A novel hydro turbine with the ultra-small unit discharge was suggested and studied for the first time. It was specially developed for hydraulic energy recovery with a smaller flow rate and medium available head, its highlight feature is straight blades in the radial direction. In this paper, the theoretical and numerical simulation of a novel hydro turbine was studied. First, with the working process and principles introduced, the theoretical models were established for the prediction of optimal unit speed, optimal unit discharge, and performance curves. Second, numerical simulation was carried out to study the performance of the novel turbine and verify the theoretical models. Different mesh quantity cases were investigated to select an accurate configuration. The numerical simulation predicted that optimal unit speed was 57.07 r/min, and optimal unit discharge was 0.0705 [Formula: see text]/s, while the specific speed was 42.3 m kW. Comparisons between theoretical calculation and numerical simulation show the accuracy of the theoretical models. Finally, a tail energy recovery runner was proposed to improve the performance of the novel turbine, and maximum efficiency was improved from 79.6% to 84.5%. The novel hydro turbine fills the blank working area between Pelton turbine and Francis turbine, and could be easily applied to engineering with low cost and simple manufacture.

To improve the identification accuracy of the vibration states of hydraulic units, an improved artificial rabbits optimization algorithm (IARO) adopting an adaptive weight adjustment strategy is developed for optimizing the support vector machine (SVM) to obtain an identification model, and the vibration signals with different states are classified and identified. The variational mode decomposition (VMD) method is used to decompose the vibration signals, and the multi-dimensional time-domain feature vectors of the signals are extracted. The IARO algorithm is used to optimize the parameters of the SVM multi-classifier. The multi-dimensional time-domain feature vectors are input into the IARO-SVM model to realize the classification and identification of vibration signal states, and the results are compared with those of the ARO-SVM model, ASO-SVM model, PSO-SVM model and WOA-SVM model. The comparative results show that the average identification accuracy of the IARO-SVM model is higher at 97.78% than its competitors, which is 3.34% higher than the closest ARO-SVM model. Therefore, the IARO-SVM model has higher identification accuracy and better stability, and can accurately identify the vibration states of hydraulic units. The research can provide a theoretical basis for the vibration identification of hydraulic units.