In nuclear power systems, centrifugal pumps often need to operate under extreme conditions. However, accurately determining the cavitation status of centrifugal pumps under such extreme conditions is challenging. To improve the recognition accuracy of the three statuses of non-cavitation, incipient cavitation, and severe cavitation while improving the anti-interference capability of the monitoring system, this study extracted cavitation features from centrifugal pumps’ motor current and vibration signals under three different operational conditions. It fused the features using feature-level multi-source information fusion (MSIF) based on the backpropagation neural network (BPNN) or support vector machine (SVM) to construct a cavitation status recognition model and analyzed the results to compare with those of recognition without information fusion. The results show that, compared with one signal source, MSIF can significantly improve the recognition accuracy of cavitation statuses. Combined current and pump casing axial monitoring based on the BPNN is the optimal scheme, with an overall recognition accuracy of 97.3% for all operational conditions, compared to 73.9% for the single current signal and 89.3% for the single casing axial vibration signal. These research results can guide the monitoring of cavitation statuses in practical engineering, as well as timely intervention at incipient cavitations to reduce structural damage to centrifugal pumps and prolong service life.

The collapse of bubbles in hydraulic machinery has emerged as a prominent area of research. To grasp the interplay between bubbles, a model of double-bubbles is built. The bubble morphology, total pressure (P), and the center of mass displacement (L_com) are taken as research objects, and the influence temperature (T), and bubble radius (R), bubble distance (L) on bubble collapse is summarized. Results show that the distance between the bubbles is smaller, the total collapse time is longer. However, L_com increases when the distance is increased or decreased to some extent. Moreover, in the case of the double-bubbles model with r₁ = 10 Å, as the bubbles (r₂ = 7.5, 10, 12.5 Å) collapse, the released pressure gradually increases, then decrease, and the release pressure of the double-bubbles model (r₁ = 10 Å, r₂ = 12.5 Å) is 1.08 times that of the model (r₁ = 10 Å, r₂ = 15 Å). Based on the differential pressure parameters (∆P₁ and ∆P₂), the significance order of temperature (T), bubble distance (L), and bubble radius (r) is L ≈ r > T. The aim of the paper is to provide technical guidance and a theoretical basis for industrial applications of techniques by enhancing the theory of cavitation.

Self-oscillating cavitation jet technology has become a research hotspot of scholars in various fields. However, existing research lacks a summary of the rules of the influence of various factors on the cavitation performance, such that efficient and stable extensive engineering applications are impossible to achieve. This paper aims at optimizing the design of the self-oscillating cavitation jet nozzle (SOCJN) as the objective; this is carried out by the experimental design, optimal Latin hypercube method, and response surface method in (design of experiment) DOE methods on the basis of the ISIGHT optimization method. In addition, taking the vapor volume fraction and cavitation number as a research objective, the obtained optimal structural parameters of the nozzles are applied under the condition of clear water to establish the function mapping relationship between the external geometric characteristics and the vapor volume fraction and cavitation number; then, this is compared with the experiment. The results indicate that the second-order response surface approximate model is suitable for the SOCJN and there is an error smaller than 8% between the approximate model results and the calculated results of the nozzle response. When the diameter of the upper nozzle is D₁ = 4.7 mm, the ratio of the upper nozzle’s diameter to the lower’s diameter (D₁/D₂) is 2.6 and the ratio of the chamber length to the chamber diameter (L/D) is 0.63; pulse jets from the SOCJN have the best pitting effect on the sample at the monitoring point when the convergence angle of collision wall α is 120°. When the structural parameters of the nozzle are optimal structural parameters, the cavitation performance is the best at the initial pressure of 4.8 MPa. This research provides a reference for the optimized design of the SOCJN for industrial applications.

Self-excited oscillating jets (SOJ) are used in several practical applications. Their performances are significantly affected by structural parameters and the target distance. In this study, a geometric model of the SOJ nozzle accounting for multiple structural parameters is introduced, then the related cavitation performances and the optimal target distance are investigated using a Large-Eddy Simulation (LES) approach. Results are also provided about an experiment, which was conducted to validate the simulation results. By analyzing the evolution of the vapor volume fraction at the nozzle outlet, a discussion is presented about the effect of the aforementioned structural parameters on the cavitation performances and the target distance. It is shown that the distribution of cavitation clouds at the outlet of the SOJ nozzle displays a non-monotonic trend (first increasing, then decreasing). Under working conditions with an inlet pressure of 4 MPa, a SOJ nozzle outlet/ inlet diameter ratio (D/D) of 1.2, and a chamber diameter ratio (D/L) close to 1.8, the nozzle outlet cavitation performance attains a maximum. The optimal structural parameters correspond to the optimal target distance, which is near 50 mm. The experiments have revealed that the SOJ nozzle with the above parameters displays a good cavitation erosion effect at the target distance of 50 mm, in satisfactory agreement with the numerical simulation results.