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Modeling plume interaction and collapse for direct-injection gasoline sprays is important because of its impact on fuel-air mixing and engine performance. Nevertheless, the aerodynamic interaction between plumes and the complicated two-phase coupling of the evaporating spray has shown to be notoriously difficult to predict. With the availability of high-speed (100 kHz) Particle Image Velocimetry (PIV) experimental data, we compare velocity field predictions between plumes to observe the full temporal evolution leading up to plume merging and complete spray collapse. The target “Spray G” operating conditions of the Engine Combustion Network (ECN) is the focus of the work, including parametric variations in ambient gas temperature. We apply both LES and RANS spray models in different CFD platforms, outlining features of the spray that are most critical to model in order to predict the correct aerodynamics and fuel-air mixing.

This paper presents a unified modeling environment to simulate vehicle driving and powertrain operations within the context of the surrounding environment, including interactions between vehicles and between vehicles and the road. The goal of this framework is to facilitate the analysis of the energy impacts of vehicle connectivity and automation, as well as the development of eco-driving algorithms. Connectivity and automation indeed provide the potential to use information about the environment and future driving to minimize energy consumption. To achieve this goal, the designers of eco-driving control strategies need to simulate a wide range of driving situations, including the interactions with other vehicles and the infrastructure in a closed-loop fashion.

The 4th Workshop of the Engine Combustion Network (ECN) was held September 5-6, 2015 in Kyoto, Japan. This manuscript presents a summary of the progress in experiments and modeling among ECN contributors leading to a better understanding of soot formation under the ECN “Spray A” configuration and some parametric variants. Relevant published and unpublished work from prior ECN workshops is reviewed. Experiments measuring soot particle size and morphology, soot volume fraction (fv), and transient soot mass have been conducted at various international institutions providing target data for improvements to computational models. Multiple modeling contributions using both the Reynolds Averaged Navier-Stokes (RANS) Equations approach and the Large-Eddy Simulation (LES) approach have been submitted. Among these, various chemical mechanisms, soot models, and turbulence-chemistry interaction (TCI) methodologies have been considered.

As a result of increasingly stringent regulations and higher customer expectations, auto manufacturers have been considering numerous technology options to improve vehicle fuel economy. Transmissions have been shown to be one of the most cost-effective technologies for improving fuel economy. Over the past couple of years, transmissions have significantly evolved and impacted both performance and fuel efficiency. This study validates the shifting control of advanced automatic transmission technologies in vehicle systems by using Argonne National Laboratory's model-based vehicle simulation tool, Autonomie. Different midsize vehicles, including several with automatic transmission (6-speeds, 7-speeds, and 8-speeds), were tested at Argonne's Advanced Powertrain Research Facility (APRF). For the vehicles, a novel process was used to import test data.

The particulate matter (PM) produced from a diesel engine-simulating combustor was characterized in its morphology, microstructure, and fractal geometry by using a unique thermophoretic sampling and Transmission Electron Microscopy (TEM) system. These results revealed that diesel PM produced from the laboratory-scale burner showed similar morphological characteristics to the particulates produced from diesel engines. The flame air/fuel ratio and the particulate temperature history have significant influences on both particle size and fractal geometry. The primary particle sizes were measured to be 14.7 nm and 14.8 nm under stoichiometric and fuel-rich flame conditions, respectively. These primary particle sizes are smaller than those produced from diesel engines. The radii of gyration for the aggregate particles were 83.8 nm and 47.5 nm under these two flame conditions.

In order to ensure the safety of a structure, adequate strength for structural elements must be provided. Moreover, catastrophic deformations such as buckling must be prevented. Using the linear finite element method, deterministic buckling analysis is completed in two main steps. First, a static analysis is performed using an arbitrary ordinate applied loading pattern. Using the obtained element axial forces, the geometric stiffness of the structure is assembled. Second, an eigenvalue problem is performed between structure's elastic and geometric stiffness matrices, yielding the structure's critical buckling loads. However, these deterministic approaches do not consider uncertainty the structure's material and geometric properties. In this work, a new method for finite element based buckling analysis of a structure with uncertainty is developed. An imprecise probability formulation is used to quantify the uncertainty present in the mechanical characteristics of the structure.

