Multiple Surrogate Modeling for Axial Compressor Blade Shape Optimization

Samad, Abdus; Kim, Kwang‐Yong; Goel, Tushar; Haftka, Raphael T.; Shyy, Wei

doi:10.2514/1.28999

Cited by 180 publications

(86 citation statements)

References 8 publications

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“…For details of the surrogate construction, the readers may refer to authors previous publications [13,22,23]. The cross-validation error analysis of surrogate models was performed and model predictions were validated for unsampled designs.…”

Section: Optimization Modelmentioning

confidence: 99%

“…It was concluded that the optimum ratio of the mixing length to nozzle diameter (L m /D n ) is 2-3.5. Several approximation methods for optimization are being used and comparative studies are presented in the literatures [12][13][14] which carried out numerical analysis of the jet pump and presented an improved design, which showed higher performance in terms of mass flow ratio, pressure ratio and efficiency under specified working conditions. Eves et al [15] conducted optimization of supersonic jet pump through computational fluid dynamics (CFD) analysis and genetic algorithm to maximize the entrained flow rate under constrained primary flow; however, there is scarcity of literature on systematic multiobjective optimization of jet pump to find out the optimal performance and corresponding design parameters.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Energy Efficient Design of a Jet Pump by Ensemble of Surrogates and Evolutionary Approach

Husain

Sonawat

Mohan

et al. 2016

International Journal of Fluid Machinery and Systems

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Energy systems working coherently in different conditions may not have a specific design which can provide optimal performance. A system working for a longer period at lower efficiency implies higher energy consumption. In this effort, a methodology demonstrated by a jet pump design and optimization via numerical modeling for fluid dynamics and implementation of an evolutionary algorithm for the optimization shows a reduction in computational costs. The jet pump inherently has a low efficiency because of improper mixing of primary and secondary fluids, and multiple momentum and energy transfer phenomena associated with it. The high fidelity solutions were obtained through a validated numerical model to construct an approximate function through surrogate analysis. Pareto-optimal solutions for two objective functions, i.e., secondary fluid pressure head and primary fluid pressure-drop, were generated through a multi-objective genetic algorithm. For the jet pump geometry, a design space of several design variables was discretized using the Latin hypercube sampling method for the optimization. The performance analysis of the surrogate models shows that the combined surrogates perform better than a single surrogate and the optimized jet pump shows a higher performance. The approach can be implemented in other energy systems to find a better design.

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Section: Optimization Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Energy Efficient Design of a Jet Pump by Ensemble of Surrogates and Evolutionary Approach

Husain

Sonawat

Mohan

et al. 2016

International Journal of Fluid Machinery and Systems

Self Cite

View full text Add to dashboard Cite

show abstract

“…Takasaki, Takao, and Setoguchi (2014) discussed the effect of blade shape on the performance of a reaction turbine, and found that the blade with linearly varying thickness from the mean radius to the tip gives a higher efficiency, but it does not overcome the weakness of stalling. Samad, Kim, Goel, Haftka, and Shyy (2008) found that the adiabatic efficiency and pressure ratio of the compressor increases if stacking and thickness is modified in a compressor. Sarraf, Nouri, Ravelet, and Bakir (2011) found that the nominal point is shifted toward lower flow rate for the thick blade and the aerodynamic characteristics of an axial fan are steeper and the pressure fluctuations are lower.…”

Section: Introductionmentioning

confidence: 99%

“…Multiple surrogates modeling is implemented in many problems (Husain & Kim, 2010;Samad et al, 2008). The predicted error sum of squares (PRESS) through the kfold cross validation strategy finds the RMS error and filters out the inaccurate surrogates (Viana, Haftka, & Steffen, 2009).…”

Section: Introductionmentioning

confidence: 99%

Shape optimization of a bidirectional impulse turbine via surrogate models

Ezhilsabareesh

Rhee

Samad

2017

Engineering Applications of Computational Fluid Mechanics

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In this work, a bidirectional impulse turbine used in a wave energy device is simulated using a CFD technique and a shape optimization performed with multiple surrogates-assisted multi-objective evolutionary algorithm. The surrogates have been generated using the validated CFD technique. The objectives for the optimization are to maximize the shaft power and to minimize the pressure drop across the turbine. The hub and the tip thickness of the rotor blade profiles are modified and considered as the design variables. The Latin hypercube sampling technique generated design points are evaluated in the CFD solver, the objective responses are used to construct multiple surrogates, and a Pareto optimal front is generated through a multi-objective optimization approach. The turbine efficiency, which is the function of pressure drop and shaft power, is relatively increased by 10.4%.

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“…Currently, the common fitness estimation methods include the fitness inheritance and the application of surrogate model [11][12][13][14][15][16][17]. However, which method will perform better in fitness estimation?…”

Section: Introductionmentioning

confidence: 99%

Efficient Query for Historical Data in Evolutionary Algorithm

Tian¹,

Pan²,

Huang³

2017

Proceedings of the 2017 International Conference on Electronic Industry and Automation (EIA 2017)

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Abstract-For the time-consuming problem in calculating the fitness value, this paper proposes a hash bucket with precision mechanism for a quick query of the data in the neighborhood of a particle. In order to establish a balance between the calculation accuracy and utility, it uses the hash tables with precision mechanism to solve the problem in the storage and query of historical calculation data so that the neighborhood of a to-be-evaluated individual can be determined more accurately to reduce the error in estimating the fitness value. Moreover, it uses the typical reference functions to separately test the effectiveness and accuracy of the algorithms based on the values obtained in different dimensions. The test result proves that compared with the other algorithms described in this paper, our algorithm can provide a better solution in the context of the same number of times for fitness calculation.

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Multiple Surrogate Modeling for Axial Compressor Blade Shape Optimization

Cited by 180 publications

References 8 publications

Energy Efficient Design of a Jet Pump by Ensemble of Surrogates and Evolutionary Approach

Energy Efficient Design of a Jet Pump by Ensemble of Surrogates and Evolutionary Approach

Shape optimization of a bidirectional impulse turbine via surrogate models

Efficient Query for Historical Data in Evolutionary Algorithm

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