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Nearly a third of the fuel energy is wasted through the exhaust of a vehicle. An efficient waste heat recovery process will undoubtedly lead to improved fuel efficiency and reduced greenhouse gas (GHG) emissions. Currently, there are multiple waste heat recovery technologies that are being investigated in the auto industry. One innovative waste heat recovery approach uses Thermoacoustic Converter (TAC) technology. Thermoacoustics is the field of physics related to the interaction of acoustic waves (sonic power) with heat flows. As in a heat engine, the TAC produces electric power where a temperature differential exists, which can be generated with engine exhaust (hot side) and coolant (cold side). Essentially, the TAC converts exhaust waste heat into electricity in two steps: 1) the exhaust waste heat is converted to acoustic energy (mechanical) and 2) the acoustic energy is converted to electrical energy.

Diesel exhaust after treatment solutions using injection, such as urea-based SCR and lean NOx trap systems, effectively reduce the emission NOx level in various light vehicles, commercial vehicles, and industrial applications. The performance of the injector is crucial for successfully utilizing this type of technology, and a simulation tool plays an important role in the virtual design, that the performance of the injector is evaluated to reach the optimized design. The virtual test methodology using CFD to capture the fluid dynamics of the injector internal flow has been previously developed and validated for quantifying the dosing rate of the test injector. In this study, the capability of the virtual test methodology was extended to determine the spray angle of the test injector, and the effect of the manufacturing process on the injector internal nozzle flow characteristics was investigated using the enhanced virtual test methodology.

Fatigue, creep, oxidation, or their combinations have long been recognized as the principal failure mechanisms in many high-temperature applications such as exhaust manifolds and thermal regeneration units used in commercial vehicle aftertreatment systems. Depending on the specific materials, loading, and temperature levels, the role of each damage mechanism may change significantly, ranging from independent development to competing and combined creep-fatigue, fatigue-oxidation, creep-fatigue-oxidation. Several multiple failure mechanisms based material damage models have been developed, and products to resist these failure mechanisms have been designed and produced. However, one of the key challenges posed to design engineers is to find a way to accelerate the durability and reliability tests of auto exhaust in component and system levels and to validate the product design within development cycle to satisfy customer and market's requirements.

Durability and reliability performance is one of the most important concerns of a recently developed Thermal Regeneration Unit for Exhaust (T.R.U.E-Clean®) for exhaust emission control. Like other ground vehicle systems, the T.R.U.E-Clean® system experiences cyclic loadings due to road vibrations leading to fatigue failure over time. Creep and oxidation cause damage at high temperature conditions which further shortens the life of the system and makes fatigue life assessment even more complex. Great efforts have been made to develop the ability to accurately and quickly assess the durability/reliability of the system in the early development stage. However, reliable and validated simplified engineering methods with rigorous mathematical and physical bases are still urgently needed to accurately manage the margin of safety and decrease the cost, whereas iterative testing is expensive and time consuming.

This paper reviews the correlation concepts and tools available, with the emphasis on their historical origins, mathematical properties and applications. Two of the most commonly used statistical correlation indicators, i.e., modal assurance criterion (MAC) for structural deformation pattern identification/correlation and the coefficient of determination (R2) for data correlation are investigated. The mathematical structure of R2 is critically examined, and the physical meanings and their implications are discussed. Based on the insights gained from these analyses, a data scatter measure and a dependency measure are proposed. The applications of the measures for both linear and nonlinear data are also discussed. Finally, several worked examples in vehicle dynamics analysis and statistical data analyses are provided to demonstrate the effectiveness of these concepts.