A Metamodeling Technique for Variable Geometry Trusses Design via Equivalent Parametric Macroelements

2009 ◽  
Vol 131 (10) ◽  
Author(s):  
Josu Aguirrebeitia ◽  
Carlos Angulo ◽  
Luis M. Macareno ◽  
Rafael Avilés
Keyword(s):  
Genetic Algorithms ◽  
Finite Elements ◽  
Elastic Energy ◽  
Computational Cost ◽  
Least Square ◽  
Optimization Process ◽  
Fe Model ◽  
Variable Geometry ◽  
Optimum Parameters ◽  
Metamodeling Technique

A metamodeling methodology is applied to reduce the large computational cost required in the design of variable geometry trusses (VGTs). Using this methodology, submodels of finite elements within a complete FE model of the VGT are substituted by groups of fewer elements called equivalent parametric macroelements (EPMs). The EPM optimum parameters are obtained using equivalence criteria based on elastic energy and inertial properties. The optimization process is performed using nonlinear least square minimization and genetic algorithms.

An Improved Kriging Assisted Multi-Objective Genetic Algorithm

2010 ◽  
Author(s):  
Mian Li
Keyword(s):  
Genetic Algorithms ◽  
Computational Cost ◽  
Population Based ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Kriging Metamodel ◽  
Metamodeling Technique ◽  
Important Challenge ◽  
Estimated Error ◽  
Adaptive Metamodeling

Although Genetic Algorithms (GAs) and Multi-Objective Genetic Algorithms (MOGAs) have been widely used in engineering design optimization, the important challenge still faced by researchers in using these methods is their high computational cost due to the population-based nature of these methods. For these problems it is important to devise MOGAs that can significantly reduce the number of simulation calls compared to a conventional MOGA. We present an improved kriging assisted MOGA, called Circled Kriging MOGA (CK-MOGA), in which kriging metamodels are embedded within the computation procedure of a traditional MOGA. In the proposed approach, the decision as to whether the original simulation or its kriging metamodel should be used for evaluating an individual is based on a new objective switch criterion and an adaptive metamodeling technique. The effect of the possible estimated error from the metamodel is mitigated by applying the new switch criterion. Three numerical and engineering examples with different degrees of difficulty are used to illustrate applicability of the proposed approach. The results show that, on the average, CK-MOGA outperforms both a conventional MOGA and our developed Kriging MOGA in terms of the number of simulation calls.

Computational Optimization of Arc Welding Parameters Using Coupled Genetic Algorithms and Finite Element Method

2013 ◽  
Author(s):  
Mohammad R. Islam ◽  
Jens Rohbrecht ◽  
Arjaan Buijk ◽  
Ehsan Namazi ◽  
Bing Liu ◽  
...  
Keyword(s):  
Finite Element ◽  
Genetic Algorithms ◽  
Arc Welding ◽  
Welding Process ◽  
Optimization Method ◽  
Optimization Techniques ◽  
Welding Parameters ◽  
Fe Model ◽  
Optimum Parameters ◽  
Arc Welding Process

An effective and rigorous approach to determine optimum welding process parameters is implementation of advanced computer aided engineering (CAE) tool that integrates efficient optimization techniques and numerical welding simulation. In this paper, an automated computational methodology to determine optimum arc welding process control parameters is proposed. It is a coupled Genetic Algorithms (GA) and Finite Element (FE) based optimization method where GA directly utilizes output responses of FE based welding simulations for iterative optimization. Effectiveness of the method has been demonstrated by predicting optimum parameters of a lap joint specimen of two thin steel plates for minimum distortion. Three dimensional FE model has been developed to simulate the arc welding process and validated by experimental results. Subsequently, it is used by GA as the evaluation model for optimization. The optimization results show that such a CAE based method can predict optimum parameters successfully with limited effort and cost.

