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A comprehensive biodiesel combustion model is presented for use in multi-dimensional engine simulations. The model incorporates realistic physical properties in a vaporization model developed for multi-component fuel sprays and applies an improved mechanism for biodiesel combustion chemistry. Previously, a detailed mechanism for methyl decanoate and methyl-9-decenoate was reduced from 3299 species to 85 species to represent the components of biodiesel fuel. In this work, a second reduction was performed to further reduce the mechanism to 69 species. Steady and unsteady spray simulations confirmed that the model adequately reproduced liquid penetration observed in biodiesel spray experiments. Additionally, the new model was able to capture expected fuel composition effects with low-volatility components and fuel blend sprays penetrating further.

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 Relevant published and unpublished work from prior ECN workshops is reviewed.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 importance of radiative heat transfer on the combustion and soot formation characteristics under nominal ECN Spray A conditions has been studied numerically. The liquid n-dodecane fuel is injected with 1500 bar fuel pressure into the constant volume chamber at different ambient conditions. Radiation from both gas-phase as well as soot particles has been included and assumed as gray. Three different solvers for the radiative transfer equation have been employed: the discrete ordinate method, the spherical-harmonics method and the optically thin assumption. The radiation models have been coupled with the transported probability density function method for turbulent reactive flows and soot, where unresolved turbulent fluctuations in temperature and composition are included and therefore capturing turbulence-chemistry-soot-radiation interactions. Results show that the gas-phase (mostly CO2 ad H2O species) has a higher contribution to the net radiation heat transfer compared to soot.

Homogeneous-charge, compression-ignition (HCCI) combustion is triggered by spontaneous ignition in dilute homogeneous mixtures. The combustion rate must be reduced by suitable solutions such as high rates of Exhaust Gas Recirculation (EGR) and/or lean mixtures. HCCI is considered a very effective way to reduce engine pollutant emissions, however only a few HCCI engines have entered into production. HCCI combustion currently cannot be extended to the whole engine operating range, especially to high loads, since the use of EGR displaces air from the cylinder, limiting engine mean effective pressure, thus the engine must be able to operate also in conventional mode. This paper concerns an innovative concept to control HCCI combustion in diesel-fuelled engines. This new combustion concept is called Homogenous Charge Progressive Combustion (HCPC). HCPC is based on split-cycle principle.

The application of close-coupled post injections in diesel engines has been proven to be an effective in-cylinder strategy for soot reduction, without much fuel efficiency penalty. But due to the complexity of in-cylinder combustion, the soot reduction mechanism of post-injections is difficult to explain. Accordingly, a simulation study using a three dimensional computational fluid dynamics (CFD) model, coupled with the SpeedChem chemistry solver and a semi-detailed soot model, was carried out to investigate post-injection in a constant volume combustion chamber, which is more simple and controllable with respect to the boundary conditions than an engine. A 2-D axisymmetric mesh of radius 2 cm and height 5 cm was used to model the spray. Post-injection durations and initial oxygen concentrations were swept to study the efficacy of post-injection under different combustion conditions.

A computational fluid dynamics (CFD) guided combustion system optimization was conducted for a heavy-duty diesel engine running with a gasoline fuel that has a research octane number (RON) of 80. The goal was to optimize the gasoline compression ignition (GCI) combustion recipe (piston bowl geometry, injector spray pattern, in-cylinder swirl motion, and thermal boundary conditions) for improved fuel efficiency while maintaining engine-out NOx within a 1-1.5 g/kW-hr window. The numerical model was developed using the multi-dimensional CFD software CONVERGE. A two-stage design of experiments (DoE) approach was employed with the first stage focusing on the piston bowl shape optimization and the second addressing refinement of the combustion recipe. For optimizing the piston bowl geometry, a software tool, CAESES, was utilized to automatically perturb key bowl design parameters. This led to the generation of 256 combustion chamber designs evaluated at several engine operating conditions.

A computational fluid dynamics (CFD) guided combustion system optimization was conducted for a heavy-duty compression-ignition engine with a gasoline-like fuel that has an anti-knock index (AKI) of 58. The primary goal was to design an optimized combustion system utilizing the high volatility and low sooting tendency of the fuel for improved fuel efficiency with minimal hardware modifications to the engine. The CFD model predictions were first validated against experimental results generated using the stock engine hardware. A comprehensive design of experiments (DoE) study was performed at different operating conditions on a world-leading supercomputer, MIRA at Argonne National Laboratory, to accelerate the development of an optimized fuel-efficiency focused design while maintaining the engine-out NOx and soot emissions levels of the baseline production engine.

