Fan Performance Scaling With Inlet Distortions

Applications such as boundary-layer-ingesting fans, and compressors in turboprop engines require continuous operation with distorted inflow. A low-speed axial fan with incompressible flow is studied in this paper. The objectives are to (1) identify the physical mechanisms which govern the fan response to inflow distortions and (2) determine how fan performance scales as the type and severity of inlet distortion varies at the design flow coefficient. A distributed source term approach to modeling the rotor and stator blade rows is used in numerical simulations in this paper. The model does not include viscous losses so that changes in diffusion factor are the primary focus. Distortions in stagnation pressure and temperature as well as swirl are considered. The key findings are that unless sharp pitchwise gradients in the diffusion response, strong radial flows, or very large distortion magnitudes are present, the response of the blade rows for strong distortions can be predicted by scaling up the response to a weaker distortion. In addition, the response to distortions which are composed of non-uniformities in several inlet quantities can be predicted by summing up the responses to the constituent distortions

Wave functions in the neighborhood of a toroidal surface; hard vs. soft constraint

The curvature potential arising from confining a particle initially in three-dimensional space onto a curved surface is normally derived in the hard constraint $q \to 0$ limit, with $q$ the degree of freedom normal to the surface. In this work the hard constraint is relaxed, and eigenvalues and wave functions are numerically determined for a particle confined to a thin layer in the neighborhood of a toroidal surface. The hard constraint and finite layer (or soft constraint) quantities are comparable, but both differ markedly from those of the corresponding two dimensional system, indicating that the curvature potential continues to influence the dynamics when the particle is confined to a finite layer. This effect is potentially of consequence to the modelling of curved nanostructures.Comment: 4 pages, no fig

A multi-stage machine learning model on diagnosis of esophageal manometry

High-resolution manometry (HRM) is the primary procedure used to diagnose esophageal motility disorders. Its interpretation and classification includes an initial evaluation of swallow-level outcomes and then derivation of a study-level diagnosis based on Chicago Classification (CC), using a tree-like algorithm. This diagnostic approach on motility disordered using HRM was mirrored using a multi-stage modeling framework developed using a combination of various machine learning approaches. Specifically, the framework includes deep-learning models at the swallow-level stage and feature-based machine learning models at the study-level stage. In the swallow-level stage, three models based on convolutional neural networks (CNNs) were developed to predict swallow type, swallow pressurization, and integrated relaxation pressure (IRP). At the study-level stage, model selection from families of the expert-knowledge-based rule models, xgboost models and artificial neural network(ANN) models were conducted, with the latter two model designed and augmented with motivation from the export knowledge. A simple model-agnostic strategy of model balancing motivated by Bayesian principles was utilized, which gave rise to model averaging weighted by precision scores. The averaged (blended) models and individual models were compared and evaluated, of which the best performance on test dataset is 0.81 in top-1 prediction, 0.92 in top-2 predictions. This is the first artificial-intelligence-style model to automatically predict CC diagnosis of HRM study from raw multi-swallow data. Moreover, the proposed modeling framework could be easily extended to multi-modal tasks, such as diagnosis of esophageal patients based on clinical data from both HRM and functional luminal imaging probe panometry (FLIP)

Ergodicity, Decisions, and Partial Information

In the simplest sequential decision problem for an ergodic stochastic process X, at each time n a decision u_n is made as a function of past observations X_0,...,X_{n-1}, and a loss l(u_n,X_n) is incurred. In this setting, it is known that one may choose (under a mild integrability assumption) a decision strategy whose pathwise time-average loss is asymptotically smaller than that of any other strategy. The corresponding problem in the case of partial information proves to be much more delicate, however: if the process X is not observable, but decisions must be based on the observation of a different process Y, the existence of pathwise optimal strategies is not guaranteed. The aim of this paper is to exhibit connections between pathwise optimal strategies and notions from ergodic theory. The sequential decision problem is developed in the general setting of an ergodic dynamical system (\Omega,B,P,T) with partial information Y\subseteq B. The existence of pathwise optimal strategies grounded in two basic properties: the conditional ergodic theory of the dynamical system, and the complexity of the loss function. When the loss function is not too complex, a general sufficient condition for the existence of pathwise optimal strategies is that the dynamical system is a conditional K-automorphism relative to the past observations \bigvee_n T^n Y. If the conditional ergodicity assumption is strengthened, the complexity assumption can be weakened. Several examples demonstrate the interplay between complexity and ergodicity, which does not arise in the case of full information. Our results also yield a decision-theoretic characterization of weak mixing in ergodic theory, and establish pathwise optimality of ergodic nonlinear filters.Comment: 45 page

