Sophie Loire

A New Mixing Diagnostic and Gulf Oil Spill Movement

Mixing Chaos Modeling the future movement of chaotic fluids is the basis for predicting the weather and ocean currents. Usually parcels of fluid are traced and geometrical and statistical approaches incorporate parameters for mixing and chaos, as well as the resulting uncertainty. Mezić et al. (p. 486, published online 2 September; see the Perspective by Thiffeault) adapted this approach to consider different mixing and stretching regimes to improve predictions. As a test, they simulated and successfully predicted the spread of oil patches from the Deepwater Horizon oil spill in a model for the Gulf of Mexico. An ocean model can account for the trajectory and fragmentation of the recent Gulf of Mexico oil spill. Chaotic advection has served as the paradigm for mixing in fluid flows with simple time dependence. Its skeletal structure is based on analysis of invariant attracting and repelling manifolds in fluid flows. Here we develop a finite-time theory for two-dimensional incompressible fluid flows with arbitrary time dependence and introduce a new mixing diagnostic based on it. Besides stretching events around attracting and repelling manifolds, this allows us to detect hyperbolic mixing zones. We used the new diagnostic to forecast the spatial location and timing of oil washing ashore in Plaquemines Parish and Grand Isle, Louisiana, and Pensacola, Florida, in May 2010 and the flow of oil toward Panama City Beach, Florida, in June 2010.

Dynamic autoinoculation and the microbial ecology of a deep water hydrocarbon irruption

The irruption of gas and oil into the Gulf of Mexico during the Deepwater Horizon event fed a deep sea bacterial bloom that consumed hydrocarbons in the affected waters, formed a regional oxygen anomaly, and altered the microbiology of the region. In this work, we develop a coupled physical–metabolic model to assess the impact of mixing processes on these deep ocean bacterial communities and their capacity for hydrocarbon and oxygen use. We find that observed biodegradation patterns are well-described by exponential growth of bacteria from seed populations present at low abundance and that current oscillation and mixing processes played a critical role in distributing hydrocarbons and associated bacterial blooms within the northeast Gulf of Mexico. Mixing processes also accelerated hydrocarbon degradation through an autoinoculation effect, where water masses, in which the hydrocarbon irruption had caused blooms, later returned to the spill site with hydrocarbon-degrading bacteria persisting at elevated abundance. Interestingly, although the initial irruption of hydrocarbons fed successive blooms of different bacterial types, subsequent irruptions promoted consistency in the structure of the bacterial community. These results highlight an impact of mixing and circulation processes on biodegradation activity of bacteria during the Deepwater Horizon event and suggest an important role for mixing processes in the microbial ecology of deep ocean environments.

Closed-form solutions in the electrical field analysis for dielectrophoretic and travelling wave inter-digitated electrode arrays

We derive closed-form solutions of electric fields, dielectrophoretic (DEP) forces, and time-averaged DEP forces in a parallel electrode array for three cases: first, the case of a two-phase DEP electrode array with a first-order approximate boundary condition; second, the case of a two-phase DEP electrode array with the exact boundary condition; and last, the case of a four-phase travelling wave DEP electrode array with a first-order approximate boundary condition. We also compare these analytic solutions with numerical solutions.

A theoretical and experimental study of ac electrothermal flows

Electrokinetic flows lead to promising utilization for mixing, concentration, pumping and have applications from basic studies of convective flows to fully integrated lab on chip developments. Despite these wide applications, electrothermal flow models have been scarcely studied. We find that the model widely used by the microfluidic community does not fit correctly the measured ac electrothermal fluid flows at higher voltages (10?Vpp and above). We thus analyse both theoretically and experimentally the importance of electrothermal coupling and the buoyancy effect. Numerical simulations are compared with micro-particle image velocimetry measurements of the vortices. Our enhanced model successfully matches our measurements over a wide range of conductivities and voltages.

Performance Study of an Adaptive Controller in the Presence of Uncertainty

This letter addresses the performance study of an adaptive controller in the presence of gain and delay uncertainties. The nonlinearities in the controller presented here do not allow an analytical study of the effect of uncertainties on performance. A numerical study is done instead using the sampling, learning, prediction, and optimization capabilities of the software Global Optimization, Sensitivity and Uncertainty in Models (GoSUM).