In this study, the combustion system of a light-duty compression ignition engine running on a market gasoline fuel with Research Octane Number (RON) of 91 was optimized using computational fluid dynamics (CFD) and Machine Learning (ML). This work was focused on optimizing the piston bowl geometry at two compression ratios (CR) (17 and 18:1) and this exercise was carried out at full-load conditions (20 bar indicated mean effective pressure, IMEP). First, a limited manual piston design optimization was performed for CR 17:1, where a couple of pistons were designed and tested. Thereafter, a CFD design of experiments (DoE) optimization was performed where CAESES, a commercial software tool, was used to automatically perturb key bowl design parameters and CONVERGE software was utilized to perform the CFD simulations. At each compression ratio, 128 piston bowl designs were evaluated.

This paper describes numerical simulations that compare the performance of two combustion CFD models against experimental data, and evaluates the effects of combustion and spray model constants on the predicted combustion and emissions under various operating conditions. The combustion models include a Characteristic Time Combustion (CTC) model and CHEMKIN with reduced chemistry models integrated in the KIVA-3Vr2 CFD code. The diesel spray process was modeled using an updated version of the KH-RT spray model that features a gas jet submodel to help reduce numerical grid dependencies, and the effects of both the spray and combustion model constants on combustion and emissions were evaluated. In addition, the performance of two soot models was compared, namely a two-step soot model, and a more detailed model that considers soot formation from PAH precursors.

The primary strength of large eddy simulation (LES) is in directly resolving the instantaneous large-scale flow features which can then be used to study critical flame properties such as ignition, extinction, flame propagation and lift-off. However, validation of the LES results with experimental or direct numerical simulation (DNS) datasets requires the determination of statistically-averaged quantities. This is typically done by performing multiple realizations of LES and performing a statistical averaging among this sample. In this study, LES of n-dodecane spray flame is performed using a well-mixed turbulent combustion model along with a dynamic structure subgrid model. A high-resolution mesh is employed with a cell size of 62.5 microns in the entire spray and combustion regions. The computational cost of each calculation was in the order of 3 weeks on 200 processors with a peak cell count of about 22 million at 1 ms.

The present experimental study explores the effects of compression ratio and piston design in a heavy-duty diesel engine operated with Reactivity Controlled Compression Ignition (RCCI) combustion. In previous studies, RCCI combustion with in-cylinder fuel blending using port-fuel-injection of a low reactivity fuel and optimized direct-injections of higher reactivity fuels was demonstrated to permit near-zero levels of NOX and PM emissions in-cylinder, while simultaneously realizing high thermal efficiencies. The present study consists of RCCI experiments at loads from 4 to 17 bar indicated mean effective pressure at engine speeds of 1,300 and 1,700 [rev/min]. The experiments used a modified piston to examine the effect of piston crevice volume, squish geometry, and compression ratio on performance and efficiency.

In this last decade, non-destructive X-ray measurement techniques have provided unique insights into the internal surface and flow characteristics of automotive injectors. This has in turn contributed to enhancing the accuracy of Computational Fluid Dynamics (CFD) models of these critical injection system components. By employing realistic injector geometries in CFD simulations, designers and modelers have identified ways to modify the injectors’ design to improve their performance. In recent work, the authors investigated the occurrence of cavitation in a heavy-duty multi-hole diesel injector operating with a high-volatility gasoline-like fuel for gasoline compression ignition applications. They proposed a comprehensive numerical study in which the original diesel injector design would be modified with the goal of suppressing the in-nozzle cavitation that occurs when gasoline fuels are used.

Reactivity Controlled Compression Ignition (RCCI) is an engine combustion strategy that utilizes in-cylinder fuel blending to produce low NOx and PM emissions while maintaining high thermal efficiency. Previous RCCI research has been investigated in single-cylinder heavy-duty engines [1, 2, 3, 4, 5, 6]. The current study investigates RCCI operation in a light-duty multi-cylinder engine over a wide number of operating points representing vehicle operation over the US EPA FTP test. Similarly, previous RCCI engine experiments have used petroleum based fuels such as ultra-low sulfur diesel fuel (ULSD) and gasoline, with some work done using high percentages of biofuels, namely E85 [7]. The current study was conducted to examine RCCI performance with moderate biofuel blends, such as E20 and B20, as compared to conventional gasoline and ULSD.

Advanced combustion systems that simultaneously address PM and NOx while retaining the high efficiency of modern diesel engines, are being developed around the globe. One of the most difficult problems in the area of advanced combustion technology development is the control of combustion initiation and retaining power density. During the past several years, significant progress has been accomplished in reducing emissions of NOx and PM through strategies such as LTC/HCCI/PCCI/PPCI and other advanced combustion processes; however control of ignition and improving power density has suffered to some degree - advanced combustion engines tend to be limited to the 10 bar BMEP range and under. Experimental investigations have been carried out on a light-duty DI multi-cylinder diesel automotive engine. The engine is operated in low temperature combustion (LTC) mode using 93 RON (Research Octane Number) and 74 RON fuel.

