93 research outputs found
Multi-locality and fusion rules on the generalized structure functions in two-dimensional and three-dimensional Navier-Stokes turbulence
Using the fusion rules hypothesis for three-dimensional and two-dimensional
Navier-Stokes turbulence, we generalize a previous non-perturbative locality
proof to multiple applications of the nonlinear interactions operator on
generalized structure functions of velocity differences. We shall call this
generalization of non-perturbative locality to multiple applications of the
nonlinear interactions operator "multilocality". The resulting cross-terms pose
a new challenge requiring a new argument and the introduction of a new fusion
rule that takes advantage of rotational symmetry. Our main result is that the
fusion rules hypothesis implies both locality and multilocality in both the IR
and UV limits for the downscale energy cascade of three-dimensional
Navier-Stokes turbulence and the downscale enstrophy cascade and inverse energy
cascade of two-dimensional Navier-Stokes turbulence. We stress that these
claims relate to non-perturbative locality of generalized structure functions
on all orders, and not the term by term perturbative locality of diagrammatic
theories or closure models that involve only two-point correlation and response
functions.Comment: 25 pages, 24 figures, resubmitted to Physical Review
Dissipation scales and anomalous sinks in steady two-dimensional turbulence
In previous papers I have argued that the \emph{fusion rules hypothesis},
which was originally introduced by L'vov and Procaccia in the context of the
problem of three-dimensional turbulence, can be used to gain a deeper insight
in understanding the enstrophy cascade and inverse energy cascade of
two-dimensional turbulence. In the present paper we show that the fusion rules
hypothesis, combined with \emph{non-perturbative locality}, itself a
consequence of the fusion rules hypothesis, dictates the location of the
boundary separating the inertial range from the dissipation range. In so doing,
the hypothesis that there may be an anomalous enstrophy sink at small scales
and an anomalous energy sink at large scales emerges as a consequence of the
fusion rules hypothesis. More broadly, we illustrate the significance of
viewing inertial ranges as multi-dimensional regions where the fully unfused
generalized structure functions of the velocity field are self-similar, by
considering, in this paper, the simplified projection of such regions in a
two-dimensional space, involving a small scale and a large scale , which
we call, in this paper, the -plane. We see, for example, that the
logarithmic correction in the enstrophy cascade, under standard molecular
dissipation, plays an essential role in inflating the inertial range in the
plane to ensure the possibility of local interactions. We have also
seen that increasingly higher orders of hyperdiffusion at large scales or
hypodiffusion at small scales make the predicted sink anomalies more resilient
to possible violations of the fusion rules hypothesis.Comment: 22 pages, resubmitted to Phys. Rev.
The role of the asymmetric Ekman dissipation term on the energetics of the two-layer quasi-geostrophic model
In the two-layer quasi-geostrophic model, the friction between the flow at
the lower layer and the surface boundary layer, placed beneath the lower layer,
is modeled by the Ekman term, which is a linear dissipation term with respect
to the horizontal velocity at the lower layer. The Ekman term appears in the
governing equations asymmetrically; it is placed at the lower layer, but does
not appear at the upper layer. A variation, proposed by Phillips and Salmon,
uses extrapolation to place the Ekman term between the lower layer and the
surface boundary layer, or at the surface boundary layer. We present
theoretical results that show that in either the standard or the extrapolated
configurations, the Ekman term dissipates energy at large scales, but does not
dissipate potential enstrophy. It also creates an approximately symmetric
stable distribution of potential enstrophy between the two layers. The behavior
of the Ekman term changes fundamentally at small scales. Under the standard
formulation, the Ekman term will unconditionally dissipate energy and also
dissipate, under very minor conditions, potential enstrophy at small scales.
However, under the extrapolated formulation, there exist small "negative
regions", which are defined over a two-dimensional phase space, capturing the
distribution of energy per wavenumber between baroclinic energy and barotropic
energy, and the distribution of potential enstrophy per wavenumber between the
upper layer and the lower layer, where the Ekman term may inject energy or
potential enstrophy.Comment: 28 pages, 3 figures, resubmitted to Physica
The effect of asymmetric large-scale dissipation on energy and potential enstrophy injection in two-layer quasi-geostrophic turbulence
In the Nastrom-Gage spectrum of atmospheric turbulence we observe a
energy spectrum that transitions into a spectrum, with increasing
wavenumber . The transition occurs near a transition wavenumber ,
located near the Rossby deformation wavenumber . The Tung-Orlando theory
interprets this spectrum as a double downscale cascade of potential enstrophy
and energy, from large scales to small scales, in which the downscale potential
enstrophy cascade coexists with the downscale energy cascade over the same
length-scale range. We show that, in a temperature forced two-layer
quasi-geostrophic model, the rates with which potential enstrophy and energy
are injected place the transition wavenumber near . We also show
that if the potential energy dominates the kinetic energy in the forcing range,
then the Ekman term suppresses the upscale cascading potential enstrophy more
than it suppresses the upscale cascading energy, a behavior contrary to what
occurs in two-dimensional turbulence. As a result, the ratio \gn/\gee of
injected potential enstrophy over injected energy, in the downscale direction,
decreases, thereby tending to decrease the transition wavenumber further.
Using a random Gaussian forcing model, we reach the same conclusion, under the
modeling assumption that the asymmetric Ekman term predominantly suppresses the
bottom layer forcing, thereby disregarding a possible entanglement between the
Ekman term and the nonlinear interlayer interaction. Based on these results, we
argue that the Tung-Orlando theory can account for the approximate coincidence
between and . We also identify certain open questions that require
further investigation via numerical simulations.Comment: 29 pages; resubmitted to J. Fluid Mec
A New Proof on Net Upscale Energy Cascade in 2D and QG Turbulence
A general proof that more energy flows upscale than downscale in
two-dimensional (2D) turbulence and barotropic quasi-geostrophic (QG)
turbulence is given. A proof is also given that in Surface QG turbulence, the
reverse is true. Though some of these results are known in restricted cases,
the proofs given here are pedagogically simpler, require fewer assumptions and
apply to both forced and unforced cases.Comment: v1: submitted to J. Fluid. Mech v2: revised and accepted by J. Fluid.
