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Production NO sensors have a strong cross-sensitivity to ammonia which limits their use for closed-loop SCR control and diagnostics since increases in sensor output can be caused by either gas component. Recently, Ammonia/NO Ratio (ANR) perturbation methods have been proposed for determining the dominant component in the post-SCR exhaust as part of the overall SCR control strategy, but these methods or the issue of sensor cross-sensitivity have not been critically evaluated or studied in their own right. In this paper the dynamic sensor direct- and cross-sensitivities are estimated from experimental FTIR data (after compensating for the dynamics of the gas sampling system) and compared to nominal values provided by the manufacturer. The ANR perturbation method and the use of different input excitations are then discussed within an analytical framework, and applied to experimental data from a large diesel engine.

Countries around the world are expected to continue to adopt more stringent emissions standards for heavy-duty markets for both oxides of nitrogen (NOx) and greenhouse gases (GHG). While there is uncertainty about the timing and extent of these regulations, it is clear that significant reductions will be required to address urban air pollution and climate change concerns. The rate and pace of technology evolution and how it will affect the energy pathways for commercial transportation and industrial use are dependent on multiple variables such as national energy and environmental policies and public-private partnerships. Although it adds complexity, the engine system has great potential to evolve as it continues to be highly integrated into the super system for which it is producing power. This paper examines the potential opportunities and challenges for engine manufacturers to continue to be the supplier of power to vehicles and equipment of the future.

Plug flow reactors, simulating engine exhaust gas, are widely used in emissions control research to gain insight into the reaction mechanisms and engineering aspects that controls activity, selectivity, and durability of catalyst components. The choice of relevant hydrocarbon (HC) species is one of the most challenging factor in such laboratory studies, given the variety of compositions that can be encountered in different application scenarios. Furthermore, this challenge is amplified by the experimental difficulties related to introducing heavier and multi-component HCs and analyzing the reaction products.

This paper presents an experimental setup and an equivalent FEM simulation methodology to accurately predict the response of Engine Control Module (ECM) assembly mounted on a commercial vehicle subjected to road vibrations. Comprehensive vibration study is carried out. It involved Modal characteristics determination followed by random vibration characterization of the ECM assembly. A hammer impact experiment is first performed in lab to estimate the natural frequencies and mode shapes of ECM assembly. Mounting conditions in test specimen are kept similar to the actual mounting settings on vehicle. Natural frequencies and mode shapes predicted from free vibration experiment are compared with finite element (FE) based modal analysis. The importance of capturing the assembly stiffness more accurately by incorporating pre-stress effects like bolt-pretension and gravity, is emphasized.

Dynamometer (dyno) durability testing plays a significant role in reliability and durability assessment of commercial engines. Frequently, durability test procedures are based on warranty history and corresponding component failure modes. Evolution of engine designs, operating conditions, electronic control features, and diagnostic limits have created challenges to historical-based testing approaches. A physics-based methodology, known as Load Matrix, is described to counteract these challenges. The technique, developed by AVL, is based on damage factor models for subsystem and component failure modes (e.g. fatigue, wear, degradation, deposits) and knowledge of customer duty cycles. By correlating dyno test to field conditions in quantifiable terms, such as customer equivalent miles, more effective and efficient durability test suites and test procedures can be utilized. To this end, application of Load Matrix to a heavy-duty diesel engine is presented.

Automotive timing gear trains, transmission gearboxes, and wind turbine gearboxes are some of the application examples known to use interference-fit to attach the gear to the rotating shaft. This paper discusses the interference-fit joint design and the finite element analysis to demonstrate the distortion. The mechanism of tooth profile distortion due to the interference-fit assembly in gear trains is discussed by demonstrating the before and after assembly gear profile measurements. An algorithm to calculate the profile slope deviation change is presented. The effectiveness of the computational algorithm to predict the distortion is demonstrated by comparing with measurements. Finally, steps to mitigate the interference assembly effects are discussed.

This paper demonstrates the use of a system level model that includes torsional models of a Cummins diesel engine and an Allison transmission to study and improve system NVH behavior. The study is a case where the two suppliers of key powertrain components, Cummins Inc. and Allison Transmission Inc., have collaborated to solve an observed NVH problem for a vehicle customer. A common commercial tool, Siemens' AMESim, was used to develop the drivetrain torsional system model. This paper describes a method of modelling and calibration of baseline engine and transmission models to identify the source of vibration. Natural frequencies, modal shapes, and forced response were calculated for each vehicle drive gear ratio to study the torsional vibration. Several parametric studies such as damping, inertia, and stiffness were carried out to understand their impact on torsional vibration of the system.

Shafaat and Kenley in 2015 identified the opportunity to improve System Engineering Standards by incorporating the design principle of learning. The System Level Assessment (SLA) Methodology is an approach that fulfills this need by efficiently capturing the learnings of a team of subject matter experts in the early stages of product system design. By gathering expertise, design considerations are identified that when used with market and business requirements improve the overall quality of the product system. To evaluate the effectiveness of this approach, the methodology has been successfully applied over 400 times within each realm of the New Product Introduction process, including most recently to a Technology Development program (in the earliest stages of the design process) to assess the viability of various electrification technologies under consideration by an automotive Tier 1 supplier.