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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 Relevant published and unpublished work from prior ECN workshops is reviewed.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.

For several years there has been a great deal of effort made in researching ways to run a compression ignition engine with simultaneously high efficiency and low emissions. Recently much of this focus has been dedicated to using gasoline-like fuels that are more volatile and less reactive than conventional diesel fuel to allow the combustion to be more premixed. One of the key challenges to using fuels with such properties in a compression ignition engine is stable engine operation at low loads. This paper provides an analysis of how stable gasoline compression ignition (GCI) engine operation was achieved down to idle speed and load on a multi-cylinder compression ignition engine using only 87 anti-knock index (AKI) gasoline. The variables explored to extend stable engine operation to idle included: uncooled exhaust gas recirculation (EGR), injection timing, injection pressure, and injector nozzle geometry.

This research investigates the potential of gasoline compression ignition (GCI) to achieve low engine-out NOx emissions with high fuel efficiency in a heavy-duty diesel engine. The experimental work was conducted in a model year (MY) 2013 Cummins ISX15 heavy-duty diesel engine, covering a load range of 5 to 15 bar BMEP at 1375 rpm. The engine compression ratio (CR) was reduced from the production level of 18.9 to 15.7 without altering the combustion bowl design. In this work, four gasolines with research octane number (RON) ranging from 58 to 93 were studied. Overall, GCI operation resulted in enhanced premixed combustion, improved NOx-soot tradeoffs, and similar or moderately improved fuel efficiency compared to diesel combustion. A split fuel injection strategy was employed for the two lower reactivity gasolines (RON80 and RON93), while the RON60 and RON70 gasolines used a single fuel injection strategy.

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.

The current study utilized 3-D computational fluid dynamics (CFD) combustion analysis to guide the development of a viable full load range combustion strategy in a light-duty gasoline compression ignition (GCI) engine. A higher reactivity gasoline that has a research octane number (RON) of 70 was used for the combustion strategy development. The engine has a geometric compression ratio of 14.5 with a piston bowl designed to accommodate different combustion strategies and injector spray patterns. Detailed combustion optimization was focused on 6 and 18 bar gross indicated mean effective pressure (IMEPg) at 1500 rpm through a Design of Experiments approach. Two different strategies were investigated: (a) a late triggering fuel injection with a wide spray angle (combustion strategy #1); and (b) an early triggering fuel injection with a narrow spray angle (combustion strategy #2).

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.

The use of gasoline in a compression ignition engine has been a research focus lately due to the ability of gasoline to provide more premixing, resulting in controlled emissions of the nitrogen oxides [NOx] and particulate matter. The present study assesses the reactivity of 93 RON [87AKI] gasoline in a GM 1.9L 4-cylinder diesel engine, to extend the low load limit. A single injection strategy was used in available experiments where the injection timing was varied from −42 to −9 deg ATDC, with a step-size of 3 deg. The minimum fueling level was defined in the experiments such that the coefficient of variance [COV] of indicated mean effective pressure [IMEP] was less than 3%. The study revealed that injection at −27 deg ATDC allowed a minimum load of 2 bar BMEP. Also, advancement in the start of injection [SOI] timing in the experiments caused an earlier CA50, which became retarded with further advancement in SOI timing.

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.

Investigating combustion characteristics of oxygenated gasoline and gasoline blended ethanol is a subject of recent interest. The non-linearity in the interaction of fuel components in the oxygenated gasoline can be studied by developing chemical kinetics of relevant surrogate of fewer components. This work proposes a new reduced four-component (isooctane, heptane, toluene, and ethanol) oxygenated gasoline surrogate mechanism consisting of 67 species and 325 reactions, applicable for dynamic CFD applications in engine combustion and sprays. The model introduces the addition of eight C1-C3 species into the previous model (Li et al; 2019) followed by extensive tuning of reaction rate constants of C7 - C8 chemistry. The current mechanism delivers excellent prediction capabilities in comprehensive combustion applications with an improved performance in lean conditions.

The accurate modeling of fuel spray behavior under diesel engine conditions requires well-characterized boundary conditions. Among those conditions, the spray cone angle is important due to its impact on the spray mixing process, flame lift-off locations and subsequent soot formation. The spray cone angle is a highly dynamic variable, but existing correlations have been developed mainly for diesel fuels at quasi-steady state and relatively low injection pressures. The objective of this study was to develop spray cone angle correlations for both diesel and a light-end gasoline fuel over a wide range of diesel-engine operating conditions that are capable of capturing both the transient and quasi-steady state processes. Two important macroscopic characteristics of solid cone sprays, the spray cone angle and spray penetration, were measured using a single-hole heavy-duty injector using two fuels at diesel engine conditions in an optical constant volume vessel.

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.

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.

Previous work has demonstrated the capabilities of gasoline compression ignition to achieve engine loads as high as 19.5 bar BMEP with a production multi-cylinder diesel engine using gasoline with an anti-knock index (AKI) of 87. In the current study, the low load limit of the engine was investigated using the same engine hardware configurations and 87 AKI fuel that was used to achieve 19.5 bar BMEP. Single injection, “minimum fueling” style injection timing and injection pressure sweeps (where fuel injection quantity was reduced at each engine operating condition until the coefficient of variance of indicated mean effective pressure rose to 3%) found that the 87 AKI test fuel could run under stable combustion conditions down to a load of 1.5 bar BMEP at an injection timing of −30 degrees after top dead center (°aTDC) with reduced injection pressure, but still without the use of intake air heating or uncooled EGR.

Global Sensitivity Analysis (GSA) is conducted for a diesel engine simulation to understand the sensitivities of various modeling constants and boundary conditions in a global manner with regards to multi-target functions such as liquid length, ignition delays, combustion phasing, and emissions. The traditional local sensitivity analysis approach, which involves sequential perturbation of model constants, does not provide a complete picture since all the parameters can be uncertain. However, this approach has been studied extensively and is advantageous from a computational point of view. The GSA simultaneously incorporates the uncertainty information for all the relevant boundary conditions, modeling constants, and other simulation parameters. A global analysis is particularly useful to address the important parameters in a model where the response of the targets to the values of the variables is highly non-linear.

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 order to achieve the targets for hydrogen engines set by the U.S. Department of Energy (DOE) - a brake thermal efficiency of 45% and nitrogen oxide (NOx) emissions below 0.07 g/mi - while maintaining the same power density as comparable gasoline engines, researchers need to investigate advanced mixture formation and combustion strategies for hydrogen internal combustion engines. Hydrogen direct injection is a very promising approach to meeting DOE targets; however, there are several challenges to be overcome in order to establish this technology as a viable pathway toward a sustainable hydrogen infrastructure. This paper describes the use of endoscopic imaging as a diagnostic tool that allows further insight into the processes that occur during hydrogen combustion. It also addresses recent progress in the development of advanced direct-injected hydrogen internal combustion engine concepts.

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.