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A Bayesian Processing Model for High Speed, Transient Engine Exhaust Characterization
| Content Provider | Semantic Scholar |
|---|---|
| Author | Wilson, David Allen, Casey |
| Copyright Year | 2017 |
| Abstract | Fourier Transform Infrared (FTIR) spectroscopy systems are powerful diagnostics for measuring the composition of engine exhaust. However, to obtain reasonable detection limits, a gas cell must be used to allow the laser beam multiple passes through the sample gas. Due to the residence time of the gas cell, FTIR measurements characterize historical flow into the cell rather than the inlet composition, making raw FTIR data unsuitable for characterizing transient emissions. To address this issue, a model-based signal processing algorithm has been developed to estimate comprehensive engine exhaust composition from FTIR measurements. A zero-dimensional fluid mixing model is central to the processor and is developed using 3-D computational fluid dynamics simulations of the FTIR gas cell. Validation of the processor with synthetic data demonstrates that the processor reduces response times to step changes in sample gas composition by up to 6 seconds compared to raw FTIR data. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://cb58ca3e-cd99-4469-8951-e18dd86e6168.filesusr.com/ugd/75411b_f491dd76c6fa4bfaaf3c4b41fe02fbef.pdf |
| Alternate Webpage(s) | https://cb58ca3e-cd99-4469-8951-e18dd86e6168.filesusr.com/ugd/75411b_5fa8cc6d33eb4b558ad4de8d1c73e004.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |