Crashworthiness optimization of circular tubes with functionally-graded thickness

2016 ◽  
Vol 33 (5) ◽  
pp. 1560-1585 ◽  
Author(s):  
Adil Baykasoglu ◽  
Cengiz Baykasoglu
Keyword(s):  
Gene Expression Programming ◽  
Functionally Graded ◽  
Least Square ◽  
Design Parameters ◽  
Optimization Approach ◽  
Content Type ◽  
Circular Tubes ◽  
Multi Objective ◽  
Crashworthiness Optimization ◽  
Graded Thickness

Purpose – The purpose of this paper is to develop a new multi-objective optimization procedure for crashworthiness optimization of thin-walled structures especially circular tubes with functionally graded thickness. Design/methodology/approach – The proposed optimization approach is based on finite element analyses for construction of sample design space and verification; gene-expression programming (GEP) for generating algebraic equations (meta-models) to compute objective functions values (peak crash force and specific energy absorption) for design parameters; multi-objective genetic algorithms for generating design parameters alternatives and determining optimal combination of them. The authors have also utilized linear and non-linear least square regression meta-models as a benchmark for GEP. Findings – It is shown that the proposed approach is able to generate Pareto optimal designs which are in a very good agreement with the actual results. Originality/value – The paper presents the application of a genetic programming-based method, namely, GEP first time in the literature. The proposed approach can be used to all kinds of related crashworthiness problems.

Multiple objective crashworthiness optimization of circular tubes with functionally graded thickness via artificial neural networks and genetic algorithms

2016 ◽  
Vol 231 (11) ◽  
pp. 2005-2016 ◽  
Author(s):  
Adil Baykasoğlu ◽  
Cengiz Baykasoğlu
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Artificial Neural Networks ◽  
Functionally Graded ◽  
Design Parameters ◽  
Multiple Objective ◽  
Circular Tubes ◽  
Crashworthiness Optimization ◽  
Artificial Neural ◽  
Graded Thickness

The objective of this paper is to develop a multiple objective optimization procedure for crashworthiness optimization of circular tubes having functionally graded thickness. The proposed optimization approach is based on finite element analyses for construction of sample design space and verification; artificial neural networks for predicting objective functions values (peak crash force and specific energy absorption) for design parameters; and genetic algorithms for generating design parameters alternatives and determining optimal combination of them. The proposed approach seaminglesly integrates artificial neural networks and genetic algorithms. Artificial neural network acts as an objective function evaluator within the multiple objective genetic algorithms. We have shown that the proposed approach is able to generate Pareto optimal designs which are in a very good agreement with the finite element results.

Multi-objective structural optimization of a wind turbine blade using NSGA-II algorithm and FSI

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ramazan Özkan ◽  
Mustafa Serdar Genç
Keyword(s):  
Structural Optimization ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Wind Turbine Blade ◽  
Design Parameters ◽  
Aerodynamic Optimization ◽  
Nsga Ii ◽  
Content Type ◽  
Multi Objective ◽  
Optimization Study

Purpose Wind turbines are one of the best candidates to solve the problem of increasing energy demand in the world. The aim of this paper is to apply a multi-objective structural optimization study to a Phase II wind turbine blade produced by the National Renewable Energy Laboratory to obtain a more efficient small-scale wind turbine. Design/methodology/approach To solve this structural optimization problem, a new Non-Dominated Sorting Genetic Algorithm (NSGA-II) was performed. In the optimization study, the objective function was on minimization of mass and cost of the blade, and design parameters were composite material type and spar cap layer number. Design constraints were deformation, strain, stress, natural frequency and failure criteria. ANSYS Composite PrepPost (ACP) module was used to model the composite materials of the blade. Moreover, fluid–structure interaction (FSI) model in ANSYS was used to carry out flow and structural analysis on the blade. Findings As a result, a new original blade was designed using the multi-objective structural optimization study which has been adapted for aerodynamic optimization, the NSGA-II algorithm and FSI. The mass of three selected optimized blades using carbon composite decreased as much as 6.6%, 11.9% and 14.3%, respectively, while their costs increased by 23.1%, 29.9% and 38.3%. This multi-objective structural optimization-based study indicates that the composite configuration of the blade could be altered to reach the desired weight and cost for production. Originality/value ACP module is a novel and advanced composite modeling technique. This study is a novel study to present the NSGA-II algorithm, which has been adapted for aerodynamic optimization, together with the FSI. Unlike other studies, complex composite layup, fiber directions and layer orientations were defined by using the ACP module, and the composite blade analyzed both aerodynamic pressure and structural design using ACP and FSI modules together.

