Search Results

High cycle-to-cycle variations (CTCV) in a Hydrogen-Fueled Internal Combustion Engine (H2-ICE), especially in the lean-burn condition, not only lower the engine’s efficiency but also increase emissions and torque variations. High CTCV are mainly due to the variations in: mixture motion within the cylinder at the time of spark, amount of air and fuel fed to the cylinder, and mixing of the fresh mixture and residual gases within the cylinder during each cycle. In this article, multiple cycle-based methodologies were compared and analyzed specifically for H2-ICEs based on systematic experimentation. The experimental test campaign was performed on a Port Fuel Injection (PFI) H2-ICE designed by PUNCH Torino and data is processed with MATLAB. A MATLAB code is also proposed as a tool for comparing multiple methodologies for the analysis of CTCV specifically for H2-ICE.

Predicting and preventing combustion anomalies leads to safe and efficient operation of the hydrogen internal combustion engine. This research presents the application of three machine learning (ML) models – K-Nearest Neighbors (KNN), Random Forest (RF) and Logistic Regression (LR) – for the prediction of combustion anomalies in a hydrogen internal combustion engine. A small experimental dataset was used to train the models and posterior experiments were used to evaluate their performance and predicting capabilities (both in operating points -speed and load- within the training dataset and operating points in other areas of the engine map). KNN and RF exhibit superior accuracy in classifying combustion anomalies in the training and testing data, particularly in minimizing false negatives, which could have detrimental effects on the engine.

Methanol is currently being evaluated as a promising alternative fuel for internal combustion engines, due to being attainable by carbon neutral or negative pathways (renewable energy and carbon capture technology). The low ignitability of methanol has made it attractive mostly as a fuel for spark ignition engines, however the low sooting properties of the fuel could potentially reduce the NOx-soot tradeoff present in compression ignition engines. In this work, using a 4-cylinder engine with compression ratio modified from 16:1 to 19:1, methanol combustion is evaluated under five operating conditions in terms of fuel consumption, criteria pollutants, CO2 emissions and engine efficiency in addition to the qualitative assessment of the combustion stability. It was found that combustion is stable at medium to high loads, with medium load NOx emissions levels at least 30% lower than the original diesel engine and comparable emissions at maximum load conditions.

Hydrogen Internal Combustion Engines (H2-ICEs) are subject to increased attention thanks to their extremely low criteria pollutant emission and near-zero CO2 tailpipe emissions. However, to further minimize exhaust emissions and increase the efficiency of a H2-ICE, it is important to carefully control the relative air-fuel ratio of operation, i.e. Lambda (λ), which will lead in turn to an optimal combustion process. The precise λ control mainly relies upon the methodology to calculate λ on board of the engine, where the availability of reliable sensors specifically-developed for hydrogen combustion is currently limited. In this article, a comparative analysis of different methodologies for the calculation of λ is performed, comparing four methodologies: exhaust gas analysis through a Spindt-Brettschneider approach (λEMI), raw Universal Exhaust Gas Oxygen (λR-UEGO), processed Universal Exhaust Gas Oxygen (λP-UEGO) and speed-density (λSD) outputs.

Thermal cabin comfort is the largest consumer of battery energy second only to propulsion in Battery Electric Vehicles (BEV’s). Accurate prediction of thermal comfort in the vehicle cabin with fast turnaround times will allow engineers to study the impact of various thermal comfort technologies and develop energy efficient Heating, Ventilation and Air Conditioning (HVAC) systems. In this study a novel data-driven model based on physics-guided Sparse Identification of Nonlinear Dynamics (SINDy) method was developed to predict Equivalent Homogeneous Temperature (EHT), Mean Radiant Temperature (MRT) and cabin air temperature under transient conditions and drive cycles. EHT is a recognized measure of the total heat loss from the human body that can be used to characterize highly non-uniform thermal environments such as a vehicle cabin. The SINDy model was trained on drive cycle data from Climatic Wind Tunnel (CWT) for a representative Battery Electric Vehicle.

Hydrogen Internal Combustion Engines (H2-ICEs) are being investigated due to their minimal criteria pollutant and zero CO2 tailpipe emissions. However, oil filters and non-hot joint gaskets have rubber material that can be damaged and deteriorate due to direct or indirect exposure to the high temperature and high-pressure hydrogen in a H2-ICE. Thus, the effects on the properties of a rubber exposed to a hydrogen environment need to be reviewed. In this review paper, the transportation, chemical and mechanical properties of a rubber exposed directly or indirectly to high temperature and high-pressure hydrogen in a H2-ICE have been reviewed. The compatibility of rubber materials used in H2-ICE has been explored. The effects of high-pressure hydrogen on the transportation, chemical and mechanical properties of NBR and HNBR have been reviewed.

