Vehicle Crashworthiness Design Via a Surrogate Model Ensemble and a Co-Evolutionary Genetic Algorithm

2005 ◽  
Author(s):  
Karim Hamza ◽  
Kazuhiro Saitou
Keyword(s):  
Genetic Algorithm ◽  
Finite Element ◽  
Surrogate Model ◽  
High Performance ◽  
Surrogate Models ◽  
Finite Element Analyses ◽  
Frontal Crash ◽  
Full Vehicle ◽  
Crashworthiness Design ◽  
Vehicle Crashworthiness

This paper presents a new method for designing vehicle structures for crashworthiness using surrogate models and a genetic algorithm. Inspired by the classifier ensemble approaches in pattern recognition, the method estimates the crash performance of a candidate design based on an ensemble of surrogate models constructed from the different sets of samples of finite element analyses. Multiple sub-populations of candidate designs are evolved, in a co-evolutionary fashion, to minimize the different aggregates of the outputs of the surrogate models in the ensemble, as well as the raw output of each surrogate. With the same sample size of finite element analyses, it is expected the method can provide wider ranges potentially high-performance designs than the conventional methods that employ a single surrogate model, by effectively compensating the errors associated with individual surrogate models. Two case studies on simplified and full vehicle models subject to full-overlap frontal crash conditions are presented for demonstration.

Design synthesis of non-symmetrically loaded high-performance disc brakes Part 1

2001 ◽  
Vol 215 (2) ◽  
pp. 101-109 ◽  
Author(s):  
M Tirovic ◽  
G Ali
Keyword(s):  
Finite Element ◽  
Design Process ◽  
Thermal Effects ◽  
High Performance ◽  
Finite Element Analyses ◽  
Design Synthesis ◽  
Critical Design ◽  
Disc Brakes ◽  
Design Requirements ◽  
Manufacturing Methods

Wheel-mounted disc brakes are exposed to severe non-symmetrical mechanical and thermal loads. The paper describes the design process for two high-performance, hub-mounted discs of different size and duty. The development has resulted in two very successful but fundamentally different hub designs and manufacturing methods. Initially, finite element analyses used in the design optimization were mainly concentrated on bulk thermal effects. Recently, in order further to improve the design process, analyses have included macro thermal effects, providing valuable results, particularly related to the prediction of disc permanent coning, one of the most critical design requirements.

Using Kriging Surrogate Models to Predict the Vibration Responses of a Submerged Riser

2020 ◽  
Author(s):  
Marcelo Damasceno ◽  
Hélio Ribeiro Neto ◽  
Tatiane Costa ◽  
Aldemir Cavalini Júnior ◽  
Ludimar Aguiar ◽  
...  
Keyword(s):  
Surrogate Model ◽  
High Performance ◽  
Computational Cost ◽  
Offshore Structures ◽  
Surrogate Models ◽  
Computational Time ◽  
Kriging Surrogate Model ◽  
Efficient Manner ◽  
Modeling Tools ◽  
Fluid Structure

Abstract Fluid-structure interaction modeling tools based on computational fluid dynamics (CFD) produce interesting results that can be used in the design of submerged structures. However, the computational cost of simulations associated with the design of submerged offshore structures is high. There are no high-performance platforms devoted to the analysis and optimization of these structures using CFD techniques. In this context, this work aims to present a computational tool dedicated to the construction of Kriging surrogate models in order to represent the time domain force responses of submerged risers. The force responses obtained from high-cost computational simulations are used as outputs for training and validated the surrogate models. In this case, different excitations are applied in the riser aiming at evaluating the representativeness of the obtained Kriging surrogate model. A similar investigation is performed by changing the number of samples and the total time used for training purposes. The present methodology can be used to perform the dynamic analysis in different submerged structures with a low computational cost. Instead of solving the motion equation associated with the fluid-structure system, a Kriging surrogate model is used. A significant reduction in computational time is expected, which allows the realization of different analyses and optimization procedures in a fast and efficient manner for the design of this type of structure.

