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Self-piercing rivets (SPR) are efficient and economical joining methods used in the manufacturing of lightweight automotive bodies. The finite element method (FEM) is a potentially effective way to assess the joining process of SPRs. However, uncertain parameters could lead to significant mismatches between the FEM predictions and physical tests. Thus, a sensitivity study on critical model parameters is important to guide the high-fidelity modeling of the SPR insertion process. In this paper, an axisymmetric FEM model is constructed to simulate the insertion process of the SPR using LS-DYNA/explicit. Then, several surrogate models are evaluated and trained using machine learning methods to represent the relations between selected inputs (e.g., material properties, interfacial frictions, and clamping force) and outputs (cross-section dimensions).

Improvement of road safety is becoming a key task for sustainable mobility in China. A joint study was conducted by China Automotive Technology and Research Center (CATARC) and Ford Motor Company to identify global experience and adopt best practices with Chinese local conditions. The project carried out: 1, Collecting road safety data in China and compare the data systems with US/UK systems; 2, Developing a method to identify most critical factors that cause accidents and fatality; 3, Reviewing experience and lessons learned in US and UK to reduce accidents and fatality; 4, Establish a strategy and recommendations for effectively improving road safety in China within a shorter time period.

Shanghai had about a half million gasoline powered motorbikes in 2000. The motorbikes have become a significant contributor to ambient air pollution in Shanghai. This study selected an electric bike (e-bike) as a potential replacement for gasoline powered motorbikes. Life cycle assessment (LCA) was carried out for the two systems in terms of energy utilization and environment implications. LCA results indicated that e-bike is not better than the motorbike in all environment categories. The e-bike consumes less energy than the motorbike during its life cycle, and emits less GWP into air, and less BOD, COD, DS and HC into water. On the other hand, it generates more solid wastes, acidification potential, and HM than the motorbike, due to electric power production. Therefore, the Shanghai government should advocate advanced batteries and clean coal fired power plant technologies while implementing an electrical vehicle plan.

Low-cost automotive catalysts have been developed that contain 20-30% of the precious metals (7-15 g/ft3) commonly used in conventional catalysts, while providing a high efficiency of emissions control and durability for vehicles operating in emerging market countries. These catalysts were reformulated by replacing the Pd, Pt and Rh mixtures with optimized mixtures of rare-earth oxides (REOs). Laboratory studies demonstrated that these aged REO catalysts (80,000 km) with unleaded Chinese fuels reduce vehicle emissions by an average of 99%, 80% and 92% for CO, HC and NOx, respectively, when operating as a three-way catalyst in a closed-loop control mode at a stoichiometric air-to-fuel ratio. REO catalysts with 7.6g/ft3 of precious metals were tested on in-use Chinese Volkswagen Santanas with carbureted engine. Several strategies for air injection were tested on these vehicles.

A life-cycle assessment (LCA) has been developed to help compare the economic, environmental and energy (EEE) impacts of converting coal to automotive fuels in China. This model was used to evaluate the total economic cost to the customer, the effect on the local and global environments, and the energy efficiencies for each fuel option. It provides a total accounting for each step in the life cycle process including the mining and transportation of coal, the conversion of coal to fuel, fuel distribution, all materials and manufacturing processes used to produce a vehicle, and vehicle operation over the life of the vehicle. The seven fuel scenarios evaluated in this study include methanol from coal, byproduct methanol from coal, methanol from methane, methanol from coke oven gas, gasoline from coal, electricity from coal, and petroleum to gasoline and diesel. The LCA results for all fuels were compared to gasoline as a baseline case.