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Efficient and accurate ordinary differential equation (ODE) solvers are necessary for powertrain and vehicle dynamics modeling. However, current commercial ODE solvers can be financially prohibitive, leading to a need for accessible, effective, open-source ODE solvers designed for powertrain modeling. Rust is a compiled programming language that has the potential to be used for fast and easy-to-use powertrain models, given its exceptional computational performance, robust package ecosystem, and short time required for modelers to become proficient. However, of the three commonly used (>3,000 downloads) packages in Rust with ODE solver capabilities, only one has more than four numerical methods implemented, and none are designed specifically for modeling physical systems. Therefore, the goal of the Differential Equation System Solver (DESS) was to implement accurate ODE solvers in Rust designed for the component-based problems often seen in powertrain modeling.

The goal of the automated mobility platforms (AMPs) initiative is to raise the bar of service regarding equity and sustainability for public mobility systems that are crucial to large facilities, and doing so using electrified, energy efficient technology. Using airports as an example, the rapid growth in air travel demand has led to facility expansions and congested terminals, which directly impacts equity (e.g., increased challenges for Passengers with Reduced Mobility [PRMs]) and sustainability—both of which are important metrics often overlooked during the engineering design process.

In 2021 the Federal Aviation Administration in collaboration with the National Research Council of Canada performed research on altitude ice crystal icing of aircraft engines using the modular compressor rig, ICE-MACR, in an altitude wind tunnel. The aim of the research campaign was to address research needs related to ice crystal icing of aircraft engines outlined in FAA publication Engine Ice Crystal Icing Technology Plan with Research Needs. This paper reports the findings on ice accretion from a configuration of ICE-MACR with two compression stages. Inherent in two-stage operation is not just additional fracturing and heating by the second stage but also higher axial velocity and potentially greater centrifuging of particles. These factors influence the accretion behavior in the test article compared to single stage accretion.

This paper presents the adhesion strength of ice on sanded and machine-finished aluminum test coupons as measured using the National Research Council of Canada (NRC) Altitude Icing Wind Tunnel (AIWT) spin rig. This rig is used to evaluate commercial and internally-developed coatings for low-adhesion properties, and the performance of ice on aluminum is required as a baseline to compare the coatings against. The tests are performed over a range of aerodynamic and icing cloud conditions, including variations in static air temperature and exposure time (and therefore accumulated ice mass). The data analysis includes an evaluation of the uncertainty in the results based on the measured ice mass repeatability and the measured shear stress repeatability.

In 2017 the National Research Council of Canada developed an evaporation model for controlling engine icing tunnels in real time. The model included simplifications to allow it to update the control system once per second, including the assumption of sea level pressure in some calculations. Recently the engine icing system was required in an altitude facility requiring operation down to static temperatures of -40°C, and up to an altitude of 9.1 km (30 kft) or 30 kPa. To accommodate the larger temperature and pressure range the model was modified by removing the assumption of sea level operation and expanding the temperature range. In addition, due to the higher concentration of water vapor that can be held by the atmosphere at lower pressures, the significance of the effect of humidity on the air properties and the effect on the model was investigated.

Future compliance to FAA 14 CFR Part 25 and EASA CS-25 Appendix O conditions has required icing wind tunnels to expand their cloud simulation envelope, and demonstrate accurate calibration of liquid water content and droplet particle size distributions under these conditions. This has led to a renewed community interest in the accuracy of these calibrations, and the potential inter-facility bias due to the choice of instrumentation and processing methods. This article provides a comparison of the response of various hot-wire liquid water content instruments under Appendix C and supercooled large droplet conditions, after an independent similar analysis at other wind tunnel facilities. The instruments are being used, or are under consideration for use, by facilities collaborating in the ICE GENESIS program.

Under contract to Transport Canada (TC) and with joint funding support from the Federal Aviation Administration (FAA), a vertical stabilizer common research model (VS-CRM) has been designed and built by the National Research Council of Canada (NRC). This model is a realistic, scaled representation of modern vertical stabilizer designs without being specific to a particular aircraft. The model was installed and tested in the NRC 3 m × 6 m Icing Wind Tunnel in late 2021/early 2022. Testing was led by APS Aviation Inc., with support from NRC and NASA, in order to observe the anti-icing fluids flow-off behavior with and without freezing or frozen precipitation during simulated take-off velocity profiles. The model dry-air aerodynamic properties were characterized using flow visualization tufts and boundary layer rakes. Using this data, a target baseline configuration was selected with a yaw angle equal to 0° and rudder deflection angle equal to -10°.

