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The purpose of this paper is to study the transition process from the crystalline particles appearing before the pump inlet to the stable operation of the pump.

Firstly, a modeling test method was put forward for the high-temperature molten salt pump. Then, according to a modeling test scheme, the experiment of the solid–liquid two-phase flow was carried out by using a model pump similar to the prototype pump. Meanwhile, the numerical method to simulate the transition process of a molten salt pump was studied, and the correctness of the numerical model was verified by the experimental results. Finally, the transition process of the molten salt pump was studied by the verified numerical model in detail.

In the simulation of the transition process, it is more accurate to judge the end of the transition process based on the unchanged particle volume fraction (PVF) at the pump outlet than on the periodic fluctuation of the outlet pressure. The outlet pressure is closely related to the PVF in the pump. The variation of the outlet pressure is slightly prior to that of the PVF at the pump outlet and mainly affected by the PVF in the impeller and volute. After 0.63 s, the PVF at each monitoring point changes periodically, and the time-averaged value does not change with time.

This study is of great significance to further improve the design method of molten salt pump and predict the abrasion characteristic of the pump due to interactions with solid particles.

A numerical method is established to simulate the transition process of a molten salt pump, and a method is proposed to verify the numerical model of two-phase flow by modeling test.

The purpose of this paper is to accurately predict the cavitation performance of a cryogenic pump and reveal the influence of the inlet pressure, the surface roughness and the flow rate on the cavitation performance.

Firstly, the Zwart cavitation model was modified by considering the thermodynamic effect. Secondly, the feasibility of the modified model was validated by the cavitation test of a hydrofoil. Thirdly, the effects of the inlet pressure, the surface roughness and the flow rate on cavitation flow in the cryogenic pump were studied by using the modified cavitation model.

The modified cavitation model can predict the cavitation performance of the cryogenic pump more accurately than the Zwart cavitation model. The thermodynamic effect inhibits cavitation development to a certain extent. The higher the vapor volume fraction, the lower the pressure and the lower the temperature. At the initial stage of the cavitation, the head increases first and then decreases with the increase of the roughness. When the cavitation develops to a certain degree, the head decreases with the increase of the roughness. With the decrease of the flow rate, the hydraulic loss increases and the cavitation at the impeller intensifies.

A cavitation model considering the thermodynamic effect is proposed. The mechanism of the influence of the roughness on the performance of the cryogenic pump is revealed from two aspects. Taking the hydraulic loss as a bridge, the relationships among flow rates, vapor volume fractions, streamlines, temperatures and pressures are established.

No adequate study on scientific analysis of surface metal fiber (SMF) arrangement of electromagnetic shielding fabric (EMSF) and influence on shielding effectiveness (SE) is available at present.

This paper recognizes the SMF region and constructs a binary feature matrix according to edge condition, width condition and gray condition using the computer image analysis technique based on the construction of the surface digitized image of the EMSF. Three parameters of coverage, dispersion and uniformity are proposed to describe the SMF arrangement. Then experiments and testing samples are designed to analyze the relationship between the three parameters and the SE.

Results show that the proposed method can accurately recognize the SMF of the EMSF, the coverage, dispersion and uniformity can describe three aspects of the SMF arrangement of percentage content, porosity and orientation, and the three parameters are positively, negatively and positively correlated to the SE, respectively.

The research in this paper provides the basis for further description of the SMF arrangement of the EMSF, possesses the significance for the study of the shielding mechanism, transmission model, electromagnetic performance and rapid non-destructive evaluation of the EMSF and provide a new idea for the study on the shielding theory and application of the EMSF.

This paper describes the organic geochemical characteristics and their roles on barium enrichment in the No. 2 Coal from Huanglong Jurassic Coalfield, China. A total of 18 bench samples were taken from Huangling Mine 2. The average content of barium (3701 mg/kg) was about 23 times higher than that of common world coals. Terrestrial higher plants were the main coal-forming parent material. Relying on the parameters of OEP, Pr/Ph and so on, there is little correlation between organic geochemical characteristics and barium enrichment. Therefore, organic material has little influence on the process of coal-forming and the enrichment of barium.

The purpose of this paper is to focus on the design of automated fabric defect detection based on cascaded low-rank decomposition and to maintain high quality control in textile manufacturing.

This paper proposed a fabric defect detection algorithm based on cascaded low-rank decomposition. First, the constructed Gabor feature matrix is divided into a low-rank matrix and sparse matrix using low-rank decomposition technique, and the sparse matrix is used as priori matrix where higher values indicate a higher probability of abnormality. Second, we conducted the second low-rank decomposition for the constructed texton feature matrix under the guidance of the priori matrix. Finally, an improved adaptive threshold segmentation algorithm was adopted to segment the saliency map generated by the final sparse matrix to locate the defect regions.

The proposed method was evaluated on the public fabric image databases. By comparing with the ground-truth, the average detection rate of 98.26% was obtained and is superior to the state-of-the-art.

