scholarly journals Multi-Objective Design Optimization of a Variable Geometry Spray Fuel Injector

2014 ◽  
Vol 136 (4) ◽  
Author(s):  
J. R. Archer ◽  
Tiegang Fang ◽  
Scott Ferguson ◽  
Gregory D. Buckner
Keyword(s):  
Design Optimization ◽  
High Performance ◽  
Pareto Frontier ◽  
Spray Angle ◽  
Design Parameters ◽  
Variable Geometry ◽  
Fuel Injector ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Design Variables

This paper explores the simulation-based design optimization of a variable geometry spray (VGS) fuel injector. A multi-objective genetic algorithm (MOGA) is interfaced with commercial computational fluid dynamics (CFD) software and high performance computing capabilities to evaluate the spray characteristics of each VGS candidate design. A three-point full factorial experimental design is conducted to identify significant design variables and to better understand possible variable interactions. The Pareto frontier of optimal designs reveals the inherent tradeoff between two performance objectives—actuator stroke and spray angle sensitivity. Analysis of these solutions provides insight into dependencies between design parameters and the performance objectives and is used to assess possible performance gains with respect to initial prototype configurations. These insights provide valuable design information for the continued development of this VGS technology.

scholarly journals Multi-Objective Design Optimization of a Shape Memory Alloy Flexural Actuator

Actuators ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 13 ◽  
Author(s):  
Casey D. Haigh ◽  
John H. Crews ◽  
Shiquan Wang ◽  
Gregory D. Buckner
Keyword(s):  
Shape Memory Alloy ◽  
Shape Memory ◽  
Design Optimization ◽  
Flexural Rigidity ◽  
Pareto Frontier ◽  
Design Parameters ◽  
Optimization Strategy ◽  
Multi Objective ◽  
Critical Design ◽  
Multi Objective Genetic Algorithm

This paper presents a computational model and design optimization strategy for shape memory alloy (SMA) flexural actuators. These actuators consist of curved SMA wires embedded within elastic structures; one potential application is positioning microcatheters inside blood vessels during clinical treatments. Each SMA wire is shape-set to an initial curvature and inserted along the neutral axis of a straight elastic member (cast polydimethylsiloxane, PDMS). The elastic structure preloads the SMA, reducing the equilibrium curvature of the composite actuator. Temperature-induced phase transformations in the SMA are achieved via Joule heating, enabling strain recovery and increased bending (increased curvature) in the actuator. Actuator behavior is modeled using the homogenized energy framework, and the effects of two critical design parameters (initial SMA curvature and flexural rigidity of the elastic sleeve) on activation curvature are investigated. Finally, a multi-objective genetic algorithm is utilized to optimize actuator performance and generate a Pareto frontier, which is subsequently experimentally validated.

A Kriging Metamodel Assisted Multi-Objective Genetic Algorithm for Design Optimization

2006 ◽  
Author(s):  
Mian Li ◽  
Genzi Li ◽  
Shapour Azarm
Keyword(s):  
Genetic Algorithm ◽  
Design Optimization ◽  
Pareto Frontier ◽  
Computational Cost ◽  
Optimization Methods ◽  
Optimization Approach ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Main Challenge ◽  
Engineering Design Problems

The high computational cost of population based optimization methods, such as multi-objective genetic algorithms, has been preventing applications of these methods to realistic engineering design problems. The main challenge is to devise methods that can significantly reduce the number of computationally intensive simulation (objective/constraint functions) calls. We present a new multi-objective design optimization approach in that kriging-based metamodeling is embedded within a multi-objective genetic algorithm. The approach is called Kriging assisted Multi-Objective Genetic Algorithm, or K-MOGA. The key difference between K-MOGA and a conventional MOGA is that in K-MOGA some of the design points or individuals are evaluated by kriging metamodels, which are computationally inexpensive, instead of the simulation. The decision as to whether the simulation or their kriging metamodels to be used for evaluating an individual is based on checking a simple condition. That is, it is determined whether by using the kriging metamodels for an individual the non-dominated set in the current generation is changed. If this set is changed, then the simulation is used for evaluating the individual; otherwise, the corresponding kriging metamodels are used. Seven numerical and engineering examples with different degrees of difficulty are used to illustrate applicability of the proposed K-MOGA. The results show that on the average, K-MOGA converges to the Pareto frontier with about 50% fewer number of simulation calls compared to a conventional MOGA.

