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One of the remaining challenges in the simulation of the aerodynamics of ground vehicles is the modeling of the airflows around the spinning tires and wheels of the vehicle. As in most advances in the development of simulation capabilities, it is the lack of appropriately detailed and accurate experimental data with which to correlate that holds back the advance of the technology. The flow around the wheels and tires and their interfaces with the vehicle body and the ground is a critical area for the development of automobiles and trucks, not just for aerodynamic forces and moments, and their result on fuel economy and vehicle handling and performance, but also for the airflows and pressures that affect brake cooling, engine cooling airflows, water spray management etc.

Engine mount is one of the temperature sensitive components in the vehicle under-hood. Due to increasing requirements for improved fuel economy, the under-hood thermal management has become very challenging in recent years. In order to study the effects of material thermal degradation on engine mount performance and durability; it is required to estimate the temperature of engine mount rubber during various driving conditions. The effect of temperature on physical properties of natural rubber can then be evaluated and the life of engine mount can be estimated. In this paper, a bench test is conducted where the engine mount is exposed to a step change in the environment around it, and the temperature of the rubber section is recorded at several points till a steady state temperature is reached. A time response curve is generated, from which a time constant is determined.

In this paper, transient component temperatures for the vehicle under-hood and underbody are estimated. The main focus is on the component temperatures as a result of radiation from exhaust, convection by underbody or under-hood air and heat conduction through the components. The exhaust surface temperature is simulated as function of time and for various vehicle duty cycles such as city traffic, road load and grade driving conditions. At each time step the radiation flux to the surrounding component is estimated, heat addition or removal by convection is evaluated based on air flow, air temperature and component surface area. Simulation results for under-hood and underbody components are compared against vehicle test data. The comparison shows very good agreement between simulated and measured component temperatures under both steady state and transient conditions.

Several popular frequency-based fatigue damage models (Wirsching and Light, Ortiz and Chen, Larsen and Lutes, Benascuitti and Tovo, Benascuitti and Tovo with α.75, Dirlik, Zhao and Baker, and Lalanne) are reviewed and assessed. Seventy power spectrum densities with varied amplitude, shape, and irregularity factors from Dirlik’s dissertation are used to study the accuracies of these methods. Recommendations on how to set up the inverse fast Fourier transform to synthesize load data and obtain accurate rainflow cycle counts are given. Since Dirlik’s method is the most commonly used one in industry, a comprehensive investigation of parameter setups for Dirlik’s method is presented. The mean error and standard deviation of the error between the frequency-based model and the rainflow cycle counting method was computed for fatigue slope exponent m ranging from 3 to 12.

This paper considers optimal power management during the establishment of an expeditionary outpost using battery and vehicle assets for electrical generation. The first step in creating a new outpost is implementing the physical protection and barrier system. Afterwards, facilities that provide communications, fires, meals, and moral boosts are implemented that steadily increase the electrical load while dynamic events, such as patrols, can cause abrupt changes in the electrical load profile. Being able to create a fully functioning outpost within 72 hours is a typical objective where the electrical power generation starts with batteries, transitions to gasoline generators and is eventually replaced by diesel generators as the outpost matures. Vehicles with power export capability are an attractive supplement to this electrical power evolution since they are usually on site, would reduce the amount of material for outpost creation, and provide a modular approach to outpost build-up.

Automobiles have a high degree of mechanical and electrical complexity. However, product complexity has the accompanying effect of requiring high levels of design and process oversight. The net result is a product creation process which is prone to creating failures. These failures typically have their origin in an overall lack of complete understanding of the system in terms of materials, geometries and energy flows. Despite all of the engineering intentions, failures are inevitable, common, and must be dealt with accordingly. In the worst case, if a failure manifests itself into an observable failure the customer may have a negative experience. Therefore, it is imperative that design engineers, suppliers along with reliability professionals be able to assess the design risk. One approach to assess risk is the use of degradation analysis. Degradation analysis often provides more information than failure time data for assessing reliability and predicting the remnant life of a system.

Engine air induction shell noise is a structure borne noise that radiates from the surface of the air induction system. The noise is driven by pulsating engine induction air and is perceived as annoying by vehicle passengers. The problem is aggravated by the vehicle design demands for low weight components packaged in an increasingly tight under hood environment. Shell noise problems are often not discovered until production intent parts are available and tested on the vehicle. Part changes are often necessary which threatens program timing. Shell noise should be analyzed in the air induction system design phase and a good shell noise analytical process and targets must be defined. Several air induction clean side ducts are selected for this study. The ducts shell noise is assessed in terms of material strength and structural stiffness. A measurement process is developed to evaluate shell noise of the air induction components. Noise levels are measured inside of the clean side ducts.

