EconPapers    
Economics at your fingertips  
 

Modelling development and optimization on hydrodynamics and energy utilization of fish culture tank based on computational fluid dynamics and machine learning

Shanhong Zhang, Guanghui Yu, Yu Guo and Yang Wang

Energy, 2023, vol. 276, issue C

Abstract: Hydrodynamics of culture tank plays an essential role in the recirculating aquaculture system (RAS), maintaining the maximum effective energy utilization rate and uniform vortex distributions for fish growth is still a great challenge. To solve this problem, a novel approach to optimize physical parameters of octagonal tank using computational fluid dynamics (CFD) and machine learning (ML) is proposed. The initial tank is regarded as the benchmark, six vital parameters of octagonal tank including inlet and outlet diameters, fillet radius, inlet height, inlet angle and inlet velocity have been numerically investigated by CFD. Modelling development and optimization based on the Artificial Neural Network (ANN) and Nondominated Sorting Genetic Algorithm ΙΙ (NSGA-Ⅱ) are developed to obtain the Pareto front for maximizing effective energy utilization rate and minimizing the vortex STD. Some key findings are found that: 1) The model provides high predictive capability: RMSE of average velocity is 0.002.2) NSGA-Ⅱ combining ANN is applied in the optimization process to obtain 78 groups of optimal Pareto front.3) A series of optimal parameters are achieved by LINMAP, the optimal parameter combination could improve the energy utilization rate up to 12.32 corresponding with the benchmark. 4) Inlet velocity of 0.1 m/s and inlet diameter of 103.9 mm in the culture tank can be more significant for improving flow uniformity but also raising effective energy utilization rate. The proposed CFD-ML model prediction and optimization have satisfactory performance in hydrodynamics of culture tank.

Keywords: Hydrodynamics; Multi-objective optimization; Physical parameters; CFD and ML; Octagonal culture tank (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S036054422300912X
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:276:y:2023:i:c:s036054422300912x

DOI: 10.1016/j.energy.2023.127518

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu (repec@elsevier.com).

 
Page updated 2024-12-28
Handle: RePEc:eee:energy:v:276:y:2023:i:c:s036054422300912x