Gasoline is a complex fuel. Many of the constituents of gasoline that are beneficial for the internal combustion engine (ICE) are expected to be challenging for on-board reformers in fuel-cell vehicles. To address these issues, the autothermal reforming of gasoline and individual components of gasoline has been investigated. The results indicate that aromatic components require higher temperatures and longer contact times to reform than paraffinic components. Napthenic components require higher temperatures to reform, but can be reformed at higher space velocities than paraffinic components. The effects of sulfur are dependent on the catalyst. These results suggest that further evolution of gasoline could reduce the demands on the reformer and provide a better fuel for a fuel-cell vehicle.

Preliminary research in our laboratory has demonstrated that boric acid is an effective lubricant with an unusual capacity to reduce the sliding friction (providing friction coefficients as low as 0.02) and wear of metallic and ceramic materials. More recent studies have revealed that water or methanol solutions of boric acid can be used to prepare strongly bonded layers of boric acid on aluminum surfaces. It appears that boric acid molecules have a strong tendency to bond chemically to the naturally oxidized surfaces of aluminum and its alloys and to make these surfaces very slippery. Recent metal-formability tests indicated that the boric acid films applied to aluminum surfaces worked quite well, improving draw scale performance by 58 to 75%.

The primary objective of the research discussed here was to compare the commercial computational fluid dynamics (CFD) software, CONVERGE, and a prevalent open-source code, OpenFOAM, with regard to their ability to predict spray and combustion characteristics. The high-fidelity data were obtained from the Engine Combustion Network (ECN) at Sandia National Laboratory in a constant-volume combustion vessel under well-defined, controlled conditions. The experiments and simulations were performed by using two diesel surrogate fuels (i.e., n-heptane and n-dodecane) under both non-reacting and reacting conditions. Specifically, ECN data on spray penetration, liquid length, vapor penetration, mixture fraction, ignition delay, and flame lift-off length (LOL) were used to validate both codes. Results indicate that both codes can predict the above experimental characteristics very well.

Over the past couple of years, numerous Hybrid Electric Vehicle (HEV) powertrain configurations have been introduced into the marketplace. Currently, the dominant architecture is the power-split configuration, notably the input splits from Toyota Motor Sales and Ford Motor Company. This paper compares two vehicle-level control strategies that have been developed to minimize fuel consumption while maintaining acceptable performance and drive quality. The first control is rules based and was developed on the basis of test data from the Toyota Prius as provided by Argonne National Laboratory's (Argonne's) Advanced Powertrain Research Facility. The second control is based on an instantaneous optimization developed to minimize the system losses at every sample time. This paper describes the algorithms of each control and compares vehicle fuel economy (FE) on several drive cycles.

At 70 miles per hour, overcoming aerodynamic drag represents about 65% of the total energy expenditure for a typical heavy truck vehicle. The goal of this US Department of Energy supported consortium is to establish a clear understanding of the drag producing flow phenomena. This is being accomplished through joint experiments and computations, leading to the intelligent design of drag reducing devices. This paper will describe our objective and approach, provide an overview of our efforts and accomplishments related to drag reduction devices, and offer a brief discussion of our future direction.

With rising oil prices, the issue of energy economy in transportation is getting much attention. At the same time, new emissions standards for tractor-trailer vehicles introduce additional challenges for the manufacturers to achieve improvements in vehicle fuel economy. As part of the U.S. Department of Energy Office of FreedomCAR and Vehicle Technologies' Heavy Vehicle Aerodynamic Drag Consortium, Argonne National Laboratory is currently developing guidelines for the use of commercial computational fluid dynamics (CFD) software to facilitate energy efficiency improvements through improved aerodynamic design of tractor-trailer vehicles. The development of these guidelines requires the consideration of the sensitivity of the accuracy of the analysis to the various modeling choices available to the end user.

This study is a continuation of previous work assessing the robustness of a Cummins XPI common rail injection system operating with gasoline-like fuel. All the hardware from the original study was retained except for the high pressure pump head and check valves which were replaced due to cavitation damage. An additional 400 hour NATO cycle was run on the refurbished fuel system to achieve a total exposure time of 800 hours and detect any other significant failure modes. As in the initial investigation, fuel system parameters including pressures, temperatures and flow rates were logged on a test bench to monitor performance over time. Fuel and lubricant samples were taken every 50 hours to assess fuel consistency, metallic wear, and interaction between fuel and oil. High fidelity driving torque and flow measurements were made to compare overall system performance when operating with both diesel and light distillate fuel.

When compared with new oil, used diesel engine oils exhibited thermal conductivity that increases as the concentration of soot increases. The magnitude of the effect depends on the oil composition, and on the size and dispersion of the soot particles. Although soot in engine oil is generally deleterious to engine performance from the standpoint of wear and deposits, no negative effects were observed on the thermal performance of the oil itself; indeed, even slight positive effects are expected for oils that maintain soot in stable dispersion. Therefore, the thermal challenge for engine oils in diesel engines that use exhaust gas recirculation will be to prevent soot deposition on engine surfaces.