A computational fluid dynamics (CFD) guided combustion system optimization was conducted for a heavy-duty compression-ignition engine with a gasoline-like fuel that has an anti-knock index (AKI) of 58. The primary goal was to design an optimized combustion system utilizing the high volatility and low sooting tendency of the fuel for improved fuel efficiency with minimal hardware modifications to the engine. The CFD model predictions were first validated against experimental results generated using the stock engine hardware. A comprehensive design of experiments (DoE) study was performed at different operating conditions on a world-leading supercomputer, MIRA at Argonne National Laboratory, to accelerate the development of an optimized fuel-efficiency focused design while maintaining the engine-out NOx and soot emissions levels of the baseline production engine.

This research proposes a control system for Spark Ignition (SI) engines with external Exhaust Gas Recirculation (EGR) based on model predictive control and a disturbance observer. The proposed Economic Nonlinear Model Predictive Controller (E-NMPC) tries to minimize fuel consumption for a number of engine cycles into the future given an Indicated Mean Effective Pressure (IMEP) tracking reference and abnormal combustion constraints like knock and combustion variability. A nonlinear optimization problem is formulated and solved in real time using Sequential Quadratic Programming (SQP) to obtain the desired control actuator set-points. An Extended Kalman Filter (EKF) based observer is applied to estimate engine states, combining both air path and cylinder dynamics. The EKF engine state(s) observer is augmented with disturbance estimation to account for modeling errors and/or sensor/actuator offset.

Piston temperature plays a major role in determining details of fuel spray vaporization, fuel film deposition and the resulting combustion in direct-injection engines. Due to different heat transfer properties that occur in optical and all-metal engines, it becomes an inevitable requirement to verify the piston temperatures in both engine configurations before carrying out optical engine studies. A novel Spot Infrared-based Temperature (SIR-T) technique was developed to measure the piston window temperature in an optical engine. Chromium spots of 200 nm thickness were vacuum-arc deposited at different locations on a sapphire window. An infrared (IR) camera was used to record the intensity of radiation emitted by the deposited spots. From a set of calibration experiments, a relation was established between the IR camera measurements of these spots and the surface temperature measured by a thermocouple.

A Machine Learning-Genetic Algorithm (ML-GA) approach was developed to virtually discover optimum designs using training data generated from multi-dimensional simulations. Machine learning (ML) presents a pathway to transform complex physical processes that occur in a combustion engine into compact informational processes. In the present work, a total of over 2000 sector-mesh computational fluid dynamics (CFD) simulations of a heavy-duty engine were performed. These were run concurrently on a supercomputer to reduce overall turnaround time. The engine being optimized was run on a low-octane (RON70) gasoline fuel under partially premixed compression ignition (PPCI) mode. A total of nine input parameters were varied, and the CFD simulation cases were generated by randomly sampling points from this nine-dimensional input space. These input parameters included fuel injection strategy, injector design, and various in-cylinder flow and thermodynamic conditions at intake valve closure (IVC).

An engine exhaust manifold undergoes repeated thermal expansion and contraction due to temperature variation. Thermomechanical fatigue (TMF) arises due to the boundary constraints on thermal expansion so that mechanical strain is introduced. Therefore, TMF evaluation is very important in engine design. In this work, the mechanical properties important for TMF assessment and modeling of a silicon (Si)- and molybdenum (Mo)-containing ductile cast iron used for exhaust manifold have been evaluated. Tensile, creep, isothermal low cycle fatigue (LCF), and TMF tests have been conducted. Parameters for material modeling, such as the viscoplastic constitutive model and the Neu-Sehitoglu TMF damage model, have been calibrated, validated, and used to evaluate the TMF life of the exhaust manifold.

Multiaxial loading on mechanical products is very common in the automotive industry, and how to design and analyze these products for durability becomes an important, urgent task for the engineering community. Due to the complex nature of the fatigue damage mechanism for a product under multiaxial state of stresses/strains which are dependent upon the modes of loading, materials, and life, modeling this behavior has always been a challenging task for fatigue scientists and engineers around the world. As a result, many multiaxial fatigue theories have been developed. Among all the theories, an existing equivalent stress theory is considered for use for the automotive components that are typically designed to prevent Case B cracks in the high cycle fatigue regime.

