Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/455061
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dc.coverage.spatialHardware implementation of artificial Neural network based mixture ratioEstimation and intelligent sensor failure Substitution for liquid rocket engines
dc.date.accessioned2023-01-30T12:14:36Z-
dc.date.available2023-01-30T12:14:36Z-
dc.identifier.urihttp://hdl.handle.net/10603/455061-
dc.description.abstractSpace exploration is the prime area of research interest in the modern world. Development of rockets has enabled the mankind to venture their horizon into the mystified space of the universe. A rocket or a launch vehicle delivers its payload to the desired orbit or on its desired trajectory. The rockets are multi-staged and every stage has one or more rocket engines. The liquid rocket engines develop thrust by burning the propellants in a particular mixture ratio. Hence, accurately estimating the Mixture Ratio of the rocket engine is very essential for its successful mission. Further to have repeated successful missions and to meet the future requirements of the human space programmes, the reliability of the rocket is to be improved to 100%. newlineThe payload mass is the carrying capacity of the rocket and it is measured in terms of weight. For the rocket, the payload can be a satellite, space probe or a spacecraft carrying humans. Maximizing the payload mass is the main driver of a rocket s revenue potential and thus, bringing down significantly the cost of access to space as well as it is the primary goal of the space programme around the world today. Reliability of the rocket is also an important factor in the process of design, as every flight involves human safety, high budget, enormous man hours and it is the pride of the country. Indeed, affordable access to space requires reliable and safe reusable transportation systems which require maximum thrust to weight ratio. The Geo Synchronous Launch Vehicle (GSLV) Mk III rocket is a three stage rocket where the second and the third stages use liquid rocket engines. The payload carrying capacity of this rocket is 4,000kg to the Geosynchronous Transfer Orbit (GTO). newline
dc.format.extentxxv,169
dc.languageEnglish
dc.relationp.157-168
dc.rightsuniversity
dc.titleHardware implementation of artificial Neural network based mixture ratioEstimation and intelligent sensor failure Substitution for liquid rocket engines
dc.title.alternative
dc.creator.researcherJessi flora J
dc.subject.keywordEngineering and Technology
dc.subject.keywordComputer Science
dc.subject.keywordComputer Science Information Systems
dc.subject.keywordArtificial Neural Network
dc.subject.keywordMixture Ratio
dc.description.note
dc.contributor.guideJeraldin auxillia D
dc.publisher.placeChennai
dc.publisher.universityAnna University
dc.publisher.institutionFaculty of Information and Communication Engineering
dc.date.registered
dc.date.completed2022
dc.date.awarded2022
dc.format.dimensions21cm
dc.format.accompanyingmaterialNone
dc.source.universityUniversity
dc.type.degreePh.D.
Appears in Departments:Faculty of Information and Communication Engineering

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01_title.pdfAttached File396.28 kBAdobe PDFView/Open
02_prelim pages.pdf2.04 MBAdobe PDFView/Open
03_content.pdf781.65 kBAdobe PDFView/Open
04_abstract.pdf503.48 kBAdobe PDFView/Open
05_chapter 1.pdf1.33 MBAdobe PDFView/Open
06_chapter 2.pdf1.13 MBAdobe PDFView/Open
07_chapter 3.pdf3.33 MBAdobe PDFView/Open
08_chapter 4.pdf1.02 MBAdobe PDFView/Open
09_chapter 5.pdf1.7 MBAdobe PDFView/Open
10_annexures.pdf352.02 kBAdobe PDFView/Open
80_recommendation.pdf161.27 kBAdobe PDFView/Open


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