Monitoring Feedwater Flow Rate and Component Thermal Performance of Pressurized Water Reactors by Means of Artificial Neural Networks

1994 ◽  
Vol 107 (1) ◽  
pp. 112-123 ◽  
Author(s):  
Kadir Kavaklioglu ◽  
Belle R. Upadhyaya
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Flow Rate ◽  
Thermal Performance ◽  
Pressurized Water Reactors ◽  
Pressurized Water ◽  
Artificial Neural ◽  
Water Reactors

scholarly journals Application of Phase Change Material and Artificial Neural Networks for Smoothing of Heat Flux Fluctuations

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3531
Author(s):  
Tomasz Tietze ◽  
Piotr Szulc ◽  
Daniel Smykowski ◽  
Andrzej Sitka ◽  
Romuald Redzicki
Keyword(s):  
Neural Networks ◽  
Heat Flux ◽  
Artificial Neural Networks ◽  
Phase Change ◽  
Mass Flow Rate ◽  
Phase Change Material ◽  
Flow Rate ◽  
Smoothing Effect ◽  
Artificial Neural ◽  
Change Material

The paper presents an innovative method for smoothing fluctuations of heat flux, using the thermal energy storage unit (TES Unit) with phase change material and Artificial Neural Networks (ANN) control. The research was carried out on a pilot large-scale installation, of which the main component was the TES Unit with a heat capacity of 500 MJ. The main challenge was to smooth the heat flux fluctuations, resulting from variable heat source operation. For this purpose, a molten salt phase change material was used, for which melting occurs at nearly constant temperature. To enhance the smoothing effect, a classical control system based on PID controllers was supported by ANN. The TES Unit was supplied with steam at a constant temperature and variable mass flow rate, while a discharging side was cooled with water at constant mass flow rate. It was indicated that the operation of the TES Unit in the phase change temperature range allows to smooth the heat flux fluctuations by 56%. The tests have also shown that the application of artificial neural networks increases the smoothing effect by 84%.

Artificial Neural Networks Modeling of Electrospun Polyurethane Nanofibers from Chloroform/Methanol Solution

2016 ◽  
Vol 41 ◽  
pp. 18-30 ◽  
Author(s):  
Saman Firoozi ◽  
Amir Amani ◽  
Mohammad Ali Derakhshan ◽  
Hossein Ghanbari
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Polymer Solution ◽  
Flow Rate ◽  
Applied Voltage ◽  
Polymer Concentration ◽  
Electrospun Nanofibers ◽  
Dominant Factor ◽  
Artificial Neural ◽  
Chloroform Methanol

In this study, electrospun nanofibers of polyurethane were prepared utilizing a new solvent system made of chloroform/methanol. Also, we planned to assess effects of four important parameters on diameter of electrospun polyurethane nanofibers using Artificial Neural Networks (ANNs). The parameters investigated included flow rate of syringe pump, distance of spinneret to collector, applied voltage and concentration of polymer solution. Diameter of obtained electrospun nanofibers was measured using scanning electron microscopy (SEM). Results showed that flow rate and distance had reverse relation with fiber diameter, while applied voltage and concentration of polymer solution directly affected the diameter. Also, polymer concentration was shown to be the dominant factor here.

scholarly journals Comparative Evaluation of Artificial Neural Networks and Data Analysis in Predicting Liposomes’ Size in a Periodic Disturbance Micromixer

2021 ◽  
Vol 4 (1) ◽  
pp. 42
Author(s):  
Ixchel Ocampo ◽  
Rubén R. Lopéz ◽  
Vahée Nerguizian ◽  
Ion Stiharu ◽  
Sergio Camacho León
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Data Analysis ◽  
Flow Rate ◽  
Periodic Disturbance ◽  
Analysis Techniques ◽  
Relevant Variables ◽  
Total Data ◽  
Liposome Size ◽  
Artificial Neural

Artificial Neural Networks (ANN) and Data analysis are powerful tools used for supporting decision-making. They have been employed in diverse fields and one of them is nanotechnology used, for example, in predicting particles size. Liposomes are nanoparticles used in different biomedical applications that can be produced in Dean Forces-based Periodic Disturbance Micromixers (PDM). In this work, ANN and data analysis techniques are used to build a liposome size prediction model by using the most relevant variables in a PDM, i.e., Flow Rate Radio (FRR) and Total Flow Rate (TFR). The ANN was designed in MATLAB and fed data from 60 experiments, which were 70% training, 15% validation and 15% testing. For data analysis, regression analysis was used. The model was evaluated; it showed 98.147% of regression number for training and 97.247% in total data compared with 78.89% regression number obtained by data analysis. These results demonstrate that liposomes’ size can be better predicted by ANN with just FRR and TFR as inputs, compared with data analysis techniques when the temperature, solvents, and concentrations are kept constant.

scholarly journals New Approach to Estimating the Saturation Flow Rate of a Shared Lane with Permitted Left Turns

2020 ◽  
Vol 32 (4) ◽  
pp. 573-583
Author(s):  
Veljko Radičević ◽  
Nikola Krstanoski ◽  
Marko Subotić
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Flow Rate ◽  
Traffic Lane ◽  
Artificial Neural ◽  
Saturation Flow Rate ◽  
Left Turns ◽  
Saturation Flow

The estimation of the saturation flow rate is of utmost importance when defining the signal plan at intersections. Because of the numerous influential factors, the values of which are hard to be determined, the subject problem is to be regarded as an extremely complex one. This research deals with the estimation of a saturation flow rate of a shared lane with permitted left turns. The suggested algorithm is based on the application of the artificial neural networks where the data for training are received by simulation. The results obtained by the neural networks are compared with multiple linear regression and the known HCM 2010 approach for determining the saturated flow of a shared lane. The testing data have shown that the approach based on the artificial neural networks foresaw statistically significantly better values than the ones obtained by multiple linear regression, with an error of 27 veh/h against 49 veh/h. The HCM 2010 approach is significantly worse than the two others included in this research. The ways of the future development of the suggested method could include additional factors, such as the grade of the traffic lane, the proximity of the bus stops, and others.

scholarly journals Development of Virtual Flow Sensor using Artificial Neural Networks

2019 ◽  
Vol 9 (2) ◽  
pp. 4081-4087
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Pressure Drop ◽  
Flow Control ◽  
Flow Rate ◽  
Functional Relation ◽  
Control Valve ◽  
Flow Sensor ◽  
Control Element ◽  
Artificial Neural

In this paper, a virtual flow sensor using artificial neural networks (ANN) is proposed to improve the efficiency of an industrial flow control loops. In conventional flow-control loop, flow meters used for sensing flow rate in the feedback path cause pressure drop in the flow. This may increase the energy usage for propelling the fluid. The functional relation between the flow rate and the physical properties of the flow through the final control element such as control valve is known and the said properties namely pressure drop, temperature, and valve position are yielded from an experimental set-up. These properties are used as training data for ANN models to yield the fluid flow rate through the control valve. Here, the ANN acts as a virtual flow sensor. The feasibility of the proposed methodology is validated by using real measurement of flow and used them to model virtual flow sensor using the multi-layer perceptron artificial neural networks (MLP-ANN) with back propagation (BP) algorithm. Moreover, its practical proof of concept is demonstrated by implementing the trained MLP-ANN on a Spartan-3E-starter Field Programmable Gate Array (FPGA) unit through a hardware co-simulation.

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