GA Optimization of Variable Angle Tow Composites in Buckling and Free Vibration Analysis through Layerwise Theory

In the current research, variable angle tow composites are used to improve the buckling and free vibration behavior of a structure. A one-dimensional (1D) Carrera Unified Formulation (CUF) is employed to determine the buckling loads and natural frequencies in Variable Angle Tow (VAT) square plates by taking advantage of the layerwise theory (LW). Subsequently, the Genetic Algorithm (GA) optimization method is applied to maximize the first critical buckling load and first natural frequency using the definition of linear fiber orientation angles. To show the power of the genetic algorithm for the VAT structure, a surrogate model using Response Surface (RS) method was used to demonstrate the convergence of the GA approach. The results showed the cost reduction for optimized VAT performance through GA optimization in combination with the 1D CUF procedure. Additionally, a Latin hypercube sampling (LHS) method with RS was used for buckling analysis. The capability of LHS sampling confirmed that it could be employed for the next stages of research along with GA.

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Optimization of the OOP Children Restraint System

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.155-156.386 ◽

2012 ◽

Vol 155-156 ◽

pp. 386-390

Author(s):

Zhong Hao Bai ◽

Jing Fei ◽

Wei Jie Ma

Keyword(s):

Genetic Algorithm ◽

Sampling Method ◽

Latin Hypercube Sampling ◽

Optimization Method ◽

Approximate Model ◽

Parameters Optimization ◽

Fe Model ◽

Restraint System ◽

Effective Protection ◽

Multi Body

Based on the study of SAE J1980-2008 and FMVSS 208, MADYMO7.1 is used to establish a Multi-body and FE model for two OOP children, and the statistic test is implemented to verify the accuracy of the model. The airbag parameters impacting OOP children greatly and their ranges are selected to determine the objective function. With the Latin Hypercube Sampling method, the Kring approximate model is constructed, and multi-island genetic algorithm is used in subsequently parameters optimization. The results show that the proposed optimization method can provide effective protection for 6-year-old OOP children.

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EFFECT OF FIBER ORIENTATION PATH ON THE BUCKLING, FREE VIBRATION, AND STATIC ANALYSES OF VARIABLE ANGLE TOW PANELS

Facta Universitatis Series Mechanical Engineering ◽

10.22190/fume200615026f ◽

2020 ◽

Vol 18 (2) ◽

pp. 165

Author(s):

Nasim Fallahi ◽

Andrea Viglietti ◽

Erasmo Carrera ◽

Alfonso Pagani ◽

Enrico Zappino

Keyword(s):

Free Vibration ◽

Fiber Orientation ◽

Mechanical Response ◽

Dimensional Model ◽

Computationally Efficient ◽

One Dimensional ◽

Variable Angle ◽

Carrera Unified Formulation ◽

Mechanical Performances ◽

Complex Phenomena

In this work, the effect of the fiber orientation on the mechanical response of variable angle tow (VAT) panels is investigated. A computationally efficient high-order one-dimensional model, derived under the framework of the Carrera unified formulation (CUF), is used. In detail, a layerwise approach is adopted to predict the complex phenomena that may appear in VAT panels. Static, free-vibration and buckling analyses are performed, considering several material properties, geometries, and boundary conditions, and the results are assessed with those obtained using existing approaches. Considering the findings of the comparative analysis, several best design practices are suggested to improve the mechanical performances of VAT panels.

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Multi-objective optimization of retaining wall using genetic algorithm

Environmental engineering ◽

10.37023/ee.8.1-2.8 ◽

2021 ◽

Vol 8 (1-2) ◽

pp. 58-65

Author(s):

Filip Dodigović ◽

Krešo Ivandić ◽

Jasmin Jug ◽

Krešimir Agnezović

Keyword(s):

Genetic Algorithm ◽

Optimization Problem ◽

Retaining Wall ◽

Silty Sand ◽

Embedment Depth ◽

Multi Objective Optimization ◽

Nsga Ii ◽

Multi Objective ◽

Definition Of ◽

The Cost

The paper investigates the possibility of applying the genetic algorithm NSGA-II to optimize a reinforced concrete retaining wall embedded in saturated silty sand. Multi-objective constrained optimization was performed to minimize the cost, while maximizing the overdesign factors (ODF) against sliding, overturning, and soil bearing resistance. For a given change in ground elevation of 5.0 m, the width of the foundation and the embedment depth were optimized. Comparing the algorithm's performance in the cases of two-objective and three objective optimizations showed that the number of objectives significantly affects its convergence rate. It was also found that the verification of the wall against the sliding yields a lower ODF value than verifications against overturning and soil bearing capacity. Because of that, it is possible to exclude them from the definition of optimization problem. The application of the NSGA-II algorithm has been demonstrated to be an effective tool for determining the set of optimal retaining wall designs.

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Optimal Eco-Driving Cycles for Conventional Vehicles Using a Genetic Algorithm

Energies ◽

10.3390/en13174362 ◽

2020 ◽

Vol 13 (17) ◽

pp. 4362

Author(s):

Subramaniam Saravana Sankar ◽

Yiqun Xia ◽

Julaluk Carmai ◽

Saiprasit Koetniyom

Keyword(s):

Genetic Algorithm ◽

Cost Function ◽

Fuel Consumption ◽

Optimization Algorithm ◽

Internal Combustion Engines ◽

Optimization Method ◽

Computational Time ◽

Weight Factor ◽

Driving Cycles ◽

The Cost

The goal of this work is to compute the eco-driving cycles for vehicles equipped with internal combustion engines by using a genetic algorithm (GA) with a focus on reducing energy consumption. The proposed GA-based optimization method uses an optimal control problem (OCP), which is framed considering both fuel consumption and driver comfort in the cost function formulation with the support of a tunable weight factor to enhance the overall performance of the algorithm. The results and functioning of the optimization algorithm are analyzed with several widely used standard driving cycles and a simulated real-world driving cycle. For the selected optimal weight factor, the simulation results show that an average reduction of eight percent in fuel consumption is achieved. The results of parallelization in computing the cost function indicates that the computational time required by the optimization algorithm is reduced based on the hardware used.

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