Aquacultural Engineering最新文献

{"title":"Analysis of effects of shellfish harvesting methods on digging shovel performance using the discrete element method","authors":"Shancan Fu ,&nbsp;Shaolei Zhang ,&nbsp;Lei Yang ,&nbsp;Haiyun Wu ,&nbsp;Zhengliang Ding ,&nbsp;Tao Zhang ,&nbsp;Yongcheng Jiang","doi":"10.1016/j.aquaeng.2025.102654","DOIUrl":"10.1016/j.aquaeng.2025.102654","url":null,"abstract":"<div><div>Mechanized harvesting has been progressively supplanting manual harvesting in the domain of shellfish harvesting. A crucial technical quandary within this area pertains to the attainment of a high harvesting rate concomitant with a low damage rate under conditions of diminished digging force and minimal soil disturbance. This study investigates the effects of two types of shellfish harvesting methods, i.e., roller-type and vibrating-type, on the performance of digging shovel, and systematically compares their characteristics of harvesting rate, forces exerted on the shellfish, digging force, kinetic energy, and soil disturbance during the harvesting process. Additionally, the study also investigates the effects of different forward speeds and soil types on the performance of roller toothed (RT) shovel, roller flat (RF) shovel, vibrating toothed (VT) shovel, and vibrating flat (VF) shovel. Test results indicate that the harvesting rate of RT shovel is lower than that of vibrating digging shovel at shallow depths, but it has a certain cost advantage due to its capability to achieve fewer small shellfish. The VT shovel exhibits a relatively high harvesting rate (78.33 %) and exerts lower forces on shellfish at deeper depths. The digging force encountered by roller-type shovels exceeds that of vibrating shovels. As the digging depth increases, the roller-type shovels have a significant influence on the soil disturbance rate, while the vibrating-type excavation shovel has a relatively large effect on the soil bulkiness. At high forward speed, the digging force and kinetic energy of RT shovel are 1.78 times and 4.85 times those of vibrating shovel respectively. The roller-type digging shovel, especially the RF shovel, is not suitable for shellfish harvesting in compacted soil, because it needs to overcome larger digging force (i.e., 103.12 N) and leads to higher soil disturbance rate (i.e., 0.74). The vibrating shovel can adapt to various loose soils other than just high-density soil, and it experiences relatively small digging force and soil disturbance rate. This study provides theoretical guidance for the optimized design and application of mechanized shellfish harvesting, and its findings can further assist engineers in developing more energy-efficient and environmentally friendly harvesting machines, thereby demonstrating the greater practical value of the research.</div></div>","PeriodicalId":8120,"journal":{"name":"Aquacultural Engineering","volume":"112 ","pages":"Article 102654"},"PeriodicalIF":4.3,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145412466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"UAV-FDFishMass: A floating dead fish mass estimation method in ponds using unmanned aerial vehicle imagery","authors":"Mingrui Kong ,&nbsp;Dingshuo Liu ,&nbsp;Yuan Hou ,&nbsp;Lan Huang ,&nbsp;Qingling Duan","doi":"10.1016/j.aquaeng.2025.102652","DOIUrl":"10.1016/j.aquaeng.2025.102652","url":null,"abstract":"<div><div>Accurately estimating the mass of floating dead fish in ponds is crucial for monitoring fish population growth, conducting ecological assessments, and facilitating insurance compensation. To address the limitations of conventional monocular and binocular devices in complex, large-scale pond aquaculture environments, as well as the low segmentation accuracy for small targets in existing mass estimation methods, this paper proposes a floating dead fish mass estimation method in ponds using UAV imagery, referred to as UAV-FDFishMass. Firstly, UAV device is employed to obtain large-scale pond images, thereby constructing an instance segmentation dataset. Then, an improved instance segmentation model enhanced for small targets is developed by incorporating a dedicated small-instance segmentation layer, and introducing SPDConv and