Application of Multi-Objective Genetic Algorithm (MOGA) for Design Optimization of Valve Timing at Various Engine Speeds

2011 ◽  
Vol 264-265 ◽  
pp. 1719-1724 ◽  
Author(s):  
A.K.M. Mohiuddin ◽  
Md. Ataur Rahman ◽  
Yap Haw Shin
Keyword(s):  
Genetic Algorithm ◽  
Design Optimization ◽  
Robust Design Optimization ◽  
Primary Concern ◽  
Variable Valve Timing ◽  
Valve Timing ◽  
Engine Valve ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Variable Valve

This paper aims to demonstrate the effectiveness of Multi-Objective Genetic Algorithm Optimization and its practical application on the automobile engine valve timing where the variation of performance parameters required for finest tuning to obtain the optimal engine performances. The primary concern is to acquire the clear picture of the implementation of Multi-Objective Genetic Algorithm and the essential of variable valve timing effects on the engine performances in various engine speeds. Majority of the research works in this project were in CAE software environment and method to implement optimization to 1D engine simulation. The paper conducts robust design optimization of CAMPRO 1.6L (S4PH) engine valve timing at various engine speeds using multiobjective genetic algorithm (MOGA) for the future variable valve timing (VVT) system research and development. This paper involves engine modelling in 1D software simulation environment, GT-Power. The GT-Power model is run simultaneously with mode Frontier to perform multiobjective optimization.

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.

Optimal design of a parallel robotic gripper using enhanced multi-objective ant lion optimizer with a sensitivity analysis approach

2020 ◽  
Vol 40 (5) ◽  
pp. 703-721
Author(s):  
Golak Bihari Mahanta ◽  
Deepak BBVL ◽  
Bibhuti B. Biswal ◽  
Amruta Rout
Keyword(s):  
Sensitivity Analysis ◽  
Distribution Function ◽  
Design Parameters ◽  
Objective Functions ◽  
Content Type ◽  
Multi Objective ◽  
The Past ◽  
Design Variables ◽  
Pick And Place ◽  
Ant Lion

Purpose From the past few decades, parallel grippers are used successfully in the automation industries for performing various pick and place jobs due to their simple design, reliable nature and its economic feasibility. So, the purpose of this paperis to design a suitable gripper with appropriate design parameters for better performance in the robotic production systems. Design/methodology/approach In this paper, an enhanced multi-objective ant lion algorithm is introduced to find the optimal geometric and design variables of a parallel gripper. The considered robotic gripper systems are evaluated by considering three objective functions while satisfying eight constraint equations. The beta distribution function is introduced for generating the initial random number at the initialization phase of the proposed algorithm as a replacement of uniform distribution function. A local search algorithm, namely, achievement scalarizing function with multi-criteria decision-making technique and beta distribution are used to enhance the existing optimizer to evaluate the optimal gripper design problem. In this study, the newly proposed enhanced optimizer to obtain the optimum design condition of the design variables is called enhanced multi-objective ant lion optimizer. Findings This study aims to obtain optimal design parameters of the parallel gripper with the help of the developed algorithms. The acquired results are investigated with the past research paper conducted in that field for comparison. It is observed that the suggested method to get the best gripper arrangement and variables of the parallel gripper mechanism outperform its counterparts. The effects of the design variables are needed to be studied for a better design approach concerning the objective functions, which is achieved by sensitivity analysis. Practical implications The developed gripper is feasible to use in the assembly operation, as well as in other pick and place operations in different industries. Originality/value In this study, the problem to find the optimum design parameter (i.e. geometric parameters such as length of the link and parallel gripper joint angles) is addressed as a multi-objective optimization. The obtained results from the execution of the algorithm are evaluated using the performance indicator algorithm and a sensitivity analysis is introduced to validate the effects of the design variables. The obtained optimal parameters are used to develop a gripper prototype, which will be used for the assembly process.

