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Tailpipe particle number (PN) emission limits for heavy-duty diesel engines have been introduced as part of the off-highway Stage V standards. To meet the required limits a diesel particulate filter (DPF) with high filtration efficiency is required. The DPF relies on formation of a soot cake layer on the channel walls to achieve this high filtration efficiency. Off highway Stage V certification cycles are significantly higher in temperature than their on-highway counterparts, leading to difficulty in creating and maintaining a soot cake in the DPF. Hence for these applications meeting particle number requirements is challenging. To meet the high filtration efficiency requirements the DPF will have to reduce mean pore size, pore standard deviation, and increase wall thickness, in turn increasing backpressure, which results in a fuel consumption penalty. Another option is to evaluate the impact of temperature stable ash accumulation on DPF filtration efficiency.

Diesel Oxidation Catalysts (DOC) are used on heavy duty diesel engine applications and experience large internal temperature variations from 150 to 600°C. The DOC oxidizes the CO and HC in the exhaust to CO2 and H2O and oxidizes NO to NO2. The oxidation reactions are functions of its internal temperatures. Hence, accurate estimation of internal temperatures is important both for onboard diagnostic and aftertreatment closed loop control strategies. This paper focuses on the development of a reduced order model and an Extended Kalman Filter (EKF) state estimator for a DOC. The reduced order model simulation results are compared to experimental data. This is important since the reduced order model is used in the EKF estimator to predict the CO, NO, NO2 and HC concentrations in the DOC and at the outlet. The estimator was exercised using transient drive cycle engine data. The closed loop EKF improves the temperature estimate inside the DOC compared to the open loop estimator.

The diesel engine emits exhaust particles that pose a unique set of measurement requirements. To document the state-of-the-art of measurement technology and to improve measurement quality, the Smoke and Particulate Panel of the Diesel Exhaust Composition group of the Coordinating Research Council reviewed published literature and particulate-sampling data generated by panel members to identify (1) the effects of key sampling parameters on measured particulate mass, (2) the causes of measurement variability, (3) the effects of dilution system design on particulate mass measurement, and (4) promising real-time mass measurement methods. The panel found greater measurement difficulty associated with particulates than for gaseous pollutants because of engine-produced variations, the sensitivity of measured particulate mass to dilution parameters, and random errors in the independent measurements which comprise a particulate measurement.

A computer simulation model to predict the thermal responses of an on-highway heavy duty diesel truck in transient operation was used to study several important cooling system design and operating variables. The truck used in this study was an International Harvester COF-9670 cab-over-chassis vehicle equipped with a McCord radiator, Cummins NTC-350 diesel engine, Kysor fan-clutch and shutter system, aftercooler, and standard cab heater and cooling system components. Input data from several portions of a Columbus to Bloomington, Indiana route were used from the Vehicle Mission Simulation (VMS) program to determine engine and vehicle operating conditions for the computer simulation model. The thermostat-fan, thermostat-shutter-fan, and thermostat-winterfront-fan systems were studied.

Physical, chemical, and biological characterization data for the particulate emissions from a Caterpillar 3208 diesel engine with and without Corning porous ceramic particulate traps are presented. Measurements made at EPA modes 3,4,5,9,lO and 11 include total hydrocarbon, oxides of nitrogen and total particulate matter emissions including the solid fraction (SOL), soluble organic fraction (SOF) and sulfate fraction (SO4), Chemical character was defined by fractionation of the SOF while biological character was defined by analysis of Ames Salmonella/ microsome bioassay data. The trap produced a wide range of total particulate reduction efficiencies (0-97%) depending on the character of the particulate. The chemical character of the SOF was significantly changed through the trap as was the biological character. The mutagenic specific activity of the SOF was generally increased through the trap but this was offset by a decrease in SOF mass emissions.