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 su...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.展开更多
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 ...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.展开更多
The design of micro-finned tube heat exchangers is a complex task due to intricate geometry, heat transfer goals, material selection, and manufacturing challenges. Nowadays, mathematical models provide valuable insigh...The design of micro-finned tube heat exchangers is a complex task due to intricate geometry, heat transfer goals, material selection, and manufacturing challenges. Nowadays, mathematical models provide valuable insights, aid in optimization, and allow us to explore various design parameters efficiently. However, existing empirical models often fall short in facilitating an optimal design because of their limited accuracy, sensitivity to assumption, and context dependency. In this scenario, the use of Machine and Deep Learning (ML and DL) methods can enhance accuracy, manage nonlinearity, adjust to varying conditions, decrease dependence on assumptions, automatically extract pertinent features, and provide scalability. Indeed, ML and DL techniques can derive valuable insights from datasets, contributing to a comprehensive understanding. By means of multiple ML and DL methods, this paper addresses the challenge of estimating key parameters in micro-finned tube heat exchangers such as the heat transfer coefficient (HTC) and frictional pressure drop (FPD). The methods have been trained and tested using an experimental dataset consisting of over a thousand data points associated with flow condensation, involving various tube geometries. In this context, the Artificial Neural Network (ANN) demonstrates superior performance in accurately estimating parameters with MAEs in the range below 4.5% for both HTC and FPD. Finally, recognizing the importance of comprehending the internal mechanisms of the black-box ANN model, the paper explores its interpretability aspects.展开更多
An experimental investigation on the boiling heat transfer and frictional pressure drop of R245fa in a 7 mm horizontal micro-fin tube was performed.The results show that in terms of flow boiling heat transfer characte...An experimental investigation on the boiling heat transfer and frictional pressure drop of R245fa in a 7 mm horizontal micro-fin tube was performed.The results show that in terms of flow boiling heat transfer characteristics,boiling heat transfer coefficient(HTC)increases with mass velocity of R245fa,while it decreases with the increment of saturation temperature and heat flux.With the increase of vapor quality,HTC has a maximum and the corresponding vapor quality is about 0.4,which varies with the operating conditions.When vapor quality is larger than the transition point,HTC can be promoted more remarkably at higher mass velocity or lower saturation temperature.Among the four selected correlations,KANDLIKAR correlation matches with 91.6%of experimental data within the deviation range of±25%,and the absolute mean deviation is 11.2%.Also,in terms of frictional pressure drop characteristics of flow boiling,the results of this study show that frictional pressure drop increases with mass velocity and heat flux of R245fa,while it decreases with the increment of saturation temperature.MULLER-STEINHAGEN-HECK correlation shows the best prediction accuracy for frictional pressure drop among the four widely used correlations.It covers 84.1%of experimental data within the deviation range of±20%,and the absolute mean deviation is 10.1%.展开更多
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 inter...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.展开更多
基金Supported by National Natural Science Foundation of China(Grant No.51375176)Guangdong Provincial Natural Science Foundation of China(Grant No.2014A030313264)Fundamental Research Funds for the Central Universities,SCUT,China(Grant No.2013ZZ017)
文摘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.
基金the Thailand Research Fund (TRF),Office of Higher Education Commission and Mahanakorn University of Technology (MUT) for financial support of this research(Grant No.MRG5480026)
文摘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.
文摘The design of micro-finned tube heat exchangers is a complex task due to intricate geometry, heat transfer goals, material selection, and manufacturing challenges. Nowadays, mathematical models provide valuable insights, aid in optimization, and allow us to explore various design parameters efficiently. However, existing empirical models often fall short in facilitating an optimal design because of their limited accuracy, sensitivity to assumption, and context dependency. In this scenario, the use of Machine and Deep Learning (ML and DL) methods can enhance accuracy, manage nonlinearity, adjust to varying conditions, decrease dependence on assumptions, automatically extract pertinent features, and provide scalability. Indeed, ML and DL techniques can derive valuable insights from datasets, contributing to a comprehensive understanding. By means of multiple ML and DL methods, this paper addresses the challenge of estimating key parameters in micro-finned tube heat exchangers such as the heat transfer coefficient (HTC) and frictional pressure drop (FPD). The methods have been trained and tested using an experimental dataset consisting of over a thousand data points associated with flow condensation, involving various tube geometries. In this context, the Artificial Neural Network (ANN) demonstrates superior performance in accurately estimating parameters with MAEs in the range below 4.5% for both HTC and FPD. Finally, recognizing the importance of comprehending the internal mechanisms of the black-box ANN model, the paper explores its interpretability aspects.
基金Project(51606162)supported by the National Natural Science Foundation of ChinaProject(2018JJ2399)supported by the Natural Science Foundation of Hunan Province,China
文摘An experimental investigation on the boiling heat transfer and frictional pressure drop of R245fa in a 7 mm horizontal micro-fin tube was performed.The results show that in terms of flow boiling heat transfer characteristics,boiling heat transfer coefficient(HTC)increases with mass velocity of R245fa,while it decreases with the increment of saturation temperature and heat flux.With the increase of vapor quality,HTC has a maximum and the corresponding vapor quality is about 0.4,which varies with the operating conditions.When vapor quality is larger than the transition point,HTC can be promoted more remarkably at higher mass velocity or lower saturation temperature.Among the four selected correlations,KANDLIKAR correlation matches with 91.6%of experimental data within the deviation range of±25%,and the absolute mean deviation is 11.2%.Also,in terms of frictional pressure drop characteristics of flow boiling,the results of this study show that frictional pressure drop increases with mass velocity and heat flux of R245fa,while it decreases with the increment of saturation temperature.MULLER-STEINHAGEN-HECK correlation shows the best prediction accuracy for frictional pressure drop among the four widely used correlations.It covers 84.1%of experimental data within the deviation range of±20%,and the absolute mean deviation is 10.1%.
文摘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.
