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In the product design stage, the effective performance analysis method is the premise to guarantee the application performance of products. However, digital simulation, as a widely used performance analysis method, has a long simulation cycle and high requirements for computer hardware performance, which leads to long product design and iteration time. Facing this problem, a product performance rapid simulation approach driven by digital twin (DT) data for variable product structures is proposed to replace the digital simulation method. Firstly, the framework for variable product structures is designed, which includes a digital simulation module, a data-driven module, and a surrogate-based simulation module. Then, module implementation of the proposed framework is introduced in detail, in which the twin data-driven product performance rapid simulation model (RSM) for variable product structures is studied based on the makeTwin reference architecture. Finally, a case study is conducted on the iterative design of a connecting rod, focusing on equivalent stress evaluation. The result shows that the proposed method is feasible, as well as costs a shorter simulation time than traditional digital simulation. [ABSTRACT FROM AUTHOR]

As mentioned in Part 1, for product iterative design, the digital simulation method, as the product performance analysis method, has the problems of time cost and computing resources. To address this issue, a product performance rapid simulation approach driven by digital twin (DT) data for variable product structures in Part 1 was proposed, which could replace the digital simulation method. Meanwhile, for performance analysis under variable operating conditions during the product design and operation phase, digital simulation also has the above problems. Therefore, based on the method for variable product structures proposed in Part 1, a product performance rapid simulation approach driven by DT data for variable operating conditions is proposed in this paper. Firstly, the framework for variable operating conditions is designed based on the makeTwin reference architecture, including twin source data acquisition for the product under different operating conditions, twin data-driven product performance rapid simulation model (RSM) construction for variable operating conditions, and rapid evaluation of product critical performance based on RSM. Then, the implementation of the framework is introduced in detail. At last, a case study for critical loosening load evaluation of bolted joint is carried out. The result indicates that the proposed method is feasible, as well as costs shorter simulation time than the traditional digital simulation method. [ABSTRACT FROM AUTHOR]

Coupling analysis plays more and more important roles in recent years for the accurately modelling of nuclear reactors. But with the different discretization methods adopted in different physical phenomena, it is difficult to couple different kinds of codes. In this paper, steady and transient-state neutronics solvers have been developed based on the finite volume method which is widely used for the simulation of fluid flow and heat and mass transfer. With the adoption of this discretization method, mesh mapping technique and data exchange become much easier for the coupling codes compared with traditional methods. The developed neutronics solvers are verified by several benchmark problems. Numerical results indicate that the developed neutronics solvers are reliable to be applied for the steady-state and transient-state neutronics analysis of nuclear reactors. [ABSTRACT FROM AUTHOR]

Suction plays an important role in the disordered manufacturing scenarios because of its single-point contact and reliability. The prediction of suction region is the primary problem needs to be solved in these applications. However, the traditional non-deep learning methods is insufficient for its poor generalization performance, and the deep learning method is inconvenient as it requires manually labeled datasets for training. Therefore, a suction region prediction method from knowledge to learning is proposed to overcome these challenges, which adopts a suction reliability matrix based on the depth image to label the disordered manufacturing datasets without manual labeling, where the effects of suction cup model, part centroid and depth are all considered. The disordered manufacturing dataset annotated is used to train the suction region prediction model based on fully convolutional neural network, which can be customized according to the model of suction cup and parts. Experiments and analysis show that the proposed method has the advantages of short detection time, high robustness and strong generalization ability. [ABSTRACT FROM AUTHOR]