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.

Digital signal processing and digital system design using discrete cosine transform [student course]

The discrete cosine transform (DCT) is an important functional block for image processing applications. The implementation of a DCT has been viewed as a specialized research task. We apply a micro-architecture based methodology to the hardware implementation of an efficient DCT algorithm in a digital design course. Several circuit optimization and design space exploration techniques at the register-transfer and logic levels are introduced in class for generating the final design. The students not only learn how the algorithm can be implemented, but also receive insights about how other signal processing algorithms can be translated into a hardware implementation. Since signal processing has very broad applications, the study and implementation of an extensively used signal processing algorithm in a digital design course significantly enhances the learning experience in both digital signal processing and digital design areas for the students.

Processing and Composition Effects on the Fracture Behavior of Spray-Formed 7XXX Series Al Alloys

The fracture properties of high-strength spray-formed Al alloys were investigated, with consideration of the effects of elemental additions such as zinc,manganese, and chromium and the influence of the addition of SiC particulate. Fracture resistance values between 13.6 and 25.6 MPa (m)1/2 were obtained for the monolithic alloys in the T6 and T7 conditions, respectively. The alloys with SiC particulate compared well and achieved fracture resistance values between 18.7 and 25.6 MPa (m)1/2. The spray-formed materials exhibited a loss in fracture resistance (KI) compared to ingot metallurgy 7075 alloys but had an improvedperformance compared to high-solute powder metallurgy alloys of similar composition. Characterization of the fracture surfaces indicated a predominantly intergranular decohesion, possibly facilitated by the presence of incoherent particles at the grain boundary regions and by the large strength differentialbetween the matrix and precipitate zone. It is believed that at the slip band-grain boundary intersection, particularly in the presence of large dispersoids and/or inclusions, microvoid nucleation would be significantly enhanced. Differences in fracture surfaces between the alloys in the T6 and T7 condition were observed and are attributed to inhomogeneous slip distribution, which results in strain localization at grain boundaries. The best overall combination of fracture resistance properties were obtained for alloys with minimum amounts of chromium and manganese additions.

Social Cognition, Face Processing, and Oxytocin Receptor Single Nucleotide Polymorphisms in Typically Developing Children

Recent research has provided evidence of a link between behavioral measures of social cognition (SC) and neural and genetic correlates. Differences in face processing and variations in the oxytocin receptor (OXTR) gene have been associated with SC deficits and autism spectrum disorder (ASD) traits. Much work has examined the qualitative differences between those with ASD and typically developing (TD) individuals, but very little has been done to quantify the natural variation in ASD-like traits in the typical population. The present study examines this variation in TD children using a multidimensional perspective involving behavior assessment, neural electroencephalogram (EEG) testing, and OXTR genotyping. Children completed a series of neurocognitive assessments, provided saliva samples for sequencing, and completed a face processing task while connected to an EEG. No clear pattern emerged for EEG covariates or genotypes for individual OXTR single nucleotide polymorphisms (SNPs). However, SNPs rs2254298 and rs53576 consistently interacted such that the AG/GG allele combination of these SNPs was associated with poorer performance on neurocognitive measures. These results suggest that neither SNP in isolation is risk-conferring, but rather that the combination of rs2254298(A/G) and rs53576(G/G) confers a deleterious effect on SC across several neurocognitive measures. Copyright 2014. Published by Elsevier Ltd.