钢框架非比例损伤识别

提出能够鉴别损伤位置和截面损伤严重程度的钢框架非比例破坏监测方法。所提出的方法由两部分组成,一是损伤识别,二是损伤严重程度分析。损伤识别和严重程度分析均以结构动力性质的变化作为损伤的判据,以此来确定损伤位置和严重程度。所提方法中损伤判别的重要特点是能够在损伤严重程度分析之前,精确地区分不同的损伤区域。与整个结构尺寸相比,损伤区域相对较小,因此,确定损伤严重程度的计算量更小。检测方法的另一个特点是有些特征值或振型对噪声相对不敏感。采用所提检测方法对几个框架进行验证,结果表明,该方法能够成功地检测及量化截面的损伤。     

客机机身剖面外形的优化设计

机身剖面外形设计是客机总体设计的一个重要方面。在分析机身剖面类型和设计流程的基础上,研究了客机机身剖面的参数化设计方法。根据客舱段的主要控制参数,利用优化方法对机身剖面外形参数进行优化设计,开发了快速生成最优客舱剖面外形的计算程序,为客舱设计提供了一个工具,应用这一设计工具进行客舱剖面外形设计,可以有效缩短设计时间。     

Adjoint Monte Carlo neutron transport using cross-section probability table representation

The probability table representation of cross-sections is generally used to deal with neutron interactions in the unresolved energy range. In the frame of neutron transport methods, the capability of the probability table representation of cross-sections on the whole neutron energy range has been mentioned by Cullen (1974) and it has been already demonstrated for the Monte Carlo transport calculations by Zheng et al. (1998). Such an advantage is also illustrated here with a simple neutron propagation configuration dealt with the TRIPOLI-4 Monte Carlo transport code.     

Nuclear data requirements for the ADS conceptual design EFIT: Uncertainty and sensitivity study

In this paper, we assess the impact of activation cross-section uncertainties on relevant fuel cycle parameters for a conceptual design of a modular European Facility for Industrial Transmutation (EFIT) with a “double strata” fuel cycle. Next, the nuclear data requirements are evaluated so that the parameters can meet the assigned design target accuracies. Different discharge burn-up levels are considered: a low burn-up, corresponding to the equilibrium cycle, and a high burn-up level, simulating the effects on the fuel of the multi-recycling scenario.     

静定桁架结构截面最优化设计的功定向配置法

以Kuhn-Tucker函数的极值条件为出发点,证明多节点位移与多杆内应力约束下结构重量最轻化问题可转化为单约束问题来求解,可将位移—重量分配准则和应力—重量分配准则转化为功定向分配准则。利用功能互等定理,将位移与应力约束统一为功的定向约束,以此为基础构造出功定向配置法,以各承受节点载荷的节点位移作为约束控制点,组成具有正定性质的位移约束;或用位移约束与应力约束的组合,构成具有正定性质的位移与应力约束;结合位移极射法和满应力法获取最优解。并用二杆和六杆静定桁架结构的算例验证相关理论和算法。     

Proportioning of reinforced concrete column sections

Relatively simplistic recommendations are widely available for the preliminary sizing of reinforced concrete columns. A simple, more precise approach is developed herein. The neutral axis depth and reinforcement required to provide a desired resistance to axial load and moment was determined analytically for the common case of symmetrically distributed reinforcement. Readily available solver routines are used to determine section dimensions required to provide the desired axial and flexural strength, for a desired reinforcement ratio. The approach is illustrated for two examples, including a case in which independent load combinations cause bending about each principal axis of the section.     

浅析钢筋混凝土框筒结构的加固

钢筋混凝土框筒结构是工业与民用建筑筑中经常采用的结构形式之一。由于抗震性能要求的提高、使用功能的改变以及施工过程中原有设计要求的变更等原因,对已建钢筋混凝土框筒结构进行结构加固设计也是工程师在实际工程中经常遇见的设计问题之一。钢筋混凝土框筒结构的加固设计,应根据工程的实际悄况选用适宜的加固方法,进行仔细的计算验证,并与实际施工方法紧密结合,在做到安全可靠的同时满足经济合理的要求。本文结合具体的工程实例,对一幢二十一层的钢筋混凝土框筒结构的加层加固设计进行了扼要的介绍,同时对增大柱截面加固法进行了初步的探讨,本文介结的结构加固方法可供类似工程的加固设计参考。     

杨树绒毛纤维的性能及其采集应用

探讨杨树绒毛纤维的性能及其采集应用情况。介绍了杨树绒毛纤维的吸湿性能、浸润性能、蓬松性、热学性能和耐洗涤性能以及在纺织中的开发及应用情况。通过对杨树绒毛纤维的采集实践对比,认为:采集穗状花序,且采用晾晒制得杨树绒毛纤维,其杂质较少,便于控制采集的质量和数量。     

Graph-based reinforcement learning for discrete cross-section optimization of planar steel frames

A combined method of graph embedding (GE) and reinforcement learning (RL) is developed for discrete cross-section optimization of planar steel frames, in which the section size of each member is selected from a prescribed list of standard sections. The RL agent aims to minimize the total structural volume under various practical constraints. GE is a method for extracting features from data with irregular connectivity. While most of the existing GE methods aim at extracting node features, an improved GE formulation is developed for extracting features of edges associated with members in this study. Owing to the proposed GE operations, the agent is capable of grasping the structural property of columns and beams considering their connectivity in a frame with an arbitrary size as feature vectors of the same size. Using the feature vectors, the agent is trained to estimate the accurate return associated with each action and to take proper actions on which members to reduce or increase their size using an RL algorithm. The applicability of the proposed method is versatile because various frames different in the numbers of nodes and members can be used for both training and application phases. In the numerical examples, the trained agents outperform a particle swarm optimization method as a benchmark in terms of both computational cost and design quality for cross-sectional design changes; the agents successfully assign reasonable cross-sections considering the geometry, connectivity, and support and load conditions of the frames.     

Resistance of stainless steel CHS columns based on cross-section deformation capacity

A conceptually new structural design approach has recently been proposed by the authors to predict the resistance of stainless steel members subjected to various types of loading with cross-sections formed from thin flat plates including angles, channels, lipped channels, I-sections and rectangular hollow sections (RHS). The proposed method does not follow the traditional cross-section classification approach, which primarily relies on the assumption of a bilinear, elastic-perfectly-plastic material model. Instead, deformation capacity of a cross-section is determined directly from the local buckling characteristics of the constituent plate elements. This is then used to obtain the corresponding local buckling stress utilising an appropriate material model. This basic concept is extended herein to predict compression resistance of stainless steel columns with circular hollow sections (CHS). Available test and finite element (FE) results have been used to develop the basic design equation to predict the compression resistance of cross-sections and to propose column curves to determine flexural buckling resistances. The predicted resistances have been compared to those obtained using the current Eurocode; the predictions are significantly more accurate and more consistent than those given by the existing Eurocode.