THE METHOD OF HYDRODYNAMIC MODELING USING A CONVOLUTIONAL NEURAL NETWORK

Abstract

Context. Solving hydrodynamic problems is associated with high computational complexity and therefore requires considerable computing resources and time. The proposed approach makes it possible to significantly reduce the time for solving such problems by applying a combination of two improved modeling methods.
 Objective. The goal is to create a comprehensive hydrodynamic modeling method that requires significantly less time to determine the dynamics of the velocity field by using the modified lattice Boltzmann method and the pressure distribution by using a convolutional neural network.
 Method. A method of hydrodynamic modeling is proposed, which realizes the synergistic effect arising from the combination of the improved lattice Boltzmann method and a convolutional neural network with a specially adapted structure. The essence of the method consists of implementing a sequence of iterations, each of which simulates the process of changing parameters when moving to the next time layer. Each iteration includes a predictor step and a corrector step. At the predictor step, the lattice Boltzmann method works, which allows us to obtain the field of fluid velocities in the working area at the next time layer using the field of velocities at the previous layer. At the corrector step, we apply an improved convolutional neural network trained on a previously created data set. Using a neural network allows us to determine the pressure distribution on a new time layer with a predetermined accuracy. After adding the fluid compressibility correction on the new time layer, we get a refined value of the velocity field, which can be used as initial data for applying the lattice Boltzmann method at the next iteration. Calculations stop when the specified number of iterations is reached.
 Results. The operation of the proposed method was studied on the example of modeling fluid movement in a fragment of the human gastrointestinal tract. The simulation results showed that the time spent implementing the simulation process was reduced by 6–7 times while maintaining acceptable accuracy for practical tasks.
 Conclusions. The proposed hydrodynamic modeling method with a convolutional neural network and the lattice Boltzmann method significantly reduces the time and computing resources required to implement the modeling process in areas with complex geometry. Further development of this method will make it possible to implement real-time hydrodynamic modeling in threedimensional domains.