Titanium tube and stainless steel tube plate were welded by an innovative friction welding of tube to tube plate using an external tool (FWTPET). Copper was used as an interlayer for joining the dissimilar materials a...Titanium tube and stainless steel tube plate were welded by an innovative friction welding of tube to tube plate using an external tool (FWTPET). Copper was used as an interlayer for joining the dissimilar materials and also to minimize the effect of intermetallics formed at the joint interface. The process parameters that govern FWTPET process are plunge rate, rotational speed, plunge depth, axial load and flash trap profile. Among them, the flash trap profile of the tube has a significant influence on the joint integrity. Various flash trap profiles like vertical slots, holes, zig-zag holes, and petals were made on the titanium tube welded to the stainless steel tube plate. Macroscopic and microscopic studies reveal defect-free joints. The presence of copper interlayer and intermetallics was evident from X-ray diffraction (XRD), scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS) studies. The microhardness survey was presented across and along the interface. A novel test procedure called “plunge shear test” was developed to evaluate the joint properties of the welded joints. The highest shear fracture load of 31.58 kN was observed on the sample having petals as flash trap profile. The sheared surfaces were further characterized using SEM for fractography.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
Using near-azeotropic refrigerant R410A as the working fluid, the experimental studies on the horizontal micro-fin tubes were conducted. Several factors affecting heat transfer coefficients were analyzed, and the char...Using near-azeotropic refrigerant R410A as the working fluid, the experimental studies on the horizontal micro-fin tubes were conducted. Several factors affecting heat transfer coefficients were analyzed, and the characteristics of flow boiling of the refrigerant in the horizontal micro-fin tubes were discussed. The local heat transfer coefficients increase with mass flux, heat flux and quality. And the heat transfer enhancement factor of those testing tubes is about 1.6 to 2.2.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
基金financial support provided by UGC-DAE-CSR (CSR-KN/CRS-04/201213/738) through fellowship
文摘Titanium tube and stainless steel tube plate were welded by an innovative friction welding of tube to tube plate using an external tool (FWTPET). Copper was used as an interlayer for joining the dissimilar materials and also to minimize the effect of intermetallics formed at the joint interface. The process parameters that govern FWTPET process are plunge rate, rotational speed, plunge depth, axial load and flash trap profile. Among them, the flash trap profile of the tube has a significant influence on the joint integrity. Various flash trap profiles like vertical slots, holes, zig-zag holes, and petals were made on the titanium tube welded to the stainless steel tube plate. Macroscopic and microscopic studies reveal defect-free joints. The presence of copper interlayer and intermetallics was evident from X-ray diffraction (XRD), scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS) studies. The microhardness survey was presented across and along the interface. A novel test procedure called “plunge shear test” was developed to evaluate the joint properties of the welded joints. The highest shear fracture load of 31.58 kN was observed on the sample having petals as flash trap profile. The sheared surfaces were further characterized using SEM for fractography.
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%.
基金Shanghai Leading Academic Discipline Project(No.T0503)
文摘Using near-azeotropic refrigerant R410A as the working fluid, the experimental studies on the horizontal micro-fin tubes were conducted. Several factors affecting heat transfer coefficients were analyzed, and the characteristics of flow boiling of the refrigerant in the horizontal micro-fin tubes were discussed. The local heat transfer coefficients increase with mass flux, heat flux and quality. And the heat transfer enhancement factor of those testing tubes is about 1.6 to 2.2.
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.
