Design Study of Dovetail Geometries of Turbine Blades Using Abaqus and Isight

2012 ◽  
Author(s):  
Youngwon Hahn ◽  
John I. Cofer
Keyword(s):  
Surrogate Model ◽  
High Reliability ◽  
Turbine Blades ◽  
Optimization Methods ◽  
Nonlinear Static Analysis ◽  
Design Parameters ◽  
Second Step ◽  
Nsga Ii ◽  
Python Script ◽  
Design Variables

Blades in gas and steam turbines continually face more challenging requirements for high reliability and efficiency. In order to meet these challenges in an increasingly competitive marketplace, blade design engineers are always looking for more efficient ways to design the blades in the shortest possible time and at the lowest possible cost while meeting multiple design objectives. In this paper, several design studies are performed using Abaqus and Isight to optimize the minimum contact pressure and stress around the dovetail of a typical turbine blade in order to achieved desired goals for stress levels. First, nine design parameters describing the dimensions of the dovetail are set up in a Python script which can be executed in Abaqus/CAE. The Python script generates the entire finite element model including boundary and loading conditions in Abaqus/CAE. A nonlinear static analysis considering centrifugal loading is performed in this work. After setting up the workflow using the Python script and Abaqus/CAE, Isight is used to automate the process to achieve the optimized dimensions of the dovetail. The optimization is performed in two steps. First, a surrogate model using the Optimal Latin Hypercube approximation method is created using tools in Isight. In this step, the surrogate model is used to determine the optimum values of the design variables, as well as the sensitivity of the design to the selected design variables. It also can be observed that the design is especially sensitive to five of the design variables. In the second step of the optimization, the five design variables to which the design is most sensitive are selected for further optimization by setting the other design variables to the optimized values obtained in the first step of the optimization. In this second step, several different optimization methods supported in Isight are used, including the NSGA-II non-dominated sorting genetic algorithm, Downhill Simplex, and an evolutionary optimization algorithm. Results from these methods are compared with those obtained using other common optimization methods in Isight.

Optimization of Supersonic Axial Turbine Blades Based on Surrogate Models

2020 ◽  
Author(s):  
Markus Waesker ◽  
Bjoern Buelten ◽  
Norman Kienzle ◽  
Christian Doetsch
Keyword(s):  
Reynolds Number ◽  
Turbine Blade ◽  
Surrogate Model ◽  
Cfd Simulation ◽  
Turbine Blades ◽  
Energy System ◽  
Design Parameters ◽  
Blade Design ◽  
Axial Turbine ◽  
Velocity Coefficient

Abstract Due to the transition of the energy system to more decentralized sector-coupled technologies, the demand on small, highly efficient and compact turbines is steadily growing. Therefore, supersonic impulse turbines have been subject of academic research for many years because of their compact and low-cost conditions. However, specific loss models for this type of turbine are still missing. In this paper, a CFD-simulation-based surrogate model for the velocity coefficient, unique incidence as well as outflow deviation of the blade, is introduced. This surrogate model forms the basis for an exemplary efficiency optimization of the “Colclough cascade”. In a first step, an automatic and robust blade design methodology for constant-channel blades based on the supersonic turbine blade design of Stratford and Sansome is shown. The blade flow is fully described by seven geometrical and three aerodynamic design parameters. After that, an automated numerical flow simulation (CFD) workflow for supersonic turbine blades is developed. The validation of the CFD setup with a published supersonic axial turbine blade (Colclough design) shows a high consistency in the shock waves, separation zones and boundary layers as well as velocity coefficients. A design of experiments (DOE) with latin hypercube sampling and 1300 sample points is calculated. This CFD data forms the basis for a highly accurate surrogate model of supersonic turbine blade flow suitable for Mach numbers between 1.1 and 1.6. The throat-based Reynolds number is varied between 1*104 and 4*105. Additionally, an optimization is introduced, based on the surrogate model for the Reynolds number and Mach number of Colclough and no degree of reaction (equal inlet and outlet static pressure). The velocity coefficient is improved by up to 3 %.

