Characterization of in-use emissions from on-highway heavy-duty diesel engines

Krishnamurthy, Mohan

doi:10.33915/etd.1384

Cited by 7 publications

(8 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The bias error is ± 3 % of the reading, for concentration values above 1000 ppm. The total uncertainty in zirconium oxide NO x sensor concentration (including both bias and random errors) is reported to be 30.4 (<1000 ppm) and ± 3.36 % of the reading, for values above 1000 ppm [17]. The Horiba BE-140 sensor has a bias error of ± 2% of full scale.…”

Section: Uncertainty Analysismentioning

confidence: 97%

“…The concentrations of gases used were dependent on the maximum concentration in the exhaust of the engine being tested. (17). Unfortunately, most stationary and portable engines in the field will not allow ready access to the engine stack for measurement of exhaust flow rates.…”

Section: General Features Of Electrochemical No Xmentioning

confidence: 99%

“…The Horiba BE-140 sensor has a bias error of ± 2% of full scale. The total uncertainty for CO 2 concentrations measured by the BE-140 sensor has been estimated to be 2.3 % full scale [17]. PM concentrations measured by the CVS method are estimated to have a total uncertainty of 1.95% [46,47].…”

Section: Uncertainty Analysismentioning

confidence: 99%

mentioning

confidence: 99%

“…Previous uncertainty analysis performed on the MEMS system(17) indicated the major uncertainties involved in the flow measurements. Results(17) have shown that the uncertainty due to flow measurement may be as high as 5% (possibly higher) in certain portions of its map. Hence, alternative methods to quantify exhaust emissions that preferably avoid measurement of exhaust flow rate such as, the Compliance Factor approach (43,44) described in this section are necessary.The section illustrates the application of this methodology to obtain estimates for Compliance Factor, F using the data gathered from a DDC Series 60 engine that was exercised on multiple ISO 8178 test cycles.…”

mentioning

confidence: 99%

See 4 more Smart Citations

Development of a test method to measure "in-use" emissions from stationary and portable diesel sources

Nayak¹

View full text Add to dashboard Cite

A cost-effective, accurate and an easy-to-use in-field test method was developed that would allow regulatory bodies, such as the California Air Resources Board (CARB) to determine in-use compliance with emission standards for newly manufactured portable and stationary engines. Engine-dynamometer/chassis dynamometer tests have been shown to be unrepresentative of actual in-field operation of the engine; hence, emissions measured during such conditions do not faithfully reflect real-world emissions. Engines may be compliant with certification standards during an engine-dynamometer test, but may still deviate from the standard by two times during an actual "in-use" operation. Hence, a test method that functions like an Inspection and Maintenance (I/M) type test method (serves as a "screening tool") and is capable of measuring in-use emissions from portable and stationary engines was developed. Only concentration measurements of pollutants such as oxides of nitrogen (NO x) and carbon dioxide (CO 2) are required to determine compliance using this "Compliance Factor, F" approach. Hence, errors introduced due to engine work output measurement and exhaust flow rate measurement are avoided. It should be noted that in-field torque measurement from mechanically controlled engines is not trivial. I also owe thanks to all the work studies at ERC including Brian Marple,

show abstract

Section: Uncertainty Analysismentioning

confidence: 97%

Section: General Features Of Electrochemical No Xmentioning

confidence: 99%

Section: Uncertainty Analysismentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

Development of a test method to measure "in-use" emissions from stationary and portable diesel sources

Nayak¹

View full text Add to dashboard Cite

show abstract

Methods for reconstruction of transient emissions from heavy -duty vehicles

Madireddy¹

View full text Add to dashboard Cite

Methods for Reconstruction of Transient Emissions from Heavy-Duty Vehicles Madhava R. Madireddy Emissions measurement analyzers give out a response that may not reflect the true instantaneous engine-out emissions. Currently, the heavy-duty diesel engines are being certified for emissions measured in a thirty second time window with certain specification requirements for the analyzers. Since these measured emissions values may not be the same as the true instantaneous emissions, integrated values for the thirty second windows may be affected by analyzer response. This document presents and examines reconstruction techniques to estimate instantaneous heavy-duty engine-out emissions. These techniques will take as the input, the continuous set of emissions data and approximate dispersion characteristics of the analyzer employed in measuring the continuous data. For this purpose, this research dealt with understanding and modeling the transient dynamics (dispersion function) of the analyzers and the sampling system to establish a relationship between the measured and instantaneous heavy-duty emissions. Four methods of reconstruction were presented in this study: Sequential Inversion Technique (SIT), Differential Coefficients Method (DCM), Inverse Fast Fourier Transform (IFFT) and Modified Deconvolution Technique (MDT). The application of each method in reconstructing real-time emissions data was presented. While SIT failed in practical applications, each of the other three methods was shown to offer advantage in the post-processing of the measured emissions data. DCM accounted for the small errors

show abstract

Development of an Artificial Neural Network to Predict In-Use Engine Emissions

Morris¹

View full text Add to dashboard Cite

A method to predict in-use diesel engine emissions is developed based on engine dynamometer and in-use data acquired at the West Virginia University Center for Alternative Fuels, Engines, and Emissions. (WVU CAFEE). The model accounts for the effects of road grade on generated emissions; a need for this model is evident in literature. Current modeling methods do not account for the effects of road grade, and have been shown to under-predict NOx by as much as 57%. It is determined through present research and a review of relevant literature that an artificial neural network (ANN) was the most applicable modeling method. A modular ANN was developed to predict the heavy duty diesel engine emissions. The two modules were trained independently, the first module was trained with data acquired through inuse testing, and the second module was trained with data acquired via engine dynamometer testing. The first module predicted the engine speed and torque associated with the inputs of road grade and vehicle speed, while the second ANN employed the first ANN's outputs, and predicts the emitted quantities of NOx, CO 2 , HC, and CO. A series of training and verification runs are conducted in order to determine the optimum ANN characteristics. Once the ANN was finalized, it was trained with and employed to predict the emissions associated with a variety of routes. When the ANN was trained with a combination of in-use and engine dynamometer data, the ANN is able to predict NOx emissions associated with that same route within 6% of the measured values. The average difference between the measured and predicted CO 2 values for the same training and verification scenario mentioned above was less than 15%. It was also demonstrated that the ANN was able to predict emissions that are associated with routes that differ from those by which it is trained. When the ANN was trained with in-use data from a specific route, it was able to predict the NOx and CO 2 emissions associated with a different route with percent differences from the measured values of 20% or less.

show abstract

Characterization of in-use emissions from on-highway heavy-duty diesel engines

Cited by 7 publications

References 14 publications

Development of a test method to measure "in-use" emissions from stationary and portable diesel sources

Development of a test method to measure "in-use" emissions from stationary and portable diesel sources

Methods for reconstruction of transient emissions from heavy -duty vehicles

Development of an Artificial Neural Network to Predict In-Use Engine Emissions

Contact Info

Product

Resources

About