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.

Multi-objective optimization of hydro-viscous flexible drive for dynamic characteristics using genetic algorithm

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jianzhong Cui ◽  
Hu Li ◽  
Dong Zhang ◽  
Yawen Xu ◽  
Fangwei Xie
Keyword(s):  
Genetic Algorithm ◽  
Dynamic Characteristics ◽  
Transmission Efficiency ◽  
Outer Radius ◽  
Transmission Model ◽  
Design Parameters ◽  
Multi Objective Optimization ◽  
Content Type ◽  
Multi Objective ◽  
Dynamic Transmission Model

Purpose The purpose of this study is to investigate the flexible dynamic characteristics about hydro-viscous drive providing meaningful insights into the credible speed-regulating behavior during the soft-start. Design/methodology/approach A comprehensive dynamic transmission model is proposed to investigate the effects of key parameters on the dynamic characteristics. To achieve a trade-off between the transmission efficiency and time proportion of hydrodynamic and mixed lubrication, a multi-objective optimization of friction pair system by genetic algorithm is presented to obtain the optimal combination of design parameters. Findings Decreasing the engagement pressure or the ratio of inner and outer radius, increasing the lubricating oil viscosity or the outer radius will result in the increase of time proportion of hydrodynamic and mixed lubrication, as well as the transmission efficiency and its maximum value. After optimization, main dynamic parameters including the oil film thickness, angular velocity of the driven disk, viscous torque and total torque show remarkable flexible transmission characteristics. Originality/value Both the dynamic transmission model and multi-objective optimization model are established to analyze the effects of main design parameters on the dynamic characteristics of hydro-viscous flexible drive.

scholarly journals A robust multi response surface approach for optimization of multistage processes

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Amir Moslemi ◽  
Mahmood Shafiee
Keyword(s):  
Robust Optimization ◽  
Response Surface ◽  
Numerical Study ◽  
Ordinary Least Squares ◽  
Response Surfaces ◽  
Estimation Methods ◽  
Design Parameters ◽  
Optimization Approach ◽  
Content Type

PurposeIn a multistage process, the final quality in the last stage not only depends on the quality of the task performed in that stage but is also dependent on the quality of the products and services in intermediate stages as well as the design parameters in each stage. One of the most efficient statistical approaches used to model the multistage problems is the response surface method (RSM). However, it is necessary to optimize each response in all stages so to achieve the best solution for the whole problem. Robust optimization can produce very accurate solutions in this case.Design/methodology/approachIn order to model a multistage problem, the RSM is often used by the researchers. A classical approach to estimate response surfaces is the ordinary least squares (OLS) method. However, this method is very sensitive to outliers. To overcome this drawback, some robust estimation methods have been presented in the literature. In optimization phase, the global criterion (GC) method is used to optimize the response surfaces estimated by the robust approach in a multistage problem.FindingsThe results of a numerical study show that our proposed robust optimization approach, considering both the sum of square error (SSE) index in model estimation and also GC index in optimization phase, will perform better than the classical full information maximum likelihood (FIML) estimation method.Originality/valueTo the best of the authors’ knowledge, there are few papers focusing on quality-oriented designs in the multistage problem by means of RSM. Development of robust approaches for the response surface estimation and also optimization of the estimated response surfaces are the main novelties in this study. The proposed approach will produce more robust and accurate solutions for multistage problems rather than classical approaches.

