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This paper describes the joint development by Tenneco and Pi Shurlok of a complete diesel engine aftertreatment system for controlling particulate matter emissions. The system consists of a DOC, DPF, sensors, controller and an exhaust fuel injection system to allow active DPF regeneration. The mechanical components were designed for flow uniformity, low backpressure and component durability. The overall package is intended as a complete PM control system solution for OEMs, which does not require any significant additions to the OEM's engine control strategies and minimizes integration complexity. Thus, to make it easier to adapt to different engine platforms, ranging from small off-road vehicle engines to large locomotive engines, model-based control algorithms were developed in preference to map-based controls.

The statistical analysis of vehicle crash accident data is generally problematic. Data from commonly used sources is almost never without error and complete. Consequently, many analyses are contaminated with modeling and system identification errors. In some cases the effect of influential factors such as crash severity (the most significant component being speed) driver behavior prior to the crash, etc. on vehicle and occupant outcome is not adequately addressed. The speed that the vehicle is traveling at the initiation of a crash is a significant contributor to occupant risk. Not incorporating it may make an accident analysis irrelevant; however, despite its importance this information is not included in many of the commonly used crash data bases, such as the Fatality Analysis Reporting System (FARS). Missing speed information can result in potential errors propagating throughout the analysis, unless a method is developed to account for the missing information.

Superior NVH performance is a key focus in the development of new powertrains. In recent years, computer simulations have gained an increasing role in the design, development, and optimization of powertrain NVH at component and system levels. This paper presents the results of a study carried out on a 4-cylinder in-line spark-ignition engine with focus on growl noise. Growl is a low frequency noise (300-700 Hz) which is primarily perceived at moderate engine speeds (2000-3000 rpm) and light to moderate throttle tip-ins. For this purpose, a coupled and fully flexible multi-body dynamics model of the powertrain was developed. Structural components were reduced using component mode synthesis and used to determine dynamics loads at various engine speeds and loading conditions. A comparative NVH assessment of various crankshaft designs, engine configurations, and in- cylinder gas pressures was carried out.

Road vibrations cause fatigue failures in vehicle components and systems. Therefore, reliable and accurate damage and life assessment is crucial to the durability and reliability performances of vehicles, especially at early design stages. However, durability and reliability assessment is difficult not only because of the unknown underlying damage mechanisms, such as crack initiation and crack growth, but also due to the large uncertainties introduced by many factors during operation. How to effectively and accurately assess the damage status and quantitatively measure the uncertainties in a damage evolution process is an important but still unsolved task in engineering probabilistic analysis. In this paper, a new procedure is developed to assess the durability and reliability performance, and characterize the uncertainties of damage evolution of components under constant amplitude loadings.

In this paper a design methodology for automotive heat exchangers has been applied which brings robustness into the design process and helps to optimize the design goals: as to maintain an optimal coolant temperature and to limit the vehicle underhood air temperature within a tolerable limit. The most influential design factors for the heat exchangers which affect the goals have been identified with that process. The paper summarizes the optimization steps necessary to meet the optimal functional goals for the vehicle as mentioned above. Taguchi's [1] Design for Six Sigma (DFSS) methods have been employed to conduct this analysis in a robust way.

In this paper, thermal models are developed based on experimental test data, and the physics of thermal systems. If experimental data is available, the data can be fitted to mathematical models that represent the system response to changes in its input parameters. Therefore, empirical models which are based on test data are developed. The concept of time constant is presented and applied to development of transient models. Mathematical models for component temperature changes during transient vehicle driving conditions are also presented. Mathematical models for climate control system warm up and cool-down are also discussed. The results show the significance of adopting this concept in analysis of vehicle test data, and in development of analytical models. The developed models can be applied to simulate the system or component response to variety of changes in input parameters. As a result, significant testing and simulation time can be saved during the vehicle development process.

Taguchi method is a technology to prevent quality problems at early stages of product development and product design. Parameter design method is an important part in Taguchi method which selects the best control factor level combination for the optimization of the robustness of product function against noise factors. The air induction system (AIS) provides clean air to the engine for combustion. The noise radiated from the inlet of the AIS can be of significant importance in reducing vehicle interior noise and tuning the interior sound quality. The porous duct has been introduced into the AIS to reduce the snorkel noise. It helps with both the system layout and isolation by reducing transmitted vibration. A CAE simulation procedure has been developed and validated to predict the snorkel noise of the porous ducted AIS. In this paper, Taguchi's parameter design method was utilized to optimize a porous duct design in an AIS to achieve the best snorkel noise performance.

