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. 相似文献
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. 相似文献
As well known, the methods of remote sensing and Bowen Ratio for retrieving surface flux are based on energy balance closure; however, in most cases, surface energy observed in experiment is lack of closure. There are two main causes for this: one is from the errors of the observation devices and the differences of their observational scale; the other lies in the effect of horizontal advection on the surface flux measurement. Therefore, it is very important to estimate the effects of horizontal advection quantitatively. Based on the local advection theory and the surface experiment, a model has been proposed for correcting the effect of horizontal advection on surface flux measurement, in which the relationship between the fetch of the measurement and pixel size for remote sensed data was considered. By means of numerical simulations, the sensitivities of the main parameters in the model and the scaling problems of horizontal advection were analyzed. At last, by using the observational data acquired in agricultural field with relatively homogeneous surface, the model was validated.
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. 相似文献
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. 相似文献