Investigation of the dilution process for measurement of particulate matter from spark-ignition engines

Measurements of particulate matter (PM) from spark ignition (SI) engine exhaust using dilution tunnels will become more prevalent as emission standards are tightened. Hence, a study of the dilution process was undertaken in order to understand how various dilution related parameters affect the accuracy with which PM sizes and concentrations can be determined. A SI and a compression ignition (CI) engine were separately used to examine parameters of the dilution process; the present work discusses the results in the context of SI exhaust dilution. A Scanning Mobility Particle Sizer (SMPS) was used to measure the size distribution, number density, and volume fraction of PM. Temperature measurements in the exhaust pipe and dilution tunnel reveal the degree of mixing between exhaust and dilution air, the effect of flowrate on heat transfer from undiluted and diluted exhaust to the environment, and the minimum permissible dilution ratio for a maximum sample temperature of 52°C. Measurements of PM concentrations as a function of dilution ratio show the competing effects of temperature and particle/vapor concentrations on particle growth dynamics, which result in a range of dilution ratios-from 13 to 18-where the effect of dilution ratio, independent of flowrate, is kept to a minimum. This range of dilution ratios is therefore optimal in order to achieve repeatable PM concentration measurements. Particle dynamics during transit through the tunnel operating at the optimal dilution ratio was found statistically insignificant compared to data scatter. Such small differences in number concentration may be qualitatively representative of particle losses for SI exhaust, but small increases in PM volume fraction during transit through the tunnel may significantly underestimate accretion of mass due to unburned hydrocarbons (HCs) emitted by SI engines. The fraction of SI-derived PM mass due to adsorbed/absorbed vapor, estimated from these data, is consistent with previous chemical analyses of PM. © 1998 Society of Automotive Engineers, Inc.

Thermodynamic formulation of flowing soft matter with transient forces

The Responsive Particle Dynamics model is a very efficient method to account for the transient forces present in complex fluids, such as solutions of entangled polymers. This coarse-grained model considers a solution of particles that are made of a core and a corona. The cores typically interact through conservative interactions, while the coronae transiently penetrate each other to form short-lived temporary interactions, typically of entropic origin. In this study, we reformulate the resulting rheological model within the general framework of nonequilibrium thermodynamics called General Equation for the Nonequilibrium Reversible-Irreversible Coupling. This allows us to determine the consistency of the model, from a mechanistic and thermodynamic point of view, and to isolate the reversible and irreversible contributions to the dynamics of the model system. © 2012 Springer-Verlag Berlin Heidelberg.

On the application of differential geometry to MDO

Multidisciplinary Design Optimization (MDO) is a methodology for optimizing large coupled systems. Over the years, a number of different MDO decomposition strategies, known as architectures, have been developed, and various pieces of analytical work have been done on MDO and its architectures. However, MDO lacks an overarching paradigm which would unify the field and promote cumulative research. In this paper, we propose a differential geometry framework as such a paradigm: Differential geometry comes with its own set of analysis tools and a long history of use in theoretical physics. We begin by outlining some of the mathematics behind differential geometry and then translate MDO into that framework. This initial work gives new tools and techniques for studying MDO and its architectures while producing a naturally arising measure of design coupling. The framework also suggests several new areas for exploration into and analysis of MDO systems. At this point, analogies with particle dynamics and systems of differential equations look particularly promising for both the wealth of extant background theory that they have and the potential predictive and evaluative power that they hold. © 2012 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.

Models and algorithms for tracking of maneuvering objects using variable rate particle filters

Standard algorithms in tracking and other state-space models assume identical and synchronous sampling rates for the state and measurement processes. However, real trajectories of objects are typically characterized by prolonged smooth sections, with sharp, but infrequent, changes. Thus, a more parsimonious representation of a target trajectory may be obtained by direct modeling of maneuver times in the state process, independently from the observation times. This is achieved by assuming the state arrival times to follow a random process, typically specified as Markovian, so that state points may be allocated along the trajectory according to the degree of variation observed. The resulting variable dimension state inference problem is solved by developing an efficient variable rate particle filtering algorithm to recursively update the posterior distribution of the state sequence as new data becomes available. The methodology is quite general and can be applied across many models where dynamic model uncertainty occurs on-line. Specific models are proposed for the dynamics of a moving object under internal forcing, expressed in terms of the intrinsic dynamics of the object. The performance of the algorithms with these dynamical models is demonstrated on several challenging maneuvering target tracking problems in clutter. © 2006 IEEE.

Imbibition dynamics of nano-particulate ink-jet drops on micro-porous media

Ink-jet printing of nano-metallic colloidal fluids on to porous media such as coated papers has become a viable method to produce conductive tracks for low-cost, disposable printed electronic devices. However, the formation of well-defined and functional tracks on an absorbing surface is controlled by the drop imbibition dynamics in addition to the well-studied post-impact drop spreading behavior. This study represents the first investigation of the real-time imbibition of ink-jet deposited nano-Cu colloid drops on to coated paper substrates. In addition, the same ink was deposited on to a non-porous polymer surface as a control substrate. By using high-speed video imaging to capture the deposition of ink-jet drops, the time-scales of drop spreading and imbibition were quantified and compared with model predictions. The influences of the coating pore size on the bulk absorption rate and nano-Cu particle distribution have also been studied.

