Integrated design optimization of voltage channel distribution and control voltages for tracking the dynamic shapes of smart plates

2010 ◽  
Vol 19 (12) ◽  
pp. 125013 ◽  
Author(s):  
Shutian Liu ◽  
Zheqi Lin
Keyword(s):  
Design Optimization ◽  
Integrated Design ◽  
Channel Distribution ◽  
And Control

Integrated design optimization of actuator structural parameters and control voltages for morphing structural shapes

2014 ◽  
Vol 22 (6) ◽  
pp. 1538-1546
Author(s):  
高仁璟 GAO Ren-jing ◽  
张莹 ZHANG Ying ◽  
吴书豪 WU Shu-hao ◽  
刘书田 LIU Shu-tian
Keyword(s):  
Design Optimization ◽  
Structural Parameters ◽  
Integrated Design ◽  
And Control

Multidisciplinary co-design optimization of the structure and control systems for large cable shovel considering cross-disciplinary interaction

2020 ◽  
Vol 234 (22) ◽  
pp. 4353-4365
Author(s):  
Xueguan Song ◽  
Tianci Zhang ◽  
Yongliang Yuan ◽  
Xiaobang Wang ◽  
Wei Sun
Keyword(s):  
Energy Consumption ◽  
Design Optimization ◽  
Integrated Design ◽  
Structure Design ◽  
Sequential Design ◽  
Simultaneous Optimization ◽  
Open Pit ◽  
Structure Control ◽  
Cable Shovel ◽  
And Control

Large cable shovel is a complex mechatronic system used for primary production in the open pit mine. For such structure-control highly coupled system, the conventional sequential design strategy (structure design followed by the control optimization in sequence) cannot manage this interaction adequately and explicitly. In addition, the large cable shovel consists of large number of sub-systems and/or disciplines, which also poses challenges to the global optimal design for large cable shovel. To enhance large cable shovel’s performance, an integrated design optimization strategy combining the structure-control simultaneous design (co-design) and the multidisciplinary design optimization is established in this study to perform the global optimization for the large cable shovel. In this proposed multidisciplinary co-design, the point-to-point trajectory planning method is extended to achieve the simultaneous optimization of the structure and control system. Besides the structure and control, the dynamics/vibration and energy consumption are taken into account in this multidisciplinary co-design. The objectives are to minimize the energy consumption per volume of ore and to minimize the excavating time. By comparing the multidisciplinary co-design and the conventional sequential design, it is found that the multidisciplinary co-design can not only make large cable shovel’s structure more compact with relatively small vibration, but also generate more flexible control speeds by making the best of the power motors.

Robust MDSDO for Co-Design of Stochastic Dynamic Systems

2019 ◽  
Vol 142 (1) ◽  
Author(s):  
Saeed Azad ◽  
Michael J. Alexander-Ramos
Keyword(s):  
Dynamic System ◽  
Design Optimization ◽  
System Design ◽  
Design Problem ◽  
Integrated Design ◽  
Problem Formulation ◽  
Robust Design Optimization ◽  
Design Problems ◽  
Embodiment Design ◽  
And Control

Abstract Optimization of dynamic engineering systems generally requires problem formulations that account for the coupling between embodiment design and control system design simultaneously. Such formulations are commonly known as combined optimal design and control (co-design) problems, and their application to deterministic systems is well established in the literature through a variety of methods. However, an issue that has not been addressed in the co-design literature is the impact of the inherent uncertainties within a dynamic system on its integrated design solution. Accounting for these uncertainties transforms the standard, deterministic co-design problem into a stochastic one, thus requiring appropriate stochastic optimization approaches for its solution. This paper serves as the starting point for research on stochastic co-design problems by proposing and solving a novel problem formulation based on robust design optimization (RDO) principles. Specifically, a co-design method known as multidisciplinary dynamic system design optimization (MDSDO) is used as the basis for an RDO problem formulation and implementation. The robust objective and inequality constraints are computed per usual as functions of their first-order-approximated means and variances, whereas analysis-based equality constraints are evaluated deterministically at the means of the random decision variables. The proposed stochastic co-design problem formulation is then implemented for two case studies, with the results indicating the importance of the robust approach on the integrated design solutions and performance measures.

