Global Entropy Solutions to the Gas Flow in General Nozzle

We are concerned with the global existence of entropy solutions for the compressible Euler equations describing the gas flow in a nozzle with general cross-sectional area, for both isentropic and isothermal fluids. New viscosities are delicately designed to obtain the uniform bound of approximate solutions. The vanishing viscosity method and compensated compactness framework are used to prove the convergence of approximate solutions. Moreover, the entropy solutions for both cases are uniformly bounded independent of time. No smallness condition is assumed on initial data. The techniques developed here can be applied to compressible Euler equations with general source terms

Factors Affecting the Growth of Multiseeded Superconducting Single Grains

© 2016 American Chemical Society.Single grain, rare earth-barium-copper oxide [(RE)BCO] bulk superconductors, fabricated either individually or assembled in large or complicated geometries, have a significant potential for a variety of potential engineering applications. Unfortunately, (RE)BCO single grains have intrinsically very low growth rates, which limits the sample size that may be achieved in a practical, top seeded melt growth process. As a result, a melt process based on the use of two or more seeds (so-called multiseeding) to control the nucleation and subsequent growth of bulk (RE)BCO superconductors has been developed to fabricate larger samples and to reduce the time taken for the melt process. However, the formation of regions that contain non-superconducting phases at grain boundaries has emerged as an unavoidable consequence of this process. This leads to the multiseeded sample behaving as if it is composed of multiple, singly seeded regions. In this work we have examined the factors that lead to the accumulation of non-superconducting phases at grain boundaries in multiseeded (RE)BCO bulk samples. We have studied the microstructure and superconducting properties of a number of samples fabricated by the multiseeded process to explore how the severity of this problem can be reduced significantly, if not eliminated completely. We conclude that, by employing the techniques described, multiseeding is a practical approach to the processing of large high performance superconducting bulk samples for engineering applications.Engineering and Physical Sciences Research Council (Grant ID: EP/K02910X/1

Composite stacks for reliable > 17 T trapped fields in bulk superconductor magnets

Funder: Siemens AG Corporate Technology eAircraftAbstract: Trapped fields of over 20 T are, in principle, achievable in bulk, single-grain high temperature cuprate superconductors. The principle barriers to realizing such performance are, firstly, the large tensile stresses that develop during the magnetization of such trapped-field magnets as a result of the Lorentz force, which lead to brittle fracture of these ceramic-like materials at high fields and, secondly, catastrophic thermal instabilities as a result of flux movement during magnetization. Moreover, for a batch of samples nominally fabricated identically, the statistical nature of the failure mechanism means the best performance (i.e. trapped fields of over 17 T) cannot be attained reliably. The magnetization process, particularly to higher fields, also often damages the samples such that they cannot repeatedly trap high fields following subsequent magnetization. In this study, we report the sequential trapping of magnetic fields of ∼ 17 T, achieving 16.8 T at 26 K initially and 17.6 T at 22.5 K subsequently, in a stack of two Ag-doped GdBa2Cu3O7-δ bulk superconductor composites of diameter 24 mm reinforced with (1) stainless-steel laminations, and (2) shrink-fit stainless steel rings. A trapped field of 17.6 T is, in fact, comparable with the highest trapped fields reported to date for bulk superconducting magnets of any mechanical and chemical composition, and this was achieved using the first composite stack to be fabricated by this technique. These post-melt-processing treatments, which are relatively straightforward to implement, were used to improve both the mechanical properties and the thermal stability of the resultant composite structure, providing what we believe is a promising route to achieving reliably fields of over 20 T

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages