Optimal design of high temperature vacuum furnace using response surface method

Li, Zhen-Zhe; Shen, Yun-De; Xu, Huilan; Lee, Jae-Woo; Heo, Kwang-Su; Seol, S.Y.

doi:10.1007/s12206-008-0617-0

Cited by 12 publications

(6 citation statements)

References 3 publications

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“…There are many kinds of approximation model, such as polynomial regression model, Kriging model and artificial neural network (ANN) method. 8,[11][12][13][14] Polynomial regression model likes the response surface model can construct an approximation model efficiently, but it has a weak point on simulating nonlinear characteristics. On the other hand, Kriging and ANN method have good characteristics on simulating nonlinear characteristics.…”

Section: Design Of Experiments Methods and Artificial Neural Networkmentioning

confidence: 99%

Multidisciplinary Design Optimization of Axial Compressor with Double Step Optimization

Lee¹,

Choi²,

Jeon³

et al. 2012

53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference

2
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0
0

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In this paper, multidisciplinary optimization for transonic low pressure compressor has been done. The rotating speed of compressor is 22,000 rpm and the rotor stage is composed of 24 blades. The optimization process is done twice to gain the accurate solution. In order to save computational cost, the first optimization process was conducted by using the Approximation model. The second optimization process was performed full numerical computation for accuracy of the optimization result. Framework is constructed using sensitivity analysis, D-optimal DOE and artificial neural network method. To obtain global optimum, genetic algorithm is used. After the optimization process, the aerodynamic efficiency is raised by 3.69% and the structural safety factor is raised by 234.4%. Nomenclature C x= Blade chord length of x th section β 1x = Inlet angle of x th section β 2x = Exit angle of x th section η = Efficiency of the compressor σ = Stress σ Y = Maximum yield stress SF = Safety factor of the compressor

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Section: Design Of Experiments Methods and Artificial Neural Networkmentioning
confidence: 99%

Multidisciplinary Design Optimization of Axial Compressor with Double Step Optimization
Lee¹,
Choi²,
Jeon³
et al. 2012
53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference
2
0
0
0
View full text Add to dashboard Cite

show abstract

“…There are many approximation models such as response surface model (RSM) [13,[21][22][23][24], ANN, or Kriging model [16]. RSM has a good advantage in that it can construct an approximation model for the design optimization efficiently using second-order polynomial.…”

Section: Artificial Neural Networkmentioning
confidence: 99%

Reliability-based design optimization of axial compressor using uncertainty model for stall margin
Hong
¹
,
Lee
²
,
Jun
³

et al. 2011
J Mech Sci Technol
16
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4
0
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Reliability-based design optimization (RBDO) of the NASA stage 37 axial compressor is performed using an uncertainty model for stall margin in order to guarantee stable operation of the compressor. The main characteristics of RBDO for the axial compressor are summarized as follows: First, the values of mass flow rate and pressure ratio in stall margin calculation are defined as statistical models with normal distribution for consideration of the uncertainty in stall margin. Second, Monte Carlo Simulation is used in the RBDO process to calculate failure probability of stall margin accurately. Third, an approximation model that is constructed by an artificial neural network is adopted to reduce the time cost of RBDO. The present method is applied to the NASA stage 37 compressor to improve the reliability of stall margin with both maximized efficiency and minimized weight. The RBDO result is compared with the deterministic optimization (DO) result which does not include an uncertainty model. In the DO case, stall margin is slightly higher than the reference value of the required constraint, but the probability of stall is 43%. This is unacceptable risk for an aircraft engine, which requires absolutely stable operation in flight. However, stall margin obtained in RBDO is 2.7% higher than the reference value, and the probability of success increases to 95% with the improved efficiency and weight. Therefore, RBDO of the axial compressor for aircraft engine can be a reliable design optimization method through consideration of unexpected disturbance of the flow conditions.

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“…To investigate the combined response of design variables, RSM is needed. It involves regression surface fitting to obtain approximate responses, design of experiments to obtain minimum variances of the responses and optimizations using the approximated responses [11].…”

Section: Introductionmentioning
confidence: 99%

A novel design procedure for tractor clutch fingers by using optimization and response surface methods
Doǧan
¹
,
Karpat
²
,
Yüce
³

et al. 2016
J Mech Sci Technol
9
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4
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This paper presents a methodology for re-designing a failed tractor transmission component subjected to cyclic loading. Unlike other vehicles, tractors cope with tough working conditions. Thus, it is necessary to re-design components by using modern optimization techniques. To extend their service life, we present a design methodology for a failed tractor clutch power take-off finger. The finger was completely re-designed using topology and shape optimization approach. Stress-life based fatigue analyses were performed. Shape optimization and response surface methodology were conducted to obtain optimum dimensions of the finger. Two design parameters were selected for the design of experiment method and 15 cases were analyzed. By using design of the experiment method, three responses were obtained: Maximum stresses, mass, and displacement depending on the selected the design parameters. After solving the optimization problem, we achieved a maximum stress and mass reduction of 14% and 6%, respectively. The stiffness was improved up to 31.6% compared to the initial design.

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Optimal design of high temperature vacuum furnace using response surface method

Cited by 12 publications

References 3 publications

Multidisciplinary Design Optimization of Axial Compressor with Double Step Optimization

Multidisciplinary Design Optimization of Axial Compressor with Double Step Optimization

Reliability-based design optimization of axial compressor using uncertainty model for stall margin

A novel design procedure for tractor clutch fingers by using optimization and response surface methods

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