On the electron boundary conditions in PIC plasma thruster plume simulations

Min Li's work has been supported by the National Natural Science Foundation of China (Grant No. 11872093). Mario Merino and Eduardo Ahedo's work has been supported by the Spanish R&D National Plan under grant number ESP2016-75887-P.The collisionless, steady state expansion of a warm electron, cold ion plasma thruster plume into vacuum is studied with an electrostatic particle-in-cell model and globally-consistent boundary conditions that discriminate between reflected and escaping electrons. As a proof of concept, several simulations are analyzed. Results from both two-dimensional planar and axisymmetric plasma plumes are discussed. In particular, the electron anisothermal and anisotropic behavior in the plume is recovered.Min Li's work has been supported by the National Natural Science Foundation of China (Grant No. 11872093). Mario Merino and Eduardo Ahedo's work has been supported by the Spanish R&D National Plan under grant number ESP2016-75887-P

CBNet: A Novel Composite Backbone Network Architecture for Object Detection

In existing CNN based detectors, the backbone network is a very important component for basic feature extraction, and the performance of the detectors highly depends on it. In this paper, we aim to achieve better detection performance by building a more powerful backbone from existing backbones like ResNet and ResNeXt. Specifically, we propose a novel strategy for assembling multiple identical backbones by composite connections between the adjacent backbones, to form a more powerful backbone named Composite Backbone Network (CBNet). In this way, CBNet iteratively feeds the output features of the previous backbone, namely high-level features, as part of input features to the succeeding backbone, in a stage-by-stage fashion, and finally the feature maps of the last backbone (named Lead Backbone) are used for object detection. We show that CBNet can be very easily integrated into most state-of-the-art detectors and significantly improve their performances. For example, it boosts the mAP of FPN, Mask R-CNN and Cascade R-CNN on the COCO dataset by about 1.5 to 3.0 percent. Meanwhile, experimental results show that the instance segmentation results can also be improved. Specially, by simply integrating the proposed CBNet into the baseline detector Cascade Mask R-CNN, we achieve a new state-of-the-art result on COCO dataset (mAP of 53.3) with single model, which demonstrates great effectiveness of the proposed CBNet architecture. Code will be made available on https://github.com/PKUbahuangliuhe/CBNet.Comment: 7 pages,6 figure

Ultrasound cavitation induced nucleation in metal solidification: an analytical model and validation by real-time experiments

Microstructural refinement of metallic alloys via ultrasonic melt processing (USMP) is an environmentally friendly and promising method. However, so far there has been no report in open literature on how to predict the solidified microstructures and grain size based on the ultrasound processing parameters.In this paper, an analytical model is developed to calculate the cavitation enhanced undercooling and the USMP refined solidification microstructure and grain size for Al-Cu alloys. Ultrafast synchrotron X-ray imaging and tomography techniques were used to collect the real-time experimental data for validating the model and the calculated results. The comparison between modeling and experiments reveal that there exists an effective ultrasound input power intensity for maximizing the grain refinement effects for the Al-Cu alloys, which is in the range of 20-45 MW/m2. In addition, a monotonous increase in temperature during USMP has negative effect on producing new nuclei, deteriorating the benefit of microstructure refinement due to the application of ultrasound