Uniformization, organization, association and use of metadata from multiple content providers and manufacturers: A close look at the Building Automation System (BAS) sector

The Internet of Things (IoT) is creating a renaissance in data analytics by connecting devices in a wide variety of industries, as well as in everyday life. However, data collected from these new and disparate sources are typically non-structured and non-uniform, which has become a significant barrier to developing scalable and repeatable analytics. The more than 5.6 million of commercial buildings in the United States of America are an illustration of this phenomena. Indeed, in the past decade, commercial, industrial, and government buildings have been outfitted with sensors and controllers, fully embracing the modern age. Successful application of analytics to systems controlling indoor building environment is providing an opportunity to realize substantial and widespread gains in energy efficiency and improved comfort as well as reductions in operating costs long after the buildings commissioning. As such, Building Automation Systems (BASs), controlling heating, ventilation and air conditioning (HVAC) equipment, provide access to some level of information, often in the form of single and overlaid trends, meter data, and key performance indicators on a dashboard. The BAS, as well as analogous approaches, enables manual analysis by trained personnel, but it is cumbersome and cannot be easily scaled. In addition, the vast diversity of equipment, controllers, and configurations among buildings as well as the heterogeneity of components within single systems have further hindered traditional automated systematic analysis. In this work, a methodology for structuring and uniformizing data applied to building environment control systems is demonstrated. The result is a system that enables scalable and repeatable advanced analytics on a macro scale, abstracting the focus from a specific building and configuration, that is likely unique, to systems of multiple equipment that is generalizable to any building.

Spatial filter averaging approach of probabilistic method to linear second-order partial differential equations of the parabolic type

A backward-in-time probabilistic method with spatial filter averaging is presented to solve linear second-order partial differential equations of the parabolic type. An advantage of this methodology is that while forward methods are subject to region with loss of density of particles and hence loss of spatial resolution of the solution, the solution given by backward methods is given on any desired grid. However, traditional backward time probabilistic method using Monte Carlo averaging are computationally expensive. We prove a convergence result and present several examples. The method leads to important improvement in computational efficiency and is expected to perform well to solve high dimensional problems where a solution is needed on a large grid.

Combustion of Methane in Microchannels

The interest in micropower generation using the high energy density provided by hydrocarbon fuels as a portable power and heat source has stimulated research on combustion in microdevices. As the length scale of a combustion channel is decreased, the surface area-to-volume ratio increases approximately inversely with the critical dimension. The resulting high surface heat loss is a limiting factor to the size of a microcombustor. However when using arrays of micro-combustors, some of the surface heat loss in a channel becomes heat source for its neighbors. Combustion of methane/air mixture in an array of channels is studied as a function of gas velocity and distance between channels and is compared to the case of a single channel. Arrays of channels are shown to have self-sustained combustion when no such combustion is possible in a single channel.Copyright

Pattern recognition and classification of HVAC rule-based faults in commercial buildings

In complex dynamical systems, traditional rule-based fault detection algorithms remain highly static, and struggle to capture important patterns or trends. Worse, many traditional methods provide an overwhelming amount of poorly labeled information, making root cause analysis extremely difficult. In order to capture localized and system wide dynamic trends, an extension to rule-based fault detection algorithms is required. In this paper, we propose a spectral clustering method as a means to recognize dynamical patterns present in commercial HVAC system fault diagnostic signals. Through applications of the Koopman operator and spectral analysis, similar patterns in HVAC device faults are automatically detected and then described using principal component analysis. This method provides an advanced extension of traditional rule-based fault detection algorithms and shows effective root cause diagnosis. Finally, this method provides a comprehensive understanding of a complex system and increases the ability for automated fault detection. We display a proof of concept by applying the proposed method to a large commercial building with an extensive system of network sensors.

Analysis of Fluid Motion in Dynamic Stall and Forced Cylinder Flow Using Koopman Operator Methods

Potential analogs between dynamics induced by periodic passage through a bifurcation critical value and the nonlinear dynamics associated with the aerodynamic dynamic stall problem are presented for the first time. Koopman operator methods are used to study the spectral features of a streamwise oscillating cylinder which exhibits wake dynamics due to externally forced oscillations through a Hopf bifurcation critical value. Koopman decomposition results show that the system transitions to a more continuous spectrum compared to the discrete spectrum associated with a stationary cylinder in post-critical flow. Finally, Fourier analysis of flow variables associated with an oscillating airfoil under dynamic stall conditions were compared with the oscillating cylinder spectra. The spectral characteristics of the two systems exhibited similar frequency broadening behavior induced by the externally forced oscillations. Therefore, the results indicate that the nonlinear dynamics associated with dynamic stall appear to have strong linkages to a system oscillating through a bifurcation critical value.Copyright