Experimental study into the flow physics of three-dimensional shock control bumps

This paper describes a fundamental experimental study of the flow structure around a single three-dimensional (3D) transonic shock control bump (SCB) mounted on a flat surface in a wind tunnel. Tests have been carried out with a Mach 1.3 normal shock wave located at a number of streamwise positions relative to the SCB. Details of the flow have been studied using the experimental techniques of schlieren photography, surface oil flow visualization, pressure sensitive paint, and laser Doppler anemometry. The results of the work build on the findings of previous researchers and shed new light on the flow physics of 3D SCBs. It is found that spanwise pressure gradients across the SCB ramp and the shape of the SCB sides affect the magnitude and uniformity of flow turning generated by the bump, which can impact on the spanwise propagation of the quasi-two-dimensional (2D) shock structure produced by a 3DSCB. At the bump crest, vortices can form if the pressure on the crest is significantly lower than at either side of the bump. The trajectories of these vortices, which are relatively weak, are strongly influenced by any spanwise pressure gradients across the bump tail. Asignificant difference between 2D and 3D SCBs highlighted by the study is the impact of spanwise pressure gradients on 3D SCB performance. The magnitude of these spanwise pressure gradients is determined largely by SCB geometry and shock position. Copyright © 2011 by the American Institute of Aeronautics and Astronautics, Inc.

A global model for effective use and evaluation of e-learning in health.

Healthcare systems worldwide face a wide range of challenges, including demographic change, rising drug and medical technology costs, and persistent and widening health inequalities both within and between countries. Simultaneously, issues such as professional silos, static medical curricula, and perceptions of "information overload" have made it difficult for medical training and continued professional development (CPD) to adapt to the changing needs of healthcare professionals in increasingly patient-centered, collaborative, and/or remote delivery contexts. In response to these challenges, increasing numbers of medical education and CPD programs have adopted e-learning approaches, which have been shown to provide flexible, low-cost, user-centered, and easily updated learning. The effectiveness of e-learning varies from context to context, however, and has also been shown to make considerable demands on users' motivation and "digital literacy" and on providing institutions. Consequently, there is a need to evaluate the effectiveness of e-learning in healthcare as part of ongoing quality improvement efforts. This article outlines the key issues for developing successful models for analyzing e-health learning.

A global model for effective use and evaluation of e-learning in health.

Healthcare systems worldwide face a wide range of challenges, including demographic change, rising drug and medical technology costs, and persistent and widening health inequalities both within and between countries. Simultaneously, issues such as professional silos, static medical curricula, and perceptions of "information overload" have made it difficult for medical training and continued professional development (CPD) to adapt to the changing needs of healthcare professionals in increasingly patient-centered, collaborative, and/or remote delivery contexts. In response to these challenges, increasing numbers of medical education and CPD programs have adopted e-learning approaches, which have been shown to provide flexible, low-cost, user-centered, and easily updated learning. The effectiveness of e-learning varies from context to context, however, and has also been shown to make considerable demands on users' motivation and "digital literacy" and on providing institutions. Consequently, there is a need to evaluate the effectiveness of e-learning in healthcare as part of ongoing quality improvement efforts. This article outlines the key issues for developing successful models for analyzing e-health learning.