An Improved Kriging-Assisted Multi-Objective Genetic Algorithm

2011 ◽  
Vol 133 (7) ◽  
Author(s):  
Mian Li
Keyword(s):  
Genetic Algorithms ◽  
Computational Cost ◽  
Population Based ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Kriging Metamodel ◽  
Metamodeling Technique ◽  
Important Challenge ◽  
Estimated Error ◽  
Adaptive Metamodeling

Although Genetic Algorithms (GAs) and Multi-Objective Genetic Algorithms (MOGAs) have been widely used in engineering design optimization, the important challenge still faced by researchers in using these methods is their high computational cost due to the population-based nature of these methods. For these problems it is important to devise MOGAs that can significantly reduce the number of simulation calls compared to a conventional MOGA. An improved kriging-assisted MOGA, called Circled Kriging MOGA (CK-MOGA), is presented in this paper, in which kriging metamodels are embedded within the computation procedure of a traditional MOGA. In the proposed approach, the decision as to whether the original simulation or its kriging metamodel should be used for evaluating an individual is based on a new and advanced objective switch criterion and an adaptive metamodeling technique. The effect of the possible estimated error from the metamodel is mitigated by applying the new switch criterion. Five numerical and engineering examples with different degrees of difficulty are used to illustrate applicability of the proposed approach, with the verification using different quality measures. The results show that, on the average, CK-MOGA outperforms both a conventional MOGA and a previously developed Kriging MOGA in terms of the number of simulation calls.

scholarly journals Application of Genetic Algorithm Elements to Modelling of Rotation Processes in Motion Transmission Including a Long Shaft

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 115
Author(s):  
Andriy Chaban ◽  
Marek Lis ◽  
Andrzej Szafraniec ◽  
Radoslaw Jedynak
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Fitness Function ◽  
Least Square ◽  
Distributed Parameter ◽  
Parameter Method ◽  
Analytical Mechanics ◽  
Parameter System ◽  
Rotational Systems ◽  
Elastic Elements

Genetic algorithms are used to parameter identification of the model of oscillatory processes in complicated motion transmission of electric drives containing long elastic shafts as systems of distributed mechanical parameters. Shaft equations are generated on the basis of a modified Hamilton–Ostrogradski principle, which serves as the foundation to analyse the lumped parameter system and distributed parameter system. They serve to compute basic functions of analytical mechanics of velocity continuum and rotational angles of shaft elements. It is demonstrated that the application of the distributed parameter method to multi-mass rotational systems, that contain long elastic elements and complicated control systems, is not always possible. The genetic algorithm is applied to determine the coefficients of approximation the system of Rotational Transmission with Elastic Shaft by equivalent differential equations. The fitness function is determined as least-square error. The obtained results confirm that application of the genetic algorithms allow one to replace the use of a complicated distributed parameter model of mechanical system by a considerably simpler model, and to eliminate sophisticated calculation procedures and identification of boundary conditions for wave motion equations of long elastic elements.

scholarly journals Conceptualizing the Moderating Role of CEO Power and Ownership Concentration in the Relationship between Audit Committee and Firm Performance: Empirical Evidence from Pakistan

2021 ◽  
Vol 13 (11) ◽  
pp. 6329
Author(s):  
Sohail Ahmad Javeed ◽  
Tze San Ong ◽  
Rashid Latief ◽  
Haslinah Muhamad ◽  
Wei Ni Soh
Keyword(s):  
Firm Performance ◽  
Audit Committee ◽  
Developing Economies ◽  
Generalized Method Of Moments ◽  
Positive Association ◽  
Least Square ◽  
Ownership Concentration ◽  
Fe Model ◽  
Ceo Power ◽  
Moderating Factors

Firms in developing economies generally find ways to enhance their reputation and growth in the international market. In this context, an Audit Committee (AC) is composed of multiple skilled members that control and monitor auditing activities and present a transparent image of their firm, which automatically attracts investors and builds investor confidence. Therefore, this study used CEO power and ownership concentration as moderating factors to examine the connection between AC and firm performance. For this purpose, this study used the data of Pakistani manufacturing firms for the period 2008 to 2018 and applied the Ordinary Least Square (OLS) method, the Fixed Effect (FE) model, and the Generalized Method of Moments (GMM). To check the robustness of the results, this study used a Feasible Generalized Least Square (FGLS) model. The findings of this study contended that AC and firm performance have a positive association with each other. Moreover, the findings revealed that CEO power positively influenced firm performance. Furthermore, lower ownership concentration is a valuable approach to maximize a firm’s performance. Importantly, the outcomes concluded that AC and firm performance have a positive connection with the moderating effects of CEO power. Moreover, AC and firm performance also have a positive association with the moderating effect of ownership concentration.

