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.

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.

Restructuring Justice: Moving Towards Gender-Responsive Programming and Alternatives to Incarceration for Non-Violent Female Offenders

The United States¿ Federal and State laws differentiate between acceptable (or, legal) and unacceptable (illegal) behavior by prescribing restrictive punishment to citizens and/or groups that violate these established rules. These regulations are written to treat every person equally and to fairly serve justice; furthermore, the sanctions placed on offenders seek to reform illegal behavior through limitations on freedoms and rehabilitative programs. Despite the effort to treat all offenders fairly regardless of social identity categories (e.g., sex, race, ethnicity, socioeconomic status, age, ability, and gender and sexual orientation) and to humanely eliminate illegal behavior, the American penal system perpetuates de facto discrimination against a multitude of peoples. Furthermore, soaring recidivism rates caused by unsuccessful re-entry of incarcerated offenders puts economic stress on Federal and State budgets. For these reasons, offenders, policy-makers, and law-abiding citizens should all have a vested interest in reforming the prison system. This thesis focuses on the failure of the United States corrections system to adequately address the gender-specific needs of non-violent female offenders. Several factors contribute to the gender-specific discrimination that women experience in the criminal justice system: 1) Trends in female criminality that skew women¿s crime towards drug-related crimes, prostitution, and property offenses; 2) Mandatory minimum sentences for drug crimes that are disproportionate to the crime committed; 3) So-called ¿gender-neutral¿ educational, vocational, substance abuse, and mental health programming that intends to equally rehabilitate men and women, but in fact favors men; and 4) The isolating nature of prison structures that inhibits smooth re-entry into society. I argue that a shift in the placement and treatment of non-violent female offenders is necessary for effective rehabilitation and for reducing recidivism rates. The first component of this shift is the design and implementation of gender- responsive treatment (GRT) rather than gender-neutral approaches in rehabilitative programming. The second shift is the utilization of alternatives to incarceration, which provide both more humane treatment of offenders and smoother reintegration to society. Drawing on recent scholarship, information from prison advocacy organizations, and research with men in an alternative program, I provide a critical analysis of current policies and alternative programs, and suggest several proposals for future gender- responsive programs in prisons and in place of incarceration. I argue that the expansion of gender-responsive programming and alternatives to incarceration respond to the marginalization of female offenders, address concerns about the financial sustainability of the United States criminal justice system, and tackle high recidivism rates.