Olcay Ersel Canyurt

Energy Demand Estimation Based on Two-Different Genetic Algorithm Approaches

Energy modeling is a subject of widespread current interest among engineers and scientists concerned with problems of energy production and consumption. Energy planning is not possible without a reasonable knowledge of past and present energy consumption and likely future demands. In this study, two forms of the energy demand equations are developed in order to improve energy demand estimation efficiency for future projections based on the genetic algorithm (GA) notion. The genetic algorithm energy demand (GAEDM) model is used to estimate Turkeys future energy demand based on gross domestic product, population, import, and export figures. Both equations proposed here are non-linear, of which one is exponential and the other is quadratic. The quadratic form of the GAEDM model provided a slightly better fit solution to the observed data and can be used with a high correlation coefficient for Turkeys future energy projections. It is expected that this study will be helpful in developing highly applicable and productive planning for energy policies.

Genetic Algorithm (GA) Approaches for the Transport Energy Demand Estimation: Model Development and Application

This study deals with estimating future transport energy demand using genetic algorithm (GA) approach. Genetic algorithm transport energy demand (GATENDM) model is developed based on socio-economic indicators (population, gross domestic product (GDP), import and export) and transportation indicators/parameters (car, bus, and truck sales). The GATENDM model developed is applied to Turkey, which is selected as an application country. This model in a quadratic form was found to provide the best fit solution to the observed data. It may be concluded that the model proposed can be used as an alternative solution and estimation technique to available estimation technique in predicting the future transportation energy utilization values of countries.

A SAND approach based on cellular computation models for analysis and optimization

Genetic algorithms (GAs) have received considerable recent attention in problems of design optimization. The mechanics of population-based search in GAs are highly amenable to implementation on parallel computers. The present article describes a fine-grained model of parallel GA implementation that derives from a cellular-automata-like computation. The central idea behind the cellular genetic algorithm (CGA) approach is to treat the GA population as being distributed over a 2-D grid of cells, with each member of the population occupying a particular cell and defining the state of that cell. Evolution of the cell state is tantamount to updating the design information contained in a cell site and, as in cellular automata computations, takes place on the basis of local interaction with neighbouring cells. A special focus of the article is in the use of cellular automata (CA)-based models for structural analysis in conjunction with the CGA approach to optimization. In such an approach, the analysis and optimization are evolved simultaneously in a unified cellular computational framework. The article describes the implementation of this approach and examines its efficiency in the context of representative structural optimization problems.

Application of Genetic Algorithm (GA) Technique on Demand Estimation of Fossil Fuels in Turkey

The main objective of the present study is to investigate Turkey’s fossil fuels demand, projection and supplies by giving the structure of the Turkish industry and Turkish economic conditions. This present study develops several scenarios to analyze fossil fuels; such as, coal, oil and natural gas consumption and make future projections based on Genetic Algorithm (GA) notion, and examines the effect of the design parameters on the fossil fuels utilization values. The models developed in the nonlinear form are applied to the coal, oil and natural gas demand of Turkey. Several Genetic Algorithm Demand Estimation Models (GA-DEM) are developed to estimate the future coal, oil and natural gas demand values based on population, Gross National Product (GNP), import, export figures. It may be concluded that the proposed models can be used as an alternative solution and estimation techniques for the future fossil fuel utilization values of any country. Oil is the most important fuel in Turkey, contributing 43% of the Total Primary Energy Supply (TPES), followed by coal (almost 30% of TPES) and natural gas (11.8%). In the study, coil, oil and natural gas consumption of Turkey are projected. Estimation shows that the coal, oil and natural gas consumption values may increase 2.82, 1.73 and 4.83 times from 2000 to 2020.© 2007 ASME

An Application of Genetic Algorithm Search Techniques to the Future Total Exergy Input/Output Estimation

