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
Using the decimetric (700–1500 MHz) radio spectrometer and the synchronous observational system with high temporal resolution at four frequencies (1420, 2130, 2840 and 4260 MHz) of Yunnan Observatory, two rare events were observed on 2001 June 24 and 1990 July 30. The former was a small radio burst exhibiting pulsations with short periods (about 29, 40 and 100 ms) in the impulsive phase. The latter was a large radio burst, which at 2840 MHz produced radio pulsations with period of about 30 ms. This paper focuses on pulsations with very short periods in the range of 29–40 ms. The mechanism of generation of such pulsations may be modulation of radio radiation by the periodic trains of whistler packets originating in unstable regions of the corona. Alternatively, these pulsations can be attributed to wave-wave non-linear interactions of electrostatic upper hybrid waves driven by beams of precipitating electrons in flaring loops. 相似文献
A type IV radio burst accompanied by several normal- and reverse-drifting type III bursts, multiple long-term quasi-periodic pulsations and spikes was observed with the radio spectrometers (1.0–2.0 and 2.6–3.8 GHz) at National Astronomical Observatories of China (NAOC) on 23 September 1998. In combination with the images of Siberian Solar Radio Telescope (SSRT) of Russia, the complex and multiple magnetic structures inferred from the radio bursts reveal the existence of both large-scale and small-scale magnetic structures. This event suggests that the geometries of coronal magnetic fields contain multiple discrete electron acceleration/injection sites at different heights, and extended open and closed magnetic field lines. It can be shown that the energetic electrons gain access to open, diverging and closed field lines thus producing different types of radio bursts. From the characteristics of position, polarization, dispersion and displacement of the sources, the model of the type IV event is supported, which involves synchrotron emission from the electrons confined by the rapid scattering through the interaction of hydromagnetic wave with particles. 相似文献
Long-term measurement of carbon metabolism of old-growth forests is critical to predict their behaviors and to reduce the uncertainties of carbon accounting under changing climate. Eddy covariance technology was applied to investigate the long-term carbon exchange over a 200 year-old Chinese broad-leaved Korean pine mixed forest in the Changbai Mountains (128°28′E and 42°24′N, Jilin Province, P. R. China) since August 2002. On the data obtained with open-path eddy covariance system and CO2 profile measurement system from Jan. 2003 to Dec. 2004, this paper reports (i) annual and seasonal variation of FNEE, FGPP and RE; (ii) regulation of environmental factors on phase and amplitude of ecosystem CO2 uptake and release Corrections due to storage and friction velocity were applied to the eddy carbon flux.
LAI and soil temperature determined the seasonal and annual dynamics of FGPP and RE separately. VPD and air temperature regulated ecosystem photosynthesis at finer scales in growing seasons. Water condition at the root zone exerted a significant influence on ecosystem maintenance carbon metabolism of this forest in winter.
The forest was a net sink of atmospheric CO2 and sequestered −449 g C·m−2 during the study period; −278 and −171 gC·m−2 for 2003 and 2004 respectively. FGPP and FRE over 2003 and 2004 were −1332, −1294 g C·m−2. and 1054, 1124 g C·m−2 respectively. This study shows that old-growth forest can be a strong net carbon sink of atmospheric CO2.
There was significant seasonal and annual variation in carbon metabolism. In winter, there was weak photosynthesis while the ecosystem emitted CO2. Carbon exchanges were active in spring and fall but contributed little to carbon sequestration on an annual scale. The summer is the most significant season as far as ecosystem carbon balance is concerned. The 90 days of summer contributed 66.9, 68.9% of FGPP, and 60.4, 62.1% of RE of the entire year.