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The present work discusses the potential benefits of using computational fluid dynamics (CFD) simulation and artificial intelligence (AI) in the design and optimization of hydrogen internal combustion engines (H2ICEs). A Machine Learning (ML) model is developed and applied to the CFD simulation data to identify optimal injection system parameters on the Sandia H2ICE Engine to improve the mixing. This approach can aid in developing predictive ML models to guide the design of future H2ICEs. For the current engine configuration, it is observed that hydrogen (H2) gas injection contributes mixing of H2 with air. If the injector parameters are optimized, mixture preparation is better and eventually combustion. A base CFD model is validated from the Sandia H2ICE engine data against Particle Image Velocimetry (PIV) data for velocity and Planar Laser Induced Fluorescence (PLIF) data for H2 mass fraction.

Mitigating human-made climate change means cutting greenhouse gas (GHG) emissions, especially carbon dioxide (CO2), which causes climate change. One approach to achieving this is to move to a carbon-free economy where carbon emissions are offset by carbon removal or sequestration. Transportation is a significant contributor to CO2 emissions, so finding renewable alternatives to fossil fuels is crucial. Green hydrogen-fueled engines can reduce the carbon footprint of transportation and help achieve a carbon-free economy. However, hydrogen combustion is challenging in an internal combustion engine due to flame instabilities, pre-ignition, and backfire. Numerical modeling of hydrogen combustion is necessary to optimize engine performance and reduce emissions. In this work, a numerical methodology is proposed to model lean hydrogen combustion in a turbocharged port fuel injection (PFI) spark-ignition (SI) engine for automotive applications.

Gasoline direct-injection (GDI) engine technology improves vehicle fuel economy while decreasing CO2 emissions. The main drawback of GDI technology is the increase in particulate emissions compared to the commonly used port fuel injection technologies. Today’s adopted strategy to limit such emissions relies upon the use of aftertreatment gasoline particulate filters (GPFs). GPFs reduce particulates resulting from fuel combustion. Soot oxidation (also known as regeneration) is required at regular intervals to clean the filter, maintain a consistent soot trapping efficiency, and avoid the formation of soot plugs in the GPF channels. In this paper, starting from a multiphysics GPF model accounting for mass, momentum, and energy transport, a sensitivity analysis is carried out to choose the best mesh refinement, time step, and relative tolerance to ensure a stable numerical solution of the transport equations during regeneration while maintaining low computational time.

An experimental investigation of non-intrusive combustion sensing was performed using a tri-axial accelerometer mounted to the engine block of a small-bore high-speed 4-cylinder compression-ignition direct-injection (CIDI) engine. This study investigates potential techniques to extract combustion features from accelerometer signals to be used for cycle-to-cycle engine control. Selection of accelerometer location and vibration axis were performed by analyzing vibration signals for three different locations along the block for all three of the accelerometer axes. A magnitude squared coherence (MSC) statistical analysis was used to select the best location and axis. Based on previous work from the literature, the vibration signal filtering was optimized, and the filtered vibration signals were analyzed. It was found that the vibration signals correlate well with the second derivative of pressure during the initial stages of combustion.

The Society of Automotive Engineers Fatigue Design & Evaluation Committee [SAE FD&E] is actively working on a total life project for weldments, in which the welding residual stress is a key contributor to an accurate assessment of fatigue life. Physics-based welding process simulation and various types of residual stress measurements were pursued to provide a representation of the residual stress field at the failure location in the fatigue samples. A well-controlled and documented robotic welding process was used for all sample fabrications to provide accurate inputs for the welding simulations. One destructive (contour method) residual stress measurement and several non-destructive residual stress measurements-surface X-ray diffraction (XRD), energy dispersive X-ray diffraction (EDXRD), and neutron diffraction (ND)-were performed on the same or similarly welded samples.

Cycle-to-cycle variation in combustion phasing and combustion rate cause knock to occur differently in every cycle. This is found to be true even if the end gas thermo-chemical time history is the same. Three cycles are shown that have matched combustion phasing, combustion rate, and time of knock onset, but have knock intensity that differs by a factor of six. Thus, the prediction of knock intensity must include a stochastic component. It is shown that there is a relationship between the maximum possible knock intensity and the unburned fuel energy at the time of knock onset. Further, for a small window of unburned energy at knock onset, the probability density function of knock intensity is self similar when scaled by the 95th percentile of the cumulative distribution, and log-normal in shape.

Numerical simulations of diesel reacting jets in a simulated engine environment were carried out to study the effect of oxygen concentration on the ignition delay time and lift-off length dynamics. A recently developed mechanism, direct integration of chemistry, and well established Lagrangian-Eulerian spray model were utilized for 3-D turbulent spray combustion simulation under engine like conditions. The simulations are able to provide a time-history of chemical species including formaldehyde CH2O intermediates and hydroxide OH radicals to facilitate development of auto-ignition and lift off length numerical diagnostics. A range of important operating points including variations in the oxygen concentration, rail pressure, and injection duration were examined. The purpose of conducting the parametric studies is to investigate the consistency of the results and provide a more comprehensive analysis than a single point condition.

A wide variety of spray models and their associated sub-models exist to assist with numerical spray development studies in the many applicable areas viz., turbines, internal combustion engines etc. The accuracy of a simulation when compared to the experiments varies, as these models chosen are varied. Also, the computational grid plays a crucial role in model correctness; a grid-converged CFD study is more valuable and assists in proper validation at later stages. Of primary relevance to this paper are the combustion models for a grid-converged Lagrangian spray modeling scenario. CONVERGE CFD code is used for simulation of split injection diesel (n-heptane) sprays and a structured methodology, using RNG k-ε turbulence model, is followed to obtain a grid-converged solution for the key Computational Fluid Dynamics (CFD) parameters viz., grid size, injected parcels and spray break-up time constant.

Using gasoline and diesel simultaneously in a dual-fuel combustion system has shown effective benefits in terms of both brake thermal efficiency and exhaust emissions. In this study, the dual-fuel approach is applied to a light-duty spark ignition (SI) gasoline direct injection (GDI) engine. Three combustion modes are proposed based on the engine load, diesel micro-pilot (DMP) combustion at high load, SI combustion at low load, and diesel assisted spark-ignition (DASI) combustion in the transition zone. Major focus is put on the DMP mode, where the diesel fuel acts as an enhancer for ignition and combustion of the mixture of gasoline, air, and recirculated exhaust gas. Computational fluid dynamics (CFD) is used to simulate the dual-fuel combustion with the final goal of supporting the comprehensive optimization of the main engine parameters.

The Formula SAE (FSAE) racing event is an annual collegiate competition in which student-designed and built, open-cockpit racecars are examined and evaluated via a variety of static and dynamic events. Cornell University has been involved in the competition since 1987, and was the first team to run its own custom-built engine control module (ECM) in 1993. This paper presents the evolution of the Cornell ECM and offers a systems-level overview of the design of the third-generation Cornell ECM 2003 (ECM03) system. The ECM03 was used in the 2003 Cornell FSAE racecar, the ARG03.

Due to their harsh operating environments, military vehicle drive trains have special requirements. These special requirements are usually represented by hill climbing ability, obstacle negotiation, battlefield cross country travel, hard acceleration, high speed, etc. These special requirements need the vehicle drive train to have a wider torque and speed range characteristics than commercial vehicles. We have proved that larger constant power ratio in electric motor can significantly enhance the vehicle acceleration performance. In other words, for the same acceleration performance, large constant power ratio can minimize the power rating of the traction motor drive, thus minimizing the power rating of the power source (batteries for instance). Actually, extension of the constant power range can also significantly enhance the gradeability, which is crucial for military vehicles.

Acid gas removal experiments are carried out in a large bench scale corona radical shower reactor-catalyst hybrid system. A simulated stationary jet engine test cell flue gas is air mixed with NO, SO2 and CH4. NO, NO2 and SO2 concentrations were measured by a Green Line gas analyzer and the trace by-products are determined by Fourier Transform Infra-Red spectroscopy (FTIR). The aerosol particles generated by the acid gases and methane related plasma processes were collected by the electrostatic precipitator operated at !19 kV dc downstream of the reactor. The size of the reactor is (10×30×100 cm) and four pipe with nozzle type radical injectors are placed in series. The corona radical shower electrode used was a 6 mm o.d. tube equipped with 28 hollow electrodes (1.2 mm i.d./1.5 mm o.d.).

Manifolding for a 600cc four-stroke engine to be used to power a vehicle entered in the 1989 student Formula SAE® competition was tuned to maximize mid-range torque. Although powerful computer programs exist to analyze manifold wave dynamics, these programs are unavailable to the public; lacking access to these types of programs, Helmholtz tuning theory was chosen for use in this work due to its straightforward computational methods. Although the assumptions of Helmholtz tuning theory may not rigorously apply at high engine speeds, it was successfully used to provide design guidelines for the selection of manifolding physical parameters, and was applied successfully to increase engine output. Intake tuning of the engine with the required restrictor and single carburetor yielded torque levels of as much as 12% higher than the unrestricted stock engine with four carburetor set-up.