Optimization of a Transonic Axial Compressor Considering Interaction of Blade and Casing Treatment to Improve Operating Stability

2011 ◽  
Author(s):  
Jin-Hyuk Kim ◽  
Kwang-Jin Choi ◽  
Kwang-Yong Kim
Keyword(s):  
Stokes Equations ◽  
Axial Compressor ◽  
Tip Clearance ◽  
Pareto Optimal ◽  
Angle Distribution ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Stall Margin ◽  
Multi Objective ◽  
Transonic Axial Compressor

A multi-objective optimization of a transonic axial compressor with circumferential casing grooves has been carried out in the present study. A hybrid multi-objective evolutionary algorithm coupled with response surface approximation is used to optimize the stall margin and design speed efficiency of the transonic axial compressor. Three-dimensional Reynolds-averaged Navier-Stokes equations with the shear stress transport turbulence model are discretized by finite volume approximations and solved on hexahedral grids for the flow analysis. The stall margin and peak adiabatic efficiency are used as objective functions for the optimization. Tip clearance and angle distribution at blade tip are considered as design variables in addition to the depth of the circumferential casing grooves which was more sensitive variable than the width in the previous work (GT2010-22396). Latin-hypercube sampling as design-of-experiments is used to generate twenty five design points within the design space. A fast non-dominated sorting genetic algorithm with an ε–constraint strategy for the local search is applied to determine the global Pareto-optimal solutions. The trade-off between two objectives is determined and discussed with respect to the representative clusters in the Pareto-optimal solutions compared to the smooth casing.

Sirocco Fan Design for Residential Ventilation Through Multi-Objective Optimization to Enhance Aerodynamic Performance

2012 ◽  
Author(s):  
Jin-Hyuk Kim ◽  
Kyung-Hun Cha ◽  
Kwang-Yong Kim
Keyword(s):  
Total Pressure ◽  
Stokes Equations ◽  
Pressure Rise ◽  
Total Efficiency ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Sirocco Fan

A multi-objective optimization of a sirocco fan for residential ventilation has been carried out in the present work. A hybrid multi-objective evolutionary algorithm combined with response surface approximation is applied to optimize the total-to-total efficiency and total pressure rise of the sirocco fan for residential ventilation. Three-dimensional Reynolds-averaged Navier-Stokes equations with the shear stress transport turbulence model are discretized by finite volume method and solved on hexahedral grids for the flow analysis. Numerical results are validated with the experimental data for the total-to-total efficiency and total pressure. The total-to-total efficiency and total pressure rise of the sirocco fan are used as objective functions for the optimization. In order to improve the total-to-total efficiency and total pressure rise of the sirocco fan, four variables defining the scroll cut-off angle, scroll diffuser expansion angle, hub ratio and the blade exit angle, respectively, are selected as the design variables in this study. Latin-hypercube sampling as design-of-experiments is used to generate the design points within the design space. A fast non-dominated sorting genetic algorithm with an ε–constraint strategy for the local search is applied to determine the global Pareto-optimal solutions. The trade-off between two objectives is determined and discussed with respect to the representative clustered optimal solutions in the Pareto-optimal solutions compared to the reference shape.

A Proposal for Passive Wall Treatment Applied in High Performance Axial Flow Compressors

2020 ◽  
Author(s):  
Rubén Bruno Díaz ◽  
Jesuino Takachi Tomita ◽  
Cleverson Bringhenti ◽  
Francisco Carlos Elizio de Paula ◽  
Luiz Henrique Lindquist Whitacker
Keyword(s):  
Stokes Equations ◽  
Axial Compressor ◽  
Axial Flow ◽  
Tip Clearance ◽  
Leakage Flow ◽  
Smooth Wall ◽  
Tip Leakage Flow ◽  
Viscous Effects ◽  
Stall Margin ◽  
Tip Leakage

Abstract Numerical simulations were carried out with the purpose of investigating the effect of applying circumferential grooves at axial compressor casing passive wall treatment to enhance the stall margin and change the tip leakage flow. The tip leakage flow is pointed out as one of the main contributors to stall inception in axial compressors. Hence, it is of major importance to treat appropriately the flow in this region. Circumferential grooves have shown a good performance in enhancing the stall margin in previous researches by changing the flow path in the tip clearance region. In this work, a passive wall treatment with four circumferential grooves was applied in the transonic axial compressor NASA Rotor 37. Its effect on the axial compressor performance and the flow in the tip clearance region was analyzed and set against the results attained for the smooth wall case. A 2.63% increase in the operational range of the axial compressor running at 100%N, was achieved, when compared with the original smooth wall casing configuration. The grooves installed at compressor casing, causes an increase in the flow entropy generation due to the high viscous effects in this gap region, between the rotor tip surface and casing with grooves. These viscous effects cause a drop in the turbomachine efficiency. For the grooves configurations used in this work, an efficiency drop of 0.7% was observed, compared with the original smooth wall. All the simulations were performed based on 3D turbulent flow calculations using Reynolds Averaged Navier-Stokes equations, and the flow eddy viscosity was determined using the two-equation SST turbulence model. The details of the grooves geometrical dimensions and its implementation are described in the paper.

scholarly journals Single- and Multi-Objective Optimization of a Dual-Chamber Microbial Fuel Cell Operating in Continuous-Flow Mode at Steady State

Processes ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. 839
Author(s):  
Ibrahim M. Abu-Reesh
Keyword(s):  
Flow Rate ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Multi Objective Optimization ◽  
Dual Chamber ◽  
Worst Case ◽  
Multi Objective ◽  
Model Based ◽  
Conflicting Objectives

