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This paper focuses on the combustion development portion of the Advanced Combustion Controls Enabling Systems and Solutions (ACCESS) project, a joint research project partially funded by a Department of Energy grant. The main goal of the project is to improve fuel economy in a gasoline fueled light-duty vehicle by 30% while maintaining similar performance and meeting SULEV emission standards for the Federal Test Procedure (FTP) cycle. In this study, several combustion modes Spark Ignited (SI), Homogeneous Charge Compression Ignition (HCCI), Spark- Assisted Compression Ignition (SACI)) were compared under various conditions (naturally aspirated, boosted, lean, and stoichiometric) to compare the methods of controlled auto-ignition on a downsized, boosted multi-cylinder engine with an advanced valvetrain system capable of operating under wide negative valve overlap (NVO) conditions.

High pressure diesel sprays were visualized under vaporizing and combusting conditions in a constant-volume combustion vessel. Near-simultaneous visualization of vapor and liquid phase fuel distribution were acquired using a hybrid shadowgraph/Mie-scattering imaging setup. This imaging technique used two pulsed LED's operating in an alternative manner to provide proper light sources for both shadowgraph and Mie scattering. In addition, combustion cases under the same ambient conditions were visualized through high-speed combustion luminosity measurement. Two single-hole diesel injectors with same nozzle diameters (100μm) but different k-factors (k0 and k1.5) were tested in this study. Detailed analysis based on spray penetration rate curves, rate of injection measurements, combustion indicators and 1D model comparison have been performed.

Recent advances in natural gas (NG) recovery technologies and availability have sparked a renewed interest in using NG as a fuel for commercial vehicles. NG can potentially provide both reduced operating cost and reductions in CO2 emissions. Commercial NG vehicles, depending on application and region, have different performance and fuel consumption targets and are subject to various emissions regulations. Therefore, different applications may require different combustion strategies to achieve specific targets and regulations. This paper summarizes an evaluation of combustion strategies and parameters available to meet these requirements while using NG in a spark ignited engine. A single-cylinder research engine using a modified diesel cylinder head was employed for this study. Both stoichiometric combustion with cooled exhaust gas recirculation (EGR) and lean-burn were evaluated.

Battery temperature variations have a strong effect on both battery aging and battery performance. Significant temperature variations will lead to different battery behaviors. This influences the performance of the Hybrid Electric Vehicle (HEV) energy management strategies. This paper investigates how variations in battery temperature will affect Lithium-ion battery aging and fuel economy of a HEV. The investigated energy management strategy used in this paper is the Equivalent Consumption Minimization Strategy (ECMS) which is a well-known energy management strategy for HEVs. The studied vehicle is a Honda Civic Hybrid and the studied battery, a BLS LiFePO4 3.2Volts 100Ah Electric Vehicle battery cell. Vehicle simulations were done with a validated vehicle model using multiple combinations of highway and city drive cycles. The battery temperature variation is studied with regards to outside air temperature.

Low Temperature Combustion (LTC) engines are promising to improve powertrain fuel economy and reduce NOx and soot emissions by improving the in-cylinder combustion process. However, the narrow operating range of LTC engines limits the use of these engines in conventional powertrains. The engine’s limited operating range can be improved by taking advantage of electrification in the powertrain. In this study, a multi-mode LTC-SI engine is integrated with a parallel hybrid electric configuration, where the engine operation modes include Homogeneous Charge Compression Ignition (HCCI), Reactivity Controlled Compression Ignition (RCCI), and conventional Spark Ignition (SI). The powertrain controller is designed to enable switching among different modes, with minimum fuel penalty for transient engine operations.

This paper investigates an optimal design of a diesel engine and after-treatment systems for a series hybrid electric forklift application. A holistic modeling approach is developed in GT-Suite® to establish a model-based hardware definition for a diesel engine and an after-treatment system to accurately predict engine performance and emissions. The used engine model is validated with the experimental data. The engine design parameters including compression ratio, boost level, air-fuel ratio (AFR), injection timing, and injection pressure are optimized at a single operating point for the series hybrid electric vehicle, together with the performance of the after-treatment components. The engine and after-treatment models are then coupled with a series hybrid electric powertrain to evaluate the performance of the forklift in the standard VDI 2198 drive cycle.

