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The fuel consumption and emissions of diesel engines is strongly influenced by the injection rate pattern, which influences the in-cylinder mixing and combustion process. Knowing the exact injection rate is mandatory for an optimal diesel combustion development. The short injection time of no more than some milliseconds prevents a direct flow rate measurement. However, the injection rate is deduced from the pressure change caused by injecting into a fuel reservoir or pipe. In an ideal case, the pressure increase in a fuel pipe correlates with the flow rate. Unfortunately, real measurement devices show measurement inaccuracies and errors, caused by non-ideal geometrical shapes as well as variable fuel temperature and fuel properties along the measurement pipe. To analyze the thermal effect onto the measurement results, an available rate measurement device is extended with a flexible heating system as well as multiple pressure and temperature sensors.

This paper presents part of the analytical work performed for the development and optimization of the 3.5L EcoBoost combustion system from Ford Motor Company. The 3.5L EcoBoost combustion system is a direct injected twin turbocharged combustion system employing side-mounted multi-hole injectors. Upfront 3D CFD, employing a Ford proprietary KIVA-based code, was extensively used in the combustion system development and optimization phases. This paper presents the effect of intake port design with various levels of tumble motion on the combustion system characteristics. A high tumble intake port design enforces a well-organized stable motion that results in higher turbulence intensity in the cylinder that in turn leads to faster burn rates, a more stable combustion and less fuel enrichment requirement at full load.

A CAE SEA-based optimization process for the enhancement of vehicle high frequency NVH applications is developed and validated. The CAE simulation, based on statistical energy analysis (SEA) theory [1], has been used to analyze high frequency NVH responses for the vehicle sound package development. However, engineers have always faced two challenges during the vehicle SEA model development. One is to create a reliable SEA model, which is correlated well with hardware test data. The other is to have a systematic approach by using the correlated model to design effective and cost efficient sound package to improve vehicle interior quietness. The optimization process presented in this paper, which integrates analysis, design sensitivity, and optimization solver, has been developed to address the challenges and to serve the needs. A non-correlated Sport Utility Vehicle (SUV) and a correlated midsize car models were used to demonstrate the capability of the proposed optimization process.

A study was conducted using Ford's nine standard CFD calibration models as described in SAE paper 940323. The models are identical from the B-pillar forward but have different back end configurations. These models were created for the purpose of evaluating the effect of back end geometry variations on aerodynamic lift and drag. Detailed experimental data is available for each model in the form of surface pressure data, surface flow visualization, and wake flow field measurements in addition to aerodynamic lift and drag values. This data is extremely useful in analyzing the accuracy of the numerical simulations. The objective of this study was to determine the capability of a digital physics based commercial CFD code, PowerFLOW ® to accurately simulate the physics of the flow field around the car-like benchmark shapes.

Software development for automotive application requires several iterations in order to tune parameters and strategy logic to operate accordantly with optimal performance. Thus, in this paper we present an optimizer method and tool used to tune calibration parameters related to torque estimation for a hybrid automatic transmission application. This optimizer aims to minimize the time invested during the software calibration and software development phases that could take significant time in order to cover the different driving conditions under which a hybrid automatic transmission can operate. For this reason, an optimization function based on the Nelder-Mead simplex algorithm using Matlab software helps to find optimized calibration values based on a cost function (square sum error minimization).

Combustion in modern spark-ignition (SI) engines is increasingly knock-limited with the wide adoption of downsizing and turbocharging technologies. Fuel autoignition conditions are different in these engines compared to the standard Research Octane Number (RON) and Motor Octane Numbers (MON) tests. The Octane Index, OI = RON - K(RON-MON), has been proposed as a means to characterize the actual fuel anti-knock performance in modern engines. The K-factor, by definition equal to 0 and 1 for the RON and MON tests respectively, is intended to characterize the deviation of modern engine operation from these standard octane tests. Accurate knowledge of K is of central importance to the OI model; however, a single method for determining K has not been well accepted in the literature.