This research investigates the energy savings achieved through eco-driving controls in connected and automated vehicles (CAVs), with a specific focus on the influence of powertrain characteristics. Eco-driving strategies have emerged as a promising approach to enhance efficiency and reduce environmental impact in CAVs. However, uncertainty remains about how the optimal strategy developed for a specific CAV applies to CAVs with different powertrain technologies, particularly concerning energy aspects. To address this gap, on-track demonstrations were conducted using a Chrysler Pacifica CAV equipped with an internal combustion engine (ICE), advanced sensors, and vehicle-to-infrastructure (V2I) communication systems, compared with another CAV, a previously studied Chevrolet Bolt electric vehicle (EV) equipped with an electric motor and battery.

The application of polyalkylene glycol (PAG) as a base stock for engine oil formulation has been explored for substantial fuel economy gain over traditional formulations with mineral oils. Various PAG chemistries were explored depending on feed stock material used for manufacturing. All formulations except one have the same additive package. The friction performance of these oils was evaluated in a motored single cylinder engine with current production engine hardware in the temperature range 40°C-120°C and in the speed range of 500 RPM-2500 RPM. PAG formulations showed up to 50% friction reduction over GF-5 SAE 5W-20 oil depending on temperature, speed, and oil chemistry. Friction evaluation in a motored I-4 engine showed up to 11% friction reduction in the temperature range 40°C-100°C over GF-5 oil. The paper will share results on ASTM Sequence VID fuel economy, Sequence IVA wear, and Sequence VG sludge and varnish tests. Chassis roll fuel economy data will also be shared.

This paper implements a coupled approach to integrate the internal nozzle flow and the ensuing fuel spray using a Volume-of-Fluid (VOF) method in the CONVERGE CFD software. A VOF method was used to model the internal nozzle two-phase flow with a cavitation description closed by the homogeneous relaxation model of Bilicki and Kestin [1]. An Eulerian single velocity field approach by Vallet et al. [2] was implemented for near-nozzle spray modeling. This Eulerian approach considers the liquid and gas phases as a complex mixture with a highly variable density to describe near nozzle dense sprays. The mean density is obtained from the Favreaveraged liquid mass fraction. The liquid mass fraction is transported with a model for the turbulent liquid diffusion flux into the gas.

The Society of Automotive Engineers Fatigue Design & Evaluation Committee [SAE FD&E] is actively working on a total life project for weldments, in which the welding residual stress is a key contributor to an accurate assessment of fatigue life. Physics-based welding process simulation and various types of residual stress measurements were pursued to provide a representation of the residual stress field at the failure location in the fatigue samples. A well-controlled and documented robotic welding process was used for all sample fabrications to provide accurate inputs for the welding simulations. One destructive (contour method) residual stress measurement and several non-destructive residual stress measurements-surface X-ray diffraction (XRD), energy dispersive X-ray diffraction (EDXRD), and neutron diffraction (ND)-were performed on the same or similarly welded samples.

Cavitation and cavitation-induced erosion have been observed in fuel injectors in regions of high acceleration and low pressure. Although these phenomena can have a large influence on the performance and lifetime of injector hardware, questions still remain on how these physics should be accurately and efficiently represented within a computational fluid dynamics model. While several studies have focused on the validation of cavitation predictions within canonical and realistic injector geometries, it is not well documented what influence the numerical and physical parameters selected to represent turbulence and phase change will have on the predictions for cavitation erosion propensity and severity. In this work, a range of numerical and physical parameters are evaluated within the mixture modeling approach in CONVERGE to understand their influence on predictions of cavitation, condensation and erosion.

Reductions in vehicle drive losses are as important to improving fuel economy as increases in powertrain efficiencies. In order to measure vehicle fuel economy, chassis dynamometer testing relies on accurate road load determinations. Road load is currently determined (with some exceptions) using established test track coastdown testing procedures. Because new vehicle technologies and usage cases challenge the accuracy and applicability of these procedures, on-road experiments were conducted using axle torque sensors to address the suitability of the test procedures in determining vehicle road loads in specific cases. Whereas coastdown testing can use vehicle deceleration to determine load, steady-state testing can offer advantages in validating road load coefficients for vehicles with no mechanical neutral gear (such as plug-in hybrid and electric vehicles).