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Due to the outstanding performance, composite materials are widely used and analyzing their properties and designing them based on performance has become a crucial task in the field of many manufacturing industries. Composite materials possess complex multiscale structures, and traditional fine-scale finite element modeling and analysis may lead to severe computational resource challenges. To overcome this difficulty, breakthroughs in key technologies of multiscale accelerated analysis algorithms are required. In this study, an innovative approach based on artificial intelligence and multiscale finite element method is presented. This approach involves partitioning the entire composite material structure into coarse grids that resemble homogenous structures of similar size, providing results consistent to fine-grid finite element analysis. By utilizing CNN for image feature recognition and employing the CGAN adversarial method, coarse-grid equivalent stiffness matrices and multiscale shape functions from completely random microstructures of composite materials can be obtained. Consequently, this enables a rapid response process from microstructure to low-resolution grid to high-resolution physical field, with remarkably accurate physical field results. Moreover, compared to traditional fine-grid finite element methods, this approach significantly reduces memory usage and computation time. This method is applicable to composite materials with varying shaped inclusions, different component properties, and diverse geometric distributions, allowing these materials to perform high-fidelity finite element calculations on coarse grids and predict their mechanical behavior. Furthermore, this breakthrough opens avenues for accelerating the optimization design of composite materials with diverse mechanical functionalities, by employing a bottom-up approach. [ABSTRACT FROM AUTHOR]

For the ongoing revolution in developing intelligent and connected vehicles (ICVs), there is a lack of research for powertrain control systems using the latest artificial intelligence and vehicle-to-everything technology that have already been widely adopted in the autonomous driving systems. In this context, recent development of deep reinforcement learning (DRL) and one of the latest computing frameworks are coupled to facilitate an onboard-based intelligent powertrain control. Taking the boost control of a diesel engine equipped with variable geometry turbocharger as an example, the results show that the final control behavior indicated by the cumulated rewards is improved by 50.43% and the learning efficiency is improved by 74.29% for the proposed curiosity-driven DRL algorithm, compared with the same structure DRL algorithm with classic random exploration policy. In addition, unlike most of the DRL-based powertrain optimization algorithms, which have only been applied to single-machine architecture, this work manages the proposed DRL algorithm in parallel and, more importantly, from an edge computing perspective. This, in addition to greatly speeding up the algorithm training, can also realize a good balance of control accuracy and generality depending upon the selected training scenario. Moreover, unlike most of the cloud computing frameworks, which require low network latency, the proposed architecture can achieve a similar final control performance even if good network communication is not allowed. Compared with other existing powertrain control methods, the proposed algorithm is able to approximate a global powertrain control optimization autonomously in a connected manner, making it attractive to current ICVs with advanced automated driving and traditional powertrain control. [ABSTRACT FROM AUTHOR]

Deep reinforcement learning (DRL), which excels at solving a wide variety of Atari and board games, is an area of machine learning that combines the deep learning approach and reinforcement learning (RL). However, to the authors' best knowledge, there seem to be few studies that apply the latest DRL algorithms on real-world powertrain control problems. If there are any, the requirement of classical model-free DRL algorithms typically for a large number of random exploration in order to realize good control performance makes it almost impossible to implement directly on a real plant. Unlike most of the other DRL studies, whose control strategies can only be trained in a simulation environment—especially when a control strategy has to be learned from scratch—in this study, a hybrid end-to-end control strategy combining one of the latest DRL approaches—i.e., a dueling deep Q-network and traditional Proportion Integration Differentiation (PID) controller—is built, assuming no fidelity simulation model exists. Taking the boost control of a diesel engine with a variable geometry turbocharger (VGT) and cooled (exhaust gas recirculation) EGR as an example, under the common driving cycle, the integral absolute error (IAE) values with the proposed algorithm are improved by 20.66% and 9.7% respectively for the control performance and generality index, compared with a fine-tuned PID benchmark. In addition, the proposed method can also improve system adaptiveness by adding another redundant control module. This makes it attractive to real plant control problems whose simulation models do not exist, and whose environment may change over time. [ABSTRACT FROM AUTHOR]