Multi Objective Design Optimization of Two Bar Truss Using NSGA II and TOPSIS

2014 ◽  
Vol 984-985 ◽  
pp. 419-424
Author(s):  
P. Sabarinath ◽  
M.R. Thansekhar ◽  
R. Saravanan
Keyword(s):  
Design Optimization ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Optimal Solutions ◽  
Objective Functions ◽  
Nsga Ii ◽  
Multi Objective ◽  
Total Displacement ◽  
Design Variables ◽  
Conflicting Objectives

Arriving optimal solutions is one of the important tasks in engineering design. Many real-world design optimization problems involve multiple conflicting objectives. The design variables are of continuous or discrete in nature. In general, for solving Multi Objective Optimization methods weight method is preferred. In this method, all the objective functions are converted into a single objective function by assigning suitable weights to each objective functions. The main drawback lies in the selection of proper weights. Recently, evolutionary algorithms are used to find the nondominated optimal solutions called as Pareto optimal front in a single run. In recent years, Non-dominated Sorting Genetic Algorithm II (NSGA-II) finds increasing applications in solving multi objective problems comprising of conflicting objectives because of low computational requirements, elitism and parameter-less sharing approach. In this work, we propose a methodology which integrates NSGA-II and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for solving a two bar truss problem. NSGA-II searches for the Pareto set where two bar truss is evaluated in terms of minimizing the weight of the truss and minimizing the total displacement of the joint under the given load. Subsequently, TOPSIS selects the best compromise solution.

The Effect of Different Optimization Methods on Robustness for Robust Optimization Design

2014 ◽  
Vol 721 ◽  
pp. 464-467
Author(s):  
Tao Fu ◽  
Qin Zhong Gong ◽  
Da Zhen Wang
Keyword(s):  
Robust Optimization ◽  
Objective Function ◽  
Robust Design ◽  
Optimization Design ◽  
Optimization Methods ◽  
Optimization Method ◽  
Design Parameters ◽  
Hybrid Particle Swarm Optimization ◽  
Design Variables ◽  
Robust Optimization Design

In view of robustness of objective function and constraints in robust design, the method of maximum variation analysis is adopted to improve the robust design. In this method, firstly, we analyses the effect of uncertain factors in design variables and design parameters on the objective function and constraints, then calculate maximum variations of objective function and constraints. A two-level optimum mathematical model is constructed by adding the maximum variations to the original constraints. Different solving methods are used to solve the model to study the influence to robustness. As a demonstration, we apply our robust optimization method to an engineering example, the design of a machine tool spindle. The results show that, compared with other methods, this method of HPSO(hybrid particle swarm optimization) algorithm is superior on solving efficiency and solving results, and the constraint robustness and the objective robustness completely satisfy the requirement, revealing that excellent solving method can improve robustness.

Accelerated 3D Aerodynamic Optimization of Gas Turbine Blades

2014 ◽  
Author(s):  
Philipp Amtsfeld ◽  
Michael Lockan ◽  
Dieter Bestle ◽  
Marcus Meyer
Keyword(s):  
Turbine Blades ◽  
Three Dimensional ◽  
Optimization Process ◽  
Design Parameters ◽  
Blade Design ◽  
Aerodynamic Optimization ◽  
Multi Stage ◽  
Design Variables ◽  
Real Flow ◽  
Blade Model

State-of-the-art aerodynamic blade design processes mainly consist of two phases: optimal design of 2D blade sections and then stacking them optimally along a three-dimensional stacking line. Such a quasi-3D approach, however, misses the potential of finding optimal blade designs especially in the presence of strong 3D flow effects. Therefore, in this paper a blade optimization process is demonstrated which uses an integral 3D blade model and 3D CFD analysis to account for three-dimensional flow features. Special emphasis is put on shortening design iterations and reducing design costs in order to obtain a rapid automatic optimization process for fully 3D aerodynamic turbine blade design which can be applied in an early design phase already. The three-dimensional parametric blade model is determined by up to 80 design variables. At first, the most important design parameters are chosen based on a non-linear sensitivity analysis. The objective of the subsequent optimization process is to maximize isentropic efficiency while fulfilling a minimal set of constraints. The CFD model contains both important geometric features like tip gaps and fillets, and cooling and leakage flows to sufficiently represent real flow conditions. Two acceleration strategies are used to cut down the turn-around time from weeks to days. Firstly, the aerodynamic multi-stage design evaluation is significantly accelerated with a GPU-based RANS solver running on a multi-GPU workstation. Secondly, a response surface method is used to reduce the number of expensive function evaluations during the optimization process. The feasibility is demonstrated by an application to a blade which is a part of a research rig similar to the high pressure turbine of a small civil jet engine. The proposed approach enables an automatic aerodynamic design of this 3D blade on a single workstation within few days.

