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.

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.

CCSD(T), W1, and other model chemistry predictions for gas-phase deprotonation reactions

A series of CCSD(T) single-point calculations on MP4(SDQ) geometries and the W1 model chemistry method have been used to calculate ΔH° and ΔG° values for the deprotonation of 17 gas-phase reactions where the experimental values have reported accuracies within 1 kcal/mol. These values have been compared with previous calculations using the G3 and CBS model chemistries and two DFT methods. The most accurate CCSD(T) method uses the aug-cc-pVQZ basis set. Extrapolation of the aug-cc-pVTZ and aug-cc-pVQZ results yields the most accurate agreement with experiment, with a standard deviation of 0.58 kcal/mol for ΔG° and 0.70 kcal/mol for ΔH°. Standard deviations from experiment for ΔG° and ΔH° for the W1 method are 0.95 and 0.83 kcal/mol, respectively. The G3 and CBS-APNO results are competitive with W1 and are much less expensive. Any of the model chemistry methods or the CCSD(T)/aug-cc-pVQZ method can serve as a valuable check on the accuracy of experimental data reported in the National Institutes of Standards and Technology (NIST) database.

Ability of the PM3 Quantum Mechanical Method to Model Intermolecular Hydrogen Bonding between Neutral Molecules

The PM3 semiempirical quantum-mechanical method was found to systematically describe intermolecular hydrogen bonding in small polar molecules. PM3 shows charge transfer from the donor to acceptor molecules on the order of 0.02-0.06 units of charge when strong hydrogen bonds are formed. The PM3 method is predictive; calculated hydrogen bond energies with an absolute magnitude greater than 2 kcal mol-' suggest that the global minimum is a hydrogen bonded complex; absolute energies less than 2 kcal mol-' imply that other van der Waals complexes are more stable. The geometries of the PM3 hydrogen bonded complexes agree with high-resolution spectroscopic observations, gas electron diffraction data, and high-level ab initio calculations. The main limitations in the PM3 method are the underestimation of hydrogen bond lengths by 0.1-0.2 for some systems and the underestimation of reliable experimental hydrogen bond energies by approximately 1-2 kcal mol-l. The PM3 method predicts that ammonia is a good hydrogen bond acceptor and a poor hydrogen donor when interacting with neutral molecules. Electronegativity differences between F, N, and 0 predict that donor strength follows the order F > 0 > N and acceptor strength follows the order N > 0 > F. In the calculations presented in this article, the PM3 method mirrors these electronegativity differences, predicting the F-H- - -N bond to be the strongest and the N-H- - -F bond the weakest. It appears that the PM3 Hamiltonian is able to model hydrogen bonding because of the reduction of two-center repulsive forces brought about by the parameterization of the Gaussian core-core interactions. The ability of the PM3 method to model intermolecular hydrogen bonding means reasonably accurate quantum-mechanical calculations can be applied to small biologic systems.

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.