In a recent study, quantitative measurements were presented of in-cylinder spatial distributions of mixture equivalence ratio in a single-cylinder light-duty optical diesel engine, operated with a non-reactive mixture at conditions similar to an early injection low-temperature combustion mode. In the experiments a planar laser-induced fluorescence (PLIF) methodology was used to obtain local mixture equivalence ratio values based on a diesel fuel surrogate (75% n-heptane, 25% iso-octane), with a small fraction of toluene as fluorescing tracer (0.5% by mass). Significant changes in the mixture's structure and composition at the walls were observed due to increased charge motion at high swirl and injection pressure levels. This suggested a non-negligible impact on wall heat transfer and, ultimately, on efficiency and engine-out emissions.

Ride Hailing service and Dynamic Shuttle are two key smart mobility practices, which provide on-demand door-to-door ride-sharing service to customers through smart phone apps. On the other hand, some big companies spend millions of dollars annually in third party vendors to offer shuttle services to pick up and drop off employees at fixed locations and provide them daily commutes for employees to and from work. Efficient fixed routing algorithms and analytics are the key ingredients for operating efficiency behind these services. They can significantly reduce operating costs by shortening bus routes and reducing bus numbers, while maintaining the same quality of service. This study developed an off-line optimization routing method for employee shuttle services including regular work shifts and demand based shifts (e.g. overtime shifts) in some regions.

This paper presents a design process with data mining technique and advanced optimization strategy. The proposed design method provides insights in three aspects. First, data mining technique is employed for analysis to identify key factors of design variables. Second, relationship between multiple types of size and shape design variables and performance responses can be analyzed. Last but not least, design preference can be initialized based on data analysis to provide priori guidance for the starting design points of optimization algorithm. An exhaust system design problem which largely contributes to the improvement of vehicular Noise, Vibration and Harshness (NVH) performance is employed for the illustration of the process. Two types of design parameters, structural variable (gauge of component) and layout variable (hanger location), are considered in the studied case.

Designing a durability test for an automatic transmission that appropriately reflects customer usage during the lifetime of the vehicle is a formidable task; while the transmission and its components must survive severe usage, overdesigning components leads to unnecessary weight, increased fuel consumption and increased emissions. Damage to transmission components is a function of many parameters including customer driving habits and vehicle and transmission characteristics such as weight, powertrain calibration, and gear ratios. Additionally, in some cases durability tests are required to verify only a subset of the total parameter space, for example, verifying only component modifications. Lastly, the ideal durability test is designed to impose the worst case loading conditions for the maximum number of internal components, be as short as practicable to reduce testing time, with minimal variability between tests in order to optimize test equipment and personnel resources.

A method of predicting brake specific fuel consumption characteristics from limited specifications of engine design has been investigated. For spark ignition engines operating on homogeneous mixtures, indicated specific fuel consumption based on gross indicated power is related to compression ratio and spark timing relative to optimum values. The influence of burn rate is approximately accounted for by the differences in spark timings required to correctly phase combustion. Data from engines of contemporary design shows that indicated specific fuel consumption can be defined as a generic function of relative spark timing, mixture air/fuel ratio and exhaust gas recirculation rate. The additional information required to generate brake specific performance maps is cylinder volumetric efficiency, rubbing friction, auxiliary loads, and exhaust back pressure characteristics.

With an emerging need for gasoline particulate filters (GPFs) to lower particle emissions from gasoline direct injection (GDI) engines, studies are being conducted to optimize GPF designs in order to balance filtration efficiency, backpressure penalty, filter size, cost and other factors. Metal fiber filters could offer additional designs to the GPF portfolio, which is currently dominated by ceramic wall-flow filters. However, knowledge on their performance as GPFs is still limited. In this study, modeling on backpressure and filtration efficiency of fibrous media was carried out to determine the basic design criteria (filtration area, filter thickness and size) for different target efficiencies and backpressures at given gas flow conditions. Filter media with different fiber sizes (8 - 17 μm) and porosities (80% - 95%) were evaluated using modeling to determine the influence of fiber size and porosity.

A passenger airbag is an important part of a vehicle restraint system which provides supplemental protection to an occupant in a crash event. New Federal Motor Vehicle Safety Standards No. 208 requires considering multiple crash scenarios at different speeds with various sizes of occupants both belted and unbelted. The increased complexity of the new requirements makes the selection of an optimal airbag shape a new challenge. The aim of this research is to present an automated optimization framework to facilitate the airbag shape design process by integrating advanced tools and technologies, including system integration, numerical optimization, robust assessment, and occupant simulation. A real-world frontal impact application is used to demonstrate the methodology.

