Search Results

Focusing on the dual-mode dual-fuel (DMDF) combustion concept, a combined optimization of the piston bowl geometry with the fuel injection strategy was conducted at low, mid, and high loads. By coupling the KIVA-3V code with the enhanced genetic algorithm (GA), a total of 14 parameters including the piston bowl geometric parameters and the injection parameters were optimized with the objective of meeting Euro VI regulations while improving the fuel efficiency. The optimal piston bowl shape coupled with the corresponding injection strategy was summarized and integrated at various loads. Furthermore, the effects of the key geometric parameters were investigated in terms of organizing the in-cylinder flow, influencing the energy distribution, and affecting the emissions. The results indicate that the behavior of the DMDF combustion mode is further enhanced in the aspects of improving the fuel economy and controlling the emissions after the bowl geometry optimization.

The non-conventional diesel nozzles have attracted more and more attention for their ability to promote jet breakup. In the present study, the internal nozzle flow and jet breakup relying on the convergent-divergent nozzle are investigated by combining the cavitation model and LES model with Multi-Fluid-Quasi-VOF model based on the OpenFOAM code. This is a novel method for which the interphase forces caused by the relative velocity of gas and liquid can be taken into account while sharpening the gas-liquid interface, which is able to accurately present the evolution processes of cavitation and jet breakup. Primarily, the numerical model was verified by the mass flow rate, spray momentum flux, discharge coefficient and effective jet velocity of the prototype Spray D nozzle from the literature.

Although turbulence plays a critical role in engines operated within low temperature combustion (LTC) regime, its interaction with chemistry on auto-ignition at low-ambient-temperature and lean-oxygen conditions remains inadequately understood. Therefore, it is worthwhile taking turbulence-chemistry interaction (TCI) into consideration in LTC engine simulation by employing advanced combustion models. In the present study, large eddy simulation (LES) coupled with linear eddy model (LEM) is performed to simulate the ignition process in n-heptane spray under engine-relevant conditions, known as Spray H. With LES, more details about unsteady spray flame could be captured compared to Reynolds-averaged Navier-Stokes equations (RANS). With LEM approach, both scalar fluctuation and turbulent mixing on sub-grid level are captured, accounting for the TCI. A skeletal mechanism is adopted in this numerical simulation, including 41 species and 124 reactions.

Syngas is considered to be a promising alternative fuel for the dual-fuel reactivity controlled compression ignition (RCCI) engine to reduce the fuel consumption and emissions. However, the optimal syngas compositions and fuel supply strategies in RCCI combustion are significantly affected by engine configurations, which have not been investigated yet. In this study, by integrating the KIVA-3V code and the non-dominated sort genetic algorithm II (NSGA-II), the optimizations for a 0.477 L single-cylinder engine with shallow/wide piston bowl (Engine A) and a 1.325 L single-cylinder engine with conventional omega-type piston (Engine B) under the syngas/diesel RCCI combustion were performed. The optimized operating parameters include the fuel-supply strategies, syngas compositions, and intake conditions. The results indicate that the fuel-supply strategy is flexible in Engine A due to the shallow/wide piston bowl and the relatively small cylinder bore.

The potential of diesel/gasoline RCCI combustion coupled with late intake valve closing (LIVC) and double direct injection of diesel for meeting high fuel efficiency with ultra-low emissions was investigated in this study. The study was aiming at high load operation in a heavy-duty diesel engine. Based on the reactivity stratification of RCCI combustion, the employment of double injection of diesel fuel provided concentration stratification of the high-reactivity fuel, which is to further realize effective control of the combustion process. Meanwhile, late intake valve closing (LIVC) strategy is introduced to control the maximum in-cylinder pressure and nitrogen oxides (NOx) emissions.

Dimethyl ether (DME) attracts increasing attentions in recent years, because it can reduce the carbon monoxide (CO), unburned hydrocarbon (HC), and soot emissions for engines as the transportation fuel or the fuel additive. In this paper, a reduced DME oxidation mechanism is developed using the decoupling methodology. The rate constants of the fuel-related reactions are optimized using the non-dominated sorting genetic algorithm II (NSGA-II) to reproduce the ignition delay times in shock tubes and major species concentrations in jet-stirred reactors (JSR) over low-to-high temperatures. In NSGA-II, the range of the rate constants was considered to ensure the reliability of the optimized mechanism. Moreover, an improved objective function was proposed to maintain the faithfulness of the optimized mechanism to the original reaction mechanism, and a new method was presented to determine the optimal solution from the Pareto front.

Flash boiling spray is effective in improving the atomization and evaporation characteristics for gasoline direct injection engines. However, for a multi-hole injector the morphology structure of spray has an obvious change with the fuel temperature increasing or the ambient pressure decreasing, which influences the process of mixture formation and flame propagation. Specially, the spray collapses with both long penetration and a narrow spray angle above certain high superheat degree, which deteriorates air/fuel mixing and hence increases emissions. It is not desired for engine applications while the mechanism of spray structure transformation for multi-hole injector still remains unclear. In the present study, a systematic flash boiling spray model for multi-hole injector is built to investigate the flash boiling spray of multi-hole injector.

