Aquaculture International, Journal Year: 2023, Volume and Issue: 32(3), P. 3017 - 3040
Published: Dec. 11, 2023
Language: Английский
Aquaculture International, Journal Year: 2023, Volume and Issue: 32(3), P. 3017 - 3040
Published: Dec. 11, 2023
Language: Английский
Ocean Engineering, Journal Year: 2025, Volume and Issue: 319, P. 120196 - 120196
Published: Jan. 4, 2025
Language: Английский
Citations
0Energy, Journal Year: 2025, Volume and Issue: unknown, P. 135618 - 135618
Published: March 1, 2025
Language: Английский
Citations
0Computers and Electronics in Agriculture, Journal Year: 2025, Volume and Issue: 234, P. 110290 - 110290
Published: March 23, 2025
Language: Английский
Citations
0Aquaculture International, Journal Year: 2025, Volume and Issue: 33(4)
Published: April 15, 2025
Language: Английский
Citations
0Journal of Oceanology and Limnology, Journal Year: 2024, Volume and Issue: 42(5), P. 1695 - 1709
Published: Aug. 10, 2024
Language: Английский
Citations
3Aquacultural Engineering, Journal Year: 2023, Volume and Issue: 104, P. 102388 - 102388
Published: Dec. 16, 2023
Language: Английский
Citations
5Engineering Applications of Computational Fluid Mechanics, Journal Year: 2024, Volume and Issue: 18(1)
Published: July 4, 2024
In the fish passage facility design, understanding coupled effects of hydrodynamics on behaviour is particularly important. The flow field caused by movement however are usually obtained via time-consuming transient numerical simulation. Hence, a hybrid deep neural network (HDNN) approach designed to predict unsteady around fish. basic architecture HDNN includes UNet convolution (UConv) module and bidirectional convolutional long-short term memory (BiConvLSTM) module. Specifically, UConv extracts crucial features from graph, while BiConvLSTM learns evolution low-dimensional spatio-temporal for prediction. results showcase that achieves accurate multi-step rolling predictions effect fields under different tail-beat frequency conditions. average standard deviation PSNR SSIM proposed model 60 time-step entire sequences four test sets being respectively larger than 34 dB 0.9. delivers speedup over 130 times compared simulator. Moreover, demonstrates commendable generalisation capabilities, enabling prediction spatial–temporal within even at unknown frequencies.
Language: Английский
Citations
1Journal of marine science and technology, Journal Year: 2023, Volume and Issue: 31(4)
Published: Jan. 1, 2023
The study aims to investigate the effect of fish movement on flow field in aquaculture tank a recirculating water system. Herein, based Navier-Stokes equations and RNG k-ε turbulence model, with was numerically simulated using multiple reference frame (MRF) model compared numerical simulation results fishless tank. revealed that overall mean velocity decreased significantly when swam counter-currently fixed trajectory, increased slightly number increased. When same side-by-side distribution greater than back-and-forth top-and-bottom distributions. Under influence counter-current swimming, uniformity reduced, intensity increased, at wall low-flow area appeared center demonstrated necessity considering impact swimming within an This consideration is crucial seeking raise welfare, improve production operation management optimize structure
Language: Английский
Citations
2Aquaculture, Journal Year: 2024, Volume and Issue: unknown, P. 741770 - 741770
Published: Oct. 1, 2024
Language: Английский
Citations
0Aquaculture International, Journal Year: 2023, Volume and Issue: 32(3), P. 3017 - 3040
Published: Dec. 11, 2023
Language: Английский
Citations
0