Computer program for the design of axial-flow turbines

Computer program, capable of analyzing single and multispool units, computes absolute and relative flow fields within the turbine at the first stator inlet, at each interblade row plane, and at the final rotor exit. No simplifying assumptions are made which would result in restrictive design

The analysis of geometry and design-point performance of axial-flow turbines using specified meridional velocity gradients. Part 2 - Design examples

Computer program for design of axial flow turbines with velocity distribution gradients at stator and rotor exit

Analysis of geometry and design point performance of axial flow turbines. Part 3 - Design analysis of selected examples Final report

Computerized design of axial flow turbines using stream filament approach to design specification

Analysis of geometry and design point performance of axial flow turbines. 1 - Development of the analysis method and the loss coefficient correlation

Stream-filament analysis procedure and correlation of total pressure loss coefficients to form basis of computer program to investigate design point performance of axial turbine

An extended space approach for particle Markov chain Monte Carlo methods

In this paper we consider fully Bayesian inference in general state space models. Existing particle Markov chain Monte Carlo (MCMC) algorithms use an augmented model that takes into account all the variable sampled in a sequential Monte Carlo algorithm. This paper describes an approach that also uses sequential Monte Carlo to construct an approximation to the state space, but generates extra states using MCMC runs at each time point. We construct an augmented model for our extended space with the marginal distribution of the sampled states matching the posterior distribution of the state vector. We show how our method may be combined with particle independent Metropolis-Hastings or particle Gibbs steps to obtain a smoothing algorithm. All the Metropolis acceptance probabilities are identical to those obtained in existing approaches, so there is no extra cost in term of Metropolis-Hastings rejections when using our approach. The number of MCMC iterates at each time point is chosen by the used and our augmented model collapses back to the model in Olsson and Ryden (2011) when the number of MCMC iterations reduces. We show empirically that our approach works well on applied examples and can outperform existing methods.Comment: 35 pages, 2 figures, Typos corrected from Version

Entrainment coefficient and effective mass for conduction neutrons in neutron star crust: II Macroscopic treatment

Phenomena such as pulsar frequency glitches are believed to be attributable to differential rotation of a current of ``free'' superfluid neutrons at densities above the ``drip'' threshold in the ionic crust of a neutron star. Such relative flow is shown to be locally describable by adaption of a canonical two fluid treatment that emphasizes the role of the momentum covectors constructed by differentiation of action with respect to the currents, with allowance for stratification whereby the ionic number current may be conserved even when the ionic charge number Z is altered by beta processes. It is demonstrated that the gauge freedom to make different choices of the chemical basis determining which neutrons are counted as ``free'' does not affect their ``superfluid'' momentum covector, which must locally have the form of a gradient (though it does affect the ``normal'' momentum covector characterising the protons and those neutrons that are considered to be ``confined'' in the nuclei). It is shown how the effect of ``entrainment'' (whereby the momentum directions deviate from those of the currents) is controlled by the (gauge independent) mobility coefficient K, estimated in recent microscopical quantum mechanical investigations, which suggest that the corresponding (gauge dependent) ``effective mass'' m* of the free neutrons can become very large in some layers. The relation between this treatment of the crust layers and related work (using different definitions of ``effective mass'') intended for the deeper core layers is discussed.Comment: 21 pages Latex. Part II of article whose Part I (Simple microscopic models) is given by nucl-th/0402057. New version extended to include figure

Bayesian Covariance Matrix Estimation using a Mixture of Decomposable Graphical Models

Estimating a covariance matrix efficiently and discovering its structure are important statistical problems with applications in many fields. This article takes a Bayesian approach to estimate the covariance matrix of Gaussian data. We use ideas from Gaussian graphical models and model selection to construct a prior for the covariance matrix that is a mixture over all decomposable graphs, where a graph means the configuration of nonzero offdiagonal elements in the inverse of the covariance matrix. Our prior for the covariance matrix is such that the probability of each graph size is specified by the user and graphs of equal size are assigned equal probability. Most previous approaches assume that all graphs are equally probable. We give empirical results that show the prior that assigns equal probability over graph sizes outperforms the prior that assigns equal probability over all graphs, both in identifying the correct decomposable graph and in more efficiently estimating the covariance matrix. The advantage is greatest when the number of observations is small relative to the dimension of the covariance matrix. The article also shows empirically that there is minimal change in statistical efficiency in using the mixture over decomposable graphs prior for estimating a general covariance compared to the Bayesian estimator by Wong et al. (2003), even when the graph of the covariance matrix is nondecomposable. However, our approach has some important advantages over that of Wong et al. (2003). Our method requires the number of decomposable graphs for each graph size. We show how to estimate these numbers using simulation and that the simulation results agree with analytic results when such results are known. We also show how to estimate the posterior distribution of the covariance matrix using Markov chain Monte Carlo with the elements of the covariance matrix integrated out and give empirical results that show the sampler is computationally efficient and converges rapidly. Finally, we note that both the prior and the simulation method to evaluate the prior apply generally to any decomposable graphical model.Covariance selection; Graphical models; Reduced conditional sampling; Variable selection