928 resultados para TRANSIENT ELECTROLUMINESCENCE


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Model based calibration has gained popularity in recent years as a method to optimize increasingly complex engine systems. However virtually all model based techniques are applied to steady state calibration. Transient calibration is by and large an emerging technology. An important piece of any transient calibration process is the ability to constrain the optimizer to treat the problem as a dynamic one and not as a quasi-static process. The optimized air-handling parameters corresponding to any instant of time must be achievable in a transient sense; this in turn depends on the trajectory of the same parameters over previous time instances. In this work dynamic constraint models have been proposed to translate commanded to actually achieved air-handling parameters. These models enable the optimization to be realistic in a transient sense. The air handling system has been treated as a linear second order system with PD control. Parameters for this second order system have been extracted from real transient data. The model has been shown to be the best choice relative to a list of appropriate candidates such as neural networks and first order models. The selected second order model was used in conjunction with transient emission models to predict emissions over the FTP cycle. It has been shown that emission predictions based on air-handing parameters predicted by the dynamic constraint model do not differ significantly from corresponding emissions based on measured air-handling parameters.

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This is the first part of a study investigating a model-based transient calibration process for diesel engines. The motivation is to populate hundreds of parameters (which can be calibrated) in a methodical and optimum manner by using model-based optimization in conjunction with the manual process so that, relative to the manual process used by itself, a significant improvement in transient emissions and fuel consumption and a sizable reduction in calibration time and test cell requirements is achieved. Empirical transient modelling and optimization has been addressed in the second part of this work, while the required data for model training and generalization are the focus of the current work. Transient and steady-state data from a turbocharged multicylinder diesel engine have been examined from a model training perspective. A single-cylinder engine with external air-handling has been used to expand the steady-state data to encompass transient parameter space. Based on comparative model performance and differences in the non-parametric space, primarily driven by a high engine difference between exhaust and intake manifold pressures (ΔP) during transients, it has been recommended that transient emission models should be trained with transient training data. It has been shown that electronic control module (ECM) estimates of transient charge flow and the exhaust gas recirculation (EGR) fraction cannot be accurate at the high engine ΔP frequently encountered during transient operation, and that such estimates do not account for cylinder-to-cylinder variation. The effects of high engine ΔP must therefore be incorporated empirically by using transient data generated from a spectrum of transient calibrations. Specific recommendations on how to choose such calibrations, how many data to acquire, and how to specify transient segments for data acquisition have been made. Methods to process transient data to account for transport delays and sensor lags have been developed. The processed data have then been visualized using statistical means to understand transient emission formation. Two modes of transient opacity formation have been observed and described. The first mode is driven by high engine ΔP and low fresh air flowrates, while the second mode is driven by high engine ΔP and high EGR flowrates. The EGR fraction is inaccurately estimated at both modes, while EGR distribution has been shown to be present but unaccounted for by the ECM. The two modes and associated phenomena are essential to understanding why transient emission models are calibration dependent and furthermore how to choose training data that will result in good model generalization.

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This is the second part of a study investigating a model-based transient calibration process for diesel engines. The first part addressed the data requirements and data processing required for empirical transient emission and torque models. The current work focuses on modelling and optimization. The unexpected result of this investigation is that when trained on transient data, simple regression models perform better than more powerful methods such as neural networks or localized regression. This result has been attributed to extrapolation over data that have estimated rather than measured transient air-handling parameters. The challenges of detecting and preventing extrapolation using statistical methods that work well with steady-state data have been explained. The concept of constraining the distribution of statistical leverage relative to the distribution of the starting solution to prevent extrapolation during the optimization process has been proposed and demonstrated. Separate from the issue of extrapolation is preventing the search from being quasi-static. Second-order linear dynamic constraint models have been proposed to prevent the search from returning solutions that are feasible if each point were run at steady state, but which are unrealistic in a transient sense. Dynamic constraint models translate commanded parameters to actually achieved parameters that then feed into the transient emission and torque models. Combined model inaccuracies have been used to adjust the optimized solutions. To frame the optimization problem within reasonable dimensionality, the coefficients of commanded surfaces that approximate engine tables are adjusted during search iterations, each of which involves simulating the entire transient cycle. The resulting strategy, different from the corresponding manual calibration strategy and resulting in lower emissions and efficiency, is intended to improve rather than replace the manual calibration process.

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Decision trees have been proposed as a basis for modifying table based injection to reduce transient particulate spikes during the turbocharger lag period. It has been shown that decision trees can detect particulate spikes in real time. In well calibrated electronically controlled diesel engines these spikes are narrow and are encompassed by a wider NOx spike. Decision trees have been shown to pinpoint the exact location of measured opacity spikes in real time thus enabling targeted PM reduction with near zero NOx penalty. A calibrated dimensional model has been used to demonstrate the possible reduction of particulate matter with targeted injection pressure pulses. Post injection strategy optimized for near stoichiometric combustion has been shown to provide additional benefits. Empirical models have been used to calculate emission tradeoffs over the entire FTP cycle. An empirical model based transient calibration has been used to demonstrate that such targeted transient modifiers are more beneficial at lower engine-out NOx levels.

