Physical and numerical modeling of horizontal gas-hydrate wells

Methane hydrate, which is usually found under deep seabed or permafrost zones, is a potential energy resource for future years. Depressurization of horizontal wells bored in methane hydrate layer is considered as one possible method for hydrate dissociation and methane extraction from the hosting soil. Since hydrate is likely to behave as a bonding material to sandy soils, supported well construction is necessary to avoid well-collapse due to the loss of the apparent cohesion during depressurization. This paper describes both physical and numerical modeling of such horizontal support wells. The experimental part involves depressurization of small well models in a large pressure cell, while the numerical part simulates the corresponding problem. While the experiment models simulate only gas saturated initial conditions, the numerical analysis simulates both gas-saturated and more realistic water-saturated conditions based on effective stress coupled flow-deformation formulation of these three phases. © 2006 Taylor & Francis Group.

1.3-μm Quantum-well InGaAsP, AlGaInAs, and InGaAsN laser material gain: A theoretical study

Due to the keen interest in improving the high-speed and high-temperature performance of 1.3-μm wavelength lasers, we compare, for the first time, the material gain of three different competing active layer materials, namely InGaAsP-InGaAsP, AlGaInAs-AlGaInAs, and InGaAsN-GaAs. We present a theoretical study of the gain of each quantum-well material system and present the factors that influence the material gain performance of each system. We find that AIGaInAs and InGaAsN active layer materials have substantially better material gain performance than the commonly used InGaAsP, both at room temperature and at high temperature.

Risk-sensitive optimal feedback control accounts for sensorimotor behavior under uncertainty

Many aspects of human motor behavior can be understood using optimality principles such as optimal feedback control. However, these proposed optimal control models are risk-neutral; that is, they are indifferent to the variability of the movement cost. Here, we propose the use of a risk-sensitive optimal controller that incorporates movement cost variance either as an added cost (risk-averse controller) or as an added value (risk-seeking controller) to model human motor behavior in the face of uncertainty. We use a sensorimotor task to test the hypothesis that subjects are risk-sensitive. Subjects controlled a virtual ball undergoing Brownian motion towards a target. Subjects were required to minimize an explicit cost, in points, that was a combination of the final positional error of the ball and the integrated control cost. By testing subjects on different levels of Brownian motion noise and relative weighting of the position and control cost, we could distinguish between risk-sensitive and risk-neutral control. We show that subjects change their movement strategy pessimistically in the face of increased uncertainty in accord with the predictions of a risk-averse optimal controller. Our results suggest that risk-sensitivity is a fundamental attribute that needs to be incorporated into optimal feedback control models. © 2010 Nagengast et al.