68 research outputs found
Clockworked VEVs and Neutrino Mass
In this paper we present an augmented version of the Abelian scalar clockwork
model to generate geometrically suppressed vacuum expectation values (vev) of
the pseudo Nambu-Goldstone bosons, that we call the clockworked vevs. We
briefly comment on generalization of the setup and possible 5D UV realizations.
We demonstrate how tiny neutrino mass can be generated by clockworking a weak
scale vev.Comment: 13 pages, 2 captioned figures, 1 table, further clarifications added
in text, references updated, matches version published in JHE
Persistence analysis of velocity and temperature fluctuations in convective surface layer turbulence
Persistence is defined as the probability that the local value of a
fluctuating field remains at a particular state for a certain amount of time,
before being switched to another state. The concept of persistence has been
found to have many diverse practical applications, ranging from non-equilibrium
statistical mechanics to financial dynamics to distribution of time scales in
turbulent flows and many more. In this study, we carry out a detailed analysis
of the statistical characteristics of the persistence probability density
functions (PDFs) of velocity and temperature fluctuations in the surface layer
of a convective boundary layer, using a field-experimental dataset. Our results
demonstrate that for the time scales smaller than the integral scales, the
persistence PDFs of turbulent velocity and temperature fluctuations display a
clear power-law behaviour, associated with self-similar eddy cascading
mechanism. Moreover, we also show that the effects of non-Gaussian temperature
fluctuations act only at those scales which are larger than the integral
scales, where the persistence PDFs deviate from the power-law and drop
exponentially. Furthermore, the mean time scales of the negative temperature
fluctuation events persisting longer than the integral scales are found to be
approximately equal to twice the integral scale in highly convective
conditions. However, with stability this mean time scale gradually decreases to
almost being equal to the integral scale in the near neutral conditions.
Contrarily, for the long positive temperature fluctuation events, the mean time
scales remain roughly equal to the integral scales, irrespective of stability
Towards Solving the Navier-Stokes Equation on Quantum Computers
In this paper, we explore the suitability of upcoming novel computing
technologies, in particular adiabatic annealing based quantum computers, to
solve fluid dynamics problems that form a critical component of several science
and engineering applications. We start with simple flows with well-studied flow
properties, and provide a framework to convert such systems to a form amenable
for deployment on such quantum annealers. We analyze the solutions obtained
both qualitatively and quantitatively as well as the sensitivities of the
various solution selection schemes on the obtained solution
Level-crossings reveal organized coherent structures in a turbulent time series
In turbulent flows, energy production is associated with highly organized
structures, known as coherent structures. Since these structures are
three-dimensional, their detection remains challenging in the most common
situation, when single-point temporal measurements are considered. While
previous research on coherent structure detection from time series employs a
thresholding approach, the thresholds are ad-hoc and vary significantly from
one study to another. To eliminate this subjective bias, we introduce the
level-crossing method and show how specific features of a turbulent time series
associated with coherent structures can be objectively identified, without
assigning a prior any arbitrary threshold. By using two wall-bounded turbulence
time series datasets, we successfully extract through level-crossing analysis
the impacts of coherent structures on turbulent dynamics, and therefore, open
an alternative avenue in experimental turbulence research. By utilizing this
framework further we identify a new metric, characterized by a statistical
asymmetry between peaks and troughs of a turbulent signal, to quantify
inner-outer interaction in wall turbulence. Moreover, a connection is
established between extreme value statistics and level-crossing analysis,
thereby allowing additional possibilities to study extreme events in other
dynamical systems.Comment: This manuscript has 9 figures and 3 supplementary figure
Visibility network analysis of large-scale intermittency in convective surface layer turbulence
Large-scale intermittency is a widely observed phenomenon in convective
surface layer turbulence that induces non-Gaussian temperature statistics,
while such signature is not observed for velocity signals. Although approaches
based on probability density functions have been used so far, those are not
able to explain to what extent the signals' temporal structure impacts the
statistical characteristics of the velocity and temperature fluctuations. To
tackle this issue, a visibility network analysis is carried out on a
field-experimental dataset from a convective atmospheric surface layer flow.
Through surrogate data and network-based measures, we demonstrate that the
temperature intermittency is related to strong non-linear dependencies in the
temperature signals. Conversely, a competition between linear and non-linear
effects tends to inhibit the temperature-like intermittency behaviour in
streamwise and vertical velocities. Based on present findings, new research
avenues are likely to be opened up in studying large-scale intermittency in
convective turbulence.Comment: 4 figure
Revisiting the role of intermittent heat transport towards Reynolds stress anisotropy in convective turbulence
Thermal plumes are the energy containing eddy motions that carry heat and
momentum in a convective boundary layer. The detailed understanding of their
structure is of fundamental interest for a range of applications, from
wall-bounded engineering flows to quantifying surface-atmosphere flux
exchanges. We address the aspect of Reynolds stress anisotropy associated with
the intermittent nature of heat transport in thermal plumes by performing an
invariant analysis of the Reynolds stress tensor in an unstable atmospheric
surface layer flow, using a field-experimental dataset. Given the intermittent
and asymmetric nature of the turbulent heat flux, we formulate this problem in
an event-based framework. In this approach, we provide structural descriptions
of warm-updraft and cold-downdraft events and investigate the degree of
isotropy of the Reynolds stress tensor within these events of different sizes.
We discover that only a subset of these events are associated with the least
anisotropic turbulence in highly-convective conditions. Additionally,
intermittent large heat flux events are found to contribute substantially to
turbulence anisotropy under unstable stratification. Moreover, we find that the
sizes related to the maximum value of the degree of isotropy do not correspond
to the peak positions of the heat flux distributions. This is because, the
vertical velocity fluctuations pertaining to the sizes associated with the
maximum heat flux, transport significant amount of streamwise momentum. A
preliminary investigation shows that the sizes of the least anisotropic events
probably scale with a mixed-length scale (, where is
the measurement height and is the large-eddy length scale)
Persistence behaviour of heat and momentum fluxes in convective surface layer turbulence
The characterization of heat and momentum fluxes in wall-bounded turbulence
is of paramount importance for a plethora of applications, ranging from
engineering to Earth sciences. However, how the turbulent structures associated
with velocity and temperature fluctuations interact to produce the emergent
flux signatures, is not evident till date. In this work, we investigate this
fundamental issue by studying the switching patterns of intermittently
occurring turbulent fluctuations from one state to another, a phenomenon called
persistence. We discover that the persistence patterns for heat and momentum
fluxes are widely different. Moreover, we uncover power-law scaling and length
scales of turbulent motions that cause this behavior. Furthermore, by
separating the phases and amplitudes of flux events, we explain the origin and
differences between heat and momentum transfer efficiencies in convective
turbulence. Our findings provide new understanding on the connection between
flow organization and flux generation mechanisms, two cornerstones of
turbulence research
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