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In-cylinder pressure measurement is an important tool in internal combustion engine research and development for combustion, cycle performance, and knock analysis in spark-ignition engines. In a typical laboratory setup, a sub crank angle resolved (typically between 0.1o and 0.5o) optical encoder is installed on the engine crankshaft, and a piezoelectric pressure transducer is installed in the engine cylinder. The charge signal produced by the transducer due to changes in cylinder pressure during the engine cycle is converted to voltage by a charge amplifier, and this analog voltage is read by a high-speed data acquisition (DAQ) system at each encoder trigger pulse. The high speed of engine operation and the need to collect hundreds of engine cycles for appropriate cycle-averaging requires significant processor speed and memory, making typical data acquisition systems very expensive.

Spark ignition engines utilize catalytic converters to reform harmful exhaust gas emissions such as carbon monoxide, unburned hydrocarbons, and oxides of nitrogen into less harmful products. Aftertreatment devices require the use of expensive catalytic metals such as platinum, palladium, and rhodium. Meanwhile, tightening automotive emissions regulations globally necessitate the development of high-performance exhaust gas catalysts. So, automotive manufactures must balance maximizing catalyst performance while minimizing production costs. There are thousands of different recipes for catalytic converters, with each having a different effect on the various catalytic chemical reactions which impact the resultant tailpipe gas composition. In the development of catalytic converters, simulation models are often used to reduce the need for physical parts and testing, thus saving significant time and money.

The possibility to operate current diesel engines in dual-fuel mode with the addition of an alternative fuel is fundamental to accelerate the energy transition to achieve carbon neutrality. The simulation of the dual- fuel combustion process with 0D/1D combustion models is fundamental for the performance prediction, but still particularly challenging, due to chemical interactions of the mixture. The authors defined a novel data-driven workflow for the development of combustion reaction mechanisms and used it to generate a dual-fuel mechanism for Ammonia and Diesel Primary Reference Fuels (DPRF) suitable for efficient combustion simulations in heavy duty engines, with variable cetane number Diesel fuels. A baseline reaction mechanism was created by merging the detailed ammonia mechanism by Glarborg et al. with reaction pathways for n- hexadecane and 2,2,4,4,6,8,8-heptamethylnonane from a well-established multi-component fuel mechanism.

We propose a novel dual-fuel combustion model for simulating heavy-duty engines with the G-Equation. Dual-Fuel combustion strategies in such engines features direct injection of a high-reactivity fuel into a lean, premixed chamber which has a high resistance to autoignition. Distinct combustion modes are present: the DI fuel auto-ignites following chemical ignition delay after spray vaporization and mixing; a reactive front is formed on its surroundings; it develops into a well-structured turbulent flame, which propagates within the premixed charge. Either direct chemistry or the flame-propagation approach (G- Equation), taken alone, do not produce accurate results. The proposed Dual-Fuel model decides what regions of the combustion chamber should be simulated with either approach, according to the local flame state; and acts as a “kernel” model for the G- Equation model. Direct chemistry is run in the regions where a premixed front is not present.

Lithium-ion and Lithium polymer batteries are fast becoming ubiquitous in high-discharge rate applications for military and non-military systems. Applications such as small aerial vehicles and energy transfer systems can often function at C-rates greater than 1. To maximize system endurance and battery health, there is a need for models capable of precisely estimating the battery state-of-charge (SoC) under all temperature and loading conditions. However, the ability to perform state estimation consistently and accurately to within 1% error has remained unsolved. Doing so can offer enhanced endurance, safety, reliability, and planning, and additionally, simplify energy management. Therefore, the work presented in this paper aims to study and develop experimentally validated mathematical models capable of high-accuracy battery SoC estimation.

In the automotive industry, performing steady-state tests on an internal combustion engine can be a time consuming and costly process, but it is necessary to ensure the engine meets performance and emissions criteria set by the manufacturer and regulatory agencies. Any measures that can reduce the amount of time required to complete these testing campaigns provides significant benefits to manufacturers. The purpose of this work is then to develop a systematic approach to minimize the time required to conduct a steady-state engine test campaign using a Savitsky-Golay filter to calculate measured signal gradients for continuous steady-state detection. Experiments were conducted on an Armfield CM11-MKII Gasoline Engine test bench equipped with a 1.2L 3-cylinder Volkswagen EA111 R3 engine. The test bench utilizes throttle position control and an eddy current dynamometer braking system with automatic PID control of engine speed.

Mobility in the automotive and transportation sectors has been experiencing a period of unprecedented evolution. A growing need for efficient, clean and safe mobility has increased momentum toward sustainable technologies in these sectors. Toward this end, battery electric vehicles have drawn keen interest and their market share is expected to grow significantly in the coming years, especially in light-duty applications such as passenger cars. Although the battery electric vehicles feature high performance and zero tailpipe emission characteristics, economic and technical issues such as battery cost, driving range, recharging time and infrastructure remain main hurdles that need to be fully addressed. In particular, the low power density of the battery limits its broad adoption in heavy-duty applications such as class 8 semi-trailer trucks due to the required size and weight of the battery and electric motor.

