Safety Analysis of the Reactor Pressure Vessel of NHR-200

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Rapid droplet leads the Liquid-Infused Slippery Surfaces more slippery

The introduction of lubricant between fluid and substrate endows the Liquid-Infused Slippery Surfaces with excellent wetting properties: low contact angle, various liquids repellency, ice-phobic and self-healing. Droplets moving on such surfaces have been widely demonstrated to obey a Landau-Levich-Derjaguin (LLD) friction. Here, we show that this power law is surprisingly decreased with the droplet accelerates: in the rapid droplet regime, the slippery surfaces seem more slippery than LLD friction. Combining experimental and numerical techniques, we find that the meniscus surrounding the droplet exhibits an incompletely developed state. The Incompletely Developed Meniscus possesses shorter shear length and thicker shear thickness than the prediction of Bretherton model and therefore is responsible for the more slippery regime. With an extended Bretherton model, we not only provide an analytical description to the IDM behavior but also the friction when the Capillary Number of the moving droplet is larger than the Critical Capillary Number

Instability of electrowetting on a dielectric substrate

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/98707/1/JApplPhys_109_034309.pd

A unified solution for self-equilibrium and super-stability of rhombic truncated regular polyhedral tensegrities

AbstractAs a novel class of lightweight and reticulated structures, tensegrities have found a diversity of technologically significant applications. In this paper, we theoretically investigate the self-equilibrium and super-stability of rhombic truncated regular polyhedral (TRP) tensegrities. First, the analytical solutions are derived individually for rhombic truncated tetrahedral, cubic, octahedral, dodecahedral, and icosahedral tensegrities. Based on these solutions, we establish a unified analytical expression for rhombic TRP tensegrities. Then the necessary and sufficient condition that ensures the existence of a self-equilibrated and super-stable state is provided. The obtained solutions are helpful not only for the design of self-equilibrated and super-stable tensegrities but also for their applications in biomechanics, civil and aerospace engineering

Quantum switch for single-photon transport in a coupled superconducting transmission line resonator array

We propose and study an approach to realize quantum switch for single-photon transport in a coupled superconducting transmission line resonator (TLR) array with one controllable hopping interaction. We find that the single-photon with arbitrary wavevector can transport in a controllable way in this system. We also study how to realize controllable hopping interaction between two TLRs via a superconducting quantum interference device (SQUID). When the frequency of the SQUID is largely detuned from those of the two TLRs, the variables of the SQUID can be adiabatically eliminated and thus a controllable interaction between two TLRs can be obtained.Comment: 4 pages,3 figure

Physics-informed radial basis network (PIRBN): A local approximation neural network for solving nonlinear PDEs

Our recent intensive study has found that physics-informed neural networks (PINN) tend to be local approximators after training. This observation leads to this novel physics-informed radial basis network (PIRBN), which can maintain the local property throughout the entire training process. Compared to deep neural networks, a PIRBN comprises of only one hidden layer and a radial basis "activation" function. Under appropriate conditions, we demonstrated that the training of PIRBNs using gradient descendent methods can converge to Gaussian processes. Besides, we studied the training dynamics of PIRBN via the neural tangent kernel (NTK) theory. In addition, comprehensive investigations regarding the initialisation strategies of PIRBN were conducted. Based on numerical examples, PIRBN has been demonstrated to be more effective and efficient than PINN in solving PDEs with high-frequency features and ill-posed computational domains. Moreover, the existing PINN numerical techniques, such as adaptive learning, decomposition and different types of loss functions, are applicable to PIRBN. The programs that can regenerate all numerical results can be found at https://github.com/JinshuaiBai/PIRBN.Comment: 48 pages, 26 figure