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The development of PM and NOx reduction system with the combination of DOC included DPF and SCR catalyst in addition to the AOC sub-assembly for NH3 slip protection is described. DPF regeneration strategy and manual regeneration functionality are introduced with using ITH, HCI device on the EUI based EGR, VGT 12.3L diesel engine at the CVS full dilution tunnel test bench. With this system, PM and NOx emission regulation for JPNL was satisfied and DPF regeneration process under steady state condition and transient condition (JE05 mode) were successfully fulfilled. Manual regeneration process was also confirmed and HCI control strategy was validated against the heat loss during transient regeneration mode. Presenter Seung-il Moon

Noise concerns regarding snowmobiles have increased in the recent past. Current standards, such as SAE J192 are used as guidelines for government agencies and manufacturers to regulate noise emissions for all manufactured snowmobiles. Unfortunately, the test standards available today produce results with variability that is much higher than desired. The most significant contributor to the variation in noise measurements is the test surface. The test surfaces can either be snow or grass and affects the measurement in two very distinct ways: sound propagation from the source to the receiver and the operational behavior of the snowmobile. Data is presented for a known sound pressure speaker source and different snowmobiles on various test days and test surfaces. Relationships are shown between the behavior of the sound propagation and track interaction to the ground with the pass-by noise measurements.

Hydrogen is widely considered a promising fuel for future transportation applications for both, internal combustion engines and fuel cells. Due to their advanced stage of development and immediate availability hydrogen combustion engines could act as a bridging technology towards a wide-spread hydrogen infrastructure. Although fuel cell vehicles are expected to surpass hydrogen combustion engine vehicles in terms of efficiency, the difference in efficiency might not be as significant as widely anticipated [1]. Hydrogen combustion engines have been shown capable of achieving efficiencies of up to 45 % [2]. One of the remaining challenges is the reduction of nitric oxide emissions while achieving peak engine efficiencies. This paper summarizes research work performed on a single-cylinder hydrogen direct injection engine at Argonne National Laboratory.

An operational scheme with fuel-lean and exhaust gas dilution in spark-ignited engines increases thermal efficiency and decreases NOx emission, while these operations inherently induce combustion instability and thus large cycle-to-cycle variation in engine. In order to stabilize combustion variations, the development of an advanced ignition system is becoming critical. To quantify the impact of spark-ignition discharge, ignitability tests were conducted in an optically accessible combustion vessel to characterize the flame kernel development of lean methane-air mixture with CO₂ simulating exhaust diluent. A shrouded fan was used to generate turbulence in the vicinity of J-gap spark plug and a Variable Output Ignition System (VOIS) capable of producing a varied set of spark discharge patterns was developed and used as an ignition source. The main feature of the VOIS is to vary the secondary current during glow discharge including naturally decaying and truncated with multiple strikes.

Pre-collision systems (PCS) use forward-looking sensors to detect the location and motion of vehicles ahead and provide a sequence of actions to help the driver either avoid striking the rear-end of another vehicle or mitigate the severity of the crash. The actions include driver alerts, amplification of driver braking as distance decreases (dynamic brake support, DBS), and automatic braking if the driver has not acted or has not acted sufficiently (crash imminent braking, CIB). Recent efforts by various organizations have sought to define PCS objective test procedures and test equipment in support of consumer information programs and potential certification. This paper presents results and insights from conducting DBS and CIB tests on two production vehicles sold in the US. Eleven scenarios are used to assess the systems' performance. The two systems' performance shows that commercial systems can be quite different.

The present experimental engine efficiency study explores the effects of intake pressure and temperature, and premixed and global equivalence ratios on gross thermal efficiency (GTE) using the reactivity controlled compression ignition (RCCI) combustion strategy. Experiments were conducted in a heavy-duty single-cylinder engine at constant net load (IMEPn) of 8.45 bar, 1300 rev/min engine speed, with 0% EGR, and a 50% mass fraction burned combustion phasing (CA50) of 0.5°CA ATDC. The engine was port fueled with E85 for the low reactivity fuel and direct injected with 3.5% 2-ethylhexyl nitrate (EHN) doped into 91 anti-knock index (AKI) gasoline for the high-reactivity fuel. The resulting reactivity of the enhanced fuel corresponds to an AKI of approximately 56 and a cetane number of approximately 28. The engine was operated with a wide range of intake pressures and temperatures, and the ratio of low- to high-reactivity fuel was adjusted to maintain a fixed speed-phasing-load condition.

