• Bayesian Identification
• Cavitation
• Cavitation Instabilities
• Inducers
• Maximum Likelihood Estimation.
• Rotating Cavitation
• Turbomachines
• Turbopumps

The onset of cavitation and its induced flow instabilities is the most stringent fluid dynamic limitation to the suction performance, power density, and operational life of inducers and turbopumps used in liquid propellant rocket engines for primary space propulsion. This thesis illustrates the application of the Bayesian estimation method developed by my supervisor, professor Luca D'Agostino to the identification and characterization of flow instabilities, with special reference to rotating cavitation, in a four-bladed axial inducer, using the unsteady pressure readings of a single transducer flush-mounted on the casing just behind the leading edges of the impeller blades. This thesis is focused on the identification of cavitation-induced flow instabilities of the DAPAMITO4 inducer which is a four-bladed, high-head, axial inducer with a tapered hub and variable pitch and manufactured at ALTA S.p.A.


The typical trapezoidal pressure distribution in the blade channels is parameterized and modulated in time and space for theoretically reproducing the expected pressure generated by known forms of cavitation instabilities (cavitation auto-oscillations, n-lobed rotating cavitation, higher-order surge/rotating cavitation modes). The Fourier spectra of the theoretical pressure so obtained in the rotating frame are transformed in the stationary frame, frequency broadened to better approximate the experimental results, and parametrically fitted to the measured auto-correlation spectra by maximum likelihood estimation with equal and independent Gaussian errors.


Each form of instability generates a characteristic distribution of sidebands in addition to its fundamental frequency. The identification makes use of this information for effective detection, discrimination, and characterization of multiple simultaneous flow instabilities/perturbations. The same information also allows for effectively bypassing the aliasing limitations of traditional cross-correlation methods in the discrimination of multiple-lobed azimuthal instabilities from dual-sensor measurements on the same axial station of the machine. The method returns both the estimates of the model parameters and their standard deviations, providing the information needed for the assessment of the accuracy and statistical significance of the results. The results are consistent with the available data obtained from traditional pressure cross-correlation techniques. The proposed approach represents therefore a promising tool for improving the sensitivity and cost-effectiveness of experimental research on flow instabilities in high-performance turbopumps.