1. Introduction
In 2020, the Food and Agriculture Organization of the United Nations (FAO) pointed out that the world’s capture fisheries and aquaculture production have shown a rising trend in the past 80 years, and how to efficiently transport fish has become a key to fetching fish [
1]. Therefore, there is a need for fish pumps capable of effectively carrying out live fish catching work, to replace the traditional fish transportation method. According to the working principle, fish pumps can be generally divided into vacuum fish pumps, centrifugal fish pumps, air-lift fish pumps and jet fish pumps [
2]. The vacuum fish pump sucks and discharges the fish water mixture through negative vacuum pressure. Although resulting in slight fish loss, its intermittent suction and discharge work methods lead to low efficiency and high power consumption [
3]. Centrifugal fish pumps are divided into submersible and fixed types depending on the installation position. They rely on the centrifugal force induced by the specially designed high-speed rotating impeller to suck the fish and water mixture, but this working principle causes a high damage rate to fish [
4]. Air-lift fish pumps first use a blower to generate negative pressure in a pipe. When the wind speed in the tube is greater than the suspension speed of the fish, fish are sucked into the fish pump. The fish loss is modest, but the efficiency is low [
5]. The jet fish pump is a particular type of annular jet pump that utilizes high-speed annular jet flow to entrain a mixture flow of water and fish, and it can continuously transport a variety of fish [
6]. The structure of the jet fish pump is simple without any rotating impellers, so the mechanical damage risk for fish is relatively low. Therefore, compared with other fish pumps, jet fish pumps have better performance for live fish transportation.
Jet fish pumps were firstly used in the United States as early as 1922. Yet, it was not until recent decades that scholars paid attention to the research on jet fish pumps. The preliminary research studied the design of jet fish pumps affected by structural parameters, for example, the throat [
7]. After that, following studies focused on the fish in jet fish pumps. Xiao et al. [
3] used a high-speed camera to record the fish locomotion characteristics in a jet fish pump and theoretically analyzed the force on fish. According to this experiment, the team of Long [
2] numerically studied the internal flow of jet fish pumps and discussed potential risks for fish injuries. In the same year, Wu et al. [
8] experimentally studied changes in blood indexes to obtain the stress response of grass carp after passing through jet fish pumps. A year later, Xu et al. [
6] conducted a series of experiments to research the transport capacity of jet fish pumps for different kinds of fish and their diversities of physiological changes were analyzed. Due to the irregular movement of fish in jet fish pumps, the experiment was the main method to study the locomotion of stressed fish before 2018. In 2019, Xu et al. [
9] made a significant step toward the numerical simulation of stressed fish locomotion, developing an image-based numerical simulation method to study the locomotion of fish in jet fish pumps. Based on this method, rich flow details could be obtained, including the distribution of pressure and velocity around fish.
Yet, the working principle of jet fish pumps causes several unavoidable complicated hydraulic factors; shear flow and pressure gradient are typical injury risk sources for fish. Experiments are the common research method to study shear flow. By exposing the fish to a submerged jet, Neitzel et al. [
10] studied the effect of shear flows on various types of juvenile fish and introduced a strain rate as an index of the shear intensity to describe the hydraulic force experienced by a fish in the shear environment. They [
11] found that different fish species had different sensitivities to strain rate and fish could be damaged when strain rates were more than 500 s
−1. Guensch et al. [
12] checked the effects of shear flows and found that eye injuries and operculum injuries were common. Pressure gradients including compression and decompression are common hydraulic factors. Rapid decompression is dangerous for fish because it can cause barotrauma [
13]. Typical symptoms of barotrauma include exophthalmia, protrusions of the everted stomach and gonads, overexpansion or rupture of the swim bladder, displacement of internal organs and rupturing of blood vessels and kidneys [
14]. Some fish have even been killed by the barotrauma in the process of decompression [
15]. Therefore, these dangerous hydraulic factors should merit more attention. Xu et al. [
16,
17] numerically studied the effects of pressure gradients and shear flow on fish and revealed the mechanism of fish external damage caused by these hydraulic factors. These hydraulic factors are the root of fish injury and are mainly determined by structural parameters. Thus, further research on jet fish pumps relies on the optimization of fish injury and transportation performance. For optimization, the biggest challenge lies in the nonlinear relationship between hydraulic factors and structural parameters. Additionally, the change of structural parameters also affects the efficiency and transportation performance of jet fish pumps. Therefore, it is a multi-parameter and multi-objective optimization problem aiming at high pump efficiency and low fish loss.
It is hard to directly establish this nonlinear relationship using traditional methods, so an advanced optimization method is needed. Currently, BP (back-propagation) neural network technology is a good choice to develop complex relationships between multiple objectives [
18]. A BP neural network was considered with arbitrarily complex pattern classification capabilities and excellent multi-dimensional function mapping capabilities [
19]. This technology does not need to determine the mathematical equation of the mapping relationship between input and output in advance. It only learns some rules through its own training and obtains the result closest to the expected output value when the input value is given. As for multi-objective optimization, evolutionary algorithms such as NSGA-II have become the main method for multi-objective optimization problems currently [
20]. NSGA-II is a non-dominated sorting genetic algorithm with an elite strategy [
21]. NSGA-II uses crowdedness and crowdedness comparison operators to make the individuals in the Pareto domain evenly distributed in the entire Pareto domain [
22].
In this research, the flow characteristics in jet fish pumps with different structural parameters were numerically studied. After that, corresponding fish damage was evaluated, according to hydrodynamic theory and multi-objective optimization theory. The BP neural network was used to establish the internal mapping between structural parameters and fish damage. Aiming at reducing fish loss while keeping efficiency without significant decrease, an NSGA-II multi-objective genetic algorithm was used to solve this mapping relationship and the optimized structural parameters of the jet fish pump were obtained.