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Diesel-fueled heavy-duty vehicles (HDVs) can be retrofitted with conversion kits to operate as dual-fuel vehicles in which partial diesel usage is offset by a gaseous fuel such as compressed natural gas (CNG). The main purpose of installing such a conversion kit is to reduce the operating cost of HDVs. Additionally, replacing diesel partially with a low-carbon fuel such as CNG can potentially lead to lower carbon dioxide (CO2) emissions in the tail-pipe. The main issue of CNG-diesel dual-fuel vehicles is the methane (CH4, the primary component of CNG) slip. CH4 is difficult to oxidize in the exhaust after-treatment (EAT) system and its slip may offset the advantage of lower CO2 emissions of natural gas combustion as CH4 is a strong greenhouse gas (GHG). The objective of this study is to compare the emissions of an HDV with a CNG conversion kit operating in diesel and dual-fuel mode during highway operation.

Heavy-duty (HD) vehicles are a crucial part of the transportation sector; however, strict governmental regulations will require future HD vehicles to meet even more rigid NOx emission standards than what already exist. The use of natural gas (NG) as the primary fuel in HD vehicles can immediately reduce the NOx emissions through lower flame temperatures as compared to traditional diesel and can serve as a precursor to even less carbon intensive fuels as they become more readily available. Pilot ignited direct injection natural gas (PIDING) engine technology is one example of how NG can be used in HD vehicles while maintaining diesel-like efficiency. However, NOx emissions still need to be mitigated to avoid negative air quality effects. Exhaust gas recirculation (EGR) is known to reduce in-cylinder temperatures and thus reduce in-cylinder NOx emissions in diesel engines, but the effects of EGR are not as well understood in PIDING engines.

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.

Ice and snow accretion on aircraft surfaces imposes operational and safety challenges, severely impacting aerodynamic performance of critical aircraft structures and equipment. For optimized location-based ice sensing and integrated ‘smart’ de-icing systems of the future, microwave resonant-based planar sensors are presented for their high sensitivity and versatility in implementation and integration. Here, a conformal, planar complementary split ring resonator (CSRR) based microwave sensor is presented for robust detection of localized ice and snow accretion. The sensor has a modified thick aluminum-plate design and is coated with epoxy for greater durability. The fabricated sensor operates at a resonant frequency of 1.18 GHz and a resonant amplitude of -33 dB. Monitoring the resonant frequency response of the sensor, the freezing and thawing process of a 0.1 ml droplet of water is monitored, and a 60 MHz downshift is observed for the frozen droplet.

This article presents an autonomous steering control scheme for articulated heavy vehicles (AHVs). Despite economic and environmental benefits in freight transportation, lateral stability is always a concern for AHVs in high-speed highway operations due to their multi-unit vehicle structures, and high centers of gravity (CGs). In addition, North American harsh winter weather makes the lateral stability even more challenging. AHVs often experience amplified lateral motions of trailing vehicle units in high-speed evasive maneuvers. AHVs represent a 7.5 times higher risk than passenger cars in highway operation. Human driver errors cause about 94% of traffic collisions. However, little attention has been paid to autonomous steering control of AHVs.

Heavy-duty diesel trucking is responsible for 25%-30% of the road transportation CO2 emissions in North America. Retrofitting class-8 trucks with a complementary hydrogen fuelling system makes it possible to co-combust hydrogen and diesel in the existing internal combustion engine (ICE), thus minimizing the costs associated with switching to non-ICE platforms and reducing the barrier for the implementation of low-carbon gaseous fuels such as hydrogen. This retrofitting approach is evaluated based on the exhaust emissions of a converted truck with several thousand kilometres of road data. The heavy-duty truck used here was retrofitted with an air-intake hydrogen injection system, onboard hydrogen storage tanks, and a proprietary hydrogen controller enabling it to operate in hydrogen-diesel co-combustion (HDC) mode.

Road vehicles have been shown to experience measurable changes in aerodynamic performance when travelling in everyday safe-distance driving conditions, with a major contributor being the lower effective wind speed associated with the wakes from forward vehicles. Using a novel traffic-wake-generator system, a comprehensive test program was undertaken to examine the influence of traffic wakes on the aerodynamic performance of heavy-duty vehicles (HDVs). The experiments were conducted in a large wind tunnel with four primary variants of a high-fidelity 30%-scale tractor-trailer model. Three high-roof-tractor models (conventional North-American sleeper-cab and day-cab, and a zero-emissions-cab style) paired with a standard dry-van trailer were tested, along with a low-roof day-cab tractor paired with a flat-bed trailer.

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 paper presents a sensitivity-based input selection algorithm and a layered modeling approach for improving Gaussian Process Regression (GPR) modeling with hyperparameter optimization for engine model development with data sets of 120 training points or less. The models presented here are developed for a Pilot-Ignited Direct-Injected Natural Gas (PIDING) engine. A previously developed GPR modeling method with hyperparameter optimization produced some models with normalized root mean square error (nRMSE) over 0.2. The input selection method reduced the overall error by 0.6% to 18.85% while the layered modeling method improved the error for carbon monoxide (CO) by 52.6%, particulate matter (PM) by 32.5%, and nitrogen oxides (NOX) by 29.8%. These results demonstrate the importance of selecting only the most relevant inputs for machine learning models.

