Aeorodynamic characteristics of an air-exchanger system for the 40- by 80-foot wind tunnel at Ames Research Center

A 1/50-scale model of the 40- by 80-Foot Wind Tunnel at Ames Research Center was used to study various air-exchange configurations. System components were tested throughout a range of parameters, and approximate analytical relationships were derived to explain the observed characteristics. It is found that the efficiency of the air exchanger could be increased (1) by adding a shaped wall to smoothly turn the incoming air downstream, (2) by changing to a contoured door at the inlet to control the flow rate, and (3) by increasing the size of the exhaust opening. The static pressures inside the circuit then remain within the design limits at the higher tunnel speeds if the air-exchange rate is about 5% or more. Since the model is much smaller than the full-scale facility, it is not possible to completely duplicate the tunnel, and it will be necessary to measure such characteristics as flow rate and tunnel pressures during implementation of the remodeled facility. The aerodynamic loads estimated for the inlet door and for nearby walls are also presented

Invariant, super and quasi-martingale functions of a Markov process

We identify the linear space spanned by the real-valued excessive functions of a Markov process with the set of those functions which are quasimartingales when we compose them with the process. Applications to semi-Dirichlet forms are given. We provide a unifying result which clarifies the relations between harmonic, co-harmonic, invariant, co-invariant, martingale and co-martingale functions, showing that in the conservative case they are all the same. Finally, using the co-excessive functions, we present a two-step approach to the existence of invariant probability measures

Fisher information and asymptotic normality in system identification for quantum Markov chains

This paper deals with the problem of estimating the coupling constant $\theta$ of a mixing quantum Markov chain. For a repeated measurement on the chain's output we show that the outcomes' time average has an asymptotically normal (Gaussian) distribution, and we give the explicit expressions of its mean and variance. In particular we obtain a simple estimator of $\theta$ whose classical Fisher information can be optimized over different choices of measured observables. We then show that the quantum state of the output together with the system, is itself asymptotically Gaussian and compute its quantum Fisher information which sets an absolute bound to the estimation error. The classical and quantum Fisher informations are compared in a simple example. In the vicinity of $\theta=0$ we find that the quantum Fisher information has a quadratic rather than linear scaling in output size, and asymptotically the Fisher information is localised in the system, while the output is independent of the parameter.Comment: 10 pages, 2 figures. final versio

F/A-18 1/9th scale model tail buffet measurements

Wind tunnel tests were carried out on a 1/9th scale model of the F/A-18 at high angles of attack to investigate the characteristics of tail buffet due to bursting of the wing leading edge extension (LEX) vortices. The tests were carried out at the Aeronautical Research Laboratory low-speed wind tunnel facility and form part of a collaborative activity with NASA Ames Research Center, organized by The Technical Cooperative Program (TTCP). Information from the program will be used in the planning of similar collaborative tests, to be carried out at NASA Ames, on a full-scale aircraft. The program covered the measurement of unsteady pressures and fin vibration for cases with and without the wing LEX fences fitted. Fourier transform methods were used to analyze the unsteady data, and information on the spatial and temporal content of the vortex burst pressure field was obtained. Flow visualization of the vortex behavior was carried out using smoke and a laser light sheet technique

Effects of Long-Term Use of Nonoxynol-9 on Vaginal Flora

OBJECTIVE—Products containing nonoxynol-9 have been used as spermicidal contraceptives for many years, but limited data have been published describing the long-term effects of nonoxynol-9 use on the vaginal microbial ecosystem. This longitudinal study was conducted to examine the effects of nonoxynol-9 on the vaginal ecology. METHODS—Vaginal swabs were obtained from 235 women enrolled in a randomized clinical trial before initiation of use of 1 of 5 different formulations of nonoxynol-9 for contraception, and up to 3 more samples were gathered over 7 months of use. The swab samples were evaluated in a single laboratory. The prevalence of several constituents of the normal vaginal flora was evaluated. The associations between nonoxynol-9 dosage, formulation, average product use per week, and number of sex acts per week were calculated. RESULTS—The changes in prevalence of vaginal microbes after nonoxynol-9 use were minimal for each of the different nonoxynol-9 formulations. However, when both nonoxynol-9 concentration and number of product uses are taken into account, nonoxynol-9 did have dose-dependant effects on the increased prevalence of anaerobic gram-negative rods (odds ratio [OR] 2.4, 95% confidence interval [CI] 1.1–5.3), H2O2-negative lactobacilli (OR 2.0, 95% CI 1.0–4.1), and bacterial vaginosis (OR 2.3, 95% CI 1.1–4.7). CONCLUSION—This study demonstrated that most nonoxynol-9 users experienced minimal disruptions in their vaginal ecology. There were no differences between the different formulations evaluated with respect to changes in vaginal microflora. However, independent of the nonoxynol-9 formulation, there was a dose-dependent effect with increased exposure to nonoxynol-9 on the risk of bacterial vaginosis and its associated flora

Random billiards with wall temperature and associated Markov chains

By a random billiard we mean a billiard system in which the standard specular reflection rule is replaced with a Markov transition probabilities operator P that, at each collision of the billiard particle with the boundary of the billiard domain, gives the probability distribution of the post-collision velocity for a given pre-collision velocity. A random billiard with microstructure (RBM) is a random billiard for which P is derived from a choice of geometric/mechanical structure on the boundary of the billiard domain. RBMs provide simple and explicit mechanical models of particle-surface interaction that can incorporate thermal effects and permit a detailed study of thermostatic action from the perspective of the standard theory of Markov chains on general state spaces. We focus on the operator P itself and how it relates to the mechanical/geometric features of the microstructure, such as mass ratios, curvatures, and potentials. The main results are as follows: (1) we characterize the stationary probabilities (equilibrium states) of P and show how standard equilibrium distributions studied in classical statistical mechanics, such as the Maxwell-Boltzmann distribution and the Knudsen cosine law, arise naturally as generalized invariant billiard measures; (2) we obtain some basic functional theoretic properties of P. Under very general conditions, we show that P is a self-adjoint operator of norm 1 on an appropriate Hilbert space. In a simple but illustrative example, we show that P is a compact (Hilbert-Schmidt) operator. This leads to the issue of relating the spectrum of eigenvalues of P to the features of the microstructure;(3) we explore the latter issue both analytically and numerically in a few representative examples;(4) we present a general algorithm for simulating these Markov chains based on a geometric description of the invariant volumes of classical statistical mechanics

Convergence to equilibrium for many particle systems

The goal of this paper is to give a short review of recent results of the authors concerning classical Hamiltonian many particle systems. We hope that these results support the new possible formulation of Boltzmann's ergodicity hypothesis which sounds as follows. For almost all potentials, the minimal contact with external world, through only one particle of $N$, is sufficient for ergodicity. But only if this contact has no memory. Also new results for quantum case are presented

On the flow-level stability of data networks without congestion control: the case of linear networks and upstream trees

In this paper, flow models of networks without congestion control are considered. Users generate data transfers according to some Poisson processes and transmit corresponding packet at a fixed rate equal to their access rate until the entire document is received at the destination; some erasure codes are used to make the transmission robust to packet losses. We study the stability of the stochastic process representing the number of active flows in two particular cases: linear networks and upstream trees. For the case of linear networks, we notably use fluid limits and an interesting phenomenon of "time scale separation" occurs. Bounds on the stability region of linear networks are given. For the case of upstream trees, underlying monotonic properties are used. Finally, the asymptotic stability of those processes is analyzed when the access rate of the users decreases to 0. An appropriate scaling is introduced and used to prove that the stability region of those networks is asymptotically maximized

CLTs and asymptotic variance of time-sampled Markov chains

For a Markov transition kernel P and a probability distribution μ on nonnegative integers, a time-sampled Markov chain evolves according to the transition kernel Pμ = Σkμ(k)Pk. In this note we obtain CLT conditions for time-sampled Markov chains and derive a spectral formula for the asymptotic variance. Using these results we compare efficiency of Barker's and Metropolis algorithms in terms of asymptotic variance

Noisy Monte Carlo: Convergence of Markov chains with approximate transition kernels

Monte Carlo algorithms often aim to draw from a distribution $\pi$ by simulating a Markov chain with transition kernel $P$ such that $\pi$ is invariant under $P$. However, there are many situations for which it is impractical or impossible to draw from the transition kernel $P$. For instance, this is the case with massive datasets, where is it prohibitively expensive to calculate the likelihood and is also the case for intractable likelihood models arising from, for example, Gibbs random fields, such as those found in spatial statistics and network analysis. A natural approach in these cases is to replace $P$ by an approximation $\hat{P}$. Using theory from the stability of Markov chains we explore a variety of situations where it is possible to quantify how 'close' the chain given by the transition kernel $\hat{P}$ is to the chain given by $P$. We apply these results to several examples from spatial statistics and network analysis.Comment: This version: results extended to non-uniformly ergodic Markov chain