Model-based neural network correlation for refrigerant mass flow rates through adiabatic capillary tubes

2007 ◽  
Vol 30 (4) ◽  
pp. 690-698 ◽  
Author(s):  
Chun-Lu Zhang ◽  
Ling-Xiao Zhao
Keyword(s):  
Neural Network ◽  
Mass Flow ◽  
Capillary Tubes ◽  
Flow Rates ◽  
Model Based ◽  
Mass Flow Rates

scholarly journals Artificial neural networks trained through deep reinforcement learning discover control strategies for active flow control

2019 ◽  
Vol 865 ◽  
pp. 281-302 ◽  
Author(s):  
Jean Rabault ◽  
Miroslav Kuchta ◽  
Atle Jensen ◽  
Ulysse Réglade ◽  
Nicolas Cerardi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Reinforcement Learning ◽  
Flow Control ◽  
Mass Flow ◽  
Active Flow Control ◽  
Flow Rates ◽  
Mass Flow Rates ◽  
Artificial Neural ◽  
Active Flow

We present the first application of an artificial neural network trained through a deep reinforcement learning agent to perform active flow control. It is shown that, in a two-dimensional simulation of the Kármán vortex street at moderate Reynolds number ($Re=100$), our artificial neural network is able to learn an active control strategy from experimenting with the mass flow rates of two jets on the sides of a cylinder. By interacting with the unsteady wake, the artificial neural network successfully stabilizes the vortex alley and reduces drag by approximately 8 %. This is performed while using small mass flow rates for the actuation, of the order of 0.5 % of the mass flow rate intersecting the cylinder cross-section once a new pseudo-periodic shedding regime is found. This opens the way to a new class of methods for performing active flow control.

EXPERIMENTAL INVESTIGATION OF R134a FLOWING THROUGH ADIABATIC HELICALLY COILED CAPILLARY TUBES

2012 ◽  
Vol 20 (01) ◽  
pp. 1250001 ◽  
Author(s):  
JATUPORN KAEW-ON ◽  
SAKARIN CHINGULPITAK ◽  
SOMCHAI WONGWISES
Keyword(s):  
Mass Flow ◽  
Flow Characteristic ◽  
Flow Conditions ◽  
Capillary Tubes ◽  
Local Pressure ◽  
Flow Rates ◽  
Temperature Distributions ◽  
Coil Diameter ◽  
Copper Tubing ◽  
Mass Flow Rates

The effects of the relevant parameters on the flow characteristic of R134a flowing through adiabatic helical capillary tubes were experimentally studied. The capillary tubes' diameter, coil diameter, and parameters relating to flow conditions such as inlet pressures and degree of subcooling were the major parameters investigated. The test section was made from copper tubing with inner diameters of 1.07, 1.27, and 1.62 mm. The coil diameters were 25, 50, and 100 mm. The local pressure and temperature distributions along the length of the capillary tubes were measured at inlet pressures ranging from 10 to 14 bar, mass flow rates from 8 to 20 kg/h, and degrees of subcooling from 0.5°C to 15°C. The metastable flow and the delay of vaporization of R134a are also presented and discussed. The results showed that the capillary diameter had more of a significant effect on the mass flow rate than the other variables.

scholarly journals Net-exergetic, hydraulic and thermal optimization of coaxial heat exchangers using fixed flow conditions instead of fixed flow rates

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Tobias Blanke ◽  
Markus Hagenkamp ◽  
Bernd Döring ◽  
Joachim Göttsche ◽  
Vitali Reger ◽  
...  
Keyword(s):  
Reynolds Number ◽  
Laminar Flow ◽  
Mass Flow ◽  
Flow Rate ◽  
Heat Exchangers ◽  
Circular Ring ◽  
Flow Conditions ◽  
Flow Rates ◽  
Mass Flow Rates ◽  
Pipe Radius

AbstractPrevious studies optimized the dimensions of coaxial heat exchangers using constant mass flow rates as a boundary condition. They show a thermal optimal circular ring width of nearly zero. Hydraulically optimal is an inner to outer pipe radius ratio of 0.65 for turbulent and 0.68 for laminar flow types. In contrast, in this study, flow conditions in the circular ring are kept constant (a set of fixed Reynolds numbers) during optimization. This approach ensures fixed flow conditions and prevents inappropriately high or low mass flow rates. The optimization is carried out for three objectives: Maximum energy gain, minimum hydraulic effort and eventually optimum net-exergy balance. The optimization changes the inner pipe radius and mass flow rate but not the Reynolds number of the circular ring. The thermal calculations base on Hellström’s borehole resistance and the hydraulic optimization on individually calculated linear loss of head coefficients. Increasing the inner pipe radius results in decreased hydraulic losses in the inner pipe but increased losses in the circular ring. The net-exergy difference is a key performance indicator and combines thermal and hydraulic calculations. It is the difference between thermal exergy flux and hydraulic effort. The Reynolds number in the circular ring is instead of the mass flow rate constant during all optimizations. The result from a thermal perspective is an optimal width of the circular ring of nearly zero. The hydraulically optimal inner pipe radius is 54% of the outer pipe radius for laminar flow and 60% for turbulent flow scenarios. Net-exergetic optimization shows a predominant influence of hydraulic losses, especially for small temperature gains. The exact result depends on the earth’s thermal properties and the flow type. Conclusively, coaxial geothermal probes’ design should focus on the hydraulic optimum and take the thermal optimum as a secondary criterion due to the dominating hydraulics.

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