Development of a model-based transient calibration process for diesel engine electronic control module tables – Part 1: data requirements, processing, and analysis

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.

A Decision-Tree-Based Approach to Smoke Spike Detection in a Heavy-Duty Diesel Engine

Smoke spikes occurring during transient engine operation have detrimental health effects and increase fuel consumption by requiring more frequent regeneration of the diesel particulate filter. This paper proposes a decision tree approach to real-time detection of smoke spikes for control and on-board diagnostics purposes. A contemporary, electronically controlled heavy-duty diesel engine was used to investigate the deficiencies of smoke control based on the fuel-to-oxygen-ratio limit. With the aid of transient and steady state data analysis and empirical as well as dimensional modeling, it was shown that the fuel-to-oxygen ratio was not estimated correctly during the turbocharger lag period. This inaccuracy was attributed to the large manifold pressure ratios and low exhaust gas recirculation flows recorded during the turbocharger lag period, which meant that engine control module correlations for the exhaust gas recirculation flow and the volumetric efficiency had to be extrapolated. The engine control module correlations were based on steady state data and it was shown that, unless the turbocharger efficiency is artificially reduced, the large manifold pressure ratios observed during the turbocharger lag period cannot be achieved at steady state. Additionally, the cylinder-to-cylinder variation during this period were shown to be sufficiently significant to make the average fuel-to-oxygen ratio a poor predictor of the transient smoke emissions. The steady state data also showed higher smoke emissions with higher exhaust gas recirculation fractions at constant fuel-to-oxygen-ratio levels. This suggests that, even if the fuel-to-oxygen ratios were to be estimated accurately for each cylinder, they would still be ineffective as smoke limiters. A decision tree trained on snap throttle data and pruned with engineering knowledge was able to use the inaccurate engine control module estimates of the fuel-to-oxygen ratio together with information on the engine control module estimate of the exhaust gas recirculation fraction, the engine speed, and the manifold pressure ratio to predict 94% of all spikes occurring over the Federal Test Procedure cycle. The advantages of this non-parametric approach over other commonly used parametric empirical methods such as regression were described. An application of accurate smoke spike detection in which the injection pressure is increased at points with a high opacity to reduce the cumulative particulate matter emissions substantially with a minimum increase in the cumulative nitrogrn oxide emissions was illustrated with dimensional and empirical modeling.

Development of a model-based transient calibration process for diesel engine electronic control module tables – Part 2: modelling and optimization

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.

Quantum Energy Teleportation Between Spin Particles in a Gibbs State

Energy in a multipartite quantum system appears from an operational perspective to be distributed to some extent non-locally because of correlations extant among the system's components. This non-locality allows users to transfer, in effect, locally accessible energy between sites of different system components by local operations and classical communication (LOCC). Quantum energy teleportation is a three-step LOCC protocol, accomplished without an external energy carrier, for effectively transferring energy between two physically separated, but correlated, sites. We apply this LOCC teleportation protocol to a model Heisenberg spin particle pair initially in a quantum thermal Gibbs state, making temperature an explicit parameter. We find in this setting that energy teleportation is possible at any temperature, even at temperatures above the threshold where the particles' entanglement vanishes. This shows for Gibbs spin states that entanglement is not fundamentally necessary for energy teleportation; correlation other than entanglement can suffice. Dissonance-quantum correlation in separable states-is in this regard shown to be a quantum resource for energy teleportation, more dissonance being consistently associated with greater energy yield. We compare energy teleportation from particle A to B in Gibbs states with direct local energy extraction by a general quantum operation on B and find a temperature threshold below which energy extraction by a local operation is impossible. This threshold delineates essentially two regimes: a high temperature regime where entanglement vanishes and the teleportation generated by other quantum correlations yields only vanishingly little energy relative to local extraction and a second low-temperature teleportation regime where energy is available at B only by teleportation.

Scale Up of PLA Nanoparticle Synthesis by Solvent Displacement and Functionalization of the Particles with a Silica Shell for Targeted Drug Delivery Applications

Biodegradable polymer nanoparticles have the properties necessary to address many of the issues associated with current drug delivery techniques including targeted and controlled delivery. A novel drug delivery vehicle is proposed consisting of a poly(lactic acid) nanoparticle core, with a functionalized, mesoporous silica shell. In this study, the production of PLA nanoparticles is investigated using solvent displacement in both a batch and continuous manner, and the effects of various system parameters are examined. Using Pluronic F-127 as the stabilization agent throughout the study, PLA nanoparticles are produced through solvent displacement with diameters ranging from 200 to 250 nm using two different methods: dropwise addition and in an impinging jet mixer. The impinging jet mixer allows for easy scale-up of particle production. The concentration of surfactant and volume of quench solution is found to have minimal impact on particle diameter; however, the concentration of PLA is found to significantly impact the diameter mean and polydispersity. In addition, the stability of the PLA nanoparticles is observed to increase as residual THF is evaporated. Lastly, the isolated PLA nanoparticles are coated with a silica shell using the Stöber Process. It is found that functionalizing the silica with a phosphonic silane in the presence of excess Pluronic F-127 decreases coalescence of the particles during the coating process. Future work should be conducted to fine-tune the PLA nanoparticle synthesis process by understanding the effect of other system parameters and in synthesizing mesoporous silica shells.

The Investigation of Uniform, Monodisperse Crystalline Particles via the Evaporation of Small Droplets

Many industrial solids processes require the production of disperse particles. In industries such as food, personal care, and pharmaceuticals, particle formation is widely used to produce solid products or to separate substances in intermediate process steps. The most important characteristics known to impact the effectiveness of a solid product are purity, size, internal structure, and morphology. These characteristics are essential to maintain optimal operation of subsequent process steps and for obtaining the desired high quality product. This thesis aims to aid in the advancement of particle production technology by (1) investigating the use of a vibrating orifice aerosol generator (VOAG) for collecting data to predict particle attributes including morphology, size, and internal structure as a function of processing parameters such as solvent, solution concentration, air flow rate, and initial droplet size, as well as to (2) determine the extent to which uniform droplet evaporation can be a tool to achieve novel particle morphologies, controlled sizes, or internal structures (crystallinity and crystal form). Experimental results for succinic acid, L-serine, and L-glutamic acid suggest that particles of controlled characteristics can indeed be produced by this method. Analysis by scanning electron microscopy (SEM), nanoindentation, and X-ray diffraction (XRD) shows that various sizes, internal structures, and morphologies can be obtained using the VOAG. Furthermore, unique morphologies and unexpected internal structures were able to be achieved for succinic acid, providing an added benefit to particle formation by this method.

Design and Use of an Alternative Fuel Testing Apparatus and Assessment of the Feasibility of Biodiesel

Petroleum supply and environmental pollution issues constantly increase interest in renewable low polluting alternative fuels. Published test results show decreased pollution with similar power output and fuel consumption from Internal Combustion Engines (ICE) burning alternative fuels. More specifically, diesel engines burning biodiesel derived from plant oils and animal fats not only reduce harmful exhaust emissions but are renewable and environmentally friendly. To validate these claims and assess the feasibility of alternative fuels, independent engine dynamometer and emissions testing was performed. A testing apparatus capable of making relevant measurements was designed, built, and used to test and determine the feasibility of biodiesel. The apparatus marks the addition of a valuable testing tool to the University and provides a foundation for future experiments. This thesis will discuss the background of biodiesel, testing methods, design and function of the testing apparatus, experimental results, relevant calculations, and conclusions.

Mapping the Operation of the Miniature Combustion Aerosol Standard (Mini-CAST) Soot Generator

The Jing Ltd. miniature combustion aerosol standard (Mini-CAST) soot generator is a portable, commercially available burner that is widely used for laboratory measurements of soot processes. While many studies have used the Mini-CAST to generate soot with known size, concentration, and organic carbon fraction under a single or few conditions, there has been no systematic study of the burner operation over a wide range of operating conditions. Here, we present a comprehensive characterization of the microphysical, chemical, morphological, and hygroscopic properties of Mini-CAST soot over the full range of oxidation air and mixing N-2 flow rates. Very fuel-rich and fuel-lean flame conditions are found to produce organic-dominated soot with mode diameters of 10-60nm, and the highest particle number concentrations are produced under fuel-rich conditions. The lowest organic fraction and largest diameter soot (70-130nm) occur under slightly fuel-lean conditions. Moving from fuel-rich to fuel-lean conditions also increases the O:C ratio of the soot coatings from similar to 0.05 to similar to 0.25, which causes a small fraction of the particles to act as cloud condensation nuclei near the Kelvin limit (kappa similar to 0-10(-3)). Comparison of these property ranges to those reported in the literature for aircraft and diesel engine soots indicates that the Mini-CAST soot is similar to real-world primary soot particles, which lends itself to a variety of process-based soot studies. The trends in soot properties uncovered here will guide selection of burner operating conditions to achieve optimum soot properties that are most relevant to such studies.

Analysis and Prediction of Transient Opacity Spikes Using Dimensional Modeling

Dimensional modeling, GT-Power in particular, has been used for two related purposes-to quantify and understand the inaccuracies of transient engine flow estimates that cause transient smoke spikes and to improve empirical models of opacity or particulate matter used for engine calibration. It has been proposed by dimensional modeling that exhaust gas recirculation flow rate was significantly underestimated and volumetric efficiency was overestimated by the electronic control module during the turbocharger lag period of an electronically controlled heavy duty diesel engine. Factoring in cylinder-to-cylinder variation, it has been shown that the electronic control module estimated fuel-Oxygen ratio was lower than actual by up to 35% during the turbocharger lag period but within 2% of actual elsewhere, thus hindering fuel-Oxygen ratio limit-based smoke control. The dimensional modeling of transient flow was enabled with a new method of simulating transient data in which the manifold pressures and exhaust gas recirculation system flow resistance, characterized as a function of exhaust gas recirculation valve position at each measured transient data point, were replicated by quasi-static or transient simulation to predict engine flows. Dimensional modeling was also used to transform the engine operating parameter model input space to a more fundamental lower dimensional space so that a nearest neighbor approach could be used to predict smoke emissions. This new approach, intended for engine calibration and control modeling, was termed the "nonparametric reduced dimensionality" approach. It was used to predict federal test procedure cumulative particulate matter within 7% of measured value, based solely on steady-state training data. Very little correlation between the model inputs in the transformed space was observed as compared to the engine operating parameter space. This more uniform, smaller, shrunken model input space might explain how the nonparametric reduced dimensionality approach model could successfully predict federal test procedure emissions when roughly 40% of all transient points were classified as outliers as per the steady-state training data.