Experimental verification of textured mechanical seal designed using multi-objective optimization

2019 ◽  
Vol 71 (6) ◽  
pp. 766-771 ◽  
Author(s):  
Xiuying Wang ◽  
Michael Khonsari ◽  
Siyuan Li ◽  
Qingwen Dai ◽  
Xiaolei Wang
Keyword(s):  
Leakage Rate ◽  
Free Form ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Mechanical Seals ◽  
Content Type ◽  
Multi Objective ◽  
Load Carrying ◽  
And Control ◽  
Good Agreement

Purpose This study aims to simultaneously enhance the load-carrying capacity and control the leakage rate of mechanical seals by optimizing the texture shape. Design/methodology/approach A multi-objective optimization approach is implemented to determine the optimal “free-form” textures and optimal circular dimples. Experiments are conducted to validate the simulation results. Findings The experimental coefficient of friction (COF) and leakage rate are in good agreement with the calculated results. In addition, the optimal “free-form” texture shows a lower COF and a lower leakage in most cases. Originality/value This work provides a method to optimize the surface texture for a better combination performance of mechanical seals.

A Parametric Optimization Approach of an Induction Heating System for Energy Consumption Reduction

2017 ◽  
Author(s):  
Paolo Cicconi ◽  
Anna Costanza Russo ◽  
Mariorosario Prist ◽  
Francesco Ferracuti ◽  
Michele Germani ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Induction Heating ◽  
Heating System ◽  
High Energy ◽  
Design Parameters ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Molding Process ◽  
Multi Objective ◽  
Induction Heating System

Nowadays, electromagnetic high-frequency induction is very used for different non-contact heating applications such as the molding process. Every molding process requires the preheating and the thermal maintenance of the molds, to enhance the filling phase and the quality of the final products. In this context, an induction heating system, mostly, is a customized equipment. The design and definition of an induction equipment depends on the target application. This technology is highly efficient and performant, however it provides a high-energy consumption. Therefore, optimization strategies are very suitable to reduce energy cost and consumption. The proposed paper aims to define a method to optimize the induction heating of a mold in terms of time, consumption, and achieved temperature. The proposed optimization method involves genetic algorithms to define the design parameters related to geometry and controller. A test case describes the design of an induction heating system for a polyurethane molding process, which is the soles foaming. This case study deals with the multi-objective optimization of parameters such as the geometrical dimensions, the inductor sizing, and the controller setting. The multi-objective optimization aims to reduce the energy consumption and to increase the wall temperature of the mold.

Multiobjective approach developed for optimizing the dynamic behavior of incremental linear actuators

2014 ◽  
Vol 33 (3) ◽  
pp. 953-964 ◽  
Author(s):  
Imen Amdouni ◽  
Lilia El Amraoui ◽  
Frédéric Gillon ◽  
Mohamed Benrejeb ◽  
Pascal Brochet
Keyword(s):  
Multiobjective Optimization ◽  
Dynamic Model ◽  
Dynamic Behavior ◽  
Pareto Front ◽  
Computing Time ◽  
Design Parameters ◽  
Parallel Optimization ◽  
Optimization Approach ◽  
Content Type ◽  
Linear Actuators

Purpose – The purpose of this paper is to develop an optimal approach for optimizing the dynamic behavior of incremental linear actuators. Design/methodology/approach – First, a parameterized design model is built. Second, a dynamic model is implemented. This model takes into account the thrust force computed from a finite element model. Finally, the multiobjective optimization approach is applied to the dynamic model to optimize control as well as design parameters. Findings – The Pareto front resulting from the optimization approach (or the parallel optimization approach,) is better than the Pareto, which is obtained from the only application of MultiObjective Genetic Algorithm (MOGA) method (or parallel MOGA with the same number of optimization approach objective function evaluations). The only use of MOGA can reach the region near an optimal Pareto front, but it consumes more computing time than the multiobjective optimization approach. At each flowchart stage, parallelization leads to a significant reduction of computing time which is halved when using two-core machine. Originality/value – In order to solve the multiobjective problem, a hybrid algorithm based on MOGA is developed.

