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The internal combustion engine (ICE) has long dominated the heavy-duty sector by using liquid fossil fuels such as diesel but global commitments by countries and OEMs to reduce lifecycle carbon dioxide (CO2) emissions has garnered interest in alternative fuels like hydrogen. Hydrogen is a unique gaseous fuel that contains zero carbon atoms and has desired thermodynamic properties of high energy density per unit mass and high flame speeds. However, there are challenges related to its adoption to the heavy-duty sector as a drop-in fuel replacement for compression ignition (CI) diesel combustion given its high autoignition resistance. To overcome this fundamental barrier, engine manufacturers are exploring dual fuel combustion engines by substituting a fraction of the diesel fuel with hydrogen which enables fuel flexibility when there is no infrastructure and retrofittability to existing platforms.

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.

The Center for Transportation Research (CTR) Vehicle Systems team modified a Nissan CK-2 Continuously Variable Transmission (CVT) for a diesel hybrid powertrain application. Mechanical and electrical modifications were made to the CVT, both internal and external to the transmission. The goal of this experiment was to investigate and demonstrate the potential of CVT for diesel engines hybrid electric vehicles (HEVs) in fuel economy and emissions. The test set-up consisted of a diesel engine coupled to an electric motor driving a Continuously Variable Transmission (CVT). This hybrid drive is connected to a dynamometer and a DC electrical power source creating a vehicle context by combining advanced computer models and emulation techniques. The experiment focuses on the impact particular transmission control strategies have on measured fuel economy and emissions specifically, nitrogen oxides (NOx) and particulate matter (PM).

An extensive numerical study of two-phase flow inside the nozzle holes and the issuing jets for a multi-hole direct injection gasoline injector is presented. The injector geometry is representative of the Spray G nozzle, an eight-hole counter-bored injector, from the Engine Combustion Network (ECN). Homogeneous Relaxation Model (HRM) coupled with the mixture multiphase approach in the Eulerian framework has been utilized to capture the phase change phenomena inside the nozzle holes. Our previous studies have demonstrated that this approach is capable of capturing the effect of injection transients and thermodynamic conditions in the combustion chamber, by predicting phenomenon such as flash boiling. However, these simulations were expensive, especially if there is significant interest in predicting the spray behavior as well.

Reynolds-averaged Navier-Stokes (RANS) modeling is expected to deliver an ensemble-averaged result for the majority of turbulent flows. This could lead to the conclusion that multi-cycle internal combustion engine (ICE) simulations performed using RANS must exhibit a converging numerical solution after a certain number of consecutive cycles. However, for some engine configurations unsteady RANS simulations are not guaranteed to deliver an ensemble-averaged result. In this paper it is shown that, when using RANS modeling to simulate multiple engine cycles, the cycle-to-cycle variations (CCV) generated from different initial conditions at each cycle are not damped out even after a large number of cycles. A single-cylinder GDI research engine is simulated using RANS modeling and the numerical results for 20 consecutive engine cycles are evaluated for two specific operating conditions.

Analytical studies of oxygen-enriched diesel engine combustion have indicated the various benefits as well as the need for using cheaper fuels with water addition. To verify analytical results, a series of single-cylinder diesel engine tests were conducted to investigate the concepts of oxygen enriched air (OEA) for combustion with water emulsified fuels. Cylinder pressure traces were obtained for inlet oxygen levels of 21% to 35% and fuel emulsions with water contents of 0% to 20%. Data for emulsified fuels included no. 2 and no. 4 diesel fuels. The excess oxygen for the tests was supplied from compressed bottled oxygen connected to the intake manifold. The cylinder pressure data was collected with an AVL pressure transducer and a personal computer-based data logging system. The crank angle was measured with an optical encoder. In each data run, 30 consecutive cycles were recorded and later averaged for analysis.

The availability and low price of personal computers with suitable interface equipment has made it practical to use such a system for cylinder pressure data acquisition. With this objective, procedures have been developed to measure and record cylinder pressure on an individual crank angle basis and obtain an average cylinder pressure trace using an Apple II Plus personal computer. These procedures as well as methods for checking the quality of cylinder pressure data are described. A simplified heat release analysis technique for an approximate first look at the data quality is presented. Comparisons are made between the result of this analysis, the Krieger-Borman heat release analysis which uses complete chemical equilibrium. The comparison is made to show the suitability of the simplified analysis in judging the quality of the pressure data.

