Modeling of active holonic control systems for intelligent buildings

Building facilities have become important infrastructures for modern productive plants dedicated to services. In this context, the control systems of intelligent buildings have evolved while their reliability has evidently improved. However, the occurrence of faults is inevitable in systems conceived, constructed and operated by humans. Thus, a practical alternative approach is found to be very useful to reduce the consequences of faults. Yet, only few publications address intelligent building modeling processes that take into consideration the occurrence of faults and how to manage their consequences. In the light of the foregoing, a procedure is proposed for the modeling of intelligent building control systems, considersing their functional specifications in normal operation and in the of the event of faults. The proposed procedure adopts the concepts of discrete event systems and holons, and explores Petri nets and their extensions so as to represent the structure and operation of control systems for intelligent buildings under normal and abnormal situations. (C) 2012 Elsevier B.V. All rights reserved.

Modeling random corrosion processes via polynomial chaos expansion

Polynomial Chaos Expansion (PCE) is widely recognized as a flexible tool to represent different types of random variables/processes. However, applications to real, experimental data are still limited. In this article, PCE is used to represent the random time-evolution of metal corrosion growth in marine environments. The PCE coefficients are determined in order to represent data of 45 corrosion coupons tested by Jeffrey and Melchers (2001) at Taylors Beach, Australia. Accuracy of the representation and possibilities for model extrapolation are considered in the study. Results show that reasonably accurate smooth representations of the corrosion process can be obtained. The representation is not better because a smooth model is used to represent non-smooth corrosion data. Random corrosion leads to time-variant reliability problems, due to resistance degradation over time. Time variant reliability problems are not trivial to solve, especially under random process loading. Two example problems are solved herein, showing how the developed PCE representations can be employed in reliability analysis of structures subject to marine corrosion. Monte Carlo Simulation is used to solve the resulting time-variant reliability problems. However, an accurate and more computationally efficient solution is also presented.

Parallel modeling of the electric field distribution in the brain

The term "Brain Imaging" identi�es a set of techniques to analyze the structure and/or functional behavior of the brain in normal and/or pathological situations. These techniques are largely used in the study of brain activity. In addition to clinical usage, analysis of brain activity is gaining popularity in others recent �fields, i.e. Brain Computer Interfaces (BCI) and the study of cognitive processes. In this context, usage of classical solutions (e.g. f MRI, PET-CT) could be unfeasible, due to their low temporal resolution, high cost and limited portability. For these reasons alternative low cost techniques are object of research, typically based on simple recording hardware and on intensive data elaboration process. Typical examples are ElectroEncephaloGraphy (EEG) and Electrical Impedance Tomography (EIT), where electric potential at the patient's scalp is recorded by high impedance electrodes. In EEG potentials are directly generated from neuronal activity, while in EIT by the injection of small currents at the scalp. To retrieve meaningful insights on brain activity from measurements, EIT and EEG relies on detailed knowledge of the underlying electrical properties of the body. This is obtained from numerical models of the electric �field distribution therein. The inhomogeneous and anisotropic electric properties of human tissues make accurate modeling and simulation very challenging, leading to a tradeo�ff between physical accuracy and technical feasibility, which currently severely limits the capabilities of these techniques. Moreover elaboration of data recorded requires usage of regularization techniques computationally intensive, which influences the application with heavy temporal constraints (such as BCI). This work focuses on the parallel implementation of a work-flow for EEG and EIT data processing. The resulting software is accelerated using multi-core GPUs, in order to provide solution in reasonable times and address requirements of real-time BCI systems, without over-simplifying the complexity and accuracy of the head models.