Multiobjective Design Optimization of Microchannel Cooling System Using High Performance Thermal Vias in LTCC Substrates

2013 ◽  
Vol 10 (1) ◽  
pp. 40-47 ◽  
Author(s):  
Aparna Aravelli ◽  
Singiresu S. Rao ◽  
Hari K. Adluru
Keyword(s):  
Heat Transfer ◽  
Design Optimization ◽  
High Performance ◽  
Cooling System ◽  
Optimization Technique ◽  
Optimization Theory ◽  
Thermal Design ◽  
Design Variables ◽  
Microchannel Cooling ◽  
Multiobjective Design

Increased heat generation in semiconductor devices for demanding applications leads to the investigation of highly efficient cooling solutions. Effective options for thermal management include passing of cooling liquid through the microchannel heat sink and using highly conductive materials. In the author's previous work, experimental and computational analyses were performed on LTCC substrates using embedded silver vias and silver columns forming microchannels. This novel technique of embedding silver vias along with forced convection using a coolant resulted in higher heat transfer rates. The present work investigates the design optimization of this cooling system (microheat exchanger) using systems optimization theory. A new multiobjective optimization problem was formulated for the heat transfer in the LTCC model using the log mean temperature difference (LMTD) method of heat exchangers. The goal is to maximize the total heat transferred and to minimize the coolant pumping power. Structural and thermal design variables are considered to meet the manufacturability and energy requirements. Pressure loss and volume of the silver metal are used as constraints. A hybrid optimization technique using sequential quadratic programming (SQP) and branch and bound method of integer programming has been developed to solve the microheat exchanger problem. The optimal design is presented and sensitivity analysis results are discussed.

Multi Objective Design Optimization of Helical Gear Pair Using Adaptive Parameter Harmony Search Algorithm

2015 ◽  
Vol 813-814 ◽  
pp. 1032-1036
Author(s):  
P. Sabarinath ◽  
M.R. Thansekhar ◽  
R. Jeganathan ◽  
R. Saravanan
Keyword(s):  
Design Optimization ◽  
Search Algorithm ◽  
Harmony Search ◽  
Helical Gear ◽  
Harmony Search Algorithm ◽  
Gear Pair ◽  
Multi Objective ◽  
Gear Design ◽  
Design Variables ◽  
Helical Gear Pair

Mechanical design engineers design products by selecting the best possible materials and geometries that satisfies the specific operational requirements of the design. It involves lot of creativity and aesthetics to make better designs. A gear design makes the designer to compromise many design variables so as to arrive the best performance of a gear set. The best possible way for multi variable, Multiobjective gear design is to try design optimization. For many complex engineering optimization problems multi objective design optimization methods are used to simplify the design problem. In this paper, multiobjective design of helical gear pair transmission with objective functions namely volume of the small and large helical gear and opposite number of overlap ratio is taken into account. The design variables considered are normal module, helix angle, gear width coefficient and teeth number of small helical gear. A recent meta-heuristic algorithm namely parameter adaptive harmony search algorithm is applied to solve this problem using the weighted sum approach. It is evident from the results that the proposed approach is performing better than other algorithms.

Design Optimization of Folding Solar Powered Autonomous Underwater Vehicles Using Origami Architecture

2017 ◽  
Author(s):  
Doe Young Hur ◽  
Edwin Peraza Hernandez ◽  
Edgar Galvan ◽  
Darren Hartl ◽  
Richard Malak
Keyword(s):  
Design Optimization ◽  
Design Process ◽  
Autonomous Underwater Vehicles ◽  
Electrical Power ◽  
Active Material ◽  
Pareto Frontier ◽  
Underwater Vehicles ◽  
Hydrodynamic Performance ◽  
Design Parameters ◽  
Solar Panels

Recently, the importance of design process with unknown parameter increased. On the other hand, the design of Autonomous Underwater Vehicles (AUVs) is a difficult challenge since it requires the consideration of various aspects such as mission range, controllability, energy source, and carrying capacity. A design process for novel type of AUV constructed using an origami-based structure that includes active material actuators and solar panels is proposed in this paper. To increase the efficiency in the three-dimensional shape modeling of the AUV, the shape of the outer surface is parameterized by a finite set of variables using shape functions. Here, the AUV should operate underwater via electrical power with the batteries being charged periodically using solar panels. The ability of the AUV to transport cargo such as instrumentation is also addressed. The design parameters include the total height and width of the AUV. As these dimensions of the AUV might vary in a non-preferential manner based on particular mission goals, these dimensions are considered as design parameters in a multi-objective optimization setting. The Predictive Parameterized Pareto Genetic Algorithm (P3GA) is selected as the optimization method to determine a Pareto frontier of design options with desired characteristics for a variety of missions for the AUV. The evaluation of each AUV design entails quantitative assessment of the origami fold pattern determined using a method developed by the authors and Computational Fluid Dynamics (CFD) analysis. The development of a design process that addresses the design optimization of the AUV considering its hydrodynamic performance and origami aspects is the main topic of this paper.

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