This paper illustrates a method to determine the experimental uncertainties in the measurement of tailpipe emissions of carbon dioxide, carbon monoxide, nitrogen oxides, hydrocarbons, and particulates of medium-, and heavy-duty vehicles when tested on a heavy-duty chassis dynamometer and full-scale dilution tunnel. Tests are performed for different chassis dynamometer driving cycles intended to simulate a wide range of operating conditions. Vehicle exhaust is diluted in the dilution tunnel by mixing with conditioned air. Samples are drawn through probes for raw exhaust, diluted exhaust and particulates and measured using laboratory grade emission analyzers and a microbalance. At the end of a driving cycle, results are reported for the above emissions in grams/mile for raw continuous, dilute continuous, dilute bag, and particulate measurements. An analytical method is developed in the present study to estimate the measurement uncertainties in emissions for a test cycle, due to the buildup of measurement uncertainties as they propagate through the system. The linearity, repeatability and noise of the measuring instruments of the system, as specified in instrument's manuals are used in the calculations. It is found that measurement uncertainties are lower for raw continuous measurements, and higher for dilute bag measurements. Analysis shows that uncertainty in concentration measurement is the major component of total uncertainty, and that uncertainty is a function of the duration of the test and the ratio of measured value to full scale range of the instrument. A computer generated Monte Carlo simulation is used to validate the present analysis. Data from one steady state propane injection test and a transient driving cycle are used to compare results from our analysis and Monte Carlo simulation. It is found that the Monte Carlo simulation shows good agreement with the results from the uncertainty analysis. Uncertainties in emissions measurement from three dynamometer- based tests are discussed. A list of abbreviations used is given at the end of the paper.
All Science Journal Classification (ASJC) codes
- Automotive Engineering
- Safety, Risk, Reliability and Quality
- Industrial and Manufacturing Engineering