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环簇式塞式喷管在固体火箭发动机上应用探讨 总被引:1,自引:0,他引:1
对可应用于固体火箭发动机上的3种塞式喷管的结构特点进行了比较,重点讨论环簇式塞式喷管结构性能。基于目前的设计方法,确定了环簇式塞式喷管与钟形喷管的性能比较方法,进行了尺寸及重量分析,并给出了其在战略导弹第一级发动机和高空发动机的应用算例。结果表明,内喷管喉径是影响环簇式塞式喷管尺寸大小的最主要设计参数;在单元数足够多时,环簇式塞式喷管可比相同面积比的钟形喷管的尺寸更小,重量更轻,推力效率更高;明确了环簇式塞式喷管实际应用所需解决的关键问题。 相似文献
提出了一种基于Nvidia公司Fermi架构图形处理单元(GPU,Graphic Processing Unit)的分层低密度奇偶校验LDPC(Low-Density Parity-Check)码译码算法的译码器结构优化设计.利用GPU架构的并行性特点,采用帧间与层内双重并行的处理方式,充分利用流多处理器硬件资源,有效缓解了分层译码算法并行度受限的问题.此外,通过采取片上constant memory存储器压缩存储校验矩阵以及利用片外global memory存储器对译码迭代信息进行联合访问的优化方法,有效降低了访存延迟,提高了译码吞吐率.测试结果表明,通过采用多帧并行处理和存储器访问优化可以提升基于GPU的LDPC译码器吞吐率14.9~34.8倍. 相似文献
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This study investigates cooperative guidance problems for multiple missiles with fixed and switching directed communication topologies.A two-step guidance strategy is proposed to realize the simultaneous attack.In the first step,a distributed cooperative guidance law is designed using local neighboring information for multiple missiles to achieve consensus on range-to-go and leading angle.The second step begins when the consensus of multiple missiles is realized.During the second step,multiple missiles disconnect from each other and hit the target using the proportional navigation guidance law.First,based on the local neighboring communications,a sufficient condition for multiple missiles to realize simultaneous attack with a fixed communication topology is presented,where the topology is only required to have a directed spanning tree.Then,the results are extended to the case of switching communication topologies.Finally,numerical simulations are provided to validate the theoretical results. 相似文献
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This article presents two new kinds of artificial neural network (ANN) response surface methods (RSMs): the ANN RSM based on early stopping technique (ANNRSM-1), and the ANN RSM based on regularization theory (ANNRSM-2). The following improvements are made to the conventional ANN RSM (ANNRSM-0): 1) by monitoring the validation error during the training process, ANNRSM-1 determines the early stopping point and the training stopping point, and the weight vector at the early stopping point, which corresponds to the ANN model with the optimal generalization, is finally returned as the training result; 2) according to the regularization theory, ANNRSM-2 modifies the conventional training performance function by adding to it the sum of squares of the network weights, so the network weights are forced to have smaller values while the training error decreases. Tests show that the performance of ANN RSM becomes much better due to the above-mentioned improvements: first, ANNRSM-1 and ANNRSM-2 approximate to the limit state function (LSF) more accurately than ANNRSM-0; second, the estimated failure probabilities given by ANNRSM-1 and ANNRSM-2 have smaller errors than that obtained by ANNRSM-0; third, compared with ANNRSM-0, ANNRSM-1 and ANNRSM-2 require much fewer data samples to achieve stable failure probability results. 相似文献
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