A Machine Learning (ML) approach is presented to correlate in-cylinder images of early flame kernel development within a spark-ignited (SI) gasoline engine to early-, mid-, and late-stage flame propagation. The objective of this study was to train machine learning models to analyze the relevance of flame surface features on subsequent burn rates. Ultimately, an approach of this nature can be generalized to flame images from a variety of sources. The prediction of combustion phasing was formulated as a regression problem to train predictive models to supplement observations of early flame kernel growth. High-speed images were captured from an optically accessible SI engine for 357 cycles under pre-mixed operation. A subset of these images was used to train three models: a linear regression model, a deep Convolutional Neural Network (CNN) based on the InceptionV3 architecture and a CNN built with assisted learning on the VGG19 architecture.

Sector mesh modeling is the dominant computational approach for combustion system design optimization. The aim of this work is to quantify the errors descending from the sector mesh approach through three geometric modeling approaches to an optical diesel engine. A full engine geometry mesh is created, including valves and intake and exhaust ports and runners, and a full-cycle flow simulation is performed until fired TDC. Next, an axisymmetric sector cylinder mesh is initialized with homogeneous bulk in-cylinder initial conditions initialized from the full-cycle simulation. Finally, a 360-degree azimuthal mesh of the cylinder is initialized with flow and thermodynamics fields at IVC mapped from the full engine geometry using a conservative interpolation approach. A study of the in-cylinder flow features until TDC showed that the geometric features on the cylinder head (valve tilt and protrusion into the combustion chamber, valve recesses) have a large impact on flow complexity.

As emerging automated vehicle technology is making advances in safety and reliability, engineers are also exploring improvements in energy efficiency with this new paradigm. Powertrain efficiency receives due attention, but also impactful is finding ways to reduce driving losses in coordinated-driving scenarios. Efforts focused on simulation to quantify road load improvements require a sufficient amount of background validation work to support them. This study uses a practical approach to directly quantify road load changes by testing the coordinated driving of two vehicles on a test track at various speeds (64, 88, 113 km/h) and vehicle time gaps (0.3 to 1.3 s). Axle torque sensors were used to directly measure the load required to maintain steady-state speeds while following a lead vehicle at various gap distances.

Powertrain electrification could be a key enabler for compliance with future exhaust emission standards and carbon dioxide (CO2) emissions limits or a customer facing product differentiator. The main objective of this study was to assess the potential of electrified propulsion systems in achieving a substantial reduction in CO2 emissions when applied to a representative full-size heavy-duty (HD) truck compared to the baseline configuration. A representative full-size HD four-wheel drive (4WD) truck of adjusted loaded vehicle weight (ALVW) 4082 kg or 9000 lbs with a 6.6 L diesel engine was simulated with various electrified drive configurations over the combined US FTP-72 (Federal Test Procedure) cycle and the Highway Fuel Economy Test (HWFET). Every hybrid vehicle configuration used in the study was designed using representative battery pack and electric drive components.

Diesel piston bowl geometry can affect turbulent mixing and therefore it impacts heat-release rates, thermal efficiency, and soot emissions. The focus of this work is on the effects of bowl geometry and injection timing on turbulent flow structure. This computational study compares engine behavior with two pistons representing competing approaches to combustion chamber design: a conventional, re-entrant piston bowl and a stepped-lip piston bowl. Three-dimensional computational fluid dynamics (CFD) simulations are performed for a part-load, conventional diesel combustion operating point with a pilot-main injection strategy under non-combusting conditions. Two injection timings are simulated based on experimental findings: an injection timing for which the stepped-lip piston enables significant efficiency and emissions benefits, and an injection timing with diminished benefits compared to the conventional, re-entrant piston.

In light-duty direct-injection (DI) diesel engines, combustion chamber geometry influences the complex interactions between swirl and squish flows, spray-wall interactions, as well as late-cycle mixing. Because of these interactions, piston bowl geometry significantly affects fuel efficiency and emissions behavior. However, due to lack of reliable in-cylinder measurements, the mechanisms responsible for piston-induced changes in engine behavior are not well understood. Non-intrusive, in situ optical measurement techniques are necessary to provide a deeper understanding of the piston geometry effect on in-cylinder processes and to assist in the development of predictive engine simulation models. This study compares two substantially different piston bowls with geometries representative of existing technology: a conventional re-entrant bowl and a stepped-lip bowl. Both pistons are tested in a single-cylinder optical diesel engine under identical boundary conditions.

In light- and medium-duty diesel engines, piston bowl shape influences thermal efficiency, either due to changes in wall heat loss or to changes in the heat release rate. The relative contributions of these two factors are not clearly described in the literature. In this work, two production piston bowls are adapted for use in a single cylinder research engine: a conventional, re-entrant piston, and a stepped-lip piston. An injection timing sweep is performed at constant load with each piston, and heat release analyses provide information about thermal efficiency, wall heat loss, and the degree of constant volume combustion. Zero-dimensional thermodynamic simulations provide further insight and support for the experimental results. The effect of bowl geometry on wall heat loss depends on injection timing, but changes in wall heat loss cannot explain changes in efficiency.