ASF modules. Finally, the morphological features of dead fish length are calibrated using a correction factor, and a Bayesian-optimized LightGBM model is employed to fit the dead fish mass, thereby enabling mass estimation of floating dead fish in ponds. The proposed model is validated using a self-constructed dataset for floating dead fish mass estimation in ponds. The instance segmentation model achieves a [email protected] of 98.63 %. For mass estimation, the model yields a MAPE of 5.90 %, and a R² of 0.9390. The proposed method outperforms alternative approaches, such as linear regression, BPNN, and XGBoost, across all evaluation metrics. Experimental results demonstrate that the proposed method can accurately estimate the mass of floating dead fish in pond environments. Furthermore, this study holds significant implications for both food security and fisheries development.</div></div>","PeriodicalId":8120,"journal":{"name":"Aquacultural Engineering","volume":"112 ","pages":"Article 102652"},"PeriodicalIF":4.3,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145412469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of variable selection and model complexity on the prediction of water quality parameters for Penaeus vannamei aquaculture in a short dataset context","authors":"Vinícius Fellype Cavalcanti de França,&nbsp;Luis Otavio Brito da Silva,&nbsp;Humber Agrelli de Andrade","doi":"10.1016/j.aquaeng.2025.102640","DOIUrl":"10.1016/j.aquaeng.2025.102640","url":null,"abstract":"<div><div>Aquaculture is expanding rapidly worldwide, increasing the demand for efficient water quality management in shrimp farming. In this study, we evaluated the impact of variable selection and model complexity on the prediction of the mean of water parameters using machine learning. Two variable selection approaches were applied: a Granger causality test to capture temporal predictability, and a backward procedure based on the Akaike Information Criterion to balance model fit and complexity. An experimental dataset of 106 observations of temperature, dissolved oxygen, salinity and pH was standardised and modelled using a linear regression and a random forest regressor. Model performance was assessed by cross-validation using mean squared error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) as metrics. Our results showed a significant superiority of linear regressor over the random forest, suggesting that simpler models may be more effective with limited datasets than more complex models.</div></div>","PeriodicalId":8120,"journal":{"name":"Aquacultural Engineering","volume":"112 ","pages":"Article 102640"},"PeriodicalIF":4.3,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145217589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The effect of Spirulina platensis on water quality factors, biofloc composition, growth efficiency, fatty acid and amino acid profile of fillets, carcass composition, antioxidant activities, bacterial population, and gene expersion in common carp (Cyprinus carpio) reared in a system without water exchange","authors":"Roholamin Alishahi ,&nbsp;Manizheh Biabani Asrami ,&nbsp;Raheb Mahforouzi ,&nbsp;Saeid Vahdat ,&nbsp;Sakineh Yeganeh","doi":"10.1016/j.aquaeng.2025.102660","DOIUrl":"10.1016/j.aquaeng.2025.102660","url":null,"abstract":"<div><div>This study investigated the effects of <em>Spirulina platensis</em> integration in a biofloc technology (BFT) system on water quality, biofloc composition, growth performance, fillet nutritional profiles, antioxidant activities, and gut microbiota in common carp (<em>C. carpio</em>)(100 g) under zero-water exchange conditions over 60 days. Four treatments were evaluated: Control (no biofloc or <em>Spirulina</em>), biofloc (BFT without <em>Spirulina</em>), biofloc+<em>Spirulina</em> (BFT with weekly <em>Spirulina</em> at 0.1 g/L), and <em>Spirulina</em> (<em>Spirulina</em> without BFT). biofloc+<em>Spirulina</em> exhibited superior growth metrics, including weight gain (204.43 g), specific growth rate (2.76 %/day), and feed conversion ratio (1.82), alongside enhanced fillet protein (55.58 %) and polyunsaturated fatty acid content (e.g., DHA 15.62 % in <em>Spirulina</em>) (P &lt; 