Effects of total pressures and equivalence ratios on kerosene/air rotating detonation engines using a paralleling CE/SE method

In this paper, the kerosene/air rotating detonation engines(RDE) are numerically investigated, and the emphasis is laid on the effects of total pressures and equivalence ratios on the operation characteristics of RDE including the initiation, instabilities, and propulsive performance. A hybrid MPI + OpenMP parallel computing model is applied and it is proved to be able to obtain a more effective parallel performance on high performance computing(HPC) systems. A series of cases with the total pressure of 1 MPa, 1.5 MPa, 2 MPa, and the equivalence ratio of 0.9, 1, 1.4 are simulated. On one hand, the total pressure shows a significant impact on the instabilities of rotating detonation waves. The instability phenomenon is observed in cases with low total pressure (1 MPa) and weakened with the increase of the total pressure. The total pressure has a small impact on the detonation wave velocity and the specific impulse. On the other hand, the equivalence ratio shows a negligible influence on the instabilities, while it affects the ignition process and accounts for the detonation velocity deficit. It is more difficult to initiate rotating detonation waves directly in the lean fuel operation condition. Little difference was observed in the thrust with different equivalence ratios of 0.9, 1, and 1.4. The highest specific impulse was obtained in the lean fuel cases, which is around 2700 s. The findings could provide insights into the understanding of the operation characteristics of kerosene/air RDE.     

Multi-scale traffic vehicle detection based on faster R-CNN with NAS optimization and feature enrichment

It well known that vehicle detection is an important component of the field of object detection. However, the environment of vehicle detection is particularly sophisticated in practical processes. It is compara-tively difficult to detect vehicles of various scales in traffic scene images, because the vehicles partially obscured by green belts, roadblocks or other vehicles, as well as influence of some low illumination weather. In this paper, we present a model based on Faster R-CNN with NAS optimization and feature enrichment to realize the effective detection of multi-scale vehicle targets in traffic scenes. First, we proposed a Retinex-based image adaptive correction algorithm (RIAC) to enhance the traffic images in the dataset to reduce the influence of shadow and illumination, and improve the image quality. Second, in order to improve the feature expression of the backbone network, we conducted Neural Architecture Search (NAS) on the backbone network used for feature extraction of Faster R-CNN to generate the optimal cross-layer connection to extract multi-layer features more effectively. Third, we used the object Feature Enrichment that combines the multi-layer feature information and the context information of the last layer after cross-layer connection to enrich the information of vehicle targets, and improve the robustness of the model for challenging targets such as small scale and severe occlusion. In the imple-mentation of the model, K-means clustering algorithm was used to select the suitable anchor size for our dataset to improve the convergence speed of the model. Our model has been trained and tested on the UN-DETRAC dataset, and the obtained results indicate that our method has art-of-state detection performance.     

A method to determine the shell layout scheme for equipment battlefield damage tests under artillery fire

In this paper,a new method for determining the shell layout scheme is proposed,which can make the equipment damage data by the battlefield damage test resemble as close as possible the actual combat data.This method is based on the analysis of the impact point distribution and effective damage area of equipment.In order to obtain the position of the impact points,an impact point distribution model under artillery fire was established.Similarly,in order to obtain the effective damage area of equipment,the concepts of generalized damage area and task-based equipment functional damage probability were demonstrated,and the corresponding calculation model was established.Through case analysis,the shell layout scheme was effectively obtained,verifying the correctness of the proposed method.     

多维六角形节块动力学程序系统的开发及验证

以多维六角形节块中子动力学程序HENKO的理论模型为基础,研制成功六角形组件的反应堆多维瞬态分析程序系统,提出了一种处理控制棒尖点问题的方法.通过VVER一号国际基准问题的验算,验证了本程序的正确性.     

一种基于兼容度的组合评估方法

分析了单一评估方法的局限性,基于兼容度准则提出一种将综合指数法和功效评分法等常用评估方法组合而成的一种综合评估方法,采用该方法对潜艇声纳的作战性能进行了评估分析.算例分析表明,该方法对于提高评估结果的可信性具有明显的作用.     

未确知性信息的证据理论合成及其应用

针对在装备管理中所采用的专家评估法存在着未确知性现象,提出了一种基于D-S证据合成理论来融合未确知性信息的方法.从而,将每名专家的意见综合为专家组的一致意见,使评估结果更合理.最后,通过实例说明该方法的有效性.     

线性方程组二级迭代法的收敛速度

二级迭代法由内、外迭代和内迭代次数三部分组成。给出了线性方程组二级迭代法R1-收敛因子的一个上界,这个上界由内、外迭代的R1-收敛因子和内迭代次数所决定,其主部为外迭代的R1-收敛因子。在矩阵单调性条件下,对于任何内迭代方法和任意内迭代次数,证明了外迭代的R1-收敛因子也是二级迭代法R1-收敛因子的下界。所得结果反映了内、外迭代的收敛速度以及内迭代次数对于二级迭代法收敛速度的综合影响。     

兵团发展循环经济的对策研究

循环经济是一种新型的经济发展模式,也是我国实现可持续发展的重要途径。兵团发展循环经济应从以下几方面入手:转变观念;制定循环经济的发展规划;确定循环经济发展的试点;探索“政府引导、市场推进”的循环经济运行机制;突出重点,注重效率,逐步推进;开发建立循环经济的绿色支撑体系。     

改进BP网络的舰船装备保障资源保障能力评估

针对单独运用BP网络评估舰船装备保障资源保障能力时存在的不足,提出了一种基于改进BP网络的舰船装备保障资源保障能力评估模型。首先构建了保障能力评估的指标体系,在此基础上,采用主成分分析法对原始输入变量进行预处理,选择输入变量的主成分作为神经网络输入,一方面减少了输入变量的维数,消除了各输入变量的相关性;另一方面提高了网络的收敛性和稳定性,同时也简化了网络的结构。然后将遗传优化算法与BP网络结合,利用GA的全局搜索能力优化BP网络的结构参数。最后进行实例计算,结果表明该方法有效克服了纯BP算法局部收敛、泛化能力弱等问题,新模型采用的算法具有收敛速度快,结果精度高的优点,适用于对保障资源保障能力的分析和评估。     

基于Z形式化规范的多Agent协作模型研究

用形式化规范语言Z定义了多A gen t协作模型的基本元素,包括:A gen t、目标、角色、交互协议和过程,给出了应满足的一致性约束,并通过城市区域的连锁超市物流调度系统说明了该形式化规范为多A gen t协作提供了一致性和连贯性。