scholarly journals Automatic Genetic Optimization Approach to 2D Blade Profile Design for Steam Turbines

1997 ◽  
Author(s):  
M. A. Trigg ◽  
G. R. Tubby ◽  
A. G. Sheard
Keyword(s):  
Systematic Approach ◽  
Optimization Approach ◽  
Genetic Optimization ◽  
Steam Turbines ◽  
Blade Profile ◽  
Minimum Loss ◽  
Profile Design ◽  
Viscous Compressible Flow ◽  
Lower Loss ◽  
Profile Loss

In this paper a systematic approach to the optimization of 2D blade profiles is presented. A genetic optimizer has been developed which modifies the blade profile and calculates its profile loss. This process is automatic, producing profile designs significantly faster and with significantly lower loss than has previously been possible. The optimizer developed uses a genetic algorithm to optimize a 2D profile, defined using 17 parameters, for minimum loss with a given flow condition. The optimizer works with a “population” of 2D profiles with varied parameters. A CFD mesh is generated for each profile, and the result is analyzed using a 2D blade to blade solver, written for steady viscous compressible flow, to determine profile loss. The loss is used as the measure of a profile’s “fitness”. The optimizer uses this information to select the members of the next population, applying crossovers, mutations, and elitism in the process. Using this method the optimizer tends towards the best values for the parameters defining the profile with minimum loss.

Automatic Genetic Optimization Approach to Two-Dimensional Blade Profile Design for Steam Turbines

1999 ◽  
Vol 121 (1) ◽  
pp. 11-17 ◽  
Author(s):  
M. A. Trigg ◽  
G. R. Tubby ◽  
A. G. Sheard
Keyword(s):  
Systematic Approach ◽  
Optimization Approach ◽  
Genetic Optimization ◽  
Two Dimensional ◽  
Steam Turbines ◽  
Blade Profile ◽  
Minimum Loss ◽  
Viscous Compressible Flow ◽  
Lower Loss ◽  
Profile Loss

In this paper a systematic approach to the optimization of two-dimensional blade profiles is presented. A genetic optimizer has been developed that modifies the blade profile and calculates its profile loss. This process is automatic, producing profile designs significantly faster and with significantly lower loss than has previously been possible. The optimizer developed uses a genetic algorithm to optimize a two-dimensional profile, defined using 17 parameters, for minimum loss with a given flow condition. The optimizer works with a “population” of two-dimensional profiles with varied parameters. A CFD mesh is generated for each profile, and the result is analyzed using a two-dimensional blade-to-blade solver, written for steady viscous compressible flow, to determine profile loss. The loss is used as the measure of a profile’s “fitness” The optimizer uses this information to select the members of the next population, applying crossovers, mutations, and elitism in the process. Using this method, the optimizer tends toward the best values for the parameters defining the profile with minimum loss.

scholarly journals Optimization of stator blade profile design for last stages of steam turbines based on the features of coarse droplets movement in inter-blade channels

2019 ◽  
Vol 1359 ◽  
pp. 012038 ◽  
Author(s):  
R A Alekseev ◽  
V G Gribin ◽  
A A Tishchenko ◽  
I Yu Gavrilov ◽  
V A Tishchenko ◽  
...  
Keyword(s):  
Steam Turbines ◽  
Blade Profile ◽  
Stator Blade ◽  
Profile Design

scholarly journals The active working shoot modeling profile of the centrifugal microturbine

2018 ◽  
Vol 226 ◽  
pp. 01009
Author(s):  
Nikolai N. Efimov ◽  
Sergei V. Skubienko ◽  
Vadim V. Kopitsa ◽  
Igor Y. Kolodyazhny ◽  
Evgeny A. Anisimov ◽  
...  
Keyword(s):  
Initial Data ◽  
Single Stage ◽  
Steam Turbines ◽  
Blade Profile ◽  
Traditional Methods ◽  
Distributed Power ◽  
Software Simulation ◽  
Advantages And Disadvantages ◽  
Profile Design ◽  
Optimum Profile

For the small distributed power, microturbines of electric 30-500 kW capacity are used: gas piston engines, gas and steam turbines, each of which has certain advantages and disadvantages. In the work, the active working blade profile is simulated for a single-stage, two-stream, centripetal microturbine, in order to determine the optimum profile design satisfying the reliability conditions and economy. The basis is a humidsteam microturbine of a horizontal electric version with a capacity of 30 kW. The initial data for software simulation were the microturbineactive stage characteristics, determined by the steam turbines calculation traditional methods.

Development of an Experimental Correlation for Transonic Turbine Flow

1987 ◽  
Vol 109 (2) ◽  
pp. 246-250 ◽  
Author(s):  
F. Martelli ◽  
A. Boretti
Keyword(s):  
Experimental Data ◽  
Operating Conditions ◽  
Geometric Parameters ◽  
Blade Profile ◽  
Profile Losses ◽  
Experimental Correlation ◽  
Transonic Turbine ◽  
The Relationship ◽  
Profile Loss ◽  
Correlation Procedure

Optimization of transonic turbine bladings over a broad range of operating conditions calls for better understanding of the relationship between blade profile loss and cascade geometric parameters. In fact, many of the experimental correlations published to date have failed to take into due consideration transonic effects, while others have considered far too few of the numerous geometric parameters affecting profile loss in transonic flows. Through examination of the experimental data gathered by some 20 authors regarding the effects of the most significant blading geometric parameters on profile losses, a loss correlation procedure has been developed. The procedure is especially advantageous in that it allows continuous updating as new experimental data become available.

An Iterative Method for Blade Profile Loss Model Adaptation Using Streamline Curvature

2007 ◽  
Author(s):  
Vassilios Pachidis ◽  
Pericles Pilidis ◽  
Ioannis Templalexis ◽  
Luca Marinai
Keyword(s):  
Experimental Data ◽  
Axial Flow ◽  
Public Domain ◽  
Performance Comparison ◽  
Model Adaptation ◽  
Blade Profile ◽  
Loss Model ◽  
Streamline Curvature ◽  
Loss Models ◽  
Profile Loss

The various incidence, deviation and loss models used in through-flow analysis methods, such as Streamline Curvature, are nothing more than statistical curve fits. A closer look at public domain data reveals that these statistical correlations and curve fits are usually based on experimental cascade data that actually display a fairly large scatter, resulting in a relatively high degree of uncertainty. This usually leads to substantial differences between the calculated and actual performances of a given gas turbine engine component. Typically, matching calculated results from a throughflow analysis against experimental data requires the combination of various correlations available in the public domain, through a very tedious, complex and time consuming ‘trial and error’ process. This particular study supports the view that it might actually be much more time-effective to “adopt” a given loss model against experimental data through an iterative, physics-based approach, rather than try to identify the best combination of available correlations. For example, the well-established “Swan’s model” for calculating the blade profile loss factor in subsonic and transonic axial flow compressors depends strongly on approximate correlations for calculating the blade wake momentum thickness, and therefore represents such a case. This study demonstrates this by looking into an iterative approach to blade profile loss model adaptation that can provide a relatively simple and quick, but also physics-based way of ‘calibrating’ profile loss models against available experimental data for subsonic applications. This paper presents in detail all the analysis necessary to support the above concept and discusses Swan’s model in particular as an example. Finally, the paper discusses the performance comparison of a two-dimensional, Streamline Curvature compressor model against experimental data before and after the adaptation of that particular loss model.

scholarly journals Results of Experiments for Determining the Influence of Blade Profile Changes and Manufacturing Tolerances on the Efficiency, the Enthalpy Drop, and the Mass Flow of Multi-Stage Axial Turbines

1970 ◽  
Author(s):  
K. Bammert ◽  
H. Stobbe
Keyword(s):  
Mass Flow ◽  
Turbine Blades ◽  
Production Costs ◽  
Steam Turbines ◽  
Blade Profile ◽  
Multi Stage ◽  
Profile Changes ◽  
Manufacturing Tolerances ◽  
Axial Turbines ◽  
The Given

When gas and steam turbines are in use, the blade profiles can be thinned by corrosion or erosion and thickened under the influence of deposit formation, thus causing a reduction in efficiency and lifetime. During the production of turbine blades, it is possible that the profiles often become thinner or thicker than the given specified profiles, also causing a decline in efficiency. In addition, the production costs of turbine blades are, to a considerable extent, dependent on the manufacturing tolerances. This report details the effects of thinning and thickening of the profiles on the efficiency, the drop, and the mass flow of multi-stage axial turbines.

scholarly journals Developing a Genetic Fuzzy System Model for Cost-Benefit Analysis.

2014 ◽  
Vol 13 (4) ◽  
pp. 4416-4421 ◽  
Author(s):  
Saurabh Singh ◽  
Praveen Kumar Shukla ◽  
Rashmi Ranjan ◽  
Anurag Kumar
Keyword(s):  
Soft Computing ◽  
Fuzzy System ◽  
Systematic Approach ◽  
Cost Benefit Analysis ◽  
Cost Benefit ◽  
System Model ◽  
Genetic Optimization ◽  
Benefit Analysis ◽  
Genetic Fuzzy System ◽  
The Cost

Cost benefit analysis is a systematic approach for calculation and analyzing the cost of a project. Soft computing approaches are also applicable to deal with cost benefit analysis. In this paper Mamdani fuzzy system has been developed for cost benefit analysis. The genetic optimization of the model is carried out. The interpretability and accuracy features are also analyzed.

scholarly journals An Optimization Approach Applied to Fair Division Transportation Funding Allocation Models

2014 ◽  
Author(s):  
Carlos M. Chang ◽  
Edith Montes
Keyword(s):  
Optimization Technique ◽  
Fair Division ◽  
Budget Constraints ◽  
Optimization Approach ◽  
Genetic Optimization ◽  
Funding Allocation ◽  
Allocation Model ◽  
Transportation Funding

The problem of multiple necessities and limited funds is common in the transportation field. Funding allocation for a transportation agency often involves prioritizing the allocation of funds across a number of participants who have their own needs and preferences. If a participant believes that the final allocation is unfair, then this perception could result in the generation of envy. In this paper, a genetic optimization technique is applied to a Fair Division Transportation Funding Allocation Model (FDTFAM) to minimize the total envy based on the participant’s own priorities and the budget constraints.

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