We present an analysis of fluid flow and heat transfer through a single horizontal channel with permeable walls which are at different temperatures. The problem is set in the context of hot dry rock geothermal energy extraction where water, introduced through an injection well, passes through a horizontal fracture by which transfer of heat is facilitated through advection of the fluid flowing toward the recovery well. We consider the walls of the fracture to have properties of a permeable medium and we study the effect of slip boundary conditions on velocity and temperature profiles for low Reynolds number (< 7) based on a similarity solution and perturbation expansion. We show that the velocity and heat transfer profiles are altered with the channel width, the permeability and a slip coefficient α, which is a dimensionless constant related to the inherent properties of the channel.

This brief article presents a quantitative analysis of the ability of eight published empirical ground-motion prediction equations (GMPEs) for subduction earthquakes (interface and intraslab) to estimate observed earthquake ground motions on the islands of the Lesser Antilles (specifically Guadeloupe, Martinique, Trinidad and Dominica). In total, over 300 records from 22 earthquakes from various seismic networks are used within the analysis. It is found that most of the GMPEs tested perform poorly, which is mainly due to a larger variability in the observed ground motions than predicted by the GMPEs, although two recent GMPEs derived using Japanese strong-motion data provide reasonably good predictions. Analyzing separately the interface and intraslab events does not significant modify the results. Therefore, it is concluded that seismic hazard assessments for this region should use a variety of GMPEs in order to capture this large epistemic uncertainty in earthquake ground-motion prediction for the Lesser Antilles.

[1] Enhanced Geothermal Systems (EGS) are based on the premise that heat can be extracted from hot dry rocks located at significant depths by circulating fluid through fracture networks in the system. Heated fluid is recovered through production wells and the energy is extracted in a heat exchange chamber. There is much published research on flow through fractures, and many models have been developed to describe an effective permeability of a fracture or a fracture network. In these cases however, the walls of the fracture were modelled as being impermeable. In this paper, we have extended our previous work on fractures with permeable walls, and we introduce a correction factor to the equation that governs fracture permeability. The solution shows that the effective fracture permeability for fractures with permeable walls depends not only on the height of the channel, but also on the wall permeability and the wall Reynolds number of the fluid. We show that our solution reduces to the established solution when the fracture walls become impermeable. We also extend the discussion to cover the effective permeability of a system of fractures with permeable walls. Citation: Mohais, R., C. Xu, P. A. Dowd, and M. Hand (2012), Permeability correction factor for fractures with permeable walls, Geophys. Res. Lett., 39, L03403,

This paper investigates the use of Random Dynamic Neighborhoods in Particle Swarm Optimization (PSO) for the purpose of training fixed-architecture neural networks to classify a real-world data set of seismological data. Instead of the ring or fully-connected neighborhoods that are typically used with PSOs, or even more complex graph structures, this work uses directed graphs that are randomly generated using size and uniform out-degree as parameters. Furthermore, the graphs are subjected to dynamism during the course of a run, thereby allowing for varying information exchange patterns. Neighborhood re-structuring is applied with a linearly decreasing probability at each iteration. Several experimental configurations are tested on a training portion of the data set, and are ranked according to their abilities to generalize over the entire set. Comparisons are performed with standard PSOs as well as several static non-random neighborhoods.

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