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The emerging need of building an efficient Electric Vehicle (EV) charging infrastructure requires the investigation of all aspects of Vehicle-Grid Integration (VGI), including the impact of EV charging on the grid, optimal EV charging control at scale, and communication interoperability. This paper presents a cloud-based simulation and testing platform for the development and Hardware-in-the-Loop (HIL) testing of VGI technologies. Although the HIL testing of a single charging station has been widely performed, the HIL testing of spatially distributed EV charging stations and communication interoperability is limited. To fill this gap, the presented platform is developed that consists of multiple subsystems: a real-time power system simulator (OPAL-RT), ISO 15118 EV Charge Scheduler System (EVCSS), and a Smart Energy Plaza (SEP) with various types of charging stations, solar panels, and energy storage systems.

The use of virtual prototyping early in the design stage of a product has gained popularity due to reduced cost and time to market. The state of the art in vehicle simulation has reached a level where full vehicles are analyzed through simulation but major difficulties continue to be present in interfacing the vehicle model with accurate powertrain models and in developing adequate formulations for the contact between tire and terrain (specifically, scenarios such as tire sliding on ice and rolling on sand or other very deformable surfaces). The proposed work focuses on developing a ground vehicle simulation capability by combining several third party packages for vehicle simulation, tire simulation, and powertrain simulation. The long-term goal of this project consists in promoting the Digital Car idea through the development of a reliable and robust simulation capability that will enhance the understanding and control of off-road vehicle performance.

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.

Reduced basis techniques and the method of mode-acceleration are applied to transient analysis of simple beam and truss structures. It is noted that the method of mode-acceleration effectively recovers the first Ritz vector used in Ritz procedures. The theoretical basis for Lanczos algorithms that generate Ritz vectors is explained. Both mode-acceleration and Ritz procedures are found, generally, to be more accurate than mode-displacement in calculation of transient shear stresses, moments and normal stresses. Reanalysis using Ritz vectors is discussed following Kitis and Pilkey [9] and Noor and Lowder [10].

In this work, the effects of ozone, hydrogen, carbon monoxide, and exhaust gas recirculation (EGR) addition to Haltermann gasoline combustion were investigated. For these additives, laminar and turbulent flame speeds were experimentally determined using spherically propagating premixed flames in a constant volume combustion vessel. Two initial mixture pressures of Po = 1 and 5 bar, two initial mixture temperatures of 358 and 373 K and a range of equivalence ratios (Ф) from 0.5 to 1 were investigated. The additives were added as single, binary and ternary mixtures to Haltermann gasoline over a wide range of concentrations. For the stoichiometric mixture, the addition of 10% H2, 5% CO and 1000 ppm O3 shows remarkable enhancement (80%) in SL0compared to neat Haltermann gasoline. In addition, for this same blend, increasing the mixture initial temperature and pressure results in a significant increase in SL0compared to the neat gasoline.

The Indicated Mean Effective Pressure (IMEP) is a very important engine parameter, giving significant information about the quality of the cycle that transforms heat into mechanical work. For this reason, modern data acquisition systems display, on line, the cylinder pressure variation together with the corresponding IMEP. The paper presents a very simple algorithm for the calculation of IMEP, based on the correlation between IMEP and the gas pressure torque. It was found that that the IMEP may be calculated by a very simple formula involving only two harmonic components of the cylinder pressure variation. The computation of the two harmonic components is very easily performed because it does not involve the calculation of an average pressure and the cylinder volume variation. The method was experimentally validated showing differences less than 0.2% with respect to the IMEP calculated by the traditional method.

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).

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.

This paper focuses on the role and significance of linear momentum and kinetic energy in controlling air bags aboard vehicles. Among the results of the study are analytic and geometric models that characterize crash behavior and control algorithms that quantify crash severity and identify crash configurations. These results constitute an effective basis for crash-data design and air-bag control.

A new three-dimensional human head finite element model, consisting of the scalp, skull, dura, falx, tentorium, pia, CSF, venous sinuses, ventricles, cerebrum (gray and white matter), cerebellum, brain stem and parasagittal bridging veins has been developed and partially validated against experimental data of Nahum et al (1977). A frontal impact and a sagittal plane rotational impact were simulated and impact responses from a homogeneous brain were compared with those of an inhomogeneous brain. Previous two-dimensional simulation results showed that differentiation between the gray and white matter and the inclusion of the ventricles are necessary in brain modeling to match regions of high shear stress to locations of diffuse axonal injury (DAI). The three-dimensional simulation results presented here also showed the necessity of including these anatomical features in brain modeling.

Many approaches have been taken to determine the burning velocity in internal combustion engines. Experimentally, the burning velocity has been determined in optically accessible gasoline engines by tracking the propagation of the flame front from the spark plug to the end of the combustion chamber. These experiments are costly as they require special imaging techniques and major modifications in the engine structure. Another approach to determine the burning velocity is from 3D CFD simulation models. These models require basic information about the mechanisms of combustion which are not available for distillate fuels in addition to many assumptions that have to be made to determine the burning velocity. Such models take long periods of computational time for execution and have to be calibrated and validated through experimentation.

