Search

The fixed-point stopping of high-speed trains in stations is generally accomplished through manual operation in China. This situation often leads to a failure to stop at fixed-point signs and causes a fluctuation in the longitudinal acceleration due to the lack of experience of the drivers. To achieve precise, stable, and automatic stopping, a distributed multiparticle precise stopping control model based on the distributed model predictive control (MPC) algorithm is developed in this paper. A two-level hierarchical control structure for the subcontroller of each vehicle is adopted to bring itself to a controlled stop. In the upper control of subcontroller, the MPC algorithm is designed in turn based on the multiparticle mechanism model of the train. In the lower control of subcontroller, the target input from the upper control is converted and distributed. The controlled object, a comprehensive numerical computing model including the spatial dynamic model of the train and its electropneumatic blending braking model, is established and controlled by the corresponding subcontroller and employed to verify the performance of the controller. The influence of the model control parameters on the stopping performance is discussed, and the optimal combination of control parameters is selected. The proposed control model using the optimization parameters is tested and verified through the comprehensive numerical computing model. The results indicate that the stopping error is 0.0075 m, which is much less than the accuracy requirements for fixed-point stopping. The computing time of each subcontroller in real-time is stable at 0.09 s. The coupler impact force between two adjacent vehicles can also be effectively inhibited and eliminated. Its control performance outperforms a proportional-integral-derivative (PID) algorithm. The proposed precise stopping model and comprehensive numerical computing model provide references for the application and algorithm optimization of automatic train operation technology in high-speed trains. [ABSTRACT FROM AUTHOR]

Understanding the impact of engineering design on product competitions is imperative for product designers to better address customer needs and develop more competitive products. In this paper, we propose a dynamic network-based approach for modeling and analyzing the evolution of product competitions using multi-year buyer survey data. The product co-consideration network, formed based on the likelihood of two products being co-considered from survey data, is treated as a proxy of products' competition relations in a market. The separate temporal exponential random graph model (STERGM) is employed as the dynamic network modeling technique to model the evolution of network as two separate processes: link formation and link dissolution. We use China's automotive market as a case study to illustrate the implementation of the proposed approach and the benefits of dynamic network models compared to the static network modeling approach based on an exponential random graph model (ERGM). The results show that since STERGM takes preexisting competition relations into account, it provides a pathway to gain insights into why a product may maintain or lose its competitiveness over time. These driving factors include both product attributes (e.g., fuel consumption) as well as current market structures (e.g., the centralization effect). With the proposed dynamic network-based approach, the insights gained from this paper can help designers better interpret the temporal changes of product competition relations to support product design decisions. [ABSTRACT FROM AUTHOR]

Rare earth elements (REE) are crucial materials in an incredible array of consumer goods, energy system components, and military defense applications. However, the global production and entire value chain for REE is dominated by China, with the U.S. currently 100% import reliant for these critical materials. Traditional mineral ores including those previously mined in the U.S., however, have several challenges. Chief among these is that the content of the most critical and valuable of the rare earths is deficient, making mining uneconomical. Further, the supply of these most critical rare earths is nearly 100% produced in China from a single resource that is only projected to last another 10-20 years. The U.S. currently considers the rare earths market an issue of national security. It is imperative that alternative domestic sources of rare earths be identified and methods developed to produce them. Recently, coal and coal byproducts have been identified as one of these promising alternative resources. This paper details the results of a study on characterization of North Dakota lignite and lignite-related feedstocks as an assessment of their feasibility for REE recovery. The abundance, distribution, and modes of occurrence of the REE in the samples collected were determined in this initial study to inform the selection of appropriate extraction and concentration methods to recover the REE. Materials investigated include the lignite coals, clay-rich sediments associated with the coal seams, and materials associated with a lignite beneficiation system and power plant. The results show that high REE levels exist both in lignite coals and associated sediments. The form of the REE in the clay materials is primarily as ultrafine mineral grains. In the lignite coals, approximately 80-95% of the rare earths content is organically associated, primarily as coordination complexes. [ABSTRACT FROM AUTHOR]