High pressure diesel sprays were visualized under vaporizing and combusting conditions in a constant-volume combustion vessel. Near-simultaneous visualization of vapor and liquid phase fuel distribution were acquired using a hybrid shadowgraph/Mie-scattering imaging setup. This imaging technique used two pulsed LED's operating in an alternative manner to provide proper light sources for both shadowgraph and Mie scattering. In addition, combustion cases under the same ambient conditions were visualized through high-speed combustion luminosity measurement. Two single-hole diesel injectors with same nozzle diameters (100μm) but different k-factors (k0 and k1.5) were tested in this study. Detailed analysis based on spray penetration rate curves, rate of injection measurements, combustion indicators and 1D model comparison have been performed.

Since transient vehicle HVAC computational fluids (CFD) simulations take too long to solve in a production environment, the goal of this project is to automatically create a lumped-parameter flow network from a steady-state CFD that solves nearly instantaneously. The data mining algorithm k-means is implemented to automatically discover flow features and form the network (a reduced order model). The lumped-parameter network is implemented in the commercial thermal solver MuSES to then run as a fully transient simulation. Using this network a “localized heat transfer coefficient” is shown to be an improvement over existing techniques. Also, it was found that the use of the clustering created a new flow visualization technique. Finally, fixing clusters near equipment newly demonstrates a capability to track localized temperatures near specific objects (such as equipment in vehicles).

The incorporation of detailed chemistry models in internal combustion engine simulations is becoming mandatory as local, globally lean, low-temperature combustion strategies are setting the path towards a more efficient and environmentally sustainable use of energy resources in transportation. In this paper, we assessed the computational efficiency of a recently developed sparse analytical Jacobian chemistry solver, namely ‘SpeedCHEM’, that features both direct and Krylov-subspace solution methods for maximum efficiency for both small and large mechanism sizes. The code was coupled with a high-dimensional clustering algorithm for grouping homogeneous reactors into clusters with similar states and reactivities, to speed-up the chemical kinetics solution in multi-dimensional combustion simulations.

An experimental study has been conducted to provide insight into heat transfer to the piston of a light-duty single-cylinder research engine under Conventional Diesel (CDC), Homogeneous Charge Compression Ignition (HCCI), and Reactivity Controlled Compression Ignition (RCCI) combustion regimes. Two fast-response surface thermocouples embedded in the piston top measured transient temperature. A commercial wireless telemetry system was used to transmit thermocouple signals from the moving piston. A detailed comparison was made between the different combustion regimes at a range of engine speed and load conditions. The closed-cycle integrated and peak heat transfer rates were found to be lower for HCCI and RCCI when compared to CDC. Under HCCI operation, the peak heat transfer rate showed sensitivity to the 50% burn location.

The effects of imaging system resolution and laser sheet thickness on the measurement of the Batchelor scale were investigated in a single-cylinder optical engine. The Batchelor scale was determined by fitting a model spectrum to the dissipation spectrum that was obtained from fuel tracer planar laser-induced fluorescence (PLIF) images of the in-cylinder scalar field. The imaging system resolution was quantified by measuring the step-response function; the scanning knife edge technique was used to measure the 10-90% clip width of the laser sheet. In these experiments, the spatial resolution varied from a native resolution of 32.0 μm to 137.4 μm, and the laser sheet thickness ranged from 108 μm to 707 μm. Thus, the overall resolution of the imaging system was made to vary by approximately a factor of four in the in-plane dimension and a factor of six in the out-of-plane dimension.

Reactivity Controlled Compression Ignition (RCCI) is an engine combustion strategy that utilizes in-cylinder fuel blending to produce low NOx and PM emissions while maintaining high thermal efficiency. The current study investigates RCCI and conventional diesel combustion (CDC) operation in a light-duty multi-cylinder engine over transient operating conditions using a high-bandwidth, transient capable engine test cell. Transient RCCI and CDC combustion and emissions results are compared over an up-speed change from 1,000 to 2,000 rev/min. and a down-speed change from 2,000 to 1,000 rev/min. at a constant 2.0 bar BMEP load. The engine experiments consisted of in-cylinder fuel blending with port fuel-injection (PFI) of gasoline and early-cycle, direct-injection (DI) of ultra-low sulfur diesel (ULSD) for the RCCI tests and the same ULSD for the CDC tests.

