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Testing of ducted fuel injection (DFI) in a single-cylinder engine with production-like hardware previously showed that adding a duct structure increased soot emissions at the full load, rated speed operating point [1]. The authors hypothesized that the DFI flame, which travels faster than a conventional diesel combustion (CDC) flame, and has a shorter distance to travel, was being re-entrained into the on-going fuel injection around the lift-off length (LOL), thus reducing air entrainment into the on-going injection. The engine operating condition and the engine combustion chamber geometry were duplicated in a constant pressure vessel. The experimental setup used a 3D piston section combined with a glass fire deck allowing for a comparison between a CDC flame and a DFI flame via high-speed imaging. CH* imaging of the 3D piston profile view clearly confirmed the re-entrainment hypothesis presented in the previous engine work.

A multi-year Power System R&D project was initiated with the objective of developing an off-road hybrid heavy-duty concept diesel engine with front end accessory drive-integrated energy storage. This off-road hybrid engine system is expected to deliver 15-20% reduction in fuel consumption over current Tier 4 Final-based diesel engines and consists of a downsized heavy-duty diesel engine containing advanced combustion technologies, capable of elevated peak cylinder pressures and thermal efficiencies, exhaust waste heat recovery via SuperTurbo™ turbocompounding, and hybrid energy recovery through both mechanical (high speed flywheel) and electrical systems. The first year of this project focused on the definition of the hybrid elements using extensive dynamic system simulation over transient work cycles, with hybrid supervisory controls development focusing on energy recovery and transient load assist, in Caterpillar’s DYNASTY™ software environment.

With the rapid development of artificial intelligence, autonomous driving technology will finally reshape an automotive industry. Although fully autonomous cars are not commercially available to common consumers at this stage, partially autonomous vehicles, which are defined as level 2 and level 3 autonomous vehicles by SAE J3016 standard, are widely tested by automakers and researchers. A typical Human-Machine-Interface (HMI) for a vehicle takes a form to support a human domination role. Although modern driving assistance systems allow vehicles to take over control at certain scenarios, the typical human-machine-interface has not changed dramatically for a long time. With deep learning neural network technologies penetrating into automotive applications, multi-modal communications between a driver and a vehicle can be enabled by a cost-effective solution.

The application of Artificial Intelligence (AI) in the automotive industry can dramatically reshape the industry. In past decades, many Original Equipment Manufacturers (OEMs) applied neural network and pattern recognition technologies to powertrain calibration, emission prediction and virtual sensor development. The AI application is mostly focused on reducing product development and validation cost. AI technologies in these applications demonstrate certain cost-saving benefits, but are far from disruptive. A disruptive impact can be realized when AI applications finally bring cost-saving benefits directly to end users (e.g., automation of a vehicle or machine operation could dramatically improve the efficiency). However, there is still a gap between current technologies and those that can fully give a vehicle or machine intelligence, including reasoning, knowledge, planning and self-learning.

Despite numerous research efforts, there is no reliable and widely accepted tool for the prediction of erosion prone material surfaces due to collapse of cavitation bubbles. In the present paper an Erosion Aggressiveness Index (EAI) is proposed, based on the pressure loads which develop on the material surface and the material yield stress. EAI depends on parameters of the liquid quality and includes the fourth power of the maximum bubble radius and the bubble size number density distribution. Both the newly proposed EAI and the Cavitation Aggressiveness Index (CAI), which has been previously proposed by the authors based on the total derivative of pressure at locations of bubble collapse (DP/Dt>0, Dα/Dt<0), are computed for a cavitating flow orifice, for which experimental and numerical results on material erosion have been published. The predicted surface area prone to cavitation damage, as shown by the CAI and EAI indexes, is correlated with the experiments.

Although soot-formation processes in diesel engines have been well characterized during the mixing-controlled burn, little is known about the distribution of soot throughout the combustion chamber after the end of appreciable heat release during the expansion and exhaust strokes. Hence, the laser-induced incandescence (LII) diagnostic was developed to visualize the distribution of soot within an optically accessible single-cylinder direct-injection diesel engine during this period. The developed LII diagnostic is semi-quantitative; i.e., if certain conditions (listed in the Appendix) are true, it accurately captures spatial and temporal trends in the in-cylinder soot field. The diagnostic features a vertically oriented and vertically propagating laser sheet that can be translated across the combustion chamber, where “vertical” refers to a direction parallel to the axis of the cylinder bore.

