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The National Highway Traffic Safety Administration (NHTSA) plays a crucial role in guiding the formulation of Corporate Average Fuel Economy (CAFE) standards, and at the forefront of this regulatory process stands Argonne National Laboratory (Argonne). Argonne, a U.S. Department of Energy (DOE) research institution, has developed Autonomie—an advanced and comprehensive full-vehicle simulation tool that has solidified its status as an industry standard for evaluating vehicle performance, energy consumption, and the effectiveness of various technologies. Under the purview of an Inter-Agency Agreement (IAA), the DOE Argonne Site Office (ASO) and Argonne have assumed the responsibility of conducting full-vehicle simulations to support NHTSA's CAFE rulemaking initiatives. This paper introduces an innovative approach that hinges on a large-scale simulation process, encompassing standard regulatory driving cycles tailored to various vehicle classes and spanning diverse timeframes.

Roundabouts are intersections at which automated cars seem currently not performing sufficiently well. Actually, sometimes, they get stuck and the traffic flow is seriously reduced. To overcome this problem a V2N-N2V (vehicle-to-network-network-to-vehicle) communication scheme is proposed. Cars communicate via 5G with an edge computer. A cooperative machine-learning algorithm orchestrates the traffic. Automated cars are instructed to accelerate or decelerate with the triple aim of improving the traffic flow into the roundabout, keeping safety constraints, and providing comfort for passengers on board of automated vehicles. In the roundabout, both automated cars and human-driven cars run. The roundabout scenario has been simulated by SUMO. Additionally, the scenario has been reconstructed into a dynamic driving simulator, with a real human driver in a virtual reality environment. The aim was to check the human perception of traffic flow, driving safety and driving comfort.

The National Highway Traffic Safety Administration (NHTSA) has been leading U.S. efforts related to the rulemaking process for Corporate Average Fuel Economy (CAFE) standards. Argonne National Laboratory, a U.S. Department of Energy (DOE) national laboratory, has developed a full-vehicle simulation tool called Autonomie that has become one of the industry standard tools for analyzing vehicle performance, energy consumption, and technology effectiveness. Through an Interagency Agreement, the DOE Argonne Site Office and Argonne National Laboratory have been tasked with conducting full vehicle simulation to support NHTSA CAFE rulemaking. This paper presents an innovative approach focused on large-scale simulation processes spanning standard regulatory driving cycles, diverse vehicle classes, and various timeframes. A key element of this approach is Autonomie’s capacity to integrate advanced engine technologies tailored to specific vehicle classes and powertrains.

The internal combustion engine (ICE) has long dominated the heavy-duty sector by using liquid fossil fuels such as diesel but global commitments by countries and OEMs to reduce lifecycle carbon dioxide (CO2) emissions has garnered interest in alternative fuels like hydrogen. Hydrogen is a unique gaseous fuel that contains zero carbon atoms and has desired thermodynamic properties of high energy density per unit mass and high flame speeds. However, there are challenges related to its adoption to the heavy-duty sector as a drop-in fuel replacement for compression ignition (CI) diesel combustion given its high autoignition resistance. To overcome this fundamental barrier, engine manufacturers are exploring dual fuel combustion engines by substituting a fraction of the diesel fuel with hydrogen which enables fuel flexibility when there is no infrastructure and retrofittability to existing platforms.

Hydrogen is anticipated to play a pivotal role as a green energy carrier in both heavy industry and transportation. Utilizing hydrogen directly in internal combustion engines (ICE) could offer several advantages compared to alternative technologies. To achieve this objective, a proper understanding of the physical mechanisms and dynamics involved in the injection of this fuel is needed. This study applied high-fidelity computational fluid dynamics (CFD) simulations to describe the flow characteristics of hydrogen injection using hollow- and single- and multi-solid-cone injectors and their effect on mixing quality and characteristics in a constant volume quiescent environment. A reference hollow-cone configuration was used to validate the model. The results indicate that solid-cone configurations achieve greater penetration due to the flow patterns they generate. However, an increase in the number of holes leads to reduced penetration length, projected area, and induced turbulence.

The employment of hydrogen as energy carrier for transportation sector represents a significant challenge for powertrains. Spark-ignition (SI) engines are feasible and low-cost devices to convert the hydrogen chemical energy into mechanical work. However, significant efforts are needed to successfully retrofit the available configurations. The computational fluid dynamics (CFD) modelling represents a useful tool to support experiments, clarifying the impact of the engine characteristics on both the mixture preparation and the combustion development. In this work, a CFD investigation is carried out on typical light-duty SI engine configurations, exploring the two main strategies of hydrogen addition: port fuel injection (PFI) and direct injection (DI). The purpose is to assess the behaviour of widely-used numerical models and methodologies when hydrogen is employed instead of traditional carbon-based fuels.