This paper focuses on the engine design portion of the Advanced Combustion Controls Enabling Systems and Solutions (ACCESS) project, a joint research project partially funded by a Department of Energy grant. The main goal of the project is to improve fuel economy in a gasoline fueled light-duty vehicle by 25% while maintaining similar performance and meeting SULEV emission standards. A Cadillac CTS with a high-feature naturally-aspirated 3.6L V6 engine was chosen as the baseline vehicle. To achieve the target fuel economy improvement over the baseline engine configuration, gasoline turbocharged direct-injection (GTDI) technology was utilized for engine downsizing in combination with part-load lean homogeneous charge compression ignition (HCCI) operation for further fuel economy gains. The GM 2.0L I4 GTDI Ecotec engine was used as the platform for the basis of this design.

The need for improved emissions and fuel economy are placing increasingly severe performance requirements on compression ignition engines. These are satisfied in part by advanced fuel injection equipment that provide multiple injections and increased injection pressures along with higher operating temperature. Fuel composition is also changing, with increased use of non-traditional feedstocks combined with a range of additive chemistries to restore or enhance fuel quality. Within this environment, a number of worldwide automotive companies have noted a trend towards increased Internal Injector Deposits (IID). Little quantitative information to understand the root cause is available, largely due to difficulty in reproducing the issue under controlled conditions. The present study details the results of an accelerated test methodology, which is used to evaluate the interrelated effects of fuel composition and operating environment.

Current developments in fuels and emissions regulations are resulting in an increasingly severe operating environment for diesel fuel injection systems. The formation of deposits within the holes or on the outside of the injector nozzle can affect the overall system performance. The rate of deposit formation is affected by a number of parameters, including operating conditions and fuel composition. For the work reported here an accelerated test procedure was developed to evaluate the relative importance of some of these parameters in a high pressure common rail fuel injection system. The resulting methodology produced measurable deposits in a custom-made injector nozzle on a single-cylinder engine. The results indicate that fuels containing 30%v/v and 100% Fatty Acid Methyl Ester (FAME) that does not meet EN 14214 produced more deposit than an EN590 petroleum diesel fuel.

A previously developed CFD-based optimization tool is utilized to find optimal engine operating conditions with respect to fuel consumption and emissions. The optimization algorithm employed is based on the steepest descent method where an adaptive cost function is minimized along each line search using an effective backtracking strategy. The adaptive cost function is based on the penalty method, where the penalty coefficient is increased after every line search. The parameter space is normalized and, thus, the optimization occurs over the unit cube in higher-dimensional space. The application of this optimization tool is demonstrated for the Sulzer S20, a central-injection, non-road DI diesel engine. The optimization parameters are the start of injection of the two pulses of a split injection system, the duration of each pulse, the exhaust gas recirculation rate, the boost pressure and the compression ratio.

The predictive capability of Lagrangian and Eulerian multi-dimensional computational fluid dynamics models accounting for the onset and development of cavitation inside Diesel nozzle holes is assessed against experimental data. These include cavitation images available from a real-size six-hole mini-sac nozzle incorporating a transparent window as well as high-speed/CCD images and LDV measurements of the liquid velocity inside an identical large-scale fully transparent nozzle replica. Results are available for different cavitation numbers, which correspond to different cavitation regimes forming inside the injection hole. Discharge coefficient measurements for various real-size nozzles operating under realistic injection pressures are also compared and match well with models' predictions.

A global optimization method has been developed for an engine simulation code and utilized in the search of optimal fuel injection strategies. This method uses a Lagrange interpolation function which interpolates engine output data generated at the vertices and the intermediate points of the input parameters. This interpolation function is then used to find a global minimum over the entire parameter set, which in turn becomes the starting point of a CFD-based optimization. The CFD optimization is based on a steepest descent method with an adaptive cost function, where the line searches are performed with a fast-converging backtracking algorithm. The adaptive cost function is based on the penalty method, where the penalty coefficient is increased after every line search. The parameter space is normalized and, thus, the optimization occurs over the unit cube in higher-dimensional space.

This paper attempts to integrate a derivative-free probability analysis method to Reliability-Based Robust Design Optimization (RBRDO). The Eigenvector Dimension Reduction (EDR) method is used for the probability analysis method. It has been demonstrated that the EDR method is more accurate and efficient than the Second-Order Reliability Method (SORM) for reliability and quality assessment. Moreover, it can simultaneously evaluate both reliability and quality without any extra expense. Two practical engineering problems (vehicle side impact and layered bonding plates) are used to demonstrate the effectiveness of the EDR method.

