Multi-Objective Aerodynamic Optimization of Axial Turbine Blades Using a Novel Multilevel Genetic Algorithm

Öksüz, Özhan; Akmandor, I. Sinan

doi:10.1115/1.3213558

Cited by 16 publications

(7 citation statements)

References 16 publications

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“…As a result, total-to-total efficiency increased by 1.68 percent, and 1.28 percent improvement is achieved for total-to-static efficiency. Öksüz and Akmandor [17] utilized a Genetic Algorithm (GA) coupled with a surrogate model in their optimization process and defined two objective functions: adiabatic efficiency and torque. In another work presented by Li et al [18] a long blade of steam turbine stage was optimized.…”

Section: Introductionmentioning

confidence: 99%

Performance Improvement of an Industrial Axial Turbine: Two-Stage Power Turbine Aerodynamic Optimization

Amini

Khavari

Alizadeh

2022

Preprint

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For high-aspect-ratio turbine blades, profile loss is dominant and its reduction is important for improving performance. In this paper, a two-stage low-pressure turbine (power turbine) is optimized in two steps: cycle optimization and aerodynamic optimization. In the cycle optimization step, the thermodynamic boundary condition of the turbine is modified. In the second step, which is the main focus of this study, the aerodynamic improvement of a turbine using a two-dimensional optimization technique is performed. The Genetic Algorithm (GA) is used in the optimization process to identify variables that satisfy a defined objective function which is subject to some constraints. The Artificial Neutral Network (ANN) is introduced to correlate variables with the defined objective functions and constraints. Maximum velocity and its location on the blade surface determine transition location and diffusion factor which directly affect total pressure losses. Therefore, in the optimization process, maximum velocity and its location are controlled and defined as objective functions. Profile shape at any given radius is represented with the camber-thickness method. The flow solver used for the aerodynamic analysis of profiles is the cascade analysis code MISES. After improving the performance of vanes and blades’ 2D sections, to be confident for performance enhancement of final power turbine ANSYS CFX is used. Finally, the aerodynamic characteristics of the power turbine using optimized geometries are discussed and compared to the original one. The results demonstrate a 1.19 percent improvement in power turbine efficiency at the same pressure ratio.

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Section: Introductionmentioning

confidence: 99%

Performance Improvement of an Industrial Axial Turbine: Two-Stage Power Turbine Aerodynamic Optimization

Amini

Khavari

Alizadeh

2022

Preprint

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“…Design optimisation processes are largely portable across similar applications, thus research can be conducted on optimisation techniques in axial turbines for different applications. Multiple sources are present, however, it is clear that the design optimisation topic is highly sensitive to manufacturers, with the majority of the sources being obsolete [5,6], as highlighted by the most recent paper on the topic [7]. The most notable common trait in previous examples of design optimisation is the fact that most of them were carried out using genetic algorithms (GAs).…”

Section: Introductionmentioning

confidence: 99%

“…Öksüz and Akmandor published their MDO using a GA approach on an axial turbine in 2010 [7]. The authors optimised an axial turbine and stator profiles to maximise efficiency and torque.…”

Section: Introductionmentioning

confidence: 99%

Turbocharger Axial Turbines for High Transient Response, Part 2: Genetic Algorithm Development for Axial Turbine Optimisation

et al. 2019

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In a previous paper [1], a preliminary design methodology was proposed for the design of an axial turbine, replacing a conventional radial turbine used in automotive turbochargers, to achieve improved transient response, due to the intrinsically lower moment of inertia. In this second part of the work, the focus is on the optimisation of this preliminary design to improve on the axial turbine efficiency using a genetic algorithm in order to make the axial turbine a more viable proposition for turbocharger turbine application. The implementation of multidisciplinary design optimisation is essential to the aerodynamic shape optimisation of turbocharger turbines, as changes in blade geometry lead to variations in both structural and aerodynamics performance. Due to the necessity to have multiple design objectives and a significant number of variables, genetic algorithms seem to offer significant advantages. However, large generation sizes and simulation run times could result in extensively long periods of time for the optimisation to be completed. This paper proposes a dimensioning of a multi-objective genetic algorithm, to improve on a preliminary blade design in a reasonable amount of time. The results achieved a significant improvement on safety factor of both blades whilst increasing the overall efficiency by 2.55%. This was achieved by testing a total of 399 configurations in just over 4 h using a cluster network, which equated to 2.73 days using a single computer.

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“…However, extracting high-pressure cooling air from the compressors also deteriorates the gas-turbine cycle efficiency, and decreasing the coolant fraction while keeping the hot component under a critical temperature is important. In the traditional three-dimensional (3D) blade redesign procedure, the turbines are always optimized for a given mass flow rate and expansion ratio to achieve higher aerodynamic efficiency, without considering the possible coolant-requirement change caused by the aerodynamic redesign [2]. Therefore, the traditional redesign method can lead to a turbine design with higher component efficiency but may not maximize the gas-turbine cycle efficiency, which is also highly influenced by the coolant fraction.…”

Section: Introductionmentioning

confidence: 99%

Three-dimensional optimal design of a cooled turbine considering the coolant-requirement change

Wang

et al. 2019

Open Physics

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Cooling technology is widely applied in modern turbines to protect the turbine blades, and extracting high-pressure cooling air from a compressor exerts a remarkable influence on the gas-turbine performance. However, the three-dimensional optimal design of a turbine in modern industrial practice is usually carried out by pursuing high component efficiency without considering possible changes in coolant requirement; hence, it may not exactly lead to improvement in the gas-turbine cycle efficiency. In this study, the turbine stator was twisted and leaned to achieve higher comprehensive efficiency, which is the cycle-based efficiency definition for a cooled turbine that considers both turbine aerodynamic performance and coolant requirement. First, the influence of twist and compound lean on turbine aerodynamic performance, considering stator-hub leakage, was investigated. Then, a method to predict the coolant requirement for turbines with different stator designs was applied, to evaluate coolant-requirement change at the design condition. The optimized turbines were finally compared to demonstrate the necessity of considering the coolant-requirement change in the optimal design. This indicated that proper twisting to open the throat area in the stator hub and compound lean to the pressure surface side could help improve the cooled-turbine comprehensive efficiency.

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Multi-Objective Aerodynamic Optimization of Axial Turbine Blades Using a Novel Multilevel Genetic Algorithm

Cited by 16 publications

References 16 publications

Performance Improvement of an Industrial Axial Turbine: Two-Stage Power Turbine Aerodynamic Optimization

Performance Improvement of an Industrial Axial Turbine: Two-Stage Power Turbine Aerodynamic Optimization

Turbocharger Axial Turbines for High Transient Response, Part 2: Genetic Algorithm Development for Axial Turbine Optimisation

Three-dimensional optimal design of a cooled turbine considering the coolant-requirement change

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