journal article·2024-06

Unveiling thermal stresses in RETaO4 (RE = Nd, Sm, Eu, Gd, Tb, Dy, Ho and Er) by first-principles calculations and finite element simulations

Mengdi Gan, Xiaoyu Chong, Tianlong Lu, Chao Yang, Wei Yu, Shun-Li Shang, Yi Wang, Zi-Kui Liu, Jing Feng

Acta Materialia· Volume 271
Publisher page

Abstract

Thermal stress (σ) plays a critical role in regulating the stability and durability of thermal barrier coatings (TBCs) during service. However, its measurements are limited due to technical challenges. Here, thermal stresses in RETaO 4 (RE = Nd, Sm, Eu, Gd, Tb, Dy, Ho, and Er) TBCs have been evaluated by a cross-scale model, integrating first-principles calculations and finite element simulations (FEM). The isobaric heat capacity, thermal expansion coefficients, density, Young`s modulus, and Poisson`s ratio of RETaO 4 as a function of temperature have been determined by the quasi-harmonic approach (QHA) and the quasi-static approach (QSA). Taking these properties as the input data of FEM, the σ of TBCs with RETaO 4 as ceramic coatings is estimated. It indicates that the maximum tensile stress exists at the ceramic and thermal-grown oxide coating interface. Notably, TBCs_NdTaO 4 exhibits the highest tensile stress, reaching up to 2093 MPa, while TBCs_GdTaO 4 has the lowest value at 1880 MPa. Furthermore, the key factors affecting σ are estimated using interpretable machine learning based on the decision tree algorithm. The RETaO 4 with small Poisson`s ratio, strong electronegativity, small heat capacity, and thermal expansion coefficients is helpful to achieving low σ in TBCs. GdTaO 4, YTaO 4 and (Ho 0.5Er 0.5)TaO 4 emerge as more favorable options as ceramic layer due to their lower σ in TBCs. This present cross-scale model provides a successful tool for predicting σ and the reverse design of TBCs materials based on low σ in TBCs.
Source: expertdata · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Nuclear materials and radiation effects · Nuclear Materials and Properties · Luminescence Properties of Advanced Materials

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

References (84)

