Aquaculture International, Год журнала: 2023, Номер 32(3), С. 3017 - 3040
Опубликована: Дек. 11, 2023
Язык: Английский
Aquaculture International, Год журнала: 2023, Номер 32(3), С. 3017 - 3040
Опубликована: Дек. 11, 2023
Язык: Английский
Journal of Oceanology and Limnology, Год журнала: 2024, Номер 42(5), С. 1695 - 1709
Опубликована: Авг. 10, 2024
Язык: Английский
Процитировано
3Ocean Engineering, Год журнала: 2025, Номер 319, С. 120196 - 120196
Опубликована: Янв. 4, 2025
Язык: Английский
Процитировано
0Energy, Год журнала: 2025, Номер unknown, С. 135618 - 135618
Опубликована: Март 1, 2025
Язык: Английский
Процитировано
0Computers and Electronics in Agriculture, Год журнала: 2025, Номер 234, С. 110290 - 110290
Опубликована: Март 23, 2025
Язык: Английский
Процитировано
0Aquaculture International, Год журнала: 2025, Номер 33(4)
Опубликована: Апрель 15, 2025
Язык: Английский
Процитировано
0Aquacultural Engineering, Год журнала: 2025, Номер unknown, С. 102577 - 102577
Опубликована: Май 1, 2025
Язык: Английский
Процитировано
0Aquacultural Engineering, Год журнала: 2023, Номер 104, С. 102388 - 102388
Опубликована: Дек. 16, 2023
Язык: Английский
Процитировано
5Engineering Applications of Computational Fluid Mechanics, Год журнала: 2024, Номер 18(1)
Опубликована: Июль 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.
Язык: Английский
Процитировано
1Journal of marine science and technology, Год журнала: 2023, Номер 31(4)
Опубликована: Янв. 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
Язык: Английский
Процитировано
3Aquaculture, Год журнала: 2024, Номер unknown, С. 741770 - 741770
Опубликована: Окт. 1, 2024
Язык: Английский
Процитировано
0