The influence of petrogenetic processes on the composition of Ocean Island Basalts

Ocean Island Basalts (OIB) provide important information on the chemical and physical characteristics of their mantle sources. However, the geochemical composition of a generated magma is significantly affected by partial melting and/or subsequent fractional crystallization processes. In addition, the isotopic composition of an ascending magma may be modified during transport through the oceanic crust. The influence of these different processes on the chemical and isotopic composition of OIB from two different localities, Hawaii and Tubuai in the Pacific Ocean, are investigated here. In a first chapter, the Os-isotope variations in suites of lavas from Kohala Volcano, Hawaii, are examined to constrain the role of melt/crust interactions on the evolution of these lavas. As 187Os/188Os sensitivity to any radiogenic contaminant strongly depend on the Os content in the melt, Os and other PGE variations are investigated first. This study reveals that Os and other PGE behavior change during the Hawaiian magma differentiation. While PGE concentrations are relatively constant in lavas with relatively primitive compositions, all PGE contents strongly decrease in the melt as it evolved through ~ 8% MgO. This likely reflects the sulfur saturation of the Hawaiian magma and the onset of sulfide fractionation at around 8% MgO. Kohala tholeiites with more than 8% MgO and rich in Os have homogeneous 187Os/188Os values likely to represent the mantle signature of Kohala lavas. However, Os isotopic ratios become more radiogenic with decreasing MgO and Os contents in the lavas, which reflects assimilation of local crust material during fractional crystallization processes. Less than 8% upper oceanic crust assimilation could have produced the most radiogenic Os-isotope ratios recorded in the shield lavas. However, these small amounts of upper crust assimilation have only negligible effects on Sr and Nd isotopic ratios and therefore, are not responsible for the Sr and Nd isotopic heterogeneities observed in Kohala lavas. In a second chapter, fractional crystallization and partial melting processes are constrained using major and trace element variations in the same suites of lavas from Kohala Volcano, Hawaii. This inverse modeling approach allows the estimation of most of the trace element composition of the Hawaiian mantle source. The calculated initial trace element pattern shows slight depletion of the concentrations from LREE to the most incompatible elements, which indicates that the incompatible element enrichments described by the Hawaiian melt patterns are entirely produced by partial melting processes. The “Kea trend” signature of lavas from Kohala Volcano is also confirmed, with Kohala lavas having lower Sr/Nd and La/Th ratios than lavas from Mauna Loa Volcano. Finally, the magmatic evolution of Tubuai Island is investigated in a last chapter using the trace element and Sr, Nd, Hf isotopic variations in mafic lava suites. The Sr, Nd and Hf isotopic data are homogeneous and typical for the HIMU-type OIB and confirms the cogenetic nature of the different mafic lavas from Tubuai Island. The trace element patterns show progressive enrichment of incompatible trace elements with increasing alkali content in the lavas, which reflect progressive decrease in the degree of partial melting towards the later volcanic events. In addition, this enrichment of incompatible trace elements is associated with relative depletion of Rb, Ba, K, Nb, Ta and Ti in the lavas, which require the presence of small amount of residual phlogopite and of a Ti-bearing phase (ilmenite or rutile) during formation of the younger analcitic and nephelinitic magmas.

Modeling of deep-convective

Deep convection by pyro-cumulonimbus clouds (pyroCb) can transport large amounts of forest fire smoke into the upper troposphere and lower stratosphere. Here, results from numerical simulations of such deep convective smoke transport are presented. The structure, shape and injection height of the pyroCb simulated for a specific case study are in good agreement with observations. The model results confirm that substantial amounts of smoke are injected into the lower stratosphere. Small-scale mixing processes at the cloud top result in a significant enhancement of smoke injection into the stratosphere. Sensitivity studies show that the release of sensible heat by the fire plays an important role for the dynamics of the pyroCb. Furthermore, the convection is found to be very sensitive to background meteorological conditions. While the abundance of aerosol particles acting as cloud condensation nuclei (CCN) has a strong influence on the microphysical structure of the pyroCb, the CCN effect on the convective dynamics is rather weak. The release of latent heat dominates the overall energy budget of the pyroCb. Since most of the cloud water originates from moisture entrained from the background atmosphere, the fire-released moisture contributes only minor to convection dynamics. Sufficient fire heating, favorable meteorological conditions, and small-scale mixing processes at the cloud top are identified as the key ingredients for troposphere-to-stratosphere transport by pyroCb convection.

