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This study explores the dynamics of turbulent flow around a sphere at a Reynolds number of R e = 10 3 using large-eddy simulation, focusing on the intricate connection between vortices and strain within the recirculation bubble of the wake. Employing a relatively new subgrid-scale modeling approach based on scale adaptivity, this research implements a functional relation to compute k sgs that encompasses both vortex-stretching and strain rate mechanisms essential for the energy cascade process. The effectiveness of this approach is analyzed in the wake of the sphere, particularly in the recirculation bubble, at the specified Reynolds number. It is also evaluated in comparison with two different subgrid-scale models through detailed analysis of the coherent structures within the recirculation bubble. These models—scale-adaptive, k-Equation, and dynamic k-Equation—are assessed for their ability to capture the complex flow dynamics near the wake. The findings indicate that while all models proficiently simulate key turbulent wake features such as vortex formation and kinetic energy distribution, they exhibit unique strengths and limitations in depicting specific flow characteristics. The scale-adaptive model shows a good ability to dynamically adjust to local flow conditions, thereby enhancing the representation of turbulent structures and eddy viscosity. Similarly, the dKE model exhibits advantages in energy dissipation and vortex dynamics due to its capability to adjust coefficients dynamically based on local conditions. The comparative analysis and statistical evaluation of vortex stretching and strain across models deepen the understanding of turbulence asymmetries and intensities, providing crucial insights for advancing aerodynamic design and analysis in various engineering fields and laying the groundwork for further sophisticated turbulence modeling explorations. [ABSTRACT FROM AUTHOR]

Most fishery regulations aim to control angler harvest. Yet, we lack a basic understanding of what actually determines the angler's decision to harvest or release fish caught. We used XGBoost, a machine learning algorithm, to develop a predictive angler harvest-release model by taking advantage of an extensive recreational fishery data set (24 water bodies, 9 years, and 193 523 fish). We were able to successfully predict the harvest-release outcome for 99% of fish caught in the training data set and 96% of fish caught in the test data set. Unsuccessful predictions were mostly attributed to predicting harvest of fish that were released. Fish length was the most essential feature examined for predicting angler harvest. Other important predictive harvest-release features included the number of individuals of the same species caught, geographic location of an angler's residence, distance traveled, and time spent fishing. The XGBoost algorithm was able to effectively predict the harvest-release decision and revealed hidden and intricate relationships that are often unaccounted for with classical analysis techniques. Exposing and accounting for these angler-fish intricacies is critical for fisheries conservation and management. La plupart des reglements relatifs a la peche visent a controler les prises des pecheurs a la ligne. Une comprehension de base de ce qui determine reellement la decision d'un pecheur de conserver ou de relacher un poisson peche manque toutefois. Nous avons utilise XGBoost, un logarithme d'apprentissage automatique, pour elaborer un modele predictif de decisions de pecheurs de conserver ou relacher un poisson en tirant parti d'un vaste ensemble de donnees de peche sportive (24 plans d'eau, 9 annees, 193 523 poissons). Nous avons ete en mesure de predire avec succes le resultat (conserver ou relacher) pour 99 % des poissons peches dans l'ensemble de donnees d'entrainement et 96 % des poissons peches dans l'ensemble de donnees experimental. Les predictions inexactes etaient pour la plupart de poissons conserves qui avaient en fait ete relaches. La longueur du poisson est l'aspect examine le plus important pour la prediction de la conservation par les pecheurs. D'autres aspects importants pour predire la conservation ou le lacher comprennent le nombre de specimens de la meme espece peches, l'emplacement geographique de la residence du pecheur, la distance parcourue et le temps passe a pecher. L'algorithme XGBoost est arrive a predire efficacement les decisions de conserver ou de relacher et a fait ressortir des relations cachees et complexes dont les methodes d'analyse classiques ne tiennent souvent pas compte. La reconnaissance et la prise en consideration de ces facteurs complexes associes aux pecheurs et aux poissons sont d'importance cle pour la conservation et la gestion des ressources halieutiques. [Traduit par la Redaction], Introduction Once a fish is caught by an angler, will it be harvested or released? Currently, we lack a basic understanding of this very important decision process. Fish harvest by [...]

Networks used to study interactions could provide insights to fisheries. We compiled data from 27 297 interviews of anglers across waterbodies that ranged in size from 1 to 12 113 ha. Catch rates of fish species among anglers grouped by species targeted generally differed between angling methods (bank or boat). We constructed angler-catch bipartite networks (angling method specific) between anglers and fish and measured several network metrics. There was considerable variation in networks among waterbodies, with multiple metrics influenced by waterbody size. Number of species-targeting angler groups and number of fish species caught increased with increasing waterbody size. Mean number of links for species-targeting angler groups and fish species caught also increased with waterbody size. Connectance (realized proportion of possible links) of angler-catch interaction networks decreased slower for boat anglers than for bank anglers with increasing waterbody size. Network specialization (deviation of number of interactions from expected) was not significantly related to waterbody size or angling methods. Application of bipartite networks in fishery science requires careful interpretation of outputs, especially considering the numerous confounding factors prevalent in recreational fisheries. Les reseaux utilises pour etudier les interactions pourraient fournir de l'information utile sur les peches. Nous avons compile des donnees de 27 297 entrevues de pecheurs a la ligne dans differents plans d'eau allant de 1 ha a 12 113 ha. Les taux de prise de differentes especes de poisson de pecheurs regroupes selon l'espece ciblee varient generalement selon la methode de peche (de la rive ou d'une embarcation). Nous avons construit des reseaux bipartites pecheur-prises (selon la methode de peche) entre les pecheurs et les poissons et mesure plusieurs parametres de ces reseaux. Il y a des variations considerables des reseaux entre plans d'eau, plusieurs parametres etant influences par la taille de ces derniers. Le nombre de groupes de pecheurs ciblant des especes precises et le nombre d'especes de poissons pechees augmentent avec la taille du plan d'eau. Le nombre moyen de liens pour les groupes de pecheurs ciblant des especes et les especes pechees augmente egalement avec la taille du plan d'eau. La connectance (proportion de liens possibles realises) des reseaux d'interactions pecheur-prises diminue plus lentement pour les pecheurs en embarcation que pour les pecheurs de la rive pour des plans d'eau de plus en plus grands. La specialisation des reseaux (ecart entre le nombre d'interactions observees et prevues) n'est pas significativement reliee a la taille du plan d'eau ou a la methode de peche. L'application de reseaux bipartites aux sciences halieutiques necessite une interpretation soigneuse des donnees de sortie, etant donne, notamment, les nombreux facteurs de confusion qui caracterisent les peches recreatives. [Traduit par la Redaction], Introduction In recreational fisheries, anglers' influence extends beyond the species that they target (Cooke and Cowx 2004, 2006; Lewin et al. 2006). The influence of recreational angling on targeted species [...]