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.

Correlation of Pullout Force of Kirschner (K-) Wire to Other Factors Indicative of Bone Quality

More than 250,000 hip fractures occur annually in the United States and the most common fracture location is the femoral neck, the weakest region of the femur. Hip fixation surgery is conducted to repair hip fractures by using a Kirschner (K-) wire as a temporary guide for permanent bone screws. Variation has been observed in the force required to extract the K-wire from the femoral head during surgery. It is hypothesized that a relationship exists between the K-wire pullout force and the bone quality at the site of extraction. Currently, bone mineral density (BMD) is used as a predictor for bone quality and strength. However, BMD characterizes the entire skeletal system and does not account for localized bone quality and factors such as lifestyle, nutrition, and drug use. A patient’s BMD may not accurately describe the quality of bone at the site of fracture. This study aims to investigate a correlation between the force required to extract a K-wire from femoral head specimens and the quality of bone. A procedure to measure K-wire pullout force was developed and tested with pig femoral head specimens. The procedure was implemented on 8 human osteoarthritic femoral head specimens and the average pullout force for each ranged from 563.32 ± 240.38 N to 1041.01 ± 346.84 N. The data exhibited significant variation within and between each specimen and no statistically significant relationships were determined between pullout force and patient age, weight, height, BMI, inorganic to organic matter ratio, and BMD. A new testing fixture was designed and manufactured to merge the clinical and research environments by enabling the physician to extract the K-wire from each bone specimen himself. The new device allows the physician to gather tactile feedback on the relative ease of extraction while load history is recorded similar to the previous procedure for data acquisition. Future work will include testing human bones with the new device to further investigate correlations for predicting bone quality.