scholarly journals Topology and Response Surface Optimization of a Bicycle Crank Arm with Multiple Load Cases

2020 ◽  
Vol 10 (6) ◽  
pp. 2201 ◽  
Author(s):  
Ahmad Yusuf Ismail ◽  
Gangta Na ◽  
Bonyong Koo
Keyword(s):  
Topology Optimization ◽  
Response Surface ◽  
Response Surface Method ◽  
High Performance ◽  
Experimental Validation ◽  
Three Dimensional ◽  
Optimization Methods ◽  
Stress Constraint ◽  
Element Analysis ◽  
Surface Method

This paper presents an application of topology optimization and response surface method to optimize the geometry of a bicycle crank arm and the experimental validation of it. This is purposely to reduce the crank arm mass and create a preliminary design of a lightweight structure necessary for the high-performance bicycle development. A three-dimensional bike crank arm model was made in the SpaceClaim software followed by a static finite element analysis using ANSYS Workbench 2019 R1. A multiple cycling load was applied simultaneously in seven crank angles of 30, 45, 60, 90, 120, 135, and 150° relative to the horizontal position to create the multiple loads to the crank. From there, topology optimization was then conducted to investigate the effect of mass constraint, stress constraint, angle of cycling, and crank materials on the topological pattern result. To minimize stress concentration at corners, a shape optimization using the response surface method was conducted and obtained the final geometry. From the result, it is shown that both optimization methods not only successfully reduce the crank arm mass and provide several optimum design options but also are able to reduce the maximum stress in the crank arm up to 20% after the optimization process. The experimental validation using a newly developed wireless measurement system shows a considerable agreement to the numerical results.

scholarly journals Reliability-Based Topology Optimization Using Stochastic Response Surface Method with Sparse Grid Design

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Qinghai Zhao ◽  
Xiaokai Chen ◽  
Zheng-Dong Ma ◽  
Yi Lin
Keyword(s):  
Topology Optimization ◽  
Reliability Analysis ◽  
Response Surface ◽  
Response Surface Method ◽  
Computational Efficiency ◽  
Sparse Grid ◽  
Stochastic Response ◽  
Stochastic Response Surface Method ◽  
Surface Method ◽  
Grid Design

A mathematical framework is developed which integrates the reliability concept into topology optimization to solve reliability-based topology optimization (RBTO) problems under uncertainty. Two typical methodologies have been presented and implemented, including the performance measure approach (PMA) and the sequential optimization and reliability assessment (SORA). To enhance the computational efficiency of reliability analysis, stochastic response surface method (SRSM) is applied to approximate the true limit state function with respect to the normalized random variables, combined with the reasonable design of experiments generated by sparse grid design, which was proven to be an effective and special discretization technique. The uncertainties such as material property and external loads are considered on three numerical examples: a cantilever beam, a loaded knee structure, and a heat conduction problem. Monte-Carlo simulations are also performed to verify the accuracy of the failure probabilities computed by the proposed approach. Based on the results, it is demonstrated that application of SRSM with SGD can produce an efficient reliability analysis in RBTO which enables a more reliable design than that obtained by DTO. It is also found that, under identical accuracy, SORA is superior to PMA in view of computational efficiency.

A Study on Improvement of Aerodynamic Performance for 100HP Axial Fan Blade and Guide Vane Using Response Surface Method

2019 ◽  
Author(s):  
Young-Seok Choi ◽  
Yong-In Kim ◽  
Sung Kim ◽  
Seul-Gi Lee ◽  
Hyeon-Mo Yang ◽  
...  
Keyword(s):  
Objective Function ◽  
Response Surface ◽  
Response Surface Method ◽  
Guide Vane ◽  
Three Dimensional ◽  
Flow Characteristics ◽  
Internal Flow ◽  
Aerodynamic Performance ◽  
Axial Fan ◽  
Surface Method

Abstract This paper describes the numerical optimization of an axial fan focused on the blade and guide vane (GV). For numerical analysis, three-dimensional (3D) steady-state Reynolds-averaged Navier-Stokes (RANS) equations with the shear stress transport (SST) turbulence model are discretized by the finite volume method (FVM). The objective function is enhancement of aerodynamic performance with specified total pressure. To select the design variables which have main effect to the objective function, 2k factorial design is employed as a method for design of experiment (DOE). In addition, response surface method (RSM) based on the central composite design applied to carry out the single-objective optimization. Effects on the components such as bell mouth and hub cap are considered with previous analysis. The internal flow characteristics between base and optimized model are analyzed and discussed.

