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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.

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.

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.

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.

Continued improvement in the combustion process of internal combustion engines is necessary to reduce fuel consumption, CO2 emissions, and criteria emissions for automotive transportation around the world. In this paper, test results for the Gen3X Gasoline Direct Injection Compression Ignition (GDCI) engine are presented. The engine is a 2.2L, four-cylinder, double overhead cam engine with compression ratio ~17. It features a “wetless” combustion system with a high-pressure direct injection fuel system. At low load, exhaust rebreathing and increased intake air temperature were used to promote autoignition and elevate exhaust temperatures to maintain high catalyst conversion efficiency. For medium-to-high loads, a new GDCI-diffusion combustion strategy was combined with advanced single-stage turbocharging to produce excellent low-end torque and power. Time-to-torque (TT) simulations indicated 90% load response in less than 1.5 seconds without a supercharger.

The present study helps to understand the local combustion characteristics of PREmixed Mixture Ignition in the End-gas Region (PREMIER) combustion mode while using increasing amount of natural gas as a diesel substitute in conventional CI engine. In order to reduce NOx emission and diesel fuel consumption micro-pilot diesel injection in premixed natural gas-air mixture is a promising technique. New strategy has been employed to simulate dual fuel combustion which uses well established combustion models. Main focus of the simulation is at detection of an end gas ignition, and creating an unified modeling approach for dual fuel combustion. In this study G-equation flame propagation model is used with detailed chemistry in order to detect end-gas ignition in overall low temperature combustion. This combustion simulation model is validated using comparison with experimental data for dual fuel engine.

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.

The automotive industry is facing tremendous challenges to improve fuel economy and emissions of the internal combustion engine. In the US, 2025 standards for fuel economy and CO2 emissions are extremely stringent. Simultaneously, vehicles must comply with new US Tier 3 emissions standards. In all market segments, there is a need for very clean and efficient engines operating on gasoline fuels. Gasoline Direct Injection Compression Ignition (GDCI) has been under development for several years and significant progress has been realized. As part of two US DOE programs, Delphi has developed a third generation GDCI engine that utilizes partially premixed compression ignition. The engine features an innovative “wetless”, low-temperature, combustion system with the latest high-pressure GDi injection system. The system was developed using extensive simulation and engine testing.

Recent advancements in the combustion control of new generation engines can benefit from real time, precise sensing of the cylinder pressure profile to facilitate successful combustion feedback. Currently, even laboratory-grade pressure sensors can deliver pressure traces with insufficient signal-to-noise quality due to electrical or combustion-induced signal interference. Consequently, for example, calculation of compression and expansion polytropic indices may require statistical averaging over several cycles to deliver required information. This lag in the resultant feedback may become a concern when the calculated combustion metric is used for feedback control, especially in the case of transients. The method described in this paper involves a special digital filter offering excellent performance which facilitates reduced-error calculation of individual polytropic indices.

Three jet fuel surrogates were compared against their target fuels in a compression ignited optical engine under a range of start-of-injection temperatures and densities. The jet fuel surrogates are representative of petroleum-based Jet-A POSF-4658, natural gas-derived S-8 POSF-4734 and coal-derived Sasol IPK POSF-5642, and were prepared from a palette of n-dodecane, n-decane, decalin, toluene, iso-octane and iso-cetane. Optical chemiluminescence and liquid penetration length measurements as well as cylinder pressure-based combustion analyses were applied to examine fuel behavior during the injection and combustion process. HCHO* emissions obtained from broadband UV imaging were used as a marker for low temperature reactivity, while 309 nm narrow band filtered imaging was applied to identify the occurrence of OH*, autoignition and high temperature reactivity.

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.

A correlation was developed to predict the ignition delay of PRF blends at a wide range of engine-relevant operating conditions. Constant volume simulations were performed using Cantera coupled with a reduced reaction mechanism at a range of initial temperatures from 570-1860K, initial pressures from 10-100atm, oxygen mole percent from 12.6% to 21%, equivalence ratios from 0.30-1.5, and PRF blends from PRF0 to PRF100. In total, 6,480 independent ignition delay simulations were performed. The correlation utilizes the traditional Arrhenius formulation; with equivalence ratio (φ), pressure (p), and oxygen mole percentage (xo2) dependencies. The exponents α, β, and γ were fitted to a third order polynomial with respect to temperature with an exponential roll-off to a constant value at low temperatures to capture the behavior expressed by the reaction mechanism. The location and rate of the roll-off functions were modified by linear functions of PRF.

The 4th Workshop of the Engine Combustion Network (ECN) was held September 5-6, 2015 in Kyoto, Japan. This manuscript presents a summary of the progress in experiments and modeling among ECN contributors leading to a better understanding of soot formation under the ECN “Spray A” configuration and some parametric variants. Relevant published and unpublished work from prior ECN workshops is reviewed. Experiments measuring soot particle size and morphology, soot volume fraction (fv), and transient soot mass have been conducted at various international institutions providing target data for improvements to computational models. Multiple modeling contributions using both the Reynolds Averaged Navier-Stokes (RANS) Equations approach and the Large-Eddy Simulation (LES) approach have been submitted. Among these, various chemical mechanisms, soot models, and turbulence-chemistry interaction (TCI) methodologies have been considered.

The second generation 1.8L Gasoline Direct Injection Compression Ignition (GDCI) engine was built and tested using RON91 gasoline. The engine is intended to meet stringent US Tier 3 emissions standards with diesel-like fuel efficiency. The engine utilizes a fulltime, partially premixed combustion process without combustion mode switching. The second generation engine features a pentroof combustion chamber, 400 bar central-mounted injector, 15:1 compression ratio, and low swirl and squish. Improvements were made to all engine subsystems including fuel injection, valve train, thermal management, piston and ring pack, lubrication, EGR, boost, and aftertreatment. Low firing friction was a major engine design objective. Preliminary test results indicated good improvement in brake specific fuel consumption (BSFC) over the first generation GDCI engines, while meeting targets for engine out emissions, combustion noise and stability.