Search Results

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.

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.

The US Department of Energy’s Co-Optimization of Engine and Fuels Initiative (Co-Optima) investigated how unique properties of bio-blendstocks considered within Co-Optima help address emissions challenges with mixing controlled compression ignition (i.e., conventional diesel combustion) and enable advanced compression ignition modes suitable for implementation in a diesel engine. Additionally, the potential synergies of these Co-Optima technologies in hybrid vehicle applications in the medium- and heavy-duty sector was also investigated. In this work, vehicles system were simulated using the Autonomie software tool for quantifying the benefits of Co-Optima engine technologies for medium-duty trucks. A Class 6 delivery truck with a 6.7 L diesel engine was used for simulations over representative real-world and certification drive cycles with four different powertrains to investigate fuel economy, criteria emissions, and performance.

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.

Hydrogen Internal Combustion Engines (H2-ICEs) are being investigated due to their minimal criteria pollutant and zero CO2 tailpipe emissions. However, oil filters and non-hot joint gaskets have rubber material that can be damaged and deteriorate due to direct or indirect exposure to the high temperature and high-pressure hydrogen in a H2-ICE. Thus, the effects on the properties of a rubber exposed to a hydrogen environment need to be reviewed. In this review paper, the transportation, chemical and mechanical properties of a rubber exposed directly or indirectly to high temperature and high-pressure hydrogen in a H2-ICE have been reviewed. The compatibility of rubber materials used in H2-ICE has been explored. The effects of high-pressure hydrogen on the transportation, chemical and mechanical properties of NBR and HNBR have been reviewed.

Prior research studies have investigated a wide variety of gasoline compression ignition (GCI) injection strategies and the resulting fuel stratification levels to maintain control over the combustion phasing, duration, and heat release rate. Previous GCI research at the US Department of Energy’s Oak Ridge National Laboratory has shown that for a combustion mode with a low degree of fuel stratification, called “partial fuel stratification” (PFS), gasoline range fuels with anti-knock index values in the range of regular-grade gasoline (~87 anti-knock index or higher) provides very little controllability over the timing of combustion without significant boost pressures. On the contrary, heavy fuel stratification (HFS) provides control over combustion phasing but has challenges achieving low temperature combustion operation, which has the benefits of low NOX and soot emissions, because of the air handling burdens associated with the required high exhaust gas recirculation rates.

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.

A new analytical method to aid in the understanding of the organic carbon (OC) phase of particulate matter (PM) from advanced compression ignition (ACI) operating modes, is presented. The presence of NO2 and unburned fuel aromatics in ACI emissions, and the low exhaust temperatures that result from this low temperature combustion strategy, provide the right conditions for the formation of carboxylic acids and nitroaromatic compounds. These polar compounds contribute to OC in the PM and are not typically measured using nonpolar solvent extraction methods such as the soluble organic fraction (SOF) method. The new extraction and detection method employs capillary electrophoresis with electrospray ionization mass spectrometry (CE-ESI MS) and was specifically developed to determine polar organic compounds in the ACI PM emissions. The new method identified both nitrophenols and aromatic carboxylic acids in the ACI PM.

Multimode engine operation uses two or more combustion modes to maximize engine efficiency across the operational range of a vehicle to achieve higher overall vehicle fuel economy than is possible with a single combustion mode. More specifically for this study, multimode solutions are explored that make use of boosted SI under high load operation and other advanced combustion modes such as advanced compression ignition (ACI) under part-load conditions to enable additional engine efficiency improvements across a broader range of the engine operating map. ACI combustion has well-documented potential to improve efficiency and emissions under part-load operation but poses challenges that limit full engine speed-load range. This study investigates the potential impact of ACI operational range on simulated fuel economy to help focus research on areas with the most opportunity for improving fuel economy.

Polyoxymethylene dimethyl ethers (PODEs) have shown promise as candidates for diesel fuel blendstocks due to their low sooting tendency, high cetane number, and diesel-comparable boiling point range. However, there is a lack of literature regarding compatibility of PODEs with common automotive elastomers, which would be a prerequisite to their adoption into the marketplace. To address this need, an exposure study and complementary solubility analysis were undertaken. A commercially available blend of PODEs with polymerization degree ranging from 3 to 6 was blended with diesel certification fuel at 0, 33, 50, 67, at 100% by mass. Elastomer coupons were exposed to the various blends for a period of 4 weeks and evaluated for volume swell.

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.

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.

Low temperature combustion (LTC) strategies present a means of reducing soot and oxides of nitrogen (NOx) emissions while simultaneously increasing efficiency relative to conventional combustion modes. By sufficiently premixing fuel and air before combustion, LTC strategies avoid high fuel-to-air equivalence ratios that lead to soot production. Dilution of the mixture lowers the combustion temperatures to reduce NOx production and offers thermodynamic advantages for improved efficiency. However, issues such as high heat release rates (HRRs), incomplete combustion, and difficulty in controlling the timing of combustion arise with low equivalence ratios and combustion temperatures. Ignition delay (the time until the start of combustion) is a way to quantify the time available for fuel and air to mix inside the cylinder before combustion. Previous studies have used ignition delay to explain trends seen in LTC such as combustion stability and HRRs.

Reactivity Controlled Compression Ignition (RCCI) is an approach to increase engine efficiency and lower engine-out emissions by using in-cylinder stratification of fuels with differing reactivity (i.e., autoignition characteristics) to control combustion phasing. Stratification can be altered by varying the injection timing of the high-reactivity fuel, causing transitions across multiple regimes of combustion. When injection is sufficiently early, combustion approaches a highly-premixed autoignition regime, and when it is sufficiently late it approaches more mixing-controlled, diesel-like conditions. Engine performance, emissions, and control authority over combustion phasing with injection timing are most favorable in between, within the RCCI regime.

Engine experiments were conducted on a heavy-duty single-cylinder engine to explore the effects of charge preparation, fuel stratification, and premixed fuel chemistry on the performance and emissions of Reactivity Controlled Compression Ignition (RCCI) combustion. The experiments were conducted at a fixed total fuel energy and engine speed, and charge preparation was varied by adjusting the global equivalence ratio between 0.28 and 0.35 at intake temperatures of 40°C and 60°C. With a premixed injection of isooctane (PRF100), and a single direct-injection of n-heptane (PRF0), fuel stratification was varied with start of injection (SOI) timing. Combustion phasing advanced as SOI was retarded between -140° and -35°, then retarded as injection timing was further retarded, indicating a potential shift in combustion regime. Peak gross efficiency was achieved between -60° and -45° SOI, and NOx emissions increased as SOI was retarded beyond -40°, peaking around -25° SOI.