A direct solution framework for structural optimization problems with interval uncertainties

Fu, Chunming; Liu, Zhiwen; Deng, Jian

doi:10.1016/j.apm.2019.11.029

Cited by 7 publications

(2 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The section applies the proposed algorithm into the structural design of AR glasses controller in order to illustrate its applicability in engineering structural design. The optimization model is established as follows [21]:…”

Section: An Engineeringmentioning

confidence: 99%

Reliability-based Design Optimization of Structures with Disturbed Design Variables and Dependent Uncertainties

Guan

Wan

2022

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

To ensure the safety of engineering structures under disturbed design variables and dependent uncertainties, a new reliability-based design optimization method was proposed in this paper. The optimization model was firstly established by adding the additional constraints that considers the disturbance of design variables. Then, the multidimensional parallelepiped model was used to describe the uncertainties and to further process their correlation. Further, the structural reliability was analyzed by interval analysis and reliability-based possibility degree of interval model. Finally, the solving algorithm was constructed. Such the algorithm was applied to a numerical example and the structural design of augmented reality glasses, among which the former demonstrated the effectiveness of proposed algorithm in dealing with disturbance of design variables and correlation between uncertainties, while the latter showed the applicability of the algorithm in engineering structural design.

show abstract

Section: An Engineeringmentioning

confidence: 99%

Reliability-based Design Optimization of Structures with Disturbed Design Variables and Dependent Uncertainties

Guan

Wan

2022

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

show abstract

“…Inuiguchi et al [23] proposed a linear multi-objective strategy based on maximum and minimum regret criteria to solve the problem of interval uncertainty in the objective function. Fu et al [24] developed a multi-objective direct structural optimization method for solving interval uncertainty. This method uses the satisfaction value of the interval possibility model to deal with non-linear uncertain constraints, and it judges the feasibility and infeasibility of individual design vectors.…”

Section: Introductionmentioning

confidence: 99%

Optimization Design for the Planetary Gear Train of an Electric Vehicle under Uncertainties

Chen

Lin

et al. 2022

Actuators

View full text Add to dashboard Cite

The planetary gear train is often used as the main device for decelerating and increasing the torque of the drive motor of electric vehicles. Considering the lightweight requirement and existing uncertainty in structural design, a multi-objective uncertainty optimization design (MUOD) framework is developed for the planetary gear train of the electric vehicle in this study. The volume and transmission efficiency of the planetary gear train are taken into consideration as optimization objectives. The manufacturing size, material, and load input of the planetary gear train are considered as uncertainties. An approximate direct decoupling model, based on subinterval Taylor expansion, is applied to evaluate the propagation of uncertainties. To improve the convergence ability of the multi-objective evolutionary algorithm, the improved non-dominated sorting genetic algorithm II (NSGA-II) is designed by using chaotic and adaptive strategies. The improved NSGA-II has better convergence efficiency than classical NSGA-II and multi-objective particle swarm optimization (MOPSO). In addition, the multi-criteria decision making (MCDM) method is applied to choose the most satisfactory solution in Pareto sets from the multi-objective evolutionary algorithm. Compared with the multi-objective deterministic optimization design (MDOD), the proposed MUOD framework has better reliability than MDOD under different uncertainty cases. This MUOD method enables further guidance pertaining to the uncertainty optimization design of transportation equipment, containing gear reduction mechanisms, in order to reduce the failure risk.

show abstract

Non-probabilistic optimization model of engineering structures with dependent interval variables

Guan

Wan

et al. 2022

Applied Mathematical Modelling

View full text Add to dashboard Cite

A direct solution framework for structural optimization problems with interval uncertainties

Cited by 7 publications

References 31 publications

Reliability-based Design Optimization of Structures with Disturbed Design Variables and Dependent Uncertainties

Reliability-based Design Optimization of Structures with Disturbed Design Variables and Dependent Uncertainties

Optimization Design for the Planetary Gear Train of an Electric Vehicle under Uncertainties

Non-probabilistic optimization model of engineering structures with dependent interval variables

Contact Info

Product

Resources

About