Advanced internal combustion engines, although generally more efficient than conventional combustion engines, often encounter limitations in multi-cylinder applications due to variations in the combustion process. This study leverages experimental data from an inline 6-cylinder heavy-duty dual fuel engine equipped with a fully-flexible variable intake valve actuation system to study cylinder-to-cylinder variations in power production. The engine is operated with late intake valve closure timings in a dual-fuel combustion mode featuring a port-injection and a direct-injection fueling system in order to improve fuel efficiency and engine performance. Experimental results show increased cylinder-to-cylinder variation in IMEP as IVC timing moves from 570°ATDC to 610°ATDC, indicating an increasingly uneven fuel distribution between cylinders.

Expanding upon the authors’ previous work which utilized a GT-Power model of the Cooperative Fuels Research (CFR) engine under Research Octane Number (RON) conditions, this work defines the boundary conditions of the CFR engine under Motored Octane Number (MON) test conditions. The GT-Power model was validated against experimental CFR engine data for primary reference fuel (PRF) blends between 60 and 100 under standard MON conditions, defining the full range of interest of MON for gasoline-type fuels. The CFR engine model utilizes a predictive turbulent flame propagation sub-model, and a chemical kinetic solver for the end-gas chemistry. The validation was performed simultaneously for thermodynamic and chemical kinetic parameters to match in-cylinder pressure conditions, burn rate, and knock point prediction with experimental data, requiring only minor modifications to the flame propagation model from previous model iterations.

This paper describes a configuration and controller, designed using Autonomie,1 for dual-motor battery electric vehicle (BEV) heavy-duty trucks. Based on the literature and current market research, this model was designed with two electric motors, one on the front axle and the other on the rear axle. A rule-based control algorithm was designed for the new dual-motor BEV, based on the model, and the control parameters were optimized by using a genetic algorithm (GA). The model was simulated in diverse driving cycles and gradeability tests. The results show both a good following of the desired cycle and achievement of truck gradeability performance requirements. The simulation results were compared with those of a single-motor BEV and showed reduced energy consumption with the high-efficiency operation of the two motors.

The U.S. Department of Energy, Vehicle Technologies Office (U.S. DOE-VTO) has been developing more energy-efficient and environmentally friendly highway transportation technologies that would enable the United States to burn less petroleum on the road. System simulation is an accepted approach for evaluating the fuel economy potential of advanced (future) technology targets. U.S. DOE-VTO defines the targets for advancement in powertrain technologies (e.g., engine efficiency targets, battery energy density, lightweighting, etc.) Vehicle system simulation models based on these targets have been generated in Autonomie, reflecting the different EPA classifications of vehicles for different advanced timeframes as part of the DOE Benefits and Scenario (BaSce) Analysis. It is also important to evaluate the progress of these component technical targets compared to existing technologies available in the market.

A thermophoretic particulate sampling device was used to investigate the detailed morphology and microstructure of diesel particulates at various engine-operating conditions. A 75 HP Caterpillar single-cylinder direct-injection diesel engine was operated to sample particulate matter from the high-temperature exhaust stream. The morphology and microstructure of the collected diesel particulates were analyzed using a high-resolution transmission electron microscope and subsequent image processing/data acquisition system. The analysis revealed that spherical primary particles were agglomerated together to form large aggregate clusters for most of engine speed and load conditions. Measured primary particle sizes ranged from 34.4 to 28.5 nm at various engine-operating conditions. The smaller primary particles observed at high engine-operating conditions were believed to be caused by particle oxidation at the high combustion temperature.

Detailed properties of particulate matter (PM) emissions from a gasoline direct injection (GDI) engine were analyzed in terms of size, morphology, and nanostructures, as gasoline and its ethanol blend E20 were used as a fuel. PM emissions were sampled from a 0.55L single-cylinder GDI engine by means of a scanning mobility particle sizer (SMPS) for size measurements and a self-designed thermophoretic sampling device for the subsequent analyses of size, morphology and nanostructures using a transmission electron microscope (TEM). The particle sizes were evaluated with variations of air-fuel equivalence ratio and fuel injection timing. The most important result from the SMPS measurements was that the number of nucleation-mode nanoparticles (particularly those smaller than 10 - 15 nm) increased significantly as the fuel injection timing was advanced to the end-of-injection angle of 310° bTDC.

