Hydrogen production by steam reforming of DME in a large scale CFB reactor. Part I:computational model and predictions

This study presents a computational fluid dynamic (CFD) study of Dimethyl Ether steam reforming (DME-SR) in a large scale Circulating Fluidized Bed (CFB) reactor. The CFD model is based on Eulerian-Eulerian dispersed flow and solved using commercial software (ANSYS FLUENT). The DME-SR reactions scheme and kinetics in the presence of a bifunctional catalyst of CuO/ZnO/Al2O3+ZSM-5 were incorporated in the model using in-house developed user-defined function. The model was validated by comparing the predictions with experimental data from the literature. The results revealed for the first time detailed CFB reactor hydrodynamics, gas residence time, temperature distribution and product gas composition at a selected operating condition of 300 °C and steam to DME mass ratio of 3 (molar ratio of 7.62). The spatial variation in the gas species concentrations suggests the existence of three distinct reaction zones but limited temperature variations. The DME conversion and hydrogen yield were found to be 87% and 59% respectively, resulting in a product gas consisting of 72 mol% hydrogen. In part II of this study, the model presented here will be used to optimize the reactor design and study the effect of operating conditions on the reactor performance and products.

Bayesian and Frequentist Two-Sample Predictions of the Inverse Weibull Model Based on Generalized Order Statistics

2000 Mathematics Subject Classification: 62E16,62F15, 62H12, 62M20.

Development of safety performance functions for empirical Bayes estimation of crash reduction factors

Crash reduction factors (CRFs) are used to estimate the potential number of traffic crashes expected to be prevented from investment in safety improvement projects. The method used to develop CRFs in Florida has been based on the commonly used before-and-after approach. This approach suffers from a widely recognized problem known as regression-to-the-mean (RTM). The Empirical Bayes (EB) method has been introduced as a means to addressing the RTM problem. This method requires the information from both the treatment and reference sites in order to predict the expected number of crashes had the safety improvement projects at the treatment sites not been implemented. The information from the reference sites is estimated from a safety performance function (SPF), which is a mathematical relationship that links crashes to traffic exposure. The objective of this dissertation was to develop the SPFs for different functional classes of the Florida State Highway System. Crash data from years 2001 through 2003 along with traffic and geometric data were used in the SPF model development. SPFs for both rural and urban roadway categories were developed. The modeling data used were based on one-mile segments that contain homogeneous traffic and geometric conditions within each segment. Segments involving intersections were excluded. The scatter plots of data show that the relationships between crashes and traffic exposure are nonlinear, that crashes increase with traffic exposure in an increasing rate. Four regression models, namely, Poisson (PRM), Negative Binomial (NBRM), zero-inflated Poisson (ZIP), and zero-inflated Negative Binomial (ZINB), were fitted to the one-mile segment records for individual roadway categories. The best model was selected for each category based on a combination of the Likelihood Ratio test, the Vuong statistical test, and the Akaike's Information Criterion (AIC). The NBRM model was found to be appropriate for only one category and the ZINB model was found to be more appropriate for six other categories. The overall results show that the Negative Binomial distribution model generally provides a better fit for the data than the Poisson distribution model. In addition, the ZINB model was found to give the best fit when the count data exhibit excess zeros and over-dispersion for most of the roadway categories. While model validation shows that most data points fall within the 95% prediction intervals of the models developed, the Pearson goodness-of-fit measure does not show statistical significance. This is expected as traffic volume is only one of the many factors contributing to the overall crash experience, and that the SPFs are to be applied in conjunction with Accident Modification Factors (AMFs) to further account for the safety impacts of major geometric features before arriving at the final crash prediction. However, with improved traffic and crash data quality, the crash prediction power of SPF models may be further improved.

Using individual tracking data to validate the predictions of species distribution models

The authors would like to thank the College of Life Sciences of Aberdeen University and Marine Scotland Science which funded CP's PhD project. Skate tagging experiments were undertaken as part of Scottish Government project SP004. We thank Ian Burrett for help in catching the fish and the other fishermen and anglers who returned tags. We thank José Manuel Gonzalez-Irusta for extracting and making available the environmental layers used as environmental covariates in the environmental suitability modelling procedure. We also thank Jason Matthiopoulos for insightful suggestions on habitat utilization metrics as well as Stephen C.F. Palmer, and three anonymous reviewers for useful suggestions to improve the clarity and quality of the manuscript.

