Search Results

Optimization of the engine cold start is critical for gasoline direct injection (GDI) engines to meet increasingly stringent emission regulations, since the emissions during the first 20 seconds of the cold start constitute more than 80% of the hydrocarbon (HC) emissions for the entire EPA FTP75 drive cycle. However, Direct Injection Spark Ignition (DISI) engine cold start optimization is very challenging due to the rapidly changing engine speed, cold thermal environment and low cranking fuel pressure. One approach to reduce HC emissions for DISI engines is to adopt retarded spark so that engines generate high heat fluxes for faster catalyst light-off during the cold idle. This approach typically degrades the engine combustion stability and presents additional challenges to the engine cold start. This paper describes a CFD modeling based approach to address these challenges for the Ford 3.5L V6 EcoBoost engine cold start.

In order to detect degradation of engine oil lubricant, bench testing along with a number of diesel-powered Ford trucks were instruments and tested. The purpose of the bench testing was primarily to determine performance aspects such as repeatability, hysteresis effects and so on. Vehicle testing was conducted by designing and installing a separate oil reservoir along with a circulation system which was mounted in the vicinity of the oil pan. An innovative oil sensor was directly installed on the reservoir which can measure five (5) independent oil parameters (viscosity, density, permittivity, conductance, temperature). In addition, the concept is capable of detecting the oil level continuously during normal engine operation. The sensing system consists of an ultrasonic transducer for the oil level detection as well as a Tuning Fork mechanical resonator for the oil condition measurement.

A systematic methodology has been employed to develop the Duratec 3.5L EcoBoost combustion system, with focus on the optimization of the combustion system including injector spray pattern, intake port design, piston geometry, cylinder head geometry. The development methodology was led by CFD (Computational Fluid Dynamics) modeling together with a testing program that uses optical, single-cylinder, and multi-cylinder engines. The current study shows the effect of several spray patterns on air-fuel mixing, in-cylinder flow development, surface wetting, and turbulence intensity. A few sets of injector spray patterns are studied; some that have a wide total cone angle, some that have a narrow cone angle and a couple of optimized injector spray patterns. The effect of the spray pattern at part load, full load and cold start operation was investigated and the methodology for choosing an optimized injector is presented.

This paper describes a CFD modeling based approach to address design challenges in GDI (gasoline direct injection) engine combustion system development. A Ford in-house developed CFD code MESIM (Multi-dimensional Engine Simulation) was applied to the study. Gasoline fuel is multi-component in nature and behaves very differently from the single component fuel representation under various operating conditions. A multi-component fuel model has been developed and is incorporated in MESIM code. To apply the model in engine simulations, a multi-component fuel recipe that represents the vaporization characteristics of gasoline is also developed using a numerical model that simulates the ASTM D86 fuel distillation experimental procedure. The effect of the multi-component model on the fuel air mixture preparations under different engine conditions is investigated. The modeling approach is applied to guide the GDI engine piston designs.

Advanced High Strength Steels (AHSS) have been implemented in the automotive industry to balance the requirements for vehicle crash safety, emissions, and fuel economy. With lower ductility compared to conventional steels, the fracture behavior of AHSS components has to be considered in vehicle crash simulations to achieve a reliable crashworthiness prediction. Without considering the fracture behavior, component fracture cannot be predicted and subsequently the crash energy absorbed by the fractured component can be over-estimated. In full vehicle simulations, failure to predict component fracture sometimes leads to less predicted intrusion. In this paper, the feasibility of using computer simulations in predicting fracture during crash deformation is studied.

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).

A novel surrogate model is presented, which predicts the engine-out Particle Number (PN) emissions of a light-duty, spray-guided, turbo-charged, GDI engine. The model is developed through extensive CFD analysis, carried out using the Siemens Simcenter STAR-CD, and considers a range of part-load operating conditions and single-variable sweeps where control parameters such as start of injection and injection pressure are varied in isolation. The work is attached to the Ford-led APC6 DYNAMO project, which aims to improve efficiency and reduce harmful emissions from the next generation of gasoline engines. The CFD work focused on the air exchange, fuel spray and mixture preparation stages of the engine cycle. A combined Rosin-Rammler and Reitz-Diwakar model, calibrated over a wide range of injection pressure, is used to model fuel atomization and secondary droplets break-up.

