A computational fluid dynamic investigation of inhomogeneous hydrogen flame acceleration and transition to detonation

Gas explosions in homogeneous reactive mixtures have been widely studied both experimentally and numerically. However, in practice and industrial applications, combustible mixtures are usually inhomogeneous and subject to vertical concentration gradients. Limited studies have been conducted in such context which resulted in limited understanding of the explosion characteristics in such situations. The present numerical investigation aims to study the dynamics of Deflagration to Detonation Transition (DDT) in inhomogeneous hydrogen/air mixtures and examine the effects of obstacle blockage ratio in DDT. VCEFoam, a reactive density-based solver recently assembled by the authors within the frame of OpenFOAM CFD toolbox has been used. VCEFoam uses the Harten–Lax–van Leer–Contact (HLLC) scheme fr the convective fluxes contribution and shock capturing. The solver has been verified by comparing its predictions with the analytical solutions of two classical test cases. For validation, the experimental data of Boeck et al. (1) is used. The experiments were conducted in a rectangular channel the three different blockage ratios and hydrogen concentrations. Comparison is presented between the predicted and measured flame tip velocities. The shaded contours of the predicted temperature and numerical Schlieren (magnitude of density gradient) will be analysed to examine the effects of the blockage ratio on flame acceleration and DDT

Supervised versus unsupervised categorization: Two sides of the same coin?

Supervised and unsupervised categorization have been studied in separate research traditions. A handful of studies have attempted to explore a possible convergence between the two. The present research builds on these studies, by comparing the unsupervised categorization results of Pothos et al. (submitted; 2008) with the results from two procedures of supervised categorization. In two experiments, we tested 375 participants with nine different stimulus sets, and examined the relation between ease of learning of a classification, memory for a classification, and spontaneous preference for a classification. After taking into account the role of the number of category labels (clusters) in supervised learning, we found the three variables to be closely associated with each other. Our results provide encouragement for researchers seeking unified theoretical explanations for supervised and unsupervised categorization, but raise a range of challenging theoretical questions

Deconstructing the effect of self-directed study on episodic memory

Self-directed learning is often associated with better long-term memory retention, however, the mechanisms that underlie this advantage remain poorly understood. This series of experiments was designed to “deconstruct” the notion of self-directed learning in order to better identify the factors most responsible for these improvements to memory. In particular, we isolate the memory advantage that comes from controlling the content of study episodes from the advantage that comes from controlling the timing of those episodes. Across four experiments, self-directed learning significantly enhanced recognition memory relative to passive observation. However, the advantage for self-directed learning was found to be present even under extremely minimal conditions of volitional control (simply pressing a button when ready to advance to the next item). Our results suggest that improvements to memory following self-directed encoding may be related to the ability to coordinate stimulus presentation with the learner’s current preparatory or attentional state, and highlight the need to consider the range of cognitive control processes involved in and influenced by self-directed study