The transient response of an engine with both High Pressure (HP) and Low Pressure (LP) EGR loops was compared by conducting step changes in EGR fraction at a constant engine speed and load. The HP EGR loop performance was shown to be closely linked to turbocharger performance, whereas the LP EGR loop was relatively independent of turbocharger performance and vice versa. The same experiment was repeated with the variable geometry turbine vanes completely open to reduce turbocharger action and achieve similar EGR rate changes with the HP and LP EGR loops. Under these conditions, the increased loop volume of the LP EGR loop prolonged the response of intake O2 concentration following the change in air-fuel ratio. The prolonged change of intake O2 concentration caused emissions to require more time to reach steady state as well. Strong coupling between the HP EGR loop and turbochargers was again observed using a hybrid EGR strategy.

Computer programs and models are playing an increasing role in simulating vehicle crashworthiness, dynamic, and fuel efficiency. To maximize the effectiveness of these models, the validity and predictive capabilities of these models need to be assessed quantitatively. For a successful implementation of Computer Aided Engineering (CAE) models as an integrated part of the current vehicle development process, it is necessary to develop objective validation metric that has the desirable metric properties to quantify the discrepancy between multiple tests and simulation results. However, most of the outputs of dynamic systems are multiple functional responses, such as time history series. This calls for the development of an objective metric that can evaluate the differences of the multiple time histories as well as the key features under uncertainty.

A copula-based approach for model bias characterization was previously proposed [18] aiming at improving prediction accuracy compared to other model characterization approaches such as regression and Gaussian Process. This paper proposes an adaptive copula-based approach for model bias identification to enhance the available methodology. The main idea is to use cluster analysis to preprocess data, then apply the copula-based approach using information from each cluster. The final prediction accumulates predictions obtained from each cluster. Two case studies will be used to demonstrate the superiority of the adaptive copula-based approach over its predecessor.

To date, model validation metric is prominently designed for non-dynamic model responses. Though metrics for dynamic responses are also available, they are specifically designed for the vehicle impact application and uncertainties are not considered in the metric. This paper proposes the validation metric for general dynamic system responses under uncertainty. The metric makes use of the popular U-pooling approach and extends it for dynamic responses. Furthermore, shape deviation metric was proposed to be included in the validation metric with the capability of considering multiple dynamic test data. One vehicle impact model is presented to demonstrate the proposed validation metric.

Simulation based design optimization has become the common practice in automotive product development. Increasing computer models are developed to simulate various dynamic systems. Before applying these models for product development, model validation needs to be conducted to assess their validity. In model validation, for the purpose of obtaining results successfully, it is vital to select or develop appropriate metrics for specific applications. For dynamic systems, one of the key obstacles of model validation is that most of the responses are functional, such as time history curves. This calls for the development of a metric that can evaluate the differences in terms of phase shift, magnitude and shape, which requires information from both time and frequency domain. And by representing time histories in frequency domain, more intuitive information can be obtained, such as magnitude-frequency and phase-frequency characteristics.

Design optimization methods are commonly used for weight reduction subjecting to multiple constraints in automotive industry. One of the major challenges remained is to deal with a large number of design variables for large-scale design optimization problems effectively. In this paper, a new approach based on fuzzy rough set is proposed to address this issue. The concept of rough set theory is to deal with redundant information and seek for a reduced design variable set. The proposed method first exploits fuzzy rough set to screen out the insignificant or redundant design variables with regard to the output functions, then uses the reduced design variable set for design optimization. A vehicle body structure is used to demonstrate the effectiveness of the proposed method and compare with a traditional weighted sensitivity based main effect approach.

One of the major challenges in multiobjective, multidisciplinary design optimization (MDO) is the long computational time required in evaluating the new designs' performances. To shorten the cycle time of product design, a data mining-based strategy is developed to improve the efficiency of heuristic optimization algorithms. Based on the historical information of the optimization process, clustering and classification techniques are employed to identify and eliminate the low quality and repetitive designs before operating the time-consuming design evaluations. The proposed method improves design performances within the same computation budget. Two case studies, one mathematical benchmark problem and one vehicle side impact design problem, are conducted as demonstration.

Ultrasonic fatigue tests (testing frequency around 20 kHz) have been conducted on four different cast aluminum alloys each with a distinct composition, heat treatment, and microstructure. Tests were performed in dry air, laboratory air and submerged in water. For some alloys, the ultrasonic fatigue lives were dramatically affected by the environment humidity. The effects of different factors like material composition, yield strength, secondary dendrite arm spacing and porosity were investigated; it was concluded that the material strength may be the key factor influencing the environmental humidity effect in ultrasonic fatigue testing. Further investigation on the effect of chemical composition, especially copper content, is needed.