Using the Natural Period of a Structure as an Indicator of the Significance of Second-Order Effects

This study investigates the feasibility of predicting the momentamplification in beam-column elements of steel moment-resisting frames using the structure's natural period. Unlike previous methods, which perform moment-amplification on a story-by-story basis, this study develops and tests two models that aim to predict a global amplification factor indicative of the largest relevant instance of local moment amplification in the structure. To thisend, a variety of two-dimensional frames is investigated using first and secondorder finite element analysis. The observed moment amplification is then compared with the predicted amplification based on the structure's natural period, which is calculated by first-order finite element analysis. As a benchmark, design moment amplification factors are calculated for each story using the story stiffness approach, and serve to demonstrate the relativeconservatism and accuracy of the proposed models with respect to current practice in design. The study finds that the observed moment amplification factors may vastly exceed expectations when internal member stresses are initially very small. Where the internal stresses are small relative to the member capacities, thesecases are inconsequential for design. To qualify the significance of the observed amplification factors, two parameters are used: the second-order moment normalized to the plastic moment capacity, and the combined flexural and axial stress interaction equations developed by AISC

Development of Dynamic Constraint Models for a Model Based Transient Calibration Process

Model based calibration has gained popularity in recent years as a method to optimize increasingly complex engine systems. However virtually all model based techniques are applied to steady state calibration. Transient calibration is by and large an emerging technology. An important piece of any transient calibration process is the ability to constrain the optimizer to treat the problem as a dynamic one and not as a quasi-static process. The optimized air-handling parameters corresponding to any instant of time must be achievable in a transient sense; this in turn depends on the trajectory of the same parameters over previous time instances. In this work dynamic constraint models have been proposed to translate commanded to actually achieved air-handling parameters. These models enable the optimization to be realistic in a transient sense. The air handling system has been treated as a linear second order system with PD control. Parameters for this second order system have been extracted from real transient data. The model has been shown to be the best choice relative to a list of appropriate candidates such as neural networks and first order models. The selected second order model was used in conjunction with transient emission models to predict emissions over the FTP cycle. It has been shown that emission predictions based on air-handing parameters predicted by the dynamic constraint model do not differ significantly from corresponding emissions based on measured air-handling parameters.

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.