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It is widely recognized that internal combustion engines (ICE) are needed for transport worldwide for years to come, however, demands on ICE fuel efficiency, emissions, cost, and performance are extremely challenging. Gasoline compression ignition (GCI) is one approach to achieve demanding efficiency and emissions targets. At Aramco Research Center-Detroit, an advanced, multi-cylinder GCI engine was designed and built using the latest combustion system, engine controls, and lean aftertreatment. The combustion system uses Aramco’s PPCI-diffusion process for ultra-low NOx and smoke. A P2 48V mild hybrid system was integrated on the engine for braking energy recovery and improved cold starts. For robust low-load operation, a 2-step valvetrain system was used for exhaust rebreathing. Test data showed that part-load fuel consumption was reduced 7 to 10 percent relative to a competitive 2.0L European diesel engine.

Currently, on-road transport contributes nearly 12% of India’s total energy related carbon dioxide (CO2) emissions that are expected to be doubled by 2040. Following the global trends of increasingly stringent greenhouse gas emissions (GHG) and criteria emissions, India will likely impose equivalent Bharat Stage (BS) regulations mandating simultaneous reduction in CO2 emissions and nearly 90% lower nitrogen oxides (NOx) from the current BS-VI levels. Consequently, Indian automakers would likely face tremendous challenges in meeting such emission reduction requirements while balancing performance and the total cost of ownership (TCO) trade-offs. Therefore, it is conceivable that cost-effective system improvements for the existing internal combustion engine (ICE) powertrains would be of high strategic importance for the automakers.

Following global trends of increasingly stringent greenhouse gas (GHG) and criteria pollutant regulations, India will likely introduce within the next decade equivalent Bharat Stage (BS) regulations for Diesel engines requiring simultaneous reduction in CO2 emissions and up to 90% reduction in NOx emission from current BS-VI levels. Consequently, automakers are likely to face tremendous challenges in meeting such emission reduction requirements while maintaining performance and vehicle total cost of ownership (TCO), especially in the Indian market, which has experienced significant tightening of emission regulation during the past decade. Therefore, it is conceivable that cost effective approaches for improving existing diesel engines platforms for future regulations would be of high strategic importance for automakers.

Hydrogen internal combustion engines (H2ICE) offer a cost-effective solution to decarbonize transport by combining a lower carbon intensity fuel with mature and established internal combustion engine technology. While vehicles running with hydrogen have been demonstrated over the years, this fuel's physical and chemical properties require modifications and upgrades on the vehicle from an engine and system-level perspective. In addition, market-specific regulatory and economic factors can also constrain the realization of optimal hydrogen powertrain architectures. Therefore, this paper reviews the impact of hydrogen use on combustion, injection, air management, and after-treatment systems, indicating the different strategies used to enable effective H2ICE strategies from an efficiency, cost, and safety standpoint.

This study investigates the effect of exhaust rebreathe (RB) on the low-load regime of a Gasoline Compression Ignition (GCI) heavy-duty engine. For this engine, a custom-designed cam profile with a second exhaust event occurring during the intake stroke was tested under different experimental load and speed conditions. First, the study focuses on the of rebreathe on combustion and gas exchange processes in the low load range of 240-300 kPa BMEP at three key speeds: 820, 1200, and 1600 rpm. Then, a general analysis of the thermal management of this technology is assessed in the low-load map, evaluating the impact on turbine outlet temperature and after-treatment performance related to the conversion rates for NOx and total hydrocarbons (THC). The detailed analysis revealed an increase of around 9% in the trapped residuals for the RB operation, translating to an in-cylinder temperature increase and raising the exhaust temperature up to 50°C.

The pre-chamber combustion can extend the lean limit of internal combustion engines (ICE) and hence increase their overall efficiency. Compared to active pre-chambers equipped with an auxiliary fuel supply system, passive pre-chambers have lower manufacturing costs and require minimal or no design modifications to the conventional spark plug engines. The major challenge of the passive pre-chamber is to extend the lean limit as much as the active pre-chamber. Computational fluid dynamics (CFD) simulations were conducted on a light-duty single-cylinder engine geometry fitted with a passive pre-chamber and using iso-octane as fuel to investigate and optimize the passive pre-chamber fuel enrichment through the pre-chamber nozzles. The non-reacting flow simulations were performed from the intake valve open (IVO) to spark timing.

In modern compression ignition engines, the dense liquid fuel is directly injected into high pressure and temperature atmosphere, so the spray transitions from subcritical to supercritical conditions. To gain better control of the spray-combustion heat release process, it is important to have a physically accurate description of the spray development process. This work explored the effect of real-fluid thermodynamics in the computational prediction of multiphase flow for two non-ideal situations: the cryogenic nitrogen and non-cryogenic n-dodecane and ammonia sprays. Three real-fluid equations of state (EoS) such as the Soave-Redlich-Kwong (SRK), Peng-Robinson (PR), and Redlich-Kwong-Peng-Robinson (RKPR) coupled with the real-fluid Chung transport model were implemented in OpenFoam to predict the real-fluid thermodynamic properties. Validations against the CoolProp database were conducted.

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.

Flame lift-off length (FLOL), ignition delay time (IDT), liquid length (LL), and Soot are essential parameters defining spray combustion characteristics. They help understand the combustion dynamics and validate the spray and combustion models for numerical simulations. However, obtaining extensive data from experiments is costlier and time-consuming. Machine learning (ML) models have advanced to the point where they could create efficient models that could be used as surrogates for experiments. In this study, five different ML algorithms have been trained using the experimental dataset available through the engine combustion network (ECN) community. A novel genetic algorithm-based hyperparameter optimization code has been used to optimize the models to improve prediction accuracy. The model performances were compared, and the better model was chosen as an experimental surrogate to predict FLOL, IDT, LL, and Soot.

