Silicon carbide fiber and silicon carbide matrix (SiCf/SiCm) tubes produced through the chemical vapor infiltration process have become a candidate cladding material in nuclear applications. The performance of this composite is influenced by many variables such as braiding angle, porosity, material properties, etc., which vary over a range of values due to the inherent fluctuations in the manufacturing process. In this study, the variability in elastic constants of SiCf/SiCm composite has been quantified through multiscale finite element (FE) simulations, variable screening, and high-fidelity surrogate modeling. The key variables dominantly affecting the elastic constants of SiCf/SiCm tubes were identified using global sensitivity analysis. A surrogate to the high-fidelity FE-based model was used in Monte Carlo simulations to generate a hundred thousand samples from which the uncertainty in elastic constants was assessed. It turned out that the coefficient of variation was less than 10%.
DjurovicBRadjenSRadenkovicM, et al.Chernobyl and Fukushima nuclear accidents: what have we learned and what have we done?Vojnosanit Pregl2016; 73: 484–490.
2.
YunDLuCZhouZ, et al.Current state and prospect on the development of advanced nuclear fuel system materials: a review. Mater Rep Energ2021; 1: 100007.
3.
KimHHKimJHMoonJY, et al.High-temperature oxidation behavior of Zircaloy-4 and Zirlo in steam ambient. J Mater Sci Technol2010; 26: 827–832.
KatohYOzawaKShihC, et al.Continuous SiC fiber, CVI SiC matrix composites for nuclear applications: properties and irradiation effects. J Nucl Mater2014; 448: 448–476.
7.
KoyanagiTKatohYSinghG, et al.SiC / SiC Cladding materials properties handbook. ORNL/TM-2017/385, Prep US Dep energy Nucl Technol Res Dev Adv Fuels Campaign M3-FT17OR020202104, (Aug 2017)2017; 55.
8.
DeckCPJacobsenGMSheederJ, et al.Characterization of SiC-SiC composites for accident tolerant fuel cladding. J Nucl Mater2015; 466: 667–681.
9.
IchikawaH. Development of high performance SiC fibers derived from polycarbosilane using electron beam irradiation curing-a review. J Ceram Soc Japan2006; 114: 455–460.
10.
RoySGebertJMStasiukG, et al.Complete determination of elastic moduli of interpenetrating metal/ceramic composites using ultrasonic techniques and micromechanical modelling. Mater Sci Eng A2011; 528: 8226–8235.
11.
WannerA. Elastic modulus measurements of extremely porous ceramic materials by ultrasonic phase spectroscopy. Mater Sci Eng A1998; 248: 35–43.
12.
ZieglerTNeubrandARoyS, et al.Elastic constants of metal/ceramic composites with lamellar microstructures: finite element modelling and ultrasonic experiments. Compos Sci Technol2009; 69: 620–626.
13.
KarkkainenRLSankarBV. A direct micromechanics method for analysis of failure initiation of plain weave textile composites. Compos Sci Technol2006; 66: 137–150.
14.
ZhaiJZengTXuGD, et al.A multi-scale finite element method for failure analysis of three-dimensional braided composite structures. Compos B Eng2017; 110: 476–486.
15.
YamadaRIgawaNTaguchiT, et al.Highly thermal conductive, sintered SiC fiber-reinforced 3D-SiC/SiC composites: experiments and finite-element analysis of the thermal diffusivity/conductivity. J Nucl Mater2002; 307–311: 1215–1220.
16.
ChateauCGélébartLBornertM, et al.Micromechanical modeling of the elastic behavior of unidirectional CVI SiC/SiC composites. Int J Sol Struct2015; 58: 322–334.
17.
MeyerPWaasAM. Mesh-objective two-scale finite element analysis of damage and failure in ceramic matrix composites. Integr Mater Manuf Innov2015; 4: 63–80.
18.
McLendonRWhitcombJD. Characteristic progressive damage modes in a plain weave textile composite under multiaxial loads. J Compos Mater2017; 51: 1539–1556. DOI: 10.1177/0021998316662132.
19.
Giselle Fernández-GodinoMParkCKimNH, et al.Issues in deciding whether to use multifidelity surrogates. AIAA J2019; 57: 2039–2054.
20.
ZhangYKimNHParkC, et al.Multifidelity surrogate based on single linear regression. AIAA J2018; 56: 4944–4952.
21.
MelchersRE. Importance sampling in structural systems. Struct Saf1989; 6: 3–10.
DeyAMahadevanS. Ductile structural system reliability analysis using adaptive importance sampling. Struct Saf1998; 20: 137–154.
24.
Le MaîtreOPNajmHNGhanemRG, et al.Multi-resolution analysis of Wiener-type uncertainty propagation schemes. J Comput Phys2004; 197: 502–531.
25.
Le MatreOPReaganMTNajmHN, et al.A stochastic projection method for fluid flow: II. Random process. J Comput Phys2002; 181: 9–44.
26.
GoelTHafktaRTShyyW. Comparing error estimation measures for polynomial and Kriging approximation of noise-free functions. Struct Multidiscip Optim2009; 38: 429–442.
