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Since transient vehicle HVAC computational fluids (CFD) simulations take too long to solve in a production environment, the goal of this project is to automatically create a lumped-parameter flow network from a steady-state CFD that solves nearly instantaneously. The data mining algorithm k-means is implemented to automatically discover flow features and form the network (a reduced order model). The lumped-parameter network is implemented in the commercial thermal solver MuSES to then run as a fully transient simulation. Using this network a “localized heat transfer coefficient” is shown to be an improvement over existing techniques. Also, it was found that the use of the clustering created a new flow visualization technique. Finally, fixing clusters near equipment newly demonstrates a capability to track localized temperatures near specific objects (such as equipment in vehicles).

This paper presents a combined numerical and experimental investigation of the characteristics of spark discharge in a spark-ignition engine. The main objective of this work is to gain insights into the spark discharge process and early flame kernel development. Experiments were conducted in an inert medium within an optically accessible constant-volume combustion vessel. The cross-flow motion in the vessel was generated using a previously developed shrouded fan. Numerical modeling was based on an existing discharge model in the literature developed by Kim and Anderson. However, this model is applicable to a limited range of gas pressures and flow fields. Therefore, the original model was evaluated and improved to predict the behavior of spark discharge at pressurized conditions up to 45 bar and high-speed cross-flows up to 32 m/s. To accomplish this goal, a parametric study on the spark channel resistance was conducted.

Computational fluid dynamics of gas-fueled large-bore spark ignition engines with pre-chamber ignition can speed up the design process of these engines provided that 1) the reliability of the results is not affected by poor meshing and 2) the time cost of the meshing process does not negatively compensate for the advantages of running a computer simulation. In this work a flame propagation model that runs with arbitrary hybrid meshes was developed and coupled with the KIVA4-MHI CFD solver, in order to address these aims. The solver follows the G-Equation level-set method for turbulent flame propagation by Tan and Reitz, and employs improved numerics to handle meshes featuring different cell types such as hexahedra, tetrahedra, square pyramids and triangular prisms. Detailed reaction kinetics from the SpeedCHEM solver are used to compute the non-equilibrium composition evolution downstream and upstream of the flame surface, where chemical equilibrium is instead assumed.

Autonomous vehicle operation is dependent upon accurate position estimation and thus a major concern of implementing the autonomous navigation is obtaining robust and accurate data from sensors. This is especially true, in case of Inertial Measurement Unit (IMU) sensor data. The IMU consists of a 3-axis gyro, 3-axis accelerometer, and 3-axis magnetometer. The IMU provides vehicle orientation in 3D space in terms of yaw, roll and pitch. Out of which, yaw is a major parameter to control the ground vehicle’s lateral position during navigation. The accelerometer is responsible for attitude (roll-pitch) estimates and magnetometer is responsible for yaw estimates. However, the magnetometer is prone to environmental magnetic disturbances which induce errors in the measurement.

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.

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.

Lookup tables and functions are widely used in real-time embedded automotive applications to conserve scarce processor resources. To minimize the resource utilization, these lookup tables (LUTs) commonly use custom data structures. The lookup function code is optimized to process these custom data structures. The legacy routines for these lookup functions are very efficient and have been in production for many years. These lookup functions and the corresponding data structures are typically used for calibration tables. The third-party calibration tools are specifically tailored to support these custom data structures. These tools assist the calibrators in optimizing the control algorithm performance for the targeted environment for production. Application software typically contains a mix of both automatically generated software and manually developed code. Some of the same calibration tables may be used in both auto generated and hand-code [ 1 ] [ 2 ].

This paper analyzes the control of the series-parallel powersplit used in the 2001 Michigan Tech FutureTruck. An electronic throttle controller is implemented and a new control algorithm is proposed and tested. A vehicle simulation has been created in MATLAB and the control algorithm implemented within the simulation. A program written in C has also been created that implements the control algorithm in the test vehicle. The results from both the simulation and test vehicle are presented and discussed and show a 15% increase in fuel economy. With the increase in fuel economy, and through the use of the original exhaust after treatment, lower emissions are also expected.

