Zhenyu Wang’s research while affiliated with China Academy of Chinese Medical Sciences and other places

Hydrogen serves as an efficient energy vector with advantages such as high energy density, greenness, and cleanliness. Hydrogen generation from water electrolysis with renewable energy is an effective approach for achieving renewable energy consumption and green hydrogen energy production. Polymer electrolyte membrane water electrolysis (PEMWE) is capable of presenting the merits of high current density, high productivity, superior gas purity, low energy consumption and high safety. The development of PEMWE is an important part of achieving the coupling of renewable energy, electric energy and hydrogen energy. As a crucial component of PEMWE, bipolar plates (BPs) constitute the mechanical support of the whole cell and provide a channel for electron transport and material supply. These channels determine the electrochemical and hydrodynamic response of a PEMWE. This work reviews the latest developments and applications of BPs, with a focus on the challenges of flow field structure and material fabrication. The specific content covers the BP matrix, types of surface layers, and effect of flow field design on mass transfer. Extended-term growth and feasibility studies of BPs, which can provide a reference and guidance for the configuration of high-behavior flow fields in PEMWEs in the long run, are envisioned.

This study investigates the tracking of underwater cables using autonomous underwater vehicles (AUVs) equipped with side-scan sonar (SSS). AUV motion stability is crucial for effective SSS imaging, which is essential for continuous cable tracking. Traditional methods that derive AUV guidance rates directly from measured cable states often cause unnecessary jitter when imaging, complicating accurate detection. To address this, we propose a non-myopic receding-horizon optimization (RHO) strategy designed to maximize cable imaging quality while considering AUV maneuvering constraints. This strategy identifies the optimal heading decision sequence over a future horizon, ensuring stable and efficient cable tracking. We also employ a long short-term memory (LSTM) network to predict future cable states, further minimizing AUV motion instability during abrupt path changes. Given the computational limitations of AUVs, we have developed an efficient decision-making framework that can execute resource-intensive algorithms in real time. Finally, the robustness and effectiveness of the proposed algorithm were validated through comparative experiments. The results demonstrate that the proposed method outperforms existing methods in key metrics such as cable-tracking accuracy and AUV motion stability. This ensures that the AUV can acquire high-quality acoustic images of the submarine cable in an optimal state, enhancing the continuity and reliability of cable-tracking tasks.

This study explores the impact of magnetic fields on the operation and performance of proton exchange membrane fuel cells (PEMFCs) with various flow fields. Different positions and intensities of magnetic fields were applied to investigate their effects. The performance of PEMFCs with different flow fields was then tested under different magnetic field strengths and arrangements. The experimental results demonstrate that, under the same conditions, the power density of the PEMFCs increases when subjected to magnetic fields of varying strengths (180mT, 220mT, and 260mT) compared to no magnetic field. Furthermore, the maximum power density of the cells increases with higher applied magnetic field strengths. The experiments also compared the performance of the fuel cell's cathode, anode, and bipolar operations with magnetic field arrangements in different flow fields. The results reveal that the performance enhancement of the fuel cell with magnetic field arrangement in the cathode is greater than that with magnetic field arrangement in the anode and bipolar. Specifically, when a magnetic field of 260mT is loaded onto the cathode for fuel cells with parallel, wave, and M−type flow fields, the maximum power density (MPD) increases by 55%, 23.9%, and 23.22%, respectively. In conclusion, the utilization of magnetic fields can enhance the performance of PEMFCs under operating conditions.

Submarine cables and pipelines laid on the seafloor are critical infrastructures for energy transportation and communication transmission and must be inspected in a timely manner to determine maintenance needs. Autonomous inspection of cables (including pipelines) and their surroundings using autonomous underwater vehicles (AUVs) is difficult because of sensor myopia and deviations in cable routing. This study proposes an automatic cable tracking method (ACTM) based on side-scan sonar (SSS) that endows AUVs with autonomous decision-making capabilities to achieve fast localization and stable tracking of submarine cables with large uncertainties. Compared with the traditional predefined waypoint method, the proposed method decomposes the tasks into a series of independent behaviors according to operational objectives; thus, the AUV can perform online dynamic task replanning and realize an autonomous coverage search of the target region. When a cable is detected, the cable-tracking task is constructed as a path-following control problem on the horizontal plane. To reduce uncertainties caused by environmental noise and sensor measurement errors, a uniform motion model based on target tracking theory was used to model the virtual mass abstracted from the cable, and the states of the cable were estimated using the Kalman filter. Subsequently, an adaptive line-of-sight guidance algorithm for the SSS is established to dynamically adjust the look-ahead distance by introducing states such as cable curvature and angular change rate, which somewhat alleviates the tracking loss caused by the SSS short-sightedness. The proposed ACTM effectiveness and robustness were validated through numerical simulations and field tests. The results showed that the proposed method can achieve automatic search and adaptive tracking of cables, further reducing the role of humans in the task.

The endurance of a deep-sea AUV is closely related to its sailing resistance, the amount of carrying batteries and equipment load, which involves interactions among multiple disciplines. In this paper, in order to develop the conceptual design of a long-range AUV in the early stage, a multidisciplinary optimization design framework is presented for decision-makers to explore the given design space, which takes into account the coupling between the disciplines of hull form, structural design and energy use. A Self-adaptive Surrogate Ensemble (SASE) method is proposed to replace the expensive process of hydrodynamic analysis, a recommended approach by the China Classification Society (CCS) specification is applied to carry out the design of metallic pressure hulls, and the classical lamination theory and Tsai–Wu criteria are adopted in the design of composite pressure hulls. Finally, the evaluation model of AUV endurance is created from the perspective of energy capacity and consumption. The conceptual design of a 200 kg-class AUV is executed to maximize the endurance based on the proposed multidisciplinary optimization design framework. The results show that the most important factors that affect AUV endurance are the velocity and diameter, and the optimum velocity of the AUV increases with the load power. The Sea-Whale 2000 AUV was developed based on the optimal result and the excellent endurance performance in the sea trial validated the effectiveness of the proposed design method in the preliminary design process.

Sailing speed performance is a crucial indicator that significantly affects the trafficability, efficiency, and tracking capability of autonomous sailing monohulls during marine science missions. Considering that the design of the hull and keel of an autonomous sailing monohull is usually a task-orientated and creative process, estimating speed performance by traditional velocity prediction programs (VPPs) based on empirical formulas and gradient solvers will lead to errors. This paper proposes a generalized VPP for helping designers assess the speed performance of their autonomous sailing monohulls. We designed an enhanced genetic algorithm (GA) solver to help the VPP converge quickly without a priori performance estimation. Furthermore, we propose an innovative neighbourhood information-based optimization (NIBO) strategy to accelerate and refine the solutions using adjacent states (external conditions with the same true wind speed (TWS) or true wind angle (TWA)) instead of culminating prediction by solving each state independently. We provide an application of the proposed VPP on our prototype as an example. Moreover, the numerical and experimental results show that the proposed VPP can serve as a practical design evaluation tool, especially in the early stages of design.