In-pile heat conduction model of the dispersion nuclear fuel plate with particle agglomeration. Part II: Predicting the effective thermal conductivity under the in-pile thermal transfer pattern based on a deep neural network
Bibliographic record
Abstract
No abstract is available for this record. The assistant can identify its topic from the title, but cannot infer the study’s results.
Source: Not provided · Quality: missing
Research context
OpenAlex topics: Nuclear reactor physics and engineering · Nuclear Materials and Properties · Granular flow and fluidized beds
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (48)
Show source-provided references
1. Research on the grain growth and the mechanism of (U, Ti)O2 dispersion fuel microspheres10.1016/j.ceramint.2013.08.114 ↗
2. Fission induced swelling and creep of U-Mo alloy fuel10.1016/j.jnucmat.2013.01.346 ↗
3. Fabrication of UN-Mo cermet nuclear fuel using advanced manufacturing techniques
4. Numerical simulation of the effect of particle spatial distribution and strength on tensile behavior of particle reinforced composites10.1016/j.commatsci.2008.04.009 ↗
5. Three-dimensional visualization and microstructure-based modeling of deformation in particle-reinforced composites10.1016/j.actamat.2005.11.027 ↗
6. Study of nanoparticles aggregation/agglomeration in polymer particulate nanocomposites by mechanical properties10.1016/j.compositesa.2016.01.020 ↗
7. Effect of particle size and aggregation on thermal conductivity of metal-polymer nanocomposite10.1115/1.4034757 ↗
8. Effect of aggregation and interfacial thermal resistance on thermal conductivity of nanocomposites and colloidal nanofluids10.1016/j.ijheatmasstransfer.2007.10.017 ↗
9. Modified Maxwell model for predicting thermal conductivity of nanocomposites considering aggregation10.1088/1674-1056/26/11/114401 ↗
10. A randomly distributed filler model for heat conductivity prediction in filled composite materials considering fillers aggregation10.1080/01457632.2015.972721 ↗
11. Growth of the interaction layer around fuel particles in dispersion fuel10.1016/j.jnucmat.2008.07.035 ↗
12. Thermophysical properties of heat-treated U-7Mo/Al dispersion fuel10.1016/j.jnucmat.2018.01.027 ↗
13. Degradation of thermal conductivity of the damaged layer of tungsten irradiated by helium-plasma10.1016/j.fusengdes.2018.08.014 ↗
14. Effect of heat treatment on thermal conductivity of U-Mo/Al alloy dispersion fuel10.1023/A:1026159303485 ↗
15. The modeling and simulation of the thermal conductivity of irradiated U-Mo dispersion fuel: estimation of the thermal conductivity of the interaction layer10.1016/j.jnucmat.2018.08.014 ↗
16. Thermal conductivity prediction of UO2-BeO composite fuels and related decisive features discovery via convolutional neural network10.1016/j.actamat.2022.118352 ↗
17. Using deep neural network with small dataset to predict material defects10.1016/j.matdes.2018.11.060 ↗
18. Topological representations of crystalline compounds for the machine-learning prediction of materials properties10.1038/s41524-021-00493-w ↗
19. Machine learning prediction of mechanical properties of braided-textile reinforced tubular structures10.1016/j.matdes.2021.110181 ↗
20. Deep learning accelerated prediction of the permeability of fibrous microstructures10.1016/j.compositesa.2022.106973 ↗
21. Deep learning in neural networks: an overview10.1016/j.neunet.2014.09.003 ↗
22. Exploring the 3D architectures of deep material network in data-driven multiscale mechanics10.1016/j.jmps.2019.03.004 ↗
23. Deep learning10.1038/nature14539 ↗
24. Goodfellow · 2016
25. FireWorks: a dynamic workflow system designed for high-throughput applications10.1002/cpe.3505 ↗
26. The rise of high-throughput computing10.1631/FITEE.1800501 ↗
27. Mechanical modeling and parameter analysis for mesoscopic failure mechanism of dispersion fuels
28. A pragmatic approach to modeling thermal conductivity of irradiated UO2 fuel: review and recommendations10.1016/S0022-3115(96)00404-7 ↗
29. MacDonald · 1976
30. Modeling the influence of interaction layer formation on thermal conductivity of U-Mo dispersion fuel10.1016/j.jallcom.2014.08.123 ↗
31. Holden · 1967
32. Room-temperature microindentation behaviour of LWR-fuels, part 1: fuel microhardness10.1016/S0022-3115(03)00328-3 ↗
33. Calculation simulation of equivalent irradiation swelling for dispersion nuclear fuel
34. Development and validation of constitutive equation of HBS irradiation swelling considering hydrostatic pressure
35. Numerical simulation of fission gas swelling in sintered Uranium dioxide fuel
36. Statistical analysis of spatial distribution of pores in metal additive manufacturing
37. Quantitative characterization and modeling of composite microstructures by Voronoi cells10.1016/S1359-6454(96)00365-5 ↗
38. Tessellation-based computational methods for the characterization and analysis of heterogeneous microstructures10.1016/S0266-3538(97)00042-0 ↗
39. Statistical analysis of real and simulated fibre arrangements in unidirectional composites10.1016/j.compscitech.2013.07.030 ↗
40. Determination of the critical size of a statistical representative volume element (SRVE) for carbon reinforced polymers10.1016/j.actamat.2006.03.042 ↗
41. New derivation reduces bias and increases power of Ripley's L index10.1016/S0304-3800(98)00173-2 ↗
42. Determination of the critical size of a statistical representative volume element (SRVE) for carbon reinforced polymers10.1016/j.actamat.2006.03.042 ↗
43. Topological representations of crystalline compounds for the machine-learning prediction of materials properties10.1038/s41524-021-00493-w ↗
44. Tensile property prediction by feature engineering guided machine learning in reduced activation ferritic/martensitic steels10.1016/j.jnucmat.2019.151823 ↗
45. Predicting the onset of void swelling in irradiated metals with machine learning10.1016/j.jnucmat.2019.05.054 ↗
46. Root mean square error (RMSE) or mean absolute error (MAE)?-Arguments against avoiding RMSE in the literature10.5194/gmd-7-1247-2014 ↗
47. The probability plot correlation coefficient test for normality10.1080/00401706.1975.10489279 ↗
48. Application of sub-miniaturized bending tests to extract tensile properties from neutron-irradiated metallic alloys10.1016/j.jnucmat.2021.153320 ↗