scholarly journals Optimal Speed Control Humps Design Based on Driver Comfort

2021 ◽  
Vol 18 (3) ◽  
pp. 8941-8958
Author(s):  
Hamid Gheibollahi ◽  
Masoud Masih-Tehrani
Keyword(s):  
Genetic Algorithm ◽  
Optimal Design ◽  
Speed Control ◽  
Vertical Acceleration ◽  
Mean Square ◽  
Horizontal Acceleration ◽  
Car Suspension ◽  
Different Types ◽  
Occupant Comfort ◽  
All Speeds

The purpose of this study is to optimise the different speed control humps by considering the vertical and horizontal acceleration of the driver’s head. In previous researches, the main focus was only on vertical acceleration, but in this study, horizontal acceleration of the head is also considered. Here, the root mean square (RMS) of acceleration of head is considered as a measure of occupant comfort. The modelling is performed by a non-linear half-car suspension system (4-DOF) with a linear model of a driver (10-DOF) and a seat. The hamps under study are circular, sinusoidal, half-sinusoidal, and trapezoidal. Finally, by analysing the results, the optimal design of each type of hump is performed. The objective function used is a combination of horizontal and vertical acceleration which is performed using MATLAB genetic algorithm. The results show a significant reduction in horizontal and vertical acceleration at all speeds. From this modelling, it is possible to extract a suitable range for passing the speed of cars over different types of humps. In this study, it is shown that the acceleration values for the circular and half-sinusoidal humps at all speeds are quite close to each other.

Minimization of total trim loss occurring in pipe cutting in shipbuilding with genetic algorithm

2021 ◽  
pp. 147509022199169
Author(s):  
Abdullah Türk ◽  
Dursun Saral ◽  
Murat Özkök ◽  
Ercan Köse
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Search Space ◽  
Cutting Process ◽  
Genetic Operators ◽  
Critical Stage ◽  
Different Types ◽  
Trim Loss ◽  
Pipe Systems

Outfitting is a critical stage in the shipbuilding process. Within the outfitting, the construction of pipe systems is a phase that has a significant effect on time and cost. While cutting the pipes required for the pipe systems in shipyards, the cutting process is usually performed randomly. This can result in large amounts of trim losses. In this paper, we present an approach to minimize these losses. With the proposed method it is aimed to base the pipe cutting process on a specific systematic. To solve this problem, Genetic Algorithms (GA), which gives successful results in solving many problems in the literature, have been used. Different types of genetic operators have been used to investigate the search space of the problem well. The results obtained have proven the effectiveness of the proposed approach.

COMPUTING AN ADAPTIVE MESH IN FLUID PROBLEMS USING NEURAL NETWORK AND GENETIC ALGORITHM WITH ADAPTIVE RELAXATION

2008 ◽  
Vol 17 (06) ◽  
pp. 1089-1108 ◽  
Author(s):  
NAMEER N. EL. EMAM ◽  
RASHEED ABDUL SHAHEED
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Numerical Experiments ◽  
Back Propagation ◽  
Adaptive Mesh ◽  
Learning System ◽  
Back Propagation Algorithm ◽  
Adaptive Smoothing ◽  
Different Types ◽  
Optimal Values

A method based on neural network with Back-Propagation Algorithm (BPA) and Adaptive Smoothing Errors (ASE), and a Genetic Algorithm (GA) employing a new concept named Adaptive Relaxation (GAAR) is presented in this paper to construct learning system that can find an Adaptive Mesh points (AM) in fluid problems. AM based on reallocation scheme is implemented on different types of two steps channels by using a three layer neural network with GA. Results of numerical experiments using Finite Element Method (FEM) are discussed. Such discussion is intended to validate the process and to demonstrate the performance of the proposed learning system on three types of two steps channels. It appears that training is fast enough and accurate due to the optimal values of weights by using a few numbers of patterns. Results confirm that the presented neural network with the proposed GA consistently finds better solutions than the conventional neural network.

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