Structure optimization of the frame based on response surface method

Wang, Yahui; Cai, Zaisheng; Su, Yongqiang; Shang, Li-yang; Zhang, Tao

doi:10.1108/ijsi-07-2019-0067

Cited by 23 publications

(18 citation statements)

References 8 publications

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“…The output parameters such as maximum equivalent stress, torsional stiffness, and deformation of the bus structure under two loading conditions in the static strength analysis are displayed in Table 8. Moreover, the maximum equivalent stress of the structure should not exceed the material's yield stress and meet the strength conditions [45]. Accordingly, all maximum stresses of models were obtained below the yield strength of the material, as shown in Table 8.…”

Section: Resultsmentioning

confidence: 93%

Structural Weight and Stiffness Optimization of a Midibus Using the Reinforcement and Response Surface Optimization (RSO) Method in Static Condition

Addisu

Koricho

2022

Modelling and Simulation in Engineering

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Midibuses are medium-sized buses widely used for transportation purposes in Asia and Africa. However, most midibuses are locally built and indirectly regulated through inspecting the end product (finished bus) during licensing for the public transport business in Ethiopia. Due to lack of engineering analysis and testing, low stiffness and overweight of midibus were compromised. This research was aimed at analyzing and optimizing the midibus structure using the reinforcement and response surface optimization (RSO) method for pure bending and torsion loading cases. Results show that the maximum deformation occurred at the roof section of the original structure during both loading cases. Furthermore, the reinforcement design was found by replacing the cross section and layouts of structural members and adding reinforcements for the most suitable location of the original structure. Response surface optimization with the multiobjective genetic algorithm (MOGA) method in ANSYS DesignXplorer was performed on the reinforced structure to maximize the bending and torsional stiffness with reduced weight. The bending stiffness of the reinforced and optimized structure increased by 41.65% (1911.4 N/m) and 10.02% (651.7 N/m), respectively. In addition, the torsional rigidity or stiffness of the bus structure was improved by 12.56% (173.31 Nm/deg) via reinforcement design. Moreover, the torsional stiffness of the optimized (RSO) model was increased by 3.29% (51.07 Nm/deg). Reinforcement design was effectively reduced by 5.23% of the structure’s weight. Moreover, the RSO method has also decreased the weight of the reinforced structure by 2.64%.

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Section: Resultsmentioning

confidence: 93%

Structural Weight and Stiffness Optimization of a Midibus Using the Reinforcement and Response Surface Optimization (RSO) Method in Static Condition

Addisu

Koricho

2022

Modelling and Simulation in Engineering

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“…, 2017). In recent years, the surrogate-assisted RBMDO method, which can fit the performance function according to the relationship between input and output variables, has become a research hotspot in the RBMDO field (Wang et al. , 2020; Tang et al.…”

Section: Discussion: Future Challenges In Rbmdomentioning

confidence: 99%

“…Although numerical simulation methods such as MCS have high calculation accuracy, they require many simulation sample points (Yuan et al, 2017). In recent years, the surrogate-assisted RBMDO method, which can fit the performance function according to the relationship between input and output variables, has become a research hotspot in the RBMDO field (Wang et al, 2020;Tang et al, 2020;Slot et al, 2020;Liu et al, 2022). For small-probability failure problems, it is usually necessary to use the variance reduction method and a high-precision surrogate model to reduce computational cost.…”

Section: High-precision Surrogate Modelmentioning

confidence: 99%

Multidisciplinary design optimization of engineering systems under uncertainty: a review

Meng

Yang

et al. 2022

IJSI

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PurposeAs an advanced calculation methodology, reliability-based multidisciplinary design optimization (RBMDO) has been widely acknowledged for the design problems of modern complex engineering systems, not only because of the accurate evaluation of the impact of uncertain factors but also the relatively good balance between economy and safety of performance. However, with the increasing complexity of engineering technology, the proposed RBMDO method gradually cannot effectively solve the higher nonlinear coupled multidisciplinary uncertainty design optimization problems, which limits the engineering application of RBMDO. Many valuable works have been done in the RBMDO field in recent decades to tackle the above challenges. This study is to review these studies systematically, highlight the research opportunities and challenges, and attempt to guide future research efforts.Design/methodology/approachThis study presents a comprehensive review of the RBMDO theory, mainly including the reliability analysis methods of different uncertainties and the decoupling strategies of RBMDO.FindingsFirst, the multidisciplinary design optimization (MDO) preliminaries are given. The basic MDO concepts and the corresponding mathematical formulas are illustrated. Then, the procedures of three RBMDO methods with different reliability analysis strategies are introduced in detail. These RBMDO methods were proposed for the design optimization problems under different uncertainty types. Furtherly, an optimization problem for a certain operating condition of a turbine runner blade is introduced to illustrate the engineering application of the above method. Finally, three aspects of future challenges for RBMDO, namely, time-varying uncertainty analysis; high-precision surrogate models, and verification, validation and accreditation (VVA) for the model, are discussed followed by the conclusion.Originality/valueThe scope of this study is to introduce the RBMDO theory systematically. Three commonly used RBMDO-SORA methods are reviewed comprehensively, including the methods' general procedures and mathematical models.

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“…In view of the second-order polynomial response surface model, the approximate expression of the maximum stress S of the EMU brake module structure is established (Wang et al, 2019):…”

Section: Robust Optimization Model Based On Interval Analysis 51 Establishment Of the Optimization Modelmentioning

confidence: 99%

Robust optimization of EMU brake module based on interval analysis

Yin

Xia

2021

MMMS

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PurposeThis study aims to research the influence of non-probabilistic design variables on interval robust optimization of electric multiple units (EMU) brake module, therefore obtain the reasonable of design variables of the EMU brake module.Design/methodology/approachA robust optimization model of the EMU brake module based on interval analysis is established. This model also considers the dimension tolerance of design variables, and it uses symmetric tolerance to describe the uncertainty of design variables. The interval order relation and possibility degree of interval number are employed to deal with the uncertainty of objective function and constraint condition, respectively. On this basis, a multiobjective robust optimization model in view of interval analysis is established and applied to the robust optimization of the EMU brake module.FindingsCompared with the traditional method and the method proposed in the reference, the maximum stress fluctuation of the EMU brake module structure is smaller after using the method proposed in this paper, which indicates that the robustness of the maximum stress of the structure has been improved. In addition, the weight and strength of the structure meet the design requirements. It shows that this method and model introduced in this research have certain feasibility.Originality/valueThis study is the first attempt to apply the robust optimization model based on interval analysis to the optimization of EMU structure and obtain the optimal solution set that meets the design requirements. Therefore, this study provides an idea for nonprobabilistic robust optimization of the EMU structure.

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Structure optimization of the frame based on response surface method

Cited by 23 publications

References 8 publications

Structural Weight and Stiffness Optimization of a Midibus Using the Reinforcement and Response Surface Optimization (RSO) Method in Static Condition

Structural Weight and Stiffness Optimization of a Midibus Using the Reinforcement and Response Surface Optimization (RSO) Method in Static Condition

Multidisciplinary design optimization of engineering systems under uncertainty: a review

Robust optimization of EMU brake module based on interval analysis

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