Characteristics and Formation Mechanism for Stainless Steel Fiber with Periodic Micro-fins

Metal fibers have been widely used in many industrial applications due to their unique advantages. In certain applications, such as catalyst supports or orthopedic implants, a rough surface or tiny outshoots on the surface of metal fibers to increase surface area are needed. However, it has not been concerned about the surface morphologies of metal fiber in the current research of metal fiber manufacturing. In this paper, a special multi-tooth tool composed of a row of triangular tiny teeth is designed. The entire cutting layer of multi-tooth tool bifurcates into several thin cutting layers due to tiny teeth involved in cutting. As a result, several stainless steel fibers with periodic micro-fins are produced simultaneously. Morphology of periodic micro-fins is found to be diverse and can be classified into three categories： unilateral plane, unilateral tapering and bilateral. There are two forming mechanisms for the micro-fins. One is that periodic burrs remained on the free side of cutting layer of a tiny tooth create micro-fins of stainless steel fiber produced by the next neighboring tiny tooth; the other is that the connections between two fibers stuck together come to be micro-fins if the two fibers are finally detached. Influence of cutting conditions on formation of micro-fins is investigated. Experimental results show that cutting depth has no significant effect on micro-fin formation, high cutting speed is conducive to micro-fin formation, and feed should be between 0.12 mm/r and 0.2 mm/r to reliably obtain stainless steel fiber with micro-fins. This research presents a new pattern of stainless steel fiber characterized by periodic micro-fins formed on the edge of fiber and its manufacturing method.

Mineralogical characterization and comprehensive utilization of micro-fine tantalum–niobium ores from Songzi

The mineralogical characteristics of tantalumniobium ores from Songzi were investigated using mineral liberation analyzer （MLA） and chemical analysis. In particular, the chemical composition, phase composition, particle size, and dissemination characteristics of the ores were studied in detail. Results show that Ta2O5 and Nb2O5 have grades of 0.013 % and 0.011%, respectively. The main valuable minerals in the ores are tantalite, col- umbite, tantalum-niobite, and microlite, and the gangue minerals associated with tantalum-niobium minerals mainly include quartz, kaolinite, illite, feldspar, and mica among others. The minerals are embedded in a complex manner. Tantalum-niobium minerals with most particle sizes of -0.040 mm are disseminated in minerals, such as mica, illite, quartz, and partially intergrown with topaz and zircon, where they could not be easily liberated. Thus, the ores are classified as low-grade, micro-fine, and refractory tantalum-niobium ores. Based on this consideration, the process of classification-gravity concentration-magnetic separation-middlings regrinding and gravity concentration are finally determined and satisfactory indices are obtained. Two rough concentrates are produced： Concentrate I has Ta2O5 and Nb2O5 grades of 7.0292 % and 3.546 %, respectively, as well as recovery of 49.42 % and 35.46 %. By comparison, Concentrate Ⅱ has Ta2O5 and Nb2O5 grades of 7.0292 % and 3.546 %, respectively, as well as recovery of 49.42 % and 35.46 %.

Heat transfer characteristics in micro-fin tube equipped with double twisted tapes: Effect of twisted tape and micro-fin tube arrangements

An experimental study was carried out to investigate the influence of double twisted-tape inserts （DTs） in micro-fin tubes （MFs） on heat transfer, friction factor and thermal performance factor characteristics of the compound devices in the following configurations： （1） twisted tapes acted in the same direction （for co-swirl） while MF and twisted tapes acted in the same （parallel） direction （MF-CoDTs：P）, （2） twisted tapes acted in the same direction （for co-swirl） while micro-fin tube and twisted tapes acted in opposite directions （MF-CoDTs：O） and （3） twisted tapes acted in opposite directions for counter swirl （MF-CDTs）. The MF alone and the MF equipped with a single twisted tape in parallel/opposite arrangement were also considered for comparison. The experiments were conducted for the flows with the Reynolds numbers between 5 650 and 17 000, under uniform heat flux condition. The experimental results indicate that MF-CDTs induce stronger swirl/turbulence flow, resulting in higher heat transfer rate, friction factor and thermal performance factor than other combined devices. The thermal performance factors associated with the use of MF-CDTs were found to be higher than those associated with the uses of MF-CoDTs：P, MF-CoDTs：O and MF alone up to 9.3%, 6.5% and 56.4%, respectively. The empirical correlations developed using the present experimental data for the Nusselt number, friction factor and thermal performance factor are also reported.

A general model for flow boiling heat transfer in microfin tubes based on a new neural network architecture

A new neural network architecture,namely DimNet,was designed for correlating dimensionless quantities with power-law-like relations.Unlike common neural networks that are usually used as“black-boxes”,DimNet is interpretable as it can be converted to an explicit algebraic piecewise power-law-like function.With DimNet,a data-driven,empirical model was developed to predict the pre-dryout heat transfer coefficient of flow boiling within microfin tubes.The model was trained on a database with 7349 experimental data points for 16 refrigerants,and then optimized by comparing different sets of dominant dimensionless quantities and by adjusting the network configuration.The model exhibits an overall mean-absolute-error of 13.8%and no systematic variation with respect to the salient parameters for most conditions.Besides being statistically accurate,the model captures parametric trends of the heat transfer coefficient.The excellent prediction performance of the model was attributed to the DimNet’s ability to automatically classify the data into optimal regions and simultaneously correlate the data of each region.Therefore,the DimNet architecture is inherently suitable for modeling complex heat transfer and flow problems where multiple distinct physical regimes exist,especially for problems where a power-law-like input–output relation is desired such as convective heat transfer.