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.

Connectivity and autonomy in vehicles promise improved efficiency, safety and comfort. The increasing use of embedded systems and the cyber element bring with them many challenges regarding cyberattacks which can seriously compromise driver and passenger safety. Beyond penetration testing, assessment of the security vulnerabilities of a component must be done through the design phase of its life cycle. This paper describes the development of a benchtop testbed which allows for the assurance of safety and security of components with all capabilities from Model-in-loop to Software-in-loop to Hardware-in-loop testing. Environment simulation is obtained using the AV simulator, CARLA which provides realistic scenarios and sensor information such as Radar, Lidar etc. MATLAB runs the vehicle, powertrain and control models of the vehicle allowing for the implementation and testing of customized models and algorithms.

This paper presents an evaluation of two different Vehicle to Infrastructure (V2I) applications, namely Red Light Violation Warning (RLVW) and Green Light Optimized Speed Advisory (GLOSA). The evaluation method is to first develop and use Hardware-in-the-Loop (HIL) simulator testing, followed by extension of the HIL testing to road testing using an experimental connected vehicle. The HIL simulator used in the testing is a state-of-the-art simulator that consists of the same hardware like the road side unit and traffic cabinet as is used in real intersections and allows testing of numerous different traffic and intersection geometry and timing scenarios realistically. First, the RLVW V2I algorithm is tested in the HIL simulator and then implemented in an On-Board-Unit (OBU) in our experimental vehicle and tested at real world intersections.

Innovations in mobility are built upon a management of complex interactions between sub-systems and components. A need for CAE tools that are capable of system simulations is well recognized, as evidenced by a growing number of commercial packages. However impressive they are, the predictability of such simulations still rests on the representation of the base components. Among them, a wet clutch actuator continues to play a critical role in the next generation propulsion systems. It converts hydraulic pressure to mechanical force to control torque transmitted through a clutch pack. The actuator is typically modeled as a hydraulic piston opposed by a mechanical spring. Because the piston slides over a seal, some models have a framework to account for seal friction. However, there are few contributions to the literature that describe the effects of seals on clutch actuator behaviors.

A wet clutch model is required in automotive propulsion system simulations for enabling robust design and control development. It commonly assumes Coulomb friction for simplicity, even though it does not represent the physics of hydrodynamic torque transfer. In practice, the Coulomb friction coefficient is treated as a tuning parameter in simulations to match vehicle data for targeted conditions. The simulations tend to deviate from actual behaviors for different drive conditions unless the friction coefficient is adjusted repeatedly. Alternatively, a complex hydrodynamic model, coupled with a surface contact model, is utilized to enhance the fidelity of system simulations for broader conditions. The theory of elastic asperity deformation is conventionally employed to model clutch surface contact. However, recent examination of friction material shows that the elastic modulus of surface fibers significantly exceeds the contact load, implying no deformation of fibers.

Combined active and passive damping is a recent trend that can be an effective solution to challenging NVH problems, especially for lightweight vehicle components that demand advanced noise and vibration treatments. Compact patches are of particular interest due to their small size and cost, however, improved modeling techniques are needed at the design stage for such methods. This paper presents a refined modeling procedure for side-by-side active and passive damping patches applied to thin, plate-like, powertrain casing structures. As an example, a plate with fixed boundaries is modeled as this is representative of real-life transmission covers which often require damping treatments. The proposed model is then utilized to examine several cases of active and passive patch location, and vibration reduction is determined in terms of insertion loss for each case.

Elastomeric joints are utilized in many automotive applications, and exhibit frequency and excitation amplitude dependent properties. Current methods commonly identify only the cross-point joint property using displacement excitation at stepped single frequencies. This process is often time consuming and is limited to measuring a single dynamic stiffness term of the joint stiffness matrix. This study focuses on developing tractable laboratory inverse experiments to identify frequency dependent stiffness matrices up to 1000 Hz. Direct measurements are performed on a commercial elastomer test system and an inverse experiment consisting of an elastic beam (with a square cross section) attached to a cylindrical elastomeric joint. Sources of error in the inverse methodology are thoroughly examined and explained through simulation which include ill-conditioning of matrices and the sensitivity to modeling error.