This paper reports on an investigation into the potential for a thermoelectric generator (TEG) to improve the fuel economy of a mild hybrid vehicle. A simulation model of a parallel hybrid vehicle equipped with a TEG in the exhaust system is presented. This model is made up by three sub-models: a parallel hybrid vehicle model, an exhaust model and a TEG model. The model is based on a quasi-static approach, which runs a fast and simple estimation of the fuel consumption and CO2 emissions. The model is validated against both experimental and published data. Using this model, the annual fuel saving, CO2 reduction and net present value (NPV) of the TEG’s life time fuel saving are all investigated. The model is also used as a flexible tool for analysis of the sensitivity of vehicle fuel consumption to the TEG design parameters. The analysis results give an effective basis for optimization of the TEG design.

The application of state-of-art thermoelectric generator (TEG) in automotive engine has potential to reduce more than 2% fuel consumption and hence the CO2 emissions. This figure is expected to be increased to 5%~10% in the near future when new thermoelectric material with higher properties is fabricated. However, in order to maximize the TEG output power, there are a few issues need to be considered in the design stage such as the number of modules, the connection of modules, the geometry of the thermoelectric module, the DC-DC converter circuit, the geometry of the heat exchanger especially the hot side heat exchanger etc. These issues can only be investigated via a proper TEG model. The authors introduced four ways of TEG modelling which in the increasing complexity order are MATLB function based model, MATLAB Simscape based Simulink model, GT-power TEG model and CFD STAR-CCM+ model. Both Simscape model and GT-Power model have intrinsic dynamic model performance.

A control strategy has been designed for a city bus equipped with a pneumatic hybrid propulsion system. The control system design is based on the precise management of energy flows during both energy storage and regeneration. Energy recovered from the braking process is stored in the form of compressed air that is redeployed for engine start and to supplement the engine air supply during vehicle acceleration. Operation modes are changed dynamically and the energy distribution is controlled to realize three principal functions: Stop-Start, Boost and Regenerative Braking. A forward facing simulation model facilitates an analysis of the vehicle dynamic performance, engine transient response, fuel economy and energy usage.

The TC48 project is developing a state-of-the-art, exceptionally low cost, 48V Plug-in hybrid electric (PHEV) demonstration drivetrain suitable for electrically powered urban driving, hybrid operation, and internal combustion engine powered high speed motoring. This paper explains the motivation for the project, and presents the layout options considered and the rationale by which these were reduced. The vehicle simulation model used to evaluate the layout options is described and discussed. The modelling work was used in order to support and justify the design choices made. The design of the vehicle's control systems is discussed, presenting simulation results. The physical embodiment of the design is not reported in this paper. The paper describes analysis of small vehicles in the marketplace, including aspects of range and cost, leading to the justification for the specification of the TC48 system.

The flow performance of intake port significantly affects engine output power, fuel economy, and exhaust emissions in gasoline engines. Thus, optimal intake port geometry is desired in gasoline engines. To optimize the flow performance of intake port, a new optimization method combining genetic algorithm (GA) and artificial neural network (ANN) was proposed. First, an automatic system for generating the geometry of the tangential intake port was constructed to create various port geometries through inputting the 18 pre-defined structural parameters. Then, the effects of four critical structural parameters were investigated through numerical simulation. On the basis of the computational results, an ANN was used to model the flow performance of the intake port, and a genetic algorithm was simultaneously employed to optimize the flow performance by optimizing the four important structural parameters. Finally, the optimization results were verified through numerical simulation.

This paper presents the implementation of a vehicle and powertrain model of the parallel hybrid electric vehicle which can be used for several purposes: as a model for estimating fuel consumption, as a model for estimating performance, and as a control model for the hybrid powertrain optimisation. The model is specified as a multi-domain physical model in MATLAB Simscape, which captures the key electrical, mechanical and thermal energy flows in the vehicles. By applying hand crafted boundary conditions, this model can be simulated either in the forwards or backwards direction, and it can easily be simplified as required to address specific control problems. Modelling in the forwards direction, the driver inputs are specified, and the vehicle response is the model output. In the backwards direction, the vehicle velocity as a function of time is the specified input, and the engine torque, and fuel consumption are the model outputs.

Design of Experiments (DOE) introduces a number of design types such as space filling design and optimal design. However, optimal design type is best for a system with high prior knowledge. Meanwhile, space-filling design is good for unknown systems, which is normal for engine calibration. It would be best to have a design that can support constructive model building, where a block of engine test is run for most of the day and followed by engine modeling at the end of the day. However, this needs separate space filling design for each day and separate design is susceptible to redundant test points. Among of the five space-filling design type, Sobol sequences and Halton sequences can support constructive model building due to the deterministic random sequence characteristic. When the model is good enough for system prediction, the remaining engine test can stop and proceed to model optimization.

