Marine engineering materials are prone to serious corrosion damage, which affects the efficiency and reliability of marine equipment. The diversity of corrosion morphology makes it difficult to achieve the quantification and standardization of the microscopic local information on the corroded surface, which is of great significance to reveal the multi-scale corrosion mechanism. In this paper, an image intelligent recognition method for the corrosion damage of Q420 steel in seawater is established, which is based on the gray level co-occurrence matrix, binary image method and fractal model. Through the feature extraction of corrosion morphology, the quantitative analysis of corrosion morphology and the microscopic evaluation of corrosion characteristics are achieved. The image recognition data are consistent with the electrochemical result for most cases, which confirms the validity of this image intelligent recognition method. The average gray value and energy value of corrosion morphology reduces with the Cl⁻ concentration, indicating that the corrosion damage aggravates gradually. The increasing standard deviation and entropy reflects that the randomness of the pit distribution increases. The pitting ratio increases from 20.19% to 51.64% as the Cl⁻ concentration increases from 50% to 200% of the standard solution. However, there exists a discrepancy for high Cl⁻ concentration because of the irregular corrosion morphology and various pit depth. The fractal dimension increases with the complexity of the corroded surface at low Cl⁻ concentration, but the fractal dimension decreases at high Cl⁻ concentration because the corrosion complexity is interfered by the interconnection of corrosion holes due to the accelerated pit evolution.

When water pipelines undergo scenarios such as valve closure or leakage, they often operate in a gas-liquid two-phase flow state, which can easily cause abnormal pressure fluctuations, exacerbating the destructiveness of water hammer and affecting the safe operation of the pipeline. To study the problem of abnormal fluctuations in complex water pipelines, this paper establishes a transient flow model for gas-containing pipelines, considering unsteady friction, and solves it using the discrete gas cavity model (DGCM). It also studies the influence of factors such as valve closing time, initial flow rate, gas content rate, leakage location, and leakage amount on the end-of-valve pressure. Furthermore, it locates the leakage position using a genetic algorithm-backpropagation neural network (GA-BP neural network). The results show that increasing the valve closing time, increasing the gas content rate, decreasing the initial flow rate, and increasing the leakage amount all reduce the pressure peak inside the pipeline. The model constructed using the GA-BP neural network effectively predicts the leakage location with a mean absolute percentage error (MAPE) of 9.26%. The research results provide a reference for studies related to the safety protection of water conveyance projects.

The development of hydroelectric projects has adversely affected the reproductive activities of downstream fish species. To facilitate the natural reproduction of fish and restore spawning grounds post‐dam construction, it is imperative to explore the ecological factors crucial for their reproduction. Currently, various research methods with different advantages and limitations are employed for this purpose. Using identified spawning locations and periods as clues, we quantitatively investigate the flow velocity, water depth, water temperature, and riverbed substrate required for spawning. The results are validated using habitat simulation methods, aiming to establish a more scientific approach to explore ecological factors affecting fish reproduction. This study provides a more scientific, systematic, and detailed report on the ecological factors required for the spawning of Gymnocypris eckloni: flow velocity ranging from 0.19 to 0.97 m/s, water depth from 0.28 to 1.12 m, water temperature between 11.4 and 15.2°C, and predominantly gravel substrate. The reliability of the results was verified in another spawning ground, with good verification results. This research provides crucial data for the bio‐mimetic reproductive technology of Gymnocypris eckloni and the restoration of spawning grounds for natural fish reproduction post‐dam construction. It addresses the lack of suitable ecological factor data for protective fish species in the upper reaches of the Yellow River. The method exhibits strong scientific, accurate, and implementable characteristics.

To enhance the heat-transfer performance of scramjet engines, a numerical simulation was conducted on the heat-transfer process of RP-3 aviation kerosene under supercritical pressure within a horizontal micro-fine circular tube. The intrinsic mechanism of the heat-transfer process was analyzed, summarizing the impacts of mass flux, inlet temperature, and gravitational acceleration. Furthermore, four commonly used buoyancy criterion numbers were compared and evaluated. The results indicate that the heat-transfer process can be divided into five phases: heating inlet phase, normal heat-transfer phase, heat-transfer deterioration phase, heat-transfer enhancement phase, and high-temperature normal heat-transfer phase. The heating inlet phase is significantly influenced by the inlet temperature, while the heat-transfer deterioration is affected both by the thermal property variations of the aviation kerosene and the buoyancy effects. Lower mass flux and hypergravity conditions all exacerbate heat-transfer deterioration. Inlet temperature, however, does not affect the heat-transfer pattern. Among the criteria, Gr_q/Gr_th provides the best prediction of buoyancy effects in horizontal circular tubes.