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The study of spray-wall interaction is of great importance to understand the dynamics during fuel-surface impingement process in modern internal combustion engines. The identification of droplet post-impingement pattern (contact, transition, non-contact) and droplet characteristics can quantitatively provide an estimation of energy transfer for spray-wall interaction, thus further influencing air-fuel mixing and emissions under combusting conditions. Theoretical criteria of single droplet post-impingement pattern on a dry wall have been experimentally and numerically studied by many researchers to quantify the hydrodynamic droplet behaviors. However, apart from model fidelity, another issue is the scalability. A theoretical criterion developed from one case might not be well suited to another scenario. In this paper, a data-driven approach for single droplet-dry wall post-impingement pattern utilizing arithmetical machine learning classification methods is proposed and demonstrated.

The automotive industry is dramatically changing. Many automotive Original Equipment Manufacturers (OEMs) proposed new prototype models or concept vehicles to promote a green vehicle image. Non-traditional players bring many latest technologies in the Information Technology (IT) industry to the automotive industry. Typical vehicle’s characteristics became wider compared to those of vehicles a decade ago, and they include not only a driving range, mileage per gallon and acceleration rating, but also many features adopted in the IT industry, such as usability, connectivity, vehicle software upgrade capability and backward compatibility. Consumers expect the latest technology features in vehicles as they enjoy in using digital applications in laptops and mobile phones. These features create a huge challenge for a design of a new vehicle, especially for a human-machine-interface (HMI) system.

The impact of additive and oil chemistry on low speed pre-ignition (LSPI) was evaluated. An additive metals matrix varied the levels of zinc dialkyldithiophosphate (ZDDP), calcium sulfonate, and molybdenum within the range of commercially available engine lubricants. A separate test matrix varied the detergent chemistry (calcium vs. magnesium), lubricant volatility, and base stock chemistry. All lubricants were evaluated on a LSPI test cycle developed by Southwest Research Institute within its Pre-Ignition Prevention Program (P3) using a GM LHU 2.0 L turbocharged GDI engine. It was observed that increasing the concentration of calcium leads to an increase in the LSPI rate. At low calcium levels, near-zero LSPI rates were observed. The addition of zinc and molybdenum additives had a negative effect on the LSPI rate; however, this was only seen at higher calcium concentrations.

Based on RADAR and LiDAR measurements of deer with RADAR and LiDAR in the Spring and Fall of 2014 [1], we report the best fit statistical models. The statistical models are each based on time-constrained measurement windows, termed test-points. Details of the collection method were presented at the SAE World Congress in 2015. Evaluation of the fitness of various statistical models to the measured data show that the LiDAR intensity of reflections from deer are best estimated by the extreme value distribution, while the RCS is best estimated by the log-normal distribution. The value of the normalized intensity of the LiDAR ranges from 0.3 to 1.0, with an expected value near 0.7. The radar cross-section (RCS) varies from -40 to +10 dBsm, with an expected value near -14 dBsm.

A full calibration exercise of a diesel engine air path can take months to complete (depending on the number of variables). Model-based calibration approach can speed up the calibration process significantly. This paper discusses the overall calibration process of the air-path of the Cat® C7.1 engine using statistical machine learning tool. The standard Cat® C7.1 engine's twin-stage turbocharger was replaced by a VTG (Variable Turbine Geometry) as part of an evaluation of a novel air system. The changes made to the air-path system required a recalculation of the air path's boost set point and desired EGR set point maps. Statistical learning processes provided a firm basis to model and optimize the air path set point maps and allowed a healthy balance to be struck between the resources required for the exercise and the resulting data quality.

Conventional diesel fuel has been in the market for decades and used successfully to run diesel engines of all sizes in many applications. In order to reduce emissions and to foster energy source diversity, new fuels such as alternative and renewable, as well as new fuel formulations have entered the market. These include biodiesel, gas-to-liquid, and alternative formulations by states such as California. Performance variations in fuel economy, emissions, and compatibility for these fuels have been evaluated and debated. In some cases contradictory views have surfaced. “Sustainable”, “Renewable”, and “Clean” designations have been interchanged. Adding to the confusion, results from one fuel in one type of engine such as an older heavy-duty engine, is at times compared to that of another fuel in another type such as a modern light-duty engine. This study was an attempt to compare the performance of several fuels in identical environments, using the same engine, for direct comparison.