A two-stage design optimization framework for multifield origami-inspired structures

2021 ◽  
pp. 1045389X2110116
Author(s):  
Wei Zhang ◽  
Saad Ahmed ◽  
Jonathan Hong ◽  
Zoubeida Ounaies ◽  
Mary Frecker
Keyword(s):  
Case Studies ◽  
Computing Time ◽  
Computational Cost ◽  
High Fidelity ◽  
Fe Model ◽  
Two Stage ◽  
Baseline Design ◽  
Optimization Framework ◽  
Highly Nonlinear ◽  
Stage 1

Different types of active materials have been used to actuate origami-inspired self-folding structures. To model the highly nonlinear deformation and material responses, as well as the coupled field equations and boundary conditions of such structures, high-fidelity models such as finite element (FE) models are needed but usually computationally expensive, which makes optimization intractable. In this paper, a computationally efficient two-stage optimization framework is developed as a systematic method for the multi-objective designs of such multifield self-folding structures where the deformations are concentrated in crease-like areas, active and passive materials are assumed to behave linearly, and low- and high-fidelity models of the structures can be developed. In Stage 1, low-fidelity models are used to determine the topology of the structure. At the end of Stage 1, a distance measure [Formula: see text] is applied as the metric to determine the best design, which then serves as the baseline design in Stage 2. In Stage 2, designs are further optimized from the baseline design with greatly reduced computing time compared to a full FEA-based topology optimization. The design framework is first described in a general formulation. To demonstrate its efficacy, this framework is implemented in two case studies, namely, a three-finger soft gripper actuated using a PVDF-based terpolymer, and a 3D multifield example actuated using both the terpolymer and a magneto-active elastomer, where the key steps are elaborated in detail, including the variable filter, metrics to select the best design, determination of design domains, and material conversion methods from low- to high-fidelity models. In this paper, analytical models and rigid body dynamic models are developed as the low-fidelity models for the terpolymer- and MAE-based actuations, respectively, and the FE model of the MAE-based actuation is generalized from previous work. Additional generalizable techniques to further reduce the computational cost are elaborated. As a result, designs with better overall performance than the baseline design were achieved at the end of Stage 2 with computing times of 15 days for the gripper and 9 days for the multifield example, which would rather be over 3 and 2 months for full FEA-based optimizations, respectively. Tradeoffs between the competing design objectives were achieved. In both case studies, the efficacy and computational efficiency of the two-stage optimization framework are successfully demonstrated.

scholarly journals Numerical Modeling and Safety Design for Lithium-Ion Vehicle Battery Modules Subject to Crush Loading

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 118
Author(s):  
Feng Zhu ◽  
Runzhou Zhou ◽  
David J. Sypeck
Keyword(s):  
Failure Modes ◽  
Computational Study ◽  
Short Circuit ◽  
Computational Cost ◽  
Lithium Ion ◽  
Simplified Model ◽  
Homogeneous Material ◽  
Fe Model ◽  
Safety Design ◽  
Battery Module

In this work, a computational study was carried out to simulate crushing tests on lithium-ion vehicle battery modules. The tests were performed on commercial battery modules subject to wedge cutting at low speeds. Based on loading and boundary conditions in the tests, finite element (FE) models were developed using explicit FEA code LS-DYNA. The model predictions demonstrated a good agreement in terms of structural failure modes and force–displacement responses at both cell and module levels. The model was extended to study additional loading conditions such as indentation by a cylinder and a rectangular block. The effect of other module components such as the cover and cooling plates was analyzed, and the results have the potential for improving battery module safety design. Based on the detailed FE model, to reduce its computational cost, a simplified model was developed by representing the battery module with a homogeneous material law. Then, all three scenarios were simulated, and the results show that this simplified model can reasonably predict the short circuit initiation of the battery module.

scholarly journals Material parameter identification using finite elements with time-adaptive higher-order time integration and experimental full-field strain information

2021 ◽  
Author(s):  
Stefan Hartmann ◽  
Rose Rogin Gilbert
Keyword(s):  
Experimental Data ◽  
Finite Elements ◽  
Parameter Identification ◽  
Material Parameter ◽  
Measurement Data ◽  
Least Square ◽  
Image Correlation ◽  
Field Strain ◽  
Material Parameter Identification ◽  
Full Field

AbstractIn this article, we follow a thorough matrix presentation of material parameter identification using a least-square approach, where the model is given by non-linear finite elements, and the experimental data is provided by both force data as well as full-field strain measurement data based on digital image correlation. First, the rigorous concept of semi-discretization for the direct problem is chosen, where—in the first step—the spatial discretization yields a large system of differential-algebraic equation (DAE-system). This is solved using a time-adaptive, high-order, singly diagonally-implicit Runge–Kutta method. Second, to study the fully analytical versus fully numerical determination of the sensitivities, required in a gradient-based optimization scheme, the force determination using the Lagrange-multiplier method and the strain computation must be provided explicitly. The consideration of the strains is necessary to circumvent the influence of rigid body motions occurring in the experimental data. This is done by applying an external strain determination tool which is based on the nodal displacements of the finite element program. Third, we apply the concept of local identifiability on the entire parameter identification procedure and show its influence on the choice of the parameters of the rate-type constitutive model. As a test example, a finite strain viscoelasticity model and biaxial tensile tests applied to a rubber-like material are chosen.

scholarly journals Established Numerical Techniques for the Structural Analysis of a Regional Aircraft Landing Gear

2018 ◽  
Vol 2018 ◽  
pp. 1-21 ◽  
Author(s):  
F. Caputo ◽  
A. De Luca ◽  
A. Greco ◽  
A. Marro ◽  
A. Apicella ◽  
...  
Keyword(s):  
Finite Elements ◽  
Numerical Models ◽  
Three Dimensional ◽  
Landing Gear ◽  
Point Of View ◽  
Fe Model ◽  
One Dimensional ◽  
Reaction Forces ◽  
Local Technique ◽  
Stick Model

Usually during the design of landing gear, simplified Finite Element (FE) models, based on one-dimensional finite elements (stick model), are used to investigate the in-service reaction forces involving each subcomponent. After that, the design of such subcomponent is carried out through detailed Global/Local FE analyses where, once at time, each component, modelled with three-dimensional finite elements, is assembled into a one-dimensional finite elements based FE model, representing the whole landing gear under the investigated loading conditions. Moreover, the landing gears are usually investigated also under a kinematic point of view, through the multibody (MB) methods, which allow achieving the reaction forces involving each subcomponent in a very short time. However, simplified stick (FE) and MB models introduce several approximations, providing results far from the real behaviour of the landing gear. Therefore, the first goal of this paper consists of assessing the effectiveness of such approaches against a 3D full-FE model. Three numerical models of the main landing gear of a regional airliner have been developed, according to MB, “stick,” and 3D full-FE methods, respectively. The former has been developed by means of ADAMS® software, the other two by means of NASTRAN® software. Once this assessment phase has been carried out, also the Global/Local technique has verified with regard to the results achieved by the 3D full-FE model. Finally, the dynamic behaviour of the landing gear has been investigated both numerically and experimentally. In particular, Magnaghi Aeronautica S.p.A. Company performed the experimental test, consisting of a drop test according to EASA CS 25 regulations. Concerning the 3D full-FE investigation, the analysis has been simulated by means of Ls-Dyna® software. A good level of accuracy has been achieved by all the developed numerical methods.

Microstructure Evolution and Bond Formation at the Contact Interface During Ultrasonic Consolidation Process

2014 ◽  
Author(s):  
Song Zhang ◽  
Lili Zheng ◽  
Hui Zhang
Keyword(s):  
Finite Elements ◽  
Cellular Automata ◽  
Applied Load ◽  
Oscillation Amplitude ◽  
Travel Speed ◽  
Process Conditions ◽  
Fe Model ◽  
Consolidation Process ◽  
Ultrasonic Consolidation ◽  
Al 7075

In this paper a cellular automata-finite elements (CAFE) model is developed by combining traditional finite elements (FE) model and cellular automata (CA) model. The microstructure under different process conditions of ultrasonic consolidation (UC) process for Al 7075 is studied. It is found that higher energy input process conditions (higher applied load and sonotrode oscillation amplitude, lower sonotrode travel speed) will lead to a higher value of dynamic recrystallization (DRX) fraction for UC deposited foils. The mean dislocation density of the UC deposited material will increase with the applied load while it decreases with the increase of sonotrode travel speed and oscillation amplitude.

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