Measurements and modeling of ozone fluxes in and above Norway spruce canopies

Ozon (O3) ist ein wichtiges Oxidierungs- und Treibhausgas in der Erdatmosphäre. Es hat Einfluss auf das Klima, die Luftqualität sowie auf die menschliche Gesundheit und die Vegetation. Ökosysteme, wie beispielsweise Wälder, sind Senken für troposphärisches Ozon und werden in Zukunft, bedingt durch Stürme, Pflanzenschädlinge und Änderungen in der Landnutzung, heterogener sein. Es ist anzunehmen, dass diese Heterogenitäten die Aufnahme von Treibhausgasen verringern und signifikante Rückkopplungen auf das Klimasystem bewirken werden. Beeinflusst wird der Atmosphären-Biosphären-Austausch von Ozon durch stomatäre Aufnahme, Deposition auf Pflanzenoberflächen und Böden sowie chemische Umwandlungen. Diese Prozesse zu verstehen und den Ozonaustausch für verschiedene Ökosysteme zu quantifizieren sind Voraussetzungen, um von lokalen Messungen auf regionale Ozonflüsse zu schließen.rnFür die Messung von vertikalen turbulenten Ozonflüssen wird die Eddy Kovarianz Methode genutzt. Die Verwendung von Eddy Kovarianz Systemen mit geschlossenem Pfad, basierend auf schnellen Chemilumineszenz-Ozonsensoren, kann zu Fehlern in der Flussmessung führen. Ein direkter Vergleich von nebeneinander angebrachten Ozonsensoren ermöglichte es einen Einblick in die Faktoren zu erhalten, die die Genauigkeit der Messungen beeinflussen. Systematische Unterschiede zwischen einzelnen Sensoren und der Einfluss von unterschiedlichen Längen des Einlassschlauches wurden untersucht, indem Frequenzspektren analysiert und Korrekturfaktoren für die Ozonflüsse bestimmt wurden. Die experimentell bestimmten Korrekturfaktoren zeigten keinen signifikanten Unterschied zu Korrekturfaktoren, die mithilfe von theoretischen Transferfunktionen bestimmt wurden, wodurch die Anwendbarkeit der theoretisch ermittelten Faktoren zur Korrektur von Ozonflüssen bestätigt wurde.rnIm Sommer 2011 wurden im Rahmen des EGER (ExchanGE processes in mountainous Regions) Projektes Messungen durchgeführt, um zu einem besseren Verständnis des Atmosphären-Biosphären Ozonaustauschs in gestörten Ökosystemen beizutragen. Ozonflüsse wurden auf beiden Seiten einer Waldkante gemessen, die einen Fichtenwald und einen Windwurf trennt. Auf der straßenähnlichen Freifläche, die durch den Sturm "Kyrill" (2007) entstand, entwickelte sich eine Sekundärvegetation, die sich in ihrer Phänologie und Blattphysiologie vom ursprünglich vorherrschenden Fichtenwald unterschied. Der mittlere nächtliche Fluss über dem Fichtenwald war -6 bis -7 nmol m2 s-1 und nahm auf -13 nmol m2 s-1 um die Mittagszeit ab. Die Ozonflüsse zeigten eine deutliche Beziehung zur Pflanzenverdunstung und CO2 Aufnahme, was darauf hinwies, dass während des Tages der Großteil des Ozons von den Pflanzenstomata aufgenommen wurde. Die relativ hohe nächtliche Deposition wurde durch nicht-stomatäre Prozesse verursacht. Die Deposition über dem Wald war im gesamten Tagesverlauf in etwa doppelt so hoch wie über der Freifläche. Dieses Verhältnis stimmte mit dem Verhältnis des Pflanzenflächenindex (PAI) überein. Die Störung des Ökosystems verringerte somit die Fähigkeit des Bewuchses, als Senke für troposphärisches Ozon zu fungieren. Der deutliche Unterschied der Ozonflüsse der beiden Bewuchsarten verdeutlichte die Herausforderung bei der Regionalisierung von Ozonflüssen in heterogen bewaldeten Gebieten.rnDie gemessenen Flüsse wurden darüber hinaus mit Simulationen verglichen, die mit dem Chemiemodell MLC-CHEM durchgeführt wurden. Um das Modell bezüglich der Berechnung von Ozonflüssen zu evaluieren, wurden gemessene und modellierte Flüsse von zwei Positionen im EGER-Gebiet verwendet. Obwohl die Größenordnung der Flüsse übereinstimmte, zeigten die Ergebnisse eine signifikante Differenz zwischen gemessenen und modellierten Flüssen. Zudem gab es eine klare Abhängigkeit der Differenz von der relativen Feuchte, mit abnehmender Differenz bei zunehmender Feuchte, was zeigte, dass das Modell vor einer Verwendung für umfangreiche Studien des Ozonflusses weiterer Verbesserungen bedarf.rn

