2,462 research outputs found
The orientation of swimming bi-flagellates in shear flows
Biflagellated algae swim in mean directions that are governed by their environments. For example, many algae can swim upward on average (gravitaxis) and toward downwelling fluid (gyrotaxis) via a variety of mechanisms. Accumulations of cells within the fluid can induce hydrodynamic instabilities leading to patterns and flow, termed bioconvection, which may be of particular relevance to algal bioreactors and plankton dynamics. Furthermore, knowledge of the behavior of an individual swimming cell subject to imposed flow is prerequisite to a full understanding of the scaled-up bulk behavior and population dynamics of cells in oceans and lakes; swimming behavior and patchiness will impact opportunities for interactions, which are at the heart of population models. Hence, better estimates of population level parameters necessitate a detailed understanding of cell swimming bias. Using the method of regularized Stokeslets, numerical computations are developed to investigate the swimming behavior of and fluid flow around gyrotactic prolate spheroidal biflagellates with five distinct flagellar beats. In particular, we explore cell reorientation mechanisms associated with bottom-heaviness and sedimentation and find that they are commensurate and complementary. Furthermore, using an experimentally measured flagellar beat for Chlamydomonas reinhardtii, we reveal that the effective cell eccentricity of the swimming cell is much smaller than for the inanimate body alone, suggesting that the cells may be modeled satisfactorily as self-propelled spheres. Finally, we propose a method to estimate the effective cell eccentricity of any biflagellate when flagellar beat images are obtained haphazardly
Adaptive Detection of Structured Signals in Low-Rank Interference
In this paper, we consider the problem of detecting the presence (or absence)
of an unknown but structured signal from the space-time outputs of an array
under strong, non-white interference. Our motivation is the detection of a
communication signal in jamming, where often the training portion is known but
the data portion is not. We assume that the measurements are corrupted by
additive white Gaussian noise of unknown variance and a few strong interferers,
whose number, powers, and array responses are unknown. We also assume the
desired signals array response is unknown. To address the detection problem, we
propose several GLRT-based detection schemes that employ a probabilistic signal
model and use the EM algorithm for likelihood maximization. Numerical
experiments are presented to assess the performance of the proposed schemes
RegPT: Direct and fast calculation of regularized cosmological power spectrum at two-loop order
We present a specific prescription for the calculation of cosmological power
spectra, exploited here at two-loop order in perturbation theory (PT), based on
the multi-point propagator expansion. In this approach power spectra are
constructed from the regularized expressions of the propagators that reproduce
both the resummed behavior in the high-k limit and the standard PT results at
low-k. With the help of N-body simulations, we show that such a construction
gives robust and accurate predictions for both the density power spectrum and
the correlation function at percent-level in the weakly non-linear regime. We
then present an algorithm that allows accelerated evaluations of all the
required diagrams by reducing the computational tasks to one-dimensional
integrals. This is achieved by means of pre-computed kernel sets defined for
appropriately chosen fiducial models. The computational time for two-loop
results is then reduced from a few minutes, with the direct method, to a few
seconds with the fast one. The robustness and applicability of this method are
tested against the power spectrum cosmic emulator from which a wide variety of
cosmological models can be explored. The fortran program with which direct and
fast calculations of power spectra can be done, RegPT, is publicly released as
part of this paper.Comment: 28 pages, 15 figure
Koopman analysis of the long-term evolution in a turbulent convection cell
We analyse the long-time evolution of the three-dimensional flow in a closed
cubic turbulent Rayleigh-B\'{e}nard convection cell via a Koopman eigenfunction
analysis. A data-driven basis derived from diffusion kernels known in machine
learning is employed here to represent a regularized generator of the unitary
Koopman group in the sense of a Galerkin approximation. The resulting Koopman
eigenfunctions can be grouped into subsets in accordance with the discrete
symmetries in a cubic box. In particular, a projection of the velocity field
onto the first group of eigenfunctions reveals the four stable large-scale
circulation (LSC) states in the convection cell. We recapture the preferential
circulation rolls in diagonal corners and the short-term switching through roll
states parallel to the side faces which have also been seen in other
simulations and experiments. The diagonal macroscopic flow states can last as
long as a thousand convective free-fall time units. In addition, we find that
specific pairs of Koopman eigenfunctions in the secondary subset obey enhanced
oscillatory fluctuations for particular stable diagonal states of the LSC. The
corresponding velocity field structures, such as corner vortices and swirls in
the midplane, are also discussed via spatiotemporal reconstructions.Comment: 32 pages, 9 figures, article in press at Journal of Fluid Mechanic
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