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Powertrain simulation is becoming an increasingly valuable tool to evaluate new technologies proposed for future military vehicles. The powertrain of the M1A1 Abrams tank is currently being modeled in the Powertrain Control Research Laboratory (PCRL) at the University of Wisconsin-Madison. This powertrain model is to be integrated with other component models in an effort to produce a high fidelity simulation of the entire vehicle.

The University of Wisconsin-Madison Snowmobile Team designed and constructed a hybrid-electric snowmobile for the 2005 Society of Automotive Engineers' Clean Snowmobile Challenge. Built on a 2003 cross-country touring chassis, this machine features a 784 cc fuel-injected four-stroke engine in parallel with a 48 V electric golf cart motor. The 12 kg electric motor increases powertrain torque up to 25% during acceleration and recharges the snowmobile's battery pack during steady-state operation. Air pollution from the gasoline engine is reduced to levels far below current best available technology in the snowmobile industry. The four-stroke engine's closed-loop EFI system maintains stoichiometric combustion while dual three-way catalysts reduce NOx, HC and CO emissions by up to 94% from stock. In addition to the use of three way catalysts, the fuel injection strategy has been modified to further reduce engine emissions from the levels measured in the CSC 2004 competition.

A previously developed CFD-based optimization tool is utilized to find optimal engine operating conditions with respect to fuel consumption and emissions. The optimization algorithm employed is based on the steepest descent method where an adaptive cost function is minimized along each line search using an effective backtracking strategy. The adaptive cost function is based on the penalty method, where the penalty coefficient is increased after every line search. The parameter space is normalized and, thus, the optimization occurs over the unit cube in higher-dimensional space. The application of this optimization tool is demonstrated for the Sulzer S20, a central-injection, non-road DI diesel engine. The optimization parameters are the start of injection of the two pulses of a split injection system, the duration of each pulse, the exhaust gas recirculation rate, the boost pressure and the compression ratio.

A global optimization method has been developed for an engine simulation code and utilized in the search of optimal fuel injection strategies. This method uses a Lagrange interpolation function which interpolates engine output data generated at the vertices and the intermediate points of the input parameters. This interpolation function is then used to find a global minimum over the entire parameter set, which in turn becomes the starting point of a CFD-based optimization. The CFD optimization is based on a steepest descent method with an adaptive cost function, where the line searches are performed with a fast-converging backtracking algorithm. The adaptive cost function is based on the penalty method, where the penalty coefficient is increased after every line search. The parameter space is normalized and, thus, the optimization occurs over the unit cube in higher-dimensional space.

An experimental study was performed to investigate diesel particulate filter (DPF) performance during filtration with the use of real-time measurement equipment. Three operating conditions of a single-cylinder 2.3-liter D.I. heavy-duty diesel engine were selected to generate distinct types of diesel particulate matter (PM) in terms of chemical composition, concentration, and size distribution. Four substrates, with a range of geometric and physical parameters, were studied to observe the effect on filtration characteristics. Real-time filtration performance indicators such as pressure drop and filtration efficiency were investigated using real-time PM size distribution and a mass analyzer. Types of filtration efficiency included: mass-based, number-based, and fractional (based on particle diameter). In addition, time integrated measurements were taken with a Rupprecht & Patashnick Tapered Element Oscillating Microbalance (TEOM), Teflon and quartz filters.

The University of Wisconsin - Madison Hybrid Vehicle Team has designed, fabricated, tested and optimized a four-wheel drive, charge sustaining, split-parallel hybrid-electric crossover vehicle for entry into the 2006 Challenge X competition. This multi-year project is based on a 2005 Chevrolet Equinox platform. Trade-offs in fuel economy, greenhouse gas impact (GHGI), acceleration, component packaging and consumer acceptability were weighed to establish Wisconsin's Vehicle Technical Specifications (VTS). Wisconsin's Equinox, nicknamed the Moovada, utilizes a General Motors (GM) 110 kW 1.9 L CIDI engine coupled to GM's 6-speed F40 transmission. The rear axle is powered by a 65 kW Ballard induction motor/gearbox powered from a 44-module (317 volts nominal) Johnson Controls Inc., nickel-metal hydride hybrid battery pack. It includes a newly developed proprietary battery management algorithm which broadcasts the battery's state of charge onto the CAN network.

