Integrated Mechanical and Thermodynamic Optimization of an Engine Linkage Using a Multi-Objective Genetic Algorithm

2015 ◽  
Vol 137 (2) ◽  
Author(s):  
Thomas A. Sullivan ◽  
James D. van de ven ◽  
William F. Northrop ◽  
Kieran McCabe
Keyword(s):  
Optimization Problem ◽  
Combustion Engine ◽  
Simultaneous Optimization ◽  
Thermodynamic Efficiency ◽  
Nsga Ii ◽  
Multi Objective Genetic Algorithm ◽  
Design Variables ◽  
Transmission Angle ◽  
Combustion Simulation ◽  
Kinematic Optimization

In order to improve the thermodynamic efficiency of an internal combustion engine (ICE), a Stephenson-III six-bar linkage is optimized to serve as a replacement for the traditional slider–crank. Novel techniques are presented for formulating the design variables in the kinematic optimization that guarantee satisfaction of the Grashof condition and of transmission angle requirements without the need for an explicit constraint function. Additionally, a nested generalization of the popular NSGA-II algorithm is presented that allows simultaneous optimization of the kinematic, dynamic, and thermodynamic properties of the mechanism. This approach successfully solves the complex six-objective optimization problem, with challenges for future refinement including improvement of the combustion simulation to attain better accuracy without prohibitive computational expense.

Dynamic Optimization of a Three Translational Degrees of Freedom Parallel Robot Based on a Multi-Objective Genetic Algorithm

2015 ◽  
Vol 789-790 ◽  
pp. 723-734
Author(s):  
Xing Guo Lu ◽  
Ming Liu ◽  
Min Xiu Kong
Keyword(s):  
Genetic Algorithm ◽  
Dynamic Optimization ◽  
Degrees Of Freedom ◽  
Optimization Problem ◽  
Parallel Robot ◽  
Nsga Ii ◽  
Multi Objective ◽  
Global Dynamic ◽  
Design Variables ◽  
Dynamic Fluctuation

This work tends to deal with the multi-objective dynamic optimization problem of a three translational degrees of freedom parallel robot. Two global dynamic indices are proposed as the objective functions for the dynamic optimization: the index of dynamic dexterity, the index describing the dynamic fluctuation effects. The length of the linkages and the circumradius of the platforms were chosen as the design variables. A multi-objective optimal design problem, including constrains on the actuating and passive joint angle limits and geometrical interference is then formulated to find the Pareto solutions for the robot in a desired workspace. The Non-dominated Sorting Genetic Algorithm (NSGA-II) is adopted to solve the constrained nonlinear multi-objective optimization problem. The simulation results obtained shows that the robot can achieve better dynamic dexterity and less dynamic fluctuation simultaneously after the optimization.

Multi-Objective Optimization of a Segmented Lunar Wheel Concept

2011 ◽  
Author(s):  
Michele Faragalli ◽  
Damiano Pasini ◽  
Peter Radzizsewski
Keyword(s):  
Optimization Problem ◽  
Element Model ◽  
Multi Objective Optimization ◽  
Stress Concentrations ◽  
Multi Objective ◽  
Optimization Framework ◽  
Multi Objective Genetic Algorithm ◽  
Design Variables ◽  
Rider Comfort ◽  
Original Concept

The goal of this work is to develop a systematic method for optimizing the structural design of a segmented wheel concept to improve its operating performance. In this study, a wheel concept is parameterized into a set of size and shape design variables, and a finite element model of the wheel component is created. A multi-objective optimization problem is formulated to optimize its directional compliance and reduce stress concentrations, which has a direct affect on the efficiency, traction, rider comfort, maneuverability, and reliability of the wheel. To solve the optimization problem, a Matlab-FE simulation loop is built and a multi-objective genetic algorithm is used to find the Pareto front of optimal solutions. A trade-off design is selected which demonstrates an improvement from the original concept. Finally, recommendations will be made to apply the structural optimization framework to alternative wheel conceptual designs.

Multi-Objective Optimization of Internal Combustion Engine Valve Springs Based on NSGA-II

2014 ◽  
Vol 945-949 ◽  
pp. 473-477
Author(s):  
You Jian Wang ◽  
Guang Zhang
Keyword(s):  
Genetic Algorithm ◽  
Internal Combustion Engine ◽  
Optimization Problem ◽  
Combustion Engine ◽  
Graphical Methods ◽  
Multi Objective Optimization ◽  
Design Quality ◽  
Nsga Ii ◽  
Engine Valve ◽  
Multi Objective

The design of engine valve spring generally belongs to multi-objective optimum design. The traditional trying means and the graphical methods are difficult to solve the multi-objective optimization problem, and the traditional multi-objective algorithms have certain defects. The elitist non-dominated sorting genetic algorithm (NSGA-II) is an excellent multi-objective algorithm, which is widely used to solve problems of multi-objective optimization. This method can improve the design quality and efficiency, and it has much more engineering practical value.