Simulation of greenhouse gases following land-use change to bioenergy crops using the ECOSSE model : a comparison between site measurements and model predictions

This work contributes to the ELUM (Ecosystem Land Use Modelling & Soil Carbon GHG Flux Trial) project, which was commissioned and funded by the Energy Technologies Institute (ETI). We acknowledge the E-OBS data set from the EU-FP6 project ENSEMBLES (http://ensembles-eu.metoffice.com) and the data providers in the ECA&D project (http://www.ecad.eu).

An evaluation of physician predictions of discharge on a general medicine service.

The goal of this study was to evaluate general medicine physicians' ability to predict hospital discharge. We prospectively asked study subjects to predict whether each patient under their care would be discharged on the next day, on the same day, or neither. Discharge predictions were recorded at 3 time points: mornings (7-9 am), midday (12-2 pm), or afternoons (5-7 pm), for a total of 2641 predictions. For predictions of next-day discharge, the sensitivity (SN) and positive predictive value (PPV) were lowest in the morning (27% and 33%, respectively), but increased by the afternoon (SN 67%, PPV 69%). Similarly, for same-day discharge predictions, SN and PPV were highest at midday (88% and 79%, respectively). We found that although physicians have difficulty predicting next-day discharges in the morning prior to the day of expected discharge, their ability to correctly predict discharges continually improved as the time to actual discharge decreased. Journal of Hospital Medicine 2015;10:808-810. © 2015 Society of Hospital Medicine.

Assessment of turbulence model predictions for a centrifugal compressor simulation

Steady-state computational fluid dynamics (CFD) simulations are an essential tool in the design process of centrifugal compressors. Whilst global parameters, such as pressure ratio and efficiency, can be predicted with reasonable accuracy, the accurate prediction of detailed compressor flow fields is a much more significant challenge. Much of the inaccuracy is associated with the incorrect selection of turbulence model. The need for a quick turnaround in simulations during the design optimisation process, also demands that the turbulence model selected be robust and numerically stable with short simulation times.
In order to assess the accuracy of a number of turbulence model predictions, the current study used an exemplar open CFD test case, the centrifugal compressor ‘Radiver’, to compare the results of three eddy viscosity models and two Reynolds stress type models. The turbulence models investigated in this study were (i) Spalart-Allmaras (SA) model, (ii) the Shear Stress Transport (SST) model, (iii) a modification to the SST model denoted the SST-curvature correction (SST-CC), (iv) Reynolds stress model of Speziale, Sarkar and Gatski (RSM-SSG), and (v) the turbulence frequency formulated Reynolds stress model (RSM-ω). Each was found to be in good agreement with the experiments (below 2% discrepancy), with respect to total-to-total parameters at three different operating conditions. However, for the off-design conditions, local flow field differences were observed between the models, with the SA model showing particularly poor prediction of local flow structures. The SST-CC showed better prediction of curved rotating flows in the impeller. The RSM-ω was better for the wake and separated flow in the diffuser. The SST model showed reasonably stable, robust and time efficient capability to predict global and local flow features.

Comparison of DES predictions of turbulent flow and heat transfer in a 45° ribbed duct

Numerical predictions of the turbulent flow and heat transfer of a stationary duct with square ribs 45° angled to the main flow direction are presented. The rib height to channel hydraulic diameter is 0.1, the rib pitch to rib height is 10. The calculations have been carried out for a bulk Reynolds number of 50,000. The flows generated by ribs are dominated by separating and reattaching shear layers with vortex shedding and secondary flows in the cross-section. The hybrid RANS-LES approach is adopted to simulate such flows at a reasonable computation cost. The capability of the various versions of DES method, depending the RANS model, such as DES-SA, DES-RKE, DES-SST, have been compared and validated against the experiment. The significant effect of RANS model on the accuracy of the DES prediction has been shown. The DES-SST method, which was able to reproduce the correct physics of flow and heat transfer in a ribbed duct showed better performance than others.

Iowa Crash History 2001 to 2015: Drivers Distracted by Use of Phone or Other Device, May 11, 2016

Iowa crash history of distracted drivers by use of phone or other devices.

Iowa Motorcycle Crash History: 1906-2014, June 2, 2016

Iowa motorcycle crash history for the State of Iowa, 1906-2014

Safety Edge Crash Modification Factors, Tech Brief, 2016

This study evaluated the safety impact of the Safety Edge for construction projects in 2010 and 2011 in Iowa to assess the effectiveness of the treatment in reducing crashes.

A comparison of predictions from the buildingEXODUS evacuation model with experimental data

Abstract not available