0.05). Water quality improved markedly in biofloc+<em>Spirulina</em> treatments, with reduced TAN-N (&lt;0.1 mg/L) and nitrite (&lt;0.1 mg/L), while nitrate accumulated moderately (up to 6.95 mg/L) (P &lt; 0.05). biofloc composition was enriched in <em>Spirulina</em> treatments, with protein reaching 64.75 % (P &lt; 0.05). Antioxidant capacities were the highest in biofloc+<em>Spirulina</em> (TAC 22.55 μmol/g; DPPH scavenging 65.05 %). Gut microbiota shifted towards probiotic genera (e.g., <em>Rhodopseudomonas</em> 15 %) in biofloc treatment, modulating cytokines with elevated TNF-α (1.9 in biofloc+<em>Spirulina</em> treatment) and IL-10 (1.5 in <em>Spirulina</em> treatment). Sensory attributes improved, with overall acceptance at 6.85 in biofloc+<em>Spirulina</em>. These findings demonstrate that <em>Spirulina</em>-enhanced BFT optimises sustainability, nutritional quality, and health in carp aquaculture, promoting sustainable and cost-effective production systems.</div></div>","PeriodicalId":8120,"journal":{"name":"Aquacultural Engineering","volume":"112 ","pages":"Article 102660"},"PeriodicalIF":4.3,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145525330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A computer vision method to estimate ventilation rate of Atlantic salmon in sea fish farms","authors":"Lukas Folkman ,&nbsp;Quynh LK Vo ,&nbsp;Colin Johnston ,&nbsp;Bela Stantic ,&nbsp;Kylie A. Pitt","doi":"10.1016/j.aquaeng.2025.102645","DOIUrl":"10.1016/j.aquaeng.2025.102645","url":null,"abstract":"<div><div>The increasing demand for aquaculture production necessitates the development of innovative, intelligent tools to effectively monitor and manage fish health and welfare. While non-invasive video monitoring has become a common practice in finfish aquaculture, existing intelligent monitoring methods predominantly focus on assessing body condition or fish swimming patterns and are often developed and evaluated in controlled tank environments, without demonstrating their applicability to real-world aquaculture settings in open sea farms. This underscores the necessity for methods that can monitor physiological traits directly within the production environment of sea fish farms. To this end, we have developed a computer vision method for monitoring ventilation rates of Atlantic salmon (<em>Salmo salar</em>), which was specifically designed for videos recorded in the production environment of commercial sea fish farms using the existing infrastructure. Our approach uses a fish head detection model, which classifies the mouth state as either open or closed using a convolutional neural network. This is followed with multiple object tracking to create temporal sequences of fish swimming across the field of view of the underwater video camera to estimate ventilation rates. The method demonstrated high efficiency, achieving a Pearson correlation coefficient of 0.82 between ground truth and predicted ventilation rates in a test set of 100 fish collected independently of the training data. Our method was designed to analyse large quantities of fish efficiently to provide population-level estimates of ventilation rates, rather than longitudinal observations for individual fish. By accurately identifying pens where fish exhibit signs of respiratory distress, the method offers broad applicability and the potential to transform fish health and welfare monitoring in finfish aquaculture.</div></div>","PeriodicalId":8120,"journal":{"name":"Aquacultural Engineering","volume":"112 ","pages":"Article 102645"},"PeriodicalIF":4.3,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145263218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel detection and segmentation system for eichhornia crassipes growth rate using region vision transformer-based adaptive Yolo with Unet++","authors":"Ajitha Eliza ,&nbsp;Diwan Baskaran","doi":"10.1016/j.aquaeng.2025.102659","DOIUrl":"10.1016/j.aquaeng.2025.102659","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Eichhornia crassipes has an extreme rapid growth rate, and has capability to doubling its population in approximately two to three weeks under optimal conditions, with biomass accumulation. It is required to