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1.
针对目前工业CT图像转换为3D打印G代码方法效率低的问题,提出一种基于邻层数据匹配的工业CT图像直接转换成G代码的方法。首先采用Canny算子提取工业CT图像的轮廓,然后处理轮廓分叉问题,实现邻层间几何信息数据匹配,其次进行邻层间轮廓插值以满足3D打印层间厚度要求,从而避免"阶梯效应",最后通过填充编码得到用于3D打印的G代码。使用本文提出的方法,轮毂CT图像转换为G代码的时间为10.5 s,耗时远小于其他间接转换方法;3D打印出的轮毂无"阶梯效应",平均尺寸误差率为0.25%。实验结果表明,该方法不涉及中间格式,转换效率高,转换误差与传统方法相当,适用于具有复杂内腔结构的零件。 相似文献
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Many bridges that lie within possible tsunami inundation zones are critical links in transport networks. Some efforts have been made to determine the effects of tsunamis on bridges, but only a limited range of published design guidelines are available. Therefore, it is necessary to further investigate the effects of tsunamis on bridges. In the current study, physical modeling experiments were carried out to measure bore impact forces and pressures for various tsunami bore strengths on a bridge deck with different abutment types (wing wall and spill-through) and different opening and submergence ratios. The experiments were conducted in a wave flume with dimensions of 14 × 1.2 × 0.8 m (length × width × height), equipped with an automatic gate designed to generate a tsunami bore. The horizontal and vertical forces showed an increasing trend with increasing submergence ratio for both types of abutment. However, the horizontal force showed a decreasing trend as the opening ratio decreased, while the vertical force initially increased as the opening ratio decreased, until it reached a peak value, and then it started to decrease. The overall shapes of the results for both types of abutment are similar, with higher values for spill-through abutments due to their lower energy dissipation rates. Based on the experimental data, empirical equations are proposed for estimation of tsunami loads as a function of opening and submergence ratios. 相似文献
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《International Journal of Hydrogen Energy》2022,47(59):25007-25021
Fluid sloshing usually causes serious safety issues on the dynamic stability and propellant thermal management during the powered-flight phase of launch vehicle. With the wide using of cryogenic propellants, the coupled thermo-mechanical performance during fluid sloshing becomes more prominent. In the present study, one numerical model is established to simulate fluid sloshing by using the VOF method coupled with the mesh motion treatment. The phase change occurring within the tank is considered. Both the experimental validation and mesh sensitivity analysis are made. It shows that present numerical model is acceptable. Based on the developed numerical model, the effect of different super gravity accelerations on fluid sloshing hydrodynamic characteristic is numerically researched. The fluid pressure variation, the sloshing force and sloshing moment, the interface dynamic response and the interface shape variation are investigated, respectively. It shows that the gravity acceleration has caused obvious influences on fluid sloshing characteristic. When the gravity acceleration is higher than 4g0, fluid sloshing becomes more obvious and must be paid enough attention. With some valuable conclusions obtained, the present work is of great significance for in-depth understanding of fluid sloshing mechanism. 相似文献
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《Advanced Powder Technology》2022,33(8):103602
To operate a bag filter continuously, pulse-jet cleaning of dust particles from the filter medium is commonly required, and the pulse-jet pressure significantly affects the filter performance. In this study, the accumulation structure of residual dust particles inside and on the surface of a filter medium at different pulse-jet pressures was investigated by constructing a simple model, and the influence of the dust structure on the filter performance was clarified. Using a simple model, we determined the effective ratio of filtration area β, which represents the ratio of the filterable area to the total filtration area, the true resistance coefficient due to the primary dust layer ζp’ thinly deposited on the filter surface, and the true resistance coefficient inside the filter media itself ζf’. The effective ratio of filtration area β decreased with operation time for all pulse-jet pressures; however, it maintained a high value when the pulse-jet pressure was high. The validity of β analyzed by the model was verified using two different methods, and the results showed good agreement, indicating that the model is effective in identifying real conditions. The true resistance coefficient due to the primary dust layer ζp’ decreased as the pulse-jet pressure increased; however, the true resistance coefficient inside the filter media itself ζf’ was the highest at 0.5 MPa. In addition, the dust collection efficiency was different at each pulse-jet pressure, which was considered to be caused by the difference in the dust particle accumulation structure. 相似文献
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Aggregate question answering essentially returns answers for given questions by obtaining query graphs with unique dependencies between values and corresponding objects. Word order dependency, as the key to uniquely identify dependency of the query graph, reflects the dependencies between the words in the question. However, due to the semantic gap caused by the expression difference between questions encoded with word vectors and query graphs represented with logical formal elements, it is not trivial to match the correct query graph for the question. Most existing approaches design more expressive query graphs for complex questions and rank them just by directly calculating their similarities, ignoring the semantic gap between them. In this paper, we propose a novel Structure-sensitive Semantic Matching(SSM) approach that learns aligned representations of dependencies in questions and query graphs to eliminate their gap. First, we propose a cross-structure matching module to bridge the gap between two modalities(i.e., textual question and query graph). Then, we propose an entropy-based gated AQG filter to remove the structural noise caused by the uncertainty of dependencies. Finally, we present a two-channel query graph representation that fuses the semantics of abstract structure and grounding content of the query graph explicitly. Experimental results show that SSM could learn aligned representations of questions and query graphs to eliminate the gaps between their dependencies, and improves up to 12% (F1 score) on aggregation questions of two benchmark datasets. 相似文献
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为解决矿山充填体强度的设计问题,提高矿山充填体的强度动态调整能力,本文通过调查国内百座矿山现场充填体强度实际数据,采用SVM方法建立充填体强度智能预测模型,对70组训练样本数据进行训练,采用BP神经网络模型与SVM模型的预测结果进行比较。结果表明:SVM预测模型的最大误差为3.52%,平均误差为2.41%。BP预测模型的最大误差为10.98%,平均误差为7.01%。SVM模型比BP模型预测精度更高,误差更小。采用SVM模型对三山岛金矿充填体强度进行预测,一步骤回采矿房充填体强度1.02MPa,推荐灰砂比1:12,二步骤回采矿房充填体强度0.86MPa,推荐灰砂比1:16。现场采场充填效果良好,未发生充填体失稳现象。基于SVM的充填体强度智能匹配模型能够在满足采场稳定性的前提下,减少充填成本,提高矿山的经济效益。 相似文献