Sensitivity Analysis Of The Dimensionless Parameters In Scaling A Polymer Flooding Reservoir

A set of scaling criteria of a polymer flooding reservoir is derived from the governing equations, which involve gravity and capillary force, compressibility of water, oil, and rock, non-Newtonian behavior of the polymer solution, absorption, dispersion, and diffusion, etc. A numerical approach to quantify the dominance degree of each dimensionless parameter is proposed. With this approach, the sensitivity factor of each dimensionless parameter is evaluated. The results show that in polymer flooding, the order of the sensitivity factor ranges from 10(-5) to 10(0) and the dominant dimensionless parameters are generally the ratio of the oil permeability under the condition of the irreducible water saturation to water permeability under the condition of residual oil saturation, density, and viscosity ratios between water and oil, the reduced initial oleic phase saturation and the shear rate exponent of the polymer solution. It is also revealed that the dominant dimensionless parameters may be different from case to case. The effect of some physical variables, such as oil viscosity, injection rate, and permeability, on the dominance degree of the dimensionless parameters is analyzed and the dominant ones are determined for different cases.

Tuning The Geometrical Parameters Of Biomimetic Fibrillar Structures To Enhance Adhesion

Fibrillar structures are common features on the feet of many animals, such as geckos, spiders and flies. Theoretical analyses often use periodical array to simulate the assembly, and each fibril is assumed to be of equal load sharing (ELS). On the other hand, studies on a single fibril show that the adhesive interface is flaw insensitive when the size of the fibril is not larger than a critical one. In this paper, the Dugdale Barenblatt model has been used to study the conditions of ELS and how to enhance adhesion by tuning the geometrical parameters in fibrillar structures. Different configurations in an array of fibres are considered, such as line array, square and hexagonal patterns. It is found that in order to satisfy flaw-insensitivity and ELS conditions, the number of fibrils and the pull-off force of the fibrillar interface depend significantly on the fibre separation, the interface interacting energy, the effective range of cohesive interaction and the radius of fibrils. Proper tuning of the geometrical parameters will enhance the pull-off force of the fibrillar structures. This study may suggest possible methods to design strong adhesion devices for engineering applications.

Optimal bounded control of first-passage failure of strongly non-linear oscillators under combined harmonic and white-noise excitations

A procedure for designing the optimal bounded control of strongly non-linear oscillators under combined harmonic and white-noise excitations for minimizing their first-passage failure is proposed. First, a stochastic averaging method for strongly non-linear oscillators under combined harmonic and white-noise excitations using generalized harmonic functions is introduced. Then, the dynamical programming equations and their boundary and final time conditions for the control problems of maximizing reliability and of maximizing mean first-passage time are formulated from the averaged Ito equations by using the dynamical programming principle. The optimal control law is derived from the dynamical programming equations and control constraint. Finally, the conditional reliability function, the conditional probability density and mean of the first-passage time of the optimally controlled system are obtained from solving the backward Kolmogorov equation and Pontryagin equation. An example is given to illustrate the proposed procedure and the results obtained are verified by using those from digital simulation. (C) 2003 Elsevier Ltd. All rights reserved.

An approximate likelihood method for estimating static parameters in multi-target tracking models

This paper considers a class of dynamic Spatial Point Processes (PP) that evolves over time in a Markovian fashion. This Markov in time PP is hidden and observed indirectly through another PP via thinning, displacement and noise. This statistical model is important for Multi object Tracking applications and we present an approximate likelihood based method for estimating the model parameters. The work is supported by an extensive numerical study.