Close-packed dimers on the line: diffraction versus dynamical spectrum

The translation action of \RR^{d} on a translation bounded measure $\omega$ leads to an interesting class of dynamical systems, with a rather rich spectral theory. In general, the diffraction spectrum of $\omega$, which is the carrier of the diffraction measure, live on a subset of the dynamical spectrum. It is known that, under some mild assumptions, a pure point diffraction spectrum implies a pure point dynamical spectrum (the opposite implication always being true). For other systems, the diffraction spectrum can be a proper subset of the dynamical spectrum, as was pointed out for the Thue-Morse sequence (with singular continuous diffraction) in \cite{EM}. Here, we construct a random system of close-packed dimers on the line that have some underlying long-range periodic order as well, and display the same type of phenomenon for a system with absolutely continuous spectrum. An interpretation in terms of `atomic' versus `molecular' spectrum suggests a way to come to a more general correspondence between these two types of spectra.Comment: 14 pages, with some additions and improvement

Dietary intake of benzo(a)pyrene and risk of esophageal cancer in north of Iran

One etiologic factor for high incidence of esophageal squamous cell carcinoma (ESCC) in Golestan (Northeastern Iran) might be exposure to polycyclic aromatic hydrocarbons. We examined whether food and water are major sources of benzo(a)pyrene (BaP) exposure in this population. We used a dietary questionnaire to assess the daily intake of staple food (rice and bread) and water in 3 groups: 40 ESCC Golestan cases, 40 healthy subjects from the same area, and 40 healthy subjects from a low-risk area in Southern Iran. We measured, by high-performance liquid chromatography combined with fluorescence detection, the BaP concentration of bread, rice, and water in samples obtained from these 3 groups and calculated the daily intake of BaP. Mean BaP concentration of staple foods and water was similar and within standard levels in both areas, but the daily intake of BaP was higher in controls from the high-risk area than in controls from the low-risk area (91.4 vs. 70.6 ng/day, P < 0.01). In the multivariate regression analysis, having ESCC had no independent effect on BaP, whereas residence in the low-risk area was associated with a significant decrease in total BaP intake. Polycyclic aromatic hydrocarbons might, along with other risk factors, contribute to the high risk of ESCC in Golestan. Copyright © 2008, Taylor & Francis Group, LLC

Variability of dayside convection and motions of the cusp/cleft aurora

We present measurements of the ionospheric plasma flow over the range of invariant latitudes 71–76°, observed at 10-second resolution using both the EISCAT radars, with simultaneous observations of the 630 nm cusp/cleft aurora made by a meridian-scanning photometer at Ny Ålesund, Svalbard. A major increase in the trans-auroral voltage from 5 to 40 kV (associated with sunward convection in the early afternoon sector) is found to follow a southward motion of the aurora and coincide with the onset of regular transient auroral breakup events. It is shown that these observations are consistent with recent theoretical work on how ionospheric flows are excited by time-dependent reconnection at the dayside magnetopause

Preozonation and Prechlorination Effects on TOC Removal by Nanofiltration in Water Treatment

ABSTRACT: In this study, NF membrane was used for surface water treatment. The rejection of organic material, measured as Total organic carbon (TOC), by Nanofiltration was examined. The effects of application of pre-ozonation and pre-chlorination on TOC removal are discussed and their performances are compared with the performances of Nanofiltraion system without pretreatment process. In NF, natural organic rejection is high and no pre-treatment are required. Coagulation targets large hydrophobic organics which foul NF membranes by precipitation and gel layer formation. The results showed that TOC removal in Preozonation-coagulation was higher than prechlorinationCoagulation. In addition pretreatment increases Nanofiltration efficiency