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.

Pople's Gaussian-3 model chemistry applied to an investigation of (H2O)8 water clusters

The Gaussian-3 method developed by Pople and coworkers has been used to calculate the free energy of neutral octamer clusters of water, (H2O)8. The most energetically stable structures are in excellent agreement with those determined from experiment and those predicted from previous high-level calculations. Cubic structures are favored over noncubic structures over all temperature ranges studied. The D2d cubic structure is the lowest free energy structure and dominates the potential energy and free energy hypersurfaces from 0 K to 298 K.

A Model to Predict Percolation Threshold and Effective Conductivity of Infiltrated Electrodes for Solid Oxide Fuel Cells

We present a mechanistic modeling methodology to predict both the percolation threshold and effective conductivity of infiltrated Solid Oxide Fuel Cell (SOFC) electrodes. The model has been developed to mirror each step of the experimental fabrication process. The primary model output is the infiltrated electrode effective conductivity which provides results over a range of infiltrate loadings that are independent of the chosen electronically conducting material. The percolation threshold is utilized as a valuable output data point directly related to the effective conductivity to compare a wide range of input value choices. The predictive capability of the model is demonstrated by favorable comparison to two separate published experimental studies, one using strontium molybdate and one using La0.8Sr0.2FeO3-δ as infiltrate materials. Effective conductivities and percolation thresholds are shown for varied infiltrate particle size, pore size, and porosity with the infiltrate particle size having the largest impact on the results.

A Model to Predict Percolation Threshold and Effective Conductivity of Infiltrated Electrodes for Solid Oxide Fuel Cells

We present a mechanistic modeling methodology to predict both the percolation threshold and effective conductivity of infiltrated Solid Oxide Fuel Cell (SOFC) electrodes. The model has been developed to mirror each step of the experimental fabrication process. The primary model output is the infiltrated electrode effective conductivity which provides results over a range of infiltrate loadings that are independent of the chosen electronically conducting material. The percolation threshold is utilized as a valuable output data point directly related to the effective conductivity to compare a wide range of input value choices. The predictive capability of the model is demonstrated by favorable comparison to two separate published experimental studies, one using strontium molybdate and one using La0.8Sr0.2FeO3-delta as infiltrate materials. Effective conductivities and percolation thresholds are shown for varied infiltrate particle size, pore size, and porosity with the infiltrate particle size having the largest impact on the results. (C) 2013 The Electrochemical Society. All rights reserved.

Teacher resistance to school-based consultation with the school psychologist: An analysis according to school level and model of identification for special education services

Consultation is promoted throughout school psychology literature as a best practice in service delivery. This method has numerous benefits including being able to work with more students at one time, providing practitioners with preventative rather than strictly reactive strategies, and helping school professionals meet state and federal education mandates and initiatives. Despite the benefits of consultation, teachers are sometimes resistant to this process.This research studies variables hypothesized to lead to resistance (Gonzalez, Nelson, Gutkin, & Shwery, 2004) and attempts to distinguish differences between school level (elementary, middle and high school) with respect to the role played by these variables and to determine if the model used to identify students for special education services has an influence on resistance factors. Twenty-sixteachers in elementary and middle schools responded to a demographicquestionnaire and a survey developed by Gonzalez, et al. (2004). This survey measures eight variables related to resistance to consultation. No high school teachers responded to the request to participate. Results of analysis of variance indicated a significant difference in the teaching efficacy subscale with elementary teachers reporting more efficacy in teaching than middle school teachers. Results also indicate a significant difference in classroom managementefficacy with teachers who work in schools that identify students according to a Response to Intervention model reporting higher classroom management efficacy than teachers who work in schools that identify students according to a combined method of refer-test-place/RtI combination model. Implications, limitations and directions for future research are discussed.