A Computational Efficient Method to Assess the Sensitivity of Finite-Element Models: An Illustration With the Hemipelvis

2016 ◽  
Vol 138 (12) ◽  
Author(s):  
Dermot O'Rourke ◽  
Saulo Martelli ◽  
Murk Bottema ◽  
Mark Taylor
Keyword(s):  
Finite Element ◽  
Factorial Design ◽  
Surrogate Model ◽  
Computational Cost ◽  
Bone Geometry ◽  
Surrogate Models ◽  
Full Factorial Design ◽  
Fe Model ◽  
Kriging Surrogate Model ◽  
Full Factorial

Assessing the sensitivity of a finite-element (FE) model to uncertainties in geometric parameters and material properties is a fundamental step in understanding the reliability of model predictions. However, the computational cost of individual simulations and the large number of required models limits comprehensive quantification of model sensitivity. To quickly assess the sensitivity of an FE model, we built linear and Kriging surrogate models of an FE model of the intact hemipelvis. The percentage of the total sum of squares (%TSS) was used to determine the most influential input parameters and their possible interactions on the median, 95th percentile and maximum equivalent strains. We assessed the surrogate models by comparing their predictions to those of a full factorial design of FE simulations. The Kriging surrogate model accurately predicted all output metrics based on a training set of 30 analyses (R2 = 0.99). There was good agreement between the Kriging surrogate model and the full factorial design in determining the most influential input parameters and interactions. For the median, 95th percentile and maximum equivalent strain, the bone geometry (60%, 52%, and 76%, respectively) was the most influential input parameter. The interactions between bone geometry and cancellous bone modulus (13%) and bone geometry and cortical bone thickness (7%) were also influential terms on the output metrics. This study demonstrates a method with a low time and computational cost to quantify the sensitivity of an FE model. It can be applied to FE models in computational orthopaedic biomechanics in order to understand the reliability of predictions.

scholarly journals Model-Based Estimation of the Strength of Laser-Based Plastic-Metal Joints Using Finite Element Microstructure Models and Regression Models

Materials ◽  
2021 ◽  
Vol 14 (17) ◽  
pp. 5004
Author(s):  
Julius Moritz Berges ◽  
Kira van der Straeten ◽  
Georg Jacobs ◽  
Jörg Berroth ◽  
Arnold Gillner
Keyword(s):  
Finite Element ◽  
Surrogate Model ◽  
Joint Strength ◽  
Mechanical Design ◽  
Surrogate Models ◽  
Fe Model ◽  
Model Based ◽  
Plastic Metal ◽  
Microstructure Parameters ◽  
Micro Level

Plastic-metal joints with a laser-structured metal surface have a high potential to reduce cost and weight compared to conventional joining technologies. However, their application is currently inhibited due to the absence of simulation methods and models for mechanical design. Thus, this paper presents a model-based approach for the strength estimation of laser-based plastic-metal joints. The approach aims to provide a methodology for the efficient creation of surrogate models, which can capture the influence of the microstructure parameters on the joint strength. A parametrization rule for the shape of the microstructure is developed using microsection analysis. Then, a parameterized finite element (FE) model of the joining zone on micro level is developed. Different statistical plans and model fits are tested, and the predicted strength of the FE model and the surrogate models are compared against experiments for different microstructure geometries. The joint strength is predicted by the FE model with a 3.7% error. Surrogate modelling using half-factorial experimental design and linear regression shows the best accuracy (6.2% error). This surrogate model can be efficiently created as only 16 samples are required. Furthermore, the surrogate model is provided as an equation, offering the designer a convenient tool to estimate parameter sensitivities.

Behaviors of Composite Circular Struts in Space Environment: Analyses and Experiments

2004 ◽  
Vol 261-263 ◽  
pp. 797-802
Author(s):  
Chul Kim ◽  
Jong Heun Lee ◽  
J.H. Kim ◽  
Hoon Sang Choi
Keyword(s):  
Genetic Algorithm ◽  
Finite Element ◽  
Composite Materials ◽  
Optimal Design ◽  
Thermal Analyses ◽  
Space Environment ◽  
Stacking Sequence ◽  
Finite Element Analyses ◽  
Axial Deformation ◽  
Generic Algorithms

