scholarly journals A General Approach in Optimization of Heat Exchangers by Bio-Inspired Artificial Intelligence Methods

Energies ◽  
2019 ◽  
Vol 12 (23) ◽  
pp. 4441 ◽  
Author(s):  
Jaroslaw Krzywanski
Keyword(s):  
Heat Transfer ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Genetic Algorithms ◽  
Artificial Neural Networks ◽  
Heat Transfer Rate ◽  
Heat Exchangers ◽  
Transfer Rate ◽  
Artificial Neural ◽  
Bottom To Top

The paper introduces the artificial intelligence (AI) approach as a general method for the design and optimization study of heat exchangers. Genetic Algorithms (GA) and Artificial Neural Networks (ANN) are applied in the paper. An AGENN model, combining Genetic Algorithms with Artificial Neural Networks, was developed and validated against the desired data on a large falling film evaporator. A broad range of operating conditions and geometric configurations are considered in the study. Four kinds of tubes are deliberated, including plain and enhanced tubes. Different tube pass arrangements, i.e., top-to-bottom, bottom-to-top, and side-by-side, are discussed. Finally, the effects of liquid refrigerant mass flow rate, as well as the number of flooded tubes on the performance of the evaporator, are analyzed. The total heat transfer rate of the evaporator, predicted by the model, is in good agreement with the desired data; the maximum error is lower than ±3%. The highest heat transfer rate of the evaporator is 1140.01 kW and corresponds to Turbo EHP tubes, and bottom-to-top tubes pass arrangements, which guarantee the best thermal energy conversion. The presented approach can be referred to as a complementary technique in heat exchanger design procedures, besides the common rating and sizing tasks. It is an effective and alternative method for the existing approaches, considering the complexity of analytical and numerical techniques as well as the high costs of experiments.

scholarly journals Proposta de modelo NEAT para jogar One Night Ultimate Werewolf

2021 ◽  
Author(s):  
Rafael Ferreira Costa ◽  
Alisson Steffens Henrique ◽  
Rodrigo Lyra ◽  
Anita Maria da Rocha Fernandes ◽  
Rudimar Luis Scaranto Dazzi
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Genetic Algorithms ◽  
Artificial Neural Networks ◽  
Incomplete Information ◽  
Board Games ◽  
Artificial Neural

The use of Artificial Intelligence approaches as NPCs in games is a very common practice, as they seek to convey the impression to players that these characters are somewhat autonomous. One of the approaches used is the technique called NEAT, which consists of making use of artificial neural networks together with genetic algorithms to manage the topology, connections, and weights of a network in an adaptive way. This work presents the proposal to create an NPC for games in a subcategory of board games, those based on bluff and incomplete information. The game used as a case study is One Night Ultimate Werewolf, a social deduction game, so that information is incomplete for players, and part of them must use the bluff in order to confuse other players. The objective is to evaluate the possibility of modeling the behaviors of this type of game for the application of NEAT.

scholarly journals Artificial Intelligence to support retail sales optimization

2020 ◽  
Author(s):  
Agostino G. Bruzzone ◽  
Kirill Sinelshchikov ◽  
Marina Massei ◽  
Wolfhard Schmidt
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Genetic Algorithms ◽  
Artificial Neural Networks ◽  
Data Integration ◽  
Process Optimization ◽  
Decision Makers ◽  
Retail Sales ◽  
Artificial Neural

Presented study focuses on utilization of Artificial Intelligence (AI) in order to support data integration, sales forecasting and process optimization in retail. In particular, use of Artificial Neural Networks (ANN) and Genetic Algorithms (GA) in order to support decision makers from sales departments has evaluated.

scholarly journals Plate Heat Exchangers: Artificial Neural Networks for Their Design

2021 ◽  
Author(s):  
Bayram Kılıç
Keyword(s):  
Heat Transfer ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Heat Exchangers ◽  
Plate Heat ◽  
Learning Capacity ◽  
Two Fluids ◽  
Thermal Calculations ◽  
Different Temperatures ◽  
Artificial Neural

Heat exchangers are installation components in which two fluids with different temperatures are separated from each other with the help of plates and these plates make heat transfer between two fluids. The biggest advantage of plate heat exchangers over other type of heat exchangers is their heat transfer efficiency. The thinness of the plates separating the two fluids compared to other material alternatives increases the amount of heat transfer and thus reduces the heat losses that may occur during heat transfer. Plate heat exchangers are not only efficient but also prevent the formation of residue and dirt that can accumulate over time in the used applications. It also protects the system against excessive pressure that may occur in the installation. In this study, some information about plate heat exchangers is given such as classification, plate geometry, pressure losses, and thermal calculations. Also, the data obtained from the experimental work were used to obtain some relativity in order to use it in plate heat exchangers and artificial neural networks (ANN) method was used for this purpose. Artificial neural network method is used in many engineering applications. The most important advantages of this method are rapid formation, simple formation and high learning capacity.

Improving Pin-Fin Heat Transfer Predictions Using Artificial Neural Networks

2013 ◽  
Author(s):  
Jason K. Ostanek
Keyword(s):  
Neural Network ◽  
Heat Transfer ◽  
Neural Networks ◽  
Reynolds Number ◽  
Artificial Neural Networks ◽  
Power Law ◽  
Transfer Data ◽  
Pin Fin ◽  
The Neural Network ◽  
Artificial Neural

In much of the public literature on pin-fin heat transfer, Nusselt number is presented as a function of Reynolds number using a power-law correlation. Power-law correlations typically have an accuracy of 20% while the experimental uncertainty of such measurements is typically between 5% and 10%. Additionally, the use of power-law correlations may require many sets of empirical constants to fully characterize heat transfer for different geometrical arrangements. In the present work, artificial neural networks were used to predict heat transfer as a function of streamwise spacing, spanwise spacing, pin-fin height, Reynolds number, and row position. When predicting experimental heat transfer data, the neural network was able to predict 73% of array-averaged heat transfer data to within 10% accuracy while published power-law correlations predicted 48% of the data to within 10% accuracy. Similarly, the neural network predicted 81% of row-averaged data to within 10% accuracy while 52% of the data was predicted to within 10% accuracy using power-law correlations. The present work shows that first-order heat transfer predictions may be simplified by using a single neural network model rather than combining or interpolating between power-law correlations. Furthermore, the neural network may be expanded to include additional pin-fin features of interest such as fillets, duct rotation, pin shape, pin inclination angle, and more making neural networks expandable and adaptable models for predicting pin-fin heat transfer.

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