1,515 research outputs found
Optimal sensing for fish school identification
Fish schooling implies an awareness of the swimmers for their companions. In
flow mediated environments, in addition to visual cues, pressure and shear
sensors on the fish body are critical for providing quantitative information
that assists the quantification of proximity to other swimmers. Here we examine
the distribution of sensors on the surface of an artificial swimmer so that it
can optimally identify a leading group of swimmers. We employ Bayesian
experimental design coupled with two-dimensional Navier Stokes equations for
multiple self-propelled swimmers. The follower tracks the school using
information from its own surface pressure and shear stress. We demonstrate that
the optimal sensor distribution of the follower is qualitatively similar to the
distribution of neuromasts on fish. Our results show that it is possible to
identify accurately the center of mass and even the number of the leading
swimmers using surface only information
Machine Learning for Fluid Mechanics
The field of fluid mechanics is rapidly advancing, driven by unprecedented
volumes of data from field measurements, experiments and large-scale
simulations at multiple spatiotemporal scales. Machine learning offers a wealth
of techniques to extract information from data that could be translated into
knowledge about the underlying fluid mechanics. Moreover, machine learning
algorithms can augment domain knowledge and automate tasks related to flow
control and optimization. This article presents an overview of past history,
current developments, and emerging opportunities of machine learning for fluid
mechanics. It outlines fundamental machine learning methodologies and discusses
their uses for understanding, modeling, optimizing, and controlling fluid
flows. The strengths and limitations of these methods are addressed from the
perspective of scientific inquiry that considers data as an inherent part of
modeling, experimentation, and simulation. Machine learning provides a powerful
information processing framework that can enrich, and possibly even transform,
current lines of fluid mechanics research and industrial applications.Comment: To appear in the Annual Reviews of Fluid Mechanics, 202
A general formulation of Bead Models applied to flexible fibers and active filaments at low Reynolds number
This contribution provides a general framework to use Lagrange multipliers
for the simulation of low Reynolds number fiber dynamics based on Bead Models
(BM). This formalism provides an efficient method to account for kinematic
constraints. We illustrate, with several examples, to which extent the proposed
formulation offers a flexible and versatile framework for the quantitative
modeling of flexible fibers deformation and rotation in shear flow, the
dynamics of actuated filaments and the propulsion of active swimmers.
Furthermore, a new contact model called Gears Model is proposed and
successfully tested. It avoids the use of numerical artifices such as repulsive
forces between adjacent beads, a source of numerical difficulties in the
temporal integration of previous Bead Models.Comment: 41 pages, 15 figure
CUDA simulations of active dumbbell suspensions
We describe and analyze CUDA simulations of hydrodynamic interactions in
active dumbbell suspensions. GPU-based parallel computing enables us not only
to study the time-resolved collective dynamics of up to a several hundred
active dumbbell swimmers but also to test the accuracy of effective
time-averaged models. Our numerical results suggest that the stroke-averaged
model yields a relatively accurate description down to distances of only a few
times the dumbbell's length. This is remarkable in view of the fact that the
stroke-averaged model is based on a far-field expansion. Thus, our analysis
confirms that stroke-averaged far-field equations of motion may provide a
useful starting point for the derivation of hydrodynamic field equations.Comment: 16 pages, 4 figure
A review on deep reinforcement learning for fluid mechanics: an update
In the past couple of years, the interest of the fluid mechanics community
for deep reinforcement learning (DRL) techniques has increased at fast pace,
leading to a growing bibliography on the topic. While the capabilities of DRL
to solve complex decision-making problems make it a valuable tool for active
flow control, recent publications also demonstrated applications to other
fields, such as shape optimization or microfluidics. The present work aims at
proposing an exhaustive review of the existing literature, and is a follow-up
to our previous review on the topic. The contributions are regrouped by field
of application, and are compared together regarding algorithmic and technical
choices, such as state selection, reward design, time granularity, and more.
Based on these comparisons, general conclusions are drawn regarding the current
state-of-the-art in the domain, and perspectives for future improvements are
sketched
The use of neural network technology to model swimming performance
to identify the factors which are able to explain the performance in the 200 meters individual medley and 400 meters front crawl events in young swimmers, to model the performance in those events using non-linear mathematic methods through artificial neural networks (multi-layer perceptrons) and to assess the neural network models precision to predict the performance. A sample of 138 young swimmers (65 males and 73 females) of national level was submitted to a test battery comprising four different domains: kinanthropometric evaluation, dry land functional evaluation (strength and flexibility), swimming functional evaluation (hydrodynamics, hydrostatic and bioenergetics characteristics) and swimming technique evaluation. To establish a profile of the young swimmer non-linear combinations between preponderant variables for each gender and swim performance in the 200 meters medley and 400 meters font crawl events were developed. For this purpose a feed forward neural network was used (Multilayer Perceptron) with three neurons in a single hidden layer. The prognosis precision of the model (error lower than 0.8% between true and estimated performances) is supported by recent evidence. Therefore, we consider that the neural network tool can be a good approach in the resolution of complex problems such as performance modeling and the talent identification in swimming and, possibly, in a wide variety of sports. Key pointsThe non-linear analysis resulting from the use of feed forward neural network allowed us the development of four performance models.The mean difference between the true and estimated results performed by each one of the four neural network models constructed was low.The neural network tool can be a good approach in the resolution of the performance modeling as an alternative to the standard statistical models that presume well-defined distributions and independence among all inputs.The use of neural networks for sports sciences application allowed us to create very realistic models for swimming performance prediction based on previous selected criterions that were related with the dependent variable (performance).info:eu-repo/semantics/publishedVersio
SwimmerNET: Underwater 2D Swimmer Pose Estimation Exploiting Fully Convolutional Neural Networks
Professional swimming coaches make use of videos to evaluate their athletes' performances. Specifically, the videos are manually analyzed in order to observe the movements of all parts of the swimmer's body during the exercise and to give indications for improving swimming technique. This operation is time-consuming, laborious and error prone. In recent years, alternative technologies have been introduced in the literature, but they still have severe limitations that make their correct and effective use impossible. In fact, the currently available techniques based on image analysis only apply to certain swimming styles; moreover, they are strongly influenced by disturbing elements (i.e., the presence of bubbles, splashes and reflections), resulting in poor measurement accuracy. The use of wearable sensors (accelerometers or photoplethysmographic sensors) or optical markers, although they can guarantee high reliability and accuracy, disturb the performance of the athletes, who tend to dislike these solutions. In this work we introduce swimmerNET, a new marker-less 2D swimmer pose estimation approach based on the combined use of computer vision algorithms and fully convolutional neural networks. By using a single 8 Mpixel wide-angle camera, the proposed system is able to estimate the pose of a swimmer during exercise while guaranteeing adequate measurement accuracy. The method has been successfully tested on several athletes (i.e., different physical characteristics and different swimming technique), obtaining an average error and a standard deviation (worst case scenario for the dataset analyzed) of approximately 1 mm and 10 mm, respectively
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