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.

IMPULSE CONTROL AND COOPERATION IN CAPUCHIN MONKEYS (Cebus apella)

The benefits animals derive from living in social groups have produced the evolution of many forms of cooperative behavior. To cooperate, two or more individuals coordinate their actions to accomplish a common goal. One cognitive process that has the potential to influence cooperation is self control. Individuals delaying their impulsive choice for an immediate reward may potentially receive a larger reward later by cooperating with others. In this study, I measured whether brown capuchin monkeys (Cebus apella) were capable of impulse control and whether impulse control was related to cooperation. Impulse control and cooperation were measured using a lazy susan-like apparatus, on which animals could turn a wheel to receive food rewards. The capuchins went through two training phases that taught them how to turn the wheel efficiently to obtain rewards and how to turn the wheel to obtain the larger of two rewards. After training, I tested impulse control by giving the capuchins a choice between a smaller and a larger reward placed at shorter or more distant locations on the wheel. The capuchins demonstrated impulse control in that they tended to inhibit the impulse to select the smaller reward when it was closer and easier to reach and instead selected the larger reward when it was farther away. Cooperation was tested in all possible dyads of seven individuals, a total of 21 dyads, by allowing each dyad 10 trials to work together with effort on the lazy-susan so that each would obtain a reward. Seventeen out of 21 dyads cooperated by simultaneously moving the wheel in the same direction. The correlation between how often a particular dyad cooperated and their average impulse control score was not statistically significant, r(21) = -.125, p = .591. Capuchins demonstrated impulse control and cooperation using this novel apparatus but the two abilities were not related. Other factors such as the unique social relationship between two individuals may play a more prominent role in the motivation to cooperate rather than the cognitive capacity to inhibit behavior.

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.