The optimal stacking sequence and wall thickness of the composite strut tubes were determined to minimize thermal strains during orbital operation using generic algorithms and finite element analyses. From the results of previous thermal analyses of composite struts with various stacking sequences, the axial deformation is a matter of prime importance. For this reason, the optimization focuses to minimize the axial strains. The balanced and symmetric stacking sequences are used to minimize the radial and the twisting deformations. The genetic algorithm is known to be very effective for the discrete optimization such as stacking sequences of composite materials. As a result, the thermal deformations of the strut with an optimal stacking sequence are almost zero. The optimal strut tube consists of 6 plies and the weight of a composite strut is 22.4% that of aluminum strut. Finite element analyses showed that the optimal design of composite strut tubes withstood combined launch loads without buckling and failure. To validate the analyses, four composite struts were fabricated and their thermal strains were measured under the temperature increase of 100°C. The thermal and vibration experiments showed excellent correlations with analytical results.

scholarly journals Comparison of a Response Surface Method and Artificial Neural Network in Predicting the Aerodynamic Performance of a Wind Turbine Airfoil and Its Optimization

2020 ◽  
Vol 10 (18) ◽  
pp. 6277 ◽  
Author(s):  
Sahuck Oh
Keyword(s):  
Wind Turbine ◽  
Performance Improvement ◽  
Surrogate Model ◽  
Prediction Accuracy ◽  
High Performance ◽  
Aerodynamic Performance ◽  
Surrogate Models ◽  
Surface Method ◽  
Wind Turbine Airfoil ◽  
High Level

To find the optimal design for an engineering object, thousands of (or even more) simulations should be implemented to obtain the outcome data for the variously designed objects. However, repeating simulations this many times is impossible because a typical simulation is a computationally expensive task. Instead of conducting all the required simulations, a more efficient way is predicting the outcome from the approximation model, called the surrogate model. The response surface method (RSM) with polynomials and artificial neural network (ANN) are the most prominent methods in constructing a surrogate model in the engineering fields. In this study, the prediction accuracy of the surrogate models computed by using an RSM and ANN is compared with several datasets showing different complexities. This comparison is investigated by constructing the surrogate models in predicting aerodynamic performance of a wind turbine airfoil. In the current paper, it is verified that the prediction accuracy of the ANN-computed surrogate model is higher than the RSM-computed one when the datasets have a high level of complexity, but the opposite phenomenon is observed if the datasets have a low level of complexity. When the surrogate models with different accuracies are used to enhance the performance of a wind turbine airfoil, the surrogate model with a high level of accuracy produces the optimal design, showing a high performance improvement. The current study is expected to give guidance on how to properly choose between an RSM and ANN to construct a highly accurate surrogate model that can help in finding a design with a high performance improvement during the optimization process.

Fixture Layout Design and Optimization of Sheet Metal Assembly Based on Genetic Algorithm for Optimization Toolbox

2016 ◽  
Author(s):  
Yanfeng Xing ◽  
Fang Wang ◽  
Qing Ji
Keyword(s):  
Genetic Algorithm ◽  
Finite Element ◽  
Sheet Metal ◽  
Layout Optimization ◽  
Finite Element Analyses ◽  
Design And Optimization ◽  
Fixture Layout ◽  
Fixture Layout Optimization ◽  
Dimensional Variation

Fixture layout can affect deformation and dimensional variation of sheet metal assemblies. Conventionally, the assembly dimensions are simulated using a large number of finite element analyses, and fixture layout optimization needs significant user intervention and unaffordable iterations of finite element analyses. This paper therefore proposes a fully automated and efficient method of fixture layout optimization based on the combination of 3DCS simulation (for dimensional analyses) and GAOT, a genetic algorithm in optimization toolbox in MATLAB. The locating points, the key elements of a fixture layout, are selected from a much smaller candidate pool thanks to our proposed manufacturing constraints based filtering methods and thus the computational efficiency is greatly improved. Since MATLAB macro commands of 3DCS have been developed to calculate assembly dimensions, the optimization process is fully automated. A case study of inner hood is applied to demonstrate the proposed method. The results show that the proposed method is suitable for generating the optimal fixture layout with excellent efficiency for engineering applications.

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