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.

Fuel lean combustion and exhaust gas dilution are known to increase the thermal efficiency and reduce NOx emissions. In this study, experiments are performed to understand the effect of equivalence ratio on flame kernel formation and flame propagation around the spark plug for different low turbulent velocities. A series of experiments are carried out for propane-air mixtures to simulate engine-like conditions. For these experiments, equivalence ratios of 0.7 and 0.9 are tested with 20 percent mass-based exhaust gas recirculation (EGR). Turbulence is generated by a shrouded fan design in the vicinity of J-spark plug. A closed loop feedback control system is used for the fan to generate a consistent flow field. The flow profile is characterized by using Particle Image Velocimetry (PIV) technique. High-speed Schlieren visualization is used for the spark formation and flame propagation.

Combustion phasing of Spark Ignition (SI) engines is traditionally regulated with map-based spark timing (SPKT) control. The calibration time and effort of this feed forward SPKT control strategy becomes less favorable as the number of engine control actuators increases. This paper proposes a model based combustion phasing control frame work. The feed forward control law is obtained by real time numerical optimization utilizing a high-fidelity combustion model that is based on flame entrainment theory. An optimization routine identifies the SPKT which phases the combustion close to the target without violating combustion constraints of knock and excessive cycle-by-cycle covariance of indicated mean effective pressure (COV of IMEP). Cylinder pressure sensors are utilized to enable feedback control of combustion phasing. An Extended Kalman Filter (EKF) is applied to reject sensor noise and combustion variation from the cylinder pressure signal.

As engines are equipped with an increased number of control actuators to meet fuel economy targets they become more difficult to control and calibrate. The large number of control actuators encourages the investigation of physics-based control strategies to reduce calibration time and complexity. Of particular interest is spark timing control and calibration since it has a significant influence on engine efficiency, emissions, vibration and durability. Spark timing determination to achieve a desired combustion phasing is currently an empirical process that occurs during the calibration phase of engine development. This process utilizes a large number of stored surfaces and corrections to account for the wide range of operating environments and conditions that a given engine will experience. An obstacle to realizing feedforward physics-based combustion phasing control is the requirement for an accurate and fast combustion model.

This work involves modeling internal and near-nozzle flows of a gasoline direct injection (GDI) nozzle. The Engine Combustion Network (ECN) Spray G condition has been considered for these simulations using the nominal geometry of the Spray G injector. First, best practices for numerical simulation of the two-phase flow evolution inside and the near-nozzle regions of the Spray G injector are presented for the peak needle lift. The mass flow rate prediction for peak needle lift was in reasonable agreement with experimental data available in the ECN database. Liquid plume targeting angle and liquid penetration estimates showed promising agreement with experimental observations. The capability to assess the influence of different thermodynamic conditions on the two-phase flow nature was established by predicting non-flashing and flashing phenomena.

The influence of spark plug orientation on early flame kernel development is investigated in an optically accessible gasoline direct injection homogeneous charged spark ignition engine. This investigation provides visual understanding and statistical characterization of how spark plug orientation impacts the early flame kernel and thus combustion phasing and engine performance. The projected images of flame kernel were captured through natural flame chemiluminescence with a high-speed camera at 10,000 frames per second, and the ignition secondary discharge voltage and current were measured with a 10 MHz DAQ system. The combustion metrics were determined using measurement from a piezo-electric in-cylinder pressure transducer and real-time engine combustion analyzer. Three spark plug orientations with two different electrode designs were studied. The captured images of the flame were processed to yield 2D and 1D probability distributions.

Low Temperature Combustion (LTC) engines are promising to improve powertrain fuel economy and reduce NOx and soot emissions by improving the in-cylinder combustion process. However, the narrow operating range of LTC engines limits the use of these engines in conventional powertrains. The engine’s limited operating range can be improved by taking advantage of electrification in the powertrain. In this study, a multi-mode LTC-SI engine is integrated with a parallel hybrid electric configuration, where the engine operation modes include Homogeneous Charge Compression Ignition (HCCI), Reactivity Controlled Compression Ignition (RCCI), and conventional Spark Ignition (SI). The powertrain controller is designed to enable switching among different modes, with minimum fuel penalty for transient engine operations.