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In order to ensure the safety of a structure, adequate strength for structural elements must be provided. Moreover, catastrophic deformations such as buckling must be prevented. Using the linear finite element method, deterministic buckling analysis is completed in two main steps. First, a static analysis is performed using an arbitrary ordinate applied loading pattern. Using the obtained element axial forces, the geometric stiffness of the structure is assembled. Second, an eigenvalue problem is performed between structure's elastic and geometric stiffness matrices, yielding the structure's critical buckling loads. However, these deterministic approaches do not consider uncertainty the structure's material and geometric properties. In this work, a new method for finite element based buckling analysis of a structure with uncertainty is developed. An imprecise probability formulation is used to quantify the uncertainty present in the mechanical characteristics of the structure.

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.

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.

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.

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.

Researchers desire to evaluate system reliability uniquely and efficiently. Despite its strong technical demand, little progress has been made on system reliability analysis in the last two decades. Up to now, bound methods for system reliability prediction have been dominant. For system reliability bounds, the second order bound method gives fairly accurate prediction for system reliability assuming that the probabilities of second-order joint events are accurately obtained. Two primary challenges in system reliability analysis are evaluation of the probabilities of second-order joint events and no unique system reliability for design optimization. Firstly, the greatest technical demand is found in an accurate and efficient method to numerically evaluate the probability of a second-order joint event.

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.

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.

Due to the low cost of the battery cells and excellent performance at ambient temperature, Lithium-ion (Li-ion) battery is a promising technology for propulsion applications. However, the performance of Li-ion batteries erodes drastically at extreme temperatures (above 65 °C or below 0 °C). Therefore, in order to maintain battery life and performance, it is crucial to keep the batteries within the temperature range where their operating characteristics are optimal. The need for expensive and complex thermal management systems has in fact kept the Li-ion technology from becoming the first choice for Hybrid Electric Vehicle (HEV) applications. In this paper, we propose a Phase Change Material (PCM) for the temperature control. Due to its high heat capacity, PCM absorbs the heat dissipated by the battery. As long as the heat emitted by the battery does not melt the PCM completely, the system is stable.

This paper deals with the dynamic characterization of an automotive shock absorber, a continuation of an earlier work [1]. The objective of this on-going research is to develop a testing and analysis methodology for obtaining dynamic properties of automotive shock absorbers for use in CAE-NVH low-to-mid frequency chassis models. First, the effects of temperature and nominal length on the stiffness and damping of the shock absorber are studied and their importance in the development of a standard test method discussed. The effects of different types of input excitation on the dynamic properties of the shock absorber are then examined. Stepped sine sweep excitation is currently used in industry to obtain shock absorber parameters along with their frequency and amplitude dependence. Sine-on-sine testing, which involves excitation using two different sine waves has been done in this study to understand the effects of the presence of multiple sine waves on the estimated dynamic properties.

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.

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 Inc. has developed a new diesel engine fuel system, powered by hydraulics and controlled electronically. This Hydraulic Electronic Unit Injector, (HEUI), requires no mechanical actuating or mechanical control devices, and offers many advantages over conventional fuel injection systems. Inherent features of the HEUI Fuel System include injection pressure control independent of engine load or speed, totally flexible injection timing, and full electronic control of injection parameters. Packaging the HEUI Fuel System on an engine is simple, as the injector is compact and available in a variety of configurations. The hydraulic actuating circuit is straightforward, using lubricating oil from the engine sump. Hydraulic lines may be internal to the engine or external. This paper describes the Caterpillar HEUI Fuel System, its operating features, performance advantages, and application to diesel engines.

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.

A variable displacement engine with the capability to vary stroke length and compression ratio independent of one another has been designed, prototyped, and successfully operated. Reasons for investigation of such an engine are the potential for improvement in fuel economy and/or performance. Literature has shown that engines with variable compression ratio can significantly decrease specific fuel consumption. Engines with variability in stroke length can maintain peak efficiency running conditions by adjusting power output through displacement change verses through the efficiency detriment of throttling. The project began with the synthesis of a planar 2-dimensional rigid body mechanism. Various synthesis techniques were employed and design took place with a collection of computer software. MATLAB code performed much of the synthesis, kinematic, and dynamic analysis.

The steering assembly is a part of an automotive suspension system that provides control and stability. It provides control of direction, stability, and control over placement of the car. Optimization of the vehicle in weight results in enhanced performance and low fuel consumption, more so for an all-terrain race car. Optimization in this paper loosely refers to weight reduction and achieving the optimum stiffness to weight ratio of each component. This research encompasses various aspects linked to conceptualizing, designing, analysing, optimizing, and finally manufacturing the steering sub-system. Analytical calculations for mechanical design were performed using data from various experiments and jigs. CAD was developed using SolidWorks, and various analyses were performed using Altair HyperWorks. Finite Element Analysis (FEA) was primarily used to build stress plots and locate weak spots aiding optimization.

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.

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.