scholarly journals Using a Generalized Regression Neural Network Prediction Tool to Estimate Thermal Performance in A Heat Exchanger By using Triple Elliptical Leaf Angle Strips with Opposite Orientation and Same Direction

2019 ◽  
Vol 8 (11) ◽  
pp. 528-535
Keyword(s):  
Neural Network ◽  
Heat Transfer ◽  
Heat Exchanger ◽  
Thermal Performance ◽  
Leaf Angle ◽  
Generalized Regression Neural Network ◽  
Opposite Orientation ◽  
Statistical Tool ◽  
Pressure Drops ◽  
Generalized Regression

In our day to day hectic schedule humans have got so adaptive to technology that tremendous pressure is built on researchers to produce better equipment with greater output & easier way of human usage. One among these is Heat exchanger which is a device for trading heat and providing comfortable environment either for humans or the equipment .This paper aims at finding a solution in improvement of the thermal performance of the heat exchanger by implementing a statistical tool derived from Artificial Neural Network. The name of the tool is GRNN. (Generalized Regression Neural Network) From a sparse data of inputs (Temperatures, Angle orientation & mass flow rates) the outputs of (outlet temperatures & drop in pressure) are found out using this tool. An experiment is also conducted to find the heat transfer rates and pressure drops. To enhance the heat transfer rate three elliptical shaped leaf strips are introduced in the tube with opposite orientation and same direction. The results obtained from both the sources are compared and the percentage of error is calculated.

scholarly journals Implementation of a Neural Network Tool for Evaluation of Thermal Performance in a Heat Exchanger by using Double Elliptical Leaf Angle Strips with same Orientation and Same Direction

2019 ◽  
Vol 8 (6) ◽  
pp. 4325-4332
Keyword(s):  
Neural Network ◽  
Heat Transfer ◽  
Heat Exchanger ◽  
Pressure Drop ◽  
Heat Transfer Rate ◽  
Thermal Performance ◽  
Transfer Rate ◽  
Leaf Angle ◽  
Generalized Regression Neural Network ◽  
Double Pipe

Exchange of energy in all processes generally occur in the form of heat & work. The exchange of heat is determined by the rate of heat exchange between hot and cold body or cold and hot body. To exchange this heat we need two energy stacks such as a source & sink. So, whenever heat is rejected or accepted the energy change occurs identically i.e. amount of heat rejected is equal to amount of heat gained in an ideal case but when heat transfer rate is analyzed it is different fsor different processes such as vaporization is an instantaneous process whereas the condensation is slower and takes much more time so, with this idea that heat transfer rate can be altered individually in different processes an idea of analyzing heat exchanger by introducing elliptic double shaped leaf strips within the double pipe heat exchanger and the rate of heat transfer and pressure drop in is planned at various orientations of angles . From these obtained results neural network tool was designed for evaluating the thermal performance named the generalized regression neural network (GRNN).In this process certain input parameters are given (temperatures, mass flow rate) and instantly predefined output parameters (heat transfer rate, pressure drop) are obtained.

scholarly journals Execution of a Smart Prediction Tool to Evaluate Thermal Performance in a heat exchanger by using Single Elliptical Leaf Strips with altered Angle

2019 ◽  
Vol 8 (11) ◽  
pp. 123-131
Keyword(s):  
Heat Transfer ◽  
Heat Exchanger ◽  
Thermal Performance ◽  
Heat Exchangers ◽  
Industrial Applications ◽  
Generalized Regression Neural Network ◽  
Prediction Tool ◽  
Pressure Drops ◽  
Transfer Rates ◽  
Mass Flow Rates

Heat exchangers are prominent industrial applications where engineering science of heat transfer and Mass transfer occurs. It is a contrivance where transfer of energy occurs to get output in the form of energy transfer. This paper aims at finding a solution to improve the thermal performance in a heat exchanger by using passive method techniques. This experimental and numerical analysis deals with finding the temperature outlets of cold and hot fluid for different mass flow rates and also pressure drop in the tube and the annular side by adding an elliptical leaf strip in the pipe at various angles. The single elliptical leaf used in experiment has major to minor axes ratios as 2:1 and distance of 50 mm between two leaves are arranged at different angular orientations from 0 0 to 1800 with 100 intervals. Since it’s not possible to find the heat transfer rates and pressure drops at every orientation of elliptical leaf so a generalized regression neural network (GRNN) prediction tool is used to get outputs with given inputs to avoid experimentation. GRNN is a statistical method of determining the relationship between dependent and independent variables. The values obtained from experimentation and GRNN nearly had precise values to each other. This analysis is a small step in regard with encomiastic approach for enhancement in performance of heat exchangers

scholarly journals Execution of a Modern Prediction Tool for Evaluation of Thermal Performance in a Heat Exchanger by Expending Triple Elliptical Leaf Angle Strips with Altered Orientation and Opposite Direction

2019 ◽  
Vol 8 (3) ◽  
pp. 1781-1789
Keyword(s):  
Heat Exchanger ◽  
Thermal Performance ◽  
Opposite Direction ◽  
Heat Exchangers ◽  
Industrial Applications ◽  
Design Parameters ◽  
Leaf Angle ◽  
Statistical Tool ◽  
Experimental Values ◽  
Pipe Heat

