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INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016 open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15 |
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| Paper Title: | Modelling and Analysis of Gas Turbine Blade Behavior to Predict Turbine Blade Resonance Amplitude |
| Authors Name: | Aditya Sahu , Sajal Raj Joshi , Kavyashree B. S. , Gururaj S. P. |
| Unique Id: | IJSDR2007049 |
| Published In: | Volume 5 Issue 7, July-2020 |
| Abstract: | Turbine Gas Blades are an essential part of any turbine machine. When the blades are run in a specific amplitude with respect to the other environmental factors, the durability of the machine seems to enhance giving us maximum efficiency. When we maintain these factors, the turbine blades used in various gas turbines in power plants and in aircraft last for a longer duration of time. This ultimately helps to reduce the mechanical waste from old machines which affect the environment. Our paper aims to provide a comparative analysis between several machine learning techniques which fits best for the dataset. Machine learning is an popular application of artificial intelligence (AI) in which a computer program learns based on data provided to it.In this paper, major focus will be implementing neural network algorithms and fuzzy logic based algorithms using python language. The intersection of machine learning methods and gas turbine sensor data has expanded rapidly in the last decade to include numerous applications of regression, clustering, and even neural network algorithms. It begins with a review of several computational methods which will be used to monitor the condition of gas turbines currently employed by industry. Since it’s an interdisciplinary paper with blending of Computer Science concepts and Gas turbine engine, our main focus would be on visualizing the data set obtained by previous experiments conducted and explore beyond the normal analysis using machine learning techniques. |
| Keywords: | Neural Network, Fuzzy logic, Gas Turbine, Comparative Analysis. |
| Cite Article: | "Modelling and Analysis of Gas Turbine Blade Behavior to Predict Turbine Blade Resonance Amplitude", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.5, Issue 7, page no.366 - 371, July-2020, Available :http://www.ijsdr.org/papers/IJSDR2007049.pdf |
| Downloads: | 000337349 |
| Publication Details: | Published Paper ID: IJSDR2007049 Registration ID:192115 Published In: Volume 5 Issue 7, July-2020 DOI (Digital Object Identifier): Page No: 366 - 371 Publisher: IJSDR | www.ijsdr.org ISSN Number: 2455-2631 |
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