Abstract: This article developed an ion thruster optic system
sputter erosion depth numerical 3D model by IFE-PIC (Immersed
Finite Element-Particle-in-Cell) and Mont Carlo method, and
calculated the downstream surface sputter erosion rate of accelerator
grid; compared with LIPS-200 life test data. The results of the
numerical model are in reasonable agreement with the measured data.
Finally, we predicted the lifetime of the 20cm diameter ion thruster via
the erosion data obtained with the model. The ultimate result
demonstrated that under normal operating condition, the erosion rate
of the grooves wears on the downstream surface of the accelerator grid
is 34.6μm⁄1000h, which means the conservative lifetime until
structural failure occurring on the accelerator grid is 11500 hours.
Abstract: Using Dynamic Bayesian Networks (DBN) to model genetic regulatory networks from gene expression data is one of the major paradigms for inferring the interactions among genes. Averaging a collection of models for predicting network is desired, rather than relying on a single high scoring model. In this paper, two kinds of model searching approaches are compared, which are Greedy hill-climbing Search with Restarts (GSR) and Markov Chain Monte Carlo (MCMC) methods. The GSR is preferred in many papers, but there is no such comparison study about which one is better for DBN models. Different types of experiments have been carried out to try to give a benchmark test to these approaches. Our experimental results demonstrated that on average the MCMC methods outperform the GSR in accuracy of predicted network, and having the comparable performance in time efficiency. By proposing the different variations of MCMC and employing simulated annealing strategy, the MCMC methods become more efficient and stable. Apart from comparisons between these approaches, another objective of this study is to investigate the feasibility of using DBN modeling approaches for inferring gene networks from few snapshots of high dimensional gene profiles. Through synthetic data experiments as well as systematic data experiments, the experimental results revealed how the performances of these approaches can be influenced as the target gene network varies in the network size, data size, as well as system complexity.