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Technical Paper

Measuring Diesel Emissions with a Split Exhaust Configuration

West Virginia University evaluated diesel oxidation catalysts (DOC) and lean-NOX catalysts as part of Diesel Emissions Control-Sulfur Effects (DECSE) project. In order to perform thermal aging of the DOC and lean-NOX catalysts simultaneously and economically, each catalyst was sized to accommodate half of the engine exhaust flow. Simultaneous catalyst aging was then achieved by splitting the engine exhaust into two streams such that approximately half of the total exhaust flowed through the DOC and half through the lean-NOX catalyst. This necessitated splitting the engine exhaust into two streams during emissions measurements. Throttling valves installed in each branch of the split exhaust were adjusted so that approximately half the engine exhaust passed though the active catalyst under evaluation and into a full flow dilution tunnel for emissions measurement.
Technical Paper

Exhaust Emissions and Combustion Stability in a Bi-Fuel Spark Ignition Engine

A Saturn 1.9 liter engine has been converted for operation on either compressed natural gas or gasoline. A bi-fuel controller (BFC) that uses closed-loop control methods for both fuel delivery and spark advance has been developed. The performance and emissions during operation on each fuel have been investigated with the BFC, as well as the performance and emissions with the stock original equipment manufacturer (OEM) controller using gasoline. In-cylinder pressure was measured at a rate of 1024 points per revolution with piezoelectric pressure transducers flush-mounted in the cylinder head. The in-cylinder pressure was used in real time for ignition timing control purposes, and was stored by a data acquisition system for the investigation of engine stability and differences in the combustion properties of the fuels.
Technical Paper

Quantification of Yard Hostler Activity and the Development of a Representative Yard Hostler Cycle

Yard hostlers are tractors (switchers) used to move containers at ports and storage facilities. While many speed-time driving cycles for assessing emissions and performance from heavy-duty vehicles exist, a driving cycle representative of yard hostler activity at Port of Long Beach, CA was not available. Activity data were collected from in-use yard hostlers as they performed ship loading/unloading, rail loading/unloading and other yard routines, primarily moving containers on trailers or carts. The activity data were then used to develop four speed-time driving cycles with durations of 1200 seconds, representing light and heavy ship activities and light and heavy load rail activities. These cycles were constructed using actual speed-time data collected during activity logging and the cycles created were statistically comparable to each subset of activity data.
Technical Paper

Numerical Simulation of a Two-Stroke Linear Engine-Alternator Combination

Series hybrid electric vehicles (HEVs) require power-plants that can generate electrical energy without specifically requiring rotary input shaft motion. A small-bore working prototype of a two-stroke spark ignited linear engine-alternator combination has been designed, constructed and tested and has been found to produce as much as 316W of electrical energy. This engine consists of two opposed pistons (of 36 mm diameter) linked by a connecting rod with a permanent magnet alternator arranged on the reciprocating shaft. This paper presents the numerical modeling of the operation of the linear engine. The piston motion of the linear engine is not mechanically defined: it rather results from the balance of the in-cylinder pressures, inertia, friction, and the load applied to the shaft by the alternator, along with history effects from the previous cycle. The engine computational model combines dynamic and thermodynamic analyses.
Technical Paper

Neural Network-Based Diesel Engine Emissions Prediction Using In-Cylinder Combustion Pressure

This paper explores the feasibility of using in-cylinder pressure-based variables to predict gaseous exhaust emissions levels from a Navistar T444 direct injection diesel engine through the use of neural networks. The networks were trained using in-cylinder pressure derived variables generated at steady state conditions over a wide speed and load test matrix. The networks were then validated on previously “unseen” real-time data obtained from the Federal Test Procedure cycle through the use of a high speed digital signal processor data acquisition system. Once fully trained, the DSP-based system developed in this work allows the real-time prediction of NOX and CO2 emissions from this engine on a cycle-by-cycle basis without requiring emissions measurement.