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.

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.

A Fast 3D Inverse Design Based Multi-Objective Optimization Strategy for Design of Pumps

2009 ◽  
Author(s):  
M. Zangeneh ◽  
K. Daneshkhah
Keyword(s):  
Pareto Front ◽  
Design Method ◽  
Leading Edge ◽  
Inverse Design ◽  
Design Parameters ◽  
Optimization Strategy ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Pump Stage ◽  
Meridional Shape

A methodology for designing pumps to meet multi-objective design criteria is presented. The method combines a 3D inviscid inverse design method with a multi-objective genetic algorithm to design pumps which meet various aerodynamic and geometrical requirements. The parameterization of the blade shape through the blade loading enables 3D optimization with very few design parameters. A generic pump stage is used to demonstrate the proposed methodology. The main design objectives are improving cavitation performance and reducing leading edge sweep. The optimization is performed subject to certain constraints on Euler head, throat area, thickness and meridional shape so that the resulting pump can meet both design and off-design conditions. A Pareto Front is generated for the two objective functions and 3 different configurations on the Pareto front are selected for detailed study by 3D RANS code. The CFD results confirm the main outcomes of the optimization process.

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.

Analysis and Optimization of a Shape Memory Alloy-Based Self-Folding Sheet Considering Material Uncertainties

2015 ◽  
Author(s):  
Edwin A. Peraza Hernandez ◽  
Darren J. Hartl ◽  
Richard J. Malak ◽  
Dimitris C. Lagoudas
Keyword(s):  
Shape Memory Alloy ◽  
Shape Memory ◽  
Material Properties ◽  
Pareto Frontier ◽  
Computational Cost ◽  
Beam Theory ◽  
Middle Layer ◽  
Numerical Approach ◽  
Design Parameters ◽  
Material Uncertainties

Origami-inspired active structures have important characteristics such as reconfigurability and the ability to adopt compact flat forms for storage. A self-folding shape memory alloy (SMA)-based laminated sheet is considered in this work wherein SMA wire meshes comprise the top and bottom layers and a thermally insulating compliant elastomer comprises the middle layer. Uncertainty in various parameters (e.g. material properties) may affect the performance of the sheet, which is explored here. Different modeling approaches are studied in order to compare their accuracy and computational cost. A numerical approach based on the Euler-Bernoulli beam theory is selected due to its accuracy when compared to higher fidelity finite element simulations and its low computational cost, necessary to perform a large number of design evaluations as required for uncertainty analysis. Optimization is performed considering uncertainty in the material properties. Failure probabilities under mechanical constraints and expected values of fold curvature and blocking moment are considered during optimization of the self-folding sheet. The multiobjective genetic algorithm for technology characterization P3GA is used to obtain the Pareto dominant designs. Most designs forming the Pareto frontier have the same values for certain design parameters such as the distance between the wires in the SMA meshes non-dimensionalized by SMA wire thickness, elastomer layer thickness non-dimensionalized by SMA wire thickness, and applied temperature. The design parameter deciding the trade-off between fold curvature and blocking moment is found to be the SMA wire thickness.

Modeling and Experimental Validation of Shape Memory Alloy Bending Actuators

2012 ◽  
Author(s):  
Casey D. Haigh ◽  
John H. Crews ◽  
Shiquan Wang ◽  
Gregory D. Buckner
Keyword(s):  
Shape Memory Alloy ◽  
Shape Memory ◽  
Performance Optimization ◽  
Experimental Validation ◽  
Design Tool ◽  
Design Parameters ◽  
Bending Performance ◽  
Critical Design ◽  
Section Modulus ◽  
Composite Actuator

In this paper, we develop a computational model that can be used to investigate and optimize the performance of shape memory alloy (SMA) bending actuators. These actuators (approximately 7–21 mm in length) consist of curved SMA wires embedded within elastic sleeves and are intended for positioning and anchoring robotic catheters 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 heat-resistant silicone with varying section modulus). The elastic member preloads the SMA (or produces a stress-induced phase transformation), reducing the equilibrium curvature of the composite actuator. Temperature-induced phase transformations in the SMA (via Joule heating) enable strain recovery and increased bending (increased curvature) in the composite actuator. The homogenized energy framework is utilized to model the behavior of this composite actuator, and the effects of several critical design parameters (initial SMA curvature and section modulus of the elastic member) on the deactivated and activated curvatures are investigated. Experimental results validate the model, enabling its use as a design tool for bending performance optimization.

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.

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