protect the safety of water resources with rigorous ecological environment by detecting Eichhornia crassipes using deep learning techniques. The conventional techniques of Eichhornia crassipes paves more attention to reduce the growth rate of Eichhornia crassipes on the aquatic environment, but real-time monitoring of large areas is critical and it becomes challenging issue. Moreover, conventional models provide low accuracy and poor detection results on target species with unclear satellite image characteristics. Hence, to overcome these issues a new developed strategy for segmenting the Eichhornia crassipes is implemented to identify the growth rate and formulate effective control strategies. Initially, the required multi-spectral images are fetched from the distinct standard datasets for recognizing the Eichhornia crassipes growth rate. The acquired images are fed to the Region-Vision Transformer-based Adaptive Yolo (RViT-Yolo-Unet++) for performing joint detection and segmentation. The RViT-Yolo detection model captures long-range contextual dependencies and global relationships, which leads to more comprehensive understanding of complex patterns. RViT-Yolo improves the accuracy and speed by adapting the visual characteristics of the Eichhornia crassipes. While, the Unet++ segmentation model provide better growth rate through resource partitioning, and allows for propagation. The RViT-Yolo-Unet++ segmentation process manage its invasive growth by identifying and removing irrelevant boundaries of the image, leading improved water quality, enhanced biofuel production, and optimized pollution control. The detection and segmentation networks are serially connected to provide efficient growth rate detection results. Initially, the garnered images are applied to the Eichhornia crassipes detection module, where the RViT-Yolo is utilized for detecting the Eichhornia crassipes in a specific region. Moreover, for improving the detection performance of Eichhornia crassipes, the parameters of Yolo are optimally selected by an Improved Crayfish Optimization Algorithm (TI-COA). While optimizing Eichhornia crassipes, it enhances growth rate, and provides greater quantity of biomass production to recover the resources. Also, it improves the water quality and reduces the operating costs and making wastewater treatment more economically feasible. Subsequently, the detected region images are passed to the segmentation module, where the Unet++ model is used for segmenting the Eichhornia crassipes affected regions that help to discover the Eichhornia crassipes growth rate. Finally, the research experiments are performed for the implemented framework by comparing the best measure of accuracy shows 7.98 % Unet, 7.52 % Unet3 + , 7.06 % ResUnet, and 4.8","PeriodicalId":8120,"journal":{"name":"Aquacultural Engineering","volume":"112 ","pages":"Article 102659"},"PeriodicalIF":4.3,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145525328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dissolved oxygen in aquaculture ponds: Causal factors, predictive modeling, and intelligent monitoring","authors":"Minghao Yu ,&nbsp;Yingqi Feng ,&nbsp;Bing Ouyang ,&nbsp;Paul S. Wills ,&nbsp;Yufei Tang","doi":"10.1016/j.aquaeng.2025.102634","DOIUrl":"10.1016/j.aquaeng.2025.102634","url":null,"abstract":"<div><div>In pond aquaculture, maintaining stable Dissolved Oxygen (DO) concentrations is essential for preventing hypoxia, optimizing growth conditions, and ensuring sustainable operations. Therefore, DO prediction is a crucial aspect of intelligent aquaculture systems, directly influencing water quality, aquatic health, and overall productivity. Advances in sensor technology and data-driven modeling have significantly enhanced the ability to monitor and forecast DO levels, enabling proactive management strategies. This review presents a novel taxonomy for classifying DO prediction approaches in pond aquaculture, structured into three key areas: (1) Driven Factors of DO, examining environmental, biological, and operational influences on DO dynamics; (2) Predictive Models, methods ranging from statistical approaches to advanced deep learning, highlighting promising techniques such as physics-informed neural networks (PINNs) and transfer learning for data-scarce environments; and (3) Monitoring and Sensor Technologies, covering electrochemical and optical sensors, particularly fluorescence-based systems, integrated with Internet of Things (IoT) platforms for real-time assessment. By synthesizing these domains, the review identifies opportunities to enhance DO prediction accuracy and monitoring reliability, supporting intelligent aeration control, improved resource efficiency, and more resilient aquaculture operations.