27.
KersaudyPSudretBVarsierN, et al.A new surrogate modeling technique combining Kriging and polynomial chaos expansions – application to uncertainty analysis in computational dosimetry. J Comput Phys2015; 286: 103–117.
28.
CrestauxTLe MaîtreOMartinezJM. Polynomial chaos expansion for sensitivity analysis. Reliab Eng Syst Saf2009; 94: 1161–1172.
29.
BishopCM. Pattern recognition and machine learning. In: JordanMKleinbergJSchollkopfB (eds). Berlin, Germany: Springer, 2006. Epub ahead of print 2006. DOI: 10.1007/978-3-030-57077-4_11.
30.
SaltelliATarantolaSCampolongoF, et al.Sensitivity analysis in practice: a guide to assessing scientific models. Chichester, England: John Wiley & Sons, 2004.
31.
SaltelliA. Sensitivity analysis: could better methods be used?J Geophys Res1999; 104: 3789–3793.
32.
McKayMDBeckmanRJConoverWJ. A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics2000; 42: 55–61.
33.
CukierRILevineHBShulerKE. Nonlinear sensitivity analysis of multiparameter model systems. J Comput Phys1978; 26: 1–42.
34.
SaltelliATarantolaSChanP-S. A quantitative model-independent method for global sensitivity analysis of model output. Technometrics1999; 41: 39–56.
35.
SaltelliASobol’IM. About the use of rank transformation in sensitivity analysis of model output. Reliab Eng Syst Saf1995; 50: 225–239.
36.
ArcherGEBSaltelliASobolIM. Sensitivity measures, anova-like techniques and the use of bootstrap. J Stat Comput Simul1997; 58: 99–120.
37.
SobolIM. Senstivity estimates for nonlinear mathematical models. Math Model Comput Exp1993; 1: 407–414.
38.
SanthoshUAhmadJOjardG, et al.Effect of porosity on the nonlinear and time-dependent behavior of ceramic matrix composites. Compos Part B Eng2020; 184: 107658.
39.
GowayedYOjardGPrevostE, et al.Defects in ceramic matrix composites and their impact on elastic properties. Compos Part B Eng2013; 55: 167–175.
40.
SinghGGonczySDeckC, et al.Interlaboratory round robin study on axial tensile properties of SiC-SiC CMC tubular test specimens. Int J Appl Ceram Technol2018; 15: 1334–1349.
41.
Bernachy-BarbeFGélébartLBornertM, et al.Anisotropic damage behavior of SiC/SiC composite tubes: multiaxial testing and damage characterization. Compos A Appl Sci Manuf2015; 76: 281–288.
42.
MuraT. Micromechanics of defects in solids. Dordrecht, Netherlands: Springer. Epub ahead of print1982. DOI: 10.1007/978-94-011-9306-1.
43.
EshelbyJD. The determination of the elastic field of an ellipsoidal inclusion in an anisotronic medium. Math Proc Cambridge Philos Soc1977; 81: 283–289.
44.
Namet-NasserSHoriM. Micromechanics: overall properties of heterogeneous materials. Second Rev. Amsterdam, Netherlands: Elsevier B.V., 1999.
45.
BeckerFHopmannC. Stiffness estimates for composites with elliptic cylindrical voids. Materials (Basel)2020; 13. Epub ahead of print 2020. DOI: 10.3390/ma13061354.
46.
RohmerEMartinELorretteC. Mechanical properties of SiC/SiC braided tubes for fuel cladding. J Nucl Mater2014; 453: 16–21.
ZhuHSankarBVMarreyRV. Evaluation of failure criteria for fiber composites using finite element micromechanics. J Compos Mater1998; 32: 766–782.
54.
BarberoEJ. Finite element analysis of composite materials using abaqus. Boca Raton, USA: CRC Press, 2013.
55.
LinHBrownLPLongAC. Modelling and simulating textile structures using texgen. Adv Mater Res2011; 331: 44–47.
56.
Thandaga NagarajuHSankarBVSubhashG, et al.Effect of curvature on extensional stiffness matrix of 2-D braided composite tubes. Compos Part A Appl Sci Manuf2021; 147: 106422.
MyersRHMontgomeryDCAnderson-cookCM. Response surface methodology: process and product optimization using designed experiments. 4th ed.New Jersey, USA: John Wiley & Sons, 2016.
59.
NanceJ. Characterization of microstructural heterogeneity and mechanical properties of SiCf-SiCm composites. University of Florida: Gainesville, USA, 2022.
60.
JohnsonNL. Bivariate distributions based on simple translation systems. Biometrika1949; 36: 297–304.
61.
JohnsonANLJohnsonBYNL. Biometrika trust systems of frequency curves generated by methods of translation Published by : Oxford University Press on behalf of Biometrika Trust Stable, 2020; 36: 149–176. URL: https://www.jstor.org/stable/2332539
62.
MateusAToméM. Estimating the parameters of the Johnson’s SB distribution using an approach of method of moments. AIP Conf Proc2011; 1389: 1483–1485.
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
