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The purpose of this paper is to present a full three-dimensional (3D) computational fluid dynamics (CFD) analysis of a rectangular asymmetric 3D diffuser utilizing seven turbulence models frequently used in engineering to assess the predictive capabilities of the turbulence models for separated flows in internal flows.

The structured computational grids are generated by means of the mesh generation tool IGG software package. The computational grids are imported into the commercial CFD code Fluent. The performance of the different turbulence models adopted has been systematically assessed by comparing the numerical results with the available experimental and direct numerical simulation/large eddy simulations data.

The comparisons show that the Reynolds stress model (RSM) evidently performs better than the other turbulence models for predicting wall pressure, velocity, and vorticity fields. Moreover, only the RSM can predict the separation bubble region around the top right corner, which is consistent with the experiment. It is found that the RSM can well predict Prandtl’s secondary flow of the second kind for considering turbulence anisotropy, whereas the other models cannot.

The paper utilizes seven turbulence models frequently used in engineering in detailed numerical investigations of a real 3D diffuser to expand the scope of application for various turbulence models. The studies are valuable for the proper use of the turbulence models, allowing the designers to understand the numerical results further and contributing to the modification of the turbulence models for 3D flows.

Selective laser sintering (SLS) is an essential technology in the field of additive manufacturing. However, SLS technology is limited by the traditional point-laser sintering method and has reached the bottleneck of efficiency improvement. This study aims to develop an image-shaped laser sintering (ISLS) system based on a digital micromirror device (DMD) to address this problem. The ISLS system uses an image-shaped laser light source with a size of 16 mm × 25.6 mm instead of the traditional SLS point-laser light source.

The ISLS system achieves large-area image-shaped sintering of polymer powder materials by moving the laser light source continuously in the x-direction and updating the sintering pattern synchronously, as well as by overlapping the splicing of adjacent sintering areas in the y-direction. A low-cost composite powder suitable for the ISLS system was prepared using polyether sulfone (PES), pinewood and carbon black (CB) powders as raw materials. Large-sized samples were fabricated using composite powder, and the microstructure, dimensional accuracy, geometric deviation, density, mechanical properties and feasible feature sizes were evaluated.

The experimental results demonstrate that the ISLS system is feasible and can print large-sized parts with good dimensional accuracy, acceptable geometric deviations, specific small-scale features and certain density and mechanical properties.

This study has achieved the transition from traditional point sintering mode to image-shaped surface sintering mode. It has provided a new approach to enhance the system performance of traditional SLS.

This paper aims to propose a suitable atomizing solidification chitosan (CS) gel liquid extrusion molding technology for the three dimensional (3D) printing method, and experiments verify the feasibility of this method.

This paper mainly uses experimental means, combined with theoretical research. The preparation method, solidification forming method and 3D printing method of CS gel solution were studied. The CS gel printing mechanism and printing error sources are analyzed on the basis of the CS gel ink printing results, printing performance with different ratios of components by constructing a gel print prototype, experiments evaluating the CS gel printing technology and the effects of the process parameters on the scaffold formation.

CS printing ink was prepared; the optimal formula was found; the 3 D printing experiment of CS was completed; the optimal printing parameters were obtained; and the reliability of the forming prototype, printing ink and gel printing process was verified, which allowed for the possibility to apply the 3 D printing technology to the manufacturing of a CS gel structure.

This study can provide theoretical and technical support for the potential application of CS 3 D printed gels in tissue engineering.

This study aims to examine the problems encountered during the establishment of the Central Police Academy (CPA) under the Nationalist regime from 1936 to 1949. While the authoritarian party-state unified the police academies by forceful means, this catalyzed the cleavage between the schools of police studies and resulted in power struggles over police education, intellectual thought, collectivity and even the national reform of police administration. More than narrating the progress of power consolidation, this study attempts to identify the problems underlying the factional strife and to reveal the interwoven pattern of these power struggles, exploring the confusion regarding what the police is, a question that troubled Chinese policemen from the mainland to Taiwan.

This paper explains the emergence of the factional strife from the beginning of the preliminary growth of the Police Academies in Nanking and Chekiang. It widely makes use of the official archives from Japan Center for Asian Historical Records and Historica Academia to show the dynamic situation in police education and administration. Rather, the official publications of the Police Academies and their affiliated associations reveal the hidden political agenda behind a unified framework as the party-state claimed. Moreover, official gazettes, memorials and newspapers are also used to strengthen the core argument of this study.

This paper examines the impact of the factional strife between the police leaders Dai Li and Li Shizhen on the CPA from 1936 to 1949. It illustrates that the establishment of the CPA ostensibly unified the nationwide police force but triggered power struggles over the control of the police administration. More importantly, it also shows how the factions strove for larger shares of power under the supreme doctrines that Chiang Kai-shek and the party-state imposed.

The failure of police education to become powerful was a special case among other more typical institutions. The governors coercively merged the police academies and created robust conditions for growth under the shelter of state authority. The police force did not follow the same path of national monopoly as what recent studies found but drifted apart with its vested interests and incompatible beliefs. Hence, the greater the demand for centralized control by the state, the greater the tension of the factional strife.

Considering the problem that the high recognition rate of deep learning requires the support of mass data, this study aims to propose an insulating fault identification method based on small data set convolutional neural network (CNN).

Because of the chaotic characteristics of partial discharge (PD) signals, the equivalent transformation of the PD signal of unit power frequency period is carried out by phase space reconstruction to derive the chaotic features. At the same time, geometric, fractal, entropy and time domain features are extracted to increase the volume of feature data. Finally, the combined features are constructed and imported into CNN to complete PD recognition.

The results of the case study show that the proposed method can realize the PD recognition of small data set and make up for the shortcomings of the methods based on CNN. Also, the 1-CNN built in this paper has better recognition performance for four typical insulation faults of cable accessories. The recognition performance is improved by 4.37% and 1.25%, respectively, compared with similar methods based on support vector machine and BPNN.

In this paper, a method of insulation fault recognition based on CNN with small data set is proposed, which can solve the difficulty to realize insulation fault recognition of cable accessories and deep data mining because of insufficient measure data.