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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 performance of five positive k-factor injector tips has been assessed in this work by analyzing a comprehensive set of injected mass, momentum, and spray measurements. Using high speed shadowgraphs of the injected diesel plumes, the sensitivities of measured vapor penetration and dispersion to injection pressure (100-250MPa) and ambient density (20-52 kg/m3) have been compared with the Naber-Siebers empirical spray model to gain understanding of second order effects of orifice diameter. Varying in size from 137 to 353μm, the orifice diameters and corresponding injector tips are appropriate for a relatively wide range of engine cylinder sizes (from 0.5 to 5L). In this regime, decreasing the orifice exit diameter was found to reduce spray penetration sensitivity to differential injection pressure. The cone angle and k-factored orifice exit diameter were found to be uncorrelated.

In this paper, residual stress distributions in rectangular bars due to rolling or burnishing at very high rolling or burnishing loads are investigated by roll burnishing experiments and three-dimensional finite element analyses using ABAQUS. First, roll burnishing experiments on rectangular bars at two roller burnishing loads are presented. The results indicate the higher burnishing load induces lower residual stresses and the higher burnishing load does not improve fatigue lives. Next, in the corresponding finite element analyses, the roller is modeled as rigid and the roller rolls on the flat surface of the bar with a low coefficient of friction. The bar material is modeled as an elastic-plastic strain hardening material with a nonlinear kinematic hardening rule for loading and unloading.

Structure-borne inputs to hybrid FEA/SEA models could have significant effects on the model prediction accuracy. The purpose of this work was to obtain the structure-borne noise (SBN) inputs using a simplified transfer path analysis (TPA) and identify the significance of the structure-borne and airborne contributions to the spectator sound power of an engine with enclosure for future modeling references. Force inputs to the enclosure from the engine were obtained and used as inputs to a hybrid engine enclosure model for sound prediction.

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.

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.

Numerical models are used in this study to investigate the oil flow and heat transfer in the piston gallery of a diesel engine. An experiment is set up to validate the numerical models. In the experiment a fixed, but adjustable steel plate is instrumented and pre-heated to a certain temperature. The oil is injected vertically upwards from an underneath injector and impinges on the bottom of the plate. The reduction of the plate temperature is recorded by the thermocouples pre-mounted in the plate. The numerical models are used to predict the temperature history at the thermocouple locations and validated with the experimental data. After the rig model validation, the numerical models are applied to evaluate the oil sloshing and heat transfer in the piston gallery. The piston motion is modeled by a dynamic mesh model, and the oil sloshing is modeled by the VOF (volume of fluid) multiphase model.

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.

The induction hardening process involves a complex interaction of electromagnetic heating, rapid cooling, metallurgical phase transformations, and mechanical behavior. Many factors including induction coil design, power, frequency, scanning velocity, workpiece geometry, material chemistry, and quench severity determine a process outcome. This paper demonstrates an effective application of a numerical analysis tool for understanding of induction hardening. First, an overview of the Caterpillar induction simulation tool is briefly discussed. Then, several important features of the model development are examined. Finally, two examples illustrating the use of the computer simulation tool for solving induction-hardening problems related to cracking and distortion are presented. These examples demonstrate the tool's ability to simulate changes in process parameters and latitude of modeling steel or cast iron.

Electron beam (EB) welding process is characterized by an extremely high power density that is capable of producing weld seams which are considerably deeper than width. Unlike other welding process, heat of EB welding is provided by the kinetic energy of electrons. This paper presents a computational model for the numerical prediction of microstructure and residual stress resulting from EB welding process. Energy input is modeled as a step function within the fusion zone. The predicted values from finite element simulation of the EB welding process agree well with the experimentally measured values. The present model is used to study an axial weld failure problem.

Time-averaged temperatures at critical locations on the piston of a direct-fuel injected, two-stroke, 388 cm3, research engine were measured using an infrared telemetry device. The piston temperatures were compared to data [7] of a carbureted version of the two-stroke engine, that was operated at comparable conditions. All temperatures were obtained at wide open throttle, and varying engine speeds (2000-4500 rpm, at 500 rpm intervals). The temperatures were measured in a configuration that allowed for axial heat flux to be determined through the piston. The heat flux was compared to carbureted data [8] obtained using measured piston temperatures as boundary conditions for a computer model, and solving for the heat flux. The direct-fuel-injected piston temperatures and heat fluxes were significantly higher than the carbureted piston. On the exhaust side of the piston, the direct-fuel injected piston temperatures ranged from 33-73 °C higher than the conventional carbureted piston.

This paper identifies potential performance benefits and computational costs of applying advanced multivariable control theory concepts to coordinate the control of a general multi-degree-of-freedom fuel system. The control variables are injection duration and pressure. The focus is on the design of a robust multi-input multi-output controller using H-infinity and mu synthesis methodology to coordinate the control of injection duration and pressure; reduce overshoots and system sensitivity to parameter variations caused by component aging. Model reduction techniques are used to reduce the order of the H-infinity controller to make it practically implementable. Computer simulation is used to test the robust performance of a generic engine and fuel system model controlled by the reduced order H-infinity controller and a traditional proportional plus integral controller.

A computational methodology has been developed for loss prediction in intake regions of internal combustion engines. The methodology consists of a hierarchy of four major tasks: (1) proper computational modeling of flow physics; (2) exact geometry and high quality and generation; (3) discretization schemes for low numerical viscosity; and (4) higher order turbulence modeling. Only when these four tasks are dealt with properly will a computational simulation yield consistently accurate results. This methodology, which is has been successfully tested and validated against benchmark quality data for a wide variety of complex 2-D and 3-D laminar and turbulent flow situations, is applied here to a loss prediction problem from industry. Total pressure losses in the intake region (inlet duct, manifold, plenum, ports, valves, and cylinder) of a Caterpillar diesel engine are predicted computationally and compared to experimental data.

A nonlinear, three-dimensional finite element analysis of the cylinder head gasket joint has been developed to allow accurate prediction of global and local joint behavior during engine operation. Nonlinear material properties and load cases that simulate full cycle engine operation are the analysis foundation. The three-dimensional, nonlinear, full-cycle simulation accurately predicts cylinder head gasket joint response to assembly, thermal, and cylinder pressure loading. Predictions correlate well with measured engine test data. Analysis results include local pressure distribution and global load splits. Insight into joint loading and an improved understanding of overall joint behavior provide the basis for informed design and development decisions.

Caterpillar uses heat treatment to enhance the properties of a significant number of parts. Traditional heat treat process optimization is both time consuming and expensive when done by empirical methods. This paper describes a computer simulation of the heat treatment process, developed by Caterpillar, based upon finite element analysis. This approach combines thermal, microstructural, and stress analysis to accurately model material transformation during quenching. Examples are presented to illustrate the program.

Boundary element analysis (BEA) is an effective computer simulation program for certain applications in design engineering. The BEA technique has been used extensively at Caterpillar for structural analysis of engine and vehicle components. The time savings and modeling ease of BEA are illustrated with specific examples of engine component models. These examples represent a variety of modeling techniques, and include comparisons with measured test data.