Effects of the critical breakdown path on the ignition performance in heaterless hollow cathodes

The critical breakdown path(CBP)has a significant impact on the breakdown voltage curve and the ignition time of heaterless hollow cathodes(HHCs).To determine the pattern of the variation in the CBP position and its impact on ignition performance,a numerical model named the CBP evaluation(CBPE)was established in this paper to calculate the CBP of a HHC.The CBPE model can be used to screen various potential breakdown paths to identify those that are most likely to satisfy the Townsend breakdown conditions,which are denoted as CBPs.To verify the calculation accuracy of the CBPE model,4.5 A-level HHC ignition tests were conducted on HHCs with three different structures.By comparing the test results and the calculated results of the breakdown voltage,the calculation errors of the CBPE under three HHC conditions ranged from 1.6%to 5.8%,and the trends of the calculated results were consistent with those of the test results.The ignition test also showed the characteristics of the breakdown voltage curve and the ignition time for the three HHCs.Based on the CBPE model,an in-depth analysis was conducted on the mechanism of the patterns revealed by the tests.The main conclusions are presented as follows:(1)the CBP always shifts from the long path to the short path in the HHCs with an increasing gas flow rate;and(2)the ignition time of the HHCs depends on the position of the CBP because different CBP positions can cause different mechanisms of heat transfer from the plasma to the emitter.This study can guide the optimization of the CBP position and the corresponding ignition times of HHCs.

Reconstruction of geological surfaces using chance-constrained programming

Geological surface modeling is typically based on seismic data, well data, and models of regional geology. However, structural interpretation of these data is error-prone, especially in the absence of structural morphology information, Existing geological surface models suffer from high levels of uncertainty, which exposes oil and gas exploration and development to additional risk. In this paper, we achieve a reconstruction of the uncertainties associated with a geological surface using chance-constrained programming based on multisource data. We also quantifi ed the uncertainty of the modeling data and added a disturbance term to the objective function. Finally, we verifi ed the applicability of the method using both synthetic and real fault data. We found that the reconstructed geological models met geological rules and reduced the reconstruction uncertainty.

Seismic Attribute Analysis with Saliency Detection in Fractional Fourier Transform Domain

Most image saliency detection models are dependent on prior knowledge and demand high computational cost. However, spectral residual(SR) and phase spectrum of the Fourier transform(PFT) models are simple and fast saliency detection approaches based on two-dimensional Fourier transform without the prior knowledge. For seismic data, the geological structure of the underground rock formation changes more obviously in the time direction. Therefore, one-dimensional Fourier transform is more suitable for seismic saliency detection. Fractional Fourier transform(FrFT) is an improved algorithm for Fourier transform, therefore we propose the seismic SR and PFT models in one-dimensional FrF T domain to obtain more detailed saliency maps. These two models use the amplitude and phase information in FrFT domain to construct the corresponding saliency maps in spatial domain. By means of these two models, several saliency maps at different fractional orders can be obtained for seismic attribute analysis. These saliency maps can characterize the detailed features and highlight the object areas, which is more conducive to determine the location of reservoirs. The performance of the proposed method is assessed on both simulated and real seismic data. The results indicate that our method is effective and convenient for seismic attribute extraction with good noise immunity.

A fast and simple algorithm for calculating flow accumulation matrices from raster digital elevation

Calculating the flow accumulation matrix is an essential step for many hydrological and topographical analyses.This study gives an overview of the existing algorithms for flow accumulation calculations for singleflow direction matrices.A fast and simple algorithm for calculating flow accumulation matrices is proposed in this study.The algorithm identifies three types of cells in a flow direction matrix: source cells,intersection cells,and interior cells.It traverses all source cells and traces the downstream interior cells of each source cell until an intersection cell is encountered.An intersection cell is treated as an interior cell when its last drainage path is traced and the tracing continues with its downstream cells.Experiments are conducted on thirty datasets with a resolution of 3 m.Compared with the existing algorithms for flow accumulation calculation,the proposed algorithm is easy to implement,runs much faster than existing algorithms,and generally requires less memory space.

Prestack Multi-Gather Simultaneous Inversion of Elastic Parameters Using Multiple Regularization Constraints

Inversion of Young’s modulus,Poisson’s ratio and density from pre-stack seismic data has been proved to be feasible and effective.However,the existing methods do not take full advantage of the prior information.Without considering the lateral continuity of the inversion results,these methods need to invert the reflectivity first.In this paper,we propose multi-gather simultaneous inversion for pre-stack seismic data.Meanwhile,the total variation(TV)regularization,L_1 norm regularization and initial model constraint are used.In order to solve the objective function contains L_1norm,TV norm and L_2 norm,we develop an algorithm based on split Bregman iteration.The main advantages of our method are as follows:(1)The elastic parameters are calculated directly from objective function rather than from their reflectivity,therefore the stability and accuracy of the inversion process can be ensured.(2)The inversion results are more in accordance with the prior geological information.(3)The lateral continuity of the inversion results are improved.The proposed method is illustrated by theoretical model data and experimented with a 2-D field data.

LD-Pumped Random Fiber Laser Based on Erbium-Ytterbium Co-Doped Fiber

In this paper,a cladding-pumped erbium-ytterbium co-doped random fiber laser(EYRFL)operating at 1550 nm with high power laser diode(LD)is proposed and experimentally demonstrated for the first time.The laser cavity includes a 5-m-long erbium-ytterbium co-doped fiber that serves as the gain medium,as well as a 2-km-long single-mode fiber(SMF)to provide random distributed feedback.As a result,stable 2.14 W of 1550 nm random lasing at 9.80 W of 976 nm LD pump power and a linear output with the slope efficiency as 22.7%are generated.This simple and novel random fiber laser could provide a promising way to develop high power 1.5μm light sources.

Efficient Priority-Flood depression filling in raster digital elevation models

Depressions in raster digital elevation models(DEM)present a challenge for extracting hydrological networks.They are commonly filled before subsequent algorithms are further applied.Among existing algorithms for filling depressions,the Priority-Flood algorithm runs the fastest.In this study,we propose an improved variant over the fastest existing sequential variant of the Priority-Flood algorithm for filling depressions in floating-point DEMs.The proposed variant introduces a series of improvements and greatly reduces the number of cells that need to be processed by the priority queue(PQ),the key data structure used in the algorithm.The proposed variant is evaluated based on statistics from 30 experiments.On average,our proposed variant reduces the number of cells processed by the PQ by around 70%.The speed-up ratios of our proposed variant over the existing fastest variant of the Priority-Flood algorithm range from 31%to 52%,with an average of 45%.The proposed variant can be used to fill depressions in large DEMs in much less time and in the parallel implementation of the Priority-Flood algorithm to further reduce the running time for processing huge DEMs that cannot be dealt with easily on single computers.