Jet fish pumps are efficient hydraulic machinery for fish transportation. Yet, the complex flow phenomenon in it is the major potential risk for damage to fish. The dangerous flow phenomena for fish, such as radial pressure gradient and exposure strain rate, are usually controlled by the structural parameters of jet fish pumps. Therefore, the injury rate of fish can be theoretically decreased by the structural optimization design of jet fish pumps. However, there is a complex nonlinear relation between flow phenomena and key structural parameters. To solve this problem, the present paper established a complex mapping between flow phenomena and structural parameters, based on computational fluid dynamics and a back-propagation neural network. According to this mapping, an NSGA-II multi-objective genetic algorithm was used to optimize the structure of jet fish pumps. The results showed that the optimized jet fish pumps could reduce the internal radial pressure gradient, exposure strain rate and danger zone to 40%, 12.5% and 50% of the pre-optimization level, respectively. Therefore, the optimized jet fish pump could significantly reduce the risk of fish injuries and keep the pump efficiency at a high level. The results could provide a certain reference for relevant structural optimization problems.

More and more activities have caused significant damage to the river environment, among which a typical problem of blocked fish migration is constantly attracting people’s attention. Nowadays, fishways are essential hydraulic facilities to solve such problems. Although a different fishway has a particular blocking effect on the water flow, the flow velocity of the vital positions of fish migration in the fishway could still be relatively high locally, which may pose a certain challenge to the fish migration (the higher flow velocity could lead to the increase in migratory energy consumption of fish). Therefore, further exploration of fish passing facilities may be required. As a check valve without movable parts, the Tesla valve is expected to be used in fish passing facilities because of its substantial obstruction to the reverse flow of internal fluid. This paper conducted numerical simulation experiments on the fish passage pipeline designed based on Tesla valves using the RNG (renormalization group) k-ε model. Grass carp were selected as the primary analysis object, and the simulation results were analyzed from the perspective of turbulence characteristics. The results showed that the fish passage pipeline based on the T45-R Tesla valve was better than that on the GMF (Gamboa, Morris and Forster) Tesla valve in velocity control. The velocity at the vital position of T45-R internal fluid was about 20% lower than that of GMF. The results of the velocity cloud diagram showed apparent high-velocity and low-velocity areas in the fish passage pipeline designed based on the T45-R Tesla valve. The high-velocity area was the vital position for fish upstream, and the maximum velocity variation range in this area was 0.904~1.478 m/s. At the same time, the flow in the low-velocity area is almost static water. The analysis illustrated that the resulting velocity environment could provide conditions for grass carp to move upstream successfully. The results of turbulent kinetic energy inside the fish passage pipeline showed that the maximum value of turbulent kinetic energy was only about 0.043 m²/s², which could be friendly for fish upstream. In addition, the results show that pressure-related problems could not seem to have an excessive impact on fish migration, such as causing damage. Overall, the results further studied the feasibility of using the Tesla valve as a fish passage pipeline.

Fishways act as ecological corridors, enabling migratory fish species to surmount barriers such as weirs or dams, which are crucial for the restoration of river ecosystems. The island-type fishway is a novel design that utilizes a combination of island structures and valvular configurations to dissipate the kinetic energy of water flow, decelerate the water velocity, and thus reduce the challenge faced by fish attempting to ascend the watercourse. The impact of valvular configurations on the hydrodynamic characteristics within an island-type fishway was explored. The results showed that the main high-velocity flow exhibits a nearly “S”-shaped characteristic, while a low-velocity region develops downstream of the valvular. The valvular configuration has a significant effect on the internal flow dynamics of the island-type fishway. Specifically, the smaller the valvular arc angle, the broader the high-velocity main flow becomes, and the smaller the area of the low-velocity region. When the valvular arc angle is set at 180°, the area dominated by low flow velocities maintains a coverage of over 60%. As the valvular arc angle decreases, turbulent kinetic energy rises, leading to an approximate 70% increase in the maximum turbulent kinetic energy across different water layers relative to the model with the initial angle setting. Within the range of valvular arc angles studied, an island-type fishway with a 180° valvular arc angle is most conducive to supporting the upstream migration of fish. This study can provide a reference for the further development of island-type fishways.

With the development of hydraulic structures, rivers are becoming fragmented, and their connectivity is greatly affected. Important migratory routes through which fish complete their life processes are hindered. In severe cases, it may lead to the extinction of fish species. As facilities commonly used to assist fish upstreaming, fishways are significant for environmental remediation and have received attention from different scholars. This article proposed a new type of fishway, inspired by Tesla valves with obstructing reverse flow characteristics and classic fishway structures. Due to its characteristic of introducing a water-blocking island structure, it was denominated as an island-style fishway. This work studied an island fishway through a combination of physical models and numerical simulations. The results show that this fishway could achieve various flow patterns suitable for fish migration, and the flow rate control was stable. This study also explored the impact of island spacing arrangement on the turbulent structure of the pool chamber. It was found that when the island distance is set at 1.5d, indicators such as flow velocity and turbulent kinetic energy could achieve significant control effects. However, inappropriate d values might cause adverse effects. This research could provide reference ideas for the design of new fishways.

The rice‐fish system has gained significant interest in recent years because of its effective usage of limited land and freshwater resources. To scientifically guide the improvement of rice field fishery production, the data in this study were selected from the latest China Fishery Statistical Yearbook, and therefore the development of paddy aquaculture was investigated. In order to more precisely predict the production of rice‐fish in China, this paper introduces an artificial neural network with the SSA‐BP model, which solves the drawbacks of the BP neural network such as easy to fall into local optimum and slow convergence speed when it is used for prediction. Firstly, the SSA‐BP model incorporates the aquaculture area (split by water area), the national freshwater fish seedling output, the national end‐of‐year ownership of inland fishing vessels, the number of freshwater fisheries practitioners as input variables, and the production of rice‐fish as an output variable; secondly, the SSA optimization algorithm was used to find the optimal initial thresholds and weights for the BP neural network, and finally the SSA‐BP prediction model was constructed. The results revealed that the overall expansion of the rice field fishery was swift in the last five years, and the output of cultivated fish in China’s rice fields rose by nearly 20% yearly in the past five years. Compared with the BP neural network and GA‐BP models, the accuracy of the SSA‐BP prediction model was enhanced by 61.01% and 16.15%, respectively, which was more suited for predicting the production of rice‐fish.