Machine learning in nuclear materials research
Nuclear materials are often demanded to function for extended time in extreme
environments, including high radiation fluxes with associated transmutations, high …
environments, including high radiation fluxes with associated transmutations, high …
Machine learning (ML)‐assisted design and fabrication for solar cells
F Li, X Peng, Z Wang, Y Zhou, Y Wu… - Energy & …, 2019 - Wiley Online Library
Photovoltaic (PV) technologies have attracted great interest due to their capability of
generating electricity directly from sunlight. Machine learning (ML) is a technique for …
generating electricity directly from sunlight. Machine learning (ML) is a technique for …
Energy and exergy evaluation of the evacuated tube solar collector using Cu2O/water nanofluid utilizing ANN methods
In this paper, the improvement of the thermal characteristics of an evacuated tube solar
collector for different volumetric flow rates of the fluid (10, 30 and 50 L/h) was experimentally …
collector for different volumetric flow rates of the fluid (10, 30 and 50 L/h) was experimentally …
[HTML][HTML] Empirical data-driven multi-layer perceptron and radial basis function techniques in predicting the performance of nanofluid-based modified tubular solar …
In the present study, the modified evacuated tube solar collector (METSC) with a bypass
pipe utilizing copper oxide/distilled water (Cu 2 O/DW) nanofluid is experimented. Then, the …
pipe utilizing copper oxide/distilled water (Cu 2 O/DW) nanofluid is experimented. Then, the …
Machine learning applied to retrieval of temperature and concentration distributions from infrared emission measurements
Inversion of temperature and species concentration distributions from radiometric
measurements involves solving nonlinear, ill-posed and high-dimensional problems …
measurements involves solving nonlinear, ill-posed and high-dimensional problems …
Estimation of minimum miscibility pressure (MMP) in enhanced oil recovery (EOR) process by N2 flooding using different computational schemes
A Barati-Harooni, A Najafi-Marghmaleki… - Fuel, 2019 - Elsevier
Nitrogen is an effective agent to be considered in gas injection processes as a part of
enhanced oil recovery (EOR) process. Successful design and implementation of nitrogen …
enhanced oil recovery (EOR) process. Successful design and implementation of nitrogen …
An image-driven machine learning approach to kinetic modeling of a discontinuous precipitation reaction
Microstructure quantification is an essential component of materials science studies, yet,
there are no widely applicable, standard methodologies, for image data representation in …
there are no widely applicable, standard methodologies, for image data representation in …
Machine learning-assisted soot temperature and volume fraction fields predictions in the ethylene laminar diffusion flames
Inferring local soot temperature and volume fraction distributions from radiation emission
measurements of sooting flames may involve solving nonlinear, ill-posed and high …
measurements of sooting flames may involve solving nonlinear, ill-posed and high …
A machine learning approach to thermal conductivity modeling: A case study on irradiated uranium-molybdenum nuclear fuels
A deep neural network was developed for the purpose of predicting thermal conductivity with
a case study performed on neutron irradiated nuclear fuel. Traditional thermal conductivity …
a case study performed on neutron irradiated nuclear fuel. Traditional thermal conductivity …
A prediction model for thermal conductivity of metallic nuclear fuel based on multiple machine learning models
Y Lu, X Huang, Z Ren, D Sun, Y Guo, X Liu… - Journal of Nuclear …, 2023 - Elsevier
In this work, the presence of the BCC phase and its thermal conductivity in uranium-based
metallic nuclear fuels at higher temperatures were predicted by coupling a random forest …
metallic nuclear fuels at higher temperatures were predicted by coupling a random forest …