Since the introduction of ice crystal icing certification requirements [1], icing facilities have played an important role in demonstrating compliance of aircraft air data probes, engine probes, and increasingly, of turbine engines. Most sea level engine icing facilities use the freezing-out of a water spray to simulate ice crystal icing conditions encountered at altitude by an aircraft in flight. However, there are notable differences in the ice particles created by freeze-out versus those observed at altitude [2, 3, 4]. Freeze-out crystals are generally spherical as compared to altitude crystals which have variable crystalline shapes. Additionally, freeze-out particles may not completely freeze in their centres, creating a combination of super-cooled liquid and ice impacting engine hardware. An alternative method for generating ice crystals in a test facility is the grinding of ice blocks or cubes to create irregular shaped crystals.

As demand for consumer electric vehicles (EVs) has drastically increased in recent years, manufacturers have been working to bring heavy-duty EVs to market to compete with Class 6-8 diesel-powered trucks. Many high-profile companies have committed to begin electrifying their fleet operations, but have yet to implement EVs at scale due to their limited range, long charging times, sparse charging infrastructure, and lack of data from in-use operation. Thus far, EVs have been disproportionately implemented by larger fleets with more resources. To aid fleet operators, it is imperative to develop tools to evaluate the electrification potential of heavy-duty fleets. However, commercially available tools, designed mostly for light-duty vehicles, are inadequate for making electrification recommendations tailored to a fleet of heavy-duty vehicles.

Some CO2-reducing technologies have real-world benefits not captured by regulatory testing methods. This paper documents a two-layer heating, ventilation, and air-conditioning (HVAC) system that facilitates faster engine warmup through strategic increased air recirculation. The performance of this technology was assessed on a 2020 Hyundai Sonata. Empirical performance of the technology was obtained through dynamometer tests at Argonne National Laboratory. Performance of the vehicle across multiple cycles and cell ambient temperatures with the two-layer technology active and inactive indicated fuel consumption reduction in nearly all cases. A thermally sensitive powertrain model, the National Renewable Energy Laboratory’s FASTSim Hot, was calibrated and validated against vehicle testing data. The developed model included the engine, cabin, and HVAC system controls.

As connected and automated vehicle technologies emerge and proliferate, lower frequency vehicle trajectory data is becoming more widely available. In some cases, entire fleets are streaming position, speed, and telemetry at sample rates of less than 10 seconds. This presents opportunities to apply powertrain simulators such as the National Renewable Energy Laboratory’s Future Automotive Systems Technology Simulator to model how advanced powertrain technologies would perform in the real world. However, connected vehicle data tends to be available at lower temporal frequencies than the 1-10 Hz trajectories that have typically been used for powertrain simulation. Higher frequency data, typically used for simulation, is costly to collect and store and therefore is often limited in density and geography. This paper explores the suitability of lower frequency, high availability, connected vehicle data for detailed powertrain simulation.

Road vehicles in the real world experience aerodynamic conditions that might be unappreciated and omitted in wind-tunnel experiments or in numerical simulations. Precipitation can potentially have an impact on the aerodynamics of road vehicles. An experimental study was devised to measure, in a wind tunnel, the impact of rain on the aerodynamic forces of the DrivAer research model. In this study, a rain system was commissioned to simulate natural rain in a wind-tunnel environment for full-scale rain rates between about 8 and 250 mm/hr. A 30%-scale DrivAer model was tested with and without precipitation for two primary configurations: the notch-back and estate-back variants. In addition, mirror-removal and covered-wheel-well configurations were investigated. The results demonstrate a distinct relationship between increasing rain intensities and increased drag of the model, providing evidence that road vehicles experience higher drag when travelling in precipitation conditions.

This project’s objective was to generate experimental data to evaluate the impact of metals doped B20 on diesel particle filter (DPF) ash loading and performance compared to that of conventional petrodiesel. The effect of metals doped B20 vs. conventional diesel on a DPF was quantified in a laboratory controlled accelerated ash loading study. The ash loading was conducted on two DPFs – one using ULSD fuel and the other on B20 containing metals dopants equivalent to 4 ppm B100 total metals. Engine oil consumption and B20 metals levels were accelerated by a factor of 5, with DPFs loaded to 30 g/L of ash. Details of the ash loading experiment and on-engine DPF performance evaluations are presented in the companion paper (Part I). The DPFs were cleaned, and ash samples were taken from the cleaned material. X-ray Fluorescence (XRF), X-Ray Photoelectron Spectroscopy (XPS) and X-Ray Diffraction (XRD) were conducted on the ash samples.