A Genetic Algorithm Based Multi-Objective Optimization of Squealer Tip Geometry in Axial Flow Turbines: A Constant Tip Gap Approach

2019 ◽  
Vol 142 (2) ◽  
Author(s):  
H. Maral ◽  
C. B. Şenel ◽  
K. Deveci ◽  
E. Alpman ◽  
L. Kavurmacıoğlu ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Tip Clearance ◽  
Design Parameters ◽  
Leakage Flow ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Tip Leakage Flow ◽  
Tip Leakage ◽  
Multi Objective ◽  
Blade Tip

Abstract Tip clearance is a crucial aspect of turbomachines in terms of aerodynamic and thermal performance. A gap between the blade tip surface and the stationary casing must be maintained to allow the relative motion of the blade. The leakage flow through the tip gap measurably reduces turbine performance and causes high thermal loads near the blade tip region. Several studies focused on the tip leakage flow to clarify the flow-physics in the past. The “squealer” design is one of the most common designs to reduce the adverse effects of tip leakage flow. In this paper, a genetic-algorithm-based optimization approach was applied to the conventional squealer tip design to enhance aerothermal performance. A multi-objective optimization method integrated with a meta-model was utilized to determine the optimum squealer geometry. Squealer height and width represent the design parameters which are aimed to be optimized. The objective functions for the genetic-algorithm-based optimization are the total pressure loss coefficient and Nusselt number calculated over the blade tip surface. The initial database is then enlarged iteratively using a coarse-to-fine approach to improve the prediction capability of the meta-models used. The procedure ends once the prediction errors are smaller than a prescribed level. This study indicates that squealer height and width have complex effects on the aerothermal performance, and optimization study allows to determine the optimum squealer dimensions.

Mission architecture for Mars exploration based on small satellites and planetary drones

2016 ◽  
Vol 4 (3) ◽  
pp. 142-162 ◽  
Author(s):  
Pierpaolo Pergola ◽  
Vittorio Cipolla
Keyword(s):  
Electric Propulsion ◽  
Low Cost ◽  
Scientific Data ◽  
Reference Points ◽  
Design Parameters ◽  
Optimization Approach ◽  
Low Thrust ◽  
Content Type ◽  
Mars Atmosphere ◽  
Small Satellites

Purpose The purpose of this paper is to deal with the study of an innovative unmanned mission to Mars, which is aimed at acquiring a great amount of detailed data related to both Mars’ atmosphere and surface. Design/methodology/approach The Mars surface exploration is conceived by means of a fleet of drones flying among a set of reference points (acting also as entry capsules and charging stations) on the surface. The three key enabling technologies of the proposed mission are the use of small satellites (used in constellation with a minimum of three), the use of electric propulsion systems for the interplanetary transfer (to reduce the propellant mass fraction) and lightweight, efficient, drones designed to operate in the harsh Mars environment and with its tiny atmosphere. Findings The low-thrust Earth-Mars transfer is designed by means of an optimization approach resulting in a duration of slightly more than 27 months with a propellant amount of about 125 kg, which is compatible with the choice of considering a 500 kg-class spacecraft. Four candidate drone configurations have been selected as the result of a sensitivity analysis. Flight endurance, weight and drone size have been considered as the driving design parameters for the selection of the final configuration, which is characterized by six rotors, a total mass of about 6.5 kg and a flight endurance of 28 minutes. In the mission scenario proposed, the drone is assumed to be delivered on the Mars surface by means of a passive entry capsule, which acts also as a docking station and charging base. Such a capsule has been sized both in terms of mass (68 kg) and power (80 W), showing to be compatible with 500 kg-class spacecraft. Research limitations/implications As a general conclusion, the study shows the mission concept feasibility. Practical implications The concept would return incomparable scientific data and can be also be potentially implemented with a relatively low budget exploiting of the shelf components to the larger extent, small identical spacecraft buses and modular low-cost drones. Originality/value The innovative mission architecture proposed in this study aims at providing a complete coverage of the surface and lowest atmospheric layers. The main innovation factor of the proposed mission consists in the adoption of small multi-copter UAVs, also called “drones,” as remote-sensing platforms.

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