In internal combustion engine computational fluid dynamics (CFD) simulations, uncertainties arise from various sources, such as estimates of model parameters, experimental boundary conditions, estimates of chemical kinetic rates, etc. These uncertainties propagate through the model and may result in discrepancies compared to experimental measurements. The relative importance of the various sources of uncertainty can be quantified by performing a sensitivity analysis. In this work, global sensitivity analysis (GSA) was applied to engine CFD simulations of a low-temperature combustion concept called gasoline compression ignition, to understand the influence of experimental measurement uncertainties from various sources on specific targets of interest-spray penetration, ignition timing, combustion phasing, combustion duration, and emissions. The sensitivity of these targets was evaluated with respect to imposed uncertainties in experimental boundary conditions and fuel properties.

In-cylinder fuel blending of gasoline with diesel fuel is investigated on a multi-cylinder light-duty diesel engine as a strategy to control in-cylinder fuel reactivity for improved efficiency and lowest possible emissions. This approach was developed and demonstrated at the University of Wisconsin through modeling and single-cylinder engine experiments. The objective of this study is to better understand the potential and challenges of this method on a multi-cylinder engine. More specifically, the effect of cylinder-to-cylinder imbalances and in-cylinder charge motion as well as the potential limitations imposed by real-world turbo-machinery were investigated on a 1.9-liter four-cylinder engine. This investigation focused on one engine condition, 2300 rpm, 5.5 bar net mean effective pressure (NMEP). Gasoline was introduced with a port-fuel-injection system.

Premixed charge compression ignition (PCI) strategies offer the potential for simultaneously low NOx and soot emissions with diesel-like efficiency. However, these strategies are generally confined to low loads due to inadequate control of combustion phasing and heat-release rate. One PCI strategy, dual-fuel reactivity-controlled compression ignition (RCCI), has been developed to control combustion phasing and rate of heat release. The RCCI concept uses in-cylinder blending of two fuels with different auto-ignition characteristics to achieve controlled high-efficiency clean combustion. This study explores fuel reactivity stratification as a method to control the rate of heat release for PCI combustion. To introduce fuel reactivity stratification, the research engine is equipped with two fuel systems. A low-pressure (100 bar) gasoline direct injector (GDI) delivers iso-octane, and a higher-pressure (600 bar) common-rail diesel direct-injector delivers n-heptane.

This study uses Fourier analysis to investigate the relationship between the heat release event and the frequency composition of pressure oscillations in a variety of combustion modes. While kinetically-controlled combustion strategies such as HCCI and RCCI offer advantages over CDC in terms of efficiency and NOX emissions, their operational range is limited by audible knock and the possibility of engine damage stemming from high pressure rise rates and oscillations. Several criteria such as peak pressure rise rate, ringing intensity, and various knock indices have been developed to quantify these effects, but they fail to capture all of the dynamics required to form direct comparisons between different engines or combustion strategies. Experiments were performed with RCCI, HCCI, and CDC on a 2.44 L heavy-duty engine at 1300 RPM, generating a significant diversity of heat release profiles.

Reynolds-averaged Navier-Stokes (RANS) turbulence model has been used extensively for diesel engine simulations due to its computational efficiency and is expected to remain the workhorse computational fluid dynamics (CFD) tool for industry in the near future. Alternatively, large eddy simulations (LES) can potentially deal with complex flows and cover a large disparity of turbulence length scales, which makes this technique more and more attractive in the engine community. An n-dodecane spray flame (Spray A from Engine Combustion Network) was simulated using a dynamic structure LES model to understand the transient behavior of this turbulent flame. The liquid spray was treated with a traditional Lagrangian method and the gas-phase reaction was closed using a delta probability density function (PDF) combustion model. A 103-species skeletal mechanism was used for n-dodecane chemical kinetic model.

The combustion system of a heavy-duty diesel engine operated in a gasoline compression ignition mode was optimized using a CFD-based response surface methodology and a machine learning genetic algorithm. One common dataset obtained from a CFD design of experiment campaign was used to construct response surfaces and train machine learning models. 128 designs were included in the campaign and were evaluated across three engine load conditions using the CONVERGE CFD solver. The design variables included piston bowl geometry, injector specifications, and swirl ratio, and the objective variables were fuel consumption, criteria emissions, and mechanical design constraints. In this study, the two approaches were extensively investigated and applied to a common dataset. The response surface-based approach utilized a combination of three modeling techniques to construct response surfaces to enhance the performance of predictions.