Opposed-piston 2-stroke (OP-2S) engines have the potential to achieve higher thermal efficiency than a typical diesel engine. However, the uniflow scavenging process is difficult to control over a wide range of speeds and loads. Scavenging performance is highly sensitive to pressure dynamics, port timings, and port design. This study proposes an analysis of the effects of port vane angle on the scavenging performance of an opposed-piston 2-stroke engine via simulation. A CFD model of a three-cylinder opposed-piston 2-stroke was developed and validated against experimental data collected by Achates Power Inc. One of the three cylinders was then isolated in a new model and simulated using cycle-averaged and cylinder-averaged initial/boundary conditions. This isolated cylinder model was used to efficiently sweep port angles from 12 degrees to 29 degrees at different pressure ratios.

As the global economy grows, so does the demand for heavy-duty commercial vehicles, both on-road and off-road. Currently, these vehicles are powered almost entirely by diesel engines. There is an imminent need to reduce the greenhouse gases (GHG) from this growing sector, but alternatives to the internal combustion engine face many challenges and can increase GHG emissions. For example, through simple analysis, this work will show that a Class 8 long haul on-highway truck powered entirely by battery electrics and charged from the average US electrical grid, yields significantly higher CO2 emissions per ton-mile as compared to an engine using alternative fuels. Thus, the most pragmatic and impactful way to reduce GHG emissions in commercial vehicles is using low carbon alternative fuels, such as ethanol made from renewable sources.

The use of Thermal Barrier Coatings (TBCs) has been shown to be a promising technology to improve internal combustion engine efficiencies by reducing heat rejection to the coolant and oil. In recent studies, temperature swing coatings that have simultaneously low volumetric heat capacity and low thermal conductivity have been shown to be particularly promising in this regard. In this study, a traditional and a newer swing coating are applied to the piston of an on-road medium-duty diesel engine to assess the benefits of their use. An analytical wall temperature model is coupled to the 1-D engine simulation software GT-POWER and predictions of wall temperature, heat transfer and chemical heat release rate are presented. The swing coating is found to yield an ~1.2% efficiency benefit at the highest load condition studied alongside an 80°C improvement in exhaust temperature at the lowest load condition studied compared to a reference uncoated piston.

Expanding upon the authors’ previous work which utilized a GT-Power model of the Cooperative Fuels Research (CFR) engine under Research Octane Number (RON) conditions, this work defines the boundary conditions of the CFR engine under Motored Octane Number (MON) test conditions. The GT-Power model was validated against experimental CFR engine data for primary reference fuel (PRF) blends between 60 and 100 under standard MON conditions, defining the full range of interest of MON for gasoline-type fuels. The CFR engine model utilizes a predictive turbulent flame propagation sub-model, and a chemical kinetic solver for the end-gas chemistry. The validation was performed simultaneously for thermodynamic and chemical kinetic parameters to match in-cylinder pressure conditions, burn rate, and knock point prediction with experimental data, requiring only minor modifications to the flame propagation model from previous model iterations.

At recent American Society for Testing and Materials (ASTM) Subcommittee D02.01 meetings, committee members and attendees from the petroleum industry have reiterated a longstanding desire to see precision improvements to the ASTM D613 Standard Test Method for Cetane Number of Diesel Fuel Oil. The existing ASTM D613 precision limits were calculated using ASTM National Exchange Group (NEG) monthly test data from the mid-1970s through the early 1990s. Over the past few decades, many detailed studies were performed to identify and better understand the shortcomings of the cetane method (both engine equipment and instrumentation). Many of these studies concluded that inconsistent combustion is the main contributing factor behind the lack of precision in the cetane number method, followed by shortcomings in the instrumentation used to measure ignition delay.

This work explores the effectiveness of common rail fuel injectors equipped with Grouped Hole Nozzles (GHNs) in aiding the mixing process and reducing particulate matter (PM) emissions of Conventional Diesel Combustion (CDC) engines, while maintaining manageable Oxides of Nitrogen (NOx) levels. Parallel (pGHN), converging (cGHN) and diverging (dGHN) - hole GHNs were studied and the results were compared to a conventional, single hole nozzle (SHN) with the same flow area. The study was conducted on a single cylinder medium-duty engine to isolate the effects of the combustion from multi-cylinder effects and the conditions were chosen to be representative of a typical mid-load operating point for an on-road diesel engine. The effects of injection pressure and the Start of Injection (SOI) timing were explored and the tradeoffs between these boundary conditions are examined by using a response surface fitting technique, to identify an optimum operating condition.

