Optimization methodology assessment for the inlet velocity profile of a hydraulic turbine draft tube: part I—computer optimization techniques

Galván, Sergio; Rubio, Carlos; Pacheco, Juan de Jesús Cando; Mendoza, Crisanto; Toledo-Velázquez, Miguel

doi:10.1007/s10898-012-9946-8

Cited by 18 publications

(9 citation statements)

References 14 publications

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“…In Chen's [35] multiobjects optimization strategy, the loss coefficient is chosen as an objective to optimize the structure of a Francis turbine draft tube. In the study by Galva'n [36], the loss coefficient of the draft tube is more sensitive to the pressure distribution of the draft tube inlet, which can better describe the velocity distribution of the draft tube inlet and the flow loss caused by the change of the draft tube inlet velocity. Therefore, the loss coefficient ζ is chosen as one of the objective functions of multiobjective optimization.…”

Section: Optimization Strategymentioning

confidence: 99%

Cavitating Flow Suppression in the Draft Tube of a Cryogenic Turbine Expander through Runner Optimization

Huang

Zhu

2020

Processes

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The application of a cryogenic liquefied natural gas expander can reduce the production of flash steam and improve the efficiency of natural gas liquefaction. Like traditional hydraulic machinery, cavitation will occur during the operation of a liquefied natural gas expander, in particular, there is a strong vortex flow in the draft tube, and the cavitation phenomenon is serious. In this paper, the energy loss coefficient of the draft tube is used to describe the cavitation flow in the draft tube, and the goal of reducing the cavitation in the draft tube is achieved through the optimization design of the runner. Different runner models within the range of design parameters were obtained using the Latin hypercube test, and the relationship between design parameters and objective functions is constructed by a second-order response surface model. Finally, the optimized runners were obtained using a genetic algorithm. The effects of blade loading distribution and blade lean angles on the cavitation in the draft tube were studied. According to the optimization results, the blade loading distribution and blade lean angles are recommended in the end.

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Section: Optimization Strategymentioning

confidence: 99%

Cavitating Flow Suppression in the Draft Tube of a Cryogenic Turbine Expander through Runner Optimization

Huang

Zhu

2020

Processes

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“…For this task, the objective function to be maximized is the mean pressure recovery, since it applies where uniform axial flow pertains at the cone inlet. Then, the structure of the inlet velocity profiles is modified, Galván et al [12] demonstrated through a flow sensitivity study that the loss coefficient is the most appropriate objective function for an optimization process. Thus, the objective function to be maximized is presented in Eq.…”

Section: The Objective Functionmentioning

confidence: 99%

“…In this work, the Reynolds Average Navier-Stokes and the κ − ε turbulence model equations were used to describe an incompressible, viscous, turbulent, and steady flow, [12]. The standard model has been shown to be economical, robust, and reasonably precise, but it gives poor results for complex fluxes with severe pressure…”

Section: Numerical Modelmentioning

confidence: 99%

Automatic shape optimization of a conical-duct diffuser using a distributed computing algorithm

Herrera

Galván

Camacho

et al. 2017

J Braz. Soc. Mech. Sci. Eng.

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overcome using a distributed computing evaluation established in a computational cluster. The methodology has allowed the location of the exact opening angle, improving approximately 1.2% the diffuser performance. A quantitative and qualitative analysis of the flow has been undertaken to understand how the optimum opening angle of a diffuser modifies the flow pattern to achieve the highest performance.

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“…The migration process is controlled by two major parameters which are the rate of migration and the interval of migration. A parametric study was made by [25] to start an optimization loop with a high chance of success avoiding extensive preliminary sensitivity analysis proving also to be well suited for solving highly nonlinear problems, like the present one.…”

Section: Optimization Algorithmmentioning

confidence: 99%

“…(5) and the mean pressure recovery factor Cp m in eq. (6) were tested to select the most appropriate objective function [25].…”

Section: Objective Functionmentioning

confidence: 99%

Quantitative and qualitative analysis of the flow field development through T99 draft tube caused by optimized inlet velocity profiles

Galván

Reggio

Guibault

et al. 2015

International Journal of Fluid Machinery and Systems

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The effect of the inlet swirling flow in a hydraulic turbine draft tube is a very complex phenomenon, which has been extensively investigated both theoretically and experimentally. In fact, the finding of the optimal flow distribution at the draft tube inlet in order to get the best performance has remained a challenge. Thus, attempting to answer this question, it was assumed that through an automatic optimization process a Genetic Algorithm would be able to manage a parameterized inlet velocity profile in order to achieve the best flow field for a particular draft tube. As a result of the optimization process, it was possible to obtain different draft-tube flow structures generated by the automatic manipulation of parameterized inlet velocity profiles. Thus, this work develops a qualitative and quantitative analysis of these new draft tube flow field structures provoked by the redesigned inlet velocity profiles. The comparisons among the different flow fields obtained clearly illustrate the importance of the flow uniformity at the end of the conduit. Another important aspect has been the elimination of the re-circulating flow area which used to promote an adverse pressure gradient in the cone, deteriorating the pressure recovery effect. Thanks to the evolutionary optimization strategy, it has been possible to demonstrate that the optimized inlet velocity profile can suppress or mitigate, at least numerically, the undesirable draft tube flow characteristics. Finally, since there is only a single swirl number for which the objective function has been minimized, the energy loss factor might be slightly affected by the flow rate if the same relation of the axial-tangential velocity components is maintained, which makes it possible to scale the inlet velocity field to different operating points.

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Optimization methodology assessment for the inlet velocity profile of a hydraulic turbine draft tube: part I—computer optimization techniques

Cited by 18 publications

References 14 publications

Cavitating Flow Suppression in the Draft Tube of a Cryogenic Turbine Expander through Runner Optimization

Cavitating Flow Suppression in the Draft Tube of a Cryogenic Turbine Expander through Runner Optimization

Automatic shape optimization of a conical-duct diffuser using a distributed computing algorithm

Quantitative and qualitative analysis of the flow field development through T99 draft tube caused by optimized inlet velocity profiles

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