Modeling of transfer kinetics at the serum-cerebrospinal fluid barrier in rabbits with experimental meningitis: application to grepafloxacin

The goals of the present study were to model the population kinetics of in vivo influx and efflux processes of grepafloxacin at the serum-cerebrospinal fluid (CSF) barrier and to propose a simulation-based approach to optimize the design of dose-finding trials in the meningitis rabbit model. Twenty-nine rabbits with pneumococcal meningitis receiving grepafloxacin at 15 mg/kg of body weight (intravenous administration at 0 h), 30 mg/kg (at 0 h), or 50 mg/kg twice (at 0 and 4 h) were studied. A three-compartment population pharmacokinetic model was fit to the data with the program NONMEM (Nonlinear Mixed Effects Modeling). Passive diffusion clearance (CL(diff)) and active efflux clearance (CL(active)) are transfer kinetic modeling parameters. Influx clearance is assumed to be equal to CL(diff), and efflux clearance is the sum of CL(diff), CL(active), and bulk flow clearance (CL(bulk)). The average influx clearance for the population was 0.0055 ml/min (interindividual variability, 17%). Passive diffusion clearance was greater in rabbits receiving grepafloxacin at 15 mg/kg than in those treated with higher doses (0.0088 versus 0.0034 ml/min). Assuming a CL(bulk) of 0.01 ml/min, CL(active) was estimated to be 0.017 ml/min (11%), and clearance by total efflux was estimated to be 0.032 ml/min. The population kinetic model allows not only to quantify in vivo efflux and influx mechanisms at the serum-CSF barrier but also to analyze the effects of different dose regimens on transfer kinetic parameters in the rabbit meningitis model. The modeling-based approach also provides a tool for the simulation and prediction of various outcomes in which researchers might be interested, which is of great potential in designing dose-finding trials.

Modeling of diesel particulate filter filtration and regeneration for transient driving schedules

Due to their high thermal efficiency, diesel engines have excellent fuel economy and have been widely used as a power source for many vehicles. Diesel engines emit less greenhouse gases (carbon dioxide) compared with gasoline engines. However, diesel engines emit large amounts of particulate matter (PM) which can imperil human health. The best way to reduce the particulate matter is by using the Diesel Particulate Filter (DPF) system which consists of a wall-flow monolith which can trap particulates, and the DPF can be periodically regenerated to remove the collected particulates. The estimation of the PM mass accumulated in the DPF and total pressure drop across the filter are very important in order to determine when to carry out the active regeneration for the DPF. In this project, by developing a filtration model and a pressure drop model, we can estimate the PM mass and the total pressure drop, then, these two models can be linked with a regeneration model which has been developed previously to predict when to regenerate the filter. There results of this project were: 1 Reproduce a filtration model and simulate the processes of filtration. By studying the deep bed filtration and cake filtration, stages and quantity of mass accumulated in the DPF can be estimated. It was found that the filtration efficiency increases faster during the deep-bed filtration than that during the cake filtration. A “unit collector” theory was used in our filtration model which can explain the mechanism of the filtration very well. 2 Perform a parametric study on the pressure drop model for changes in engine exhaust flow rate, deposit layer thickness, and inlet temperature. It was found that there are five primary variables impacting the pressure drop in the DPF which are temperature gradient along the channel, deposit layer thickness, deposit layer permeability, wall thickness, and wall permeability. 3 Link the filtration model and the pressure drop model with the regeneration model to determine the time to carry out the regeneration of the DPF. It was found that the regeneration should be initiated when the cake layer is at a certain thickness, since a cake layer with either too big or too small an amount of particulates will need more thermal energy to reach a higher regeneration efficiency. 4 Formulate diesel particulate trap regeneration strategies for real world driving conditions to find out the best desirable conditions for DPF regeneration. It was found that the regeneration should be initiated when the vehicle’s speed is high and during which there should not be any stops from the vehicle. Moreover, the regeneration duration is about 120 seconds and the inlet temperature for the regeneration is 710K.

Dynamic modeling of active regeneration in catalyzed and non-catalyzed diesel particulate filters

Particulate matter (PM) emissions standards set by the US Environmental Protection Agency (EPA) have become increasingly stringent over the years. The EPA regulation for PM in heavy duty diesel engines has been reduced to 0.01 g/bhp-hr for the year 2010. Heavy duty diesel engines make use of an aftertreatment filtration device, the Diesel Particulate Filter (DPF). DPFs are highly efficient in filtering PM (known as soot) and are an integral part of 2010 heavy duty diesel aftertreatment system. PM is accumulated in the DPF as the exhaust gas flows through it. This PM needs to be removed by oxidation periodically for the efficient functioning of the filter. This oxidation process is also known as regeneration. There are 2 types of regeneration processes, namely active regeneration (oxidation of PM by external means) and passive oxidation (oxidation of PM by internal means). Active regeneration occurs typically in high temperature regions, about 500 - 600 °C, which is much higher than normal diesel exhaust temperatures. Thus, the exhaust temperature has to be raised with the help of external devices like a Diesel Oxidation Catalyst (DOC) or a fuel burner. The O2 oxidizes PM producing CO2 as oxidation product. In passive oxidation, one way of regeneration is by the use of NO2. NO2 oxidizes the PM producing NO and CO2 as oxidation products. The passive oxidation process occurs at lower temperatures (200 - 400 °C) in comparison to the active regeneration temperatures. Generally, DPF substrate walls are washcoated with catalyst material to speed up the rate of PM oxidation. The catalyst washcoat is observed to increase the rate of PM oxidation. The goal of this research is to develop a simple mathematical model to simulate the PM depletion during the active regeneration process in a DPF (catalyzed and non-catalyzed). A simple, zero-dimensional kinetic model was developed in MATLAB. Experimental data required for calibration was obtained by active regeneration experiments performed on PM loaded mini DPFs in an automated flow reactor. The DPFs were loaded with PM from the exhaust of a commercial heavy duty diesel engine. The model was calibrated to the data obtained from active regeneration experiments. Numerical gradient based optimization techniques were used to estimate the kinetic parameters of the model.

Dispersal Modeling of Fish Early Life Stages: Sensitivity with Application to Atlantic Cod in the Western Gulf of Maine

As an initial step in establishing mechanistic relationships between environmental variability and recruitment in Atlantic cod Gadhus morhua along the coast of the western Gulf of Maine, we assessed transport success of larvae from major spawning grounds to nursery areas with particle tracking using the unstructured grid model FVCOM (finite volume coastal ocean model). In coastal areas, dispersal of early planktonic life stages of fish and invertebrate species is highly dependent on the regional dynamics and its variability, which has to be captured by our models. With state-of-the-art forcing for the year 1995, we evaluate the sensitivity of particle dispersal to the timing and location of spawning, the spatial and temporal resolution of the model, and the vertical mixing scheme. A 3 d frequency for the release of particles is necessary to capture the effect of the circulation variability into an averaged dispersal pattern of the spawning season. The analysis of sensitivity to model setup showed that a higher resolution mesh, tidal forcing, and current variability do not change the general pattern of connectivity, but do tend to increase within-site retention. Our results indicate strong downstream connectivity among spawning grounds and higher chances for successful transport from spawning areas closer to the coast. The model run for January egg release indicates 1 to 19 % within-spawning ground retention of initial particles, which may be sufficient to sustain local populations. A systematic sensitivity analysis still needs to be conducted to determine the minimum mesh and forcing resolution that adequately resolves the complex dynamics of the western Gulf of Maine. Other sources of variability, i.e. large-scale upstream forcing and the biological environment, also need to be considered in future studies of the interannual variability in transport and survival of the early life stages of cod.