The demand of game-changing technologies to improve efficiency and abate emissions of heavy-duty trucks and off-road vehicles promoted the development of novel engine concepts. The Recuperated Split-Cycle (R-SC) engine allows to recover the exhaust gases energy into the air intake by separating the compression and combustion stages into two different but connected cylinders: the compressor and expander, respectively. The result is a potential increase of the engine thermal efficiency. Accordingly, the 3D-computational fluid dynamics (CFD) modelling of the gas exchange process and the combustion evolution inside the expander becomes essential to control and optimize the R-SC engine concept. This work aims to address the most challenging numerical aspects encountered in a 3D numerical simulation of an R-SC engine.

The rising environmental awareness has led to a growing interest in electric and lightweight vehicles. Four-wheeled Ultra-Efficient Lightweight Vehicles (UELVs) have the potential to improve the quality of urban life, reduce environmental impact and make efficient use of land. However, the safety of these vehicles in terms of dynamic behaviour needs to be better understood. This paper aims to provide a quantitative assessment of the handling behaviour of UELVs. An analytical single-track model and a numerical simulation by VI-CarRealTime are analysed to evaluate the dynamic performance of a UELV compared to a city car. This analysis shows that the lightweight vehicle has a higher readiness (i.e. lower reaction time to yaw rate) for step steering and lower steering effort (i.e. higher steady-state value). Experimental analysis through real-time driving sessions on the Dynamic Driving Simulator assesses vehicle responses and subjective perception for different manoeuvres.

In future decarbonized scenarios, hydrogen is widely considered as one of the best alternative fuels for internal combustion engines, allowing to achieve zero CO2 emissions at the tailpipe. However, NOx emissions represent the predominant pollutants and their production has to be controlled. In this work different strategies for the control and abatement of pollutant emissions on a H2-fueled high-performance V8 twin turbo 3.9L IC engine are tested. The characterization of pollutant production on a single-cylinder configuration is carried out by means of the 1D code Gasdyn, considering lean and homogeneous conditions. The NOx are extremely low in lean conditions with respect to the emissions legislation limits, while the maximum mass flow rate remains below the turbocharger technical constraint limit at λ=1 only.

This work presents a comprehensive numerical model for ice accretion and Ice Protection System (IPS) simulation over a 2D component, such as an airfoil. The model is based on the Myers model for ice accretion and extended to include the possibility of a heated substratum. Six different icing conditions that can occur during in-flight ice accretion with an Electro-Thermal Ice Protection System (ETIPS) activated are identified. Each condition presents one or more layers with a different water phase. Depending on the heat fluxes, there could be only liquid water, ice, or a combination of both on the substratum. The possible layers are the ice layer on the substratum, the running liquid film over ice or substratum, and the static liquid film between ice and substratum caused by ice melting. The last layer, which is always present, is the substratum. The physical model that describes the evolution of these layers is based on the Stefan problem. For each layer, one heat equation is solved.

This paper presents a novel fully-automatic remeshing procedure, based on the level-set method and Delaunay triangulation, to model three-dimensional boundary problems and generate a new conformal body-fitted mesh. The proposed methodology is applied to long-term in-flight ice accretion, which is characterized by the formation of extremely irregular ice shapes. Since ice accretion is coupled with the aerodynamic flow field, a multi-step procedure is implemented. The total icing exposure time is subdivided into smaller time steps, and at each time step a three-dimensional body-fitted mesh, suitable for the computation of the aerodynamic flow field around the updated geometry, is generated automatically. The methodology proposed can effectively deal with front intersections, as shown with a manufactured example.

We present a framework for the robust optimization of the heat flux distribution for an anti-ice electro-thermal ice protection system (AI-ETIPS) and iced airfoil performance analysis under uncertain conditions. The considered uncertainty regards a lack of knowledge concerning the characteristics of the cloud i.e. the liquid water content and the median volume diameter of water droplets, and the accuracy of measuring devices i.e., the static temperature probe, uncertain parameters are modeled as uniform random variables. A forward uncertainty propagation analysis is carried out using a Monte Carlo approach. The optimization framework relies on a gradient-free algorithm (Mesh Adaptive Direct Search) and three different problem formulations are considered in this work. Two bi-objective deterministic optimizations aim to minimize power consumption and either minimize ice formations or the iced airfoil drag coefficient.

Over the past couple of years, Argonne National Laboratory has tested, analyzed, and validated automobile models for the light duty vehicle class, including several types of powertrains including conventional, hybrid electric, plug-in hybrid electric and battery electric vehicles. Argonne’s previous works focused on the light duty vehicle models, but no work has been done on medium and heavy-duty vehicles. This study focuses on the validation of shifting control in advanced automatic transmission technologies for medium duty vehicles by using Argonne’s model-based high-fidelity, forward-looking, vehicle simulation tool, Autonomie. Different medium duty vehicles, from Argonne’s own fleet, including the Ram 2500, Ford F-250 and Ford F-350, were tested with the equipment for OBD (on-board diagnostics) signal data record. For the medium duty vehicles, a workflow process was used to import test data.

