Development of a Simple Model to Predict Spatial Distribution of Cycle-Averaged Wall Heat Flux Using Artificial Neural Networks

2003 ◽  
Author(s):  
T. Kim ◽  
J. B. Ghandhi
Keyword(s):  
Neural Networks ◽  
Heat Flux ◽  
Spatial Distribution ◽  
Artificial Neural Networks ◽  
Simple Model ◽  
Wall Heat Flux ◽  
Artificial Neural

Inverse Estimation of Hot-Wall Heat Flux Using Nonlinear Artificial Neural Networks

Measurement ◽  
2021 ◽  
pp. 109648
Author(s):  
Hui Wang ◽  
Tao Zhu ◽  
Xinxin Zhu ◽  
Kai Yang ◽  
Qiang Ge ◽  
...  
Keyword(s):  
Neural Networks ◽  
Heat Flux ◽  
Artificial Neural Networks ◽  
Wall Heat Flux ◽  
Inverse Estimation ◽  
Artificial Neural

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%.

scholarly journals Water, Energy, and Carbon with Artificial Neural Networks (WECANN): a statistically based estimate of global surface turbulent fluxes and gross primary productivity using solar-induced fluorescence

2017 ◽  
Vol 14 (18) ◽  
pp. 4101-4124 ◽  
Author(s):  
Seyed Hamed Alemohammad ◽  
Bin Fang ◽  
Alexandra G. Konings ◽  
Filipe Aires ◽  
Julia K. Green ◽  
...  
Keyword(s):  
Neural Networks ◽  
Heat Flux ◽  
Artificial Neural Networks ◽  
Eddy Covariance ◽  
Latent Heat ◽  
Gross Primary Production ◽  
Turbulent Fluxes ◽  
Uncertainty Estimates ◽  
Artificial Neural ◽  
The Impact

Abstract. A new global estimate of surface turbulent fluxes, latent heat flux (LE) and sensible heat flux (H), and gross primary production (GPP) is developed using a machine learning approach informed by novel remotely sensed solar-induced fluorescence (SIF) and other radiative and meteorological variables. This is the first study to jointly retrieve LE, H, and GPP using SIF observations. The approach uses an artificial neural network (ANN) with a target dataset generated from three independent data sources, weighted based on a triple collocation (TC) algorithm. The new retrieval, named Water, Energy, and Carbon with Artificial Neural Networks (WECANN), provides estimates of LE, H, and GPP from 2007 to 2015 at 1°  ×  1° spatial resolution and at monthly time resolution. The quality of ANN training is assessed using the target data, and the WECANN retrievals are evaluated using eddy covariance tower estimates from the FLUXNET network across various climates and conditions. When compared to eddy covariance estimates, WECANN typically outperforms other products, particularly for sensible and latent heat fluxes. Analyzing WECANN retrievals across three extreme drought and heat wave events demonstrates the capability of the retrievals to capture the extent of these events. Uncertainty estimates of the retrievals are analyzed and the interannual variability in average global and regional fluxes shows the impact of distinct climatic events – such as the 2015 El Niño – on surface turbulent fluxes and GPP.

scholarly journals Water, Energy, and Carbon with Artificial Neural Networks (WECANN): A statistically-based estimate of global surface turbulent fluxes using solar-induced fluorescence

2016 ◽  
Author(s):  
Seyed Hamed Alemohammad ◽  
Bin Fang ◽  
Alexandra G. Konings ◽  
Julia K. Green ◽  
Jana Kolassa ◽  
...  
Keyword(s):  
Neural Networks ◽  
Heat Flux ◽  
Artificial Neural Networks ◽  
Latent Heat ◽  
Gross Primary Production ◽  
Sensible Heat Flux ◽  
Turbulent Fluxes ◽  
Input Dataset ◽  
Artificial Neural ◽  
The Impact

Abstract. A new global estimate of surface turbulent fluxes, including latent heat flux (LE), sensible heat flux (H), and gross primary production (GPP) is developed using remotely sensed Solar-Induced Fluorescence (SIF) and other radiative and meteorological variables. The approach uses an artificial neural network (ANN) with a Bayesian perspective to learn from the training datasets: a target input dataset is generated using three independent data sources and a triple collocation (TC) algorithm to define a prior distribution. The new retrieval, named Water, Energy, and Carbon with Artificial Neural Networks (WECANN), provides surface turbulent fluxes from 2007 to 2015 at 1° × 1° spatial resolution and on monthly time resolution. The quality of ANN training is assessed using the target data, and the WECANN retrievals are validated using FLUXNET tower measurements across various climates and conditions. WECANN performs well in most cases and is strongly constrained by SIF information. The impact of SIF on WECANN retrievals is evaluated by removing it from the input dataset of the ANN, and it shows that SIF has significant influence, especially in regions of high vegetation cover and in humid conditions. When compared to in situ eddy covariance observations, WECANN typically outperforms other estimates, particularly for sensible and latent heat fluxes.

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