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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.

Understanding the fundamental details of drop/wall interactions is important to improving engine performance. Most of the drop-wall interactions studies are based on the impact of a single drop on the wall. To accurately mimic and model the real engine conditions, it is necessary to characterize spray/wall interactions with different impingement frequencies at a wide range of wall temperatures. In this study, a numerical method, based on Smoothed Particle Hydrodynamics (SPH), is used to simulate consecutive droplet impacts on a heated wall both below and above the Leidenfrost temperature. Impact regimes are identified for various impact conditions by analyzing the time evolution of the post-impingement process of n-heptane drops at different impingement frequencies and wall surface temperatures. For wall temperature below the Leidenfrost temperature, the recoiled film does not leave the surface.

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.

Accelerated computational optimization of a diesel engine calibration was achieved by combining Support Vector Regression models with the Particle Swarm Optimization routine. The framework utilized a full engine simulation as a surrogate for a real engine test with test parameters closely resembling a typical 4.5L diesel engine. Initial tests were run with multi-modal test problems including Rastragin's, Bukin's, Ackely's, and Schubert's functions which informed the ML model tuning hyper-parameters. To improve the performance of the engine the hybrid approach was used to optimize the Fuel Pressure, Injection Timing, Pilot Timing and Fraction, and EGR rate. Nitrogen Oxides, Particulate Matter, and Specific Fuel Consumption are simultaneously reduced. As expected, optimums reflect a late injection strategy with moderately high EGR rates.

The impact of fuel droplets on heated surfaces is of great importance in internal combustion engines. In engine computational fluid dynamics (CFD) simulations, the drop-wall interaction is usually considered by using models derived from experimental data and correlations rather than direct simulations. This paper presented a numerical method based on smoothed particle hydrodynamics (SPH), which can directly simulate the impact process of fuel droplets impinging on solid surfaces. The SPH method is a Lagrangian meshfree particle method. It discretizes fluid into a number of SPH particles and governing equations of fluid into a set of particle equations. By solving the particle equations, the movement of particles can be obtained, which represents the fluid flows. The SPH method is able to simulate the large deformation and breakup of liquid drops without using additional interface tracking techniques.

An engine-aftertreatment computational model was developed to support in-loop performance simulations of tailpipe emissions and fuel consumption associated with a range of heavy-duty (HD) truck drive cycles. For purposes of this study, the engine-out exhaust dynamics were simulated with a combination of steady-state engine maps and dynamic correction factors that accounted for recent engine operating history. The engine correction factors were approximated as dynamic first-order lags associated with the thermal inertia of the major engine components and the rate at which engine-out exhaust temperature and composition vary as combustion heat is absorbed or lost to the surroundings. The aftertreatment model included catalytic monolith components for diesel exhaust oxidation, particulate filtration, and selective catalytic reduction of nitrogen oxides (NOx) with urea.

In this study both double and triple injection strategies were used with fuel pressures up to 300 and 250 MPa, respectively. Tests were conducted at medium load conditions with cooled, high-pressure EGR at a ratio of 40% and higher. A four-cylinder production engine, featuring double turbochargers with one variable geometry turbocharger, was tested. The double injection strategy consisted of a 20% close-coupled pilot injection while the triple injection strategy introduced a post injection consisting of 10% the total cycle fuel. Results of this study do not indicate an advantage to extreme fuel pressure. The increased air entrainment reduces soot while increasing the premixed burn heat release, mean cylinder temperature, and NOx. Compared to the double injection scheme, triple injections achieved much lower soot for the same EGR rate with only a small NOx penalty.

A prototype three-way catalyst (TWC) with NOX storage component was evaluated for ammonia (NH3) generation on a 2.0-liter BMW lean burn gasoline direct injection engine as a component in a passive ammonia selective catalytic reduction (SCR) system. The passive NH3 SCR system is a potential approach for controlling nitrogen oxides (NOX) emissions from lean burn gasoline engines. In this system, NH3 is generated over a close-coupled TWC during periodic slightly-rich engine operation and subsequently stored on an underfloor SCR catalyst. Upon switching to lean, NOX passes through the TWC and is reduced by the stored NH3 on the SCR catalyst. Adding a NOX storage component to a TWC provides two benefits in the context of a passive SCR system: (1) enabling longer lean operation by storing NOX upstream and preserving NH3 inventory on the downstream SCR catalyst; and (2) increasing the quantity and rate of NH3 production during rich operation.

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.

Reliable means for on-board detection of particulate filter failures or malfunctions are needed to meet diagnostics (OBD) requirements. Detecting these failures, which result in tailpipe particulate matter (PM) emissions exceeding the OBD limit, over all operating conditions is challenging. Current approaches employ differential pressure sensors and downstream PM sensors, in combination with particulate filter and engine-out soot models. These conventional monitors typically operate over narrowly-defined time windows and do not provide a direct measure of the filter’s state of health. In contrast, radio frequency (RF) sensors, which transmit a wireless signal through the filter substrate provide a direct means for interrogating the condition of the filter itself.

The increasing use of diesel and gasoline particulate filters requires advanced on-board diagnostics (OBD) to prevent and detect filter failures and malfunctions. Early detection of upstream (engine-out) malfunctions is paramount to preventing irreversible damage to downstream aftertreatment system components. Such early detection can mitigate the failure of the particulate filter resulting in the escape of emissions exceeding permissible limits and extend the component life. However, despite best efforts at early detection and filter failure prevention, the OBD system must also be able to detect filter failures when they occur. In this study, radio frequency (RF) sensors were used to directly monitor the particulate filter state of health for both gasoline particulate filter (GPF) and diesel particulate filter (DPF) applications.

