Thermal conductivity prediction of UO2-BeO composite fuels and related decisive features discovery via convolutional neural network
Published 2022 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Thermal conductivity prediction of UO2-BeO composite fuels and related decisive features discovery via convolutional neural network
Authors
Keywords
-
Journal
ACTA MATERIALIA
Volume 240, Issue -, Pages 118352
Publisher
Elsevier BV
Online
2022-09-10
DOI
10.1016/j.actamat.2022.118352
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
- (2021) Tsz Wai Ko et al. Nature Communications
- High-throughput first-principles search for ceramic superlattices with improved ductility and fracture resistance
- (2021) Nikola Koutná et al. ACTA MATERIALIA
- Exploration of optimal microstructure and mechanical properties in continuous microstructure space using a variational autoencoder
- (2021) Yongju Kim et al. MATERIALS & DESIGN
- Deep learning model to predict complex stress and strain fields in hierarchical composites
- (2021) Zhenze Yang et al. Science Advances
- Studying the micromechanical behaviors of a polycrystalline metal by artificial neural networks
- (2021) Wei Dai et al. ACTA MATERIALIA
- First‐Principles Multiscale Modeling of Mechanical Properties in Graphene/Borophene Heterostructures Empowered by Machine‐Learning Interatomic Potentials
- (2021) Bohayra Mortazavi et al. ADVANCED MATERIALS
- Descriptive modeling of textiles using FE simulations and deep learning
- (2021) Arturo Mendoza et al. COMPOSITES SCIENCE AND TECHNOLOGY
- Mesoscopic and multiscale modelling in materials
- (2021) Jacob Fish et al. NATURE MATERIALS
- Discovering and understanding materials through computation
- (2021) Steven G. Louie et al. NATURE MATERIALS
- Electronic-structure methods for materials design
- (2021) Nicola Marzari et al. NATURE MATERIALS
- From predictive modelling to machine learning and reverse engineering of colloidal self-assembly
- (2021) Marjolein Dijkstra et al. NATURE MATERIALS
- Deep learning for visualization and novelty detection in large X-ray diffraction datasets
- (2021) Lars Banko et al. npj Computational Materials
- Mechanical behavior predictions of additively manufactured microstructures using functional Gaussian process surrogates
- (2021) Robert Saunders et al. npj Computational Materials
- Autonomous reinforcement learning agent for stretchable kirigami design of 2D materials
- (2021) Pankaj Rajak et al. npj Computational Materials
- Combinatorial study of thermal stability in ternary nanocrystalline alloys
- (2020) Sebastian A. Kube et al. ACTA MATERIALIA
- Explainable Machine Learning Algorithms For Predicting Glass Transition Temperatures
- (2020) Edesio Alcobaça et al. ACTA MATERIALIA
- Design of Nickel-Cobalt-Ruthenium Multi-Principal Element Alloys
- (2020) M.A. Charpagne et al. ACTA MATERIALIA
- Genetic algorithm-driven discovery of unexpected thermal conductivity enhancement by disorder
- (2020) Han Wei et al. Nano Energy
- Bi-directional prediction of structural characteristics and effective thermal conductivities of composite fuels through learning from finite element simulation results
- (2020) Biaojie Yan et al. MATERIALS & DESIGN
- Prediction of composite microstructure stress-strain curves using convolutional neural networks
- (2020) Charles Yang et al. MATERIALS & DESIGN
- An active learning high-throughput microstructure calibration framework for solving inverse structure–process problems in materials informatics
- (2020) Anh Tran et al. ACTA MATERIALIA
- Machine learning prediction of thermal transport in porous media with physics-based descriptors
- (2020) Han Wei et al. INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER
- Unsupervised Phase Discovery with Deep Anomaly Detection
- (2020) Korbinian Kottmann et al. PHYSICAL REVIEW LETTERS
- Deep neural network approach to estimate biaxial stress-strain curves of sheet metals
- (2020) Akinori Yamanaka et al. MATERIALS & DESIGN
- Machine learning-based glass formation prediction in multicomponent alloys
- (2020) Xiaodi Liu et al. ACTA MATERIALIA
- Intelligent layout design of curvilinearly stiffened panels via deep learning-based method
- (2020) Peng Hao et al. MATERIALS & DESIGN
- Materials informatics: From the atomic-level to the continuum
- (2019) J.M. Rickman et al. ACTA MATERIALIA
- Modern data analytics approach to predict creep of high-temperature alloys
- (2019) D. Shin et al. ACTA MATERIALIA
- Machine-learning phase prediction of high-entropy alloys
- (2019) Wenjiang Huang et al. ACTA MATERIALIA
- Characterizing surrogates to develop an additive manufacturing process for U3Si2 nuclear fuel
- (2019) Jhonathan Rosales et al. JOURNAL OF NUCLEAR MATERIALS
