Obtaining fluid flow pattern for turbine stage with neural model

• Autorzy: Butterweck Anna
• Języki publikacji: EN

Abstrakty

EN

In the paper possibility of applying neural model to obtaining patterns of proper operation for fluid flow in turbine stagefor fluid-flow diagnostics is discussed. Main differences between Computational Fluid Dynamics (CFD) solvers and neural model is given, also limitations and advantages of both are considered. Time of calculations of both methods was given, also possibilities of shortening that time with preserving the accuracy of the calculations are discussed. Gathering training data set and neural networks architecture is presented in detail. Range of work of neural model was given. Required input data for neural model and reason why it is different than in computational fluid dynamics solvers isexplained. Results obtained with neural model in 21 tests are discussed. Arithmetic mean and median of relative errors of recreating distribution of pressure and temperature are shown. Achieved results are analysed.

Słowa kluczowe

EN

turbine; diagnostics; neural model; pattern; fluid flow

• Wydawca: Faculty of Ocean Engineering and Ship Technology, Gdańsk University of Technology
• Czasopismo: Journal of Polish CIMEEAC
• Rocznik: 2019
• Tom: Vol. 14, no 1
• Strony: 45--50
• Opis fizyczny: Bibliogr. 8 poz., rys., tab.

Twórcy

autor

Butterweck Anna

• Gdańsk University of Technology, Faculty of Ocean Engineering and Ship Technology

Bibliografia

• [1] Venkatasubramanian V., Rengaswamy R., Yin K. , Surya K. N., A review of process fault detection and diagnosis Part I: Quantitative model-based methods, Computers and Chemical Engineering 27 (2003) 293-311.
• [2] Venkatasubramanian V., Rengaswamy R., Surya K. N., A review of process fault detection and diagnosis Part II: Qualitative models and search strategies, Computers and Chemical Engineering 27 (2003) 313-326.
• [3] Venkatasubramanian V., Rengaswamy R., Surya K. N., Yin K., A review of process fault detection and diagnosis Part III:Processs history based methods, Computers and Chemical Engineering 27 (2003) 327-346.
• [4] Venkatasubramanian V., Process fault detection and diagnosis: past, present and future, 2001, IFAC On-line Fault detection and supervision in the chemical process industries, Jejudo Island, Korea.
• [5] Kim SM, Joo YJ. Implementation of on-line performance monitoring system at Seoincheon and Sinincheon combined cycle power plant. Energy, 2005;30: 2383–401.
• [6] Głuch J., Ślężak –Żołna Justyna, Solving problems with patterns or heat flow diagnostics dedicated for turbine power plants, Proceedings of ASME TURBO EXPO, 2012, 969-979.
• [7] Dragan D., Fault Detection of an Industrial Heat-Exchanger: A Model-Based Approach, 2011, STROJNISKI VESTNIK-JOURNAL OF MECHANICAL ENGINEERING, vol. 57.6, 477-484.
• [8] Mohanraj M., Jayaraj S., Muraleedharan C., Applications of artificial neural networks for thermal analysis of heat exchangers - A review, 2015, INTERNATIONAL JOURNAL OF THERMAL SCIENCES, vol. 90, 150-172.