A Genetic Algorithm Approach to the Problem of Minimum Ship Wave Resistance

2002 ◽  
Vol 39 (03) ◽  
pp. 187-195
Author(s):  
Roko Dejhalla ◽  
Zoran Mrša ◽  
Senka Vukovic´
Keyword(s):  
Genetic Algorithm ◽  
Potential Flow ◽  
Three Dimensional ◽  
Optimization Procedure ◽  
Optimization Method ◽  
Wave Resistance ◽  
Ship Hull ◽  
Point Of View ◽  
Flow Solver ◽  
Genetic Algorithm Approach

A genetic algorithm-based optimization method is proposed for an optimization of a ship hull from a hydrodynamic point of view. In the optimization procedure, the wave resistance has been selected as an objective function. The genetic algorithm is coupled with a computer program for solving the three-dimensional potential flow around a ship hull. The potential flow solver is based upon the well-known Dawson method. The optimization procedure has been applied to the Series 60 CB = 0.60 hull taken as a basis hull. The computational examples show the optimization ability of the proposed method.

Two Dimensional Optimization of Centrifugal Compressor Impellers Using Online Quasi-3D Flow Solver and Genetic Algorithm

2010 ◽  
Author(s):  
Ying Ma ◽  
Abraham Engeda
Keyword(s):  
Genetic Algorithm ◽  
Centrifugal Compressor ◽  
Search Algorithm ◽  
Radial Basis Function Network ◽  
Three Dimensional ◽  
Optimization Procedure ◽  
Modeling Tools ◽  
Flow Solver ◽  
Performance Map ◽  
A Performance

Modeling tools are widely used to create a performance map and decrease design cycle time in computer-aided centrifugal compressor impeller optimization procedures. However, a high-dimensional performance map is difficult to create and application of the approximate performance map brings errors into optimization procedures. This paper presents an online flow solver optimization procedure, in which a Quasi-three dimensional flow solver is directly used to evaluate impeller performances in the genetic algorithm (GA). Also, this procedure is compared with offline flow solver optimization procedure. In offline flow optimization procedure, the flow solver is employed to calculate performances in training database for creating a performance map trained by one type of artificial neural network (ANN), radial basis function network (RBFN). This performance map is further used to calculate the performances of impeller geometries. Results of these two optimization procedures under same GA parameters setting are compared and show that online flow solver optimization procedure can find better optima than offline flow solver optimization procedure. Moreover, influences of GA operators, parameters and local search algorithm on online flow solver optimization procedure are also investigated.

Structural Optimization of Composite Panel with Lamination Parameters and Equivalent Stiffness Laminate Method

2014 ◽  
Vol 496-500 ◽  
pp. 429-435
Author(s):  
Xiao Ping Zhong ◽  
Peng Jin
Keyword(s):  
Genetic Algorithm ◽  
Structural Optimization ◽  
Composite Structure ◽  
Optimization Procedure ◽  
Optimization Method ◽  
Composite Panel ◽  
Analysis Tool ◽  
Equivalent Stiffness ◽  
Lamination Parameters ◽  
Design Variables

Firstly, a two-level optimization procedure for composite structure is investigated with lamination parameters as design variables and MSC.Nastran as analysis tool. The details using lamination parameters as MSC.Nastran input parameters are presented. Secondly, with a proper equivalent stiffness laminate built to substitute for the lamination parameters, a two-level optimization method based on the equivalent stiffness laminate is proposed. Compared with the lamination parameters-based method, the layer thicknesses of the equivalent stiffness laminate are adopted as continuous design variables at the first level. The corresponding lamination parameters are calculated from the optimal layer thicknesses. At the second level, genetic algorithm (GA) is applied to identify an optimal laminate configuration to target the lamination parameters obtained. The numerical example shows that the proposed method without considering constraints of lamination parameters can obtain better optimal results.

A Novel Combination of Adaptive Tools for Turbomachinery Airfoil Shape Optimization Using a Real-Coded Genetic Algorithm

2013 ◽  
Author(s):  
A. Safari ◽  
H. G. Lemu ◽  
M. Assadi
Keyword(s):  
Genetic Algorithm ◽  
Shape Optimization ◽  
Performance Enhancement ◽  
Flow Analysis ◽  
Three Dimensional ◽  
Pressure Rise ◽  
Computation Time ◽  
Point Of View ◽  
Compressor Rotor ◽  
Real Coded Genetic Algorithm

An automated shape optimization methodology for a typical heavy-duty gas turbine (GT) compressor rotor blade section is presented in this paper. The approach combines a Non-Uniform Rational B-Spline (NURBS) driven parametric geometry description, a two-dimensional flow analysis, and a Genetic Algorithm (GA)-based optimization route. The objective is minimizing the total pressure losses for design condition as well as maximizing the airfoils operating range which is an assessment of the off-design behavior. To achieve the goal, design optimization process is carried out by coupling an established MATLAB code for the Differential Evolution (DE)-based optimum parameterized curve fitting of the measured point cloud of the airfoils’ shape, a blade-to-blade flow analysis in COMSOL Multiphysics, and a developed real-coded GA in MATLAB script. Using the combination of these adaptive tools and methods, the first results are considerably promising in terms of computation time, ability to extend the methodology for three-dimensional and multidisciplinary approach, and last but not least airfoil shape performance enhancement from efficiency and pressure rise point of view.