Three distinct types of diesel particulate matter (PM) are generated in selected engine operating conditions of a single-cylinder heavy-duty diesel engine. The three types of PM are trapped using typical Cordierite diesel particulate filters (DPF) with different washcoat formulations and a commercial Silicon-Carbide DPF. Two systems, an external electric furnace and an in-situ burner, were used for regeneration. Furnace regeneration experiments allow the collected PM to be classified into two categories depending on oxidation mechanism: PM that is affected by the catalyst and PM that is oxidized by a purely thermal mechanism. The two PM categories prove to contribute differently to pressure drop and transient filtration efficiency during in-situ regeneration.

The demand for highly flexible manipulation of plant growth generations, modification of specific plant processes, and genetically engineered crop varieties in a controlled environment has led to the development of a Commercial Plant Biotechnology Facility (CPBF). The CPBF is a quad-middeck locker playload to be mounted in the EXPRESS Rack that will be installed in the International Space Station (ISS). The CPBF integrates proven ASTROCULTURE” technologies, state-of-the-art control software, and fault tolerance and recovery technologies together to increase overall system efficiency, reliability, robustness, flexibility, and user friendliness. The CPBF provides a large plant growing volume for the support of commercial plant biotechnology studies and/or applications for long time plant research in a reduced gravity environment.

Automation of space-based scientific operations minimizes the crew time needs for experiments while increasing the efficiency and quality of science operations. ORBITEC has completed the development of a space qualifiable prototype of a Shared Multi-Use Remote Robotics Facility (SMURRF). SMURRF, sized for a Middeck Locker (MDL) application, provides a simple, flexible, and functional manipulator to assist space operations, in manned or unmanned modes, carried out in lockers or racks onboard the Space Shuttle and the International Space Station (ISS). It will be primarily operated in an automated mode with additional remote command/control capability from the ground or from space. Ground trials have demonstrated that many operations can be autonomously performed without the presence of a human operator.

Transient engine operations are modeled and simulated with a 1-D code (GT Power) using heat release and emission data computed by a 3-D CFD code (Kiva3). During each iteration step of a transient engine simulation, the 1-D code utilizes the 3-D data to interpolate the values for heat release and emissions. The 3-D CFD computations were performed for the compression and combustion stroke of strategically chosen engine operating points considering engine speed, torque and excess air. The 3-D inlet conditions were obtained from the 1-D code, which utilized 3-D heat release data from the previous 1-D unsteady computations. In most cases, only two different sets of 3-D input data are needed to interpolate the transient phase between two engine operating points. This keeps the computation time at a reasonable level. The results are demonstrated on the load response of a generator which is driven by a medium-speed diesel engine.

Optimal fuel injection strategies are obtained with a micro-genetic algorithm and an adaptive gradient method for a nonroad, medium-speed DI diesel engine equipped with a multi-orifice, asynchronous fuel injection system. The gradient optimization utilizes a fast-converging backtracking algorithm and an adaptive cost function which is based on the penalty method, where the penalty coefficient is increased after every line search. The micro-genetic algorithm uses parameter combinations of the best two individuals in each generation until a local convergence is achieved, and then generates a random population to continue the global search. The optimizations have been performed for a two pulse fuel injection strategy where the optimization parameters are the injection timings and the nozzle orifice diameters.

Various single and split injection schemes are studied to provide a better understanding of fuel distribution during cold starting in DI diesel engines. Improved spray-wall interaction, fuel film and multicomponent vaporization models are used to analyze the combustion processes. Better combustion characteristics are obtained for the split injection schemes than with a single injection. An analysis of the fuel impingement processes identifies the mechanisms involved in producing the differences in vaporization and combustion of the fuel. A greater amount of splashing occurred for the split injections compared to a single injection. This behavior is attributed to the decreased film thickness (less dissipation of impingement energy), the decreased impingement area (obtained by increasing the impingement Weber number), and most importantly, the reduced frequency of drop impingement.

Predictive models of soot formation during Diesel combustion are of great practical interest, particularly in light of newly proposed strict regulations on particulate emissions. A modified version of the phenomenological model of soot formation developed previously has been implemented in KIVA-II CFD code. The model includes major generic processes involved in soot formation during combustion, i.e., formation of soot precursors, formation of surface growth species, soot particle nucleation, coagulation, surface growth and oxidation. The formulation of the model within the KIVA-II is fully coupled with the mass and energy balances in the system. The model performance has been tested by comparison with the results of optical in-cylinder soot measurements in a single cylinder Cummins NH Diesel engine. The predicted soot volume fraction, number density and particle size agree reasonably well with the experimental data.

