The precipitation of dissolved gas in oil is a challenging problem in pollution control of hydraulic systems. When the self-excited oscillation jet is formed, there are two low-pressure regions in the self-excited oscillation cavity, and the reduction in pressure causes the dissolved gas in the oil to precipitate out. Here, we investigated the effect of the self-excited oscillation cavity on the dissolution of dissolved gas in oil. We studied the gas precipitation performance of the self-excited oscillation cavity by simulating the pressure and velocity fields inside the cavity under different ratios of dimensionless structure parameters. The results indicated that parameter intervals for maintaining good gas precipitation performance of the self-excited oscillation cavity were d2/d1=2–2.4, D/d2=4–6, and D/L = 2. We then used a heuristic prediction algorithm (Genetic algorithm-backpropagation, GA-BP) to fit the simulation and experimental data, in which the root mean square error between the simulation and experimental data was only 2.45%. This indicated that the simulation of the flow field was reasonable, and that the GA-BP model performed well in predicting the gas precipitation performance of the self-excited oscillation cavity. Our results have important guiding significance for future studies on the gas precipitation performance of the self-excited oscillation cavity.

Accelerated life test (ALT) is commonly used to assess lifetime of mission critical and expensive products. Accurate ALT requires realistic load sequence representative of the actual and/or expected work conditions. A novel method, using the Markov information matrix, is proposed in this paper to produce the load sequence to be applied in the ALT. The proposed method provides several advantages, including improved efficiency as well as capturing dynamic characteristics of the loading conditions, such as transition frequency and cumulative time effect of the load. This paper presents the Markov method for analyzing the load sequence, in which the Markov information matrix is modified by taking into account the cumulative effect of each load level. The model is then applied to the ALT of hydraulic piston pumps, which are widely used in aircraft, in order to perform analysis of the cumulative damage. Considering the actual aircraft operation, hidden Markov model is used to generate the equivalent accelerated load sequence of a pump. The load sequence can be easily applied on a test bench for the verification and approval of pumps. Finally, an accelerated sequence design method is proposed to guide the ALT. The method ensures the consistency of the transfer frequency and the cumulative damage caused by the load, which provides an equivalent sequence for the ALT.

Low-frequency vibration control is a crucial challenge within the realm of fluid physics, and in this paper, a novel magnetic compound fluid (MCF) has been developed to produce a magnetic fluid nonlinear energy sink (MF-NES) with optimum energy consumption efficiency. The mixed doping of bi-dispersed nano-sized iron particles (NIPs) and micrometer-sized carbonyl iron particles (CIPs) can generate a load-bearing structure between the inertial mass and the MF-NES shell with a reduced friction coefficient. The effectiveness and sensitivity of the MF-NES mechanism is significantly enhanced, particularly in responding to low frequency vibrations. To predict and quantify the friction coefficient of the MF, a novel hybrid approach combining data handling and a genetic algorithm model was developed and rigorously validated through empirical data obtained from the experiments and demonstrated that the bi-dispersed NIPs/CIPs MCF had a notably lower friction coefficient, making it adept at responding to low-frequency and even ultra-low-frequency vibrations.