مورد إلكتروني
Modelling of turbine blade vibrations via computational intelligence methods
العنوان: | Modelling of turbine blade vibrations via computational intelligence methods |
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بيانات النشر: | KTH, Kraft- och värmeteknologi 2017 |
تفاصيل مُضافة: | Norton, S. Ramsay, T. Karatzas, K. Fridh, Jens E. Petrie-Repar, Paul |
نوع الوثيقة: | Electronic Resource |
مستخلص: | A method for modelling turbomachine blade vibration events is proposed, based on computational intelligence algorithms. The method utilises steady thermodynamic data and blade tip-timing data to identify high amplitude vibration events and to draw underlying relationships between steady-thermodynamic input channels and resultant blade motion characteristics. Several computational studies probe specific process aspects in order to improve model prediction accuracy and several methods of data-feature reduction are established to further enhance vibration predictions. Overall, the study shows promise of what prediction capabilities can be achieved with seemingly limited instrumentation. Drawbacks in matters of tip-timing interpretation, quality/quantity of data and process limitations are discussed. Consequential future objectives are outlined to envisage onward predictive accuracy. QC 20170815 |
مصطلحات الفهرس: | Blade tip timing, Blade vibration prediction, Computational intelligence, Health monitoring, Energy Engineering, Energiteknik, Conference paper, info:eu-repo/semantics/conferenceObject, text |
DOI: | 10.29008.etc2017-083 |
URL: | 12th European Conference on Turbomachinery Fluid Dynamics and Thermodynamics, ETC 2017 |
الإتاحة: | Open access content. Open access content info:eu-repo/semantics/restrictedAccess |
ملاحظة: | English |
أرقام أخرى: | UPE oai:DiVA.org:kth-211881 doi:10.29008/etc2017-083 Scopus 2-s2.0-85085849505 1235078500 |
المصدر المساهم: | UPPSALA UNIV LIBR From OAIster®, provided by the OCLC Cooperative. |
رقم الأكسشن: | edsoai.on1235078500 |
قاعدة البيانات: | OAIster |
DOI: | 10.29008.etc2017-083 |
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