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.

Speech Enhancement With Inventory Style Speech Resynthesis

We present a new method for the enhancement of speech. The method is designed for scenarios in which targeted speaker enrollment as well as system training within the typical noise environment are feasible. The proposed procedure is fundamentally different from most conventional and state-of-the-art denoising approaches. Instead of filtering a distorted signal we are resynthesizing a new “clean” signal based on its likely characteristics. These characteristics are estimated from the distorted signal. A successful implementation of the proposed method is presented. Experiments were performed in a scenario with roughly one hour of clean speech training data. Our results show that the proposed method compares very favorably to other state-of-the-art systems in both objective and subjective speech quality assessments. Potential applications for the proposed method include jet cockpit communication systems and offline methods for the restoration of audio recordings.

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.

Advantages and Applications of Transforming Empirical Model Input Space with Dimensional Models

Model-based calibration of steady-state engine operation is commonly performed with highly parameterized empirical models that are accurate but not very robust, particularly when predicting highly nonlinear responses such as diesel smoke emissions. To address this problem, and to boost the accuracy of more robust non-parametric methods to the same level, GT-Power was used to transform the empirical model input space into multiple input spaces that simplified the input-output relationship and improved the accuracy and robustness of smoke predictions made by three commonly used empirical modeling methods: Multivariate Regression, Neural Networks and the k-Nearest Neighbor method. The availability of multiple input spaces allowed the development of two committee techniques: a 'Simple Committee' technique that used averaged predictions from a set of 10 pre-selected input spaces chosen by the training data and the "Minimum Variance Committee" technique where the input spaces for each prediction were chosen on the basis of disagreement between the three modeling methods. This latter technique equalized the performance of the three modeling methods. The successively increasing improvements resulting from the use of a single best transformed input space (Best Combination Technique), Simple Committee Technique and Minimum Variance Committee Technique were verified with hypothesis testing. The transformed input spaces were also shown to improve outlier detection and to improve k-Nearest Neighbor performance when predicting dynamic emissions with steady-state training data. An unexpected finding was that the benefits of input space transformation were unaffected by changes in the hardware or the calibration of the underlying GT-Power model.

Training in Mental Health: Evaluation of the Telephone Conference Network

The Telephone Conference Network, sponsored by The Pennsylvania State University's Coordinating Council for Health Care, is designed as a cost-effective format for providing inservice training in geriatric mental health for individuals who serve the elderly. Institutions which subscribe to the Telephone Conference Network are equipped with a conference speaker and telephone hook-up providing a two-way line of communication, and may choose from a variety of inservice programs. Mailed evaluations were completed by participants (N=73) in the "Skills to Manage Moods" program, a series of four 1-hour sessions designed to teach participants the skills needed to help patients cope with depression and to deliver the program to others. The majority of respondents reported high levels of satisfaction with the Telephone Conference Network system and the specific program in which they participated. Although 85 percent reported that they would be able to use the skills learned in the program on the job, 50 percent reported that they would not be interested in teaching these skills to others. The convenience and efficiency of the Telephone Conference Network were the most frequently mentioned strengths of the system, while the physical facilities and the program delivery format adopted by the individual institutions were the most frequently mentioned weaknesses. These data suggested several recommendations for Network subscribers and for professionals offering telephone conference programs, including ensuring optimal class enrollment and adequate physical facilities, and participant involvement in program implementation.