Robust MDSDO for Co-Design of Stochastic Dynamic Systems

2018 ◽  
Author(s):  
Saeed Azad ◽  
Michael J. Alexander-Ramos
Keyword(s):  
Dynamic System ◽  
Design Optimization ◽  
System Design ◽  
Design Problem ◽  
Integrated Design ◽  
Problem Formulation ◽  
Robust Design Optimization ◽  
Design Problems ◽  
Embodiment Design ◽  
And Control

Optimization of dynamic engineering systems generally requires problem formulations that account for the coupling between embodiment design and control system design simultaneously. Such formulations are commonly known as combined optimal design and control (co-design) problems, and their application to deterministic systems is well-established in the literature through a variety of methods. However, an issue that has not been addressed in the co-design literature is the impact of the inherent uncertainties within a dynamic system on its integrated design solution. Accounting for these uncertainties transforms the standard, deterministic co-design problem into a stochastic one, thus requiring appropriate stochastic optimization approaches for its solution. This paper serves as the starting point for research on stochastic co-design problems by proposing and solving a novel problem formulation based on robust design optimization (RDO) principles. Specifically, a co-design method known as multidisciplinary dynamic system design optimization (MDSDO) is used as the basis for a RDO problem formulation and implementation. The robust objective and inequality constraints are computed per usual as functions of their first-order-approximated means and variances, whereas analysis-based equality constraints are evaluated deterministically at the means of the random decision variables. The proposed stochastic co-design problem formulation is then implemented for two case studies, with the results indicating a significant impact of the robust approach on the integrated design solutions and performance measures.

Integrated Design and Multi-Objective Optimization of a Single Stage Heat-Pump Turbocompressor

2013 ◽  
Author(s):  
J. Schiffmann
Keyword(s):  
Design Optimization ◽  
High Efficiency ◽  
Integrated Design ◽  
Heat Pumps ◽  
Small Scale ◽  
Multi Objective Optimization ◽  
Design Constraints ◽  
Multi Objective ◽  
Wide Range ◽  
Standard Design

Small scale turbomachines in domestic heat pumps reach high efficiency and provide oil-free solutions which improve heat-exchanger performance and offer major advantages in the design of advanced thermodynamic cycles. An appropriate turbocompressor for domestic air based heat pumps requires the ability to operate on a wide range of inlet pressure, pressure ratios and mass flows, confronting the designer with the necessity to compromise between range and efficiency. Further the design of small-scale direct driven turbomachines is a complex and interdisciplinary task. Textbook design procedures propose to split such systems into subcomponents and to design and optimize each element individually. This common procedure, however, tends to neglect the interactions between the different components leading to suboptimal solutions. The authors propose an approach based on the integrated philosophy for designing and optimizing gas bearing supported, direct driven turbocompressors for applications with challenging requirements with regards to operation range and efficiency. Using previously validated reduced order models for the different components an integrated model of the compressor is implemented and the optimum system found via multi-objective optimization. It is shown that compared to standard design procedure the integrated approach yields an increase of the seasonal compressor efficiency of more than 12 points. Further a design optimization based sensitivity analysis allows to investigate the influence of design constraints determined prior to optimization such as impeller surface roughness, rotor material and impeller force. A relaxation of these constrains yields additional room for improvement. Reduced impeller force improves efficiency due to a smaller thrust bearing mainly, whereas a lighter rotor material improves rotordynamic performance. A hydraulically smoother impeller surface improves the overall efficiency considerably by reducing aerodynamic losses. A combination of the relaxation of the 3 design constraints yields an additional improvement of 6 points compared to the original optimization process. The integrated design and optimization procedure implemented in the case of a complex design problem thus clearly shows its advantages compared to traditional design methods by allowing a truly exhaustive search for optimum solutions throughout the complete design space. It can be used for both design optimization and for design analysis.

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