The objective of this work was to identify methods of reliably predicting optimum operating conditions in an experimental compression ignition engine using multiple injections. Abstract modeling offered an efficient way to predict large volumes data, when compared with simulation, although the initial cost of constructing such models can be large. This work aims to reduce that initial cost by adding knowledge about the favorable network structures and training rules which are discovered. The data were gathered from a high pressure common rail direct injection turbocharged compression ignition engine utilizing a high EGR configuration. The range of design parameters were relatively large; 100 MPa - 240 MPa for fuel pressure, up to 62% EGR using a modified, long-route, low pressure EGR system, while the pilot timing, main timing, and pilot ratio were free within the safe operating window for the engine.

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.

Radio frequency (RF)-based sensors provide a direct measure of the particulate filter loading state. In contrast to particulate matter (PM) sensors, which monitor the concentration of PM in the exhaust gas stream for on-board diagnostics purposes, RF sensors have historically been applied to monitor and control the particulate filter regeneration process. This work developed an RF-based particulate filter control system utilizing both conventional and fast response RF sensors, and evaluated the feasibility of applying fast-response RF sensors to provide a real-time measurement of engine-out PM emissions. Testing with a light-duty diesel engine equipped with fast response RF sensors investigated the potential to utilize the particulate filter itself as an engine-out soot sensor.

Particle Swarm and the Genetic Algorithm were coupled to optimize multiple performance metrics for the combustion of neat biodiesel in a turbocharged, four cylinder, John Deere engine operating under constant partial load. The enhanced algorithm was used with five inputs including EGR, injection pressure, and the timing/distribution of fuel between a pilot and main injection. A merit function was defined and used to minimize five output parameters including CO, NOx, PM, HC and fuel consumption simultaneously. The combination of PSO and GA yielded convergence to a Pareto regime without the need for excessive engine runs. Results along the Pareto front illustrate the tradeoff between NOx and particulate matter seen in the literature.

Rapid vehicle powertrain development has become a technological breakthrough for the design and implementation of vehicles that meet and exceed the fuel efficiency, cost, and performance targets expected by today’s consumer. Recently, advances in large scale additive manufacturing have provided the means to bridge hardware-in-the-loop with preproduction mule chassis testing. This paper details a case study from Oak Ridge National Laboratory bridging the powertrain-in-the-loop development process with vehicle systems implementation using big area additive manufacturing (BAAM). For this case study, the use of a component-in-the-loop laboratory with math-based models is detailed for the design of a battery electric powertrain to be implemented in a printed prototype mule. The ability for BAAM to accelerate the mule development process via the concept of computer-aided design to part is explored.

This paper is part of a larger body of experimental and computational work devoted to studying the role of close-coupled post injections on soot reduction in a heavy-duty optical engine. It is a continuation of an earlier computational paper. The goals of the current work are to develop new CFD analysis tools and methods and apply them to gain a more in depth understanding of the different in-cylinder environments into which fuel from main- and post-injections are injected and to study how the in-cylinder flow, thermal and chemical fields are transformed between start of injection timings. The engine represented in this computational study is a single-cylinder, direct-injection, heavy-duty, low-swirl engine with optical components. It is based on the Cummins N14, has a cylindrical shaped piston bowl and an eight-hole injector that are both centered on the cylinder axis. The fuel used was n-heptane and the engine operating condition was light load at 1200 RPM.

Due to the new challenge of meeting number-based regulations for particulate matter (PM), a numerical and experimental study has been conducted to better understand particulate formation in engines fuelled with compressed natural gas. The study has been conducted on a Heavy-Duty, Euro VI, 4-cylinder, spark ignited engine, with multipoint sequential phased injection and stoichiometric combustion. For the experimental measurements two different instruments were used: a condensation particle counter (CPC) and a fast-response particle size spectrometer (DMS) the latter able also to provide a particle size distribution of the measured particles in the range from 5 to 1000 nm. Experimental measurements in both stationary and transient conditions were carried out. The data using the World Harmonized Transient Cycle (WHTC) were useful to detect which operating conditions lead to high numbers of particles. Then a further transient test was used for a more detailed and deeper analysis.

Reactivity Controlled Compression Ignition (RCCI) has been shown to be an attractive concept to achieve clean and high efficiency combustion. RCCI can be realized by applying two fuels with different reactivities, e.g., diesel and gasoline. This motivates the idea of using a single low reactivity fuel and direct injection (DI) of the same fuel blended with a small amount of cetane improver to achieve RCCI combustion. In the current study, numerical investigation was conducted to simulate RCCI and HCCI combustion and emissions with various fuels, including gasoline/diesel, iso-butanol/diesel and iso-butanol/iso-butanol+di-tert-butyl peroxide (DTBP) cetane improver. A reduced Primary Reference Fuel (PRF)-iso-butanol-DTBP mechanism was formulated and coupled with the KIVA computational fluid dynamic (CFD) code to predict the combustion and emissions of these fuels under different operating conditions in a heavy duty diesel engine.

A novel 2-zone combustion chamber concept (patent pending) was developed using multi-dimensional modeling. At minimum volume, an axial projection in the piston divides the volume into distinct zones joined by a communication channel. The projection provides a means to control the mixture formation and combustion phasing within each zone. The novel combustion system was applied to reactivity controlled compression ignition (RCCI) combustion in both light-duty and heavy-duty diesel engines. Results from the study of an 8.8 bar BMEP, 2600 RPM operating condition are presented for the light-duty engine. The results from the heavy-duty engine are at an 18.1 bar BMEP, 1200 RPM operating condition. The effect of several major design features were investigated including the volume split between the inner and outer combustion chamber volumes, the clearance (squish) height, and the top ring land (crevice) volume.