Heavy-duty commercial vehicles consume a significant amount of energy due to their large size and mass, directly leading to vehicle operators prioritizing energy efficiency to reduce operational costs and comply with environmental regulations. One tool that can be used for the evaluation of energy efficiency in heavy-duty vehicles is the evaluation of energy efficiency using vehicle modeling and simulation. Simulation provides a path for energy efficiency improvement by allowing rapid experimentation of different vehicle characteristics on fuel consumption without the need for costly physical prototyping. The research presented in this paper focuses on using real-world, sparsely sampled telematics data from a large fleet of heavy-duty vehicles to create high-fidelity models for simulation. Samples in the telematics dataset are collected sporadically, resulting in sparse data with an infrequent and irregular sampling rate.

This paper describes a system-level view of a fully automated transit system comprising a fleet of automated vehicles (AVs) in driverless operation, each with an SAE level 4 Automated Driving System, along with its related safety infrastructure and other system equipment. This AV system-level control is compared to the automatic train control system used in automated guideway transit technology, particularly that of communications-based train control (CBTC). Drawing from the safety principles, analysis methods, and risk assessments of CBTC systems, comparable functional subsystem definitions are proposed for AV fleets in driverless operation. With the prospect of multiple AV fleets operating within a single automated mobility district, the criticality of protecting roadway junctions requires an approach like that of automated fixed-guideway transit systems, in which a guideway switch zone “interlocking” at each junction location deconflicts railway traffic, affirming safe passage.

The aerodynamic forces experienced by vehicles depend on a variety of factors including wind direction, traffic, and roadside vegetation. Such complex boundary conditions often result in unsteady flow separation and the formation of large-scale coherent structures, which, in turn, significantly influence the aerodynamics of following vehicles. To gain a deeper understanding of the unsteady behaviour of such vehicle wakes under large-scale conditions, a time-resolved field measurement technique is required. Existing methods, such as tomographic particle image velocimetry and three-dimensional particle tracking velocimetry are unfortunately quite limited at these scales. Furthermore, such techniques require complex multi-camera calibrations, hazardous lasers, and optical access from many vantage points.

Strain controlled thermo-mechanical fatigue (TMF) tests were conducted on a high Silicon ductile cast iron (SiMo) as the baseline material and a similar SiMo cast iron with aluminum addition (SiMoAl). The much improved fatigue life with aluminum addition is analyzed using the integrated creep-fatigue theory (ICFT) in combination with the metallurgical analysis on the tested coupons. Addition of about 3 wt.% Aluminum significantly improved TMF life of the SiMo cast iron. The results are explained by elimination of brittleness at middle temperature range, the higher flow stress, lower creep rate and higher oxidation resistance from Al addition.

In North America, heavy-duty diesel engines for on-road use have to meet strict regulations for their emissions of nitric oxide and nitrogen dioxide (cumulatively referred to as ‘NOx’) besides other criteria pollutants. Over the next decade, regulations for NOx emissions are expected to becoming more stringent in North America. One of the major technical barriers for achieving in-use NOx emissions commensurate with the levels determined from in-laboratory test procedures required by regulations is controlling NOx emissions during cold start and engine idling. Since the exhaust gas temperature can be low during these conditions, the effectiveness of the exhaust after-treatment (EAT) system may be reduced. Under colder climate conditions like in Canada, the impact may be even more significant.

Presently, a main mobility sector objective is to reduce its impact on the global greenhouse gas emissions. While there are many techniques being explored, a promising approach to improve fuel economy is to reduce the required energy by using slipstream effects. This study analyzes the demanded engine power and mechanical energy used by heavy-duty trucks during platooning and non-platooning operation to determine the aerodynamic benefits of the slipstream. A series of platooning tests utilizing class 8 semi-trucks platooning via Cooperative Adaptive Cruise Control (CACC) are performed. Comparing the demanded engine power and mechanical energy used reveals the benefits of platooning on the aerodynamic drag while disregarding any potential negative side effects on the engine. However, energy savings were lower than expected in some cases.

Accurately characterizing vehicle drive cycles plays a fundamental role in assessing the performance of new vehicle technologies. Repeatable, short duration representative drive cycles facilitate more informed decision making, resulting in improved test procedures and more successful vehicle designs. With continued growth in the deployment of onboard telematics systems employing global positioning systems (GPS), large scale, low cost collection of real-world vehicle drive cycle data has become a reality. As a result of these technological advances, researchers, designers, and engineers are no longer constrained by lack of operating data when developing and optimizing technology, but rather by resources available for testing and simulation. Experimental testing is expensive and time consuming, therefore the need exists for a fast and accurate means of generating representative cycles from large volumes of real-world driving data.