scholarly journals Selection of the optimal type of thermal insulation structure based on the neural network modelling

2020 ◽  
Vol 216 ◽  
pp. 01037
Author(s):  
Irina Akhmetova ◽  
Elena Balzamova ◽  
Veronika Bronskaya ◽  
Denis Balzamov ◽  
Konstantin Lapin ◽  
...  
Keyword(s):  
Neural Network ◽  
Web Application ◽  
District Heating ◽  
Heat Network ◽  
Network Modelling ◽  
Query Log ◽  
Neural Network Modelling ◽  
The Neural Network ◽  
Selection Of ◽  
Optimal Type

A software package with the user interface for calculating, analyzing and predicting the parameters of cogeneration-based district heating based on the neural network modelling is presented in order to optimize and ensure the reliability of heat networks. The package is the basis for a web-application that allows to calculate the characteristics of the heat network in accordance with the model, keep a query log and provide the possibility of administration.

scholarly journals Reliability assurance of the thermal energy sources using the neural network modelling

2020 ◽  
Vol 216 ◽  
pp. 01036
Author(s):  
Irina Akhmetova ◽  
Elena Balzamova ◽  
Veronika Bronskaya ◽  
Denis Balzamov ◽  
Olga Kharitonova
Keyword(s):  
Neural Network ◽  
Software Package ◽  
Heat Supply System ◽  
Supply System ◽  
Heat Sources ◽  
Network Modelling ◽  
Neural Network Modelling ◽  
The Neural Network ◽  
Modelling Analysis ◽  
Heating Networks

A software package for neural network modelling, analysis and decision-making to improve the reliability of the heat supply system is presented. The features of heat sources and heating networks are taken into account when modelling. The trends and recommendations for improving reliability are presented.

scholarly journals Neural Network Modelling of Track Profile in Cold Spray Additive Manufacturing

Materials ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 2827 ◽  
Author(s):  
Daiki Ikeuchi ◽  
Alejandro Vargas-Uscategui ◽  
Xiaofeng Wu ◽  
Peter C. King
Keyword(s):  
Neural Network ◽  
Additive Manufacturing ◽  
Cold Spray ◽  
Network Model ◽  
Neural Network Model ◽  
Geometric Control ◽  
Geometric Feature ◽  
Network Modelling ◽  
Neural Network Modelling ◽  
The Neural Network

Cold spray additive manufacturing is an emerging technology that offers the ability to deposit oxygen-sensitive materials and to manufacture large components in the solid state. For further development of the technology, the geometric control of cold sprayed components is fundamental but not yet fully matured. This study presents a neural network predictive modelling of a single-track profile in cold spray additive manufacturing to address the problem. In contrast to previous studies focusing only on key geometric feature predictions, the neural network model was employed to demonstrate its capability of predicting complete track profiles at both normal and off-normal spray angles, resulting in a mean absolute error of 8.3%. We also compared the track profile modelling results against the previously proposed Gaussian model and showed that the neural network model provided comparable predictive accuracy, even outperforming in the predictions at cold spray profile edges. The results indicate that a neural network modelling approach is well suited to cold spray profile prediction and may be used to improve geometric control during additive manufacturing with an appropriate process planning algorithm.

scholarly journals Adaptive Control Structure with Neural Data Processing Applied for Electrical Drive with Elastic Shaft

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3389
Author(s):  
Marcin Kamiński ◽  
Krzysztof Szabat
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Dynamic Properties ◽  
Experimental Studies ◽  
Drive System ◽  
Particle Swarm Optimizer ◽  
The Neural Network ◽  
Control Concept ◽  
Constant Coefficients ◽  
Selection Of

This paper presents issues related to the adaptive control of the drive system with an elastic clutch connecting the main motor and the load machine. Firstly, the problems and the main algorithms often implemented for the mentioned object are analyzed. Then, the control concept based on the RNN (recurrent neural network) for the drive system with the flexible coupling is thoroughly described. For this purpose, an adaptive model inspired by the Elman model is selected, which is related to internal feedback in the neural network. The indicated feature improves the processing of dynamic signals. During the design process, for the selection of constant coefficients of the controller, the PSO (particle swarm optimizer) is applied. Moreover, in order to obtain better dynamic properties and improve work in real conditions, one model based on the ADALINE (adaptive linear neuron) is introduced into the structure. Details of the algorithm used for the weights’ adaptation are presented (including stability analysis) to perform the shaft torque signal filtering. The effectiveness of the proposed approach is examined through simulation and experimental studies.

Sign in / Sign up

Export Citation Format

Share Document