Egemen Ogretim

Probabilistic Design of a Near-Surface CO2 Leak Detection System

A methodology is developed for predicting the performance of near-surface CO(2) leak detection systems at geologic sequestration sites. The methodology integrates site characterization and modeling to predict the statistical properties of natural CO(2) fluxes, the transport of CO(2) from potential subsurface leakage points, and the detection of CO(2) surface fluxes by the monitoring network. The probability of leak detection is computed as the probability that the leakage signal is sufficient to increase the total flux beyond a statistically determined threshold. The methodology is illustrated for a highly idealized site monitored with CO(2) accumulation chamber measurements taken on a uniform grid. The TOUGH2 code is used to predict the spatial profile of surface CO(2) fluxes resulting from different leakage rates and different soil permeabilities. A response surface is fit to the TOUGH2 results to allow interpolation across a continuous range of values of permeability and leakage rate. The spatial distribution of leakage probability is assumed uniform in this application. Nonlinear, nonmonotonic relationships of network performance to soil permeability and network density are evident. In general, dense networks (with ∼10-20 m between monitors) are required to ensure a moderate to high probability of leak detection.

Darcy Flow in a Wavy Channel Filled with a Porous Medium

Flow in channels bounded by wavy or corrugated walls is of interest in both technological and geological contexts. This paper presents an analytical solution for the steady Darcy flow of an incompressible fluid through a homogeneous, isotropic porous medium filling a channel bounded by symmetric wavy walls. This packed channel may represent an idealized packed fracture, a situation which is of interest as a potential pathway for the leakage of carbon dioxide from a geological sequestration site. The channel walls change from parallel planes, to small amplitude sine waves, to large amplitude nonsinusoidal waves as certain parameters are increased. The direction of gravity is arbitrary. A plot of piezometric head against distance in the direction of mean flow changes from a straight line for parallel planes to a series of steeply sloping sections in the reaches of small aperture alternating with nearly constant sections in the large aperture bulges. Expressions are given for the stream function, specific discharge, piezometric head, and pressure.

Aircraft Ice Accretion Prediction Based on Neural Networks

Many experimental research efforts in the past two decades have revealed that the complete picture of aircraft ice accretion has many components, resulting in a complex physical structure. Although overwhelmingly complex, the icing phenomenon needs to be understood because of its impact on aircraft performance and safety. This requires a detailed knowledge of ice accretion physics, subsequent flow over the aircraft, and the resulting modified aircraft performance. Experimental and numerical studies to address these issues have their own advantages, disadvantages, and limitations, which further limit the analysis of the icing phenomena. The motivation behind this study is the belief that complex phenomena in nature have an orderly structure on the large scale. Based on this premise, it is thought that icing phenomena also have orderly, albeit nonlinear, behavior that can be modeled by neural networks, which have a proven capability for modeling nonlinear systems. The methodology developed in the present study incorporates the Fourier series expansion of an ice shape following a conformal mapping, which suppresses the effect of airfoil geometry, and then utilizes neural networks to model the Fourier coefficients and the downstream extent of the ice shape. The neural network can be trained to make ice accretion predictions, given a set of data including the flight and atmospheric conditions, along with the Fourier coefficients and the extent of the resulting ice shape. The neural network also provides statistical output of the relative significance of the input parameters in the training. The preliminary results show that the proposed method has reasonable capabilities and has merit for further investment, because it can be coupled with other systems to create advanced computational ice accretion models and ice protection systems. Nomenclature ai, bi = coefficients of the cosine and sine functions of the Fourier series expansion, respectively f = actual perturbation geometry from the parabola surface ˜ f = approximated perturbation geometry LWC = liquid water content of oncoming air in grams per cubic meter M = number of Fourier terms for the truncated Fourier series expansion MVD = median volumetric diameter in micrometers N = number of data points of the actual ice geometry T∞ = static temperature in the oncoming air in kelvin V∞ = free stream velocity in meters per second x‐y = data coordinates of the experimental ice shape x � ‐y � = ice shape coordinates nondimensionalized by the leading-edge radius (LER) of the airfoil ξ ‐η = coordinates of the ice shape in the transformed plane ξ � ‐η � = coordinates of the ice shape after separation