The effects of an oxidation catalytic converter (OCC), an emulsified fuel, and their combined effects on particle number and volume concentrations compared to those obtained when using a basefuel were studied. Particle size and particulate emission measurements were conducted at three operating conditions; idle (850 rpm, 35 Nm), Mode 11 (1900 rpm, 277 Nm) and Mode 9 (1900 rpm, 831 Nm) of the EPA 13 mode cycle. The individual effects of the emulsified fuel and the OCC as well as their combined effects on particle number and volume concentrations were studied at two different particle size ranges; the nuclei (less than or equal to 50 nm) and accumulation (greater than 50 nm) modes. An OCC loaded with 10 g/ft3 platinum metal (OCC1) and a 20% emulsified fuel were used for this study and a notable influence on the particle size with respect to number and volume distributions was observed.

A study was conducted to assess the effects of a water-diesel fuel emulsion with and without an oxidation catalytic converter (OCC) on steady-state heavy-duty diesel engine emissions. Two OCCs with different metal loading levels were used in this study. A 1988 Cummins L10-300 heavy-duty diesel engine was operated at the rated speed of 1900 rpm and at 75% and 25% load conditions (EPA modes 9 and 11 respectively) of the 13 mode steady-state test as well as at idle. Raw exhaust emissions' measurements included total hydrocarbons (HC), oxides of nitrogen (NOx) and nitric oxide (NO). Diluted exhaust measurements included total particulate matter (TPM) and its primary constituents, the soluble organic (SOF), sulfate (SO42-) and the carbonaceous solids (SOL) fractions. Vapor phase organic compounds (XOC) were also analyzed. The SOF and XOC samples were analyzed for selected polynuclear aromatic hydrocarbons (PAHs).

This study investigates a methodology in which the general public's subjective interpretation of vehicle handling and performance can be predicted. Several vehicle handling measurements were acquired, and associated metrics calculated, in a controlled setting. Human evaluators were then asked to drive and evaluate each vehicle in a winter driving school setting. Using the acquired data, multiple linear regression and artificial neural network (ANN) techniques were used to create and refine mathematical models of human subjective responses. It is shown that artificial neural networks, which have been trained with the sets of objective and subjective data, are both more accurate and more robust than multiple linear regression models created from the same data.

A 2-dimensional numerical model (MTU-FILTER) for a single channel of a honeycomb ceramic diesel particulate trap has been developed. The mathematical modeling of the filtration, flow, heat transfer and regeneration behavior of the particulate trap is described. Numerical results for the pressure drop and particulate mass were compared with existing experimental results. Parametric studies of the diesel particulate trap were carried out. The effects of trap size and inlet temperature on the trap performance are studied using the trap model. An approximate 2-dimensional analytical solution to the simplified Navier-Stokes equations was used to calculate the velocity field of the exhaust flow in the inlet and outlet channels. Assuming a similarity velocity profile in the channels, the 2-dimensional Navier-Stokes equations are approximated by 1-dimenisonal conservation equations, which is similar to those first developed by Bissett.

The U.S. Bureau of Mines and Michigan Technological University are collaborating to conduct laboratory evaluations of oxidation catalytic converters (OCCs) and diesel fuels to identify combinations which minimize potentially harmful emissions. The purpose is to provide technical information concerning diesel exhaust emission control to the mining industry, regulators, and vendors of fuel and emission control devices. In this study, an Engelhard PTX 10 DVC (Ultra-10)* OCC was evaluated in the exhaust stream of an indirect injection Caterpillar 3304 PCNA mining engine using a light-duty laboratory transient cycle. This cycle was selected because it causes high emissions of particle-associated organics. Results are also reported for two different fuels with similar sulfur contents (0.03-0.04 wt pct) and a cetane number of 53, but different aromatic contents (11 vs. 20 wt pct).

Verification and validation (V&V) are essential stages in the design cycle of industrial controllers to remove the gap between the designed and implemented controller. In this study, a model-based adaptive methodology is proposed to enable easily verifiable controller design based on the formulation of a sliding mode controller (SMC). The proposed adaptive SMC improves the controller robustness against major implementation imprecisions including sampling and quantization. The application of the proposed technique is demonstrated on the engine cold start emission control problem in a mid-size passenger car. The cold start controller is first designed in a single-input single-output (SISO) structure with three separate sliding surfaces, and then is redesigned based on a multiinput multi-output (MIMO) SMC design technique using nonlinear balanced realization.

