Abstract:Optimization procedure is one of the key techniques to address the computational and organizational complexities of multidisciplinary design optimization (MDO). Motivated by the idea of synthetically exploiting the advantage of multiple existing optimization procedures and meanwhile complying with the general process of satellite system design optimization in conceptual design phase, a multistage-multilevel MDO procedure is proposed in this paper by integrating multiple-discipline-feasible (MDF) and concurrent… Show more
“…The total number of iterations of the NRMCO and MRMCO approaches is nearly the same on average; these methods are the best in terms of computational efficiency in comparison to NRCO. Furthermore, BLISCO and multiple-discipline-feasible combined with concurrent subspace optimization (MDF-CSSO: Yao et al, 2012) are applied to this problem. Compared with these methods, NRMCO is more efficient and practical for the speed reducer optimization.…”
The purpose of the present work is to improve the performance of the standard collaborative optimization (CO) approach based on an existing dynamic relaxation method. This approach may be weakened by starting design points. First, a New Relaxation (NR) method is proposed to solve the difficulties in convergence and low accuracy of CO. The new method is based on the existing dynamic relaxation method and it is achieved by changing the system-level consistency equality constraints into relaxation inequality constraints. Then, a Modified Collaborative Optimization (MCO) approach is proposed to eliminate the impact of the information inconsistency between the system-level and the discipline-level on the feasibility of optimal solutions. In the MCO approach, the impact of the inconsistency is treated by transforming the discipline-level constrained optimization problems into an unconstrained optimization problem using an exact penalty function. Based on the NR method, the performance of the MCO approach carried out by solving two multidisciplinary optimization problems. The obtained results show that the MCO approach has improved the convergence of CO significantly. These results prove that the present MCO succeeds in getting feasible solutions while the CO fails to provide feasible solutions with the used starting design points.
“…The total number of iterations of the NRMCO and MRMCO approaches is nearly the same on average; these methods are the best in terms of computational efficiency in comparison to NRCO. Furthermore, BLISCO and multiple-discipline-feasible combined with concurrent subspace optimization (MDF-CSSO: Yao et al, 2012) are applied to this problem. Compared with these methods, NRMCO is more efficient and practical for the speed reducer optimization.…”
The purpose of the present work is to improve the performance of the standard collaborative optimization (CO) approach based on an existing dynamic relaxation method. This approach may be weakened by starting design points. First, a New Relaxation (NR) method is proposed to solve the difficulties in convergence and low accuracy of CO. The new method is based on the existing dynamic relaxation method and it is achieved by changing the system-level consistency equality constraints into relaxation inequality constraints. Then, a Modified Collaborative Optimization (MCO) approach is proposed to eliminate the impact of the information inconsistency between the system-level and the discipline-level on the feasibility of optimal solutions. In the MCO approach, the impact of the inconsistency is treated by transforming the discipline-level constrained optimization problems into an unconstrained optimization problem using an exact penalty function. Based on the NR method, the performance of the MCO approach carried out by solving two multidisciplinary optimization problems. The obtained results show that the MCO approach has improved the convergence of CO significantly. These results prove that the present MCO succeeds in getting feasible solutions while the CO fails to provide feasible solutions with the used starting design points.
The design of space systems is a complex and multidisciplinary process with multiple conflicting objectives, large number of design variables, and constraints that limits application of the existing multidisciplinary design optimization architectures to this class of design problems. This paper presents an enhanced multidisciplinary design optimization architecture to concurrent holistic design optimization of a satellite system. The proposed multidisciplinary design optimization architecture extends concepts of multidiscipline feasible and bi-level integrated system synthesis into a unified architecture using metamodels. The proposed architecture was evaluated and compared with the existing multidisciplinary design optimization architectures that include all-at-once, bi-level integrated system synthesis, and multidisciplinary design optimization using a remote sensing small satellite in low earth orbit. The satellite design optimization problem deals with the minimization of the total mass of the satellite, involving disciplines of mission analysis, payload, structures, attitude determination and control, communication, command and data handling, power and thermal. The computational performance and accuracy of the proposed architecture were compared with multidisciplinary design optimization benchmark problems. Then the proposed architecture is successfully applied to the satellite system design problem. The results obtained show that metamodel-based bi-level integrated system synthesis-multidisciplinary design optimization architecture presented in this paper provides an effective way of solving large-scale design problems.
“…Single level approaches employ a system optimizer for the whole problem, which is straightforward to understand and easy to implement. Multi-level approaches utilize decomposition strategies to allow disciplinary autonomy in design and optimization while managing interdisciplinary consistency via system coordination [12].…”
In five-axis machine tools, a rotary table is often used as a means for providing rotational motion and supporting the workpiece. Its rigidity, precision and carrying capacity is directly related to the machining ability and the accuracy of the NC machine tool. Traditional rotary table design is normally performed by teams, each with expertise in a specific discipline, which causes excessive iterations and cannot provide users with products of reliable working performance and bearing capacity. To achieve an optimal design with less cost and better performance, this paper considers the mutual interaction of hydrostatics and structure disciplines involved in the design of hydrostatic rotary tables, and a sensitivity-based multidisciplinary optimal design procedure of a hydrostatic rotary table is proposed. Sensitivity analysis is adopted to identify the key design parameters that have a major influence on the performance of rotary tables to improve the convergence of optimization process. The constrained multi-objective optimization problem is solved by using a particle swarm optimization approach. A hydrostatic rotary table of a five-axis heavy duty machine tool is selected as an illustration example. The results show that the proposed method can realize the multidisciplinary optimization resulting in a rotary table of good rigidity and bearing capacity.
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