Performance Prediction of Pulse Tube Refrigerator Using Artificial Neural Network

The present study proposes a numerical model to analyze the effect of four dimensional parameters on performance characteristics such as Coefficient of performance (COP), of the Inertance-Type Pulse Tube Refrigerator (ITPTR). The numerical model is validated by comparing with previously published results. The detail analysis of cool down behaviour, heat transfer at the cold end and the pressure variation inside the whole system has been carried out by using the most powerful computational fluid dynamic software package ANSYS FLUENT 13. The operating frequency for all the studied cases is (34 Hz). In fact, to get an optimum parameter experimentally is a very tedious for iterance pulse tube refrigerator job, so that the CFD approach gives a better solution. Finally, an artificial neural network (ANN) based process model is proposed to establish relation between input parameters and the responses. The model provides an inexpensive and time saving substitute to study the performance of ITPTR. The model can be used for selecting ideal process states to improve ITPTR performance.

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Artificial Neural Network–Based Numerical Model to Predict Flexural Capacity of Masonry Panels Strengthened with Textile Reinforced Mortar

Journal of Composites for Construction ◽

10.1061/(asce)cc.1943-5614.0001104 ◽

2021 ◽

Vol 25 (1) ◽

pp. 06020004

Author(s):

Smitha Gopinath ◽

Abhishek Kumar

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Numerical Model ◽

Flexural Capacity ◽

Artificial Neural ◽

Textile Reinforced Mortar

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CFD-based blade shape optimization of MGT-70(3)axial flow compressor

International Journal of Numerical Methods for Heat &amp Fluid Flow ◽

10.1108/hff-10-2018-0603 ◽

2019 ◽

Vol 30 (6) ◽

pp. 3307-3321 ◽

Cited By ~ 5

Author(s):

Mohammad Reza Pakatchian ◽

Hossein Saeidi ◽

Alireza Ziamolki

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Shape Optimization ◽

Fluid Dynamic ◽

Axial Compressor ◽

Axial Flow ◽

Design Parameters ◽

Content Type ◽

Wide Range ◽

Artificial Neural

Purpose This study aims at enhancing the performance of a 16-stage axial compressor and improving the operating stability. The adopted approaches for upgrading the compressor are artificial neural network, optimization algorithms and computational fluid dynamics. Design/methodology/approach The process starts with developing several data sets for certain 2D sections by means of training several artificial neural networks (ANNs) as surrogate models. Afterward, the trained ANNs are applied to the 3D shape optimization along with parametrization of the blade stacking line. Specifying the significant design parameters, a wide range of geometrical variations are considered by implementation of appropriate number of design variables. The optimized shapes are analyzed by applying computational fluid dynamic to obtain the best geometry. Findings 3D optimal results show improvements, especially in the case of decreasing or elimination of near walls corner separations. In addition, in comparison with the base geometry, numerical optimization shows an increase of 1.15 per cent in total isentropic efficiency in the first four stages, which results in 0.6 per cent improvement for the whole compressor, even while keeping the rest of the stages unchanged. To evaluate the numerical results, experimental data are compared with obtained data from simulation. Based on the results, the highest absolute relative deviation between experimental and numerical static pressure is approximately 7.5 per cent. Originality/value The blades geometry of an axial compressor used in a heavy-duty gas turbine is optimized by applying artificial neural network, and the results are compared with the base geometry numerically and experimentally.

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Comparative evaluation of numerical model and artificial neural network for simulating groundwater flow in Kathajodi–Surua Inter-basin of Odisha, India

Journal of Hydrology ◽

10.1016/j.jhydrol.2013.04.041 ◽

2013 ◽

Vol 495 ◽

pp. 38-51 ◽

Cited By ~ 49

Author(s):

S. Mohanty ◽

Madan K. Jha ◽

Ashwani Kumar ◽

D.K. Panda

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Numerical Model ◽

Groundwater Flow ◽

Comparative Evaluation ◽

Artificial Neural

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Spatio-temporal prediction of suspended sediment concentration in the coastal zone using an artificial neural network and a numerical model

Journal of Hydroinformatics ◽

10.2166/hydro.2012.123 ◽

2012 ◽

Vol 14 (3) ◽

pp. 574-584 ◽

Cited By ~ 2

Author(s):

B. Bhattacharya ◽

T. van Kessel ◽

D. P. Solomatine

Keyword(s):

Neural Network ◽

Time Series ◽

Artificial Neural Network ◽

Numerical Model ◽

Series Data ◽

Ann Model ◽

Pass Filter ◽

Low Pass Filter ◽

Temporal Prediction ◽

Artificial Neural

A problem of predicting suspended particulate matter (SPM) concentration on the basis of wind and wave measurements and estimates of bed shear stress done by a numerical model is considered. Data at a location at 10 km offshore from Noordwijk in the Dutch coastal area is used. The time series data have been filtered with a low pass filter to remove short-term fluctuations due to noise and tides and the resulting time series have been used to build an artificial neural network (ANN) model. The accuracy of the ANN model during both storm and calm periods was found to be high. The possibilities to apply the trained ANN model at other locations, where the model is assisted by the correctors based on the ratio of long-term average SPM values for the considered location to that for Noordwijk (for which the model was trained), have been investigated. These experiments demonstrated that the ANN model's accuracy at the other locations was acceptable, which shows the potential of the considered approach.

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