International Journal of Engineering Applied Sciences and Technology, 2020 Vol. 5, Issue 4, ISSN No. 2455-2143, Pages 404-406 Published Online August 2020 in IJEAST (http://www.ijeast.com) 404 REVIEW OF AN ALGORITHM SELECTION FOR ELECTROMAGNET Kanishk Sharma, Shripad G Desai, Navya Raj, Manish Kumar, Hetal Verma Department of Electrical Engineering, Bharati Vidyapeeth (Deemed to be) University College of Engineering, Pune, India Abstract — This paper gives an overview of a method using selection algorithm for the electromagnet design which can be used for electromagnetic launcher and allied applications. The structural constraints must be considered in the design of an electromagnet. In this paper, an algorithm based on the calculation utilizing the magnet design equations and their own constraints were utilized. This method was verified by IDE simulations, which showed that this method is more efficient than conventional calculation based selection criteria. Electromagnetic launcher can be described as an application of electromagnetic force acting on launching body, design of electromagnet necessary to sustain such magnitude of fields. Thus this paper proposes a certain electromagnet which can be utilized for such applications like SLV (Satellite Launch Vehicle), Elevators, Energy Projectiles, Weapons, EMALS etc. Keywords— Coil guns, electromagnetic launcher, electromagnet, acceleration, capacitor. I. INTRODUCTION The aim of this paper is to present experimental research information on electromagnet and related topics. Thus we hope to foster interest in the fields of physics and engineering. Our long term objective is to design and construct a multi- stage coil guns capable of firing projectiles at supersonic speeds.[1], [2] Recent advances in energy storage, switching and magnet technology make electromagnetic acceleration a viable alternative to chemical propulsion for certain tasks, and a means to perform other tasks not previously feasible. Magnetic repulsion can levitate the object and reduces stress on airframes because they can be accelerated more gradually to take-off speed. Acceleration of metallic bodies by electromagnetic induction can offer advantages of being simple, absence of heat and significant reduction of the fuel cost.[3] – [10] II. SELECTION OF TOOL For performing the algorithm we have selected Python as a programming language as no doubt it is a programming language with dynamic exposition and the reason behind selecting python as a tool for the selection of algorithms is its increased efficiency. Since there is no compilation step, the edit-test-debug cycle is incredibly fast. Amending in Python programs is simple: a midge or wrong input will never cause a sectionalization error. Moreover, when an error is discovered by an interpreter, it raises an exception. When the program doesn't catch the exception, the interpreter prints a stack trace. [1] C++ code can also be used as a tool but Python code is about more or less 8-10 times shorter than equivalent program in C++. Statistical evidence suggests what an individual Python programmer can finish in a few months’ time, two C++ programmers can't complete within a year. Python shines as a glue language, used for combination of components written in C++. [8] – [14] III. SELECTION ALGORITHM The algorithm is a calculation-based method with a predefined population size. An initial estimation is randomly generated as in many evolutionary algorithms. An individual parameter within the sample space represents a single possible solution to a particular optimization problem. Then, the algorithm tries to determine using the predefined parameters and calculate the type of electromagnet suitable for the given application. The algorithm will repeat until it reaches the possible working conditions in every situation and that too at maximum efficiency. During the data entering phase, the role of a user is to access the given situation and enter the required data so as to calculate the working conditions of any given situation and decide whether any electromagnet available for selection is suitable or not and if found suitable what would be the conditions for that are required for the electromagnet to run most efficiently within the given pre- conditions. [15] – [20] The algorithm attempts to improve the user input by selecting what is best for that particular electromagnet and what can be compromised to ensure better results and a longer life for the electromagnet. This is constructed using the mean values for each parameter within the problem space (dimension) and represents the qualities of different electromagnets from the sample space.