Heat exchangers are the basic devices which are used in many areas wherever applications of heat flow occurs. Its usage varies from common domestic devices to mighty industrial applications. The performance of the heat exchanger plays a very important role for its utilization in many aspects. This performance is not dependent on the design parameters in a particular relationship hence experimental values for thermal performance are taken by utilizing three elliptical leaf strips in a tube and pipe heat exchanger. The three elliptical leaves used in experiment has major to minor axes ratios as 2:1 and distance of 50 mm between two leaves are arranged at different angular orientations from 00 to 1800 with 100 intervals. The leaves are placed in the tube side with different orientation and opposite direction of flow and experimentation is conducted to obtain the values. Based on these datasets available a statistical tool is utilized known as GRNN for the comparison between these obtained experimental values & GRNN values. From this comparison the percentage of error between the values is identified as results

Application of Generalized Regression Neural Network in Predicting the Thermal Performance of Solar Flat Plate Collector Systems

2020 ◽  
Vol 13 (2) ◽  
Author(s):  
M. Sridharan ◽  
Shribalaji Shenbagaraj
Keyword(s):  
Neural Network ◽  
Flat Plate ◽  
Thermal Performance ◽  
Experimental Studies ◽  
Generalized Regression Neural Network ◽  
Fluid Temperature ◽  
Transient Nature ◽  
Collector System ◽  
Generalized Regression ◽  
Flat Plate Collector

Abstract This study presents a smart neural network (NN) model for estimating the thermal performance of a transient nature solar flat plate collector system (SFPCS). For this purpose, a series of experimental studies are conducted through four successive days with three different arrangements of SFPCS (standalone, series, and parallel). Experimental results of such arrangements are then used for designing a generalized regression neural network (GRNN) model. The GRNN architecture proposed in this study consists of four inputs (mass flowrate, solar irradiance, fluid temperature difference, and collector area) and two dependent outputs (power output and efficiency of SFPCS). Such GRNN architecture is trained, tested, and validated with real-time experimental transient datasets for each arrangement individually. The results of the GRNN model are in good agreement with experimental datasets. The overall accuracy of the developed GRNN model in predicting the performance of standalone, series, and parallel connected SFPCS is 98%.

scholarly journals Implementation of GRNN for Evaluating the Pressure Drop and Heat Transfer in a Heat Exchanger by Utilizing Triple Elliptical Leaf Angle Strips with Same Orientation and Opposite Direction

2019 ◽  
Vol 8 (6) ◽  
pp. 4333-4340
Keyword(s):  
Heat Transfer ◽  
Heat Exchanger ◽  
Opposite Direction ◽  
Heat Exchangers ◽  
Industrial Applications ◽  
Design Parameters ◽  
Leaf Angle ◽  
Statistical Tool ◽  
Experimental Values ◽  
Pipe Heat

Heat exchangers are the basic devices which are used in many areas wherever applications of heat flow occurs. Its usage varies from common domestic devices to mighty industrial applications. The performance of the heat exchanger shows a very important role for its utilization in many aspects. This performance is not dependent on the design parameters in a particular relationship hence experimental values for thermal performance are taken by utilizing three elliptical leaf strips in a tube and pipe heat exchanger. The three elliptical leaves used in experiment has major to minor axes ratios as 2:1 and distance of 50 mm between two leaves are arranged at different angular orientations from 00 to 1800 with 100 intervals. The leaves are placed in the tube side with same orientation and opposite direction of flow and experimentation is conducted to obtain the values. Based on these datasets available a statistical tool is utilized known as GRNN for the comparison between these obtained experimental values & GRNN values. From this comparison the percentage of error between the values is identified as result.

Ultra-Compact Micro-Scale Heat Exchanger for Advanced Thermal Management in Data Centers

2021 ◽  
Author(s):  
Raffaele L. Amalfi ◽  
Todd Salamon ◽  
Filippo Cataldo ◽  
Jackson B. Marcinichen ◽  
John R. Thome
Keyword(s):  
Heat Transfer ◽  
Heat Exchanger ◽  
Thermal Performance ◽  
Heat Exchangers ◽  
Heat Transfer Performance ◽  
Operating Conditions ◽  
Transfer Performance ◽  
Two Phase ◽  
Maximum Heat ◽  
Pressure Drops

Abstract The present study is focused on the experimental characterization of two-phase heat transfer performance and pressure drops within an ultra-compact heat exchanger (UCHE) suitable for electronics cooling applications. The UCHE is composed of a double-side-copper finned plate with an optimized geometry that enhances the heat transfer performance and flow stability, while minimizing the pressure drops. These features make the UCHE the ideal component for thermosyphon cooling systems, where low pressure drops are required to achieve high passive flow circulation rates and thus achieve high critical heat flux values. The UCHE's thermal-hydraulic performance is first evaluated in a pump-driven system at the Laboratory of Heat and Mass Transfer (LTCM-EPFL), where experiments include many configurations and operating conditions. Then, the UCHE is installed and tested as the condenser of a thermosyphon loop that rejects heat to a pumped refrigerant system at Nokia Bell Labs, in which both sides operate with refrigerants in phase change (condensation-to-boiling). Experimental results demonstrate high thermal performance with a maximum heat dissipation density of 5455 (kW/m3/K), which is significantly larger than conventional air-cooled heat exchangers and liquid-cooled small pressing depth brazed plate heat exchangers. Finally, a thermal performance analysis is presented that provides guidelines in terms of heat density dissipations at the server- and rack-level when using passive two-phase cooling.

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