Coupling of Chemical Kinetics with Computational Fluid Dynamics in a Three-Dimensional Engine Model

The role of computer modeling has grown recently to integrate itself as an inseparable tool to experimental studies for the optimization of automotive engines and the development of future fuels. Traditionally, computer models rely on simplified global reaction steps to simulate the combustion and pollutant formation inside the internal combustion engine. With the current interest in advanced combustion modes and injection strategies, this approach depends on arbitrary adjustment of model parameters that could reduce credibility of the predictions. The purpose of this study is to enhance the combustion model of KIVA, a computational fluid dynamics code, by coupling its fluid mechanics solution with detailed kinetic reactions solved by the chemistry solver, CHEMKIN. As a result, an engine-friendly reaction mechanism for n-heptane was selected to simulate diesel oxidation. Each cell in the computational domain is considered as a perfectly-stirred reactor which undergoes adiabatic constant- volume combustion. The model was applied to an ideally-prepared homogeneous- charge compression-ignition combustion (HCCI) and direct injection (DI) diesel combustion. Ignition and combustion results show that the code successfully simulates the premixed HCCI scenario when compared to traditional combustion models. Direct injection cases, on the other hand, do not offer a reliable prediction mainly due to the lack of turbulent-mixing model, inherent in the perfectly-stirred reactor formulation. In addition, the model is sensitive to intake conditions and experimental uncertainties which require implementation of enhanced predictive tools. It is recommended that future improvements consider turbulent-mixing effects as well as optimization techniques to accurately simulate actual in-cylinder process with reduced computational cost. Furthermore, the model requires the extension of existing fuel oxidation mechanisms to include pollutant formation kinetics for emission control studies.

Development and Application of Predictive Models for Survival, Growth, and Death of Enteric Pathogens in Leafy Greens Supply Chain

Leafy greens are essential part of a healthy diet. Because of their health benefits, production and consumption of leafy greens has increased considerably in the U.S. in the last few decades. However, leafy greens are also associated with a large number of foodborne disease outbreaks in the last few years. The overall goal of this dissertation was to use the current knowledge of predictive models and available data to understand the growth, survival, and death of enteric pathogens in leafy greens at pre- and post-harvest levels. Temperature plays a major role in the growth and death of bacteria in foods. A growth-death model was developed for Salmonella and Listeria monocytogenes in leafy greens for varying temperature conditions typically encountered during supply chain. The developed growth-death models were validated using experimental dynamic time-temperature profiles available in the literature. Furthermore, these growth-death models for Salmonella and Listeria monocytogenes and a similar model for E. coli O157:H7 were used to predict the growth of these pathogens in leafy greens during transportation without temperature control. Refrigeration of leafy greens meets the purposes of increasing their shelf-life and mitigating the bacterial growth, but at the same time, storage of foods at lower temperature increases the storage cost. Nonlinear programming was used to optimize the storage temperature of leafy greens during supply chain while minimizing the storage cost and maintaining the desired levels of sensory quality and microbial safety. Most of the outbreaks associated with consumption of leafy greens contaminated with E. coli O157:H7 have occurred during July-November in the U.S. A dynamic system model consisting of subsystems and inputs (soil, irrigation, cattle, wildlife, and rainfall) simulating a farm in a major leafy greens producing area in California was developed. The model was simulated incorporating the events of planting, irrigation, harvesting, ground preparation for the new crop, contamination of soil and plants, and survival of E. coli O157:H7. The predictions of this system model are in agreement with the seasonality of outbreaks. This dissertation utilized the growth, survival, and death models of enteric pathogens in leafy greens during production and supply chain.

Development and experimental validation of a knock induced damage model and real-time implementation of model-based strategies to control knock intensity in boosted gasoline engines

This PhD thesis reports the main activities carried out during the 3 years long “Mechanics and advanced engineering sciences” course, at the Department of Industrial Engineering of the University of Bologna. The research project title is “Development and analysis of high efficiency combustion systems for internal combustion engines” and the main topic is knock, one of the main challenges for boosted gasoline engines. Through experimental campaigns, modelling activity and test bench validation, 4 different aspects have been addressed to tackle the issue. The main path goes towards the definition and calibration of a knock-induced damage model, to be implemented in the on-board control strategy, but also usable for the engine calibration and potentially during the engine design. Ionization current signal capabilities have been investigated to fully replace the pressure sensor, to develop a robust on-board close-loop combustion control strategy, both in knock-free and knock-limited conditions. Water injection is a powerful solution to mitigate knock intensity and exhaust temperature, improving fuel consumption; its capabilities have been modelled and validated at the test bench. Finally, an empiric model is proposed to predict the engine knock response, depending on several operating condition and control parameters, including injected water quantity.

