State diagnosing of automatic relief valve circuit and parkiing seal of liquid rocket engine turbo pump

Aeronautical and Space-Rocket Engineering

To the 100th anniversary of B.V. Ovsyannikov


DOI: 10.34759/vst-2021-3-24-32

Аuthors

Gemranova E. A.

NPO Energomash named after academician V.P. Glushko, 1, Burdenko str., Khimki, 141400, Russia

e-mail: gemranova@yandex.ru

Abstract

As fire tests (FT) practice revealed, defects leading to destruction of engine structure elements, such as radial-thrust bearings, parking seal and blade wheel hub of the centrifugal pump occurred and developed with time in the automatic relief valve (ARV) circuit and parking seal (PS). Very often, such defects were developing in the course of several and even tens of seconds. These defects may be detected at the early stages of their development by the functional diagnostics methods employing slowly changing parameters being measured while the FT and mathematical model of the engine workflow processes.

Until recently, the computational-experimental analysis of accidents occurring in the ARV circuit and PS was performed locally, using only a mathematical model of this circuit, where the boundary conditions were assigned by empirical or approximation dependences. It is clear that integration of the ARV circuit and PS mathematical model into the math model of the engine workflow processes gives an opportunity of obtaining more complete diagnostic information about the circuit being considered. It is worth noting the inexpediency of neural network involving for this purpose due to the necessity of its training on a large number of FTs.

To increase the depth of engine diagnosing and confident control of the ARV and SS circuit state, the system of ARV and SS equations is closed by the parameters, by which this circuit is being conjugated with the engine parameters. By the model obtained in this way, a step-by-step process of the ARV and SS circuit state diagnosing is presented, starting from the moment of identifying the time of a fault occurrence and up to its localization. At each stage, special algorithms are being used to confirm the decisions made at the previous stage. The control begins with determining the moment of malfunction occurrence by measured parameters of the malfunction occurrence time instant. After this, deviations of measured parameters from the ones computed with the model are being controlled. Then it is necessary to proceed to the control of the engine characteristics deviation from those obtained while autonomous tests of units. Finally, if necessary, the control of functional relations violation by the structural exclusion method is being performed. On the example of liquid rocket engine state control during test bench fire test, the sequence of diagnostic procedures resulted in the malfunction, which caused forces unbalance on the radial-thrust bearing of the oxidizer pump and pressure increase in the cavity of the oxidizer pump control system, was detected and localized, was presented.

The stated diagnostic procedures may be employed in the analysis of a wide class of complex technical systems functioning.

Keywords:

liquid rocket engine, diagnostic model, automatic relief valve, parking seal, diagnostic signs

References

  1. Vasin A.S., Vengerskii E.V. Novye naukoemkie tekhnologii v tekhnike. Entsiklopediya, Moscow, Aspekt, 1994, vol. 1, 280 p.

  2. Bukanov V.T., Kolbasenkov A.I., Martirosov D.S. Trudy NPO Energomash im. akademika V.P. Glushko, 2012, no. 29, pp. 174-187.

  3. Kamenskii S.S., Martirosov D.S., Kolomentsev A.I. Similarity theory methods application for lpre steady- flow working procedures analysis. Aerospace MAI Journal, 2016, vol. 23, no. 1, pp. 32-37.

  4. Tirskii A.A. Razrabotka i issledovanie avtomatizirovannoi sistemy funktsional’nogo kontrolya i diagnostirovaniya ZhRD (Development and study of LRE automated functional control and diagnostics system). Doctor’s thesis, Moscow, MATI, 2001, 200 p.

  5. Meyer C.M., Zakrajsek J.F. Rocket engine failure detection using system identification techniques. NASA Contractor Report 185259. AIAA-90-1993, 18 p. DOI: 10.2514/6.1990-1993

  6. Chebaevskii V.F., Petrov V.I. Kavitatsionnye kharakteristiki vysokooborotnykh shneko-tsentrobezhnykh nasosov (Cavitation characteristics of high-speed screw-centrifugal pumps), Moscow, Mashinostroenie, 1973, 152 p.