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Long-term benefits of combination antiretroviral therapy (cART) initiation during primary HIV-1 infection are debated.

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Mild unconjugated hyperbilirubinemia seems to be more common in patients with disorders from the schizophrenic spectrum than in other psychiatric patients or in the general population and has been linked to brain alterations. This spectrum however contains a number of diagnostic entities that might not share the same etiological and environmental factors.

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Transient trapping is a new mechanism of on-line sample concentration and separation that has recently been presented. It involves the injection of a short length of micellar solution in front of the sample, making it similar to sweeping in partial-filling MEKC. Here, we examine the mechanism of transient trapping by the use of computer simulations and compare it to sweeping in MEKC for the two analytes, sulforhodamine B and 101. The simulation results confirm the mechanism for concentration and separation originally proposed. The mechanism for concentration is similar to sweeping since the analytes are picked and accumulated by the micelles that penetrate the sample zone. The mechanism for separation is however quite unique since the concentrated analytes are trapped for a few seconds on the sample/micelle boundary before they are released as the concentration of micelle is reduced as it undergoes electromigration dispersion and the analytes separate down a micelle gradient. Simulation results suggested that a significant contribution of band broadening arises from the micelle gradient, with shallower gradients resulting in broader peaks. However, this is offset by an increase in selectivity, such that resolution was enhanced even though the peaks are broader. Transient trapping analysis with similar resolution to those obtained by sweeping MEKC could be achieved in 1/10 of the time and 1/4 of the capillary length, which results in a 2-3 times increase in sensitivity.

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RATIONALE: Copeptin independently predicts functional outcome and mortality at 90 days and one-year after ischemic stroke. In patients with transient ischemic attack, elevated copeptin values indicate an increased risk of further cerebrovascular events. AIMS: The Copeptin Risk Stratification (CoRisk) study aims to validate the predictive value of copeptin in patients with ischemic stroke and transient ischemic attack. In patients with ischemic stroke, the CoRisk study aims to further explore the effect of treatment (i.e. thrombolysis) on the predictive value of copeptin. DESIGN: Prospective observational multicenter study analyzing three groups of patients, i.e. patients with ischemic stroke treated with and without thrombolysis and patients with transient ischemic attack. OUTCOMES: Primary end-point: In patients with ischemic stroke, the primary end-point includes disability (modified Rankin scale from 3 to 5) and mortality (modified Rankin scale 6) at three-months after stroke. In patients with transient ischemic attack, the primary end-point is a recurrent ischemic cerebrovascular event (i.e. ischemic stroke or recurrent transient ischemic attack). Secondary end-point: In patients with ischemic stroke, the secondary end-points include in-house complications (i.e. symptomatic intracerebral hemorrhage, malignant edema, aspiration pneumonia or seizures during hospitalization, and in-house mortality).

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Sleep-disordered breathing represents a risk factor for cardiovascular morbidity and mortality and negatively affects short-term and long-term outcome after an ischemic stroke or transient ischemic attack. The effect of continuous positive airways pressure in patients with sleep-disordered breathing and acute cerebrovascular event is poorly known. The SAS CARE 1 study assesses the effects of sleep-disordered breathing on clinical evolution, vascular functions, and markers within the first three-months after an acute cerebrovascular event. The SAS CARE 2 assesses the effect of continuous positive airways pressure on clinical evolution, cardiovascular events, and mortality as well as vascular functions and markers at 12 and 24 months after acute cerebrovascular event.

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Patients with a transient ischemic attack and an acute stroke need urgent investigations and therapy in a stroke unit. Immediate investigation of the etiology and early secondary prevention measures reduce the likelihood of recurrent and other vascular events. In selected stroke patients intravenous thrombolysis and/or endovascular therapies lead to a significant reduction of long term disabilities.

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The variability of the Atlantic meridional overturing circulation (AMOC) strength is investigated in control experiments and in transient simulations of up to the last millennium using the low-resolution Community Climate System Model version 3. In the transient simulations the AMOC exhibits enhanced low-frequency variability that is mainly caused by infrequent transitions between two semi-stable circulation states which amount to a 10 percent change of the maximum overturning. One transition is also found in a control experiment, but the time-varying external forcing significantly increases the probability of the occurrence of such events though not having a direct, linear impact on the AMOC. The transition from a high to a low AMOC state starts with a reduction of the convection in the Labrador and Irminger Seas and goes along with a changed barotropic circulation of both gyres in the North Atlantic and a gradual strengthening of the convection in the Greenland-Iceland-Norwegian (GIN) Seas. In contrast, the transition from a weak to a strong overturning is induced by decreased mixing in the GIN Seas. As a consequence of the transition, regional sea surface temperature (SST) anomalies are found in the midlatitude North Atlantic and in the convection regions with an amplitude of up to 3 K. The atmospheric response to the SST forcing associated with the transition indicates a significant impact on the Scandinavian surface air temperature (SAT) in the order of 1 K. Thus, the changes of the ocean circulation make a major contribution to the Scandinavian SAT variability in the last millennium.