</div></div>","PeriodicalId":8120,"journal":{"name":"Aquacultural Engineering","volume":"112 ","pages":"Article 102634"},"PeriodicalIF":4.3,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145061221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fractional factorial design-based evaluation of physicochemical parameters affecting biodiesel properties from Chlorella sp. PG96","authors":"Roya Parichehreh ,&nbsp;Reza Gheshlaghi ,&nbsp;Mahmood Akhavan Mahdavi ,&nbsp;Hesam Kamyab","doi":"10.1016/j.aquaeng.2025.102664","DOIUrl":"10.1016/j.aquaeng.2025.102664","url":null,"abstract":"<div><div>Biodiesel, as a renewable energy source synthesized through the transesterification of algal lipids, has received increasing attention in recent years. The quality of biodiesel derived from microalgal lipids depends largely on the composition of fatty acid methyl ester in the fuel. This research employed a fractional factorial design (2 ¹¹⁻⁷) to statistically screen eleven independent factors, including NaHCO₃, CO₂, MgSO₄.7H₂O, K₂HPO₄, NaNO₃, NH₄Cl, salinity, light spectrum, aeration rate, light intensity, and temperature, for achieving a rich fatty acid profile by <em>Chlorella</em> sp. PG96 (a strain isolated from municipal wastewater), as well as the synthesis of superior-quality biodiesel. The effects of all eleven physicochemical factors and their interactions on the growth characteristics (biomass and lipid production) of <em>Chlorella</em> sp. PG96 were thoroughly investigated in our previous study. According to the experimental results of the present study, the maximum concentrations of palmitic acid (32.23 ± 2.87 %), oleic acid (31.53 ± 3.31 %), saturated fatty acids (38.07 ± 4.03 %), and monounsaturated fatty acids (33.63 ± 3.36 %) in microalgal lipids were obtained when <em>Chlorella</em> sp. PG96 was grown at a low NaHCO₃ concentration (0 mg L⁻¹) and white light irradiation. The estimated values of biodiesel properties such as iodine value, unsaturation degree, oxidation stability, cetane number, higher heating value, density, and kinematic viscosity were all in accordance with the quality benchmarks established by ASTM and EN 14214. The findings further demonstrated that temperature and light intensity represented the key determinants influencing fatty acid composition. Aeration rate and salinity had the most significant effects on the cetane number index, whereas the oxidation stability of algal oil was markedly affected by the concentrations of NaNO₃ and NaHCO₃. Moreover, ammonium as a nitrogen source and bicarbonate as a carbon source exhibited greater significance in fatty acid biosynthesis compared with nitrate and CO₂, respectively. The interactions between NaHCO₃ and the light spectrum, as well as between NaHCO₃ and NH₄Cl, were found to be the most significant for all measured responses. It is suggested that <em>Chlorella</em> sp. PG96, when cultivated with elevated NH₄Cl concentration and light intensity but under reduced temperature (320 mg L⁻¹, 22,500 Lux, and 20 °C, respectively), may act as a promising feedstock for biodiesel production.</div></div>","PeriodicalId":8120,"journal":{"name":"Aquacultural Engineering","volume":"112 ","pages":"Article 102664"},"PeriodicalIF":4.3,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145575887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rotiferometer: An automated system for quantification of rotifer cultures","authors":"Alioune Diouf ,&nbsp;Leandre Bereziat ,&nbsp;David Nodier ,&nbsp;Marco Amaral ,&nbsp;Sinan Haliyo ,&nbsp;Abdelkrim Mannioui","doi":"10.1016/j.aquaeng.2025.102662","DOIUrl":"10.1016/j.aquaeng.2025.102662","url":null,"abstract":"<div><div>The accurate quantification and continuous monitoring of <em>Brachionus plicatilis.