Traditional Lagrangian spray modeling approaches for internal combustion engines are highly grid-dependent due to insufficient resolution in the near nozzle region. This is primarily because of inherent restrictions of volume fraction with the Lagrangian assumption together with high computational costs associated with small grid sizes. A state-of-the-art grid-convergent spray modeling approach was recently developed and implemented by Senecal et al., (ASME-ICEF2012-92043) in the CONVERGE software. The key features of the methodology include Adaptive Mesh Refinement (AMR), advanced liquid-gas momentum coupling, and improved distribution of the liquid phase, which enables use of cell sizes smaller than the nozzle diameter. This modeling approach was rigorously validated against non-evaporating, evaporating, and reacting data from the literature.

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 connectivity and sensing technologies become more mature, automated vehicles can predict future driving situations and utilize this information to drive more energy-efficiently than human-driven vehicles. However, future information beyond the limited connectivity and sensing range is difficult to predict and utilize, limiting the energy-saving potential of energy-efficient driving. Thus, we combine a conventional speed optimization planner, developed in our previous work, and reinforcement learning to propose a real-time intelligent speed optimization planner for connected and automated vehicles. We briefly summarize the conventional speed optimization planner with limited information, based on closed-form energy-optimal solutions, and present its multiple parameters that determine reference speed trajectories.

This work presents a simple fan model that is based on the actuator disk approximation, and the blade element and vortex theory of a propeller. A set of equations are derived that require as input the rotational speed of the fan, geometric fan data, and the lift and drag coefficients of the blades. These equations are solved iteratively to obtain the body forces generated by the fan in the axial and circumferential directions. These forces are used as momentum sources in a CFD code to simulate the effect of the fan in an underhood thermal management simulation. To validate this fan model, a fan experiment was simulated. The model was incorporated into the CFD code STAR-CD and predictions were generated for axial and circumferential air velocities at different radial positions and at different planes downstream of the fan. The agreement between experimental measurements and predictions is good.

Spray behavior is regarded as one of main factors which influence engine performance, fuel consumption and emissions for diesel engine. In practice, spray characteristics from each orifice from a multi-hole nozzle are normally arranged symmetrically, while the hole-to-hole spray variation is unavoidable. This variation will cause spatial uneven distribution of spray and combustion degrade, which will be no longer inconsiderable in face of the more and more stringent emission rules. In this paper, two methods including spray macro-characteristics experiment and separated fuel mass measurement are employed to test the hole-to-hole spray variation of two six-hole symmetric VCO injectors of different brands, and experiments are operated under different conditions including different injection pressures, back pressures and injection durations.

Accurate modeling of the internal flow and spray characteristics in fuel injectors is a critical aspect of direct injection engine design. However, such high-fidelity computational fluid dynamics (CFD) models are often computationally expensive due to the requirement of resolving fine temporal and spatial scales. This paper addresses the computational bottleneck issue by proposing a machine learning-based emulator framework, which learns efficient surrogate models for spatiotemporal flow distributions relevant for static coupling injection maps, namely total void fraction, velocity, and mass, within a design space of interest. Different design points involving variations of needle lift, fuel viscosity, and level of non-condensable gas in the fuel were explored in this study. An interpretable Bayesian learning strategy was employed to understand the effect of the design parameters on the void fraction fields at the exit of the injector orifice.

For several years there has been a great deal of effort made in researching ways to run a compression ignition engine with simultaneously high efficiency and low emissions. Recently much of this focus has been dedicated to using gasoline-like fuels that are more volatile and less reactive than conventional diesel fuel to allow the combustion to be more premixed. One of the key challenges to using fuels with such properties in a compression ignition engine is stable engine operation at low loads. This paper provides an analysis of how stable gasoline compression ignition (GCI) engine operation was achieved down to idle speed and load on a multi-cylinder compression ignition engine using only 87 anti-knock index (AKI) gasoline. The variables explored to extend stable engine operation to idle included: uncooled exhaust gas recirculation (EGR), injection timing, injection pressure, and injector nozzle geometry.

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.

Gasoline compression ignition (GCI) offers improved efficiency by harnessing gasoline’s low reactivity to induce an extended ignition delay that promotes partial premixing of air and fuel before combustion occurs. However, enabling GCI across the full engine operating load map poses several challenges. At high load, due to the elevated pressures and temperatures of the charge mixture, the ignition delay time shrinks, leading to diminished GCI efficiency benefits. At low load, insufficient temperatures and pressures can lead to combustion instability. Variable valve actuation offers a practical solution to these challenges by enabling effective compression ratio (ECR) control. In this paper, the effects of variable intake valve closings were investigated for high load operations in a prototype heavy-duty GCI engine, using a research octane number 93 gasoline fuel. The study focused on the 50% (B50) and the 75% (B75) load conditions at 1375 RPM.