Advantages and Applications of Transforming Empirical Model Input Space with Dimensional Models

Model-based calibration of steady-state engine operation is commonly performed with highly parameterized empirical models that are accurate but not very robust, particularly when predicting highly nonlinear responses such as diesel smoke emissions. To address this problem, and to boost the accuracy of more robust non-parametric methods to the same level, GT-Power was used to transform the empirical model input space into multiple input spaces that simplified the input-output relationship and improved the accuracy and robustness of smoke predictions made by three commonly used empirical modeling methods: Multivariate Regression, Neural Networks and the k-Nearest Neighbor method. The availability of multiple input spaces allowed the development of two committee techniques: a 'Simple Committee' technique that used averaged predictions from a set of 10 pre-selected input spaces chosen by the training data and the "Minimum Variance Committee" technique where the input spaces for each prediction were chosen on the basis of disagreement between the three modeling methods. This latter technique equalized the performance of the three modeling methods. The successively increasing improvements resulting from the use of a single best transformed input space (Best Combination Technique), Simple Committee Technique and Minimum Variance Committee Technique were verified with hypothesis testing. The transformed input spaces were also shown to improve outlier detection and to improve k-Nearest Neighbor performance when predicting dynamic emissions with steady-state training data. An unexpected finding was that the benefits of input space transformation were unaffected by changes in the hardware or the calibration of the underlying GT-Power model.

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.

Synthesis and Surface Modification of CdSe and CdS Quantum Dots Exhibiting High Quantum Yield

The thesis investigates the effect of surface treatment with various reducing and oxidizing agents on the quantum yield (QY) of CdSe and CdS quantum dots (QDs). The QDs, as synthesized by the organometallic method, contained defect sites on their surface that trapped photons and prevented their radiative recombination, therefore resulting in adecreased QY. To passivate these defect sites and enhance the QY, the QDs were treated with various reducing and oxidizing agents, including: sodium borohydride (NaBH4), calcium hydride (CaH2), hydrazine (N2H4), benzoyl peroxide (C14H10O4), and tert-butylhydroperoxide (C4H10O2). It was hypothesized that the reducing/oxidizing agents reduced the ligands on the QD surface, causing them to detach, thereby allowing oxygen from atmospheric air to bind to the exposed cadmium. This cadmium oxdide (CdO) layeraround the QD surface satisfied the defect sites and resulted in an increased QY. To correlate what effect the reducing and oxidizing agents were having on the optical properties of the QDs, we investigated these treatments on the following factors:chalcogenide (Se vs. S), ligand (oleylamine vs. OA), coordinating solvent (ODE vs.TOA), and dispersant solvent (chloroform vs. toluene) on the overall optical properties of the QDs. The QY of each sample was calculated before and after the various surface treatments from ultra-violet visible spectroscopy (UV-Vis) and fluorescence spectroscopy data to determine if the treatment was successful.From our results, we found that sodium borohydride was the most effective surface treatment, with 10 of the 12 treatments resulting in an increased QY. Hydrazine, on the other hand, was the least effective treatments, as it quenched the QD fluorescence in every case. From these observations, we hypothesize that the effectiveness of the QD surface treatments was dependent on reaction rate. More specifically, when the surface treatment reaction happened too quickly, we hypothesize that the QDs began to aggregate, resulting in a quenched fluorescence. Furthermore, we believe that the reactionrate is dependent on concentration of the reducing/oxidizing agents, solubility of the agents in each solvent, and reactivity of the agents with water. The quantum yield of the QDs can therefore be maximized by slowing the reaction rate of each surface treatment toa rate that allows for the proper passivation of defect sites.