Search Results

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.

In contrast to conventional powertrains from internal combustion engine vehicles (ICEV), the tribological performance of powertrains of electric vehicles (EVs) must be further evaluated by considering new critical operating conditions such as electrical environments. The operation of any type of electric motor produces shaft voltages and currents due to various hardware configurations and factors. Furthermore, the common application of inverters intensifies this problem. It has been reported that the induced shaft voltages and currents can cause premature failure problems in tribological components such as bearings and gears due to accelerated wear and/or fatigue. It is ascribed to effects of electric discharge machining (EDM), also named, sparking wear caused by shaft currents and poor or increasingly diminishing dielectric strength of lubricants. A great effort has been done to study this problem in bearings, but it has not yet been the case for gears.

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.

A Dual Power Split Electronic Continuously Variable Transmission (DPS-ECVT) with an input-split, output coupled, split-power-path configuration is proposed for improving overall system efficiency and range for electric vehicles. By modulating the power split ratio between the mechanical (planetary gear meshes) and electrical (Motor Generator Units) driveline components, a continuous range of gear ratios operating at higher efficiency is obtained. The proposed concept leverages two power-split units that lead to significantly reduced power flow through the electrical drivelines (compared with single speed EV transmissions as well as single power-split E-CVTs) while providing the same overall ratio spread for transmission operation.

This paper presents calculations of engine flows by using the Partially-Averaged Navier Stokes (PANS) method (Girimaji [1]; [2]). The PANS is a scale-resolving turbulence computational approach designed to resolve large scale fluctuations and model the remainder with appropriate closures. Depending upon the prescribed cut-off length (filter width) the method adjusts seamlessly from the Reynolds-Averaged Navier-Stokes (RANS) to the Direct Numerical Solution (DNS) of the Navier-Stokes equations. The PANS method was successfully used for many applications but mainly on static geometries, e.g. Basara et al. [3]; [4]. This is due to the calculation of the cut-off control parameter which requires that the resolved kinetic energy is known and this is usually obtained by suitably averaging of the resolved field. Such averaging process is expensive and impractical for engines as it would require averaging per cycles.

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.

Gasoline Direct Injection (GDI) is known to produce lower concentrations of smaller particulate matter (PM) compared to diesel combustion [1]. The lower concentration results in the absence of soot-cake formation on the filter channel wall and therefore filtration behavior deviates from the expected diesel particulate filter (DPF) performance. Therefore, studies of cake-less filtration regimes for smaller sized particulates is of interest for GDI PM mitigation. This work investigates the filtration efficiency of laboratory-generated particulates, representative of GDI-sized PM, in uncoated, commercial DPF cordierite substrates of varying porosities. Size-dependent particulate concentrations were measured using a Scanning Mobility Particle Sizer (SMPS), both upstream and downstream of the filters. By comparing these measured concentrations, the particle size-dependent filtration efficiency of filter samples was calculated.

The increasing number of gasoline direct injection (GDI) vehicles on the roads has drawn attention to their particulate matter (PM) emissions, which are greater both in number and mass than port fuel injected (PFI) spark ignition (SI) engines [1]. Regulations have been proposed and implemented to reduce exposure to PM, which has been shown to have negative impacts on both human health and the environment [2, 3]. Currently, the gasoline particulate filter (GPF) is the proposed method of reducing the amount of PM from vehicle exhaust, but modifications to improve the filtration efficiency (FE) and reduce the pressure drop across the filter are yet needed for implementation of this solution in on-road vehicles. This work evaluates the impacts of wall thickness and cell density on filtration efficiency and backpressure using a benchtop filtration system.

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.

This paper investigates the effect of the cetane number (CN) of a diesel fuel on the energy balance between a light duty (1.9L) and medium duty (4.5L) diesel engine. The two engines have a similar stroke to bore (S/B) ratio, and all other control parameters including: geometric compression ratio, cylinder number, stroke, and combustion chamber, have been kept the same, meaning that only the displacement changes between the engine platforms. Two Coordinating Research Council (CRC) diesel fuels for advanced combustion engines (FACE) were studied. The two fuels were selected to have a similar distillation profile and aromatic content, but varying CN. The effects on the energy balance of the engines were considered at two operating conditions; a “low load” condition of 1500 rev/min (RPM) and nominally 1.88 bar brake mean effective pressure (BMEP), and a “medium load” condition of 1500 RPM and 5.65 BMEP.

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 comprehensive analysis of engine performance and fuel consumption was carried out to study Low Heat Rejection (LHR) concepts in the conventional light-duty diesel engine. From most previous studies on LHR diesel engines, thermal-barrier coatings (TBCs) have been recognized as a conventional way of insulating engine parts; while for the cases studied in this paper, the LHR concept is realized by altering engine coolant temperature (ECT). This paper presents engine simulation of a multi-cylinder, four-stroke, 1.9L diesel engine operating at 1500 rpm with five cases having different ECTs. The simulated results have been validated against experimental data. Calibration strategy for the engine simulation model is detailed in a systematic methodology for a better understanding of this simulation-development process. The calibrated model predicts the performance and fuel consumption within tolerated uncertainties.

It is important for engineering firms to be able to develop forecasts of recommended courses of action based on available information. In particular, engineering firms must be able to assess the benefit of performing information-gathering actions. For example, an automobile manufacturer may use a computer simulation of a hydraulic motor and pump in the design of a new vehicle. The model may contain random variables that can be more accurately determined through expensive information-gathering actions, e.g., physical experiments, surveys, etc. To decide whether to perform these information-gathering actions, the automobile manufacturer must be able to quantify the expected value to the firm of conducting them. However, the cost of computing the expected value of information (through optimization, Monte Carlo sampling, etc.) grows exponentially with the amount of information that is to be gathered and can often exceed the cost of actually gathering the information.

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.

Research on dual-fuel engine systems is regaining interest as advances in combustion reveal enabling features for attaining high efficiencies. Although this movement is manifested by development of advanced modes of combustion (e.g., reactivity controlled compression ignition combustion, or RCCI), the possibility of gasoline / diesel conventional combustion exists, which is characterized by premixed gasoline and direct-injected diesel fuel at conventional diesel injection timing. This study evaluates the effects of operating parameter on fuel conversion efficiency for gasoline / diesel conventional combustion in a medium duty diesel engine. Through adjustment of gasoline ratio (mass basis), injection timing and rail pressure (with adjustments to diesel fuel quantity to hold torque constant), the combustion, performance and emissions are studied.

A major decision to make in design projects is the selection of the best technology to provide some needed system functionality. In making this decision, the designer must consider the range of technologies available and the performance of each. During the useful life of the product, the technologies composing the product evolve as research and development efforts continue. The performance evolution rate of one technology may be such that even though it is not initially a preferably technology, it becomes a superior technology after a few years. Quantifying the evolution of these technologies complicates the technology selection decision. The selection of energy storage technology in the design of an electric car is one example of a difficult decision involving evolving technologies.

Tradeoff studies are a common part of engineering practice. Designers conduct tradeoff studies in order to improve their understanding of how various design considerations relate to one another and to make decisions. Generally a tradeoff study involves a systematic multi-criteria evaluation of various alternatives for a particular system or subsystem. After evaluating these alternatives, designers eliminate those that perform poorly under the given criteria and explore more carefully those that remain. One limitation of current practice is that designers cannot combine the results of preexisting tradeoff studies under uncertainty. For deterministic problems, designers can use the Pareto dominance criterion to eliminate inferior designs. Prior work also exists on composing tradeoff studies performed under certainty using an extension of this criterion, called parameterized Pareto dominance.