Mech, 17 page
Locality and stability of the cascades of two-dimensional turbulence
We investigate and clarify the notion of locality as it pertains to the
cascades of two-dimensional turbulence. The mathematical framework underlying
our analysis is the infinite system of balance equations that govern the
generalized unfused structure functions, first introduced by L'vov and
Procaccia. As a point of departure we use a revised version of the system of
hypotheses that was proposed by Frisch for three-dimensional turbulence. We
show that both the enstrophy cascade and the inverse energy cascade are local
in the sense of non-perturbative statistical locality. We also investigate the
stability conditions for both cascades. We have shown that statistical
stability with respect to forcing applies unconditionally for the inverse
energy cascade. For the enstrophy cascade, statistical stability requires
large-scale dissipation and a vanishing downscale energy dissipation. A careful
discussion of the subtle notion of locality is given at the end of the paper.Comment: v2: 23 pages; 4 figures; minor revisions; resubmitted to Phys. Rev.
Is the subdominant part of the energy spectrum due to downscale energy cascade hidden in quasi-geostrophic turbulence?
In systems governing two-dimensional turbulence, surface quasi-geostrophic
turbulence, (more generally -turbulence), two-layer quasi-geostrophic
turbulence, etc., there often exist two conservative quadratic quantities, one
``energy''-like and one ``enstrophy''-like. In a finite inertial range there
are in general two spectral fluxes, one associated with each conserved
quantity. We derive here an inequality comparing the relative magnitudes of the
``energy'' and ``enstrophy'' fluxes for finite or infinitesimal dissipations,
and for hyper or hypo viscosities. When this inequality is satisfied, as is the
case of 2D turbulence,where the energy flux contribution to the energy spectrum
is small, the subdominant part will be effectively hidden. In sQG turbulence,
it is shown that the opposite is true: the downscale energy flux becomes the
dominant contribution to the energy spectrum. A combination of these two
behaviors appears to be the case in 2-layer QG turbulence, depending on the
baroclinicity of the system.Comment: 23 pages; accepted at Discrete and Continuous Dynamical Systems B;
Major revisio
Optimisation based approaches for machine learning
Machine learning has attracted a lot of attention in recent years and it has become an integral part of many commercial and research projects, with a wide range of applications. With current developments in technology, more data is generated and stored than ever before. Identifying patterns, trends and anomalies in these datasets and summarising them with simple quantitative models is a vital task. This thesis focuses on the development of machine learning algorithms based on mathematical programming for datasets that are relatively small in size. The first topic of this doctoral thesis is piecewise regression, where a dataset is partitioned into multiple regions and a regression model is fitted to each one. This work uses an existing algorithm from the literature and extends the mathematical formulation in order to include information criteria. The inclusion of such criteria targets to deal with overfitting, which is a common problem in supervised learning tasks, by finding a balance between predictive performance and model complexity. The improvement in overall performance is demonstrated by testing and comparing the proposed method with various algorithms from the literature on various regression datasets. Extending the topic of regression, a decision tree regressor is also proposed. Decision trees are powerful and easy to understand structures that can be used both for regression and classification. In this work, an optimisation model is used for the binary splitting of nodes. A statistical test is introduced to check whether the partitioning of nodes is statistically meaningful and as a result control the tree generation process. Additionally, a novel mathematical formulation is proposed to perform feature selection and ultimately identify the appropriate variable to be selected for the splitting of nodes. The performance of the proposed algorithm is once again compared with a number of literature algorithms and it is shown that the introduction of the variable selection model is useful for reducing the training time of the algorithm without major sacrifices in performance. Lastly, a novel decision tree classifier is proposed. This algorithm is based on a mathematical formulation that identifies the optimal splitting variable and break value, applies a linear transformation to the data and then assigns them to a class while minimising the number of misclassified samples. The introduction of the linear transformation step reduces the dimensionality of the examined dataset down to a single variable, aiding the classification accuracy of the algorithm for more complex datasets. Popular classifiers from the literature have been used to compare the accuracy of the proposed algorithm on both synthetic and publicly available classification datasets
Solving parametric radical equations with depth 2 rigorously using the restriction set method
We review the history and previous literature on radical equations and
present the rigorous solution theory for radical equations of depth 2,
continuing a previous study of radical equations of depth 1. Radical equations
of depth 2 are equations where the unknown variable appears under at least one
square root and where two steps are needed to eliminate all radicals appearing
in the equation. We state and prove theorems for all three equation forms with
depth 2 that give the solution set of all real-valued solutions. The theorems
are shown via the restriction set method that uses inequality restrictions to
decide whether to accept or reject candidate solutions. We distinguish between
formal solutions that satisfy the original equation in a formal sense, where we
allow some radicals to evaluate to imaginary numbers during verification, and
strong solutions, where all radicals evaluate to real numbers during
verification. Our theorems explicitly identify the set of all formal solutions
and the set of all strong solutions for each equation form. The theory
underlying radical equations with depth 2 is richer and more interesting than
the theory governing radical equations with depth 1, and some aspects of the
theory are not intuitively obvious. It is illustrated with examples of
parametric radical equations.Comment: 23 pages; 0 figures; resubmitted to International Journal of
Mathematical Education in Science and Technolog
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