In the last decade, considerable advances have been made in reliability-based design optimization (RBDO). One assumption in RBDO is that the complete information of input uncertainties are known. However, this assumption is not valid in practical engineering applications, due to the lack of sufficient data. In practical engineering design, information concerning uncertainty parameters is usually in the form of finite samples. Existing methods in uncertainty based design optimization cannot handle design problems involving epistemic uncertainty with a shortage of information. Recently, a novel method referred to as Bayesian Reliability-Based Design Optimization (BRBDO) was proposed to properly handle design problems when engaging both epistemic and aleatory uncertainties [1]. However, when a design problem involves a large number of epistemic variables, the computation task for BRBDO becomes extremely expensive.

Modern diesel engines are equipped with an increasing number of actuators set to improve human comfort and fuel consumptions while respecting the restricted emissions regulations. In spite of the great progress made in the electronic and data-processing domains, the physical-based emissions models remain time consuming and too complicated to be used in a dynamic calibrating process. Therefore, until these days, the calibration of the engine's cartographies is done manually by experimental experts on dynamic test bed, but the results are not often the best compromise in the consumption-emissions formula due to the increasing number of actuators and to the nonlinear and complex relations between the different variables involved in the combustion process. Recently, neural networks are successfully used to model dynamic multiple inputs - multiple outputs processes by learning from examples and without any additional or detailed information about the process itself.

The favorable physical properties of hydrogen (H2) make it an excellent alternative fuel for internal combustion (IC) engines and hence it is widely regarded as the energy carrier of the future. Hydrogen direct injection provides multiple degrees of freedom for engine optimization and influencing the in-cylinder combustion processes. This paper compares the results in the mixture formation and combustion behavior of a hydrogen direct-injected single-cylinder research engine using two different injector locations as well as various injector nozzle designs. For this study the research engine was equipped with a specially designed cylinder head that allows accommodating a hydrogen injector in a side location between the intake valves as well as in the center location adjacent to the spark plug.

The pursuit of new combustion concepts or modes is ongoing to meet future emissions regulations and to eliminate or at least to minimize the burden of aftertreatment systems. In this research, Premixed Low Temperature Diesel Combustion (PLTDC) was developed using a single-cylinder engine to achieve low NOx and soot emissions while maintaining fuel efficiency. Operating conditions considered were 1500 rpm, 3 bar and 6 bar IMEP. The effects of injection timing, injection pressure, swirl ratio, EGR rate, and multiple injection strategies on the combustion process have been investigated. The results show that low NOx and soot emissions can be obtained at both operating conditions without sacrificing the fuel efficiency. Low NOx and soot emissions are achieved through minimization of peak temperatures during the combustion process and homogenization of in-cylinder air-fuel mixture.

Optimal fuel injection strategies are obtained with a micro-genetic algorithm and an adaptive gradient method for a nonroad, medium-speed DI diesel engine equipped with a multi-orifice, asynchronous fuel injection system. The gradient optimization utilizes a fast-converging backtracking algorithm and an adaptive cost function which is based on the penalty method, where the penalty coefficient is increased after every line search. The micro-genetic algorithm uses parameter combinations of the best two individuals in each generation until a local convergence is achieved, and then generates a random population to continue the global search. The optimizations have been performed for a two pulse fuel injection strategy where the optimization parameters are the injection timings and the nozzle orifice diameters.

Future light, medium and heavy duty diesel engines will need to satisfy the more stringent emission levels (US 2014, Euro 6, etc.) without compromising their current performance and fuel economy, while still maintaining a competitive cost. In order to achieve this, the Fuel Injection Equipment (FIE) together with the pressure charging, cooling system, exhaust after treatment and other engine sub-systems will each play a key role. The FIE has to offer a range of flexible injection characteristics, e.g. a multiple injection train with or without separation, modulated injection pressures and rates for every injection, higher specific power output from the same injector envelope, and close control of very small fuel injection quantities. The aim of this paper is to present Delphi's developments in fuel injection strategies for light and medium duty diesel engines that will comply with future emission legislation, whilst providing higher power density and uncompromised fuel economy.

This paper describes the design, development and validation of a muffler for reducing exhaust noise from a commercial tele-handler. It also describes the procedure for modeling and optimizing the exhaust muffler along with experimental measurement for correlating the sound transmission loss (STL). The design and tuning of the tele-handler muffler was based on several factors including overall performance, cost, weight, available space, and ease of manufacturing. The analysis for predicting the STL was conducted using the commercial software LMS Virtual Lab (LMS-VL), while the experimental validation was carried out in the laboratory using the two load setup. First, in order to gain confidence in the applicability of LMS-VL, the STL of some simple expansion mufflers with and without extended inlet/outlet and perforations was considered. The STL of these mufflers were predicted using the traditional plane wave transfer matrix approach.