Use of natural gas-diesel dual-fuel (NDDF) combustion in compression ignition engines is a method of reducing the net greenhouse gas (GHG) and particulate matter (PM) emissions of these engines. Compressed natural gas (NG) is injected into the intake manifold of the engine and the air-NG mixture is ignited by a direct injection of diesel in the cylinder. One of the main challenges with NDDF combustion is the methane (primary component of NG) slip at low and medium loads, which reduces the engine efficiency and offsets the advantage of lower carbon dioxide emissions of the NG combustion. In order to address this issue, an intake manifold insert is devised with the objective to alter the intake flow profile into the engine and ultimately reduce the methane slip. This is a novel strategy for an NDDF engine since modifying the in-cylinder flow profile can intensify the mixing between diesel and air-NG mixture in order to improve the NG utilization in the cylinder.

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.

Low temperature combustion (LTC) of diesel fuel offers a path to low engine emissions of nitrogen oxides (NOx) and particulate matter (PM), especially at low loads. Borescopic optical imaging offers insight into key aspects of the combustion process without significantly disrupting the engine geometry. To assess LTC combustion, two-colour pyrometry can be used to quantify local temperatures and soot concentrations (KL factor). High sensitivity photo-multiplier tubes (PMTs) can resolve natural luminosity down to low temperatures with adequate signal-to-noise ratios. In this work the authors present the calibration and implementation of a borescope-based system for evaluating low luminosity LTC using spatially resolved visible flame imaging and high-sensitivity PMT data to quantify the luminous-area average temperature and soot concentration for temperatures from 1350-2600 K.

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.

Natural gas is an increasingly attractive fuel for marine applications due to its abundance, lower cost, and reduced CO2, NOx, SOx, and particulate matter (PM) emissions relative to conventional fuels such as diesel. Methane in natural gas is a potent greenhouse gas (GHG) and must be monitored and controlled to minimize GHG emissions. In-use GHG emissions are commonly estimated from emission factors based on steady state engine operation, but these do not consider transient operation which has been noted to affect other pollutants including PM and NOx. This study compares methane emissions from a coastal marine vessel during transient operation to those expected based on steady state emission factors. The exhaust methane concentration from a diesel pilot-ignited, low pressure natural gas-fuelled engine was measured with a wavelength modulation spectroscopy system, during periods of increasing and decreasing engine load (between 3 and 90%).

Conventional assessments of the aerodynamic performance of ground vehicles have, to date, been considered in the context of a vehicle that encounters a uniform wind field in the absence of surrounding traffic. Recent vehicle-platooning studies have revealed measurable fuel savings when following other vehicles at inter-vehicle distances experienced in every-day traffic. These energy savings have been attributed in large part to the air-wakes of the leading vehicles. This set of three papers documents a study to examine the near-to-far regions of ground-vehicle wakes (one to ten vehicle lengths), in the context of their potential influence on other vehicles. Part two of this three-part paper documents the influence of the ambient winds on the development of the wake behind a vehicle.

The emissions and efficiency of modern internal combustion engines need to be improved to reduce their environmental impact. Many strategies to address this (e.g., alternative fuels, exhaust gas aftertreatment, novel injection systems, etc.) require engine calibrations to be modified, involving extensive experimental data collection. A new approach to modeling and data collection is proposed to expedite the development of these new technologies and to reduce their upfront cost. This work evaluates a Gaussian Process Regression, Artificial Neural Network and Bayesian Optimization based strategy for the efficient development of machine learning models, intended for engine optimization and calibration. The objective of this method is to minimize the size of the required experimental data set and reduce the associated data collection cost for engine modeling.

A truck platooning system was tested using two heavy-duty tractor-trailer trucks on a closed test track to investigate the sensitivity of intentional lateral offsets over a range of intervehicle spacings. The fuel consumption for both trucks in the platoon was measured using the SAE J1321 gravimetric procedure while travelling at 65 mph and loaded to a gross weight of 65,000 lb. In addition, the SAE J1939 instantaneous fuel rate was calibrated against the gravimetric measurements and used as proxy for additional analyses. The testing campaign demonstrated the effects of intervehicle gaps, following-vehicle longitudinal control, and manual lateral control. The new results are compared to previous truck-platooning studies to reinforce the value of the new information and demonstrate similarity to past trends. Fuel savings for the following vehicle was observed to exceed 10% at closer following distances.

Natural gas (NG)-diesel dual-fuel combustion can be a suitable solution to reduce the overall CO2 emissions of heavy-duty vehicles using diesel engines. One configuration of such a dual-fuel engine can be port injection of NG to form a combustible air-NG mixture in the cylinder. This mixture is then ignited by a direct injection of diesel. Other potential advantages of such an engine include the flexibility of switching back to diesel-only mode, reduced hardware development costs and lower soot emissions. However, the trade-off is lower brake thermal efficiency (BTE) and higher hydrocarbon emissions, especially methane, at low load and/or high engine speed conditions. Advancing the diesel injection timing tends to improve the BTE but may cause the NOx emissions to increase.

A two-truck platoon based on a prototype cooperative adaptive cruise control (CACC) system was tested on a closed test track in a variety of realistic traffic and transient operating scenarios - conditions that truck platoons are likely to face on real highways. The fuel consumption for both trucks in the platoon was measured using the SAE J1321 gravimetric procedure as well as calibrated J1939 instantaneous fuel rate, serving as proxies to evaluate the impact of aerodynamic drag reduction under constant-speed conditions. These measurements demonstrate the effects of: the presence of a multiple-passenger-vehicle pattern ahead of and adjacent to the platoon, cut-in and cut-out manoeuvres by other vehicles, transient traffic, the use of mismatched platooned vehicles (van trailer mixed with flatbed trailer), and the platoon following another truck with adaptive cruise control (ACC).