Laser remelting induces grain refinement and properties enhancement in high-speed laser cladding AlCoCrFeNi high-entropy alloy coatings

Laser Remelting Induces Grain Refinement and Properties Enhancement in High-Speed Laser Cladding Alcocrfeni High-Entropy Alloy Coatings

In order to study the effect of laser remelting on the properties of H13 steel, WC/Ni-based coating was prepared on its surface by laser cladding. The macroscopic morphology, microstructure, element distribution and mechanical properties of the coatings before and after remelting were compared by means of scanning electron microscopy, energy spectrometer, XRD, hardness tester and friction and wear tester. The results show that the in-situ prepared cladding layer has no obvious defects such as cracks and pores. With the increase of laser energy density, the microstructure distribution in the remelted coating becomes more and more uniform, and the surface roughness of the coating becomes lower and lower. However, the microhardness and wear resistance of the coating have no obvious difference. When the laser energy density reaches 1500 J/mm, the ceramic particles inside the remelting coating basically disappear, the coating uniformity and surface smoothness are the best, the hardness reaches 768 HV0.5, and the wear resistance is 3.75 times that of the substrate. 

Effect of laser remelting on the organization and properties of WC/Ni-based coatings generated in situ by laser cladding

Experiments on imbibition mechanisms of fractured reservoirs by microfluidic chips

Multiple-Point Geostatistics-Based Three-Dimensional Automatic Geological Modeling and Uncertainty Analysis for Borehole Data

Effects of laser remelting on microstructural characteristics of Ni-WC composite coatings produced by laser hot wire cladding

A method of reconstructing 3D model from 2D geological cross-section based on self-adaptive spatial sampling: A case study of Cretaceous McMurray reservoirs in a block of Canada

A training image optimization method in multiple-point geostatistics and its application in geological modeling

The invention relates to a seismic reservoir inversion method based on multi-point geological statistics. A data template used in the method comprises a template core. Furthermore 30-50 data modes arerandomly selected at each to-be-estimated part as to-be-selected data modes. The to-be-selected data modes comprise a mode inner core which corresponds with the template core. The seismic reservoir inversion method has advantages of simulating the to-be-evaluated point through selecting partial data modes, improving operation efficiency, ensuring high inversion efficiency in a core mode and a peripheral mode, and improving continuity and accuracy of an inversion result.

Seismic reservoir inversion method based on multi-point geological statistics

Training images, as an important modeling parameter in the multi-point geostatistics, directly determine the effect of modeling. It’s necessary to evaluate and select the candidate training image before using the multi-point geostatistical modeling. The overall repetition probability is not sufficient to describe the relationship of single data events in the training image. Based on the understanding, a new method was presented in this paper to select the training image. As is shown in the basic idea, the repetition probability distribution of a single data event was used to characterize the type and stationarity of the sedimentary pattern in the training image. The repetition probability mean value and deviation of single data event reflected the stationarity of the geological model of the training image; the rate of data event mismatching reflected the diversity of geological patterns in training images. The selection of optimal training image was achieved by combining the probability of repeated events and the probability of overall repetition of single data events. It’s illustrated in the simulation tests that a good training image has the advantages of high repetition probability compatibility, stable distribution of repeated probability of single data event, low probability mean value, low probability deviation and low rate of mismatching. The method can quickly select the training image and provide the basic guarantee for multi-point geostatistical simulations.

/pdf/a-new-method-to-select-training-images-in-multi-point-2834zwlc0h.pdf

A New Method to Select Training Images in Multi-Point Geostatistics

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