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.

Psychological Flow In A Physical Task

Psychological flow describes the mental phenomenon that takes place during intense engagement with a task (Jackson & Csikszentmihalyi, 1999). Its components have been operationalized through the development of the Flow State Scale (Jackson & Eklund, 2002). As feedback has been shown to be a critical element for the facilitation of a flow experience (Moneta, 2012), the current study sought to investigate the effect of differential feedback on psychological flow outcomes using the FSS as the dependent variable. The feedback manipulation featured three experimental groups; control, positive, and negative. This study also accounted for the personality trait of perfectionism as a variable influencing the experience of flow. Following the completion of a personality measure, participants engaged in a bolt threading task for ten minutes, then reported the time they perceived to have spent on the task as well as the outcome of their flow experience. The feedback conditions were created by the use of different size containers for participants to place their nut and bolt pairs in, and thus feedback was inherent in the task. The study found that feedback played an important role in the outcome of a flow experience. The positive feedback condition was more conducive to flow than the negative feedback condition. Furthermore, those in the positive condition outperformed those in the negative condition during the ten minutes. Goal clarity and feedback clarity differed significantly across feedback manipulations. Perfectionism¿s impact on the outcome of flow was more pronounced in the negative feedback condition than the positive or control conditions. In settings where engagement and performance are imperative, ample attention should be given to the feedback processes present in the situation.