Search Results

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.

Controlled Auto-Ignition (CAI) also known as Homogeneous Charge Compression Ignition (HCCI) is increasingly seen as a very effective way of lowering both fuel consumption and emissions. Hence, it is regarded as one of the best ways to meet stringent future emissions legislation. It has however, still many problems to overcome, such as limited operating range. This combustion concept was achieved in a production type, 4-cylinder gasoline engine, in two separated tests: naturally aspirated and turbocharged. Very few modifications to the original engine were needed. These consisted basically of a new set of camshafts for the naturally aspirated test and new camshafts plus turbocharger for the test with forced induction. After previous experiments with naturally aspirated CAI operation, it was decided to investigate the capability of turbocharging for extended CAI load and speed range.

The proposed technique is a tailored deep neural network (DNN) training approach which uses an iterative process to support the learning of DNNs by targeting their specific misclassification and missed detections. The process begins with a DNN that is trained on freely available annotated image data, which we will refer to as the Base model, where a subset of the categories for the classifier are related to the automotive theater. A small set of video capture files taken from drives with test vehicles are selected, (based on the diversity of scenes, frequency of vehicles, incidental lighting, etc.), and the Base model is used to detect/classify images within the video files. A software application developed specifically for this work then allows for the capture of frames from the video set where the DNN has made misclassifications. The corresponding annotation files for these images are subsequently corrected to eliminate mislabels.

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.

Chopped carbon fiber sheet molding compound (SMC) material is a promising material for mass-production lightweight vehicle components. However, the experimental characterization of SMC material property is a challenging task and needs to be further investigated. There now exist two ASTM standards (ASTM D7078/D7078M and ASTM D5379/D5379M) for characterizing the shear properties of composite materials. However, it is still not clear which standard is more suitable for SMC material characterization. In this work, a comparative study is conducted by performing two independent Digital Image Correlation (DIC) shear tests following the two standards, respectively. The results show that ASTM D5379/D5379M is not appropriate for testing SMC materials. Moreover, the failure mode of these samples indicates that the failure is caused by the additional moment raised by the improper design of the fixture.

To advance vehicle lightweighting, chopped carbon fiber sheet molding compound (SMC) is identified as a promising material to replace metals. However, there are no effective tools and methods to predict the mechanical property of the chopped carbon fiber SMC due to the high complexity in microstructure features and the anisotropic properties. In this paper, a Representative Volume Element (RVE) approach is used to model the SMC microstructure. Two modeling methods, the Voronoi diagram-based method and the chip packing method, are developed to populate the RVE. The elastic moduli of the RVE are calculated and the two methods are compared with experimental tensile test conduct using Digital Image Correlation (DIC). Furthermore, the advantages and shortcomings of these two methods are discussed in terms of the required input information and the convenience of use in the integrated processing-microstructure-property analysis.

Pumping Mean Effective Pressure (PMEP) is the main factor limiting the improvement of thermal efficiency in a spark-ignition (SI) engine under low load. One of the ways to reduce the pumping loss under low load is to use Cylinder DeActivation (CDA). The CDA aims at reducing the firing density (FD) of the SI engine under low load operation and increasing the mass of air-fuel mixture within one cycle in one cylinder to reduce the throttling effect and further reducing the PMEP. The multi-stroke cycles can also reduce the firing density of the SI engine after some certain reasonable design, which is feasible to improve the thermal efficiency of the engine under low load in theory. The research was carried out on a calibrated four-cylinder SI engine simulation platform. The thermal efficiency improvements of the 6-stroke cycle and 8-stroke cycle to the engine performance were studied compared with the traditional 4-stroke cycle under low load conditions.

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.

As part of the International Lubricant Standardization and Approval Committee's (ILSAC) goal of developing a global automatic transmission fluid (ATF) specification, members have been evaluating test methods that are currently used by various automotive manufacturers for qualifying ATF for use in their respective transmissions. This report deals with comparing test methods used for determining torque capacity in friction systems (shifting clutches). Three test methods were compared, the Plate Friction Test from the General Motors DEXRON®-III Specification, the Friction Durability Test from the Ford MERCON® Specification, and the Japanese Automotive Manufacturers Association Friction Test - JASO Method 348-95. Eight different fluids were evaluated. Friction parameters used in the comparison were breakaway friction, dynamic friction torque at midpoint and the end of engagement, and the ratio of end torque to midpoint torque.

