Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - Data management and decision support for the in-flight SRM
AU - Luchinsky, Dmitry G.
AU - Smelyanskiy, Vadim N.
AU - Osipov, Slava V.
AU - Timucin, Dogan A.
AU - Lee, Sun Hwan
PY - 2007/11/5
Y1 - 2007/11/5
N2 - A novel Bayesian framework for the in-flight SRM Failure Decision and Prognostic (FD&P) is introduced and discussed. It is based on a combination of low-dimensional performance models (LPDMs) and a dynamical inference of the parameters of nonlinear flow of combustion products. To verify the method we introduce a high-fidelity model of the overpressure fault based on a system of stochastic partial differential equations (SPDEs). To analyze the deviations of the system parameters from the stable burn-back conditions of the SRM we derived a LPDM of the SRM obtained by integrating the SPDEs over the length of the combustion camera. We consider a few fault scenarios, including nozzle failure with neutral and progressive thrust curve, and nozzle blocking with time varying fault parameters to model "misses" or "false alarms". Prognostic is accomplished by building the distribution of the predicted values of the fault parameters as a function of the measurement time. We discuss how the novel Bayesian framework can be extended to encompass the pro pella nt cracking and the case breach faults of the SRM.
AB - A novel Bayesian framework for the in-flight SRM Failure Decision and Prognostic (FD&P) is introduced and discussed. It is based on a combination of low-dimensional performance models (LPDMs) and a dynamical inference of the parameters of nonlinear flow of combustion products. To verify the method we introduce a high-fidelity model of the overpressure fault based on a system of stochastic partial differential equations (SPDEs). To analyze the deviations of the system parameters from the stable burn-back conditions of the SRM we derived a LPDM of the SRM obtained by integrating the SPDEs over the length of the combustion camera. We consider a few fault scenarios, including nozzle failure with neutral and progressive thrust curve, and nozzle blocking with time varying fault parameters to model "misses" or "false alarms". Prognostic is accomplished by building the distribution of the predicted values of the fault parameters as a function of the measurement time. We discuss how the novel Bayesian framework can be extended to encompass the pro pella nt cracking and the case breach faults of the SRM.
U2 - 10.2514/6.2007-2829
DO - 10.2514/6.2007-2829
M3 - Conference contribution/Paper
AN - SCOPUS:35648972082
SN - 1563478935
SN - 9781563478932
T3 - Collection of Technical Papers - 2007 AIAA InfoTech at Aerospace Conference
SP - 1200
EP - 1220
BT - Collection of Technical Papers - 2007 AIAA InfoTech at Aerospace Conference
PB - AIAA
T2 - 2007 AIAA InfoTech at Aerospace Conference
Y2 - 7 May 2007 through 10 May 2007
ER -