Real-time control of Reactivity Controlled Compression Ignition (RCCI) during engine load and speed transient operation is challenging, since RCCI combustion phasing depends on nonlinear thermo-kinetic reactions that are controlled by dual-fuel reactivity gradients. This paper discusses the design and implementation of a real-time closed-loop combustion controller to maintain optimum combustion phasing during RCCI transient operations. New algorithms for real-time in-cylinder pressure analysis and combustion phasing calculations are developed and embedded on a Field Programmable Gate Array (FPGA) to compute RCCI combustion and performance metrics on cycle-by-cycle basis. This cycle-by-cycle data is then used as a feedback to the combustion controller, which is implemented on a real-time processor. A computationally efficient algorithm is introduced for detecting Start of Combustion (SOC) for the High Temperature Heat Release (HTHR) or main-stage heat release.

Increasing fuel injection pressure has enabled reduction of diesel emissions while retaining the advantage of the high thermal efficiency of diesel engines. With production diesel injectors operating in the range from 300 to 2400 bar, there is interest in injection pressures of 3000 bar and higher for further emissions reduction and fuel efficiency improvements. Fundamental understanding of diesel spray characteristics including very early injection and non-vaporizing spray penetration is essential to improve model development and facilitate the integration of advanced injection systems with elevated injection pressure into future diesel engines. Studies were conducted in an optically accessible constant volume combustion vessel under non-vaporizing conditions. Two advanced high pressure multi-hole injectors were used with different hole diameters, number of holes, and flow rates, with only one plume of each injector being imaged to enable high frame rate imaging.

A new press/die system for restraining force control has been developed in order to facilitate an increased level of process control in sheet metal forming. The press features a built-in system for controlling drawbead penetration in real time. The die has local force transducers built into the draw radius of the lower tooling. These sensors are designed to give process information useful for the drawbead control. This paper focuses on developing models of the drawbead actuators and the die shoulder sensors. The actuator model is useful for developing optimal control methods. The sensor characterization is necessary in order to develop a relationship between the raw sensor outputs and a definitive process characteristic such as drawbead restraining force (DBRF). Closed loop control of local specific punch force is demonstrated using the die shoulder sensor and a PID controller developed off-line with the actuator model.

Studies have been conducted at Michigan Technological University (MTU) for over twenty years on methods for characterizing and controlling particulate emissions from heavy-duty diesel engines and the resulting effects on regulated and unregulated emissions. During that time, control technologies have developed in response to more stringent EPA standards for diesel emissions. This paper is a review of: 1) modern emission control technologies, 2) emissions sampling and chemical, physical and biological characterization methods and 3) summary results from recent studies conducted at MTU on heavy-duty diesel engines with a trap and an oxidation catalytic converter (OCC) operated on three different fuels. Control technology developments discussed are particulate traps, catalysts, advances in engine design, the application of exhaust gas recirculation (EGR), and modifications of fuel formulations.

In this study, the effects of an oxidation catalytic converter (OCC) on regulated and unregulated emissions from a 1991 prototype Cummins I.10-310 diesel engine fueled with a 0.01 weight percent sulfur fuel were investigated. The OCC's effects were determined by measuring and comparing selected raw exhaust emissions with and without the platinum-based OCC installed in the exhaust system, with the engine operated at three steady-state modes. It was found that the OCC had no significant effect on oxides of nitrogen (NOX) and nitric oxide (NO) at any mode, but reduced hydrocarbon (HC) emmissions by 60 to 70 percent. The OCC reduced total particulate matter (TPM) levels by 27 to 54 percent, primarily resulting from 53 to 71 percent reductions of the soluble organic fraction (SOF). The OCC increased sulfate (SO42-) levels at two of the three modes (modes 9 and 10), but the overall SO42- contribution to TPM was less than 6 percent at all modes due to the low sulfur level of the fuel.

Suitable and effective nutrient delivery systems will be required for both long-duration studies of plant growth and for implementation of bioregenerative life support technologies involving crop production in closed micro-gravity environments. Such environments are anticipated onboard a Space Station. The development of such systems hinges on acquiring the scientific and engineering knowledge necessary to design for micro-gravity operations. We have completed the preliminary design of a flight experiment called the Nutrient Delivery Testbed-1 (NDT-1) which will provide a substantial amount of information about the behavior of such systems in the space environment. The NDT-1 package includes a computer control subsystem, two motor-driven nutrient solution reservoirs, a nutrient solution composition monitoring subsystem, a solution sampling subsystem, three different nutrient delivery systems, and a plant surrogate.