Genetic Algorithm Approach to Optimize Biodiesel Production by Ultrasonic System

2014 ◽  
Vol 9 (1) ◽  
pp. 59-70 ◽  
Author(s):  
Ebrahim Fayyazi ◽  
Barat Ghobadian ◽  
Gholamhassan Najafi ◽  
Bahram Hosseinzadeh
Keyword(s):  
Genetic Algorithm ◽  
Catalyst Concentration ◽  
Ultrasonic Method ◽  
Research Work ◽  
Optimization Method ◽  
Waste Cooking Oil ◽  
Biodiesel Production ◽  
Molar Ratio ◽  
Genetic Algorithm Approach ◽  
Mixing Method

Abstract Ultrasonic processing is an effective tool to attain required mixing while providing the necessary activation energy in the field of biofuels. In this regard, optimization of fast transesterification of waste cooking oil is very important. The goal of this research paper is therefore to determine the effect of important parameters such as methanol to oil molar ratio, catalyst concentration (potassium hydroxide), temperature, and horn position on oil conversion to methyl ester in ultrasonic mixing method. Result of experiments showed that the optimum conditions for the transesterification process have been obtained as molar ratio of alcohol to oil as 6:1, catalyst concentration of 1 wt.%, temperature as 45°C, and horn position at the interface of methanol to oil. The results show that the ultrasonic method decreases the reaction time as much as up to eight times compare to the conventional stirring. For practically evaluating the theoretical optimum point using genetic algorithm, the obtained values were verified experimentally. In order to perform this, the catalyst concentration, temperature, and the time of reaction were determined, and the values are 1%, 48°C, and 449s, respectively. For the obtained values, the biodiesel conversion was 93.2%, so that the experimental optimum value is closed to that of the theoretical values. As a result, experimental data confirmed the obtained values from optimization method in this research work.

scholarly journals Genetic Algorithm Approach to the 3D Node Localization in TDOA Systems

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3880 ◽  
Author(s):  
Javier Díez-González ◽  
Rubén Álvarez ◽  
David González-Bárcena ◽  
Lidia Sánchez-González ◽  
Manuel Castejón-Limas ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Sensor Deployment ◽  
Selection Strategy ◽  
Ground Vehicles ◽  
Node Localization ◽  
Heuristic Solution ◽  
Positioning Systems ◽  
Genetic Algorithm Approach

Positioning asynchronous architectures based on time measurements are reaching growing importance in Local Positioning Systems (LPS). These architectures have special relevance in precision applications and indoor/outdoor navigation of automatic vehicles such as Automatic Ground Vehicles (AGVs) and Unmanned Aerial Vehicles (UAVs). The positioning error of these systems is conditioned by the algorithms used in the position calculation, the quality of the time measurements, and the sensor deployment of the signal receivers. Once the algorithms have been defined and the method to compute the time measurements has been selected, the only design criteria of the LPS is the distribution of the sensors in the three-dimensional space. This problem has proved to be NP-hard, and therefore a heuristic solution to the problem is recommended. In this paper, a genetic algorithm with the flexibility to be adapted to different scenarios and ground modelings is proposed. This algorithm is used to determine the best node localization in order to reduce the Cramér-Rao Lower Bound (CRLB) with a heteroscedastic noise consideration in each sensor of an Asynchronous Time Difference of Arrival (A-TDOA) architecture. The methodology proposed allows for the optimization of the 3D sensor deployment of a passive A-TDOA architecture, including ground modeling flexibility and heteroscedastic noise consideration with sequential iterations, and reducing the spatial discretization to achieve better results. Results show that optimization with 15% of elitism and a Tournament 3 selection strategy offers the best maximization for the algorithm.

The Application and Comparison of Two Different Optimization Algorithms in the Optimization Design of Hydrofoil

2012 ◽  
Vol 178-181 ◽  
pp. 2871-2876
Author(s):  
Chao Wang ◽  
Feng Feng ◽  
Xin Chang ◽  
Chun Yu Guo ◽  
Yang Hao Liu
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Optimization Design ◽  
Simulated Annealing Algorithm ◽  
Optimization Algorithms ◽  
Optimization Method ◽  
Flow Solver ◽  
Propeller Design ◽  
Section Design ◽  
Annealing Algorithm

Hydrofoil is the important part of ship design and diverse motion equipment. The optimization design of hydrofoil section on lift-to-drag radio with genetic algorithm (GA) and simulated annealing algorithm are demonstrated, and the method on the hydrofoil section design of the propeller design will be done. Objective function and fitness of every individual are provided by flow solver of panel method. The optimization method on design of hydrofoil section on lift-to-drag is successfully used. The optimization results show the combination of optimization algorithm is feasible at the optimal design of hydrofoil sections. What’s more, a comparison between two different optimization algorithms is made, a conclusion that the simulated annealing algorithm is better then the genetic algorithm is obtained.

