Women's clubs: Dispersing Shakespeare across America

This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2011 Symbiosis - A Transatlantic Journal.This article explains the importance of women's clubs in America and their role in the dissemination of Shakespeare in the 19th and 20th centuries

Vibrational characteristics of linear space frames

Digital computer program for determining modes and frequencies of arbitrary linear space frame

The relationship between compliance gaining messages and the coordinated management of meaning

LD2668 .R4 SPCH 1988 W54Master of ArtsCommunication Studie

User's guide: SPAR processor MN analysis of inelastic three-dimensional solids, part 2

A user's guide to the processor MN is presented. The processor MN is used to analyze systems in which some or all of the three dimensional elements have nonlinear stress-strain relations. Representations of nonlinear material behavior implemented into the system are provided including Von Mises yield criterion, the Prandtl-Reuss flow rule, and the mechanical sublayer method. The primary data sets used to represent the state of the system are presented

Snap dynamics

Computer program calculates normal vibration modes of complex structures elimating excessively large amounts of input data, run time, and core storage. Provision for accuracy improvement is also included

SPAR demonstration problems

A series of examples are presented to indicate some of the principal functions of the SPAR system and to illustrate SPAR's control card-data card structure. Information in the following categories is given: (1) a description of the problem and, in most cases, comparisons with analytical solutions; (2) a list of the input cards; (3) a printout of the table of contents of the direct access library into which all SPAR output was directed; and (4) a few representative plots

The SPAR thermal analyzer: Present and future

The SPAR thermal analyzer, a system of finite-element processors for performing steady-state and transient thermal analyses, is described. The processors communicate with each other through the SPAR random access data base. As each processor is executed, all pertinent source data is extracted from the data base and results are stored in the data base. Steady state temperature distributions are determined by a direct solution method for linear problems and a modified Newton-Raphson method for nonlinear problems. An explicit and several implicit methods are available for the solution of transient heat transfer problems. Finite element plotting capability is available for model checkout and verification

SPAR thermal analysis processors reference manual, system level 16. Volume 1: Program executive. Volume 2: Theory. Volume 3: Demonstration problems. Volume 4: Experimental thermal element capability. Volume 5: Programmer reference

User instructions are given for performing linear and nonlinear steady state and transient thermal analyses with SPAR thermal analysis processors TGEO, SSTA, and TRTA. It is assumed that the user is familiar with basic SPAR operations and basic heat transfer theory

Investigating Sources of Variability and Error in Simulations of Carbon Dioxide in an Urban Region

Greenhouse gas (GHG) emissions estimation methods that use atmospheric trace gas observations, including inverse modeling techniques, perform better when carbon dioxide (CO2) fluxes are more accurately transported and dispersed in the atmosphere by a numerical model. In urban areas, transport and dispersion is particularly difficult to simulate using current mesoscale meteorological models due, in part, to added complexity from surface heterogeneity and fine spatial/temporal scales. It is generally assumed that the errors in GHG estimation methods in urban areas are dominated by errors in transport and dispersion. Other significant errors include, but are not limited to, those from assumed emissions magnitude and spatial distribution. To assess the predictability of simulated trace gas mole fractions in urban observing systems using a numerical weather prediction model, we employ an Eulerian model that combines traditional meteorological variables with multiple passive tracers of atmospheric CO2 from anthropogenic inventories and a biospheric model. The predictability of the Eulerian model is assessed by comparing simulated atmospheric CO2 mole fractions to observations from four in situ tower sites (three urban and one rural) in the Washington DC/Baltimore, MD area for February 2016. Four different gridded fossil fuel emissions inventories along with a biospheric flux model are used to create an ensemble of simulated atmospheric CO2 observations within the model. These ensembles help to evaluate whether the modeled observations are impacted more by the underlying emissions or transport. The spread of modeled observations using the four emission fields indicates the model's ability to distinguish between the different inventories under various meteorological conditions. Overall, the Eulerian model performs well; simulated and observed average CO2 mole fractions agree within 1% when averaged at the three urban sites across the month. However, there can be differences greater than 10% at any given hour, which are attributed to complex meteorological conditions rather than differences in the inventories themselves. On average, the mean absolute error of the simulated compared to actual observations is generally twice as large as the standard deviation of the modeled mole fractions across the four emission inventories. This result supports the assumption, in urban domains, that the predicted mole fraction error relative to observations is dominated by errors in model meteorology rather than errors in the underlying fluxes in winter months. As such, minimizing errors associated with atmospheric transport and dispersion may help improve the performance of GHG estimation models more so than improving flux priors in the winter months. We also find that the errors associated with atmospheric transport in urban domains are not restricted to certain times of day. This suggests that atmospheric inversions should use CO2 observations that have been filtered using meteorological observations rather than assuming that meteorological modeling is most accurate at certain times of day (such as using only mid-afternoon observations)