Time-Series Panel Analysis (TSPA): Multivariate Modeling of Temporal Associations in Psychotherapy Process.

Objective: Processes occurring in the course of psychotherapy are characterized by the simple fact that they unfold in time and that the multiple factors engaged in change processes vary highly between individuals (idiographic phenomena). Previous research, however, has neglected the temporal perspective by its traditional focus on static phenomena, which were mainly assessed at the group level (nomothetic phenomena). To support a temporal approach, the authors introduce time-series panel analysis (TSPA), a statistical methodology explicitly focusing on the quantification of temporal, session-to-session aspects of change in psychotherapy. TSPA-models are initially built at the level of individuals and are subsequently aggregated at the group level, thus allowing the exploration of prototypical models. Method: TSPA is based on vector auto-regression (VAR), an extension of univariate auto-regression models to multivariate time-series data. The application of TSPA is demonstrated in a sample of 87 outpatient psychotherapy patients who were monitored by postsession questionnaires. Prototypical mechanisms of change were derived from the aggregation of individual multivariate models of psychotherapy process. In a 2nd step, the associations between mechanisms of change (TSPA) and pre- to postsymptom change were explored. Results: TSPA allowed a prototypical process pattern to be identified, where patient's alliance and self-efficacy were linked by a temporal feedback-loop. Furthermore, therapist's stability over time in both mastery and clarification interventions was positively associated with better outcomes. Conclusions: TSPA is a statistical tool that sheds new light on temporal mechanisms of change. Through this approach, clinicians may gain insight into prototypical patterns of change in psychotherapy.

Benchmark problems for reactive transport modeling of the generation and attenuation of acid rock drainage

Acid rock drainage (ARD) is a problem of international relevance with substantial environmental and economic implications. Reactive transport modeling has proven a powerful tool for the process-based assessment of metal release and attenuation at ARD sites. Although a variety of models has been used to investigate ARD, a systematic model intercomparison has not been conducted to date. This contribution presents such a model intercomparison involving three synthetic benchmark problems designed to evaluate model results for the most relevant processes at ARD sites. The first benchmark (ARD-B1) focuses on the oxidation of sulfide minerals in an unsaturated tailing impoundment, affected by the ingress of atmospheric oxygen. ARD-B2 extends the first problem to include pH buffering by primary mineral dissolution and secondary mineral precipitation. The third problem (ARD-B3) in addition considers the kinetic and pH-dependent dissolution of silicate minerals under low pH conditions. The set of benchmarks was solved by four reactive transport codes, namely CrunchFlow, Flotran, HP1, and MIN3P. The results comparison focused on spatial profiles of dissolved concentrations, pH and pE, pore gas composition, and mineral assemblages. In addition, results of transient profiles for selected elements and cumulative mass loadings were considered in the intercomparison. Despite substantial differences in model formulations, very good agreement was obtained between the various codes. Residual deviations between the results are analyzed and discussed in terms of their implications for capturing system evolution and long-term mass loading predictions.

Atomistic Modeling of Effect of Mg on Oxygen Vacancy Diffusion in α-Alumina

Oxygen diffusion plays an important role in grain growth and densification during the sintering of alumina ceramics and governs high-temperature processes such as creep. The atomistic mechanism for oxygen diffusion in alumina is, however, still debated; atomistic calculations not being able to match experimentally determined activation energies for oxygen vacancy diffusion. These calculations are, however, usually performed for perfectly pure crystals, whereas virtually every experimental alumina sample contains a significant fraction of impurity/dopants ions. In this study, we use atomistic defect cluster and nudged elastic band (NEB) calculations to model the effect of Mg impurities/dopants on defect binding energies and migration barriers. We find that oxygen vacancies can form energetically favorable clusters with Mg, which reduces the number of mobile species and leads to an additional 1.5 eV energy barrier for the detachment of a single vacancy from Mg. The migration barriers of diffusive jumps change such that an enhanced concentration of oxygen vacancies is expected around Mg ions. Mg impurities were also found to cause destabilization of certain vacancy configurations as well as enhanced vacancy–vacancy interaction.