Machine learning methods, such as decision trees and deep neural networks, are becoming increasingly important and useful for data analysis in various scientific fields including dynamics and control, signal processing, pattern recognition, fluid mechanics, and chemical synthesis, etc. For future engine design and performance optimization, there is an urgent need for a robust predictive model which could capture the major combustion properties such as autoignition and flame propagation of multicomponent fuels under a wide range of engine operating conditions, without massive experimental measurement or computational efforts. It will be shown that these long-held limitations and challenges related to complex fuel combustion and engine research could be readily solved by implementing machine learning methods.

Given increasingly stringent emission targets, engine efficiency has become of foremost importance. While increasing engine compression ratio can lead to efficiency gains, it also leads to higher in-cylinder pressure and temperatures, thus increasing the risk of knock. One potential solution is the use of a Variable Compression Ratio system, which is capable of exploiting the advantages coming from high compression ratio while limiting its drawbacks by operating at low engine loads with a high compression ratio, and at high loads with a low compression ratio, where knock could pose a significant threat. This paper describes the design of a model for the evaluation of fuel consumption for an engine equipped with a VCR system over representative drive cycles. The model takes as inputs; a switching time for the VCR system, the vehicle characteristics, engine performance maps corresponding to two different compression ratios, and a drive cycle.

Variable Compression Ratio systems are an increasingly attractive solution for car manufacturers in order to reduce vehicle fuel consumption. By having the capability to operate with a range of compression ratios, engine efficiency can be significantly increased by operating with a high compression ratio at low loads, where the engine is normally not knock-limited, and with a low compression ratio at high load, where the engine is more prone to knock. In this way, engine efficiency can be maximized without sacrificing performance. This study aims to analyze how the effectiveness of a VCR system is affected by various powertrain and vehicle parameters. By using a Matlab model of a VCR system developed in Part 1 of this work, the influence of the vehicle characteristics, the drive cycle, and of the number of stages used in the VCR system was studied.

A new generation of low heat capacitance Thermal Barrier Coatings (TBCs) has been developed under U.S. Dept. of Energy / Advanced Research Projects Agency - Energy (ARPA-E) sponsored research. The TBCs developed under this project have significantly lower thermal conductivity of < 0.35 W/m-K, thermal heat capacitance of < 500 kJ/m3-K, and density of <0.35 g/cm3. Two different binder types were used for thermal barrier coatings applied by High Velocity Low Pressure (HVLP) spraying to the piston, cylinder head, and valve combustion surfaces of a small natural gas engine. The effects of thermal barrier coatings on engine efficiency and knock characteristics were studied in a small, high compression ratio, spark-ignition, internal combustion engine operating on methane number fuels from 60 to 100. The new TBCs with low thermal conductivity and low thermal heat capacities have been shown to increase overall engine efficiency through reduced heat transfer to the piston and cylinder head.

Dual-fuel reactivity controlled compression ignition (RCCI) combustion is a promising method to achieve high efficiency with near-zero NOx and soot emissions; however, the requirement to carry two fuels on board limits practical application. Advancements in catalytic reforming have demonstrated the ability to generate syngas (a mixture of CO and hydrogen) from a single hydrocarbon stream. This syngas mixture can then be used as the low reactivity fuel stream to enable single fuel RCCI combustion. The present effort uses a combination of engine experiments and system level modeling to investigate reformed fuel RCCI combustion. The impact of reformer composition is investigated by varying the syngas composition from 10% H2 to approximately 80% H2. The results of the investigation show that reformed fuel RCCI combustion is possible over a wide range of H2/CO ratios.

Developments in modern spark ignition (SI) engines such as intake boosting, direct-injection, and engine downsizing techniques have demonstrated improved performance and thermal efficiency, however, these strategies induce significant deviation in end-gas pressure/temperature histories from those of the traditional Research and Motor Octane Number (RON and MON) standards. Attempting to extrapolate the anti-knock performance of fuels tested under the traditional RON/MON conditions to boosted operation has yielded mixed results in both SI and advanced compression ignition (ACI) engines. This consideration motivates the present work with seeks to establish a pathway towards the development of the test conditions of a boosted octane number, which would better correlate to fuel performance at high intake pressure conditions.

Engine experiments were carried out on a heavy-duty single-cylinder engine to investigate the effects of Gasoline Compression Ignition on emissions and performance of a heavy-duty engine operating at a high load condition. Comparisons between gasoline fueled operation and diesel fueled operation are presented using a single, near top dead center injection. Although the fuel’s cetane numbers are very different, the combustion characteristics of the two fuels at high load are similar, with the gasoline-fueled case showing less than two crank angle degree longer ignition delay. Gasoline operation showed lower soot production at similar levels of NOx, initiating study of the impact of exhaust gas recirculation which spanned a range of NOx levels covering the range from minimal urea dosing to high urea dosing. A conventional soot-NOx tradeoff was found to exist with gasoline as exists with diesel.