Cost reduction in the automotive industry becomes a widely-adopted operational strategy not only for Original Equipment Manufacturers (OEMs) that take cost leader generic corporation strategy, but also for many OEMs that take differentiation generic corporation strategy. Since differentiation generic strategy requires an organization to provide a product or service above the industry average level, a premium is typically included in the tag price for those products or services. Cost reduction measures could increase risks for the organizations that pursue differentiation strategy. Although manufacturers in the automotive industry dramatically improved production efficiency in past ten years, they are still facing the pressure of cost control. The big challenge in cost control for automakers and suppliers is increasing prices of raw materials, energy and labor costs. These costs create constraints for the traditional economic expansion model.

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.

It is estimated that operating continuously on a B20 fuel containing the current allowable ASTM specification limits for metal impurities in biodiesel could result in a doubling of ash exposure relative to lube-oil-derived ash. The purpose of this study was to determine if a fuel containing metals at the ASTM limits could cause adverse impacts on the performance and durability of diesel emission control systems. An accelerated durability test method was developed to determine the potential impact of these biodiesel impurities. The test program included engine testing with multiple DPF substrate types as well as DOC and SCR catalysts. The results showed no significant degradation in the thermo-mechanical properties of cordierite, aluminum titanate, or silicon carbide DPFs after exposure to 150,000 mile equivalent biodiesel ash and thermal aging. However, exposure of a cordierite DPF to 435,000 mile equivalent aging resulted in a 69% decrease in the thermal shock resistance parameter.

Today's engine and combustion process development is closely related to the intake port layout. Combustion, performance and emissions are coupled to the intensity of turbulence, the quality of mixture formation and the distribution of residual gas, all of which depend on the in-cylinder charge motion, which is mainly determined by the intake port and cylinder head design. Additionally, an increasing level of volumetric efficiency is demanded for a high power output. Most optimization efforts on typical homogeneous charge spark ignition (HCSI) engines have been at low loads because that is all that is required for a vehicle to make it through the FTP cycle. However, due to pumping losses, this is where such engines are least efficient, so it would be good to find strategies to allow the engine to operate at higher loads.

Smaller locomotives often use mechanical transmissions instead of diesel-electric drive systems typically used in larger locomotives. This paper discusses how Model Based Design was used to develop the complete drive train control system for a 24 ton sugar cane locomotive. A complete MATLAB Simulink machine model was built to fully test and verify the shift control logic, traction control, vehicle speed limiting, and braking control for this locomotive application before it was commissioned. The model included the engine, torque converter, planetary transmission, drive line, and steel on steel driving surface. Simulation was used to debug all control code and test and refine control strategies so that the initial field commissioning in remote Australia was executed very quickly with minimal engineering support required.

In modern production diesel engine control systems, fuel path control is still largely conducted through a system of tables that set mode, timing and injection quantity and with common rail systems, rail pressure. In the hands of an experienced team, such systems have proved so far able to meet emissions standards, but they lack the analytical underpinning that lead to systematic solutions. In high degree of freedom systems typified by modern fuel injection, there is substantial scope to deploy optimising closed loop strategies during calibration and potentially in the delivered product. In an optimising controller, a digital algorithm will explicitly trade-off conflicting objectives and follow trajectories during transients that continue to meet a defined set of criteria. Such an optimising controller must be based on a model of the system behaviour which is used in real time to investigate the consequences of proposed control actions.

Increasingly, the exchanges of data in complex ECM (Electronic Control Module) systems rely on multiple communication networks across various physical and network layers. This has greatly increased system flexibility and provided an excellent medium to create well-defined exchangeable interfaces between components; however this added flexibility comes with increased network complexity. A system-level approach allows for the optimization of data exchange and network configuration as well as the development of a comprehensive network failure strategy. Many current ECM systems utilize complex multi-network communication strategies to exchange and control data to components. Recently, Caterpillar implemented an HIL (Hardware-In-the-Loop) test system that provides an approach for developing and testing a comprehensive ECM network strategy.

A framework to study the response of seated operators to whole-body vibration (WBV) is presented in this work. The framework consists of (i) a six-degree-of-freedom man-rated motion platform to play back ride files of typical heavy off-road machines; (ii) an optical motion capture system to collect 3D motion data of the operators and the surrounding environment (seat and platform); (iii) a computer skeletal model to embody the tested subjects in terms of their body dimensions, joint centers, and inertia properties; (iv) a marker placement protocol for seated positions that facilitates the process of collecting data of the lower thoracic and the lumbar regions of the spine regardless of the existence of the seatback; and (v) a computer human model to solve the inverse kinematics/dynamic problem for the joint profiles and joint torques. The proposed framework uses experimental data to answer critical questions regarding human response to WBV.

