The Computation method of Solid Ducted Rocket (SDR) is a complex problem. It needs to effectively solve the two mixing and combustion processes, and is also affected by many factors such as the overall scheme and propellant type. In order to find a suitable method, physical model, simplified hypothesis, control equation, turbulence model, combustion model, etc. were investigated. Subsequently, calculations were carried out based on the Vanka model, and finally the results of pressure, temperature and combustion efficiency were obtained. The results show that the proposed method is practicable, and the accuracy and efficiency are ideal. Combustion efficiency is only 77.4%, and the Vanka model must be further improved by the air and gas intake modes. The results can provide reference for relevant research.

Smaller time loss and smoother information communication mode is the urgent pursuit of the software R&D enterprise. Information communication is difficult to control and manage and it needs more technical to support. Data mining is an intelligent way tried to analyze knowledge and laws which hidden in massive amounts of data. Data mining technology together with share repositories can improve the intelligent degree of information communication mode. In this paper, the framework of intelligent information communication mode which based on data mining technology and share repositories is advanced, and data mining model for information communication of software development is designed. In view of the extant single decision tree algorithm existence the characteristics that counting inefficient and its learning based on supervise, a new semi-supervised learning algorithm three decision trees voting classification algorithm based on tri-training (TTVA) is proposed. This algorithm in training only requests a few labeled data, and can use massively unlabeled data repeatedly revision to the classifier. It has overcome the single decision tree algorithm shortcoming. Experiments on the real communicated data sets of software developmental item indicate that TTVA has the good identification and accuracy to the crux issues mining, and can apply to the decision analysis of the development and management of the software project. At the same time, TTVA can effectively exploit the massively unlabeled data to enhance the learning performance.

In experiment of network engineering and network construction, students are required to configure Cisco network equipment. Due to limit of lab environment, amount of equipment is not enough and some experiments require much more expensive and higher level equipment that are not practical to purchase and maintain. It is meaningful to use network equipment simulator on PCs. The proposed platform utilizes Dynamips simulator to simulate routers and switches of Cisco product series. Dynamips supports directly loading Cisco IOS images and provides configuration interface as true equipment’s. It has very good teaching effect in experiment of network engineering. And success of the proposed platform shows it can be used in other network related courses.

Smaller time loss and smoother communication pattern is the urgent pursuit in the software development enterprise. However, communication is difficult to control and manage and demands on technical support, due to the uncertainty and complex structure of data appeared in communication. Data mining is a well established framework aiming at intelligently discovering knowledge and principles hidden in massive amounts of original data. Data mining technology together with shared repositories results in an intelligent way to analyze data of communication in software development environment. We propose a data mining based algorithm to tackle the problem, adopting a co-training styled algorithm to discover pattern in software development environment. Decision tree is trained as based learners and a majority voting procedure is then launched to determine labels of unlabeled data. Based learners are then trained again with newly labeled data and such iteration stops when a consistent state is reached. Our method is naturally semi-supervised which can improve generalization ability by making use of unlabeled data. Experimental results on data set gathered from productive environment indicate that the proposed algorithm is effective and outperforms traditional supervised algorithms.