Active regeneration systems for cleaning diesel exhaust can operate at extremely high temperatures up to 1000°C. The extremely high temperatures create a unique challenge for the design of regeneration structural components near their melting temperatures. In this paper, the preparation of the sheet specimens and the test set-up based on induction heating for sheet specimens are first presented. Tensile test data at room temperature, 500, 700, 900 and 1100°C are then presented. The yield strength and tensile strength were observed to decrease with decreasing strain rate in tests conducted at 900 and 1100°C but no strain rate dependence was observed in the elastic properties for tests conducted below 900°C. The stress-life relations for under cyclic loading at 700 and 1100°C with and without hold time are then investigated. The fatigue test data show that the hold time at the maximum stress strongly affects the stress-life relation at high temperatures.

Accurate numerical prediction of an engine thermal map at a wide range of engine operating conditions can help tune engine performance parameters at an early development stage. This study documents the correlation of an engine thermal simulation using the conjugate heat transfer (CHT) methodology with thermocouple data from an engine operating in a dynamometer and a vehicle drive cell. Three different operating conditions are matched with the simulation data. Temperatures predicted by simulation at specific sections, both at the intake and the exhaust sides of the engine are compared with the measured temperatures in the same location on the operating engine.

Durability/reliability design of products, such as auto exhaust systems, is essentially based on the observation of test data and the accurate interpretation of these data. Therefore, test planning and related data analysis are critical to successful engineering designs. To facilitate engineering applications, testing and data analysis methods have been standardized over the last decades by several standard bodies such as the American Society for Testing and Materials (ASTM). However, over the last few years, several effective testing and data analysis methods have been developed, and the existing standard procedures need to be updated to incorporate the new observations, knowledge, and consensus. In this paper, the common practices and the standard test planning and data analysis procedures are reviewed first. Subsequently, the recent development in accelerated testing, equilibrium based data fitting, design curve construction, and Bayesian statistical data analysis is presented.

Catalysts and filters continue to be applied widely to meet particulate matter regulations across new and retrofit diesel engines. Soot management of the filter continues to be enhanced, including regeneration methodologies. Concerns regarding in-cylinder post-injection of fuel for active regeneration increases interests in directly injecting this fuel into the exhaust. Performance of secondary fuel injection layouts is discussed, and sensitivities on thermal uniformity are measured and analyzed, providing insight to packaging challenges and methods to characterize and improve application designs. Influences of end cone geometries, mixers, and injector mounting positions are quantified via thermal distribution at each substrate's outlet. A flow laboratory is applied for steady state characterization, repeated on an engine dynamometer, which also provides transient results across the NRTC.

Strength and fatigue life prediction is very difficult for stamped structural steel parts because the manufacturing process alters the localized material properties. Traditional tensile tests cannot be used to obtain material properties due to size limitations. Because of this, FEA predictions are most often “directional” at best. In this paper an improved prediction methodology is suggested. With a material library developed from standard homogenous test specimens, or even textbook material property tables, localized strength and plastic strain numbers can be inferred from localized hardness tests(1). The new method, using standard ABAQUS static analysis (not commercial fatigue analysis software with many unknowns), is shown to be very accurate. This paper compares the new process FEA strength and fatigue life predictions to laboratory test results using statistical confidence intervals.

This study presents a probabilistic life (failure) and damage assessment approach for components under general fatigue loadings, including constant amplitude loading, step-stress loading, and variable amplitude loading. The approach consists of two parts: (1) an empirical probabilistic distribution obtained by fitting the fatigue failure data at various stress range levels, and (2) an inverse technique, which transforms the probabilistic life distribution to the probabilistic damage distribution at any applied cycle. With this approach, closed-form solutions of damage as function of the applied cycle can be obtained for constant amplitude loading. Under step-stress and variable amplitude loadings, the damage distribution at any cycle can be calculated based on the accumulative damage model in a cycle-by-cycle manner. For Gaussian-type random loading, a cycle-by-cycle equivalent, but a much simpler closed-form solution can be derived.