The study showcases the strength of machine learning (ML) models in imitating the operation of an advanced engine concept - the gasoline compression ignition (GCI) - at low loads. The GCI engine is prone to exceeding the limits of criteria emissions at such loads, especially at the cold start when the catalyst is not activated. One proposition to accelerate catalyst light-off is using spark-ignition. This, however, adds an extra level of complexity in identifying an optimum operation point. The ML models can be a useful tool in guiding the engine calibration process. In this study, the ML models are trained on GCI engine experiments, covering different intake conditions, injection strategies, and spark settings. The models can predict seven engine performance parameters: fuel consumption, four engine-out emissions, exhaust temperature, and coefficient of variation (COV) in indicated mean effective pressure (IMEP).

The morphology of the internal flow of Spray C was numerically investigated using an Eulerian volume-of-fluid (VOF) method in the finite-volume framework. The injector geometry available in the Engine Combustion Network (ECN) was employed, and the simulations were performed under the ambient condition at 900 K and 60 bar. The simulation data were analyzed for three important events: the initial nozzle opening, steady injection, and nozzle closing. First, projected densities on XY and XZ planes are computed radially at four axial locations. Projected density at 2 mm is compared with available experimental results, which show similar results. Then, the mass flow rate is found to match the reported experimental results and the virtually generated values from CMT using an appropriate discharge coefficient. An investigation on the appropriate discharge coefficient is performed and found that Cd = 0.63 ± 0.02 is acceptable for Spray C.

Gasoline compression ignition (GCI) has been shown to offer benefits in the NOx-soot tradeoff over conventional diesel combustion while still achieving high fuel efficiency. However, due to gasoline’s low reactivity, it is challenging for GCI to attain robust ignition and stable combustion under cold operating conditions. Building on previous work to evaluate glow plug-assisted GCI combustion at cold idle, this work evaluates the use of a spark plug to assist combustion. The closed-cycle 3-D CFD model was validated against GCI test results at a compression ratio of 17.3 during extended cold idle operation under laboratory-controlled conditions. A market representative, ethanol-free, gasoline (RON92, E0) was used in both the experiment and the numerical analysis. Spark-assisted simulations were performed by incorporating an ignition model with the spark energy required for stable combustion at cold start.

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.

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.

A computational fluid dynamics (CFD) guided design optimization was conducted for the piston bowl geometry for a heavy-duty diesel engine. The optimization goal was to minimize engine-out NOx emissions without sacrificing engine peak power and thermal efficiency. The CFD model was validated with experiments and the combustion system optimization was conducted under three selected operating conditions representing low speed, maximum torque, and rated power. A hundred piston bowl shapes were generated, of which 32 shapes with 3 spray angles for each shape were numerically analyzed and one optimized design of piston bowl geometry with spray angle was selected. On average, the optimized combustion system decreased nitrogen oxide (NOx) emissions by 17% and soot emissions by 41% without compromising maximum engine power and fuel economy.

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.

Achieving robust ignitability for compression ignition of diesel engines at cold conditions is traditionally challenging due to insufficient fuel vaporization, heavy wall impingement, and thick wall films. Gasoline compression ignition (GCI) has shown the potential to offer an enhanced NOx-particulate matter tradeoff with diesel-like fuel efficiency, but it is unknown how the volatility and reactivity of the fuel will affect ignition under very cold conditions. Therefore, it is important to investigate the impact of fuel physical and chemical properties on ignition under pressures and temperatures relevant to practical engine operating conditions during cold weather. In this paper, 0-D and 3-D computational fluid dynamics (CFD) simulations of GCI combustion at cold conditions were performed.

Fuel stratification effects on the combustion and emissions behaviors for partially premixed compression ignition (PPCI) combustion of a high reactivity gasoline (research octane number of 80) was investigated using the third generation Gasoline Direct-Injection Compression Ignition (Gen3 GDCI) multi-cylinder engine. The PPCI combustion mode was achieved through a double injection strategy. The extent of in-cylinder fuel stratification was tailored by varying the start of second fuel injection timing (SOIsecond) while the first fuel injection event was held constant and occurred during the intake stroke. Based on the experimental results, three combustion characteristic zones were identified in terms of the SOIsecond - CA50 (crank angle at 50% cumulative heat release) relationship: (I) no response zone (HCCI-like combustion); (II) negative CA50 slope zone: (early PPCI mode); and (III) positive CA50 slope zone (late PPCI mode).

A detailed study of various air system configurations has been conducted for a prototype gasoline compression ignition (GCI) engine using a Cummins MY2013 ISX15 heavy-duty diesel engine as the base platform. The study evaluated the configurations with the assumption that RON80 gasoline would be used as the fuel and the combustion chamber would have a geometric compression ratio (CR) of 16.5. Using 3-D computational fluid dynamics (CFD) simulations, a high efficiency & low engine-out NOx GCI combustion recipe was developed across the five engine operating points from the heavy-duty Supplemental Emissions Test (SET) cycle: A100, B25, B50, B75, and C100. The CFD generated air-thermal boundary conditions and the combustion burn-rate & injector rate-of-injection profiles were imported into a calibrated 1-D engine model for the air-handling systems analysis.

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.