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Ammonia has received attention as an alternative hydrogen carrier and a potential fuel for thermal propulsion systems with a lower carbon footprint. One strategy for high power density in ammonia applications will be direct injection of liquid ammonia. Understanding the evaporation and mixing processes associated with this is important for model development. Additionally, as a prior step for developing new injectors, it is of interest to understand how a conventional gasoline direct injection (GDI) injector would behave when used for liquid ammonia without any modifications. Pure anhydrous ammonia, in its liquid form, was injected from a single hole GDI injector at a fuel pressure of 150 bar into an optically accessible constant volume chamber filled with nitrogen gas for ammonia spray measurements. The chamber conditions spanned a wide range of pressures from 3 − 15 bar at an increment of 1 bar or 2 bar between the test points.

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.

Several governments are increasing the blending mandate of renewable fuels to reduce the life-cycle greenhouse gas emissions of the road transport sector. Currently, ethanol is a prominent renewable fuel and is used in low-level blends, such as E10 (10 %v/v ethanol, 90 %v/v gasoline) in many parts of the world. However, the exact concentration of ethanol amongst other renewable fuel components in commercially available fuels can vary and is not known. To understand the impact of the renewable fuel content on the emissions from Euro 6d-TEMP emissions specification vehicles, this paper examines the real-driving emissions (RDE) from four 2020 to 2022 model-year vehicles run on E0 and E10 fuels. CO, CO2, NO, and NO2 were measured through a Portable Emissions Measuring System (PEMS).

Forthcoming worldwide emissions regulations will start regulating ammonia emissions from light duty vehicles. At present, most light duty vehicles are powered by gasoline spark ignition engines. Sources of ammonia emission from such engines can be in-cylinder reactions (i.e. combustion) or downstream reactions across aftertreatment devices, particularly three-way catalysts. The latter has been known to be a major source of ammonia emissions from gasoline vehicles and has been extensively investigated. The former (combustion), less so, and thus is the subject of this work. A two-zone thermodynamic spark ignition engine model with a comprehensive chemical kinetics framework (C3MechV3.3 mechanism), after being validated against experimental ammonia emissions data, is used to study ammonia formation during combustion.

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.

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.

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.

Particulate emissions from gasoline direct injection (GDI) engines continue to be a topic of substantial research interest. Forthcoming regulation both in the USA and the EU will further reduce their emission and drive innovation. Substantial research effort is spent undertaking experiments to understand, characterize, and research particle number (PN) emissions from engines and vehicles. Recent advances in computing power, data storage, and understanding of artificial intelligence algorithms now mean that these are becoming an important tool in engine research. In this work a random forest (RF) algorithm is used for the prediction of PN emissions from a highly boosted (up to 32 bar BMEP) GDI engine. Particle size, concentration, and the accumulation mode geometric standard deviation (GSD) are all predicted by the model. The results are analysed and an in depth study on parameter importance is carried out.

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.

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%).

The European Particle Measurement Program (PMP) defines the current standard for measurement of Particle Number (PN) emissions from vehicles in Europe. This specifies a 50% count efficiency (D50) at 23 nm and a 90% count efficiency (D90) at 41 nm. Particulate emissions from Gasoline Direct Injection (GDI) engines have been widely studied, but usually only in the context of PMP or similar sampling procedures. There is increasing interest in the smallest particles - i.e. smaller than 23 nm - which can be emitted from vehicles. The literature suggest that by moving D50 to 10 nm, PN emissions from GDI engines might increase by between 35 and 50% but there remains a lot of uncertainty.

The partial melting of ingested ice crystals can lead to ice accretion in aircraft compressors, but accurately measuring the relatively small fraction of liquid water content in such flows is challenging. Probe-based methods for detecting liquid water content are not suitable for deployment within turbofan engines, and thus alternatives are sought. Recent research has described approaches based on passive microwave sensing. We present here an approach based on active microwave transmission and reflection, employing a vector network analyzer. Utilization of both transmission and reflection provides additional data over and above emission or transmission only, and permits a more controllable environment than passive sensing approaches. The paper specifically addresses the question of whether such an approach is viable within the context of representative icing wind tunnel and engine flow conditions.

Ingestion of high altitude atmospheric ice particles can be hazardous to gas turbine engines in flight. Ice accretion may occur in the core compression system, leading to blockage of the core gas path, blade damage and/or flameout. Numerous engine powerloss events since 1990 have been attributed to this mechanism. An expansion in engine certification requirements to incorporate ice crystal conditions has spurred efforts to develop analytical models for phenomenon, as a method of demonstrating safe operation. A necessary component of a complete analytical icing model is a thermodynamic accretion model. Continuity and energy balances are performed using the local flow conditions and the mass fluxes of ice and water that are incident on a surface to predict the accretion growth rate.