Vehicle exhaust components and systems under fatigue loading often show multiple failure modes, which should be treated, at least theoretically, with rigorous advanced bi-modal and multi-modal statistical theories and approaches. These advanced methods are usually applied to mission-critical engineering applications such as nuclear and aerospace, in which large amounts of test data are often available. In the automotive industry, however, the sample size adopted in the product validation is usually small, thus the bi-modal and multi-modal phenomena cannot be distinguished with certainty.

In the Big Data era, the capability in statistical and probabilistic data characterization, data pattern identification, data modeling and analysis is critical to understand the data, to find the trends in the data, and to make better use of the data. In this paper the fundamental probability concepts and several commonly used probabilistic distribution functions, such as the Weibull for spectrum events and the Pareto for extreme/rare events, are described first. An event quadrant is subsequently established based on the commonality/rarity and impact/effect of the probabilistic events. Level of measurement, which is the key for quantitative measurement of the data, is also discussed based on the framework of probability. The damage density function, which is a measure of the relative damage contribution of each constituent is proposed. The new measure demonstrates its capability in distinguishing between the extreme/rare events and the spectrum events.

It is challenging to perform probabilistic analysis and design of large-scale structures because probabilistic analysis requires repeated finite element analyses of large models and each analysis is expensive. This paper presents a methodology for probabilistic analysis and reliability based design optimization of large scale structures that consists of two re-analysis methods; one for estimating the deterministic vibratory response and another for estimating the probability of the response exceeding a certain level. The deterministic re-analysis method can analyze efficiently large-scale finite element models consisting of tens or hundreds of thousand degrees of freedom and large numbers of design variables that vary in a wide range. The probabilistic re-analysis method calculates very efficiently the system reliability for many probability distributions of the design variables by performing a single Monte Carlo simulation.

An approach for Probabilistic Reanalysis (PRA) of a system is presented. PRA calculates very efficiently the system reliability or the average value of an attribute of a design for many probability distributions of the input variables, by performing a single Monte Carlo simulation. In addition, PRA calculates the sensitivity derivatives of the reliability to the parameters of the probability distributions. The approach is useful for analysis problems where reliability bounds need to be calculated because the probability distribution of the input variables is uncertain or for design problems where the design variables are random. The accuracy and efficiency of PRA is demonstrated on vibration analysis of a car and on system reliability-based optimization (RBDO) of an internal combustion engine.

In this study a dynamic simulation was conducted on a forged steel crankshaft from a single cylinder four stroke engine. Finite element analysis was performed to obtain the variation of the stress magnitude at critical locations. The dynamic analysis resulted in the development of the load spectrum applied to the crankpin bearing. This load was then applied to the FE model and boundary conditions were applied according to the engine mounting conditions. Results obtained from the aforementioned analysis were then used in optimization of the forged steel crankshaft. Geometry, material, and manufacturing processes were optimized using different geometric constraints, manufacturing feasibility, and cost. The first step in the optimization process was weight reduction of the component considering dynamic loading. This required the stress range under dynamic loading not to exceed the magnitude of the stress range in the original crankshaft.

During metal forming processes such as rolling and forging, deformable manganese sulfide (MnS) inclusions become elongated. Such elongated MnS inclusions can have considerable adverse effects on mechanical properties, if the inclusions are not aligned with the loading direction. The objectives of this study were to evaluate and compare fatigue, monotonic tensile and CVN impact behavior of SAE 4140 steel with high (0.077% S), low (0.012% S) and ultra low (0.004% S) sulfur contents at two hardness levels (40 HRC and 50 HRC). The longitudinally oriented samples at 40 HRC, where MnS inclusions were oriented along the loading direction, did not exhibit any significant sensitivity of tensile or fatigue properties to the sulfur content. For the transversely oriented MnS inclusions, however, the monotonic tensile test results indicate very low ductility of the high sulfur material at both hardness levels, where specimens failed shortly after yielding.

This paper discusses the effects of changes in specimen geometry, stress gradient, and residual stresses on fully-reversed constant amplitude uniaxial fatigue behavior of a medium carbon steel. Axial fatigue tests were performed on both flat and round specimens, while four-point rotating bending tests were performed only on round specimens. All the tests were performed using shot peened and unpeened flat and round samples, to investigate the effects of compressive residual stresses on fatigue behavior. The specimens in the rotating bending tests experienced longer life for a given stress amplitude than in the axial test. Shot-peening was found to be beneficial in the long life region, while in short life tests the shot-peened samples experienced a shorter life than the unpeened samples under both axial and bending test conditions.