DNA Fragment Assembly Using Multi-Objective Genetic Algorithms

2014 ◽  
Vol 5 (3) ◽  
pp. 84-108 ◽  
Author(s):  
Manisha Rathee ◽  
T. V. Vijay Kumar
Keyword(s):  
Optimization Problem ◽  
Nsga Ii ◽  
Fragment Assembly ◽  
Assembly Algorithm ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Dna Fragment ◽  
The Right ◽  
Dna Fragment Assembly ◽  
Single Objective

DNA Fragment Assembly Problem (FAP) is concerned with the reconstruction of the target DNA, using the several hundreds (or thousands) of sequenced fragments, by identifying the right order and orientation of each fragment in the layout. Several algorithms have been proposed for solving FAP. Most of these have solely dwelt on the single objective of maximizing the sum of the overlaps between adjacent fragments in order to optimize the fragment layout. This paper aims to formulate this FAP as a bi-objective optimization problem, with the two objectives being the maximization of the overlap between the adjacent fragments and the minimization of the overlap between the distant fragments. Moreover, since there is greater desirability for having lesser number of contigs, FAP becomes a tri-objective optimization problem where the minimization of the number of contigs becomes the additional objective. These problems were solved using the multi-objective genetic algorithm NSGA-II. The experimental results show that the NSGA-II-based Bi-Objective Fragment Assembly Algorithm (BOFAA) and the Tri-Objective Fragment Assembly Algorithm (TOFAA) are able to produce better quality layouts than those generated by the GA-based Single Objective Fragment Assembly Algorithm (SOFAA). Further, the layouts produced by TOFAA are also comparatively better than those produced using BOFAA.

Integrated Optimization of ACLD Treatment for Shape Control of Flexible Beams Using Multi-Objective Genetic Algorithm

Aerospace ◽  
2003 ◽  
Author(s):  
L. C. Hau ◽  
Eric H. K. Fung
Keyword(s):  
Genetic Algorithm ◽  
Shape Control ◽  
Optimization Problem ◽  
Viscoelastic Layer ◽  
Flexible Beams ◽  
Pareto Solution ◽  
Integrated Optimization ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Design Variables

This paper presents the use of multi-objective genetic algorithm (MOGA) to solve an integrated optimization problem for the shape control of flexible beams with Active Constrained Layer Damping (ACLD) treatment. The design objectives are to minimize the total weight of the system, the input voltage and the steady-state error between the achieved and desired shapes. Design variables include the thickness of the constraining layer and viscoelastic layer, the length and location of the ACLD patches, as well as the control gains. In order to evaluate the effect of different combinations of design variables on the system performance, the finite element method, in conjunction with the Golla-Hughes-McTavish (GHM) method, is employed to model a clamped-free beam with ACLD patches. As a result of the optimization, a Pareto solution is successfully obtained. It is shown that the MOGA is applicable to the present integrated optimization problem, and ACLD treatment is suitable for shape control of flexible structures.

Study on 2.5D C/C composite finger seal integrate optimization combined with microstructural parameters of material and macro geometry of finger seal

2015 ◽  
Vol 231 (9) ◽  
pp. 1599-1608 ◽  
Author(s):  
Hua Su ◽  
Lu Wang
Keyword(s):  
Finite Element Method ◽  
Finite Element ◽  
Composite Material ◽  
Contact Pressure ◽  
Optimization Method ◽  
Nsga Ii ◽  
Microstructural Parameters ◽  
Multi Objective Genetic Algorithm ◽  
Design Variables ◽  
Element Method

The current study focuses on performance analysis and structural optimization of the 2.5 D C/C composite finger seal. A micro/macrostructural integrated optimization method of 2.5D C/C composite finger seal is presented. Based on uniform strain assumption the stiffness average method is used to predict the elastic properties of 2.5D C/C composite material. In order to achieve the advantage of the designability of composite material, the microstructure parameters are also as design variables together with the macro structure of finger seal. Considering the two optimization objectives, leakage and contact pressure, are both implicit functions of the structure parameters of finger seal which obtained by finite element method, a Krige model is established to replace the finite element method analysis in each optimization iteration, which could improve the optimization calculating efficiency obviously. By using the multi objective genetic algorithm NSGA-II the 2.5D C/C composite finger seal optimization is implemented availably. An example is given which indicates the leakage and contact pressure of finger seal decrease significantly through the integration optimization of 2.5D C/C composite finger seal which develop a new approach to design finger seal with high performances.