Adjoint-Response Surface Method in Aerodynamic Shape Optimization of Turbomachinery Blades

2016 ◽  
Author(s):  
Xiao Tang ◽  
Jiaqi Luo ◽  
Feng Liu
Keyword(s):  
Cost Function ◽  
Response Surface ◽  
Response Surface Method ◽  
Design Space ◽  
Three Dimensional ◽  
Gradient Methods ◽  
Control Parameters ◽  
Surface Method ◽  
Optimization Stage ◽  
Turbomachinery Blades

An adjoint-response surface method is developed to give global representation of cost function in a parametrized design space for turbomachinery blades. Radial basis function (RBF) based and quadratic polynomial (QP) based response surface models are constructed using both the values of cost function and its adjoint gradients with respect to geometry control parameters. The method is tested on a quasi-three dimensional NACA0012 blade row, then applied to the transonic Rotor 67. In preliminary design optimization stage, when the number of undetermined control parameters is large, the QP based model can provide a global image of the cost function in high dimensional design space with a small amount of sample points. In two-parameter fine optimization stage, high resolution can be achieved with the RBF based models. This gradient-enhanced response surface method is useful in guiding designers to discover the global optimum which may be missed by local gradient methods in a complicated design space. It may also be used as substitute of CFD flow solver in time consuming iterative design and optimization.

Full-Space Response Surface Method for Analysis of Structural Reliability

2020 ◽  
Vol 20 (08) ◽  
pp. 2050096
Author(s):  
Lu Feng Yang ◽  
Zhao Yang Li ◽  
Bo Yu ◽  
Qiu Sheng Li
Keyword(s):  
Response Surface ◽  
Response Surface Method ◽  
Computational Efficiency ◽  
Structural Reliability ◽  
Displacement Vector ◽  
Element Analysis ◽  
Collocation Points ◽  
Surface Method ◽  
Global Stiffness Matrix ◽  
Space Response

In order to improve the computational efficiency of the response surface method (RSM) in analysis of structural reliability, a full-space response surface method (FRSM) is presented in this paper. First, a vector-type response surface is developed by expanding the stochastic nodal displacement vector along the Krylov basis vectors defined by the global stiffness matrix and the force vector. Then the effective collocation points are picked out of the candidate ones according to the linear independence of the combining row vector. Finally, the unknown coefficients of the proposed response surface are determined by means of the regression analysis. Examples show that the proposed FRSM requires much fewer effective collocation points and less times of finite element analysis, achieving much higher computational efficiency by comparing with the traditional RSM.

Towards Next Generation of High-Performance Radio Frequency Passive Components for Beyond-5G, 6G and Super-IoT – Application of Response Surface Method to the Optimization of RF-MEMS Reconfigurable Power Attenuators

2021 ◽  
Author(s):  
Jacopo Iannacci ◽  
Girolamo Tagliapietra ◽  
Alessio Bucciarelli
Keyword(s):  
Radio Frequency ◽  
Design Optimization ◽  
Response Surface ◽  
Response Surface Method ◽  
High Performance ◽  
Rf Mems ◽  
Optimization Approach ◽  
Scattering Parameter ◽  
Passive Components ◽  
Surface Method

Abstract The emerging paradigms of Beyond-5G, 6G and Super-IoT will demand for Radio Frequency (RF) passive components with pronounced performance, and RF-MEMS technology, i.e. Microsystem-based RF passives, is a good candidate to meet such a challenge. As known, RF-MEMS have a complex behavior, that crosses different physical domains (mechanical; electrical; electromagnetic), making the whole design optimization and trimming phases particularly articulated and time consuming. In this work, we propose a novel design optimization approach based on the Response Surface Method (RSM) statistical methodology, focusing the attention on a class of RF-MEMS-based programmable step power attenuators. The proposed method is validated both against physical simulations, performed with Finite Element Method (FEM) commercial software tools, as well as experimental measurements of physical devices. The case study here discussed features 3 DoFs (Degrees of Freedom), comprising both geometrical and material parameters, and aims at optimizing the RF performance of the MEMS attenuator in terms of attenuation (S21 Scattering parameter) and reflection (VSWR – Voltage Standing Wave Ratio). When validate, the proposed RSM-based method allows avoiding physical FEM simulations, thus making the design optimization considerably faster and less complex, both in terms of time and computational load.

Structural Optimization of a Machining Center Column Based on Response Surface Method

2012 ◽  
Vol 522 ◽  
pp. 663-667
Author(s):  
Ming Nan Sun ◽  
Guo Fu Yin ◽  
Teng Hu
Keyword(s):  
Finite Element ◽  
Structural Optimization ◽  
Response Surface ◽  
Response Surface Method ◽  
Surface Model ◽  
Element Analysis ◽  
Mean Frequency ◽  
Machining Center ◽  
Surface Method ◽  
Design Variables

In order to improve dynamic characteristics of a machining center column, this paper proposes a structural optimization method based on finite element method (FEM) and response surface method (RSM). In order to reduce number of design variables, the finite element analysis samples in design space are selected by using the central composite design (CCD) experiment method. On the basis of FEM results at these experiment samples, quadratic polynomials are employed to establish response surface model, which reflects the relationship between the response (mean frequency of the first four orders) and the design variables (the column structural sizes). The goal of getting maximum mean frequency is reached by using NLPQL algorithm in iSIGHT. Through the optimization, the mean frequency is increased by 8.12%.

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