A new approach for the multi-objective optimization of composite structures under the effects of uncertainty in mechanical properties, structural parameters and external loads is proposed, to guarantee higher levels o...
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A new approach for the multi-objective optimization of composite structures under the effects of uncertainty in mechanical properties, structural parameters and external loads is proposed, to guarantee higher levels of accuracy exclusively with Evolutionary Algorithms (EA). The concepts of reliability-based robust design optimization (RBRDO) are applied. Optimality is defined as the minimization of the structural weight and robustness as the minimization of the determinant of the variance-covariance matrix of the structural responses. reliability assessment is performed through a mathematical reformulation of the Performance Measure Approach, suitable for EA, where the standard normal uncertainty space was defined in directional coordinates and reduced to the surface of the hypersphere of radius beta(boolean AND a). A binary reliability constraint, that allowed avoiding unnecessary runs of the reliability inner-cycle is defined. The robustdesignoptimization cycle is solved by a multi-objective EA, based on constrained-dominance. Sensitivities of the structural responses, necessary for uncertainty analysis only, are calculated analytically by the Adjoint Variable Method. A numerical example considering a balanced angle-ply laminate shell is presented. Results show an effective convergence of the Pareto-optimal Front (POF). Uncertainty analysis shows that the variability of the critical displacements increases along the POF. For the stresses, variability is stable but of higher values. The incorporation of the reliability constraint prevents the natural decrease of the reliability index, along the POF, to reach levels too close, or inside, of the failure domain. The distribution of the reliability measures along the POF is similar and demonstrates the effects of reliability in the RBRDO procedure.
Low-cost Moon imaging micro/nano-satellites have been receiving much attention from the engineering community. The reliability-based robust design optimization (RBRDO) of these weight/size/power constrained satellites...
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Low-cost Moon imaging micro/nano-satellites have been receiving much attention from the engineering community. The reliability-based robust design optimization (RBRDO) of these weight/size/power constrained satellites is crucial. In this study, the configuration of this conceptual design problem considering uncertainty is analyzed and the interdisciplinary relations are described. Discipline models including orbit, payload, propulsion, and communication are mainly discussed. A new RBRDO approach to lunar micro/nano-satellite system design is presented, involving dimension reduction, dynamic response surface, system decoupling, and multi objective alliance search. The optimization goal includes payload cost-effective ratio and satellite mass, and five reliability constraints are considered including satellite installation volume and battery cycles. Through identifying a dominant active subspace for high-dimensional inputs, the in-loop uncertainty quantification is greatly accelerated by about two orders of magnitude. A smooth and uniformly distributed Pareto front is therefore obtained to provide beneficial Pareto-optimal solutions, which indicates that the orbit altitude gathers around 200 km and the mission cycle is nearly 2.4 years. An optimal trade-off design has the minimum satellite mass of 8.95 kg and smaller cost-effective ratio. Compared with the deterministic optimization, the RBRDO approach is preferable for designing Moon imaging micro/nano-satellites of high reliability and robustness.
Hull-form stochastic optimization methods are presented and evaluated for resistance reduction and operational efficiency (operability), addressing stochastic sea state and operations. The cost/benefit analysis of the...
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Hull-form stochastic optimization methods are presented and evaluated for resistance reduction and operational efficiency (operability), addressing stochastic sea state and operations. The cost/benefit analysis of the optimization procedure is presented by comparison of four hierarchical problems, from stochastic most general to deterministic least general. The parent hull is a high-speed catamaran, with geometrical constraints for maximum variation of length, beam, draft, and displacement. Problem 1 is used as a benchmark for the evaluation of the other problem formulations and is defined as a multi-objective stochastic optimization for resistance and operability, considering stochastic sea state and speed, but limited to head waves. Problem 2 is a multi-objective stochastic optimization for resistance and motions at fixed sea state and speed. Problem 3 is a multi-objective deterministic optimization for resistance and motions using a single regular wave at fixed speed. Problem 4 is a single-objective deterministic optimization for calm-water resistance at fixed speed. The designoptimization is based on hull-form modifications by the Karhunen-LoSve expansion of a free-form deformation, URANS-based CFD simulations, regular wave approximations for irregular waves, metamodels and multi-objective particle swarm. The designoptimization achieves an 8.7, 23, 53, and 10% average improvements for problems 1, 2, 3, and 4, respectively. Comparing to problem 1, problem 2, 3, 4 optimized designs have average performances 1, 2.1 and 1.7% worse, respectively. The most efficient problem, from the computational cost/benefit viewpoint, is problem 3. Nevertheless, problem 1 is needed to evaluate and compare the stochastic performance of the designs and finally assess the optimization cost/benefit.
In a real-world loading case (e.g., car crash accidents), energy-absorbing components are subject to oblique loads at various uncertain angles. This paper aims to investigate the behavior of such components under thre...
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In a real-world loading case (e.g., car crash accidents), energy-absorbing components are subject to oblique loads at various uncertain angles. This paper aims to investigate the behavior of such components under three-dimensional (3D) oblique loads in deterministic and probabilistic loading conditions. In this way, some square tubes are tested experimentally, and results are utilized to validate numerical models. To apply the 3D oblique load, a special test setup is designed, constructed, and installed on a universal tensile testing machine. Hammersley method is employed to design sample points. ABAQUS software is used for the finite element modeling and analysis. GEvoM software is implemented for mapping design variables onto crashworthiness characteristics including energy absorption (EA) and peak crush force (PCF). Both deterministic and reliability-basedrobustdesign (RBRD) optimizations are performed, and their results are compared with each other. The primary outcome of this research is the effect of incidence angles on the energy-absorbing characteristics, as well as some remarkable trade-off design points obtained from various multiple-criteria decision-making (MCDM) methods. It was discovered that the obtained design points of probabilistic study, which satisfied the reliability constraint, were roughly 60% more robust than deterministic points. (C) 2018 Elsevier Ltd. All rights reserved.
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