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Abstract A methodology for the development of catalyst models is presented. Also, a methodology of the implementation of such models into a modular simulation tool, which simulates the units in succession, is presented. A case study is presented illustrating how suitable models can be found and used for simulations. Such simulations illustrate the behavior of the individual units and the overall system. It is shown how, by simulating the units in succession, the entire after treatment system can be tested and optimized, because the integration makes it possible to observe the effect of the modules on one another.

Abstract Dynamic stresses exist in parts of a catalyst muffler caused by the vibration of a moving vehicle, and it is important to clarify and predict the vibration response properties for preventing fatigue failures. Assuming a vibration isolating installation in the vehicle frame, the vibration transmissibility and local dynamic stress of the catalyst muffler were examined through a vibration machine. Based on the measured data and by systematically taking vibration theories into consideration, a new prediction method of the vibration modes and parameters was proposed that takes account of vibration isolating and damping. A lumped vibration model with the six-element and one mass point was set up, and the vibration response parameters were analyzed accurately from equations of motion. In the vibration test, resonance peaks from the hanging bracket, rubber bush, and muffler parts were confirmed in three excitation drives, and local stress peaks were coordinate with them as well.

Abstract Effects of process parameters such as rotational speed, feed rate, and drill diameters on the drilling behavior of basalt-epoxy-based composites including 2.5 wt.% Al2O3 particles manufactured by mixing and compression method were investigated by Taguchi’s technique. The experimental results showed that the burr height (BH) increased considerably almost linearly with an increase in the drill diameter, while it remained stable with speed and decreased the feed rate slightly. There was an excellent correlation between the control factors and responses, BH of basalt fiber-reinforced plastics (BFRPs) through the Taguchi approach. The model had an adjusted R2 value of 96.3%. Generally, the inclusion of Al2O3 particles in BFRP increased its cutting force properties. Optimized drilling conditions for the input variables to produce the lowest response of the BH for composites were rotational speed of 560 rpm and feed rate of 0.28 mm/rev and a drill diameter of 4.5 mm.

Abstract The high quality of the machined parts in a short time is a research challenge for enhancing these parts’ operating performance. Optimizing the machining operations and adequately selecting the cutting parameters can solve this challenge. Thus, this work proposes an optimization approach of the machining process parameters of epoxy hybrid nanocomposites reinforced by multiwall carbon nanotubes (MWCNTs) and aluminum oxide (Al2O3). Cutting speed (V), feed rate (F), insert nose radius, and depth of cut (D) were the machining parameters. The roundness error and surface roughness (Ra) were selected as process response control parameters. The optimization techniques such as response surface method (RSM) and grey relation analysis (GRA) with the variance of analysis (ANOVA) were involved. Forty experimental runs were performed. The RSM optimization and ANOVA results showed that the insert nose radius and F are the most significant factors that affect the Ra.

Abstract The selective catalytic reduction (SCR) of nitrogen oxides seems to be the most promising technique to meet prospective emission regulations of diesel-driven commercial vehicles. In the case of developing cost-effective catalytic converters with comparably high activity, selectivity, and resistance against aging, ion-exchanged zeolites play a major role. This study presents, firstly, a brief literature review and subsequently a discussion of an extensive conversion analysis of exemplary Cu/ and Fe/zeolites, as well as a homogeneous admixture of both. The aging stages of SCR catalysts deserve particular attention in this study. In addition, the aging condition of the diesel oxidation catalyst (DOC) was analyzed, which influences the nitrogen dioxide (NO2) formation, because the NO2/nitrogen oxides (NOx) ratio upstream from the SCR converter could be identified as a key factor for low temperature NOx conversion.

Abstract Powertrain simulations and catalyst studies showed the efficiency credits and feasibility of onboard reforming as a way to recover waste heat from heavy duty vehicles (HDVs) fueled by natural gas (NG). Onboard reforming involves 1) injecting NG into the exhaust gas recycle (EGR) loop of the HDV, 2) reforming NG on a catalyst in the EGR loop to hydrogen and carbon monoxide, and 3) combusting the reformed fuel in the engine. The reformed fuel has increased heating value (4-10% higher LHV) and flame speed over NG, allowing stable flames in spark ignition (SI) engines at EGR levels up to 25-30%. A sulfur-tolerant reforming catalyst was shown to reform a significant amount of NG (15-30% conversion) using amounts of precious metal near the current practice for HDV emissions control (10 g rhodium). Engine simulations showed that the high EGR levels enabled by onboard reforming are used most effectively to control engine load instead of waste-gating or throttling.

Abstract Finite Element Analysis (FEA) of metal cutting is largely the domain of research organizations. Despite significant advances towards accurately modelling metal machining processes, industrial adoption of these advances has been limited. Academic studies, which mainly focused on orthogonal cutting, fail to address this discrepancy. This paper bridges the gap between simplistic orthogonal cutting models and the complex components typical in the manufacturing sector. This paper outlines how to utilize results from orthogonal cutting simulations to predict industrially relevant performance measures efficiently. In this approach, using 2D FEA cutting models a range of feed, speed and rake angles are simulated. Cutting force coefficients are then fit to the predicted cutting forces. Using these coefficients, forces for 3D cutting geometries are calculated.