Engine downsizing and super/turbocharging is currently the most followed trend in order to reduce CO2 emissions and increase the powertrain efficiency. A key challenge for achieving the desired fuel economy benefits lies in optimizing the design and control of the engine boosting system, which requires the ability to rapidly sort different design options and technologies in simulation, evaluating their impact on engine performance and fuel consumption. This paper presents a scalable modeling approach for the characterization of flow and efficiency maps for automotive turbochargers. Starting from the dimensional analysis theory for turbomachinery and a set of well-known control-oriented models for turbocharged engines simulation, a novel scalable model is proposed to predict the flow and efficiency maps of centrifugal compressors and radial inflow turbines as function of their key design parameters.

In this paper, a three-dimensional (3D) microstructure-based finite element modeling method (i.e., extrinsic modeling method) is developed, which can be used in examining the effects of porosity on the ductility/fracture of Mg castings. For this purpose, AM60 Mg tensile samples were generated under high-pressure die-casting in a specially-designed mold. Before the tensile test, the samples were CT-scanned to obtain the pore distributions within the samples. 3D microstructure-based finite element models were then developed based on the obtained actual pore distributions of the gauge area. The input properties for the matrix material were determined by fitting the simulation result to the experimental result of a selected sample, and then used for all the other samples’ simulation. The results show that the ductility and fracture locations predicted from simulations agree well with the experimental results.

This paper proposes a novel probabilistic approach for multidisciplinary design optimization (MDO) under uncertainty, especially for systems with feedback coupled analyses with multiple coupling variables. The proposed approach consists of four components: multidisciplinary analysis, Bayesian network, copula-based sampling, and design optimization. The Bayesian network represents the joint distribution of multiple variables through marginal distributions and conditional probabilities, and updates the distributions based on new data. In this methodology, the Bayesian network is pursued in two directions: (1) probabilistic surrogate modeling to estimate the output uncertainty given values of the design variables, and (2) probabilistic multidisciplinary analysis (MDA) to infer the distributions of the coupling and output variables that satisfy interdisciplinary compatibility conditions.

Vehicle to Vehicle Communication use case performance heavily relies on market penetration rate. The more vehicles support a use case, the better the customer experience. Enabling these use cases with acceptable quality on vehicles without built-in navigation systems, elaborate map matching and highly accurate sensors is challenging. This paper introduces a simulation framework to assess system performance in dependency of vehicle positioning accuracy for matching approach path traces in Decentralized Environmental Notification Messages (DENMs) in absence of navigation systems supporting map matching. DENMs are used for distributing information about hazards on the road network. A vehicle without navigation system and street map can only match its position trajectory with the trajectory carried in the DENM.

In an automotive cooling circuit, the wax melting process determines the net and time history of the energy transfer between the engine and its environment. A numerical process that gives insight into the mixing process outside the wax chamber, the wax melting process inside the wax chamber, and the effect on the poppet valve displacement will be advantageous to both the engine and automotive system design. A fully three dimensional, transient, system level simulation of an inlet controlled thermostat inside an automotive cooling circuit is undertaken in this paper. A proprietary CFD algorithm, Simerics-Sys®/PumpLinx®, is used to solve this complex problem. A two-phase model is developed in PumpLinx® to simulate the wax melting process. The hysteresis effect of the wax melting process is also considered in the simulation.

Control of vehicle powertrain thermal management systems is becoming more challenging as the number of components is growing, and as a result, advanced control methods are being investigated. Model predictive control (MPC) is particularly interesting in this application because it provides a suitable framework to manage actuator and temperature constraints, and can potentially leverage preview information if available in the future. In previous SAE publications (2015-01-0336 and 2016-01-0215), a robust MPC control formulation was proposed, and both simulation and powertrain thermal lab test results were provided. In this work, we discuss the controller deployment in a vehicle; where controller validation is done through road driving and on a wind tunnel chassis dynamometer. This paper discusses challenges of linear MPC implementation related to nonlinearities in this over-actuated thermal system.