喷射成形A356铝合金组织及力学性能的研究

采用自行开发的控制往复喷射成形专利技术,以产业化规模制备了铝合金锭坯,研究了对传统铸造A356铝合金的组织及其力学性能的影响.结果表明:喷射成形态合金组织均匀,致密度达到95.8%;挤压态合金中均匀分布大量细小Si颗粒;固溶温度为540℃,时效温度为160℃,时效10 h时硬度达到峰值,继续延长时效时间对硬度值影响甚微;经过固溶时效处理的A356铝合金常温抗拉强度σb=337 MPa,屈服强度σ0.2=281 MPa,伸长率δ5=18.3%,分别提高21.2%,35.7%,205%.     

第62届世界铸造会议宣读论文综述(有色金属部分)

详细介绍了第 62届世界铸造会议中有关有色合金铸造的 6篇宣读论文。“铝合金压铸的压力传递与铸件质量的关系”一文提出一种新的测量铝合金压铸型腔中压力传递的方法 ,搞清了压力传递、流速与铸件质量之间的关系 ,获得了本届会议的优秀论文奖。“挤压压铸和半固态铸造生产高质量、复杂的铸件”一文展示了应用压铸高技术 ,生产出力学性能极高的汽车用复杂压铸件。“监控金属型、压铸模温度的智能系统”一文提出的智能控温系统 ,测温可靠 ,能自动控制铸型温度 ,铸型中不需开冷却水孔道。“制造金属基复合材料的复合铸造工艺 (Hybrid)”一文介绍了一种挤压铸造和熔模铸造相结合生产金属基复合材料的新工艺。“喷铸法制取铝基SiCp增强复合材料的微观组织和磨耗特性”一文指出用喷铸法制取的铝基SiCp增强复合材料 ,具有很高的耐磨性能。“不同凝固条件下 ,Na ,Ca含量对加Sb变质的 3 5 6合金力学性能的影响”一文的作者通过大量试验 ,搞清了Na ,Ca的有害作用 ,指出 3 5 6合金的σb 与δ之间存在下列关系 :σb(MPa) =2 5 .87lgδ( % ) 12 8.85。     

喷射成形过程中雾化熔滴的凝固行为

通过对高温合金喷射成形过喷粉末凝固组织的分析，结合热传输分析结果，研究了喷射形过程中雾化熔滴的生核与生长动力学行为。研究结果表明：雾化过程中以异质生核为主，先凝固小颗粒与大尺寸熔滴相碰撞而成晶核在生核机制中起重要作用，不同粒度粉末均呈快速凝固枝晶生长形态，并给出了组织细化程度与冷却速度之间的定量关系。     

镁合金表面冷喷涂快凝Zn-Al合金粉末的研究

对镁合金表面冷喷涂快凝Zn-Al合金粉末工艺进行了研究，观察了涂层微观组织，并分析了涂层与基体之间的结合，测定了涂层的硬度和涂层与基体的结合强度。结果表明，采用冷喷涂技术制备的快凝Zn—Al合金粉末涂层组织致密，基体与涂层的结合处只产生塑性变形并且无熔化现象，涂层硬度比基体硬度显著提高。     

基于位置信息预测的船舶机会网络路由

机会网络利用节点移动形成的通信机会来逐步传输消息.然而当机会网络应用在海洋上时,由于海域广阔,节点分布稀疏,所以船舶之间相遇通信的概率较低,同时由于船速较慢,固定航道,传统机会网络路由协议执行效率不高.利用船舶位置信息和航行方向等,结合插值算法预测节点未来可能到达的位置,并对Spray and Wait协议改进,从而增加数据传递到目的船舶的可能性和效率,提高了船舶机会网络连通性.最后,通过仿真实验与目前比较流行的机会网络路由协议进行比较,取得较好的实验结果.     