Abstract In an automated machining process, monitoring the conditions of the tool is essential for deciding to replace or repair the tool without any manual intervention. Intelligent models built with sensor information and machine learning techniques are predicting the condition of the tool with good accuracy. In this study, statistical models are developed to identify the conditions of the abrasive grinding wheel using the Acoustic Emission (AE) signature acquired during the surface grinding operation. Abrasive grinding wheel conditions are identified using the abrasive wheel wear plot established by conducting experiments. The piezoelectric sensor is used to capture the AE from the grinding process, and statistical features of the abrasive wheel conditions are extracted in time and wavelet domains of the signature. Machine learning algorithms, namely, Classification and Regression Trees (CART) and Support Vector Classifiers (SVC), are used to build statistical models.

Abstract Catalytic converters, a primary component in most automotive emissions control systems, do not function well until they are heated substantially above ambient temperature. As the primary energy for catalyst heating comes from engine exhaust gases, plug-in hybrid electric vehicles (PHEVs) that have the potential for short and infrequent use of their onboard engine may have limited energy available for catalytic converter heating. This article presents a comparison of multiple hybrid supervisory control strategies to determine the ability to avoid engine cold starts during a blended charge-depleting propulsion mode. Full vehicle and catalytic converter simulations are performed in parallel with engine dynamometer testing in order to examine catalyst temperature variations during the course of the US06 City drive cycle. Emissions and energy consumption (E&EC) calculations are also performed to determine the effective number of engine starts during the drive cycle.

Abstract Under contract to the EPA, Eastern Research Group analyzed light-duty vehicle OBD monitor readiness and diagnostic trouble codes (DTCs) using inspection and maintenance (I/M) data from four states. Results from roadside pullover emissions and OBD tests were also compared with same-vehicle I/M OBD results from one of the states. Analysis focused on the evaporative emissions control (evap) system, the catalytic converter (catalyst), the exhaust gas recirculation (EGR) system and the oxygen sensor and oxygen sensor heater (O2 system). Evap and catalyst monitors had similar overall readiness rates (90% to 95%), while the EGR and O2 systems had higher readiness rates (95% to 98%). Approximately 0.7% to 2.5% of inspection cycles with a “ready” evap monitor had at least one stored evap DTC, but DTC rates were under 1% for the catalyst and EGR systems, and under 1.1% for the O2 system, in the states with enforced OBD programs.

Abstract Regulations limiting GreenHouse Gases (GHG) from Heavy-Duty (HD) commercial vehicles in the United States (US) and European Union will phase in between the 2024 and 2030 model years. These mandates require efficiency improvements at both the engine and vehicle levels, with the most stringent reductions required in the heaviest vehicles used for long-haul applications. At the same time, a 90% reduction in oxides of nitrogen (NOx) will be required as part of new regulations from the California Air Resources Board. Any technologies applied to improve engine efficiency must therefore not come at the expense of increased NOx emissions. Research into advanced engine architectures and components has identified improved turbomachine efficiency as one of the largest potential contributors to engine efficiency improvement. However this comes at the cost of a reduced capability to drive high-pressure Exhaust Gas Recirculation (EGR).

Abstract Reactivity-controlled compression ignition (RCCI) is a dual fuel low temperature combustion (LTC) strategy which results in a wider operating load range, near-zero oxides of nitrogen (NOx) and particulate matter (PM) emissions, and higher thermal efficiency. One of the major shortcomings in RCCI is a higher unburned hydrocarbon (HC) and carbon monoxide (CO) emissions. Unlike conventional combustion, aftertreatment control of HC and CO emissions is difficult to achieve in RCCI owing to lower exhaust gas temperatures. In conventional RCCI, an early direct injection (DI) of low volatile diesel fuel into the premixed gasoline-air mixture in the combustion chamber results in charge stratification and fuel spray wall wetting leading to higher HC and CO emissions. To address this limitation, a homogeneous charge reactivity-controlled compression ignition (HCRCCI) strategy is proposed in the present work, wherein the DI of diesel fuel is eliminated.

Abstract In order to meet the worldwide increasingly stringent particulate matter (PM) and particulate number (PN) emission limits, the diesel particulate filter (DPF) is widely used today and has been considered to be an indispensable feature of modern diesel engines. To estimate the soot loading amount in the DPF accurately and in real-time is a key function of realizing systematic and efficient applications of diesel engines, as starting the thermal regeneration of DPF too early or too late will lead to either fuel economy penalty or system reliability issues. In this work, an open-loop and on-line approach to estimating the DPF soot loading on the basis of soot mass balance is developed and experimentally investigated, through establishing and combining prediction models of the NOx and soot emissions out of the engine and a model of the catalytic soot oxidation characteristics of passive regeneration in the DPF.