Microbial fuel cells (MFCs) are a promising technology for bioenergy generation and wastewater treatment. Various parameters affect the performance of dual-chamber MFCs, such as substrate flow rate and concentration. Performance can be assessed by power density ( PD ), current density ( CD ) production, or substrate removal efficiency ( SRE ). In this study, a mathematical model-based optimization was used to optimize the performance of an MFC using single- and multi-objective optimization (MOO) methods. Matlab’s fmincon and fminimax functions were used to solve the nonlinear constrained equations for the single- and multi-objective optimization, respectively. The fminimax method minimizes the worst-case of the two conflicting objective functions. The single-objective optimization revealed that the maximum PD ,   CD , and SRE were 2.04 W/m2, 11.08 A/m2, and 73.6%, respectively. The substrate concentration and flow rate significantly impacted the performance of the MFC. Pareto-optimal solutions were generated using the weighted sum method for maximizing the two conflicting objectives of PD and CD in addition to PD and SRE   simultaneously. The fminimax method for maximizing PD and CD showed that the compromise solution was to operate the MFC at maximum PD conditions. The model-based optimization proved to be a fast and low-cost optimization method for MFCs and it provided a better understanding of the factors affecting an MFC’s performance. The MOO provided Pareto-optimal solutions with multiple choices for practical applications depending on the purpose of using the MFCs.

Analysis and Optimization of Staggered Partial Diffuser Vanes in a Centrifugal Pump

2020 ◽  
Vol 142 (5) ◽  
Author(s):  
Hyeon-Seok Shim ◽  
Sang-Hoon Kim ◽  
Kwang-Yong Kim
Keyword(s):  
Centrifugal Pump ◽  
Stokes Equations ◽  
Maximum Pressure ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Objective Functions ◽  
Baseline Design ◽  
Operating Range ◽  
Full Height

Abstract A performance analysis and three-objective design optimization were performed for the staggered partial diffuser vanes in a centrifugal pump using three-dimensional Reynolds-averaged Navier–Stokes equations. First, the performance of the diffuser vanes was evaluated for four different arrangements: full-height diffuser vanes, vaneless diffuser, half vanes attached to the hub, half vanes attached to the shroud, and staggered vanes attached alternately to the hub and the shroud. The staggered partial diffuser vanes were optimized using the following design variables: the installation angle of the vanes, the heights of the vanes attached to the hub and shroud, and the angle of rotation of the straight part on the pressure surface of the vanes. The objective functions were the hydraulic efficiency, the flowrate of the maximum pressure recovery, and the operating range of the diffuser. The Kriging model was used to construct surrogate models of the objective functions based on the results at the design points obtained by Latin hypercube sampling. The Pareto-optimal solutions were obtained by a multi-objective genetic algorithm (MOGA). The representative Pareto-optimal solutions for the staggered diffuser vanes obtained by the K-means clustering showed the improved performances in terms of both the hydraulic performance and operating range compared with the full-height diffuser vanes and the baseline design.

New Dynamic Multi-Objective Constrained Optimization Evolutionary Algorithm

2015 ◽  
Vol 32 (05) ◽  
pp. 1550036 ◽  
Author(s):  
Chun-An Liu ◽  
Yuping Wang ◽  
Aihong Ren
Keyword(s):  
Constrained Optimization ◽  
Evolutionary Algorithm ◽  
Optimization Problem ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Constrained Optimization Problem ◽  
Multi Objective ◽  
Time Period ◽  
Pareto Fronts

For dynamic multi-objective constrained optimization problem (DMCOP), it is important to find a sufficient number of uniformly distributed and representative dynamic Pareto optimal solutions. In this paper, the time period of the DMCOP is first divided into several random subperiods. In each random subperiod, the DMCOP is approximately regarded as a static optimization problem by taking the time subperiod fixed. Then, in order to decrease the amount of computation and improve the effectiveness of the algorithm, the dynamic multi-objective constrained optimization problem is further transformed into a dynamic bi-objective constrained optimization problem based on the dynamic mean rank variance and dynamic mean density variance of the evolution population. The evolution operators and a self-check operator which can automatically checkout the change of time parameter are introduced to solve the optimization problem efficiently. And finally, a dynamic multi-objective constrained optimization evolutionary algorithm is proposed. Also, the convergence analysis for the proposed algorithm is given. The computer simulations are made on four dynamic multi-objective optimization test functions and the results demonstrate that the proposed algorithm can effectively track and find the varying Pareto optimal solutions or the varying Pareto fronts with the change of time.

scholarly journals GENERATING PARETO OPTIMAL SOLUTIONS OF MULTI-OBJECTIVE LFPP WITH INTERVAL COEFFICIENTS USING E-CONSTRAINT METHOD

2015 ◽  
Vol 20 (3) ◽  
pp. 329-345 ◽  
Author(s):  
Suvasis Nayak ◽  
Akshay Ojha
Keyword(s):  
Closed Interval ◽  
Fuzzy Programming ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Multi Objective ◽  
Decision Variables ◽  
Interval Coefficients ◽  
Constraint Method ◽  
Linear Fractional Programming Problem

This paper illustrates a procedure to generate pareto optimal solutions of multi-objective linear fractional programming problem (MOLFPP) with closed interval coefficients of decision variables both in objective and constraint functions. E-constraint method is applied to produce pareto optimal solutions comprising most preferred solution to satisfy all objectives. A numerical example is solved using our proposed method and the result so obtained is compared with that of fuzzy programming which justifies the efficiency and authenticity of the proposed method.

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