Fuel-injection schedules that use two injection events per cycle (“dual-injection” approaches) have the potential to simultaneously attenuate engine-out soot and NOx emissions. The extent to which these benefits are due to enhanced mixing, low-temperature combustion modes, altered combustion phasing, or other factors is not fully understood. A traditional single-injection, an early-injection-only, and two dual-injection cases are studied using a suite of imaging diagnostics including spray visualization, natural luminosity imaging, and planar laser-induced fluorescence (PLIF) imaging of nitric oxide (NO). These data, coupled with heat-release and efficiency analyses, are used to enhance understanding of the in-cylinder processes that lead to the observed emissions reductions.

A slipstream of exhaust from a Caterpillar 3126B engine was diverted into a plasma-catalytic NOx control system in the space velocity range of 7,000 to 100,000 hr-1. The stream was first fed through a non-thermal plasma that was formed in a coaxial cylinder dielectric barrier discharge reactor. Plasma treated gas was then passed over a catalyst bed held at constant temperature in the range of 573 to 773 K. Catalysts examined consisted of γ-alumina, indium-doped γ-alumina, and silver-doped γ-alumina. Road and rated load conditions resulted in engine out NOx levels of 250 - 600 ppm. The effects of hydrocarbon level, catalyst temperature, and space velocity are discussed where propene and in one case ultra-low sulfur diesel fuel (late cycle injection) were the reducing agents used for NOx reduction. Results showed NOx reduction in the range of 25 - 97% depending on engine operating conditions and management of the catalyst and slipstream conditions.

Most of the history of systems engineering has been focused on processes for engineering a single complex system. However, most large enterprises design, manufacture, operate, sell, or support not one product but multiple product lines of related but varying systems. They seek to optimize time to market, costs of development and production, leverage of intellectual assets, best use of talented human resources, overall competitiveness, overall profitability and productivity. Optimizing globally across multiple product lines does not follow from treating each system family member as an independently engineered system or product. Traditional systems engineering principles can be generalized to apply to families. This article includes a multi-year case study of the actual use of a generic model-based systems engineering methodology for families, Systematica™, across the embedded electronic systems products of one of the world's largest manufacturers of heavy equipment.

The NOx reduction performance under lean conditions over γ-alumina was evaluated using a micro-reactor system and a non-thermal plasma-equipped bench test system. Various alumina samples were obtained from alumina manufacturers to assess commercial alumina materials. In addition, γ-alumina samples were synthesized at Caterpillar with a sol-gel technique in order to control alumina properties. The deNOx performances of the alumina samples were compared. The alumina samples were characterized with analytical techniques such as inductively coupled plasma (ICP) emission spectroscopy, temperature programmed desorption (TPD) and surface area measurements (BET) to understand physical and chemical properties. The information derived from these techniques was correlated with the NOx reduction performance to identify key parameters of γ-alumina for optimizing materials for lean-NOx and plasma assisted catalysis.

In recent years, Nearfield Acoustical Holography (NAH) has been used to identify stationary vehicle exterior noise sources. However that application has usually been limited to individual components. Since powertrain noise sources are hidden within the engine compartment, it is difficult to use NAH to identify those sources and the associated partial field that combine to create the complete exterior noise field of a motor vehicle. Integrated Nearfield Acoustical Holography (INAH) has been developed to address these concerns: it is described here. The procedure entails sensing the sources inside the engine compartment by using an array of reference microphones, and then calculating the associated partial radiation fields by using NAH. In the second part of this paper, the use of farfield arrays is considered. Several array techniques have previously been applied to identify noise sources on moving vehicles.

Engineering new technology and products challenges managers to balance design innovation and program risk. To do this, managers need methods to judge future results to avoid program and product disasters. Besides the traditional prediction tools of schedule, simulations and “iron tests”, process control standards (with measurements) can also be applied to the development programs to mitigate risks. This paper briefly discusses the theory and case history behind some new process control methods and standards currently in place at Caterpillar's Electrical & Electronics department. Process standards reviewed in this paper include process mapping, ISO9001, process controls, and process improvement models (e.g. SEI's CMMs.)