Implementation of a coupled plastic damage distinct lattice spring model for dynamic crack propagation in geomaterials

This paper studies dynamic crack propagation by employing the distinct lattice spring model (DLSM) and 3‐dimensional (3D) printing technique. A damage‐plasticity model was developed and implemented in a 2D DLSM. Applicability of the damage‐plasticity DLSM was verified against analytical elastic solutions and experimental results for crack propagation. As a physical analogy, dynamic fracturing tests were conducted on 3D printed specimens using the split Hopkinson pressure bar. The dynamic stress intensity factors were recorded, and crack paths were captured by a high‐speed camera. A parametric study was conducted to find the influences of the parameters on cracking behaviors, including initial and peak fracture toughness, crack speed, and crack patterns. Finally, selection of parameters for the damage‐plasticity model was determined through the comparison of numerical predictions and the experimentally observed cracking features.     

A machine learning approach for predicting computational intensity and domain decomposition in parallel geoprocessing

ABSTRACT

High performance computing is required for fast geoprocessing of geospatial big data. Using spatial domains to represent computational intensity (CIT) and domain decomposition for parallelism are prominent strategies when designing parallel geoprocessing applications. Traditional domain decomposition is limited in evaluating the computational intensity, which often results in load imbalance and poor parallel performance. From the data science perspective, machine learning from Artificial Intelligence (AI) shows promise for better CIT evaluation. This paper proposes a machine learning approach for predicting computational intensity, followed by an optimized domain decomposition, which divides the spatial domain into balanced subdivisions based on the predicted CIT to achieve better parallel performance. The approach provides a reference framework on how various machine learning methods including feature selection and model training can be used in predicting computational intensity and optimizing parallel geoprocessing against different cases. Some comparative experiments between the approach and traditional methods were performed using the two cases, DEM generation from point clouds and spatial intersection on vector data. The results not only demonstrate the advantage of the approach, but also provide hints on how traditional GIS computation can be improved by the AI machine learning.     

建鲤基因组DNA的RAPD分析

用 1 5个随机引物对建鲤基因组DNA进行RAPD分析 ,结果显示 ,建鲤群体内的遗传相似系数为 0 91 81± 0 0 73 8,多态位点比例为 0 41 67,平均杂合度为 0 1 884。表明建鲤种质较纯 ,群体内的遗传变异程度较小。建鲤优良性状产生的分子学基础是等位基因的杂合程度较高     

江汉盆地钻孔测井资料确定地应力最大水平主压应力方向

利用钻孔测井资料并运用地层倾角测量信息分析法，给出了江汉盆地地应力最大水平主压应力方向为ＮＥ６０～６５°     

DEVELOPMENT STRATEGIES OF WATER AND LAND RESOURCES IN THE HEXI REGION,CHINA

ＤＥＶＥＬＯＰＭＥＮＴＳＴＲＡＴＥＧＩＥＳＯＦＷＡＴＥＲＡＮＤＬＡＮＤＲＥＳＯＵＲＣＥＳＩＮＴＨＥＨＥＸＩＲＥＧＩＯＮ，ＣＨＩＮＡ肖洪浪，高前兆，李福兴ＤＥＶＥＬＯＰＭＥＮＴＳＴＲＡＴＥＧＩＥＳＯＦＷＡＴＥＲＡＮＤＬＡＮＤＲＥＳＯＵＲＣＥＳＩＮＴＨＥ...     

法库地区大型韧性剪切带特征及成因机制

通过对辽北法库地区构造岩的观察和研究，确定了本区存在一大型推覆韧性剪切带。根据同构造侵入岩的同位素年龄，确定该韧性剪切带形成时期为海西－印支期，这与天山－兴安褶皱系最终形成时间相一致     

A New Solar Radio Spectrometer at 1.10-2.06 GHz and First Observational Results

An improved Solar Radio Spectrometer working at 1.10-2.06 GHz with much improved spectral and temporal resolution, has been accomplished by the National Astronomical Observatories and Hebei Semiconductor Research Institute, based on an old spectrometer at 1-2 GHz. The new spectrometer has a spectral resolution of 4 MHz and a temporal resolution of 5ms, with an instantaneous detectable range from 0.02 to 10 times of the quiet Sun flux. It can measure both left and right circular polarization with an accuracy of 10% in degree of polarization. Some results of preliminary observations that could not be recorded by the old spectrometer at 1-2 GHz are presented.     

毫秒级快速尖峰辐射事件的时间和频率特性

介绍了太阳22周峰年期间云南天文台米波射电频谱仪的观测结果。主要对1557个单个Spike的时间和频率分布作了统计分析，着重指出太阳快速精细结构的观测特征并定性地指出其产生机制。     

滇西北和邻区均衡重力异常特征及其壳内构造

本文以爱黎-海斯卡宁均衡补偿假设模式为基础,利用圆模板编制了滇西北及邻区均衡重力异常图。分析了异常的基本特征与壳内构造的关系,并初步探讨了均衡重力异常和强震活动的相关性。     

Silicon limitation on primary production and its destiny in Jiaozhou Bay, China——Ⅳ：Study on cross-bay transect from estuary to ocean

The authors analyzed the data collected in the Ecological Station Jiaozhou Bay from May 1991 to November 1994, including 12 seasonal investigations, to determine the characteristics, dynamic cycles and variation trends of the silicate in the bay. The results indicated that the rivers around Jiaozhou Bay provided abundant supply of silicate to the bay. The silicate concentration there depended on river flow variation. The horizontal variation of silicate concentration on the transect showed that the silicate concentration decreased with distance from shorelines. The vertical variation of it showed that silicate sank and deposited on the sea bottom by phytoplankton uptake and death, and zooplankton excretion. In this way, silicon would endlessly be transferred from terrestrial sources to the sea bottom. The silicon took up by phytoplankton and by other biogeochemical processes led to insufficient silicon supply for phytoplankton growth. In this paper, a 2D dynamic model of river flow versus silicate concentration was established by which silicate concentrations of 0.028–0.062 μmol/L in seawater was yielded by inputting certain seasonal unit river flows (m³/s), or in other words, the silicate supply rate; and when the unit river flow was set to zero, meaning no river input, the silicate concentrations were between 0.05–0.69 μmol/L in the bay. In terms of the silicate supply rate, Jiaozhou Bay was divided into three parts. The division shows a given river flow could generate several different silicon levels in corresponding regions, so as to the silicon-limitation levels to the phytoplankton in these regions. Another dynamic model of river flow versus primary production was set up by which the phytoplankton primary production of 5.21–15.55 (mgC/m²·d)/(m³/s) were obtained in our case at unit river flow values via silicate concentration or primary production conversion rate. Similarly, the values of primary production of 121.98–195.33 (mgC/m²·d) were achieved at zero unit river flow condition. A primary production conversion rate reflects the sensitivity to silicon depletion so as to different phytoplankton primary production and silicon requirements by different phytoplankton assemblages in different marine areas. In addition, the authors differentiated two equations (Eqs. 1 and 2) in the models to obtain the river flow variation that determines the silicate concentration variation, and in turn, the variation of primary production. These results proved further that nutrient silicon is a limiting factor for phytoplankton growth. This study was funded by NSFC (No. 40036010), and the Director's Fund of the Beihai Sea Monitoring Center, the State Oceanic Administration.