed during the stage of tool selection in order to effectively utilize the production-process-data(PPD) to drive the selection of tools, and to improve the precision of tool selection. An intelligent tool selection model was built according to the mapping characteristics of PPD during the process of tool selection. On this basis, key influencing factors were analyzed and calculated, then the topological relationship among data was mined. According to the cosine similarity algorithm, optimization algorithm of tool selection was derived. By inputting the PPD as impact factor, outputting the tool selection importance value, this optimization algorithm can support the users to select tools. A tool selection optimization system was developed based on Java by using the intelligent tool selection model. The feasibility and effectiveness of the optimization method were verified by this system.
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