The cascaded low-rank decomposition was first proposed and applied into the fabric defect detection. The quantitative value shows the effectiveness of the detection method. Hence, the proposed method can be used for accurate defect detection and automated analysis system.

Fabric defect detection plays an important role in textile quality control. The purpose of this paper is to propose a fabric defect detection algorithm via context-based local texture saliency analysis.

In the proposed algorithm, a target image is first divided into blocks, then the Local Binary Pattern (LBP) technique is used to extract the texture features of blocks. Second, for a given image block, several other blocks are randomly chosen for calculating the LBP contrast between a given block and the randomly chosen blocks. Based on the obtained contrast information, a saliency map is produced. Finally, saliency map is segmented by using an optimal threshold, which is obtained by an iterative approach.

The experimental results show that the proposed algorithm, integrating local texture features and global image texture information, can detect texture defects effectively.

In this paper, a novel fabric defect detection algorithm via context-based local texture saliency analysis is proposed.

Fabric defect detection plays an important role in textile quality control. The purpose of this paper is to propose a fabric defect detection algorithm using learned dictionary-based visual saliency.

First, the test fabric image is splitted into image blocks, and the learned dictionary with normal samples and defective sample is constructed by selecting the image block local binary pattern features with highest or lowest similarity comparing with the average feature vector; second, the first L largest correlation coefficients between each test image block and the dictionary are calculated, and other correlation coefficients are set to zeros; third, the sum of the non-zeros coefficients corresponding to defective samples is used to generate saliency map; finally, an improve valley-emphasis method can efficiently segment the defect region.

Experimental results demonstrate that the generated saliency map by the proposed method can efficiently outstand defect region comparing with the state-of-the-art, and segment results can precisely localize defect region.

In this paper, a novel fabric defect detection scheme is proposed via learned dictionary-based visual saliency.

Hexahedral meshing is one of the most important steps in performing an accurate simulation using the finite element analysis (FEA). However, the current hexahedral meshing method in the industry is a nonautomatic and inefficient method, i.e. manually decomposing the model into suitable blocks and obtaining the hexahedral mesh from these blocks by mapping or sweeping algorithms. The purpose of this paper is to propose an almost automatic decomposition algorithm based on the 3D frame field and model features to replace the traditional time-consuming and laborious manual decomposition method.

The proposed algorithm is based on the 3D frame field and features, where features are used to construct feature-cutting surfaces and the 3D frame field is used to construct singular-cutting surfaces. The feature-cutting surfaces constructed from concave features first reduce the complexity of the model and decompose it into some coarse blocks. Then, an improved 3D frame field algorithm is performed on these coarse blocks to extract the singular structure and construct singular-cutting surfaces to further decompose the coarse blocks. In most modeling examples, the proposed algorithm uses both types of cutting surfaces to decompose models fully automatically. In a few examples with special requirements for hexahedral meshes, the algorithm requires manual input of some user-defined cutting surfaces and constructs different singular-cutting surfaces to ensure the effectiveness of the decomposition.

Benefiting from the feature decomposition and the 3D frame field algorithm, the output blocks of the proposed algorithm have no inner singular structure and are suitable for the mapping or sweeping algorithm. The introduction of internal constraints makes 3D frame field generation more robust in this paper, and it can automatically correct some invalid 3–5 singular structures. In a few examples with special requirements, the proposed algorithm successfully generates valid blocks even though the singular structure of the model is modified by user-defined cutting surfaces.

The proposed algorithm takes the advantage of feature decomposition and the 3D frame field to generate suitable blocks for a mapping or sweeping algorithm, which saves a lot of simulation time and requires less experience. The user-defined cutting surfaces enable the creation of special hexahedral meshes, which was difficult with previous algorithms. An improved 3D frame field generation method is proposed to correct some invalid singular structures and improve the robustness of the previous methods.

The purpose of this paper is to find an efficient fabric defect detection algorithm by means of exploring the sparsity characteristics of main local binary pattern (MLBP) extracted from the original fabric texture.

In the proposed algorithm, original LBP features are extracted from the fabric texture to be detected, and MLBP are selected by occurrence probability. Second, a dictionary is established with MLBP atoms which can sparsely represent all the LBP. Then, the value of the gray-scale difference between gray level of neighborhood pixels and the central pixel, and the mean of the difference which has the same MLBP feature are calculated. And then, the defect-contained image is reconstructed as normal texture image. Finally, the residual is calculated between reconstructed and original images, and a simple threshold segmentation method can divide the residual image, and the defective region is detected.

The experiment result shows that the fabric texture can be more efficiently reconstructed, and the proposed method achieves better defect detection performance. Moreover, it offers empirical insights about how to exploit the sparsity of one certain feature, e.g. LBP.

Because of the selected research approach, the results may lack generalizability in chambray. Therefore, researchers are encouraged to test the proposed propositions further.

In this paper, a novel fabric defect detection method which extracts the sparsity of MLBP features is proposed.