Runze (China)

The after flow induced by underwater explosion (UNDEX) plays an important role on the damage of ship structures, particularly in the near-field explosion. In this paper, the damage experiments of the hull girder were conducted at detonation distances of R/R0 = 6, 10, and 17, and a dynamic model was established to study the hull girder deformation considering the after flow. It shows that the numerical simulations have a good agreement with the experimental results of the hull girder deformation, which increases nonlinearly as the distance decreases due to the contribution of the after flow to the nonlinear growth of structural damage. It further demonstrates that the after flow has the most pronounced influence on the overall deformation at the closest distance (R/R0 = 6), with the after flow caused by bubble expansion playing a dominant role. Critically, due to the most contribution of the after flow to the total wall pressure and its continuous energy input, the after flow increases the maximum deformation by 79.3% compared with the numerical results that exclude it at this distance. Notably, the after flow caused by bubble expansion alone accounts for an additional 87.1% final deformation increment. This research provides significant insights into the structural damage mechanism caused by near-field UNDEX loads.

With the rapid advancement of AI technologies, generative models have been increasingly employed in the exploration of novel materials. By integrating traditional computational approaches such as density functional theory (DFT) and molecular dynamics (MD), existing generative models — including diffusion models and autoregressive models — have demonstrated remarkable potential in the discovery of novel materials. However, their efficiency in goal-directed materials design remains suboptimal. In this work we developed a highly transferable, efficient and robust conditional generation framework, PODGen, by integrating a general generative model with multiple property prediction models. Based on PODGen, we designed a workflow for the high-throughput crystals conditional generation which is used to search new topological insulators (TIs). Our results show that the success rate of generating TIs using our framework is approximately 5 times higher than that of the unconstrained approach. This demonstrates that conditional generation significantly enhances the efficiency of targeted material discovery. Using this method, we generated tens of thousands of new topological materials and conducted further first-principles calculations on those with promising application potential. Furthermore, we identified promising, synthesizable topological (crystalline) insulators such as CsHgSb, NaLaB12, Bi4Sb2Se3, Be3Ta2Si and Be2W.

To improve the load frequency control (LFC) performance of power systems incorporating virtual power plants (VPPs) while reducing network resource consumption, a model predictive control (MPC) method based on a mixed time/event-triggered mechanism (MTETM) is proposed. This mechanism integrates an event-triggered mechanism (ETM) with a time-triggered mechanism (TTM), where ETM avoids unnecessary signal transmission and TTM ensures fundamental control performance. Subsequently, for the LFC system incorporating VPPs, a state hard constrained MPC problem is formulated and transformed into a “min-max” optimisation problem. Through linear matrix inequalities, the original optimisation problem is equivalently transformed into an auxiliary optimisation problem, with the optimal control law solved via rolling optimisation. Theoretical analysis demonstrates that the proposed auxiliary optimisation problem possesses recursive feasibility, whilst the closed-loop system satisfies input-to-state stability. Finally, validation through case studies of two regional power systems demonstrates that the MPC approach based on MTETM outperforms the ETM-based MPC approach in terms of control performance while maintaining a triggering rate of 33.3%. Compared with the TTM-based MPC algorithm, the MTETM-based MPC method reduces the triggering rate by 66.7%, while maintaining nearly equivalent control performance. Consequently, the results validate the effectiveness of the MTETM-based MPC approach in conserving network resources while maintaining control performance.