In order to improve fuel economy, engine manufacturers are investigating various technologies that reduce pumping work in spark ignition engines. Current cylinder pressure analysis methods do not allow valid comparison of pumping work reduction strategies. Existing methods neglect valve timing effects which occur during the expansion and compression strokes, but are actually part of the gas exchange process. These additional pumping work contributions become more significant when evaluating non-standard valve timing concepts. This paper outlines a new analysis method for calculating the pumping work and indicated work of a 4-stroke internal combustion engine. Corrections to PMEP and IMEP are introduced which allow the valid comparison of pumping work and indicated efficiency between engines with different pumping work reduction strategies.

Capacity planning is one of the major factors in saving capital and avoiding unnecessary costs in any manufacturing system particularly large original equipment manufacturers (OEMs). However, many manufacturing systems still suffer from huge costs incurred due to a lack of applying a robust capacity planning optimization model. Most of the developed models in literature do not consider real-life situations in manufacturing systems and, hence, are not easy to implement. In this paper, a novel capacity planning optimization model considers various important features of a manufacturing system. The objective function of the model is to minimize the weighted sum of the total number of assets and changeovers. A unique feature of the developed model is the capability of providing the number of additional required assets of each type in case the existing assets are not capable of covering the entire demand.

There is keen interest in understanding the origins of engine-out unburned hydrocarbons emitted during SI engine cold start. This is especially true for the first few firing cycles, which can contribute disproportionately to the total emissions measured over standard drive cycles such as the US Federal Test Procedure (FTP). This study reports on the development of a novel methodology for capturing and quantifying unburned hydrocarbon emissions (HC), CO, and CO2 on a cycle-by-cycle basis during an engine cold start. The method was demonstrated by applying it to a 4 cylinder 2 liter GTDI (Gasoline Turbocharged Direct Injection) engine for cold start conditions at an ambient temperature of 22°C. For this technique, the entirety of the engine exhaust gas was captured for a predetermined number of firing cycles.

The capability to drive NVH quality into vehicle frame design is often compromised by the lack of available predictive tools that can be developed and applied within the timeframe during which key architectural design decisions are required. To address this need, a new parametric frame modeling approach was developed and is presented in this paper. This fully parameterized model is capable of fast modal, static stiffness & weight assessments, as well as DSA/optimization for frame design changes. This tool has been proven to be effective in improving speed, quality and impact of NVH hardware decisions.

Transmission system models require a high level of fidelity and details in order to capture the transient behaviors in drivability and fuel economy simulations. Due to model fidelity, manufacturing tolerances, frictional losses and other noise sources, parametrization and tuning of a large number of parameters in the plant model is very challenging and time consuming. In this paper, we used particle swarm optimization as the key algorithm to fast correlate the open-loop performance of an automatic transmission system plant model to vehicle launch and coast down test data using vehicle control inputs. During normal operations, the model correlated well with test data. For error states, due to the lack of model fidelity, the model cannot reproduce the same error state quantitatively, but provided a valuable methodology for qualitatively identifying error states at the early stages.

The objective of this paper is to develop a test capable of ranking lift-gates based on strain concentration levels reflected in fringe characteristics in the known stress/strain concentration and fracture vicinity. First, the system (lift gate glass, adhesive and appliqué) is chosen as test sample since the subsystem (local appliqué) does not exhibit the failure mode observed in the field test. Subsequently, it has been identified that the thermal component (rather than mechanical) is the predominant load by laser scanning vibrometry and confirmed via field test data. Next, digital shearography has been selected as the measurement and visualization tool of strain distribution due to its various advantages such as full field view and non-contact advantages. Finally, the test method has been applied to rank and optimize the structural configuration around appliqués' to reduce / eliminate failure.

This Paper introduces a modular, flexible and user-friendly dynamic powertrain model of the US Army's High Mobility Multi-Wheeled Vehicle (HMMWV). It includes the DDC 6.5L diesel engine, Hydra-matic 4L80-E automatic transmission, Torsen differentials, transfer case, and flexible drive and axle shafts. This model is used in a case study on transmission optimization design to demonstrate an application of the model. This study shows how combined optimization of the transmission hardware (clutch capacity) and control strategy (shift time) can be explored, and how the models can help the designer understand dynamic interactions as well as provide useful design guidance early in the system design phase.