Comparisons of static, quasi-static and dynamic 3D porous media scale network models for two-phase immiscible flow in porous media

A dynamic 3D pore-scale network model is formulated for investigating the effect of interfacial tension and oil-water viscosity during chemical flooding. The model takes into account both viscous and capillary forces in analyzing the impact of chemical properties on flow behavior or displacement configuration, while the static model with conventional invasion percolation algorithm incorporates the capillary pressure only. From comparisons of simulation results from these models. it indicates that the static pore scale network model can be used successfully when the capillary number is low. With the capillary increases due to the enhancement of water viscosity or decrease of interfacial tension, only the quasi-static and dynamic model can give insight into the displacement mechanisms.

Injection locked Fabry-Perot laser diodes for WDM passive optical network spare function

In this paper, a protection scheme for transmitters in wavelength-division-multiplexing passive optical network (WDM-PON) has been proposed and demonstrated. If any downstream transmitter encounters problems at the central office (CO), the interrupted communication can be restored immediately by injecting a Fabry-Perot laser diode (FP-LD) with the upstream lightwave corresponding to the failure transmitter. Compared with the conventional methods, this proposed architecture provides a cost-effective and reliable protection scheme employing a common FP-LD. In the experiment, a 1 36 protection capability was implemented with a 2.5 Gbit/s downstream transmission capability. (C) 2009 Elsevier B.V. All rights reserved.

Packing LUT clusters with network flow programming

This paper describes a two-step packing algorithm for LUT clusters of which the LUT input multipliers are depopulated. In the first step, a greedy algorithm is used to search for BLE locations and cluster inputs. If the greedy algorithm fails, the second step with network flow programming algorithm is employed. Numerical results illustrate that our two-step packing algorithm obtains better packing density than one-step greedy packing algorithm.

SOC dynamic power management using artificial neural network

Dynamic Power Management (DPM) is a technique to reduce power consumption of electronic system by selectively shutting down idle components. In this article we try to introduce back propagation network and radial basis network into the research of the system-level power management policies. We proposed two PM policies-Back propagation Power Management (BPPM) and Radial Basis Function Power Management (RBFPM) which are based on Artificial Neural Networks (ANN). Our experiments show that the two power management policies greatly lowered the system-level power consumption and have higher performance than traditional Power Management(PM) techniques-BPPM is 1.09-competitive and RBFPM is 1.08-competitive vs. 1.79 . 1.45 . 1.18-competitive separately for traditional timeout PM . adaptive predictive PM and stochastic PM.

adaptive location update mechanism for network-constrained moving objects in changeful traffic conditions

Natl Chiao Tung Univ, Dept Comp Sci