Contact: 414.288.2160 casey.allen@marquette.edu
Themes
ENGINE in the
URban ecosystem
One of the primary research themes in our lab is to determine how the internal combustion engine can be used as a diagnostic tool in the urban ecosystem.
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We envision the development of a "living lab" that combines real-time emissions data from vehicles with remote pollution measurements from IoT (Internet of Things) sensors.
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This source-receptor system will transform our understanding of urban pollution flows and weather patterns, and the data will critically inform emissions inventories, autonomous vehicle routing algorithms and urban design methodologies.
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Our lab is pursuing this future by: (1) developing comprehensive emissions diagnostics, (2) exploring strategies for instantaneous, comprehensive emissions predictions, and (3) testing autonomous engine control strategies.
ADVANCED FUELS
The advanced combustion strategies of tomorrow's high-efficiency engines will require optimized fuel blends. Our lab develops the experimental and simulation tools necessary to identify these fuels.
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While true that battery-electric and fuel cell powertrains are making inroads in the vehicle market, analysts estimate the internal combustion (IC) engine will be the dominant powertrain for the next several decades. In fact, the IC engine has entered a golden era, where highly-efficient, clean combustion strategies are now possible because of advances in sensing, control, and modeling.
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The "right fuels" must be identified to enable advanced combustion strategies. To this end, we (1) investigate the ignition chemistry of advanced fuels, (2) develop robust combustion simulation tools, and (3) optimize experimental/ simulation methods to maximize scientific insight from combustion data.
Topics
Selected research topics described below. Additional summary of published work appears here.
Transient Emission Measurement & Modeling
Emissions from internal combustion engines are closely scrutinized, and regulations will only become more stringent in the coming years as governments place new restrictions on specific chemical pollutants and their emitted mass. Drive cycles and fleet routing must be optimized to meet these regulations, which will require accurate, real-time predictions of combustion emissions. To address these needs, our lab has developed a technique for high-speed, comprehensive emissions measurements. This technique allows emission events during short, transient engine conditions to be accurately characterized. The comprehensive nature of the technique provides detailed speciation data to identify the most toxic and environmentally-damaging pollutants. We are using this technique to build real-time emissions models.
Recent published/presented work in this area:
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Experimental Validation of an Unscented Kalman Filter for Estimating Transient Engine Exhaust Composition with Fourier Transform Infrared Spectroscopy , D. Wilson, C. Allen​
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A New Measurement Model for an Unscented Kalman Filter for Effective Rise Time Reduction of Fourier Transform Infrared Spectroscopy Measurements, Central States Section of the Combustion Institute, 2018 Spring Technical Meeting, D. Wilson, C. Allen​
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A Comprehensive Characterization of Spark Ignited Exhaust Emissions during Transient Load Cycles, Central States Section of the Combustion Institute, 2018 Spring Technical Meeting, D. Lehmier, C. Allen
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A Bayesian Estimation Model for Transient Engine Exhaust Characterization using Fourier Transform Infrared Spectroscopy, Energy & Fuels (Energy & Fuels, 2017), D. Wilson, C. Allen​
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A Bayesian Processing Model for High Speed, Transient Engine Exhaust Characterization, U.S. National Combustion Meeting (April 2017), D. Wilson, C. Allen​
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See Publications for additional works
Optimal Control for Autonomous Applications
The autonomous revolution brings with it an exciting opportunity to minimize fuel consumption and emissions by taking the driver (and their poor driving habits!) out of the control loop. Our lab is developing engine/throttle control strategies that can minimize fuel consumption for a known engine load profile. In other words, if the external engine load due to terrain, wind resistance, etc. is known from vehicle sensors, GPS data, or other communication links, we calculate and execute control moves to optimize performance over the short-term horizon.
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Our lab has recently developed the specialized infrastructure needed to develop and test these control strategies (see Facilities for a detailed description). Test results indicate that the optimal control approach can save up to 6% on fuel consumption while minimally increasing travel time (~2%). The sample results at right compare an "Optimal Control" case where fuel consumption is optimized and a "PI Engine Speed Control" case where engine speed is maintained constant. On average, the fuel flow rate (bottom subplot) is less in the optimal control case, demonstrating the value of the approach.
Recent published/presented work in this area:
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Speciated Transient Gasoline Engine Emissions Under Optimally-Controlled Speed-Load Trajectories, Applied Energy (Submitted, In Review), D. Lehmier, J. Rehn III, W. Herzberg, D. Wilson, C. Allen
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Development of an Optimal Controller and Validation Platform for Fuel Efficient Engine Control, J. Rehn
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See Publications for additional works
Fuel Ignition Characterization
Optimized fuel blends play a critical role in driving high efficiencies from new engine technologies. Identification of these fuels requires new and advanced experimental methods and analysis tools, which can provide deeper insight into combustion test data. Our lab's fuel tests are focused on ignition behavior in the low-temperature, high-pressure region -- conditions which are readily probed with a rapid compression machine (RCM). Interpretation of RCM data has been a point of controversy in
the literature, and our recent focus has been to develop simulation and analysis tools to aid in the interpretation. We recently coupled a multi-zone model (MZM; originally developed by Dr. Scott Goldsborough) with Cantera (open source chemical kinetics package) to create a robust RCM simulation tool. The MZM simulates the influence of temperature stratification that develops during an RCM experiment, which allows accurate simulation of both (1) reaction quenching experiments and (2) multi-stage ignition behavior without the need for time-consuming CFD simulations. Our lab is using this tool to model speciation data gathered from the RCM and to investigate the link between global ignition metrics and elementary reactions.
Recent published/presented work in this area:
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On the Influence of Initial Conditions and Facility Effects on Rapid Compression Machine Data (Submitted),
J. Ezzell, C. Allen
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A Comparison of Sensitivity Metrics for Two-Stage Ignition Behavior in Rapid Compression Machines, Fuel (2017),
D. Wilson, C. Allen
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Application of a Multi-Zone Model for the Prediction of Species Concentrations in Rapid Compression Machine Experiments, Combustion and Flame (2016), D. Wilson, C. Allen
Conventional and Bio-Derived Jet Fuel Surrogate Modeling in Low Temperature and Lean Combustion, Energy & Fuels (2015), A. Oldani, D. Valco, K. Min, J. Edwards, C. Kweon, C. Allen, T. Lee
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See Publications for additional works