This work presents a methodology to determine the off-cycle fuel economy benefit of a 2-Layer HVAC system which reduces ventilation and heat rejection losses of the heater core versus a vehicle using a standard system. Experimental dynamometer tests using EPA drive cycles over a broad range of ambient temperatures were conducted on a highly instrumented 2016 Lexus RX350 (3.5L, 8 speed automatic). These tests were conducted to measure differences in engine efficiency caused by changes in engine warmup due to the 2-Layer HVAC technology in use versus the technology being disabled (disabled equals fresh air-considered as the standard technology baseline). These experimental datasets were used to develop simplified response surface and lumped capacitance vehicle thermal models predictive of vehicle efficiency as a function of thermal state.

This work is an extension to a previously reported work on chemical kinetic mechanism reduction scheme for large-scale mechanisms. Here, Perfectly Stirred Reactor (PSR) was added as a criterion of data source for mechanism reduction instead of using only auto-ignition condition. As a result, a reduced n-hexadecane mechanism with 79 species for diesel fuel surrogate was successfully derived from the detailed mechanism. Following that, the reduced n-hexadecane mechanism was validated under auto-ignition and PSR conditions using zero-dimensional (0-D) closed homogeneous batch reactor in CHEMKIN-PRO software. Agreement was achieved between the reduced and detailed mechanisms in ignition timing predictions and the reduced n-hexadecane mechanism was able to reproduce species concentration profiles with a maximum error of 40%. Accordingly, two-dimensional (2-D) Computational Fluid Dynamic (CFD) simulations were performed to study the spray combustion phenomena within a constant volume bomb.

Fuel injection characteristics, in particular the atomization and penetration of the fuel droplets in the region close to the nozzle orifice, are known to affect emission and particulate formation in Diesel engines. It is also well established that the primary fuel atomization process is induced by aerodynamics in the near nozzle region as well as cavitation and turbulence from the injector nozzle. Typical breakup models in the literature however, do not consider the effects of cavitation and turbulence from nozzle injector. In this paper, a comprehensive primary breakup model incorporating the inner nozzle flow effects such as cavitation and turbulence along with aerodynamically induced breakup is developed and incorporated in the CONVERGE CFD code. This new primary breakup model is tested in a constant volume spray chamber against various spray data available in the literature.

The CFR engine is the widely accepted platform to test standard Research Octane Number (RON) and Motored Octane Number (MON) for determining anti-knock characteristics of motor fuels. With increasing interest in engine downsizing, up-torquing, and alternative fuels for modern spark ignition (SI) engines, there is a need to better understand the conditions that fuels are subjected to in the CFR engine during octane rating. To take into account fuel properties, such as fuel heat of vaporization, laminar flame speed and auto-ignition chemistry; and understand their impacts on combustion knock, it is essential to estimate accurate cylinder conditions. In this study, the CFR F1/F2 engine was modeled using GT-Power with the Three Pressure Analysis (TPA) and the model was validated for different fuels and engine conditions.

Low temperature combustion through in-cylinder blending of fuels with different reactivity offers the potential to improve engine efficiency while yielding low engine-out NOx and soot emissions. A Navistar MaxxForce 13 heavy-duty compression ignition engine was modified to run with two separate fuel systems, aiming to utilize fuel reactivity to demonstrate a technical path towards high engine efficiency. The dual-fuel engine has a geometric compression ratio of 14 and uses sequential, multi-port-injection of a low reactivity fuel in combination with in-cylinder direct injection of diesel. Through control of in-cylinder charge reactivity and reactivity stratification, the engine combustion process can be tailored towards high efficiency and low engine-out emissions. Engine testing was conducted at 1200 rpm over a load sweep.

Response Surface Methodology (RSM) techniques are applied to develop brake specific fuel consumption (BSFC) maps of a test vehicle over standard drive cycles under various ambient conditions. This technique allows for modeling and predicting fuel consumption of an engine as a function of engine operating conditions. Results will be shown from Federal Test Procedure engine starts of 20°C, and colder conditions of -7°C. Fueling rates under a broad range of engine temperatures are presented. Analysis comparing oil and engine coolant as an input factor of the model is conducted. Analysis comparing the model to experimental datasets, as well as some details into the modeling development, will be presented. Although the methodology was applied to data collected from a vehicle, the same technique could be applied to engines run on dynamometers.

Design and optimization of automotive engines present unique challenges on account of the large design space and conflicting constraints. A notable example of such a problem is optimizing the fuel consumption and reducing emissions over the drive cycle of an automotive engine. There are over twenty design variables (including operating conditions and geometry) for the above-mentioned problem. Conducting design, analyses, and optimization studies over such a large parametric space presents a serious computational challenge. The large design parameter space precludes the use of detailed numerical or experimental investigations. Physics-based reduced-order models can be used effectively in the design and optimization of such problems.