- Exploiting machine learning for end-to-end drug discovery and development
- (2019) Sean Ekins et al. NATURE MATERIALS
- Designing high ductility in magnesium alloys
- (2019) Rasool Ahmad et al. ACTA MATERIALIA
- A map of the inorganic ternary metal nitrides
- (2019) Wenhao Sun et al. NATURE MATERIALS
- Physically informed artificial neural networks for atomistic modeling of materials
- (2019) G. P. Purja Pun et al. Nature Communications
- Strain anisotropy and magnetic domain structures in multiferroic heterostructures: High-throughput finite-element and phase-field studies
- (2019) Jian-Jun Wang et al. ACTA MATERIALIA
- Machine Learning Interatomic Potentials as Emerging Tools for Materials Science
- (2019) Volker L. Deringer et al. ADVANCED MATERIALS
- Predicting the effective thermal conductivity of composites from cross sections images using deep learning methods
- (2019) Qingyuan Rong et al. COMPOSITES SCIENCE AND TECHNOLOGY
- A novel deep learning based method for the computational material design of flexoelectric nanostructures with topology optimization
- (2019) Khader M. Hamdia et al. FINITE ELEMENTS IN ANALYSIS AND DESIGN
- Voxelated soft matter via multimaterial multinozzle 3D printing
- (2019) Mark A. Skylar-Scott et al. NATURE
- Material structure-property linkages using three-dimensional convolutional neural networks
- (2018) Ahmet Cecen et al. ACTA MATERIALIA
- Enhanced thermal conductivity accident tolerant fuels for improved reactor safety – A comprehensive review
- (2018) Wei Zhou et al. ANNALS OF NUCLEAR ENERGY
- Early progress on additive manufacturing of nuclear fuel materials
- (2018) A. Bergeron et al. JOURNAL OF NUCLEAR MATERIALS
- Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics
- (2018) Linfeng Zhang et al. PHYSICAL REVIEW LETTERS
- Mechanistic origin and prediction of enhanced ductility in magnesium alloys
- (2018) Zhaoxuan Wu et al. SCIENCE
- High temperature thermal physical performance of BeO/UO 2 composites prepared by spark plasma sintering (SPS)
- (2018) Bingqing Li et al. SCRIPTA MATERIALIA
- Printing ferromagnetic domains for untethered fast-transforming soft materials
- (2018) Yoonho Kim et al. NATURE
- Establishing structure-property localization linkages for elastic deformation of three-dimensional high contrast composites using deep learning approaches
- (2018) Zijiang Yang et al. ACTA MATERIALIA
- Synthesis and preservation of graphene-supported uranium dioxide nanocrystals
- (2016) Hanyu Ma et al. JOURNAL OF NUCLEAR MATERIALS
- Printing soft matter in three dimensions
- (2016) Ryan L. Truby et al. NATURE
- Development Status of Accident-tolerant Fuel for Light Water Reactors in Korea
- (2016) Hyun-Gil Kim et al. Nuclear Engineering and Technology
- Fabrication methods and thermal hydraulics analysis of enhanced thermal conductivity UO2–BeO fuel in light water reactors
- (2015) Wenzhong Zhou et al. ANNALS OF NUCLEAR ENERGY
- Deep learning
- (2015) Yann LeCun et al. NATURE
- Spark plasma sintering of diamond-reinforced uranium dioxide composite fuel pellets
- (2015) Zhichao Chen et al. NUCLEAR ENGINEERING AND DESIGN
- Forward for special JNM issue on accident tolerant fuels for LWRs
- (2014) Jon Carmack et al. JOURNAL OF NUCLEAR MATERIALS
- On the possibility of using uranium-beryllium oxide fuel in a VVER reactor
- (2014) A. A. Kovalishin et al. PHYSICS OF ATOMIC NUCLEI
- Materials challenges in nuclear energy
- (2013) S.J. Zinkle et al. ACTA MATERIALIA
- The influence of SiC particle size and volume fraction on the thermal conductivity of spark plasma sintered UO2–SiC composites
- (2013) Sunghwan Yeo et al. JOURNAL OF NUCLEAR MATERIALS
- The effect of fuel thermal conductivity on the behavior of LWR cores during loss-of-coolant accidents
- (2013) Kurt A. Terrani et al. JOURNAL OF NUCLEAR MATERIALS
- Preliminary assessment of accident-tolerant fuels on LWR performance during normal operation and under DB and BDB accident conditions
- (2013) L.J. Ott et al. JOURNAL OF NUCLEAR MATERIALS
- The high-throughput highway to computational materials design
- (2013) Stefano Curtarolo et al. NATURE MATERIALS
- Impact of thermal conductivity models on the coupling of heat transport and oxygen diffusion in UO2 nuclear fuel elements
- (2012) Bogdan Mihaila et al. JOURNAL OF NUCLEAR MATERIALS
- Nuclear Fuel in a Reactor Accident
- (2012) Peter C. Burns et al. SCIENCE
- Modeling and Measurement of Thermal Properties of Ceramic Composite Fuel for Light Water Reactors
- (2008) Ryan Latta et al. HEAT TRANSFER ENGINEERING
Discover Peeref hubs
Discuss science. Find collaborators. Network.
Join a conversationFind the ideal target journal for your manuscript
Explore over 38,000 international journals covering a vast array of academic fields.
Search