Chemical Characterization of Urban Background Aerosol Using Online And Filter Methods

In order to evaluate the influence of ambient aerosol particles on cloud formation, climate and human health, detailed information about the concentration and composition of ambient aerosol particles is needed. The dura-tion of aerosol formation, growth and removal processes in the atmosphere range from minutes to hours, which highlights the need for high-time-resolution data in order to understand the underlying processes. This thesis focuses on characterization of ambient levels, size distributions and sources of water-soluble organic carbon (WSOC) in ambient aerosols. The results show that in the location of this study typically 50-60 % of organic carbon in fine particles is water-soluble. The amount of WSOC was observed to increase as aerosols age, likely due to further oxidation of organic compounds. In the boreal region the main sources of WSOC were biomass burning during the winter and secondary aerosol formation during the summer. WSOC was mainly attributed to a fine particle mode between 0.1 - 1 μm, although different size distributions were measured for different sources. The WSOC concentrations and size distributions had a clear seasonal variation. Another main focus of this thesis was to test and further develop the high-time-resolution methods for chemical characterization of ambient aerosol particles. The concentrations of the main chemical components (ions, OC, EC) of ambient aerosol particles were measured online during a year-long intensive measurement campaign conducted on the SMEAR III station in Southern Finland. The results were compared to the results of traditional filter collections in order to study sampling artifacts and limitations related to each method. To achieve better a time resolution for the WSOC and ion measurements, a particle-into-liquid sampler (PILS) was coupled with a total organic carbon analyzer (TOC) and two ion chromatographs (IC). The PILS-TOC-IC provided important data about diurnal variations and short-time plumes, which cannot be resolved from the filter samples. In summary, the measurements made for this thesis provide new information on the concentrations, size distribu-tions and sources of WSOC in ambient aerosol particles in the boreal region. The analytical and collection me-thods needed for the online characterization of aerosol chemical composition were further developed in order to provide more reliable high-time-resolution measurements.

Particle filters for structural system identification using multiple test and sensor data: A combined computational and experimental study

The problem of structural system identification when measurements originate from multiple tests and multiple sensors is considered. An offline solution to this problem using bootstrap particle filtering is proposed. The central idea of the proposed method is the introduction of a dummy independent variable that allows for simultaneous assimilation of multiple measurements in a sequential manner. The method can treat linear/nonlinear structural models and allows for measurements on strains and displacements under static/dynamic loads. Illustrative examples consider measurement data from numerical models and also from laboratory experiments. The results from the proposed method are compared with those from a Kalman filter-based approach and the superior performance of the proposed method is demonstrated. Copyright (C) 2009 John Wiley & Sons, Ltd.

Pseudo-time particle filtering for diffuse optical tomography

We recast the reconstruction problem of diffuse optical tomography (DOT) in a pseudo-dynamical framework and develop a method to recover the optical parameters using particle filters, i.e., stochastic filters based on Monte Carlo simulations. In particular, we have implemented two such filters, viz., the bootstrap (BS) filter and the Gaussian-sum (GS) filter and employed them to recover optical absorption coefficient distribution from both numerically simulated and experimentally generated photon fluence data. Using either indicator functions or compactly supported continuous kernels to represent the unknown property distribution within the inhomogeneous inclusions, we have drastically reduced the number of parameters to be recovered and thus brought the overall computation time to within reasonable limits. Even though the GS filter outperformed the BS filter in terms of accuracy of reconstruction, both gave fairly accurate recovery of the height, radius, and location of the inclusions. Since the present filtering algorithms do not use derivatives, we could demonstrate accurate contrast recovery even in the middle of the object where the usual deterministic algorithms perform poorly owing to the poor sensitivity of measurement of the parameters. Consistent with the fact that the DOT recovery, being ill posed, admits multiple solutions, both the filters gave solutions that were verified to be admissible by the closeness of the data computed through them to the data used in the filtering step (either numerically simulated or experimentally generated). (C) 2011 Optical Society of America

Spatial Filter Based Bessel-Like Beam for Improved Penetration Depth Imaging in Fluorescence Microscopy

Monitoring and visualizing specimens at a large penetration depth is a challenge. At depths of hundreds of microns, several physical effects (such as, scattering, PSF distortion and noise) deteriorate the image quality and prohibit a detailed study of key biological phenomena. In this study, we use a Bessel-like beam in-conjugation with an orthogonal detection system to achieve depth imaging. A Bessel-like penetrating diffractionless beam is generated by engineering the back-aperture of the excitation objective. The proposed excitation scheme allows continuous scanning by simply translating the detection PSF. This type of imaging system is beneficial for obtaining depth information from any desired specimen layer, including nano-particle tracking in thick tissue. As demonstrated by imaging the fluorescent polymer-tagged-CaCO3 particles and yeast cells in a tissue-like gel-matrix, the system offers a penetration depth that extends up to 650 mu m. This achievement will advance the field of fluorescence imaging and deep nano-particle tracking.

A scaled unscented transformation based directed Gaussian sum filter for nonlinear dynamic system identification

Impoverishment of particles, i.e. the discretely simulated sample paths of the process dynamics, poses a major obstacle in employing the particle filters for large dimensional nonlinear system identification. A known route of alleviating this impoverishment, i.e. of using an exponentially increasing ensemble size vis-a-vis the system dimension, remains computationally infeasible in most cases of practical importance. In this work, we explore the possibility of unscented transformation on Gaussian random variables, as incorporated within a scaled Gaussian sum stochastic filter, as a means of applying the nonlinear stochastic filtering theory to higher dimensional structural system identification problems. As an additional strategy to reconcile the evolving process dynamics with the observation history, the proposed filtering scheme also modifies the process model via the incorporation of gain-weighted innovation terms. The reported numerical work on the identification of structural dynamic models of dimension up to 100 is indicative of the potential of the proposed filter in realizing the stated aim of successfully treating relatively larger dimensional filtering problems. (C) 2013 Elsevier Ltd. All rights reserved.

A Kushner-Stratonovich Monte Carlo filter applied to nonlinear dynamical system identification

A Monte Carlo filter, based on the idea of averaging over characteristics and fashioned after a particle-based time-discretized approximation to the Kushner-Stratonovich (KS) nonlinear filtering equation, is proposed. A key aspect of the new filter is the gain-like additive update, designed to approximate the innovation integral in the KS equation and implemented through an annealing-type iterative procedure, which is aimed at rendering the innovation (observation prediction mismatch) for a given time-step to a zero-mean Brownian increment corresponding to the measurement noise. This may be contrasted with the weight-based multiplicative updates in most particle filters that are known to precipitate the numerical problem of weight collapse within a finite-ensemble setting. A study to estimate the a-priori error bounds in the proposed scheme is undertaken. The numerical evidence, presently gathered from the assessed performance of the proposed and a few other competing filters on a class of nonlinear dynamic system identification and target tracking problems, is suggestive of the remarkably improved convergence and accuracy of the new filter. (C) 2013 Elsevier B.V. All rights reserved.

Iterated stochastic filters with additive updates for dynamic system identification: Annealing-type iterations and the filter bank

A nonlinear stochastic filtering scheme based on a Gaussian sum representation of the filtering density and an annealing-type iterative update, which is additive and uses an artificial diffusion parameter, is proposed. The additive nature of the update relieves the problem of weight collapse often encountered with filters employing weighted particle based empirical approximation to the filtering density. The proposed Monte Carlo filter bank conforms in structure to the parent nonlinear filtering (Kushner-Stratonovich) equation and possesses excellent mixing properties enabling adequate exploration of the phase space of the state vector. The performance of the filter bank, presently assessed against a few carefully chosen numerical examples, provide ample evidence of its remarkable performance in terms of filter convergence and estimation accuracy vis-a-vis most other competing filters especially in higher dimensional dynamic system identification problems including cases that may demand estimating relatively minor variations in the parameter values from their reference states. (C) 2014 Elsevier Ltd. All rights reserved.

System reliability analysis of granular filter for protection against piping in dams

Granular filters are provided for the safety of water retaining structure for protection against piping failure. The phenomenon of piping triggers when the base soil to be protected starts migrating in the direction of seepage flow under the influence of seepage force. To protect base soil from migration, the voids in the filter media should be small enough but it should not also be too small to block smooth passage of seeping water. Fulfilling these two contradictory design requirements at the same time is a major concern for the successful performance of granular filter media. Since Terzaghi era, conventionally, particle size distribution (PSD) of granular filters is designed based on particle size distribution characteristics of the base soil to be protected. The design approach provides a range of D15f value in which the PSD of granular filter media should fall and there exist infinite possibilities. Further, safety against the two critical design requirements cannot be ensured. Although used successfully for many decades, the existing filter design guidelines are purely empirical in nature accompanied with experience and good engineering judgment. In the present study, analytical solutions for obtaining the factor of safety with respect to base soil particle migration and soil permeability consideration as proposed by the authors are first discussed. The solution takes into consideration the basic geotechnical properties of base soil and filter media as well as existing hydraulic conditions and provides a comprehensive solution to the granular filter design with ability to assess the stability in terms of factor of safety. Considering the fact that geotechnical properties are variable in nature, probabilistic analysis is further suggested to evaluate the system reliability of the filter media that may help in risk assessment and risk management for decision making.

An Ensemble Kushner-Stratonovich-Poisson Filter for Recursive Estimation in Nonlinear Dynamical Systems

We propose a Monte Carlo filter for recursive estimation of diffusive processes that modulate the instantaneous rates of Poisson measurements. A key aspect is the additive update, through a gain-like correction term, empirically approximated from the innovation integral in the time-discretized Kushner-Stratonovich equation. The additive filter-update scheme eliminates the problem of particle collapse encountered in many conventional particle filters. Through a few numerical demonstrations, the versatility of the proposed filter is brought forth.

Subgrid scale fluid velocity timescales seen by inertial particles in large-eddy simulation of particle-laden turbulence

Large-eddy simulation (LES) has emerged as a promising tool for simulating turbulent flows in general and, in recent years,has also been applied to the particle-laden turbulence with some success (Kassinos et al., 2007). The motion of inertial particles is much more complicated than fluid elements, and therefore, LES of turbulent flow laden with inertial particles encounters new challenges. In the conventional LES, only large-scale eddies are explicitly resolved and the effects of unresolved, small or subgrid scale (SGS) eddies on the large-scale eddies are modeled. The SGS turbulent flow field is not available. The effects of SGS turbulent velocity field on particle motion have been studied by Wang and Squires (1996), Armenio et al. (1999), Yamamoto et al. (2001), Shotorban and Mashayek (2006a,b), Fede and Simonin (2006), Berrouk et al. (2007), Bini and Jones (2008), and Pozorski and Apte (2009), amongst others. One contemporary method to include the effects of SGS eddies on inertial particle motions is to introduce a stochastic differential equation (SDE), that is, a Langevin stochastic equation to model the SGS fluid velocity seen by inertial particles (Fede et al., 2006; Shotorban and Mashayek, 2006a; Shotorban and Mashayek, 2006b; Berrouk et al., 2007; Bini and Jones, 2008; Pozorski and Apte, 2009).However, the accuracy of such a Langevin equation model depends primarily on the prescription of the SGS fluid velocity autocorrelation time seen by an inertial particle or the inertial particle–SGS eddy interaction timescale (denoted by $\delt T_{Lp}$ and a second model constant in the diffusion term which controls the intensity of the random force received by an inertial particle (denoted by C_0, see Eq. (7)). From the theoretical point of view, dTLp differs significantly from the Lagrangian fluid velocity correlation time (Reeks, 1977; Wang and Stock, 1993), and this carries the essential nonlinearity in the statistical modeling of particle motion. dTLp and C0 may depend on the filter width and particle Stokes number even for a given turbulent flow. In previous studies, dTLp is modeled either by the fluid SGS Lagrangian timescale (Fede et al., 2006; Shotorban and Mashayek, 2006b; Pozorski and Apte, 2009; Bini and Jones, 2008) or by a simple extension of the timescale obtained from the full flow field (Berrouk et al., 2007). In this work, we shall study the subtle and on-monotonic dependence of $\delt T_{Lp}$ on the filter width and particle Stokes number using a flow field obtained from Direct Numerical Simulation (DNS). We then propose an empirical closure model for $\delta T_{Lp}$. Finally, the model is validated against LES of particle-laden turbulence in predicting single-particle statistics such as particle kinetic energy. As a first step, we consider the particle motion under the one-way coupling assumption in isotropic turbulent flow and neglect the gravitational settling effect. The one-way coupling assumption is only valid for low particle mass loading.

An improved technique to measure axial variation in wall flow along the channels of a diesel particulate filter

A technique to measure wall flow variation in Diesel Particle Filters (DPFs) is described. In a recent paper, it was shown how the flow distribution in DPFs could be measured in a non-destructive manner. This involved measuring the progressive dilution of a tracer gas introduced at the "outlet" channel upstream end. In the present paper, a significant further improvement to this technique is described, in which only a single probe is required, rather than the two of the previous technique. The single, traversable, probe consists of a controllable flow sink, and slightly downstream, a tracer gas supply. By controlling the sink flow rate such that a very small concentration of tracer gas is aspirated into it, the total flow up to that location in the channel is determined. Typical results showing the axial variation in the wall flow for known wall blockage cases are presented. It is suggested that this technique could be used to interpret the soot loading in the filter channels in a non-intrusive way.

A backward-simulation based Rao-Blackwellized particle smoother for conditionally linear Gaussian models

In this article, we develop a new Rao-Blackwellized Monte Carlo smoothing algorithm for conditionally linear Gaussian models. The algorithm is based on the forward-filtering backward-simulation Monte Carlo smoother concept and performs the backward simulation directly in the marginal space of the non-Gaussian state component while treating the linear part analytically. Unlike the previously proposed backward-simulation based Rao-Blackwellized smoothing approaches, it does not require sampling of the Gaussian state component and is also able to overcome certain normalization problems of two-filter smoother based approaches. The performance of the algorithm is illustrated in a simulated application. © 2012 IFAC.

Di-nuclear systems studied with the velocity filter SHIP

Various nuclear reactions like quasi-fission, fusion-fission or particle and cluster evaporation from excited compound nuclei were studied in heavy-ion reactions at the velocity filter SHIP of GSI. The velocity filter offers the possibility to detect all reaction products under zero degree relative to the beam direction. Together with the measurement of the product velocity distribution this allows for an identification of the underlying reaction mechanism. This article is focussed on reactions of Mg-25 and Ni-64 beams on Pb-206,Pb-207 targets at energies of 5.9 x A MeV and 8.7 x A MeV. Besides evaporation residues from Mg-25 + Pb-206 collisions we found evidence for rotation and quasi-fission of nuclear molecules formed in the entrance channel after the capture stage. The break-up of the systems showed a preferred clustering leading to isotopes in the region 84 <= Z <= 88 and 122 <= N <= 127 of the chart of nuclei.

Comparison of Experimental PIV Data and CFD Simulations for Flow in a Diesel Particulate Filter Inlet Diffuser

Flow maldistribution of the exhaust gas entering a Diesel Particulate Filter (DPF) can cause uneven soot distribution during loading and excessive temperature gradients during the regeneration phase. Minimising the magnitude of this maldistribution is therefore an important consideration in the design of the inlet pipe and diffuser, particularly in situations where packaging constraints dictate bends in the inlet pipe close to the filter, or a sharp diffuser angle. This paper describes the use of Particle Image Velocimetry (PIV) to validate a Computational Fluid Dynamic (CFD) model of the flow within the inlet diffuser of a DPF so that CFD can be used with confidence as a tool to minimise this flow maldistribution. PIV is used to study the flow of gas into a DPF over a range of steady state flow conditions. The distribution of flow approaching the front face of the substrate was of particular interest to this study. Optically clear diffusing cones were designed and placed between pipe and substrate to allow PIV analysis to take place. Stereoscopic PIV was used to eliminate any error produced by the optical aberrations caused by looking through the curved wall of the inlet cone. In parallel to the experiments, numerical analysis was carried out using a CFD program with an incorporated DPF model. Boundary conditions for the CFD simulations were taken from the experimental data, allowing an experimental validation of the numerical results. The CFD model incorporated a DPF model, the cement layers seen in segmented filters and the intumescent matting that is commonly used to pack the filter into a metal casing. The mesh contained approximately 580,000 cells and used the realizable ?-e turbulence model. The CFD simulation predicted both pressure drop across the DPF and the velocity field within the cone and at the DPF face with reasonable accuracy, providing confidence in the use the CFD in future work to design new, more efficient cones.

Analyse selbsterregter Strömungsfelder im Verdichter-Leitrad durch Kopplung von Particle Image Velocimetry und Hitzdrahtanemometrie mittels Fast Fourier Transformation

Der Einsatz der Particle Image Velocimetry (PIV) zur Analyse selbsterregter Strömungsphänomene und das dafür notwendige Auswerteverfahren werden in dieser Arbeit beschrieben. Zur Untersuchung von solchen Mechanismen, die in Turbo-Verdichtern als Rotierende Instabilitäten in Erscheinung treten, wird auf Datensätze zurückgegriffen, die anhand experimenteller Untersuchungen an einem ringförmigen Verdichter-Leitrad gewonnen wurden. Die Rotierenden Instabilitäten sind zeitabhängige Strömungsphänomene, die bei hohen aerodynamischen Belastungen in Verdichtergittern auftreten können. Aufgrund der fehlenden Phaseninformation kann diese instationäre Strömung mit konventionellen PIV-Systemen nicht erfasst werden. Die Kármánsche Wirbelstraße und Rotierende Instabilitäten stellen beide selbsterregte Strömungsvorgänge dar. Die Ähnlichkeit wird genutzt um die Funktionalität des Verfahrens anhand der Kármánschen Wirbelstraße nachzuweisen. Der mittels PIV zu visualisierende Wirbeltransport erfordert ein besonderes Verfahren, da ein externes Signal zur Festlegung des Phasenwinkels dieser selbsterregten Strömung nicht zur Verfügung steht. Die Methodik basiert auf der Kopplung der PIV-Technik mit der Hitzdrahtanemometrie. Die gleichzeitige Messung mittels einer zeitlich hochaufgelösten Hitzdraht-Messung ermöglicht den Zeitpunkten der PIV-Bilder einen Phasenwinkel zuzuordnen. Hierzu wird das Hitzdrahtsignal mit einem FFT-Verfahren analysiert, um die PIV-Bilder entsprechend ihrer Phasenwinkel zu gruppieren. Dafür werden die aufgenommenen Bilder auf der Zeitachse der Hitzdrahtmessungen markiert. Eine systematische Analyse des Hitzdrahtsignals in der Umgebung der PIV-Messung liefert Daten zur Festlegung der Grundfrequenz und erlaubt es, der markierten PIV-Position einen Phasenwinkel zuzuordnen. Die sich aus den PIV-Bildern einer Klasse ergebenden Geschwindigkeitskomponenten werden anschließend gemittelt. Aus den resultierenden Bildern jeder Klasse ergibt sich das zweidimensionale zeitabhängige Geschwindigkeitsfeld, in dem die Wirbelwanderung der Kármánschen Wirbelstraße ersichtlich wird. In hierauf aufbauenden Untersuchungen werden Zeitsignale aus Messungen in einem Verdichterringgitter analysiert. Dabei zeigt sich, dass zusätzlich Filterfunktionen erforderlich sind. Im Ergebnis wird schließlich deutlich, dass die Übertragung der anhand der Kármánschen Wirbelstraße entwickelten Methode nur teilweise gelingt und weitere Forschungsarbeiten erforderlich sind.