Conventional diesel fuel has been in the market for decades and used successfully to run diesel engines of all sizes in many applications. In order to reduce emissions and to foster energy source diversity, new fuels such as alternative and renewable, as well as new fuel formulations have entered the market. These include biodiesel, gas-to-liquid, and alternative formulations by states such as California. Performance variations in fuel economy, emissions, and compatibility for these fuels have been evaluated and debated. In some cases contradictory views have surfaced. “Sustainable”, “Renewable”, and “Clean” designations have been interchanged. Adding to the confusion, results from one fuel in one type of engine such as an older heavy-duty engine, is at times compared to that of another type such as a modern light-duty. This study was an attempt to compare the performance of several fuels in an identical environment, using the same engine, for direct comparison.

Analyzing the combustion characteristics, engine performance, and emissions pathways of the internal combustion (IC) engine requires management of complex and an increasing quantity of data. With this in mind, effective management to deliver increased knowledge from these data over shorter timescales is a priority for development engineers. This paper describes how this can be achieved by combining conventional engine research methods with the latest developments in process informatics and statistical analysis. Process informatics enables engineers to combine data, instrumental and application models to carry out automated model development including optimization and validation against large data repositories of experimental data.

Building on two decades of expertise, a fourth generation fleet test protocol is presented for assessing the response of engine performance to gasoline additive treatment. In this case, the ability of additives to remove pre-existing deposit from the intake systems of port fuel injected vehicles has been examined. The protocol is capable of identifying real benefits under realistic market conditions, isolating fuel performance from other effects thereby allowing a direct comparison between different fuels. It is cost efficient and robust to unplanned incidents. The new protocol has been applied to the development of a candidate fuel additive package for the North American market. A vehicle fleet of 5 quadruplets (5 sets of 4 matched vehicles, each set of a different model) was tested twice, assessing the intake valve clean-up performance of 3 test fuels relative to a control fuel.

In-cylinder ion sensing is a subject of interest due to its application in spark-ignited (SI) engines for feedback control and diagnostics including: combustion knock detection, rate and phasing of combustion, and mis-fire On Board Diagnostics (OBD). Further advancement and application is likely to continue as the result of the availability of ignition coils with integrated ion sensing circuitry making ion sensing more versatile and cost effective. In SI engines, combustion knock is controlled through closed loop feedback from sensor metrics to maintain knock near the borderline, below engine damage and NVH thresholds. Combustion knock is one of the critical applications for ion sensing in SI engines and improvement in knock detection offers the potential for increased thermal efficiency. This work analyzes and characterizes the ionization signal in reference to the cylinder pressure signal under knocking and non-knocking conditions.

Sensory jury testing was utilized to characterize vibration levels perceived by the operator, with respect to levels measured using instrumentation, in order to develop a tool for the evaluation of vibration at the operator interfaces. Details of the jury testing and jury data processing method are highlighted as well as the refinement of vibration characterization for a specific application. The vibration at user interface locations of both snowmobiles and ATVs was measured along with subjective feedback from a panel of jurists. Statistical analysis was performed on the jury data to provide both a qualitative and quantitative number to represent the opinion of the jury. Correlations were developed between the measured levels of vibration and the opinions of the jury. Finally, a set of correlation functions suitable for design predictions was developed.

This paper presents an innovative approach for quality engineering using the Eigenvector Dimension Reduction (EDR) Method. Currently industry relies heavily upon the use of the Taguchi method and Signal to Noise (S/N) ratios as quality indices. However, some disadvantages of the Taguchi method exist such as, its reliance upon samples occurring at specified levels, results to be valid at only the current design point, and its expensiveness to maintain a certain level of confidence. Recently, it has been shown that the EDR method can accurately provide an analysis of variance, similar to that of the Taguchi method, but is not hindered by the aforementioned drawbacks of the Taguchi method. This is evident because the EDR method is based upon fundamental statistics, where the statistical information for each design parameter is used to estimate the uncertainty propagation through engineering systems.

Beginning June 1, 2006, 80% of the highway diesel fuel produced in the United States had to contain 15 ppm sulfur or less. To account for sulfur test method variability, the United States Environmental Protection Agency (US EPA) allowed a 2 ppm compliance margin, meaning that in an EPA enforcement action fuel measuring 17 ppm or less would still be deemed compliant since the true sulfur level could still be 15 ppm. Concern was voiced over the appropriateness of the 2 ppm compliance margin, citing recent American Society for Testing and Materials (ASTM) round-robin and crosscheck test program results that showed sulfur test lab-to-lab variability (reproducibility) on the order of 4 to 5 ppm depending on test method.

A 28-vehicle fleet test was executed to verify and quantify the NOX emissions reductions achieved through the use of Infineum's Vektron 6913 gasoline additive. The fleet composition and experimental design were finalized in collaborative discussions with US Environmental Protection Agency (EPA) Office of Transportation & Air Quality (OTAQ) and consultation / advice from several major US automotive manufacturers. The test was conducted over a period of five months at Southwest Research Institute. Statistical analysis of the emissions data indicated a 10% average fleet reduction in NOX emissions without any negative impact on other criteria pollutants (CO, HC) or fuel economy.