基于迭代式动态规划的影像密集匹配

针对动态规划影像密集匹配中因匹配的整体相关性导致的误匹配点连带扩散效应问题,提出了一种基于迭代式动态规划的影像密集匹配算法。该算法在影像密集匹配过程中引入了视差方向一致性、视差突变性作为迭代判定准则,通过对动态规划匹配结果中不满足迭代条件的候选匹配点子集进行分析,在候选匹配点子集中识别出误匹配点并去除之,反复迭代直至满足迭代准则,从而解决了误匹配点的连带扩散效应问题。该算法已成功应用于嫦娥三号遥操作项目,经在轨应用的检验表明,提出的基于迭代式动态规划的影像密集匹配算法能够极大地降低立体影像匹配中密集同名点的误匹配率。     

超音速扩压器喷雾冷却数值仿真研究

为了探讨研究喷雾冷却中冷却水流量、喷射速度及喷嘴轴向分布位置等因素对超音速扩压器壁面温度的影响,借助CFD计算软件FLUENT,采用标准k-ε湍流模型,对喷雾冷却下超音速扩压器内流场进行了数值仿真。通过计算,得到了扩压器内压强分布及壁面温度分布。分析了喷雾冷却中冷却水流量、喷射速度及喷嘴轴向位置对超音速扩压器壁面温度的影响。研究表明：随着冷却水流量的增大,扩压器壁面温度降低。同时,扩压器入口处静压升高,对应仿真高度降低;减小冷却水喷射速度,可以有效降低扩压器入口处壁面温度;喷嘴的轴向分布位置对扩压器壁面温度有较大影响,仿真结果证明,喷嘴沿轴向呈对数分布有利于降低扩压器壁面温度。     

An Online Algorithm for the Dynamic Maximal Dense Tree Problem

Abstract. The Online Maximal Dense Tree problem is as follows: given a weighted directed graph and a source node, users issue online requests for connection to the source node. A request can either be accepted or rejected (the admission control decision). If the connection request is accepted, it must be connected to the source or to a node previously connected to the source (the routing decision). The objective is to maximize the total number of connections while keeping the connection density , i.e. the ratio of accepted requests to the weight of the spanning tree, sufficiently high. The primary motivation for the Maximal Dense Tree problem is the Online Capacitated Multicast admission control and routing problem. In the Online Capacitated Multicast problem, we are given a communication network with limited link capacities and a set of signal source nodes. Users generate online requests for connection to the signal sources, and the network administrator has to make the admission control and routing decisions. The goal of the network administrator is to maximize the total number of users connected subject to the network capacity constraints. The Online Maximal Dense Tree problem is also faced by a cable TV operator who wishes to connect as many customers as possible while keeping down the amount of wiring per customer. Informally, the Online Maximal Dense Tree algorithm must ``gamble' on certain geographic areas, connecting nodes which are unprofitable to start with, in the hope that eventually enough requests will arrive in its vicinity to make the investment profitable. In this paper we present a randomized online algorithm for the Maximal Dense Tree problem that guarantees acceptance of a (1- ɛ) factor of the requests accepted by the optimum offline algorithm with the expectation of density being at most polylogarithmically lower than that of the offline algorithm. This yields an online capacitated multicast algorithm whose throughput is only poly-logarithmically lower than that of the optimum offline algorithm. Previous work on multicast routing and maximal dense tree problems either made probabilistic assumptions or resulted in linear performance gaps with the offline algorithm. Attempts to solve the Online Maximal Dense Tree problem have also lead to the development of the first polylogarithmic approximation algorithms for the k -MST and the Prize Collecting Salesman problems [AABV].     

Multi-scale-based joint super-resolution and inverse tone-mapping with data synthesis for UHD HDR video

The performance of current joint super-resolution (SR) and inverse tone-mapping (ITM) frameworks is limited since they only account for information in small local receptive fields. Moreover, the SDR images in the existing dataset for joint SR-ITM are oversaturated compared to the HDR ground truths. The models trained on this dataset produce HDR images with color shift. This paper proposes a multi-scale-based joint SR-ITM model to reconstruct HR HDR videos. Image features are downsampled to different resolutions to increase the local receptive fields. Thus the proposed model can react to more complex patterns of input images. And we design a novel multi-path residual dense block (MRDB) as the model’s fundamental component to extract features. The proposed MRDBs can improve performance by combining dense feature learning and novel multi-path residual learning. Experiments show that the proposed model outperforms the state-of-the-art methods in terms of quantitative and qualitative results. Furthermore, we propose a data synthesis pipeline to generate perceptually identical SDR images from their HDR counterparts. These SDR-HDR pairs are used to create a new dataset. Experiments demonstrate that models trained with our new dataset prevent color shift and preserve creative intent.