scholarly journals Improved Immune Genetic Algorithm to Optimize the Design of Wing Structure of Competition Aircraft

2021 ◽  
Vol 2137 (1) ◽  
pp. 012075
Author(s):  
Xi Feng ◽  
Yafeng Zhang
Keyword(s):  
Genetic Algorithm ◽  
Aircraft Design ◽  
Flight Test ◽  
Optimization Methods ◽  
Optimization Method ◽  
Design Parameters ◽  
Immune Genetic Algorithm ◽  
Maximum Deformation ◽  
Design Variables ◽  
Wing Structure

Abstract An improved immune genetic algorithm is used to design and optimize the wing structure parameters of a competition aircraft. According to the requirements of aircraft design, multi-objective optimization index is established. On this basis, the basic steps of using immune algorithm to optimize the main design parameters of aircraft wing structure are proposed, and the optimization of the wing parameters of a competition aircraft is used as an example for simulation calculation. The design variables in the optimization are the size of the wing components, and the optimization goal is to minimize the weight of the wing and the maximum deformation of the wing structure. Research shows that compared with traditional optimization methods; the improved immune genetic algorithm is a very effective optimization method. At the same time, a prototype is made to check the validity and feasibility of the design. Flight test results show that the optimization method is very effective. Although the method is proposed for competition aircraft, it is also applicable to other types of aircraft.

scholarly journals Multi-Objective Optimization of Two-Stage Helical Gear Train Using NSGA-II

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
R. C. Sanghvi ◽  
A. S. Vashi ◽  
H. P. Patolia ◽  
R. G. Jivani
Keyword(s):  
Bending Strength ◽  
Gear Train ◽  
Design Parameters ◽  
Uniform Motion ◽  
Nsga Ii ◽  
Surface Fatigue ◽  
Face Width ◽  
Design Variables ◽  
Load Carrying ◽  
Do So

Gears not only transmit the motion and power satisfactorily but also can do so with uniform motion. The design of gears requires an iterative approach to optimize the design parameters that take care of kinematics aspects as well as strength aspects. Moreover, the choice of materials available for gears is limited. Owing to the complex combinations of the above facts, manual design of gears is complicated and time consuming. In this paper, the volume and load carrying capacity are optimized. Three different methodologies (i) MATLAB optimization toolbox, (ii) genetic algorithm (GA), and (iii) multiobjective optimization (NSGA-II) technique are used to solve the problem. In the first two methods, volume is minimized in the first step and then the load carrying capacities of both shafts are calculated. In the third method, the problem is treated as a multiobjective problem. For the optimization purpose, face width, module, and number of teeth are taken as design variables. Constraints are imposed on bending strength, surface fatigue strength, and interference. It is apparent from the comparison of results that the result obtained by NSGA-II is more superior than the results obtained by other methods in terms of both objectives.

Optimum design parameters and mechanical properties of polymeric nanocomposites using NSGA-II optimization method

2020 ◽  
pp. 002199832096077
Author(s):  
Mahlatse Rabothata ◽  
Jacob Muthu ◽  
Leon Wegner
Keyword(s):  
Mechanical Properties ◽  
Tensile Strength ◽  
Elastic Modulus ◽  
Optimization Model ◽  
Optimization Method ◽  
Design Parameters ◽  
Published Data ◽  
Polymeric Nanocomposites ◽  
Nsga Ii ◽  
Design Variables

The aim of this work was to develop a method for optimizing both the design parameters and the mechanical properties of polymer-based nanocomposites using multi-objective optimization (MOO) methods. The objective was to maximize both the elastic modulus and the tensile strength of nanocomposites simultaneously by varying the design parameters. The Ji and Zare models were selected as the objective functions for the elastic modulus and tensile strength of polymer nanocomposites, respectively. For this purpose, the NSGA-II approach implemented in MATLAB was used to obtain optimal solutions of the design variables. The optimization model was able to successfully find optimum solutions of the design variables and the overall optimization results were found to be in good agreement with the available published data. In addition, the proposed optimization model was found to be sufficiently accurate in finding the optimum values of the design variables for improving the mechanical properties of nanocomposites.

