journal article·2022-08

Bi-objective optimization of thermal conductivity and thermal stress of UO2–Mo–Nb composite through Gaussian process regression and NSGA-II method

Biaojie Yan, Zhiyi Wang, Tao Fa, Liang Cheng, Pengcheng Zhang

Ceramics International· Volume 48· Issue 16
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OpenAlex topics: Nuclear Materials and Properties · Nuclear reactor physics and engineering · Nuclear Engineering Thermal-Hydraulics

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References (39)

Show source-provided references
1. Enhanced thermal conductivity accident tolerant fuels for improved reactor safety – a comprehensive review10.1016/j.anucene.2018.04.040 ↗
2. Review of manufacturing technologies for coated accident tolerant fuel cladding10.1016/j.jnucmat.2022.153562 ↗
3. Recent studies on potential accident-tolerant fuel-cladding systems in light water reactors10.1007/s41365-020-0741-9 ↗
4. Thermal conductivity of UO2-BeO pellet10.1080/18811248.1996.9731875 ↗
5. Fabrication of micro-cell UO2–Mo pellet with enhanced thermal conductivity10.1016/j.jnucmat.2015.04.003 ↗
6. Fabrication of UO2-Mo composite fuel with enhanced thermal conductivity from sol-gel feedstock10.1016/j.jnucmat.2019.04.011 ↗
7. Densification behaviour of UO2/Mo core-shell composite pellets with a reduced coefficient of thermal expansion10.1016/j.ceramint.2019.10.204 ↗
8. Numerical investigation of the thermal conductivity of UO2 – Mo microplate fuel pellets to realize enhanced heat transfer in the fuel radial direction10.1016/j.jnucmat.2021.153075 ↗
9. Evaluation of thermophysical properties of UO2-10 Vol% Mo nuclear fuel pellets10.1016/j.jnucmat.2021.153444 ↗
10. SiC whisker strengthened UO2–Mo composite pellets with superior thermal-mechanical properties10.1016/j.ceramint.2019.08.039 ↗
11. UO2–Mo–Be composites for Accident Tolerant Fuel: SPS fabrication, microcracks-free in as-fabricated state and superior thermal conductivity10.1016/j.ceramint.2020.08.064 ↗
12. 2016
13. Machine learning in materials genome initiative: a review,10.1016/j.jmst.2020.01.067 ↗
14. Machine-learning and high-throughput studies for high-entropy materials10.1016/j.mser.2021.100645 ↗
15. Machine learning in nuclear materials research10.1016/j.cossms.2021.100975 ↗
16. A machine learning assisted approach for textile formability assessment and design improvement of composite components,10.1016/j.compositesa.2019.05.027 ↗
17. Machine learning assisted multiscale modeling of composite phase change materials for Li-ion batteries' thermal management10.1016/j.ijheatmasstransfer.2021.121199 ↗
18. Thermal conductivity of Cu-matrix composites reinforced with coated SiC particles: numerical modeling and experimental verification10.1016/j.ijheatmasstransfer.2022.122633 ↗
19. Investigation of the residual stress in UO2-Mo composites via a neutron diffraction method10.1016/j.ceramint.2020.03.137 ↗
20. Bi-directional prediction of structural characteristics and effective thermal conductivities of composite fuels through learning from finite element simulation results10.1016/j.matdes.2020.108483 ↗
21. Optimization of thermal conductivity of UO2–Mo composite with continuous Mo channel based on finite element method and machine learning10.1016/j.ijheatmasstransfer.2020.120067 ↗
22. Designing staggered platelet composite structure with Gaussian process regression based Bayesian optimization10.1016/j.compscitech.2021.109254 ↗
23. Predictive modelling for contact angle of liquid metals and oxide ceramics by comparing Gaussian process regression with other machine learning methods10.1016/j.ceramint.2021.09.146 ↗
24. Multi-objective optimization of 3D printed liquid cooled heat sink with guide vanes for targeting hotspots in high heat flux electronics10.1016/j.ijheatmasstransfer.2021.122287 ↗
25. Multi-objective optimization of mini U-channel cold plate with SiO2 nanofluid by RSM and NSGA-II10.1016/j.energy.2021.123039 ↗
26. Sensitivity analysis and multi-objective optimization of double-ceramic-layers thermal barrier system10.1016/j.ceramint.2019.05.278 ↗
27. Kingery · 1976
28. Uranium dioxide properties for LWR fuel rods10.1016/0029-5493(75)90024-2 ↗
29. Temperature dependence of the Young's modulus and shear modulus of pure nickel, platinum, and molybdenum10.1007/BF02644859 ↗
30. High temperature elastic properties of polycrystalline niobium, tantalum, and vanadium10.1007/BF02811703 ↗
31. Mechanical properties of niobium radio-frequency cavities10.1016/j.msea.2015.06.095 ↗
32. The thermal expansion of solid UO2 and (U, Pu) mixed oxides — a review and recommendations,10.1016/0022-3115(88)90315-7 ↗
33. Assessments of molar volume and thermal expansion for selected bcc, fcc and hcp metallic elements10.1016/j.calphad.2005.05.001 ↗
34. The role of defects on thermophysical properties: thermal expansion of V, Nb, Ta, Mo and W10.1016/S0927-796X(98)00011-4 ↗
35. Thermophysical properties of uranium dioxide10.1016/S0022-3115(99)00273-1 ↗
36. Thermal conductivity of the elements10.1063/1.3253100 ↗
37. Bergman · 2011
38. Rasmussen · 2004
39. A fast and elitist multiobjective genetic algorithm: NSGA-II,10.1109/4235.996017 ↗