Fishway design method based on computational fluid dynamics and convolutional neural network

Abstract

The invention discloses a fishway design method based on computational fluid dynamics and a convolutional neural network. The fishway design method comprises the following four stages: a first stage,synchronously measuring and recording flow field information and a target fish swimming track; a second stage: establishing a fish perception model; a third stage: establishing a stimulation responseneural network model of the target fish in the changed water flow environment; and a fourth stage: applying the stimulation response neural network model obtained in the third stage to the fishway ofwhich the geometric body type is changed or the water flow boundary condition is changed. The changed fishway flow field is obtained through the computational fluid dynamics technology, and the motiontrails of fishes are predicted through the stimulation response neural network model in combination with the flow field. According to the fishway design method, through deep learning of the neural network on the superposed transient flow field and the target fish trajectory, the response relationship of the target fish to water flow stimulation is established, and then fish motion trajectory prediction is realized.

Description

technical field [0001] The invention belongs to the field of water conservancy and hydropower engineering environmental protection, and in particular relates to a fishway design method based on computational fluid dynamics and a convolutional neural network. Background technique [0002] The massive construction of water conservancy facilities will inevitably reduce the connectivity of river and lake water systems, leading to drastic changes and fragmentation of the habitat environment on which local fish depend, resulting in a significant reduction in the number of fish, or even extinction. Fishway, as an ecological compensation measure to alleviate such problems, has been widely used at home and abroad. However, there is still a large distance between the actual use effect of many fishways and the design expectations. The main reasons are: the designer does not know what kind of fishway water flow the target fish prefers, and there are differences in the individual charact...

AI Technical Summary

Problems solved by technology

The traditional design method does not consider the response of individual fish to the hydraulic stimulation of the complex flow field around them in the changing swimming environment, which makes the design have great limitations and is difficult to promote

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