Genetic Algorithm for Material Cost Minimization of External Strengthening System with Fiber Reinforced Polymer

2012 ◽  
Vol 468-471 ◽  
pp. 1817-1822
Author(s):  
Md. Moshiur Rahman ◽  
Mohd Zamin Jumaat ◽  
Md. Akter Hosen
Keyword(s):  
Genetic Algorithm ◽  
Cost Minimization ◽  
Optimization Procedure ◽  
Minimum Cost ◽  
Optimization Method ◽  
Epoxy Adhesive ◽  
Fiber Reinforced Polymer ◽  
Fiber Reinforced ◽  
Limit States ◽  
Reinforced Polymer

An optimization procedural method for designing fiber reinforced polymer (FRP) plate for strengthening reinforced concrete beam is presented. The optimization procedure is formulated to find the design variables leading to the minimum cost of structural strengthening system using CFRP plate with constraints imposed based on TR55 code provisions. Genetic algorithm based approach is utilized to solve the optimization task. The cost of FRP plate and epoxy adhesive is included in the formulation of the objective function. The ultimate limit states and the serviceability limit states are included in formulation of constraints. A numerical example is given to show the validity of the proposed optimization method.

Optimization of a Centrifugal Pump Impeller: Coupling a Hybrid Stochastic Method to a Potential Flow Solver

2009 ◽  
Author(s):  
Nikolas Antonakis
Keyword(s):  
Potential Flow ◽  
Design Space ◽  
Hybrid Approach ◽  
Cost Effective ◽  
Optimization Methods ◽  
Optimization Method ◽  
Population Based ◽  
Pump Impeller ◽  
Flow Solver ◽  
Improved Performance

An optimization process is employed to improve the performance of an industrial radial flow pump impeller. A hybrid optimization scheme is coupled to a cost effective potential flow solver that computes the flow through the blade channel. The impeller geometry is parameterized to reproduce variations over a wide design space and the objective function is evaluated at each iteration to account for the performance of each candidate blade. The concept of the hybrid approach is to employ a global stochastic optimization method for the diversification of the design space and a deterministic local method for efficiently intensifying the search towards the optimum. The trend of computer industry to multi-core processors is a promising platform for population based optimization methods and even on a quad core the timescales needed to solve the problem with the proposed methodology are reasonable. Results present an optimised impeller with improved performance but also a clear trade-off among contradictory design objectives.

A Genetic Algorithm Approach to Weld Pattern Optimization in Sheet Metal Assembly

2003 ◽  
Author(s):  
Gene Y. Liao
Keyword(s):  
Genetic Algorithm ◽  
Sheet Metal ◽  
Optimization Method ◽  
Structural Deformation ◽  
Assembly Process ◽  
Sheet Metal Parts ◽  
Final Assembly ◽  
Welding Operation ◽  
Genetic Algorithm Approach ◽  
Sheet Metal Assembly

In sheet metal assembly process, welding operation joins two or more sheet metal parts together. Since sheet metals are subject to dimensional variation resulted from manufacturing randomness, gap may be generated at each weld pair prior to welding. These gaps are forced to close during a welding operation and accordingly undesirable structural deformation results. Optimizing the welding pattern (the number and locations of weld pairs) of an assembly process was proven to significantly improve the quality of final assembly. This paper presents a Genetic Algorithm (GA)-based optimization method to automatically search for the optimal weld pattern so that the assembly deformation is minimized. Application result of a real industrial part demonstrated that the proposed algorithm effectively achieve the objective.

scholarly journals Genetic Algorithm-Based Digital Test Optimization Method and its Application to Yield Improvement

2021 ◽  
Author(s):  
Y.H. Chan ◽  
S.H. Goh
Keyword(s):  
Genetic Algorithm ◽  
Optimization Methods ◽  
Optimization Method ◽  
Turnaround Time ◽  
Third Party ◽  
Product Launch ◽  
Technology Scaling ◽  
Product Test ◽  
Genetic Algorithm Approach ◽  
Test Optimization

Abstract Narrowing design and manufacturing process margins with technology scaling are one of the causes for a reduction in IC chip test margin. This situation is further aggravated by the extensive use of third-party design blocks in contemporary system-on-chips which complicates chip timing constraint. Since a thorough timing verification prior to silicon fabrication is usually not done due to aggressive product launch schedules and escalating design cost, occasionally, a post-silicon timing optimization process is required to eliminate false fails encountered on ATE. An iterative two-dimensional shmoo plots and pin margin analysis are custom optimization methods to accomplish this. However, these methods neglect the interdependencies between different IO timing edges such that a truly optimized condition cannot be attained. In this paper, we present a robust and automated solution based on a genetic algorithm approach. Elimination of shmoo holes and widening of test margins (up to 2x enhancements) are demonstrated on actual product test cases. Besides test margin optimization, this method also offers insights into the criticality of test pins to accelerate failure debug turnaround time.

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