Resistive field generation in intense proton beam interaction with solid targets

The Brown-Preston-Singleton(BPS)stopping power model is added to our previously developed hybrid code to model ion beam-plasma interaction.Hybrid simulations show that both resistive field and ion scattering effects are important for proton beam transport in a solid target,in which they compete with each other.When the target is not completely ionized,the self-generated resistive field effect dominates over the ion scattering effect.However,when the target is completely ionized,this situation is reversed.Moreover,it is found that Ohmic heating is important for higher current densities and materials with high resistivity.The energy fraction deposited as Ohmic heating can be as high as 20%-30%.Typical ion divergences with half-angles of about 5°-10°will modify the proton energy deposition substantially and should be taken into account.

射频等离子系统中微弧放电的解析模型

最近我们在实验中发现:射频等离子体系统中的高电位可以引发微弧放电,这种微弧放电现象不是发生在射频系统的输入极上而是发生在接地极上。这种相对于电极呈非对称分布的微弧放电现象不是我们期待的结果,也不能用现有的理论来解释它。本文在Ch ild-L angm u ir鞘层模型理论和电流连续性理论的基础上,推导建立了一个用于表述射频等离子体系统中的这种非对称型微弧放电现象的解析模型。从我们的推导过程可以发现鞘层内外的电位差取决于接地极鞘层面积与输入极鞘层面积之比。当接地极的鞘层面积大于输入极鞘层面积时,接地极鞘层电位差的最小值成为高电位,从而引发电弧放电。而输入极鞘层电位差的最小值不发生变化。这个模型和推导结果与实验现象和P IC仿真数据有较好的一致性。

Prediction of effective diffusivity of porous media using deep learning method based on sample structure information self-amplification

Effective diffusivity is one of the basic transport coefficients used to describe the mass transport capability of a porous medium.In this study,a deep learning method based on a convolutional neural network(CNN)with sam-ple structure information self-amplification is proposed to predict the effective diffusivity of a porous medium,which is considerably influenced by the morphological and topological parameters of the porous medium.In this method,the geometric structures of three-dimensional(3D)porous media are reproduced via a stochastic reconstruction method.Datasets of the effective diffusivities of the reconstructed porous media were first estab-lished by the pore-scale lattice Boltzmann method(LBM)simulation.A large number of geometric structures of 3D porous media are obtained using the proposed sample structure information self-amplification approach.The 3D geometric structure information and corresponding effective diffusivities are directionally applied to a CNN for training and prediction.The effective diffusivities of media with porosities ranging from 0.48 to 0.58 are employed as training datasets,and the effective diffusivities of media with a broader porosity range of 0.39 to 0.79 are predicted by CNN.The CNN model can achieve a fast and accurate prediction of the effective diffusivity.The relative error between the CNN and LBM is 0.026%–8.95%with porosities ranging from 0.39 to 0.79.For a typical case with a porosity of 0.5,the computation time required by the CNN model is only 3×10^(−4) h,while the computation time for the same case is 16.96 h using the LBM.These findings indicate that the proposed deep learning method has a powerful learning ability;it is time-saving,provides accurate predic-tions,and can serve as a promising and powerful tool to predict the transport coefficients of complex porous media.