Transient Smoke Reduction Using a Hybrid Combination of Dimensional and Empirical Modeling

Brahma, Indranil

doi:10.4271/2013-01-0348

Cited by 3 publications

(1 citation statement)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…4–7 Since emission compliance technologies have matured and manufacturers of these technologies have consolidated, engine manufacturers have discovered that engine calibration can be a competitive advantage, see, for example, work by Kerekes et al 8 The traditional “art” of engine calibration practiced by a few experts with limited knowledge sharing has been transformed into an area of systematic scientific enquiry with its own conferences, for example, the design of experiments (DoEs) conference organized by IAV GmbH 9 and the dedicated calibration/control sessions at the Society of Automotive Engineers (SAE) World Congress. 10 Several commercial products such as the AVL “Cameo,” the MathWorks “Model-Based Calibration (MBC)” toolbox, the IAV “EasyDoE” and the Ricardo “Global DoE Toolkit” are available for model-based calibration, in addition to other related products that enable automated data acquisition for engine calibration; see work by Koegeler et al 11 for an example of calibration performed with a commercial product. Many manufacturers have developed corresponding in-house calibration tools.…”

Section: Introductionmentioning

confidence: 99%

Advantages and applications of transforming empirical model input space with dimensional models

Brahma

2014

International Journal of Engine Research

Self Cite

View full text Add to dashboard Cite

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.

show abstract