This content is not included in your SAE MOBILUS subscription, or you are not logged in.
An Investigation into the Use of the EGR Cooler Pressure Drop to Measure EGR Flow Rate
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 14, 2015 by SAE International in United States
Annotation ability available
EGR flow rate measurements on production engines are commonly made using orifices or flow nozzles. These devices increase the exhaust pressure resulting in an increase in fuel consumption. Further, they are accurate and recommended only for steady state flow, and not pulsating flow encountered in engines. In this work measurements made at the EGR cooler, such as the pressure drop across it and the inlet and outlet temperatures, have been examined for their ability to predict mass flow rate through the cooler. Direct measurements of pulsating flow through an EGR cooler were made by routing all of the engine exhaust flow through the cooler while making accurate measurements of the air and fuel flowing into the engine. Based on dimensional arguments, the flow resistance of the EGR cooler was characterized by a loss coefficient within the standard head loss energy equation. This loss coefficient could not be characterized by the Reynolds number; rather it was found to be a function of the pulsation frequency and the average pressure drop across the EGR cooler. Mass flow rate predictions, made using the loss coefficient, were within 4% error, on average. The loss coefficient appeared to be unaffected by the temperature drop across the cooler and no functional dependence was found. Given that flow obstructing devices such as orifices and nozzles are not recommended for measuring pulsating flow in general, EGR coolers or other heat exchangers such as Charge Air Coolers appears to be an alternative solution that produces similar accuracy with more computation but at a lower initial cost and resulting in higher fuel efficiency.
CitationBrahma, I., Ofili, O., Campbell, M., Chiang, H. et al., "An Investigation into the Use of the EGR Cooler Pressure Drop to Measure EGR Flow Rate," SAE Technical Paper 2015-01-1639, 2015, https://doi.org/10.4271/2015-01-1639.
- Brahma, I., & Chi, J. N. (2012). “Development of a model-based transient calibration process for diesel engine electronic control module tables-Part 1: data requirements, processing, and analysis”, International Journal of Engine Research, 13(1): pp. 77-96
- Brahma, I., & Chi, J. N. (2012). “Development of a model-based transient calibration process for diesel engine electronic control module tables-Part 2: Modeling and optimization”, International Journal of Engine Research, 13(2): pp. 147-168
- Brahma I. (2014). “Analysis and prediction of transient opacity spikes using dimensional modeling”, International Journal of Engine Research, 15(3): pp. 263-81.
- Brahma, I., Schmidt, J., Confair, R., Kurtz, J. et al., “An Investigation into the Accuracy of Orifice Based Flow Estimates for Pulsating Compressible Flows,” SAE Int. J. Engines 7(1):313-322, 2014, doi:10.4271/2014-01-1154.
- Crowe, Elger & Robertson, Engineering Fluid Mechanics, 8th edition, ISBN 0-470-08639-4, John Wiley & Sons, 2005
- Holman, J.P., Experimental Methods for Engineers, 6th edition, ISBN 0-07-029666-9, McGraw-Hill, 1994
- Miller, R. W., Flow Measurement Engineering Handbook, McGraw-Hill, 1983.
- Cusick, C. F., Flow Meter Engineering Handbook, Honeywell Inc., 1977.
- Bean, H. S., Fluid Meters, 6th ed., ASME New York, 1971.
- Cunningham, R.G., “Orifice Meters with Supercritical Compressible Flow”, Trans. ASME, Vol. 73, pp. 625-638, 1951
- Engineering Department - Crane Corporation, “Flow of Fluids through Valves, Fittings and Pipes”, Technical Paper No. 410, 1988
- Meters Fluid, American Society of Mechanical Engineers, Part 1 - 6th Edition, New York, 1971
- Brahma, I., Ofili, O., Campbell, M., Chiang, H., Giralo, V., Stryker, P., Johnson, D. and Clark, A., (2014). “Characterization of the Orifice Discharge Coefficient for Pulsating Compressible Flow”, Manuscript in Preparation
- 16 Fox, R. W., Pritchard, P. J., & McDonald, A. T., Introduction to Fluid Mechanics, 7th edition, ISBN-13: 9780471742999, John Wiley & Sons, 2009
- Weiss, S.M. and Kulikowski, C. A., ‘Computer Systems That Learn: Classification and Prediction Methods from Statistics, Neural Nets, Machine Learning and Expert Systems’, Morgan Kaufmann San Mateo, CA, 1990