Diagnostic errors and repetitive sequential classifications in on-line process control by attributes

The procedure of on-line process control by attributes, known as Taguchi`s on-line process control, consists of inspecting the mth item (a single item) at every m produced items and deciding, at each inspection, whether the fraction of conforming items was reduced or not. If the inspected item is nonconforming, the production is stopped for adjustment. As the inspection system can be subject to diagnosis errors, one develops a probabilistic model that classifies repeatedly the examined item until a conforming or b non-conforming classification is observed. The first event that occurs (a conforming classifications or b non-conforming classifications) determines the final classification of the examined item. Proprieties of an ergodic Markov chain were used to get the expression of average cost of the system of control, which can be optimized by three parameters: the sampling interval of the inspections (m); the number of repeated conforming classifications (a); and the number of repeated non-conforming classifications (b). The optimum design is compared with two alternative approaches: the first one consists of a simple preventive policy. The production system is adjusted at every n produced items (no inspection is performed). The second classifies the examined item repeatedly r (fixed) times and considers it conforming if most classification results are conforming. Results indicate that the current proposal performs better than the procedure that fixes the number of repeated classifications and classifies the examined item as conforming if most classifications were conforming. On the other hand, the preventive policy can be averagely the most economical alternative rather than those ones that require inspection depending on the degree of errors and costs. A numerical example illustrates the proposed procedure. (C) 2009 Elsevier B. V. All rights reserved.

Distributed power control algorithm for multiple access systems based on Verhulst model

In this paper the continuous Verhulst dynamic model is used to synthesize a new distributed power control algorithm (DPCA) for use in direct sequence code division multiple access (DS-CDMA) systems. The Verhulst model was initially designed to describe the population growth of biological species under food and physical space restrictions. The discretization of the corresponding differential equation is accomplished via the Euler numeric integration (ENI) method. Analytical convergence conditions for the proposed DPCA are also established. Several properties of the proposed recursive algorithm, such as Euclidean distance from optimum vector after convergence, convergence speed, normalized mean squared error (NSE), average power consumption per user, performance under dynamics channels, and implementation complexity aspects, are analyzed through simulations. The simulation results are compared with two other DPCAs: the classic algorithm derived by Foschini and Miljanic and the sigmoidal of Uykan and Koivo. Under estimated errors conditions, the proposed DPCA exhibits smaller discrepancy from the optimum power vector solution and better convergence (under fixed and adaptive convergence factor) than the classic and sigmoidal DPCAs. (C) 2010 Elsevier GmbH. All rights reserved.

Optimal release strategies for biological control agents: an application of stochastic dynamic programming to population management

1. Establishing biological control agents in the field is a major step in any classical biocontrol programme, yet there are few general guidelines to help the practitioner decide what factors might enhance the establishment of such agents. 2. A stochastic dynamic programming (SDP) approach, linked to a metapopulation model, was used to find optimal release strategies (number and size of releases), given constraints on time and the number of biocontrol agents available. By modelling within a decision-making framework we derived rules of thumb that will enable biocontrol workers to choose between management options, depending on the current state of the system. 3. When there are few well-established sites, making a few large releases is the optimal strategy. For other states of the system, the optimal strategy ranges from a few large releases, through a mixed strategy (a variety of release sizes), to many small releases, as the probability of establishment of smaller inocula increases. 4. Given that the probability of establishment is rarely a known entity, we also strongly recommend a mixed strategy in the early stages of a release programme, to accelerate learning and improve the chances of finding the optimal approach.

A landscape-scale test of the predictive ability of a spatially explicit model for population viability analysis