This paper traces the evolution of the ASTM Test Monitoring Center (TMC) from its modest beginnings in 1976 to the present. Formed as an unbiased and non-aligned group within ASTM Subcommittee D02.B, the TMC operates a reference oil based calibration system that serves both the producers and users of automotive lubricants. Governed by the ASTM Test Monitoring Board, the center's primary mission is to calibrate engine dynamometer test stands used to conduct various ASTM test methods for evaluating lubricant performance. The core services of the TMC have remained the same over its nearly 25 year history. The center stores and distributes ASTM reference oils and is responsible for assuring, through the use of analytical testing, the quality and consistency of the oils. The number of reference oils handled by the TMC has steadily increased over time such that today the center inventories some 100 different formulations having a total volume of 65,000 gallons.

The U.S. Environmental Protection Agency (EPA) formed a Heavy-Duty Engine Working Group (HDEWG) in the Mobile Sources Technical Advisory Subcommittee in 1995. The goal of the HDEWG was to help define the role of the fuel in meeting the future emissions standards in advanced technology engines (beyond 2004 regulated emissions levels). A three-phase program was developed. This paper presents the results of the statistical analysis of the data collected in the Phase II program. Included is a description of the design of the fuel test matrix, and a listing of the regression equations developed to predict emissions as a function of fuel density, cetane number, monoaromatics, and polyaromatics. Also included is a description of selected analyses of the emissions from a smaller set of fuel data that allowed direct comparison of the effects of natural and boosted cetane number.

A number of laboratory-scale test methods are available to predict the effects of fuel lubricity on injection system wear. Anecdotal evidence exists to indicate that these methods produce poor correlation with pump wear, particularly for fuels that contain lubricity additives. The issue is further complicated by variations in the lubricity requirements of full-scale equipment and the test methodologies used to evaluate the pumps. However, the cost of performing full-scale equipment testing severely limits the quantity of data available for validation of the laboratory procedures at any single location. In the present study, the technical literature was reviewed and all previously published data was combined to form a single database of 175 pump stand results. This volume of data allows far more accurate statistical analysis than is possible with tests performed at a single location. The results indicate differences in the effectiveness of the standardized laboratory-scale methods.

The Port Fuel Injector (PFI) Deposit Test is a performance-based test procedure developed by the Coordinating Research Council and adopted by state and federal regulatory agencies for fuel qualification in the United States. To date, Southwest Research Institute (SwRI) has performed over 375 PFI tests between 1991 and 1998 for various clients. This paper details the analyses of these tests. Of the 375 tests, 199 were performed as keep-clean tests and 176 were performed as clean-up tests. The following areas of interest are discussed in this paper: Keep-clean versus clean-up test procedures Linearity of deposit formation Injector position effects as related to fouling Dirtyup / cleanup phenomena Seasonal effects This paper draws the conclusion that it is easier to keep new injectors from forming deposits than it is to clean up previously formed deposits. It was found that injector deposit formation is generally non-linear.

An appropriate test procedure, based on a duty cycle representative of real in-use operation, is an essential tool for characterizing engine emissions. A study has been performed to develop and validate a snowmobile engine test procedure for measurement of exhaust emissions. Real-time operating data collected from four instrumented snowmobiles were combined into a composite database for analysis and formulation of a snowmobile engine duty cycle. One snowmobile from each of four manufacturers (Arctic Cat, Polaris, Ski-Doo, and Yamaha) was included in the data collection process. Snowmobiles were driven over various on- and off-trail segments representing five driving styles: aggressive (trail), moderate (trail), double (trail with operator and one passenger), freestyle (off trail), and lake driving. Statistical analysis of this database was performed, and a five-mode steady-state snowmobile engine duty cycle was developed.

A Coordinating Research Council sponsored test program was conducted to determine the effects of diesel fuel properties on emissions from two heavy-duty diesel engines designed to meet EPA emission requirements for 1994. Results for a prototype 1994 DDC Series 60 were reported in SAE Paper 941020. This paper reports the results from a prototype 1994 Navistar DTA-466 engine equipped with an exhaust catalyst. A set of ten fuels having specific variations in cetane number, aromatics, and oxygen were used to study effects of these fuel properties on emissions. Using glycol diether compounds as an oxygenated additive, selected diesel fuels were treated to obtain 2 and 4 mass percent oxygen. Cetane number was increased for selected fuels using a cetane improver. Emissions were measured during transient FTP operation of the Navistar engine tuned for a nominal 5 g/hp-hr NOx, then repeated using a 4 g/hp-hr NOx calibration.