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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.

A 1-dimensional analytic solution has been developed to evaluate the pressure drop and filtration performance of ceramic wall-flow partial diesel particulate filters (PFs). An axially resolved mathematical model for the static pressure and velocity profiles prevailing inside wall-flow filters, with such unique plugging configurations, is being proposed for the first time. So far, the PF models that have been developed are either iterative/numerical in nature [1], or based on commercial CFD packages [7]. In comparison, an analytic solution approach is a transparent and computationally inexpensive tool that is capable of accurately predicting trends as well as, offering explanations to fundamental performance behavior. The simple mathematical expressions that have been obtained facilitate rational decision-making when designing partial filters, and could also reduce the complexity of OBD logic necessary to control onboard filter performance.

Wheel loader subsystems are multi-domain in nature, including controls, mechanisms, hydraulics, and thermal. This paper describes the process of developing a multi-domain simulation of a wheel loader. Working hydraulics, kinematics of the working tool, driveline, engine, and cooling system are modeled in LMS Imagine.Lab Amesim. Contacts between boom/bucket and bucket/ground are defined to constrain the movement of the bucket and boom. The wheel loader has four heat exchangers: charge air cooler, radiator, transmission oil cooler, and hydraulic oil cooler. Heat rejection from engine, energy losses from driveline, and hydraulic subsystem are inputs to the heat exchangers. 3D CFD modeling was done to calibrate airflows through heat exchangers in LMS Amesim. CFD modeling was done in ANSYS FLUENT® using a standard k - ε model with detailed fan and underhood geometry.

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.

This paper presents the business purpose, software architecture, technology integration, and applications of the Cummins Vehicle Mission Simulation (VMS) software. VMS is the value-based analysis tool used by the marketing, sales, and product engineering functions to simulate vehicle missions quickly and to gauge, communicate, and improve the value proposition of Cummins engines to customers. VMS leverages the best of software architecture practices and proven technologies available today. It consists of a close integration of MATLAB and Simulink with Java, XML, and JDBC technologies. This Windows compatible application software uses stand-alone mathematical models compiled using Real Time Workshop. A built-in MySQL database contains product data for engines, driveline components, vehicles, and topographic routes. This paper outlines the database governance model that facilitates effective management, control, and distribution of engine and vehicle data across the enterprise.

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.

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.

An analysis of vehicle engine cooling airflow by means of a one-dimensional, transient, compressible flow model was carried out and revealed that similarity theory could be applied to investigate the variation of the airflow with ambient and operating conditions. It was recognized that for a given vehicle engine cooling system, the cooling airflow behavior could be explained using several dimensionless parameters that involve the vehicle speed, fan speed, heat transfer rate through the radiator, ambient temperature and pressure, and the system characteristic dimension. Using the flow resistance and fan characteristics measured from a prototype cooling system and the computer simulation for the one-dimensional compressible flow model, a quantitative correlation of non-dimensional mass flow rate to three dimensionless parameters for a prototype heavy-duty truck was established. The results are presented in charts, tables, and formulas.

A one-dimensional incompressible flow model covering the mechanisms involved in the airflow through an automotive radiator-shroud-fan system with no heat transfer was developed. An analytical expression to approximate the experimentally determined fan performance characteristics was used in conjunction with an analytical approach for this simplified cooling airflow model, and the solution is discussed with illustrations. A major result of this model is a closed form equation relating the transient velocity of the air to the vehicle speed, pressure rise characteristics and speed of the fan, as well as the dimensions and resistance of the radiator. This provides a basis for calculating cooling airflow rate under various conditions. The results of the incompressible flow analysis were further compared with the computational results obtained with a previously developed one-dimensional, transient, compressible flow model.

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.

Interest in operational cost reduction is driving engine manufacturers to consider low-cost fuel substitution in heavy-duty diesel engines. These dual-fuel (DF) engines could be operated either in diesel-only mode or operated with premixed natural gas (NG) ignited by a pilot flame of compression-ignited direct-injected diesel fuel. Under certain conditions, dual-fuel operation can result in increased cycle-to-cycle variability (CCV) during combustion. CFD can greatly help in understanding and identifying critical parameters influencing CCV. Innovative modelling techniques and large computing resources are needed to investigate the factors affecting CCV in dual-fuel engines. This paper discusses the use of the High Performance Computing resource Titan, at Oak Ridge National Laboratory, to investigate CCV of a dual-fuel engine.

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.

The increasing complexity of vehicle engine cooling systems results in additional system interactions. Design and evaluation of such systems and related interactions requires a fully coupled detailed engine and cooling system model. The Vehicle Engine Cooling System Simulation (VECSS) developed at Michigan Technological University was enhanced by linking with GT-POWER for the engine/cycle analysis model. Enhanced VECSS (E-VECSS) predicts the effects of cooling system performance on engine performance including accessory power and fuel conversion efficiency. Along with the engine cycle, modeled components include the engine manifolds, turbocharger, radiator, charge-air-cooler, engine oil circuit, oil cooler, cab heater, coolant pump, thermostat, and fan. This tool was then applied to develop and simulate an actively controlled electric cooling system for a 12.7 liter diesel engine.

We report experimental observations of cyclic combustion variability during the transition between propagating flame combustion and homogeneous charge compression ignition (HCCI) in a single-cylinder, stoichiometrically fueled, spark-assisted gasoline engine. The level of internal EGR was controlled with variable valve actuation (VVA), and HCCI combustion was achieved at high levels of internal EGR using the VVA system. Spark-ignition was used for conventional combustion and was optionally available during HCCI. The transition region between purely propagating combustion and HCCI was mapped at multiple engine speeds and loads by incrementally adjusting the internal EGR level and capturing data for 2800 sequential cycles. These measurements revealed a complex sequence of high COV, cyclic combustion variations when operating between the propagating flame and HCCI limits.

The objective of this study is the development of a computationally efficient CFD-based tool with the capability of finding optimal engine operating conditions with respect to emissions and fuel consumption. The approach taken uses a conjugate gradient method, where the line search is performed with a backtracking algorithm. The initial backtracking step employs an adaptive step size mechanism which depends on the steepness of the search direction. The engine simulations are performed with a KIVA-3-based code which is equipped with well-established spray, combustion and emission models. The cost function is based on the idea of the penalty method and is minimized over the unit cube in n-dimensional space, which represents the set of normalized injection parameters under investigation. The application of this optimization tool is demonstrated for the Sulzer S20, a central-injection, non-road DI diesel engine.