1. Although population viability analysis (PVA) is widely employed, forecasts from PVA models are rarely tested. This study in a fragmented forest in southern Australia contrasted field data on patch occupancy and abundance for the arboreal marsupial greater glider Petauroides volans with predictions from a generic spatially explicit PVA model. This work represents one of the first landscape-scale tests of its type. 2. Initially we contrasted field data from a set of eucalypt forest patches totalling 437 ha with a naive null model in which forecasts of patch occupancy were made, assuming no fragmentation effects and based simply on remnant area and measured densities derived from nearby unfragmented forest. The naive null model predicted an average total of approximately 170 greater gliders, considerably greater than the true count (n = 81). 3. Congruence was examined between field data and predictions from PVA under several metapopulation modelling scenarios. The metapopulation models performed better than the naive null model. Logistic regression showed highly significant positive relationships between predicted and actual patch occupancy for the four scenarios (P = 0.001-0.006). When the model-derived probability of patch occupancy was high (0.50-0.75, 0.75-1.00), there was greater congruence between actual patch occupancy and the predicted probability of occupancy. 4. For many patches, probability distribution functions indicated that model predictions for animal abundance in a given patch were not outside those expected by chance. However, for some patches the model either substantially over-predicted or under-predicted actual abundance. Some important processes, such as inter-patch dispersal, that influence the distribution and abundance of the greater glider may not have been adequately modelled. 5. Additional landscape-scale tests of PVA models, on a wider range of species, are required to assess further predictions made using these tools. This will help determine those taxa for which predictions are and are not accurate and give insights for improving models for applied conservation management.

Rabbit Retinal Neovascularization Induced by Latex Angiogenic-Derived Fraction: An Experimental Model

Purpose: To create a retinal neovascularization experimental model using intravitreal injection of microspheres loaded with latex-derived angiogenic fraction. Methods: Thirty-two albino New Zealand rabbits, divided in 4 groups of 8 animals, were enrolled in this study. Rabbits in groups I, II, and III received one intravitreal injection of PLGA (L-lactide-co-glycolide) microspheres with 10, 30, and 50 mu g of latex-derived angiogenic fraction into their right eyes, respectively, and group IV received 0.1 ml of microspheres without the angiogenic fraction. Weekly follow-up with ophthalmoscopy and fluorescein angiography was performed; the rabbits were sacrificed in the 4th week and their eyes processed for light microscopy. Results: All eyes from group I demonstrated increased retinal vascular tortuosity, observed from 14 days after injection and maintained for 28 days, otherwise without new vessels detection. All group II eyes showed vascular changes similar to group I. Fifty percent of the eyes from group II rabbits developed retinal neovascularization 21 days after injection. All eyes from group III demonstrated significant vascular tortuosity and retinal new vessels 2 weeks after injection, progressing to fibrovascular proliferation and tractional retinal detachment. No vascular changes or retinal new vessels were observed in group IV eyes. Light microscopy confirmed the existence of new vessels previously seen on fluorescein angiography, in retinal sections adjacent to the optic disc, not observed in sections at the same area in the control group. Conclusion: Thirty- and 50-mu g microspheres containing latex-derived angiogenic fraction injected into the vitreous cavity induced retinal neovascularization in rabbits.

Differential regulation of TRP channels in a rat model of neuropathic pain

Neuropathic pain is a chronic disease resulting from dysfunction of the nervous system often due to peripheral nerve injury. Hypersensitivity to sensory Stimuli (mechanical, thermal or chemical) is a common source of pain in patients and ion channels involved in detecting these Stimuli are possible candidates for inducing and/or maintaining the pain. Transient receptor potential (TRP) channels expressed on nociceptors respond to different sensory stimuli and a few of them have been studied previously in the models of neuropathic pain. Using real-time PCR for quantification of all known TRP channels we identified several TRP channels, which have not been associated with nociception OF neuropathic pain before, to be expressed in the DRG and to be differentially regulated after spared nerve injury (SNI). Of all TRP channel members, TRPML3 showed the most dramatic change in animals exhibiting neuropathic pain behaviour compared to control animals. fit situ hybridisation showed a widespread increase of expression ill neurons of small, medium and large cell sizes, indicating expression ill multiple subtypes. Co-localisation of TRPML3 with CGRP, NF200 and IB4 staining confirmed a broad Subtype distribution. Expression studies during development showed that TRPML3 is all embryonic channel that is induced upon nerve injury in three different nerve injury models investigated. Thus. the current results link for the first time a re-expression of TRPML3 with the development of neuropathic pain conditions. In addition, decreased mRNA levels after SNI were seen for TRPM6, TRPM8, TRPV1, TRPA1, TRPC3, TRPC4 and TRPC5. (C) 2009 International Association for the Study of Pain. Published by Elsevier B.V. All rights reserved.