Pressure Sensitive Paint (PSP) has been used for several years by the aircraft industry in transonic wind tunnel testing where the oxygen concentrations are low and the luminescence of the paint is easily recorded. Extending PSP to slower speeds where the oxygen concentrations are closer to atmospheric conditions is much more challenging. For the past few years, work has been underway at both Wright Patterson Air Force Base and Ford Motor Company to advance PSP techniques for testing at slower speeds. The CRADA (Cooperative Research and Development Agreement) provided a way for comparisons to be made of the different PSP systems that were being investigated. This paper will report on PSP tests conducted as part of the CRADA.

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.

A correlation of aerodynamic wind tunnels was initiated between Chrysler, Ford and General Motors under the umbrella of the United States Council for Automotive Research (USCAR). The wind tunnels used in this correlation were the open jet tunnel at Chrysler's Aero Acoustic Wind Tunnel (AAWT), the open jet tunnel at the Jacobs Drivability Test Facility (DTF) that Ford uses, and the closed jet tunnel at General Motors Aerodynamics Laboratory (GMAL). Initially, existing non-competitive aerodynamic data was compared to determine the feasibility of facility correlation. Once feasibility was established, a series of standardized tests with six vehicles were conducted at the three wind tunnels. The size and body styles of the six vehicles were selected to cover the spectrum of production vehicles produced by the three companies. All vehicles were tested at EPA loading conditions. Despite the significant differences between the three facilities, the correlation results were very good.

Self-piercing rivet (SPR) joints are a key joining technology for lightweight materials, and they have been widely used in automobile manufacturing. Manual visual crack inspection of SPR joints could be time-consuming and relies on high-level training for engineers to distinguish features subjectively. This paper presents a novel machine learning-based crack detection method for SPR joint button images. Firstly, sub-images are cropped from the button images and preprocessed into three categories (i.e., cracks, edges and smooth regions) as training samples. Then, the Artificial Neural Network (ANN) is chosen as the classification algorithm for sub-images. In the training of ANN, three pattern descriptors are proposed as feature extractors of sub-images, and compared with validation samples. Lastly, a search algorithm is developed to extend the application of the learned model from sub-images into the original button images.

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.

The wide applications of automatic sensing devices and data acquisition systems in automotive manufacturing have resulted in a data-rich environment, which demands new data mining methodologies for effective data fusion and information integration to support decision making. This paper presents a new methodology for developing a diagnostic system using manufacturing system data for high-value assets in automotive manufacturing. The proposed method extends the basic attributes control charts with the following key elements: optimal feature subset selection considering multiple features and correlation structure, balancing the type I and type II errors in decision making, on-line process monitoring using adaptive modeling with control charts, and diagnostic performance assessment using shift and trend detection. The performance of the developed diagnostic system can be continuously improved as the knowledge of machine faults is automatically accumulated during production.

A fuel vapor system model (FVSMOD) has been developed to simulate vehicle evaporative emission control system behavior. The fuel system components incorporated into the model include the fuel tank and pump, filler cap, liquid supply and return lines, fuel rail, vent valves, vent line, carbon canister and purge line. The system is modeled as a vented system of liquid fuel and vapor in equilibrium, subject to a thermal environment characterized by underhood and underbody temperatures and heat transfer parameters assumed known or determined by calibration with experimental liquid temperature data. The vapor/liquid equilibrium is calculated by simple empirical equations which take into account the weathering of the fuel, while the canister is modeled as a 1-dimensional unsteady absorptive and diffusive bed. Both fuel and canister submodels have been described in previous publications. This paper presents the system equations along with validation against experimental data.

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.

The use of finite element modeling (FEM) tools is part of the most of the current product development projects of the automotive industry companies, replacing an important part of the physical tests with lower costs, higher speed and with increasing accuracy by each day. In addition to this, computer-aided engineering (CAE) tools can be either used after the product is released, at any moment of the product life, in many different situation as a new feature release, to validate a more cost-efficient design proposal or to help on solving some manufacturing problem or even a vehicular field issue. Different from the phase where the product is still under development, when standard virtual test procedures are performed in order to validate the vehicle project, in this case, where engineers expertise plays a very important role, before to proceed with any standard test it is fundamental to understand the physics of the phenomena that is causing the unexpected behavior.

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.

The Environmental Protection Agency (EPA) and California Air Resources Board (CARB) requirements for high mileage durability of emission components make it necessary to ensure the mechanical robustness of metallic catalytic converters. In addition, the robustness of design features must be assessed in the early design development phase without resorting to vehicle fleet testing. By following established reliability methods, a new approach for time and cost efficient accelerated durability testing was developed, which can account for the combined effects of critical stressors of a metallic catalytic converter. This paper describes the methodology used to determine the critical stressors and their levels in actual operating conditions which were determined by analyzing a broad range of vehicle test information. This information was used to develop a temperature profile and a high vibration load profile for the new life test method.