In modeling the characteristics of a discharging valve in a hydrodynamic retarder, it is commonly required to determine the value of the flow area to calculate the force on the spool. However, the flow area often relies heavily on empirical or simulation data, which leads to increased uncertainty and computational cost, especially with the variation in the spool displacement. To overcome these shortcomings, Res-SE-U-Nets (networks that combine residual connections, squeeze-and-excitation blocks, and U-Net) are used to reconstruct the velocity field, and they have shown exceptional performance in image-to-image mapping tasks. The dataset of computational fluid dynamics (CFD) results for the velocity field is collected and verified using particle image velocimetry (PIV). The results show that Res-SE-U-Nets can capture the location information of the flow field using a training set of only 120 data points. By utilizing location information in velocity field reconstruction, the flow area can be directly obtained under different spool displacements and pressures to calculate the spool force. The valve characteristics calculated with this method show an error of less than 2% when compared with CFD results, which confirms the validity and effectiveness of this method. The proposed method, which utilizes location information extracted from flow field prediction results, is capable of calculating valve characteristics. This approach also demonstrates the feasibility of using Res-SE-U-Nets for flow field reconstruction.

Temperature rise is a salient characteristic of the braking device of heavy-duty vehicles (HDVs) during long downhill braking. The temperature-independent effect on braking performance is an inevitable challenge for the longitudinal controller, which also causes the overheating of mechanical or hydrodynamic braking devices and may bring about brake failures for heavy-duty vehicles. To this end, a universal Bi-level control framework combining a temperature hierarchy system performance prediction method and a deep reinforcement learning (DRL)-based controller is proposed for long downhill braking of heavy-duty vehicles. Firstly, the temperature-independent characteristic is clustered to predict the braking performance under various rotating speeds. Secondly, a data-driven model for temperature rising is built for the long-time braking thermal prediction and estimates the safety remaining braking time using the auxiliary braking. Thirdly, a temperature-hierarchy environmental perceptive control framework with Double Deep Q Network (DDQN) algorithm is exploited to achieve the target speed tracking accuracy. Thermal safety is ensured with the application of fast calculating for the thermal rising, along with the effective estimation of remaining braking performance on endurance braking. The proposed Bi-level longitudinal controller is compared with the average-temperature strategy and the classic PID strategy to validate its superiority in terms of speed-tracking accuracy on robust conditions. The simulation results show that the proposed strategy improves the speed tracking accuracy by 37.31% on constant slope conditions and 68.11% on varying slope conditions compared with the classic PID strategy. Furthermore, a processor-in-the-loop test experiment verifies its real-time application.

Traditional forward design method for blade of hydrodynamic torque converter is limited by empirical design so that it is difficult to produce innovative blade design results. Based on the adjoint sensitivity, an unconventional optimization blade design method, Adjoint Fluid Topology Optimization method was proposed. Based on this method, a directional topological optimization of stator blade design was carried out as an example to achieve a better torque ratio performance for torque converter under stalling condition. Such optimization method can achieve irregular stator blade with better performance. Moreover, the flow energy distribution before and after such topology optimization are further studied, the results show that the optimized torque ratio is increased by 9.99% under stalling condition when comparing with the original design result, and the irregular blade result has a more reasonable distribution of flow energy, indicating that fluid topology optimization can not only get rid of the restriction of forward blade design method and provide an innovation way for irregular blade design, but also improve the potential performance of turbomachinery.