Modeling is the primary point to conveying product characteristics. Accurately capturing the image needs of users for modeling is an important way to effectively improve product design efficiency. Quantitative theory type I is a method to solve the internal law between the user’s target image and the product modeling characteristics by building a mathematical model. Aiming at the problems of abundant product modeling design elements, diversified combination methods, and high design cost due to the subjective ambiguity of user images, a topological design method of product modeling based on quantification theory I is proposed. This method uses the semantic difference method and statistical method to obtain the quantitative data of perceptual semantic features of product modeling to represent the explicit and implicit needs of users. Based on the topological transformation, the topological analysis and modeling of the two types of requirements are carried out. The mapping model of product modeling features and style images is constructed using quantitative theory type I, and the topologic value of each modeling design element is calculated. The above method can effectively solve the mapping problem between product modeling features and style images. On this basis, this research provides a decision-making basis for product modeling design. Using a wine jug as a product exemplar for this research, the scheme design and evaluation are carried out to verify the effectiveness and feasibility of the product modeling topological design method.

The simulation test of passive ejection type high-altitude solid rocket motor (SRM) was simulated by computer. For SRM passive ejector type high-altitude test, the tempering in the process of engine ignition and flameout poses a serious threat to the nozzle of the engine test device and circuit arrangement in the upper compartment. Using the N-S equation and Spalart-Allmaras model: numerical simulation on the flow structure of engine pressurize, decompression and stratochamber air phase in the testing process, and flow field of the diffuser and the cabin altitude simulation, analyzing the flow field structure of high-altitude experiment stages. The results showed the rough coincidence between the simulation curve and the measured data, with slight difference at the initial stage of air supply, the measured data and the simulation data reach the overall high degree of coincidence. During the process of building a compression and decompression, the adiabatic measures should be taken to prevent high temperature gas from damaging the engine, especially the nozzle and the head ring, in addition, the reasonable design of the nozzle contour surface can control gas separation tempering, reducing the influence caused by tempering. Stability of the flow field in the high altitude chamber of the pressure will not affect the engine.

Mining association rules is a very important aspect in data mining fields. The process to mine association rules not only take much time, but also take huge computing source. How to fast and efficiently find the large itemsets is a crucial point in the association rule algorithms. This paper will focus on two algorithms research and implementation in parallel computing environments. One is Bitmap Combination algorithm, the other is Bitmap FP-Growth algorithm. Compared to Apriori algorithm, both Bitmap Combination and Bitmap FP-Growth algorithms don't need generate candidate items, avoids costly database scans. Both algorithms need to translate the original database to Bitmap format, analyze bit distribution to reduce database size and apply high-speed bit calculation to improve the algorithms. The divide-and-conquer replace generation-and-test idea as the basic strategy. Bitmap Combination Algorithm shows the quick combination skills between any two, three, four and more rows, then screening the qualified itemsets. Bitmap FP-Growth Algorithm apply special bit calculation to recursively mine association rules. Based on the experimental results in this paper, both algorithms greatly improve the efficiency and performance of mining association rules, especially provide the possibility to mine association rules in highly parallel computing environments.

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