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Teaching Engineering And Technology Students To Develop Genetic Algorithms For The Design Of Energy Systems

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2009 Annual Conference & Exposition


Austin, Texas

Publication Date

June 14, 2009

Start Date

June 14, 2009

End Date

June 17, 2009



Conference Session

Engineering and Mathematics Potpourri

Tagged Division


Page Count


Page Numbers

14.1133.1 - 14.1133.13



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Paper Authors

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Murray Teitell DeVry University, Long Beach

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NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract

Teaching Engineering and Technology Students to

Develop Genetic Algorithms for the Design of Energy Systems

Introduction Delivering the energy required by industry and the consumer at a reasonable price is a major problem facing the United States and the international community. The United States needs a comprehensive plan to meet its energy needs for the next 50 years. Popular goals are focused on limiting energy consumption, increasing renewable sources, promoting conservation, and making energy conversion more efficient. To muster political support, there has to be an emphasis on safety, ethics, and maximizing domestic resources. New energy technology is continually being introduced: e. g. ultracapacitors, efficient batteries, solar cells, fusion reactors.1,2,3 Energy plans need to take countenance of these new technologies on the horizon. In order to prepare engineering students to develop energy plans, they should be exposed to methods in their educational programs. How do you find the best solutions for complex energy systems? What kinds of algorithms are appropriate for this type of problem? These are the questions the author posed to his mechatronics class? Mechatronics4 is a subject that joins electrical engineering with mechanical engineering. Energy systems are mechatronics systems in that they are part mechanical and part electrical and electronic. The students’ challenge was to optimize an energy plan for the U. S. for the next 50 years. The class divided themselves into different factions. Since genetic algorithms lend themselves to systems that have indefinite factors, this was the category of algorithm that was chosen for this investigation. A population of different energy resources was compiled. For each faction, a spreadsheet was created which contained a detailed summary of the energy plan components. Each faction then created and applied a genetic algorithm to their starting plans. Genetic algorithms are based on Darwin’s Theory of Evolution.5 An algorithm is a strategy for solving a problem. Using genetic algorithms, the solutions evolve by making a series of prescribed changes and then select those changes that allow the system to best adapt to the environment. Genetic algorithms were first proposed by John Holland.6,7 Current State of U. S. Energy The components of the energy plan included wind power from wind mills, fossil fuels for transportation, solar energy including solar panels and thermal solar, nuclear reactors, nuclear fusion, steam power plants. At the time of the class, gasoline prices in the US were at an all time high so one of the goals was to make the U.S. less dependent on fossil fuels and in particular foreign sources of fossil fuels. The total energy expended by the United States for all purposes in 2006 was approximately 100 quadrillion BTUs or 105 exajoules.8 This is the total energy that the U.S. population consumed and which was supplied from all sources. This included the energy derived from fossil fuels for power and transportation which was about 90%.8 The rest of the energy came from nuclear, wind, solar, hydroelectric and hydrothermal sources.

Teitell, M. (2009, June), Teaching Engineering And Technology Students To Develop Genetic Algorithms For The Design Of Energy Systems Paper presented at 2009 Annual Conference & Exposition, Austin, Texas. 10.18260/1-2--5184

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