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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.

In experimental work, it is well known that measured values are not exact. Statistical treatment of uncertainty in measured values is common. The propagation of uncertainty in measured values into calculated values is examined. This technique of uncertainty analysis presented provides a rigorous mathematical technique for the analysis of uncertainty propagation from experimentally measured values into calculated results. The method of uncertainty analysis is introduced in general and an application to the momentum equation is examined to demonstrate the technique. The example presented demonstrates the effectiveness of the technique by demonstrating the growth of uncertainty in the calculated results around a singularity in the equations.

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 relationship of slip resistance (or coefficient of friction) to safe climbing system maneuvers on high profile vehicles has become an issue because of its possible connection to falls of drivers. To partially address this issue, coefficients of friction were measured for seven of the more popular fabricated metal step materials. Evaluated on these steps were four types of shoe materials (crepe, leather, ribbed-rubber, and oil-resistant-rubber) and three types of contaminant conditions (dry, wet-water, and diesel fuel). The final factor evaluated was the direction of sole force application. Results showed that COF varied primarily as a function of sole material and the presence of contaminants. Unexpectedly, few effects were attributible to the metal step materials. Numerous statistical interactions suggested that adequate levels of COF are more likely to be attained by targeting control on shoe soles and contaminants rather than the choice of a particular step material.

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.

Two structure-borne and two airborne paths were measured on 99 “identical” Isuzu RODEOs and 57 “identical” Isuzu pickup trucks. Significant effort was made to control measurement variability but not environmental (climate) variations. A record was kept of the tests of a reference vehicle over the variation of environmental factors. The frequency response functions (FRFs) of the reference vehicle varied by approximately 2-4 dB over the frequency range 0-500 Hz for the structure-borne paths and over 0-1000 Hz for the airborne paths due to measurement and environmental variations. The FRFs of the fleet varied by as much as 5-10 dB over the same frequency range. In this paper, the vehicle tests are described. The reference and the fleet data are shown in raw form. Reduced data and implications of the results are also discussed.