Multiple states in turbulent large-aspect ratio thermal convection: What determines the number of convection rolls?

Recent findings suggest that wall-bounded turbulent flow can take different statistically stationary turbulent states, with different transport properties, even for the very same values of the control parameters. What state the system takes depends on the initial conditions. Here we analyze the multiple states in large-aspect ratio ($\Gamma$) two-dimensional turbulent Rayleigh--B\'enard flow with no-slip plates and horizontally periodic boundary conditions as model system. We determine the number $n$ of convection rolls, their mean aspect ratios $\Gamma_r = \Gamma /n$, and the corresponding transport properties of the flow (i.e., the Nusselt number $Nu$), as function of the control parameters Rayleigh ($Ra$) and Prandtl number. The effective scaling exponent $\beta$ in $Nu \sim Ra^\beta$ is found to depend on the realized state and thus $\Gamma_r$, with a larger value for the smaller $\Gamma_r$. By making use of a generalized Friedrichs inequality, we show that the elliptical instability and viscous damping determine the $\Gamma_r$-window for the realizable turbulent states. The theoretical results are in excellent agreement with our numerical finding $2/3 \le \Gamma_r \le 4/3$, where the lower threshold is approached for the larger $Ra$. Finally, we show that the theoretical approach to frame $\Gamma_r$ also works for free-slip boundary conditions.Comment: 6 pages, 5 figure

Uncertainty in Economic Growth and Inequality

A step to consilience, starting with a deconstruction of the causality of uncertainty that is embedded in the fundamentals of growth and inequality, following a construction of aggregation laws that disclose the invariance principle across heterogeneous individuals, ending with a reconstruction of metric models that yields deeper structural connections via U.S. GDP and income data

Growth Estimators and Confidence Intervals for the Mean of Negative Binomial Random Variables with Unknown Dispersion

The Negative Binomial distribution becomes highly skewed under extreme dispersion. Even at moderately large sample sizes, the sample mean exhibits a heavy right tail. The standard Normal approximation often does not provide adequate inferences about the data's mean in this setting. In previous work, we have examined alternative methods of generating confidence intervals for the expected value. These methods were based upon Gamma and Chi Square approximations or tail probability bounds such as Bernstein's Inequality. We now propose growth estimators of the Negative Binomial mean. Under high dispersion, zero values are likely to be overrepresented in the data. A growth estimator constructs a Normal-style confidence interval by effectively removing a small, pre--determined number of zeros from the data. We propose growth estimators based upon multiplicative adjustments of the sample mean and direct removal of zeros from the sample. These methods do not require estimating the nuisance dispersion parameter. We will demonstrate that the growth estimators' confidence intervals provide improved coverage over a wide range of parameter values and asymptotically converge to the sample mean. Interestingly, the proposed methods succeed despite adding both bias and variance to the Normal approximation

Implementing the multimodel generalized beta estimator in stata and its application

The multimodel generalized beta estimator (MGBE) described by von Hippel, Scarpino and Hola (2014) provides researchers with an improved way to estimate inequality from binned incomes. To extend the application of MGBE, the mgbe command is developed in Stata. In this report, the implementation and performance of mgbe are discussed.Statistic

Functional inequalities for modified Bessel functions

In this paper our aim is to show some mean value inequalities for the modified Bessel functions of the first and second kinds. Our proofs are based on some bounds for the logarithmic derivatives of these functions, which are in fact equivalent to the corresponding Tur\'an type inequalities for these functions. As an application of the results concerning the modified Bessel function of the second kind we prove that the cumulative distribution function of the gamma-gamma distribution is log-concave. At the end of this paper several open problems are posed, which may be of interest for further research.Comment: 14 page

Some completely monotonic functions involving the qq-gamma function

We present some completely monotonic functions involving the $q$-gamma function that are inspired by their analogues involving the gamma function.Comment: 8 page