Multi-Objective Optimization of Gas Blend Alternative Refrigerants for Vapor-Compression Refrigeration Systems

2015 ◽  
Author(s):  
Mohamed El-Morsi ◽  
Karim Hamza
Keyword(s):  
Optimization Problem ◽  
Primary Objective ◽  
Exhaustive Search ◽  
Coefficient Of Performance ◽  
Alternative Refrigerants ◽  
Hydrocarbon Gases ◽  
Nsga Ii ◽  
Multi Objective ◽  
Mixing Ratios ◽  
Design Variables

This paper presents a theoretical study on optimizing the mixing ratios of hydrocarbon blends to be used as refrigerants in existing refrigeration equipment. The primary objective is to maximize the coefficient of performance. The gas blending optimization problem is posed in a multi-objective framework, where the optimization seeks to generate Pareto optimal solutions that span the trade-off frontier between coefficient of performance versus deviation from a desired volumetric refrigeration capacity, while adhering to a maximum compression ratio. Design variables in the optimization are the mass fractions of hydrocarbon gases in the blend. A domain reduction scheme is introduced, which allows for efficient conduction of exhaustive search, with up to three hydrocarbon gases in the blend. While exhaustive search guarantees that the obtained solutions are global optima, the computational resources it requires scale poorly as the number of design variables increase. Two alternative approaches, (multi-start SQP) and (NSGA-II) are also tested for solving the optimization problem. Numerical simulation case studies for replacement of R12, R22 and R134a with hydrocarbon blends of isobutane, propane and propylene show agreement between solution methods that good compromises are possible to achieve, but a small loss in coefficient of performance is inevitable.

Shape Optimization of Hydraulic Structures: an Example of an Optimum Design of a Fish Passage

10.29007/2k64 ◽  
2018 ◽  
Author(s):  
Pat Prodanovic ◽  
Cedric Goeury ◽  
Fabrice Zaoui ◽  
Riadh Ata ◽  
Jacques Fontaine ◽  
...  
Keyword(s):  
Numerical Model ◽  
Shape Optimization ◽  
Objective Function ◽  
Optimum Design ◽  
Optimization Problem ◽  
A Priori ◽  
Coupled System ◽  
Fish Passage ◽  
Hydraulic Structures ◽  
Design Variables

This paper presents a practical methodology developed for shape optimization studies of hydraulic structures using environmental numerical modelling codes. The methodology starts by defining the optimization problem and identifying relevant problem constraints. Design variables in shape optimization studies are configuration of structures (such as length or spacing of groins, orientation and layout of breakwaters, etc.) whose optimal orientation is not known a priori. The optimization problem is solved numerically by coupling an optimization algorithm to a numerical model. The coupled system is able to define, test and evaluate a multitude of new shapes, which are internally generated and then simulated using a numerical model. The developed methodology is tested using an example of an optimum design of a fish passage, where the design variables are the length and the position of slots. In this paper an objective function is defined where a target is specified and the numerical optimizer is asked to retrieve the target solution. Such a definition of the objective function is used to validate the developed tool chain. This work uses the numerical model TELEMAC- 2Dfrom the TELEMAC-MASCARET suite of numerical solvers for the solution of shallow water equations, coupled with various numerical optimization algorithms available in the literature.

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.

scholarly journals Modeling and Optimizing the Multi-Objective Portfolio Optimization Problem with Trapezoidal Fuzzy Parameters

2021 ◽  
Vol 26 (2) ◽  
pp. 36
Author(s):  
Alejandro Estrada-Padilla ◽  
Daniela Lopez-Garcia ◽  
Claudia Gómez-Santillán ◽  
Héctor Joaquín Fraire-Huacuja ◽  
Laura Cruz-Reyes ◽  
...  
Keyword(s):  
Steady State ◽  
Portfolio Optimization ◽  
Optimization Problem ◽  
Solution Process ◽  
Density Estimator ◽  
Nsga Ii ◽  
Spatial Spread ◽  
Multi Objective ◽  
Significant Difference ◽  
Portfolio Optimization Problem

A common issue in the Multi-Objective Portfolio Optimization Problem (MOPOP) is the presence of uncertainty that affects individual decisions, e.g., variations on resources or benefits of projects. Fuzzy numbers are successful in dealing with imprecise numerical quantities, and they found numerous applications in optimization. However, so far, they have not been used to tackle uncertainty in MOPOP. Hence, this work proposes to tackle MOPOP’s uncertainty with a new optimization model based on fuzzy trapezoidal parameters. Additionally, it proposes three novel steady-state algorithms as the model’s solution process. One approach integrates the Fuzzy Adaptive Multi-objective Evolutionary (FAME) methodology; the other two apply the Non-Dominated Genetic Algorithm (NSGA-II) methodology. One steady-state algorithm uses the Spatial Spread Deviation as a density estimator to improve the Pareto fronts’ distribution. This research work’s final contribution is developing a new defuzzification mapping that allows measuring algorithms’ performance using widely known metrics. The results show a significant difference in performance favoring the proposed steady-state algorithm based on the FAME methodology.

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