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.

Developmental Brain Dysfunction: Revival and Expansion of Old Concepts Based on New Genetic Evidence

Neurodevelopmental disorders can be caused by many different genetic abnormalities that are individually rare but collectively common. Specific genetic causes, including certain copy number variants and single-gene mutations, are shared among disorders that are thought to be clinically distinct. This evidence of variability in the clinical manifestations of individual genetic variants and sharing of genetic causes among clinically distinct brain disorders is consistent with the concept of developmental brain dysfunction, a term we use to describe the abnormal brain function underlying a group of neurodevelopmental and neuropsychiatric disorders and to encompass a subset of various clinical diagnoses. Although many pathogenic genetic variants are currently thought to be variably penetrant, we hypothesise that when disorders encompassed by developmental brain dysfunction are considered as a group, the penetrance will approach 100%. The penetrance is also predicted to approach 100% when the phenotype being considered is a specific trait, such as intelligence or autistic-like social impairment, and the trait could be assessed using a continuous, quantitative measure to compare probands with non-carrier family members rather than a qualitative, dichotomous trait and comparing probands with the healthy population. Copyright 2013 Elsevier Ltd. All rights reserved.

The Repairing of Misconceptions through the Use of Inquiry-Based Activities

Misconceptions exist in all fields of learning and develop through a person’s preconception of how the world works. Students with misconceptions in chemical engineering are not capable of correctly transferring knowledge to a new situation and will likely arrive at an incorrect solution. The purpose of this thesis was to repair misconceptions in thermodynamics by using inquiry-based activities. Inquiry-based learning is a method of teaching that involves hands-on learning and self-discovery. Previous work has shown inquiry-based methods result in better conceptual understanding by students relative to traditional lectures. The thermodynamics activities were designed to guide students towards the correct conceptual understanding through observing a preconception fail to hold up through an experiment or simulation. The developed activities focus on the following topics in thermodynamics: “internal energy versus enthalpy”, “equilibrium versus steady state”, and “entropy”. For each topic, two activities were designed to clarify the concept and assure it was properly grasped. Each activity was coupled with an instructions packet containing experimental procedure as well as pre- and post-analysis questions, which were used to analyze the effect of the activities on the students’ responses. Concept inventories were used to monitor students’ conceptual understanding at the beginning and end of the semester. The results did not show a statistically significant increase in the overall concept inventory scores for students who performed the activities compared to traditional learning. There was a statistically significant increase in concept area scores for “internal energy versus enthalpy” and “equilibrium versus steady state”. Although there was not a significant increase in concept inventory scores for “entropy”, written analyses showed most students’ misconceptions were repaired. Students transferred knowledge effectively and retained most of the information in the concept areas of “internal energy versus enthalpy” and “equilibrium versus steady state”.