A significant number of historical engine powerloss events have recently been attributed to ingestion of high altitude ice crystals, prompting regulators to expand engine certification envelopes to incorporate ‘ice crystal icing’ conditions. There has been a resulting effort by OEMs and academia to develop analytical and semi-empirical models for the phenomenon, partly through use of rig testing. The current study presents results and analysis of experiments conducted in the National Research Council’s Research Altitude Test Facility (RATFac). The experiments used a simplified compressor stator vane test article, designed to produce data to build semi-empirical models and validate an existing ice crystal icing code. Accretion growth rates, extracted from backlit shadowgraphy, are presented as a function of test condition, and the algorithm of a new image processing technique using Canny filtering is discussed.

Numerous turbofan power-loss events have occurred in high altitude locations in the presence of ice crystals. It is theorized that ice crystals enter the engine core, partially melt in the compressor and then accrete onto stator blade surfaces. This may lead to engine rollback, or shed induced blade damage, surge and/or flameout. The first generation of ice crystal icing predictive models use a single flow field where there is no accretion to calculate particle trajectories and accretion growth rates. Recent work completed at the University of Oxford has created an algorithm to automatically detect the edge of accretion from experimental video data. Using these accretion profiles, numerical simulations were carried out at discrete points in time using a manual meshing process.

High altitude ice crystals can pose a threat to aircraft engine compression and combustion systems. Cases of engine damage, surge and rollback have been recorded in recent years, believed due to ice crystals partially melting and accreting on static surfaces (stators, endwalls and ducting). The increased awareness and understanding of this phenomenon has resulted in the extension of icing certification requirements to include glaciated and mixed phase conditions. Developing semi-empirical models is a cost effective way of enabling certification, and providing simple design rules for next generation engines. A comprehensive ice crystal icing model is presented in this paper, the Ice Crystal Icing ComputationaL Environment (ICICLE). It is modular in design, comprising a baseline code consisting of an axisymmetric or 2D planar flowfield solution, Lagrangian particle tracking, air-particle heat transfer and phase change, and surface interactions (bouncing, fragmentation, sticking).

In-cylinder flow motion has a significant effect on mixture preparation and combustion. Therefore, it is vital that CFD engine simulations are capable of accurately predicting the in-cylinder velocity fields. High-speed planar Particle Image Velocimetry (PIV) experiments have been performed on a single-cylinder GDI optical engine in order to validate CFD simulations for a range of engine conditions. Novel metrics have been developed to quantify the differences between experimental and simulated velocity fields in both alignment and magnitude. The Weighted Relevance Index (WRI) is a variation of the standard Relevance Index that accounts for the local velocity magnitudes to provide a robust comparison of the alignment between two vector fields. Similarly, the Weighted Magnitude Index (WMI) quantifies the differences in the local magnitudes of the two velocity fields.

In-cylinder temperatures and their cyclic variations strongly influence many aspects of internal combustion engine operation, from chemical reaction rates determining the production of NOx and particulate matter to the tendency for auto-ignition leading to knock in spark ignition engines. Spatially resolved measurements of temperature can provide insights into such processes and enable validation of Computational Fluid Dynamics simulations used to model engine performance and guide engine design. This work uses a combination of Two-Colour Planar Laser Induced Fluorescence (TC-PLIF) and Laser Induced Grating Spectroscopy (LIGS) to measure the in-cylinder temperature distributions of a firing optically accessible spark ignition engine. TC-PLIF performs 2-D temperature measurements using fluorescence emission in two different wavelength bands but requires calibration under conditions of known temperature, pressure and composition.

Particulate emissions from Gasoline Direct Injection (GDI) engines have been an important topic of recent research interest due to their known environmental effects. This review paper will characterise the influence of different gasoline direct injection fuel systems on particle number (PN) emissions. The findings will be reviewed for engine and vehicle measurements with appropriate driving cycles (especially real driving cycles) to evaluate effects of the fuel injection systems on PN emissions. Recent technological developments alongside the trends of the influence of system pressure and nozzle design on injector tip wetting and deposits will be considered. Besides the engine and fuel system it is known that fuel composition will have an important effect on GDI engine PN emissions. The evaporation qualities of fuels have a substantial influence on mixture preparation, as does the composition of the fuel itself.

Knowledge of fuel mass injected in an individual cycle is important for engine performance and modeling. At the moment, such measurements are not possible on engine or in real time. In this article, a new method using Coriolis flow meters (CFMs) and a new, patented, signal processing technique, known as the Prism, are introduced. CFMs are extensively used for flow measurement both in the automotive industry and further afield and, when coupled with the Prism, have the potential to make these challenging high-speed measurements. A rig-based feasibility study was conducted injecting very small quantities of diesel (3 mg) at pressures of up to 1000 bar at simulated engine speeds of up to 4000 rpm. The results show that these small quantities can in principle be measured. The results also reveal a previously unknown behavior of CFMs when measuring very low flow rates at high speed.