It has been recognized that density, viscosity, surface tension, and volatility of liquid fuel are of great importance on the atomization and vaporization characteristics of biodiesel spray. This paper presents a comprehensive physical property prediction of biodiesel fuel for spray modeling with most recently developed property prediction models. The temperature-dependent properties of a soy methyl ester (SME) biodiesel were well predicted by the updated prediction methods. Then, the physical properties of the SME biodiesel were added into the KIVA-3V fuel library. By using the well predicted fuel properties, the spray behaviors of SME were successfully simulated by the KIVA-3V code under late-cycle post-injection, conventional diesel injection, and early-injection engine-relevant conditions. The simulation results agree reasonably well with the available experimental liquid penetrations under conditions of various ambient densities and temperatures.

Spray behaviors of pure biodiesel and its blend with conventional diesel have been substantially studied in the last decade. However, the studies on the spray behaviors of pure fatty acid methyl esters (FAMEs) are scarce. The primary components of most biodiesel fuels are methyl palmitate (C16:0), methyl stearate (C18:0), methyl oleate (C18:1), methyl linoleate (C18:2) and methyl linolenate (C18:3), and methyl laurate (C12:0) is also the dominant component of some biodiesels. In this study, the spray behaviors of the aforementioned six FAMEs in biodiesel fuels under engine-relevant conditions were numerically studied using the KIVA-3V code. The physical properties needed for spray modeling were predicted with most recently developed property prediction models and added into the fuel library of KIVA-3V. The transient behaviors of liquid penetrations and vaporization characteristics of these FAMEs were numerically studied under various engine-relevant conditions.

Unlike RANS method, LES method needs more time and much more grids to accurately simulate the spray process. In KIVA, spray process was modeled by Lagrangain-drop and Eulerian-fluid method. The coarse grid can cause errors in predicting the droplet-gas relative velocity, so for reducing grid dependency due to the relative velocity effects, an improved spray model based on a gas-jet theory is used in this work and in order to validate the model seven different size grids were used. In this work, the local dense grid was used to reduce the computation cost and obtain accurate results that also were compared with entire dense grid. Another method to improve computation efficiency is the MUSCL (Monotone Upstream-centered Schemes for Conservation Laws) differencing scheme that was implemented into KIVA3V-LES code to calculate the momentum convective term and reduce numerical errors.

In order to identify predictive models for a diesel engine combustion process, combustion cylinder pressure together with other fuel path variables such as rail pressure, injector current and sleeve pressure of 1000 continuous cycles were sampled and collected at high resolution. Using these engine steady state test data, three types of modeling approach have been studied. The first is the Auto-Regressive-Moving-Average (ARMA) model which had limited prediction ability for both peak combustion pressure (Pmax) and Indicated Mean Effective Pressure (IMEP). By applying correlation analysis, proper inputs were found for a linear predictive model of Pmax and IMEP respectively. The prediction performance of this linear model is excellent with a 30% fit number for both Pmax and IMEP. Further nonlinear modeling work shows that even a nonlinear Neural Network (NN) model does not have improved prediction performance compared to the linear predictive model.

Recovering the braking energy and reusing it can significantly improve the fuel economy of a vehicle which is subject to frequent braking events such as a city bus. As one way to achieve this goal, pneumatic hybrid technology converts kinetic energy to pneumatic energy by compressing air into tanks during braking, and then reuses the compressed air to power an air starter to realize a regenerative Stop-Start function. Unlike the pure electric or hybrid electric passenger car, the pneumatic hybrid city bus uses the rear axle to achieve regenerative braking function. In this paper we discuss research into the blending of pneumatic regenerative braking and mechanical frictional braking at the rear axle. The aim of the braking function is to recover as much energy as possible and at the same time distribute the total braking effort between the front and rear axles to achieve stable braking performance.

In this work, large eddy simulation (LES) with a K-equation subgrid turbulent kinetic energy model is implemented into the CFD code KIVA3V to study the features of liquid fuel spray and combustion using gradually varying grid in a constant volume chamber. The characteristic time-scale combustion model (CTC) incorporating a turbulent timescale is adopted to predict the combustion process and the SHELL auto-ignition model is used to predict auto-ignition. Combustion is also simulated using Parallel Detailed Chemistry with Lu's n-heptane reduced mechanism (58 species), which has been added into the KIVA3V-LES code. The computational results are compared with Sandia experimental data for non-reacting and reacting cases. As a result, LES can capture the complex structure of the spray and temperature distribution as well as the trend of ignition delay and flame lift-off length variations. Better results are obtained using the Parallel Detailed Chemistry than the CTC model.