Abstract Cycle-by-cycle combustion prediction in real time during engine operation can serve as a vital input for operating at optimal performance conditions and for emission control. In this work, a real-time capable physics-based combustion model has been proposed for the prediction of the heat release rate in a direct injection diesel engine. The model extends the approaches proposed earlier in the literature by considering spray dynamics such as spray penetration and Sauter mean diameter in order to calculate the mass of evaporated fuel from the spray. Wall impingement of the liquid spray is predicted by considering the liquid length based on the prevailing in-cylinder conditions. These effects are considered even after the hydraulic end of injection till the last droplet of fuel impinges on the combustion chamber wall. The fuel evaporated from the wall film and its contribution to the kinetic energy of the charge are also considered.

Abstract This experimental study aims to evaluate the engine performance and emissions when exhaust gas recirculation (EGR) and e-boosting are used in a gasoline compression ignition (GCI) engine operating at 2000 rpm and 800-900 kPa indicated mean effective pressure (IMEP) conditions. In an automotive size common-rail diesel engine architecture, a partially premixed charge-based GCI combustion was realized implementing triple injections with a split ratio of 50%, 10%, and 40% and injection timings of 170, 40, and 9-6 crank angle degrees (°CA) before top dead center (bTDC). The previous tests performed in the same engine suggested this injection strategy could achieve further nitrogen oxides (NOx) reduction if EGR is utilized with the help of intake air boosting to compensate for the loss in power output and engine efficiency. In the present study, the GCI engine is set up with a conventional EGR system and a supercharger driven by an electric motor (or an e-booster).

Abstract Combining ball milling with stir casting in the synthesis of nanocomposites is found effective in increasing the strength and ductility of the nanocomposites. In the first step, the nanoparticles used as reinforcement are generated by milling a mixture of aluminum (Al) and manganese dioxide (MnO2) powders. A mixture of Al and MnO2 powders are mixed in the ratio of 1:2.4 by weight and milled at 300 rpm in a high-energy planetary ball mill for different durations of 120 min, 240 min, and 360 min to generate nano-sized alumina (Al2O3) particles. It is supposed that the powders have two different roles during milling, firstly, to generate nano-sized Al2O3 by oxidation at the high-energy impact points due to collision between Al and MnO2 particles, and secondly, to keep nano-sized Al2O3 particles physically separate by the presence of coarser particles.

Abstract The efficiency of the traditional machining process becomes limited because of the mechanical properties and complexity of the geometric shape of the processed materials. This difficulty is resolved through the nonconventional machining process. Electric Discharge Machining (EDM) process is one of the popular nonconventional machining processes among all nonconventional machining processes for processing such materials. The main objective of the present research work is to evaluate the effect of percentage weight fraction of reinforcement and process parameters on machining responses during EDM of aluminum (Al) 7075-reinforced boron carbide (B4C) and graphite metal matrix composite (MMC) and optimization of the result.

Abstract The impact of Laser Beam Machining (LBM) process parameters on Surface Roughness (SR) and kerf width during machining is investigated in this work. Stainless Steel is a material that is resistant to corrosion. LBM is a nontraditional machining method in which material is removed by melting and vaporizing metal when a laser beam collides with the metal surface. There are numerous process variables that influence the quality of the LBM-cut machined surface. However, the most essential factors are laser power, cutting speed, assist gas pressure, nozzle distance, focus length, pulse frequency, and pulse width. SR, Material Removal Rate (MRR), and kerf width and heat affected zone are significant performance indicators in LBM. The influence of LBM process parameters on SR and kerf width while machining stainless steel material is investigated in this study.

Abstract This work presents an experimental analysis of the bulk content characterization of 2024 T351 Aluminum alloy under cyclic loadings used for precision applications such as balancing, optical, and laser instruments. Test samples with various machining directions (longitudinal and orthogonal) are formed using a CNC milling machine. Inelastic and plastic deformations in the nanoscale are the investigated characteristics of interest; hence, the fabric’s time constant at a fixed quarter-hour span. Samples with specific geometry are subjected to a tensile stress range of 10-150 N/mm2 provided by an electromagnetic test device. It should be said that all types of deformations considered were measured with and without loading using interferometers and capacitive sensors. Experiments are performed under constant temperature-stable housing whereas experimental measurements are recorded within the residual strain range of 10 microns.

Abstract Upcoming engine generations are characterized by both a general trend of increased specific-power and higher efficiency. This leads to increased thermal loads, compromising reliability, and simultaneously to a limited amount of heat under ordinary engine use. Heat is a valuable resource in providing passenger comfort and emission control. For these reasons the subject of engine thermal management is receiving increasing attention. This work presents a comprehensive study of the complete engine thermal behavior at relevant running conditions: rated-power, peak-torque and ordinary use. The work is further extended to the engine warm-up period. The result is a high-resolution complete engine thermal model, capable of simultaneously reporting the local temperature of any engine part, and the global engine heat balance at any engine load.