</em> rotifer cultures are essential for aquaculture and aquatic animal research laboratories. Manual counting methods are labor-intensive, error-prone, and inefficient for large-scale operations, necessitating automated solutions. This study presents the Rotiferometer, an automated and cost-effective system that integrates mechanical design, deep learning, and automation for precise rotifer detection, classification and counting. Using a YOLOv8 model, the system achieves a mean average precision ([email protected]) of 94.7 % in distinguishing gravid and non-gravid rotifers. It proceeds by scanning a 1 mL Sedgewick Rafter slide under 3 min, ensuring rapid and accurate enumeration. A strong correlation was observed between manual and Rotiferometer counts, (with R2 values of 0.9729 and 0.9868 for gravid (egg-bearing) and non-gravid (non-egg-bearing) rotifers, respectively), confirming the system’s accuracy. Additionally, the analysis of operator variability using the Rotiferometer delivered consistent results regardless of the user, minimizing the need for specialized expertise. Finally, a 45-day monitoring experiment with the Rotiferometer effectively tracked rotifer population changes, identifying key phases of growth, decline, and recovery. These results highlight the device’s potential to enhance rotifer culture management by providing real-time, reliable, and automated monitoring, thereby optimizing aquaculture productivity and research efficiency.</div></div>","PeriodicalId":8120,"journal":{"name":"Aquacultural Engineering","volume":"112 ","pages":"Article 102662"},"PeriodicalIF":4.3,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145462656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An experimental study on the internal flow field characteristics in aquaculture vessel tanks under sloshing conditions","authors":"Chunhui Zhao ,&nbsp;Guoqiang Li ,&nbsp;Haixiang Xu ,&nbsp;Yonghe Xie ,&nbsp;Panpan Jia ,&nbsp;Peng Xu","doi":"10.1016/j.aquaeng.2025.102619","DOIUrl":"10.1016/j.aquaeng.2025.102619","url":null,"abstract":"<div><div>An experimental platform was established to investigate the internal flow field of an aquaculture vessel under rolling and heaving motions. The effects of different jet angles (<em>α</em>_<em>jet</em>) and velocities (<em>v</em>_<em>jet</em>), roll angles (<em>θ</em>) and heave amplitude (<em>Z</em>_<em>a</em>), and motion periods (<em>T</em>) were explored. The results show that in the stationary state, the flow field in the aquaculture tank was stable, and the jet conditions affected the flow field characteristics and stability. Under heaving motions, the average velocity (<em>v</em>_<em>avg</em>) exhibited an inverse relationship with <em>Z</em>_<em>a</em>, while the coefficient of variation (COV) demonstrated a non-monotonic trend, with its variation pattern influenced by <em>T</em>. Notably, the heave motion had a negative correlation effect on the <em>v</em>_<em>avg</em> across the entire tank area. With rolling motions, the flow field changes were related to the motion trajectory of the tank. The <em>v</em>_<em>jet</em> had a significant influence on the <em>v</em>_<em>avg</em> which increased as <em>θ</em> and decreased as <em>T</em> increased. Under the combined motion of the two degrees-of-freedom, the <em>v</em>_<em>avg</em> increased with <em>θ</em>. Additionally, an increase in <em>T</em> led to a corresponding enhancement in flow field stability. Under combined heave-roll motion conditions, the <em>v</em>_<em>avg</em> demonstrated a 20–30 % reduction compared to static conditions at identical jet parameters (<em>α</em>_<em>jet</em>, <em>v</em>_<em>jet</em>). These results furnish empirical evidence for refining the hydrodynamic behavior of aquaculture vessels in seaway conditions (e.g., rolling/heaving), thereby enhancing their operational adaptability in fisheries.</div></div>","PeriodicalId":8120,"journal":{"name":"Aquacultural Engineering","volume":"112 ","pages":"Article 102619"},"PeriodicalIF":4.3,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144997564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}