Perception in adverse weather conditions is one of the most prominent challenges for automated driving features. The sensors used for mid-to-long range perception most impacted by weather (i.e., camera and LiDAR) are susceptible to data degradation, causing potential system failures. This research series aims to better understand sensor data degradation characteristics in real-world, dynamic environmental conditions, focusing on adverse weather. To achieve this, a dataset containing LiDAR (Velodyne VLP-16) and camera (Mako G-507) data was gathered under static scenarios using a single vehicle target to quantify the sensor detection performance. The relative position between the sensors and the target vehicle varied longitudinally and laterally. The longitudinal position was varied from 10m to 175m at 25m increments and the lateral position was adjusted by moving the sensor set angle between 0 degrees (left position), 4.5 degrees (center position), and 9 degrees (right position).

Wheel and wheelhouses contribute up to 20-30% of the aerodynamic drag of passenger cars. Simulating the flow field around wheels is challenging due to the complexity of the flow structures generated by tires and rims, wheel rotation, tire deformation and contact with the ground. High accuracy is usually obtained with transient simulations that treat rim rotation with the Sliding Mesh (SM) approach, which is also computationally expensive. Previous studies have confirmed that the application of a tangential velocity component to the rim surface is unphysical for open rims, while a Moving Reference Frame (MRF) is lacking accuracy and the averaged results depend on the initial spokes position. These methods do not consider the dynamic nature of the problem. This work proposes the use of the Actuator Line (AL) and Rotor Disk (RD) approaches as alternatives for simulating open rims with much lower computational cost.

The tightening trend of regulations on the levels of admitted pollutant emissions has given a great spur to the research work in the field of combustion and after-treatment devices. Despite the improvements that can be applied to the development of the combustion process, pollutant emissions cannot be reduced to zero; for this reason, the aftertreatment system will become a key component in the path to achieving near-zero emission levels. This study focuses on the numerical analysis and optimization of different metallic substrates, specifically developed for three-way catalyst (TWC) and Diesel oxidation catalyst (DOC) applications, to improve their thermal efficiency by reducing radial thermal losses through the outer mantle. The optimization process relies on computational fluid dynamics (CFD) simulations supported by experimental measurements to validate the numerical models carried out under uncoated conditions, where chemical reactions do not occur.

This paper introduces a new systematic workflow for the rapid evaluation of energy-efficient automated driving controls in real vehicles in controlled laboratory conditions. This vehicle-in-the-loop (VIL) workflow, largely standardized and automated, is reusable and customizable, saves time and minimizes costly dynamometer time. In the first case study run with the VIL workflow, an automated car driven by an energy-efficient driving control previously developed at Argonne used up to 22 % less energy than a conventional control. In a VIL experiment, the real vehicle, positioned on a chassis dynamometer, has a digital twin that drives in a virtual world that replicates real-life situations, such as approaching a traffic signal or following other vehicles.

In the recent years, the interest in heavy-duty engines fueled with Compressed Natural Gas (CNG) is increasing due to the necessity to comply with the stringent CO2 limitation imposed by national and international regulations. Indeed, the reduced number of carbon atoms of the NG molecule allows to reduce the CO2 emissions compared to a conventional fuel. The possibility to produce synthetic methane from renewable energy sources, or bio-methane from agricultural biomass and/or animal waste, contributes to support the switch from conventional liquid fuels to CNG. To drive the engine development and reduce the time-to-market, the employment of numerical analysis is mandatory. This requires a continuous improvement of the simulation models toward real predictive analyses able to reduce the experimental R&D efforts. In this framework, 1D numerical codes are fundamental tools for system design, energy management optimization, and so on.

This study presents experimental and numerical examination of directly injected (DI) propane and iso-octane, surrogates for liquified petroleum gas (LPG) and gasoline, respectively, at various engine like conditions with the overall objective to establish the baseline with regards to fuel delivery required for future high efficiency DI-LPG fueled heavy-duty engines. Sprays for both iso-octane and propane were characterized and the results from the optical diagnostic techniques including high-speed Schlieren and planar Mie scattering imaging were applied to differentiate the liquid-phase regions and the bulk spray phenomenon from single plume behaviors. The experimental results, coupled with high-fidelity internal nozzle-flow simulations were then used to define best practices in CFD Lagrangian spray models.

Modeling combustion of transportation fuels remains a difficult task due to the extremely large number of species constituting commercial gasoline and diesel. However, for this purpose, multi-component surrogate fuel models with a reduced number of key species and dedicated reaction subsets can be used to reproduce the physical and chemical traits of diesel and gasoline, also allowing to perform CFD calculations. Recently, a detailed surrogate fuel kinetic model, named C3 mechanism, was developed by merging high-fidelity sub-mechanisms from different research groups, i.e. C0-C4 chemistry (NUI Galway), linear C6-C7 and iso-octane chemistry (Lawrence Livermore National Laboratory), and monocyclic aromatic hydrocarbons (MAHs) and polycyclic aromatic hydrocarbons (PAHs) (ITV-RWTH Aachen and CRECK modelling Lab-Politecnico di Milano).