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.

Testing the TICT State Hypothesis: an Investigation into the Solvatochromic Behavior of 2-Naphthalenylmethylene Malononitrile

Towards the goal of investigating the possible Twisted Intramolecular Charge Transfer (TICT) state mechanism of fluorescence emission, two aromatic dicyanovinyl compounds, 2-(naphthalene-2-ylmethylene) malononitrile (DCN) and a rigidified analogue, 3,4-dihydrophenanthren-1(2H)-ylidene)malononitrile (RDCN) were synthesized and their absorption and steady-state fluorescence emission spectra characterized. The spectral characterization was divided into two studies: first, DCN and RDCN were characterized in liquid solvents of increasing polarity; second, DCN and RDCN were characterized in viscous solvents and rigid glass media. The absorption spectra for both DCN and RDCN in all solvents demonstrated little to no solvatochromism. Emission results for DCN and RDCN in liquid solvents of increasing polarity showed DCN possessing strong solvatochromism while RDCN showed much less solvatochromism. Using the Lippert-Mataga equation, the difference between the ground and excited state dipole moment for DCN was estimated to be 8.4 + 0.4 Debye and between ~3.0 to 5.0 Debye for RDCN. Quantum yield measurements for DCN and RDCN in hexane, diethyl ether and acetonitrile were less than 0.01 and independent of polarity for both both solvents, with DCN generally possessing a quantum yield 3-4 times greater than RDCN. Experiments in glass media for DCN and RDCN showed a lessening of their solvatochromic character in both polar and non-polar glasses. These data provide strong evidence for a link between molecular flexibility and solvatochromism. However, while these data are consistent with a TICT state hypothesis for the emission mechanism, an alternative mechanism proposed by Maroncelli et al.10 involving rotation about the dicyanovinyl double bond in the excited state remains a possibility as well.