Since 1975, there has been a great deal of interest, particularly during the past decade, in the promising genetic algorithm (GA) and its application to various disciplines from medicine to cogeneration. However, the studies performed on energy-related GA modeling are relatively low in numbers. The main objective of the present study is to develop the exergy input/output estimation equations in order to estimate the future projections based on the GA notion. In this regard, the GA Future Total EXergy Input/Output Estimation Models (GAFTEXIEM/GAFTEXOEM) are used to estimate total exergy input/output demand of Turkey, which is selected as an application country, based on the economic and social indicators of gross domestic product (GDP), population, import, export and house production figures. The future prediction of Turkeys total exergy input/output values are projected between 2003 and 2023. It may be concluded that the models proposed here can be used as an alternative solution and estimation techniques to available estimation techniques. It is also expected that this study will be helpful in developing highly applicable and productive planning for energy policies.

A new approach for calculating the stiffness of bolted connections

The stiffness of bolted joint influences the transfer of initial preload and operating force. The determination of stiffness without the gasket is fairly difficult to obtain, because the compression region spreads out between the bolt head and the nut. Bolted joints including bolts and members act as elastic springs under operating conditions. The affected area is not uniform. The stiffness of the bolted joints can be determined using finite element analysis and soft computing techniques. The prediction of the non-dimensional joint stiffness is developed in this paper by using genetic algorithm methods. The proposed model was in good agreement with the experimental results and previous literature and may be considered as a simple and suitable guideline for the design of bolted joints.

Joint strength of friction stir welded AISI 304 austenitic stainless steels

Abstract In this study, AISI 304 (X5CrNi18-10) austenitic stainless steels were joined by means of friction stir welding. The welded joint strength of stainless steels was influenced by many factors, such as different tool rotational speeds, traverse speeds, compressive tool forces, and tool angles, etc. There is a strong interrelation among the friction stir welding design parameters. The effects of design parameters on the welded joint were analyzed using a genetic algorithm. Appropriate design parameter configurations led to fine-grained microstructures that resulted in higher tensile strength joints compared to the base material. The best design configuration that led to 1.16 times higher strength than the base material was achieved with 47.5 mm min−1 traverse speed, a rotational speed of 1 180 min−1, compressive tool force of 7 kN and tool tilt angle of 2.0°.

The effects of tool rotation speed and traverse speed on friction stir welding of AISI 304 austenitic stainless steel

Abstract In this study, AISI 304 (X5CrNi18-10, material identification number 1.4301) austenitic stainless steels, 3.0 mm thick, were joined by friction stir welding by applying different tool rotation speeds and traverse speeds, compressive forces and tool angles. The strength of the welded joints was improved by selecting suitable welding parameters. The maximum notch impact toughness was achieved on samples produced with 950 rpm rotation speed, 60 mm min−1 traverse speed, 9 kN compressive force, and 1.5° tool tilt angle. The maximum tensile strength of the weld zone was obtained on samples welded with 47.5 mm min−1 traverse speed, a rotational speed of 750 rpm, compressive force of 9 kN and tool tilt angle of 1.5°. The traverse speed of 47.5 mm min−1 was found to optimize the results of tensile strength and impact tests. Fine-grained microstructures occurred in the welded area. The weld joints obtained with friction stir welding have lower tensile strength compared to that of the base material. The experimental results indicate that AISI 304 austenitic stainless steels can be successfully joined considering both the strength of the welded joint and the appearance of the welding bead by selecting proper tool material and welding parameters using friction stir welding.

Modeling and Application of Genetic Algorithm (GA) Approach to Estimating the Future Total Energy Input Values

Abstract This study deals with the modeling and developing the energy input estimation equations in order to estimate the future projections based on genetic algorithm (GA) notion. In this regard, the GA future prediction of total energy input estimation model (GAFPTEIEM) is used to better estimate the future energy input demand based on the economic and social indicators of gross domestic product (GDP), population, import, export, house production, cement production, and basic house appliances consumption figures. The models proposed here can be used as an alternative solution and estimation techniques to available estimation techniques. It is also expected that this study will be helpful in developing highly applicable and productive planning for energy policies.