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As greenhouse gas regulations continue to tighten, more opportunities to improve engine efficiency emerge, including exhaust gas heat recovery. Upon cold starts, engine exhaust gases downstream of the catalysts are redirected with a bypass valve into a heat exchanger, transferring its heat to the coolant to accelerate engine warm-up. This has several advantages, including reduced fuel consumption, as the engine’s efficiency improves with temperature. Furthermore, this accelerates readiness to defrost the windshield, improving both safety as well as comfort, with greater benefits in colder climates, particularly when combined with hybridization’s need for engine on-time solely for cabin heating. Such products have been in the market now for several years; however they are bulky, heavy and expensive, yielding opportunities for competitive alternatives.

A novel Spatially Optimized Diffusion Alloy (SODA) material has been developed and applied to exhaust systems, which are an aggressive environment subject to high temperatures and loads, as well as excessive corrosion. Traditional stainless steels disperse chromium homogeneously throughout the material, with varying amounts ranging from 10% to 20% dependent upon its grade (e.g. 409, 436, 439, 441, and 304). SODA steels, however, offer layered concentrations of chromium, enabling an increased amount along the outer surface for much needed corrosion resistance and aesthetics. This outer layer, typically about 70μm thick, exceeds 20% of chromium concentration locally, but is less than 3% in bulk, offering selective placement of the chromium to minimize its overall usage. Since this layer is metallurgically bonded, it cannot delaminate or separate from its core, enabling durable protection throughout manufacturing processes and full useful life.

The approaches of obtaining both fatigue strength distribution and fatigue life distribution for a given set of fatigue S-N data are reviewed in this paper. A new fatigue S-N curve transformation technique, which is based on the fundamental statistics definition and some reasonable assumptions, is specifically developed in this paper to transform a fatigue life distribution to a fatigue strength distribution. The procedures of applying the technique to multiple-stress level, two-stress level, and one-stress level fatigue S-N data are presented.

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.

A probabilistic distribution function roughly consists of two parts: the middle part and the tails. The fatigue life distribution at a stress/load level is often described with two-parameter lognormal or Weibull distribution functions, which are more suitable for modeling the mean (middle) behaviors. The domains of the conventional probabilistic distribution functions are often unbounded, either infinite small (0 for the two-parameter Weibull) or infinite large or both. For most materials in low- and medium-cycle fatigue regimes, the domains of fatigue lives are usually bounded, and the inclusion of the bounds in a probabilistic model is often critical in some applications, such as product validation and life management. In this paper, four- and five-parameter Weibull distribution functions for the probabilistic distributions with bounds are developed. Finally, the applications of these new models in fatigue data analysis and damage assessment are provided and discussed.

Life testing or test-to-failure method and binomial testing method are the two most commonly used methods in product validation and reliability demonstration. The two-parameter Weibull distribution function is often used in the life testing and almost exclusively used in the extended time testing, which can be considered as an accelerated testing method by appropriately extending the testing time but with significantly reduced testing samples. However, the fatigue data from a wide variety of sources indicate that the three-parameter Weibull distribution function with a threshold parameter at the left tail is more appropriate for fatigue life data with large sample sizes. The uncertainties introduced from the assumptions about the underlying probabilistic distribution would significantly affect the interpretation of the test data and the assessment of the performance of the accelerated binomial testing methods, therefore, the selection of a probabilistic model is critically important.

External Exhaust Gas Recirculation (EGR) has been used on diesel engines for decades and has also been used on gasoline engines in the past. It is recently reintroduced on gasoline engines to improve fuel economy at mid and high engine load conditions, where EGR can reduce throttling losses and fuel enrichment. Fuel enrichment causes fuel penalty and high soot particulates, as well as hydrocarbon (HC) emissions, all of which are limited by emissions regulations. Under stoichiometric conditions, gasoline engines can be operated at high EGR rates (> 20%), but more than diesel engines, its intake gas including external EGR needs extreme cooling (down to ~50°C) to gain the maximum fuel economy improvement. However, external EGR and its problems at low temperatures (fouling, corrosion & condensation) are well known.

Product validation and reliability demonstration require testing of limited samples and probabilistic analyses of the test data. The uncertainties introduced from the tests with limited sample sizes and the assumptions made about the underlying probabilistic distribution will significantly impact the results and the results interpretation. Therefore, understanding the nature of these uncertainties is critical to test method development, uncertainty reduction, data interpretation, and the effectiveness of the validation and reliability demonstration procedures. In this paper, these uncertainties are investigated with the focuses on the following two aspects: (1) fundamentals of the RxxCyy criterion used in both the life testing and the binomial testing methods, (2) issues and benefits of using the two-parameter Weibull probabilistic distribution function.

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.

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.

The Diesel engine combustion process results in harmful exhaust emissions, mainly composed of Particulate Matter (PM), Hydro Carbon (HC), Carbon monoxide (CO) and Nitrogen Oxides (NOx). Several technologies have been developed in the past decades to control these diesel emissions. One of the promising and well matured technology of reducing NOx is to implement Selective Catalytic Reduction (SCR) using ammonia (NH3) as the reducing agent. For an effective SCR system, the aqueous urea solutions should be fully decomposed into ammonia and it should be well distributed across the SCR. In the catalyst, all the ammonia is utilized for NOx reduction process. In the design stage, it is more viable to implement Computational Fluid Dynamics (CFD) for design iterations to determine an optimized SCR system based on SCR flow distribution. And in later stage, experimental test is required to predict the after-treatment system performance based on NOx reduction.

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.

With introductions of stringent diesel engine emission regulations, the DOC and DPF systems have become the mainstream technology to eliminate soot particles through diesel combustion under various operation conditions. Urea-based SCR has been the mainstream technical direction to reduce NOx emissions. For both technologies, low-temperature conditions or cold start conditions pose challenges to activate DOC or SCR emission-reduction performance. To address this issue, mini or full flow burner systems may be used to increase exhaust temperature to reach DOC light-off or SCR initiation temperature by combustion of diesel fuel. In essence, the burner systems incorporate a fuel injector, spray atomization, proper fuel / air mixing mechanisms, and combustion control as independent heat sources.

To meet EPA Tier IV large diesel engine emission targets, intensive development efforts are necessary to achieve NOx reduction and Particulate Matter (PM) reduction targets [1]. With respect to NOx reduction, liquid urea is typically used as the reagent to react with NOx via SCR catalyst [2]. Regarding to PM reduction, additional heat is required to raise exhaust temperature to reach DPF active / passive regeneration performance window [3]. Typically the heat can be generated by external diesel burners which allow diesel liquid droplets to react directly with oxygen in the exhaust gas [4]. Alternatively the heat can be generated by catalytic burners which enable diesel vapor to react with oxygen via DOC catalyst mostly through surface reactions [5].

In order to satisfy tightening global emissions regulations, diesel truck manufacturers are striving to meet increasingly stringent Oxides of Nitrogen (NOx) reduction standards. The majority of heavy duty diesel trucks have integrated urea SCR NOx abatement strategies. To this end, aftertreatment systems need to be properly engineered to achieve high conversion efficiencies. A EuroV intent urea SCR system is evaluated and failed to meet NOx conversion targets with severe urea deposit formation. Systematic enhancements of the design have been performed to enable it to meet targets, including emission reduction efficiency via improved reagent mixing, evaporation, distribution, back pressure, and removing of urea deposits. Multiple urea mixers, injector mounting positions and various system layouts are developed and evaluated, including both CFD analysis and full scale laboratory tests.

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.