Hybridization is a mainstream technology for automobiles, and its application is rapidly expanding in other fields. Marine propulsion is one such field that could benefit from electrification of the powertrain. In particular, for boats to sail in enclosed waterways, such as harbors, channels, lagoons, a pure electric mode would be highly desirable. The main challenge to accomplish hybridization is the additional weight of the electric components, in particular the batteries. The goal of this project is to replace a conventional 4-stroke turbocharged Diesel engine with a hybrid powertrain, without any penalty in terms of weight, overall dimensions, fuel efficiency, and pollutant emissions. This can be achieved by developing a new generation of 2-Stroke Diesel engines, and coupling them to a state-of-the art electric system. For the thermal units, two alternative designs without active valve train are considered: opposed piston and loop scavenged engines.

This work reports a CFD study on a 2-stroke (2-S) opposed piston high speed direct injection (HSDI) Diesel engine. The engine main features (bore, stroke, port timings, et cetera) are defined in a previous stage of the project, while the current analysis is focused on the assembly made up of scavenge ports, manifold and cylinder. The first step of the study consists in the construction of a parametric mesh on a simplified geometry. Two geometric parameters and three different operating conditions are considered. A CFD-3D simulation by using a customized version of the KIVA-4 code is performed on a set of 243 different cases, sweeping all the most interesting combinations of geometric parameters and operating conditions. The post-processing of this huge amount of data allow us to define the most effective geometric configuration, named baseline.

Growing awareness about CO2 emissions and their environmental implications are leading to an increase in the importance of thermal efficiency as criteria to design internal combustion engines (ICE). Heat transfer to the combustion chamber walls contributes to a decrease in the indicated efficiency. A strategy explored in this study to mitigate this efficiency loss is to promote low swirl conditions in the combustion chamber by using low swirl ratios. A decrease in swirl ratio leads to a reduction in heat transfer, but unfortunately, it can also lead to worsening of combustion development and a decrease in the gross indicated efficiency. Moreover, pumping work plays also an important role due to the effect of reduced intake restriction to generate the swirl motion. Current research evaluates the effect of a dedicated injection strategy to enhance combustion process when low swirl is used.

A quasi-dimensional model for a direct injection diesel engine was developed based on experiments at Sandia National Laboratory. The Sandia researchers obtained images describing diesel spray evolution, spray mixing, premixed combustion, mixing controlled combustion, soot formation, and NOx formation. Dec [1] combined all of the available images to develop a conceptual diesel combustion model to describe diesel combustion from the start of injection up to the quasi-steady form of the jet. The end of injection behavior was left undescribed in this conceptual model because no clear image was available due to the chaotic behavior of diesel combustion. A conceptual end-of-injection diesel combustion behavior model was developed to capture diesel combustion throughout its life span. The compression, expansion, and gas exchange stages are modeled via zero-dimensional single zone calculations.

It is widely understood that cold ambient temperatures negatively impact vehicle system efficiency. This is due to a combination of factors: increased friction (engine oil, transmission, and driveline viscous effects), cold start enrichment, heat transfer, and air density variations. Although the science of quantifying steady-state vehicle component efficiency is mature, transient component efficiencies over dynamic ambient real-world conditions is less understood and quantified. This work characterizes wheel assembly efficiencies of a conventional and electric vehicle over a wide range of ambient conditions. For this work, the wheel assembly is defined as the tire side axle spline, spline housing, bearings, brakes, and tires. Dynamometer testing over hot and cold ambient temperatures was conducted with a conventional and electric vehicle instrumented to determine the output energy losses of the wheel assembly in proportion to the input energy of the half-shafts.

Two-stroke diesel engines could be a promising solution for reducing carbon dioxide (CO2) emissions from light-duty vehicles. The main objective of this study was to assess the potential of two-stroke engines in achieving a substantial reduction in CO2 emissions compared to four-stroke diesel baselines. As part of this study 1-D models were developed for loop scavenged two-stroke and opposed piston two-stroke diesel engine concepts. Based on the engine models and an in-house vehicle model, projections were made for the CO2 emissions for a representative light-duty vehicle over the New European Driving Cycle and the Worldwide Harmonized Light Vehicles Test Procedure. The loop scavenged two-stroke engine had about 5-6% lower CO2 emissions over the two driving cycles compared to a state of the art four-stroke diesel engine, while the opposed piston diesel engine had about 13-15% potential benefit.

Engine-out soot emissions are the result of a complex balance between in-cylinder soot formation and oxidation. Soot is formed in the diffusion flame, just after the lift-off length (LOL). Size and mass of soot particles increase through the diffusion flame and finally they are partially oxidized at the flame front. Therefore, engine-out soot emissions depend on the amount of soot formed and oxidized inside the combustion chamber. There is a considerable amount of work in the literature on characterization of soot formation. However, there is a clear lack of published research related to the characterization of soot oxidation. Thus, the main objective of the current research is to provide more knowledge and insight into the soot oxidation processes. For this purpose, a combination of theoretical and experimental tools were used. In particular, in-cylinder optical thickness (KL) was quantified with an optoelectronic sensor that uses two-color pyrometry.