Toroidal propellers, a new type of drone propellers capable of significantly reducing noise, offer new possibilities for future low-altitude flying platforms. In this study, a numerical model was established to analyze the aerodynamic noise of the toroidal propeller under normal atmospheric conditions. The aerodynamic calculations for the toroidal and benchmark propellers were performed to obtain noise source information using a transient large eddy simulation. The hybrid computational aeroacoustic method was employed to calculate the noise spectrum at different speeds and locations. Moreover, an experimental system for propellers was designed and built within an anechoic chamber to investigate the aerodynamic performance and noise characteristics at various speeds. The noise reduction effect of the toroidal propeller compared to the benchmark propeller was analyzed, taking into account the sound pressure level, lift coefficient, and figure of merit. At the same thrust level, the lift coefficient of the toroidal propeller increased by 187% relative to the benchmark propeller. The radial and axial sound pressure levels decreased by 5.2 dB(A) and 19.6 dB(A), respectively. The toroidal propeller significantly reduced noise while improving aerodynamic performance. This study provides a theoretical basis, experimental methods, and data support for the aerodynamic and noise calculations of toroidal propellers. It has significant engineering implications for the development of low-noise propellers for drones.

The stability of brake control is an important guarantee for the safety of heavy-duty vehicles (HDVs) at high speeds. However, the electro-hydraulic actuation braking systems often exhibit a significant delay in seconds, which makes braking performance forecasting and control difficult. To address the torque tracking control problem with time delay, a deep inference and control method is proposed. First, a theoretical delay time under different rotating speeds is identified with a data-driven model. Then, a fast end-to-end prediction model is established to estimate the torque performance of the next step with delay information. The deep Q-network (DQN) learning approach is proposed to learn the experimental data by exploring and seeking the optimal control strategy in the time delay environment. A comparative simulation of the proposed DQN-based controller with or without considering time delay, and the rule-based method with or without considering time delay is implemented, and an online processor-in-the-loop(PIL) test with the edge computing device NVIDIA Jetson Xavier NX is performed on the robustness condition. The simulation results and PIL test results demonstrate that the proposed control framework achieves a great improvement in the torque tracking effect with time efficiency.

Heavy cavitation in torque converters can have a significant effect on hydrodynamic performance, particularly with regards to the torque capacity. The objective of this study is to therefore investigate the effects of pump and turbine blade geometries on cavitation in a torque converter and improve the torque capacity without increasing the torus dimension. A steady-state homogeneous computational fluid dynamics (CFD) model was developed and validated against test data at stall operating condition. A full flow passage with a fixed turbine-stator domain was used to improve the convergence and accuracy of the cavitation model. Cavitation analysis was carried out with various pump and turbine blade geometries. It was found that there is a threshold point for pump blade exit angle in terms of its effect on torque capacity due to heavy cavitation. Further increasing the pump blade exit angle past this point will worsen cavitation condition and decrease torque capacity. The study also shows that a higher turbine blade exit angle, i.e., lower stator incidence angle, could reduce flow separation at the stator suction surface and consequently abate cavitation. A base high-capacity torque converter was upgraded utilizing the cavitation model, and the resulting design exhibited a 20.7% improvement in capacity constant without sacrificing other performance metrics.

Purpose: The purpose of this paper is to study the transient cavitation process in torque converters with a particular focus on cavitation suppression with a passive flow control technique. Design/methodology/approach: The transient fluid field in a torque converter was simulated by RANS-based computational fluid dynamics (CFD) in a full three-dimensional (3D) model. A homogeneous Rayleigh–Plesset cavitation model was used to simulate the transient cavitation process and the results were validated with test data. Various secondary flow passages (SFP) were applied to the stator blade. The cavitation behavior and hydrodynamic performance were simulated and compared to investigate the effect of SFP geometries on cavitation suppression. Findings: Presented results show that cavitation in the torque converter is highly unstable at stall operating condition because of the combination of a high incidence angle and high flow velocity. The addition of an SFP to the stator blade produces a disruption of the re-entrant jet and reduces the overall degree of cavitation, consequently inhibiting the unstable cavitation and reducing performance degradation. Originality/value: This paper provides unique insights into the complicated transient cavitation flow patterns found in torque converters and introduces effective passive flow control techniques useful to researchers and engineers in the areas of fluid dynamics and turbomachinery.

Hydraulic torque converter is a kind of high speed rotating machine using viscosity hydraulic oil as working medium, and its internal flow field is very complex. Thereby cavitation can occur easily in the working process, resulting in severe degradation of torque converter performance, noise, vibration and even failure. In order to reveal the effect of rotating speeds on the cavitation characteristics, a full flow passage geometry and a computational fluid dynamics (CFD) model with cavitation were developed to analyze the flow behavior in the torque converter. The results show that cavitation occurs when the speed difference between pump and turbine exceeds 1400 rpm for the basic model torque converter, which could be used as a useful indicator for the occurrence and degree of severity of flow cavitation. The increase of pump rotating speed or the decrease of speed ratio will intensify cavitation, which reduces the hydraulic transmission capacity and efficiency by over 20%, and seriously alters the shape, size, vapor volume fraction and region of cavitation bubbles. In extreme cases, more than 80% of the area on the suction side of the stator blade could be covered by cavitation bubbles. Moreover, the increase of pump rotating speed also changes the critical cavitation number and extends the cavitation range towards high speed ratio conditions not previously affected. These findings can provide guidance on how to choose the operating conditions of the hydraulic torque converter and how to improve its hydrodynamic performance and stability.

Cavitation in torque converters may cause degradation in hydrodynamic performance, severe noise, or even blade damage. Researches have highlighted that the stator is most susceptible to the occurrence of cavitation due to the combination of high flow velocities and high incidence angles. The objective of this study is to therefore investigate the effects of cavitation on hydrodynamic performance as well as the influence of stator blade geometry on cavitation. A steady-state homogeneous computational fluid dynamics (CFD) model was developed and validated against test data. It was found that cavitation brought severe capacity constant degradation under low-speed ratio (SR) operating conditions and vanished in high-speed ratio operating conditions. A design of experiments (DOE) study was performed to investigate the influence of stator design variables on cavitation over various operating conditions, and it was found that stator blade geometry had a significant effect on cavitation behavior. The results show that stator blade count and leaning angle are important variables in terms of capacity constant loss, torque ratio (TR) variance, and duration of cavitation. Large leaning angles are recommended due to their ability to increase the cavitation number in torque converters over a wide range of SRs, leading to less stall capacity loss as well as a shorter duration of cavitation. A reduced stator blade count is also suggested due to a reduced TR loss and capacity loss at stall.