Investigation of Relative Humidity and Induced-Vortex Effects on Aircraft Icing

Two new mechanisms for downstream ice growth (i.e., downstream of the primary ice shape) in aircraft icing scenarios were investigated. The first mechanism is local variation of relative humidity with its potential for water deposition due to supersaturation. The second mechanism is induced-vortex effects due to their potential impact on droplet paths. It was shown that for rough surfaces with an extended period of exposure, relative humidity effects can lead to additional growth. The resultant frost is a sandpaperlike roughness that can severely degrade the aerodynamic performance of the wings. It was also shown that the vortices induced by the existing ice-shape features are capable of altering the droplet paths. As a result, impingements occur beyond the limits predicted by the methods in other icing prediction codes.

Effects of Atmospheric Dynamics on CO2 Seepage at Mammoth Mountain, California USA

In the past few decades, atmospheric effects on the variation of seepage from soil have been studied in disciplines such as volcanology, environmental protection, safety and health hazard avoidance. Recently, monitoring of potential leakage from the geologic sequestration of carbon has been added to this list. Throughout these diverse fields, barometric pumping and presence of steady winds are the two most commonly investigated atmospheric factors. These two factors have the effect of pumping gas into and out of the unsaturated zone, and sweeping the gas in the porous medium. This study focuses on two new factors related to atmosphere in order to explain the CO2 seepage anomalies observed at the Horseshoe Lake tree kill near Mammoth Mountain, CA, where the temporal variation of seepage due to a storm event could not be explained by the two commonly studied effects. First, the interaction of the lower atmospheric dynamics and the ground topography is considered for its effect on the seepage variation over ...

Mechanisms for Downstream Ice Growth

Even though aircraft icing has been an active area of research for many years due to its public safety ramifications, there are still gaps in the knowledge base, such as in the ice accretion process. Ice prediction codes have been developed and generally can capture the gross features of the ice shape for many areas of parameter space. However, there are still features of the ice shapes that are not captured and not well understood. For example, current icing codes have difficulty in predicting the ice or frost that develops beyond the impingement limits from a standard trajectory analysis. This is a strong indicator that there are other physical mechanisms that lead to ice growth in these areas, which require further investigation. The present study focuses on the effects of relative humidity and shed vortices from the ice surface on the downstream ice growth. Relative humidity was found to be a secondary effect in the direct impingement regions due to the time scales involved. However, exposure to supersaturated air for long periods can lead to localized ice/frost growth aft of the primary ice shape. It was also found that the vortices shed from ice surface or vortices entrapped within the ice roughness can alter the trajectory paths of the droplets and potentially change the ice growth process. The altered paths result in impingement on areas that are beyond the direct impingement region.

Ice Accretion Prediction Based on Neural Networks

Many experimental research efforts in the last two decades have revealed that the complete picture of aircraft ice accretion has many components resulting in a complex physical structure. Although overwhelmingly complex, the icing phenomena need to be understood due to the impact on the aircraft performance. This requires a detailed knowledge of the ice accretion physics, the subsequent flow over the aircraft, and the resulting modified aircraft performance. The experimental and numerical studies to address these issues have their own advantages, disadvantages, and limitations, which further limit the analysis of icing phenomena. The motivation behind this study is the belief that complex phenomena in nature have an orderly structure in the large-scale. Based on this premise, it is thought that icing phenomena also have an orderly, albeit non-linear, behavior which can be modeled by neural networks, which have a proven capability for modeling non-linear systems. The developed methodology in the present study involves the Fourier series expansion of the ice shape after a conformal mapping, which clears the effect of airfoil geometry, and use of the neural networks to model the Fourier coefficients and the extent of the ice shape. The neural network can be trained to make ice accretion predictions, given a set of data including the flight and the atmospheric conditions, the Fourier coefficients and extent of the resulting ice shape. It also provides a statistical output of the relative significance of the input parameters in the training. The preliminary results show that the proposed method has an reasonable accuracy and has merit for further investment since it can be coupled with other systems to create advanced computational ice accretion models and ice protection systems.