The effect of musculoskeletal pain on motor activity and control

Aberrant movement patterns and postures are obvious to clinicians managing patients with musculoskeletal pain. However, some changes in motor function that occur in the presence of pain are less apparent. Clinical and basic science investigations have provided evidence of the effects of nociception on aspects of motor function. Both increases and decreases in muscle activity have been shown, along with alterations in neuronal control mechanisms, proprioception, and local muscle morphology. Various models have been proposed in an attempt to provide an explanation for some of these changes. These include the vicious cycle and pain adaptation models. Recent research has seen the emergence of a new model in which patterns of muscle activation and recruitment are altered in the presence of pain (neuromuscular activation model). These changes seem to particularly affect the ability of muscles to perform synergistic functions related to maintaining joint stability and control. These changes are believed to persist into the period of chronicity. This review shows current knowledge of the effect of musculoskeletal pain on the motor system and presents the various proposed models, in addition to other shown effects not covered by these models. The relevance of these models to both acute and chronic pain is considered. It is apparent that people experiencing musculoskeletal pain exhibit complex motor responses that may show some variation with the time course of the disorder. (C) 2001 by the American Pain Society.

Murine ventricular L-type Ca2+ current is enhanced by zinterol via beta(1)-adrenoceptors, and is reduced in TG4 mice overexpressing the human beta(2)-adrenoceptor

1 The functional coupling of B-2-adrenoceptors (beta (2)-ARs) to murine L-type Ca2+ current (I-Ca(L)) was investigated with two different approaches. The beta (2)-AR signalling cascade was activated either with the beta (2)-AR selective agonist zinterol (myocytes from wild-type mice), or by spontaneously active, unoccupied beta (2)-ARs (myocytes from TG4 mice with 435 fold overexpression of human beta (2)-ARs). Ca2+ and Ba2+ currents were recorded in the whole-cell and cell-attached configuration of the patch- clamp technique, respectively. 2 Zinterol (10 muM) significantly increased I-Ca(L) amplitude of wild-type myocytes by 19+/-5%, and this effect was markedly enhanced after inactivation of Gi-proteins with pertussis-toxin (PTX; 76+/-13% increase). However, the effect of zinterol was entirely mediated by the beta (1)-AR subtype, since it was blocked by the beta (1)-AR selective antagonist CGP 20712A (300 nM). The beta (2)-AR selective antagonist ICI 118,551 (50 nM) did not affect the response of I-Ca(L) to zinterol. 3 In myocytes with beta (2)-AR overexpression I-Ca(L) was not stimulated by the activated signalling cascade. On the contrary, I-Ca(L) was lower in TG4 myocytes and a significant reduction of single-channel activity was identified as a reason for the lower whole-cell I-Ca(L). The beta (2)-AR inverse agonist ICI 118,551 did not further decrease I-Ca(L). PTX-treatment increased current amplitude to values found in control myocytes. 4 In conclusion, there is no evidence for beta (2)-AR mediated increases of I-Ca(L) in wild-type mouse ventricular myocytes. Inactivation of Gi-proteins does not unmask beta (2)-AR responses to zinterol, but augments beta (1)-AR mediated increases of I-Ca(L). In the mouse model of beta (2)-AR overexpression I-Ca(L) is reduced due to tonic activation of Gi-proteins.

MRI based diffusion and perfusion predictive model to estimate stroke evolution

In this study we present a novel automated strategy for predicting infarct evolution, based on MR diffusion and perfusion images acquired in the acute stage of stroke. The validity of this methodology was tested on novel patient data including data acquired from an independent stroke clinic. Regions-of-interest (ROIs) defining the initial diffusion lesion and tissue with abnormal hemodynamic function as defined by the mean transit time (MTT) abnormality were automatically extracted from DWI/PI maps. Quantitative measures of cerebral blood flow (CBF) and volume (CBV) along with ratio measures defined relative to the contralateral hemisphere (r(a)CBF and r(a)CBV) were calculated for the MTT ROIs. A parametric normal classifier algorithm incorporating these measures was used to predict infarct growth. The mean r(a)CBF and r(a)CBV values for eventually infarcted MTT tissue were 0.70 +/-0.19 and 1.20 +/-0.36. For recovered tissue the mean values were 0.99 +/-0.25 and 1.87 +/-0.71, respectively. There was a significant difference between these two regions for both measures (P

Exact form factors for the Josephson tunneling current and relative particle number fluctuations in a model of two coupled Bose-Einstein condensates

Form factors are derived for a model describing the coherent Josephson tunneling between two coupled Bose-Einstein condensates. This is achieved by studying the exact solution of the model within the framework of the algebraic Bethe ansatz. In this approach the form factors are expressed through determinant representations which are functions of the roots of the Bethe ansatz equations.