  7. Chebaevskii V.F., Petrov V.I. Kavitatsiya v vysokooborotnykh lopastnykh nasosakh (Cavitation in high-speed vane pumps), Moscow, Mashinostroenie, 1982, 192 p.

  8. Galeev A.G., Ivanov V.N., Katenin A.V. et al. Metodologiya eksperimental’noi otrabotki ZhRD i DU, osnovy provedeniya ispytanii i ustroistva ispytatel’nykh stendov (Experimental testing methodology of LRE and propulsion systems, testing and of test benches devices basics), Kirov, Mezhdunarodnyi tsentr nauchno-issledovatel’skikh proektov, 2015, 435 p.

  9. Belyaev E.N., Chvanov V.K., Chervakov V.V. Matematicheskoe modelirovanie rabochego protsessa zhidkostnykh raketnykh dvigatelei (Mathematical modeling of Liquid Rocket Engine workflow), Moscow, MAI, 1999, 226 p.

  10. Kolomentsev A.I., Martirosov D.S. Metody funktsional’noi diagnostiki dvigatelei letatel’nykh apparatov (Functional diagnostics methods of flying vehicles engines), Moscow, MAI, 2002, 112 p.

  11. Grebenyuk A.T., Kanalin Yu.I., Poletaev N.P. Trudy NPO Energomash im. akademika V.P.Glushko, 2014, no. 31, pp. 131-145.

  12. Vidishev V.I., Kanalin Yu.I., Kukharev V.N., Malikova S.A., Poletaev N.P. Trudy NPO Energomash im. akademika V.P.Glushko, 2000, no. 18, pp. 100-114.

  13. Demiyanenko Yu.V., Dmitrenko A.I., Pershin V.K. Boost Turbopump Assemblies for Hydrogen-oxygen Liquid Propellant Rocket Engines. 40th AIAA/ASME/SAE/ASEE Joint Propulsion Conference and Exhibit (11-14 July 2004; Fort Lauderdale, Florida). AIAA 2004-3685. DOI: 10.2514/6.2004-3685

  14. Dmitrenko A.I., Pershin V.K. Patent RU 2099567 C1, 20.12.1997.

  15. Demiyanenko Yu.V., Dmitrenko A.I., Pershin V.K., Grebennikov D.Yu. Investigation of the Performance of a Thrust Balance Device for a Centrifugal Pump Rotor. 40th AIAA/ASME/SAE/ASEE Joint Propulsion Conference and Exhibit (11-14 July 2004; Fort Lauderdale, Florida). AIAA 2004-3689. DOI: 10.2514/6.2004-3689

  16. Moshiri B., Jazbi S.A. Fault Detection and Isolation with RBF Neural Network. IFAC Proceedings Volumes, 1997, vol. 30, no. 25, рр. 91-96. DOI: 10.1016/S1474-6670(17)41306-1

  17. Koppen-Seliger B., Frank P.M. Fault detection and isolation in technical processes with neural network. 34th IEEE Conference on Decision and Control (13-15 December 1995; New Orleans, LA, USA). DOI: 10.1109/CDC.1995.480701

  18. Levochkin P.S., Martirosov D.S., Bukanov V.T. Vestnik Moskovskogo gosudarstvennogo tekhnicheskogo universiteta im N.E. Baumana. Seriya Mashinostroenie, 2013, no. 1(90), pp. 72-78.

  19. Martirosov D.S. Diagnostirovanie slozhnykh tekhnicheskikh sistem na osnove matematicheskikh modelei fizicheskikh protsessov i izmeryaemykh parametov metodom strukturnogo isklyucheniya (Complex technical systems diagnosing based on mathematical models of physical processes and measured parameters by the structural exclusion method), Moscow, MAI, 1998, 53 p.

  20. Martsinkovskii V.A., Vorona P.N. Nasosy atomnykh elektrostantsii (Nuclear Power Plant Pumps), Moscow, Energoatomizdat, 1987, 256 p.

  21. Gear CW. Differential-algebraic equation index transformations. SIAM Journal on Scientific and Statistical Computing, 1988, vol. 9, no. 1, pp. 39-47. DOI: 10.1137/0909004

mai.ru — informational site of MAI

Copyright © 1994-2024 by MAI