The objective of this project was to model and study the interior noise in an Off-Highway Truck cab using Statistical Energy Analysis (SEA). The analysis was performed using two different modeling techniques. In the first method, the structural members of the cab were modeled along with the panels and the interior cavity. In the second method, the structural members were not modeled and only the acoustic cavity and panels were modeled. Comparison was done between the model with structural members and without structural members to evaluate the necessity of modeling the structure. Correlation between model prediction of interior sound pressure and test data was performed for eight different load conditions. Power contribution analysis was performed to find dominant paths and 1/3rd octave band frequencies.

A study of partially premixed combustion (PPC) with non-oxygenated 91 pump octane number1 (PON) commercially available gasoline was performed using a heavy-duty (HD) compression-ignition (CI) 2.44 l Caterpillar 3401E single-cylinder oil test engine (SCOTE). The experimental conditions selected were a net indicated mean effective pressure (IMEP) of 11.5 bar, an engine speed of 1300 rev/min, an intake temperature of 40°C with intake and exhaust pressures of 200 and 207 kPa, respectively. The baseline case for all studies presented had 0% exhaust gas recirculation (EGR), used a dual injection strategy a -137 deg ATDC pilot SOI and a -6 deg ATDC main start-of-injection (SOI) timing with a 30/70% pilot/main fuel split for a total of 5.3 kg/h fueling (equating to approximately 50% load). Combustion and emissions characteristics were explored relative to the baseline case by sweeping main and pilot SOI timings, injection split fuel percentage, intake pressure, load and EGR levels.

The University of Wisconsin Hybrid Vehicle Team has implemented and optimized a four-wheel drive, charge sustaining, split-parallel hybrid-electric crossover vehicle for entry into the 2008 ChallengeX competition. This four year project is based on a 2005 Chevrolet Equinox platform. Fuel economy, greenhouse gas impact (GHGI), acceleration, component packaging and consumer acceptability were appropriately weighted to determine powertrain component selections. Wisconsin's Equinox, nicknamed the Moovada, is a split-parallel hybrid utilizing a General Motors (GM) 110 kW 1.9L CDTi (common rail diesel turbo injection) engine coupled to an F40 6-speed manual transmission. The rear axle is powered by a SiemensVDO induction motor/gearbox power-limited to 65 kW by a 40-module (288 volts nominal) Johnson Controls Inc, nickel-metal hydride battery pack.

In modern production diesel engine control systems, fuel path control is still largely conducted through a system of tables that set mode, timing and injection quantity and with common rail systems, rail pressure. In the hands of an experienced team, such systems have proved so far able to meet emissions standards, but they lack the analytical underpinning that lead to systematic solutions. In high degree of freedom systems typified by modern fuel injection, there is substantial scope to deploy optimising closed loop strategies during calibration and potentially in the delivered product. In an optimising controller, a digital algorithm will explicitly trade-off conflicting objectives and follow trajectories during transients that continue to meet a defined set of criteria. Such an optimising controller must be based on a model of the system behaviour which is used in real time to investigate the consequences of proposed control actions.

Today's engine and combustion process development is closely related to the intake port layout. Combustion, performance and emissions are coupled to the intensity of turbulence, the quality of mixture formation and the distribution of residual gas, all of which depend on the in-cylinder charge motion, which is mainly determined by the intake port and cylinder head design. Additionally, an increasing level of volumetric efficiency is demanded for a high power output. Most optimization efforts on typical homogeneous charge spark ignition (HCSI) engines have been at low loads because that is all that is required for a vehicle to make it through the FTP cycle. However, due to pumping losses, this is where such engines are least efficient, so it would be good to find strategies to allow the engine to operate at higher loads.

Smaller locomotives often use mechanical transmissions instead of diesel-electric drive systems typically used in larger locomotives. This paper discusses how Model Based Design was used to develop the complete drive train control system for a 24 ton sugar cane locomotive. A complete MATLAB Simulink machine model was built to fully test and verify the shift control logic, traction control, vehicle speed limiting, and braking control for this locomotive application before it was commissioned. The model included the engine, torque converter, planetary transmission, drive line, and steel on steel driving surface. Simulation was used to debug all control code and test and refine control strategies so that the initial field commissioning in remote Australia was executed very quickly with minimal engineering support required.

Analyzing the combustion characteristics, engine performance, and emissions pathways of the internal combustion (IC) engine requires management of complex and an increasing quantity of data. With this in mind, effective management to deliver increased knowledge from these data over shorter timescales is a priority for development engineers. This paper describes how this can be achieved by combining conventional engine research methods with the latest developments in process informatics and statistical analysis. Process informatics enables engineers to combine data, instrumental and application models to carry out automated model development including optimization and validation against large data repositories of experimental data.