scholarly journals Energy and Efficiency Evaluation of Feedback Branch Design in Thermoacoustic Stirling-Like Engines

Energies ◽  
2019 ◽  
Vol 12 (20) ◽  
pp. 3867 ◽  
Author(s):  
Iniesta ◽  
Olazagoitia ◽  
Vinolas ◽  
Gros
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
High Reliability ◽  
Reduction Process ◽  
Acoustic Power ◽  
Great Difficulty ◽  
Design Parameters ◽  
Internal Diameter ◽  
Design Variables ◽  
External Combustion

Stirling-like thermoacoustic generators are external combustion engines that provide useful acoustic power in the absence of moving parts with high reliability and respect for the environment. The study of these systems involves a great complexity since the parameters that describe them, besides being numerous, present a high degree of coupling between them. This implies a great difficulty in characterizing the effects of any parametric variation on the performance of these devices. Due to the huge amount of data to analyze, the experiments and simulations required to address the problem involve high investments in time and resources, sometimes unaffordable. This article presents, how a sensitivity analysis applying the response surface methodology can be applied to optimize the feedback branch of a thermoacoustic Stirling-like engine. The proposed study is made by evaluating the comparative relevance of seven design variables. The dimensional reduction process identifies three significant factors: the frequency of operation, the internal diameter of compliance, and the inertance. Subsequently, the Response Surface Methodology is applied to assess the interaction effects of these three design parameters on the efficiency of the thermoacoustic engine, and an improvement of 6% has been achieved. The enhanced values given by the response surface methodology are validated using the DeltaEC software.

Fuzzy multi-objective location and routing problem

2020 ◽  
Vol 39 (3) ◽  
pp. 3259-3273
Author(s):  
Nasser Shahsavari-Pour ◽  
Najmeh Bahram-Pour ◽  
Mojde Kazemi
Keyword(s):  
Firefly Algorithm ◽  
High Reliability ◽  
Research Area ◽  
Nsga Ii ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Location Allocation ◽  
Multi Objective ◽  
Location Routing ◽  
The Cost

The location-routing problem is a research area that simultaneously solves location-allocation and vehicle routing issues. It is critical to delivering emergency goods to customers with high reliability. In this paper, reliability in location and routing problems was considered as the probability of failure in depots, vehicles, and routs. The problem has two objectives, minimizing the cost and maximizing the reliability, the latter expressed by minimizing the expected cost of failure. First, a mathematical model of the problem was presented and due to its NP-hard nature, it was solved by a meta-heuristic approach using a NSGA-II algorithm and a discrete multi-objective firefly algorithm. The efficiency of these algorithms was studied through a complete set of examples and it was found that the multi-objective discrete firefly algorithm has a better Diversification Metric (DM) index; the Mean Ideal Distance (MID) and Spacing Metric (SM) indexes are only suitable for small to medium problems, losing their effectiveness for big problems.

Multiobjective optimization of hard coating parameters designing for damping of the bladed disk

2017 ◽  
Vol 232 (9) ◽  
pp. 1609-1619 ◽  
Author(s):  
Yugang Chen ◽  
Jingyu Zhai ◽  
Qingkai Han
Keyword(s):  
Pareto Front ◽  
Engineering Application ◽  
Front Surface ◽  
Model Material ◽  
Superior Performance ◽  
Hard Coating ◽  
Damping Capacity ◽  
Nsga Ii ◽  
Bladed Disk ◽  
Design Variables

In this paper, the damping capacity and the structural influence of the hard coating on the given bladed disk are optimized by the non-dominated sorting genetic algorithm (NSGA-II) coupled with the Kriging surrogate model. Material and geometric parameters of the hard coating are taken as the design variables, and the loss factors, frequency variations and weight gain are considered as the objective functions. Results of the bi-objective optimization are obtained as curved line of Pareto front, and results of the triple-objective optimization are obtained as Pareto front surface with an obvious frontier. The results can give guidance to the designer, which can help to achieve more superior performance of hard coating in engineering application.

Sign in / Sign up

Export Citation Format

Share Document