Statistics and dynamics of turbulence-flame alignment in premixed combustion

The geometric alignment of turbulent strain-rate structures with premixed flames greatly influences the results of the turbulence-flame interaction. Here, the statistics and dynamics of this alignment are experimentally investigated in turbulent premixed Bunsen flames using high-repetition-rate stereoscopic particle image velocimetry. In all cases, the statistics showed that the most extensive principal strain-rate associated with the turbulence preferentially aligned such that it was more perpendicular than parallel to the flame surface normal direction. The mean turbulence-flame alignment differed between the flames, with the stronger flames (higher laminar flame speed) exhibiting stronger preferential alignment. Furthermore, the preferential alignment was greatest on the reactant side of the mean flame brush. To understand these differences, individual structures of fluid-dynamic strain-rate were tracked through time in a Lagrangian manner (i.e., by following the fluid elements). It was found that the flame surface affected the orientation of the turbulence structures, with the majority of structures rotating as they approached the flame such that their most extensive principal strain-rate was perpendicular to the flame normal. The maximum change in turbulent structure orientation was found to decrease with the strength of the structure, increase with the strength of the flame, and exhibit similar trends when the structure strength and flame strength were represented by a Karlovitz number. The mean change in orientation decreased from the unburnt to burnt side of the flame brush and appears to be influenced by the overall flame shape. © 2011 The Combustion Institute.

Forecasting high-frequency futures returns using online langevin dynamics

Forecasting the returns of assets at high frequency is the key challenge for high-frequency algorithmic trading strategies. In this paper, we propose a jump-diffusion model for asset price movements that models price and its trend and allows a momentum strategy to be developed. Conditional on jump times, we derive closed-form transition densities for this model. We show how this allows us to extract a trend from high-frequency finance data by using a Rao-Blackwellized variable rate particle filter to filter incoming price data. Our results show that even in the presence of transaction costs our algorithm can achieve a Sharpe ratio above 1 when applied across a portfolio of 75 futures contracts at high frequency. © 2011 IEEE.

Studying adsorbent dynamics on a quartz crystal resonator using its nonlinear electrical response

The quartz crystal resonator has been traditionally employed in studying surface-confined physisorbed films and particles by measuring dissipation and frequency shifts. However, theoretical interpretation of the experimental observations is often challenged due to limited understanding of physical interaction mechanisms at the interfaces involved. Here we model a physisorbed interaction between particles and gold electrode surface of a quartz crystal and demonstrate how the nonlinear modulation of the electric response of the crystal due to the nonlinear interaction forces may be used to study the dynamics of the particles. In particular, we show that the graphs of the deviation in the third Fourier harmonic response versus oscillation amplitude provide important information about the onset, progress and nature of sliding of the particles. The graphs also present a signature of the surface-particle interaction and could be used to estimate the interaction energy profile. Interestingly, the insights gained from the model help to explain some of the experimental observations with physisorbed streptavidin-coated polystyrene microbeads on quartz resonators. © 2012 Elsevier B.V. All rights reserved.

Static and dynamic errors in particle tracking microrheology.

Particle tracking techniques are often used to assess the local mechanical properties of cells and biological fluids. The extracted trajectories are exploited to compute the mean-squared displacement that characterizes the dynamics of the probe particles. Limited spatial resolution and statistical uncertainty are the limiting factors that alter the accuracy of the mean-squared displacement estimation. We precisely quantified the effect of localization errors in the determination of the mean-squared displacement by separating the sources of these errors into two separate contributions. A "static error" arises in the position measurements of immobilized particles. A "dynamic error" comes from the particle motion during the finite exposure time that is required for visualization. We calculated the propagation of these errors on the mean-squared displacement. We examined the impact of our error analysis on theoretical model fluids used in biorheology. These theoretical predictions were verified for purely viscous fluids using simulations and a multiple-particle tracking technique performed with video microscopy. We showed that the static contribution can be confidently corrected in dynamics studies by using static experiments performed at a similar noise-to-signal ratio. This groundwork allowed us to achieve higher resolution in the mean-squared displacement, and thus to increase the accuracy of microrheology studies.

Bayesian inference and learning in Gaussian process state-space models with Particle MCMC

State-space models are successfully used in many areas of science, engineering and economics to model time series and dynamical systems. We present a fully Bayesian approach to inference and learning (i.e. state estimation and system identification) in nonlinear nonparametric state-space models. We place a Gaussian process prior over the state transition dynamics, resulting in a flexible model able to capture complex dynamical phenomena. To enable efficient inference, we marginalize over the transition dynamics function and, instead, infer directly the joint smoothing distribution using specially tailored Particle Markov Chain Monte Carlo samplers. Once a sample from the smoothing distribution is computed, the state transition predictive distribution can be formulated analytically. Our approach preserves the full nonparametric expressivity of the model and can make use of sparse Gaussian processes to greatly reduce computational complexity.