Show source-provided references
1. Thermal-barrier coatings for more efficient gas-turbine engines10.1557/mrs.2012.232 ↗
2. Advanced structural ceramics in aerospace propulsion10.1038/nmat4687 ↗
3. No easy solutions for aerospace10.1038/nmat4706 ↗
4. Understanding a way to fly high10.1038/nmat4699 ↗
5. Influence of HfO2 alloying effect on microstructure and thermal conductivity of HoTaO4 ceramics10.1007/s40145-019-0336-2 ↗
6. Thermal barrier coating materials10.1016/S1369-7021(05)70934-2 ↗
7. Advances on strategies for searching for next generation thermal barrier coating materials10.1016/j.jmst.2018.11.016 ↗
8. (La0.2Ce0.2Nd0.2Sm0.2Eu0.2)2Zr2O7: a novel high-entropy ceramic with low thermal conductivity and sluggish grain growth rate10.1016/j.jmst.2019.05.054 ↗
9. (Ho0.25Lu0.25Yb0.25Eu0.25)2SiO5 high-entropy ceramic with low thermal conductivity, tunable thermal expansion coefficient, and excellent resistance to CMAS corrosion10.1007/s40145-022-0609-z ↗
10. Composition optimization, high-temperature stability, and thermal cycling performance of Sc-doped Gd2Zr2O7 thermal barrier coatings: theoretical and experimental studies10.1007/s40145-021-0549-z ↗
11. Low thermal conductivity oxides10.1557/mrs.2012.234 ↗
12. The effect of zirconia substitution on the high-temperature transformation of the monoclinic-prime phase in yttrium tantalate10.1016/j.jeurceramsoc.2018.04.002 ↗
13. Microstructure and thermal properties of RETaO4 (RE=Nd, Eu, Gd, Dy, Er, Yb, Lu) as promising thermal barrier coating materials10.1016/j.scriptamat.2016.08.019 ↗
14. Thermal expansion performance and intrinsic lattice thermal conductivity of ferroelastic RETaO4 ceramics10.1111/jace.16328 ↗
15. Failure mechanisms associated with the thermally grown oxide in plasma-sprayed thermal barrier coatings10.1016/S1359-6454(00)00171-3 ↗
16. Parametric study for optimal design of an air plasma sprayed thermal barrier coating system with respect to thermal stress10.1016/j.surfcoat.2017.02.012 ↗
17. Effect of five kinds of pores shape on thermal stress properties of thermal barrier coatings by finite element method10.1016/j.ceramint.2017.04.139 ↗
18. The influence of bondcoat and topcoat mechanical properties on stress development in thermal barrier coating systems10.1016/j.actamat.2009.01.017 ↗
19. Finite element simulation of residual stress and failure mechanism in plasma sprayed thermal barrier coatings using actual microstructure as the representative volume10.1016/j.surfcoat.2016.02.028 ↗
20. Exploration of V–Cr–Fe–Co–Ni high-entropy alloys with high yield strength: a combination of machine learning and molecular dynamics simulation10.1016/j.commatsci.2022.111888 ↗
21. A property-oriented design strategy for high performance copper alloys via machine learning10.1038/s41524-019-0227-7 ↗
22. Machine learning-evolutionary algorithm enabled design for 4D-printed active composite structures10.1002/adfm.202109805 ↗
23. Predicting lattice thermal conductivity via machine learning: a mini review10.1038/s41524-023-00964-2 ↗
24. Predicting lattice thermal conductivity from fundamental material properties using machine learning techniques10.1039/D2TA08721A ↗
25. Machine learning for predicting thermal transport properties of solids10.1016/j.mser.2021.100642 ↗
26. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set10.1103/PhysRevB.54.11169 ↗
27. Generalized gradient approximation made simple10.1103/PhysRevLett.77.3865 ↗
28. From ultrasoft pseudopotentials to the projector augmented-wave method10.1103/PhysRevB.59.1758 ↗
29. Special points for Brillouin-zone integrations–a reply10.1103/PhysRevB.16.1748 ↗
30. First-principles calculations of lattice dynamics and thermal properties of polar solids10.1038/npjcompumats.2016.6 ↗
31. First principles phonon calculations in materials science10.1016/j.scriptamat.2015.07.021 ↗
32. A structural modeling approach to solid solutions based on the similar atomic environment10.1063/5.0014094 ↗
33. Elasticity of hexagonal BeO10.1088/0953-8984/13/2/302 ↗
34. The elastic behaviour of a crystalline aggregate10.1088/0370-1298/65/5/307 ↗
35. Berechnung der fließgrenze von mischkristallen auf grund der plastizitätsbedingung für einkristable
36. Tailoring the anisotropic mechanical properties of hexagonal M7X3 (M=Fe, Cr, W, Mo; X=C, B) by multialloying10.1016/j.actamat.2019.03.015 ↗
37. Changes of alloying elements on elasticity and solid solution strengthening of α-Ti alloys: a comprehensive high-throughput first-principles calculations10.1007/s12598-022-01996-1 ↗
38. First-principle prediction of structural and mechanical properties in NbMoTaWRex refractory high-entropy alloys with experimental validation10.1007/s12598-022-02054-6 ↗
39. Temperature-dependent elastic stiffness constants of α- and θ-Al2O3 from first-principles calculations10.1088/0953-8984/22/37/375403 ↗
40. Thermodynamic properties of Al, Ni, NiAl, and Ni3Al from first-principles calculations10.1016/j.actamat.2004.02.014 ↗
41. First-principles calculations of phonon and thermodynamic properties in the boron-alkaline earth metal binary systems: B-Ca, B-Sr, and B-Ba10.1103/PhysRevB.75.024302 ↗
42. Computational thermodynamics and its applications10.1016/j.actamat.2020.08.008 ↗
43. Gibbs2: a new version of the quasi-harmonic model code. I. Robust treatment of the static data10.1016/j.cpc.2011.04.016 ↗
44. Effects of alloying elements and temperature on the elastic properties of dilute Ni-base superalloys from first-principles calculations10.1063/1.4749406 ↗
45. First-principles thermodynamics from phonon and Debye model: application to Ni and Ni3Al10.1016/j.commatsci.2009.12.006 ↗
46. Thermodynamic properties of the Yb-Sb system predicted from first-principles calculations10.1016/j.actamat.2021.117169 ↗
47. A first-principles approach to finite temperature elastic constants10.1088/0953-8984/22/22/225404 ↗
48. Calculated thermal properties of metals10.1103/PhysRevB.37.790 ↗
49. Morelli · 2006 · 37
50. Theoretical study on crystal structures, elastic stiffness, and intrinsic thermal conductivities of β-, γ-, and δ-Y2Si2O710.1557/jmr.2015.1 ↗
51. Analysis of thermal field on integrated LED light source based on COMSOL multi-physics finite element simulation10.1016/j.phpro.2011.11.024 ↗
52. Machine learning for nanoplasmonics10.1038/s41565-022-01284-0 ↗
53. Machine learning identified materials descriptors for ferroelectricity10.1016/j.actamat.2021.116815 ↗
54. Identifying Pb-free perovskites for solar cells by machine learning10.1038/s41524-019-0177-0 ↗
55. Construction of decision trees by using feature importance value for improved learning performance
56. First-principles calculations of the high-temperature phase transformation in yttrium tantalate10.1103/PhysRevB.90.094102 ↗
57. Phase stability and mechanical properties of the monoclinic, monoclinic-prime and tetragonal REMO4 (M = Ta, Nb) from first-principles calculations10.3390/coatings12010073 ↗
58. Elastic anisotropy and thermodynamics properties of BiCu2PO6, BiZn2PO6 and BiPb2PO6 ceramics materials from first-principles calculations10.1016/j.ceramint.2019.12.089 ↗
59. The chemical consequences of the gradual decrease of the ionic radius along the Ln-series10.1016/j.ccr.2019.213146 ↗
60. Thermodynamics of solids10.1063/1.3050727 ↗
61. Thermal conductivity of single- and multi-phase compositions in the ZrO2–Y2O3–Ta2O5 system10.1016/j.jeurceramsoc.2014.03.013 ↗
62. Understanding the ultralow lattice thermal conductivity of monoclinic RETaO4 from acoustic-optical phonon anti-crossing property and a comparison with ZrO210.1111/jace.18988 ↗
63. Low thermal conductivity and anisotropic thermal expansion of ferroelastic (Gd1−xYx)TaO4 ceramics10.1007/s40145-022-0641-z ↗
64. Theoretical investigation of phonon contributions to thermal expansion coefficients for rare earth monosilicates RE2SiO5 (RE = Dy, Ho, Er, Tm, Yb and Lu)10.1016/j.jeurceramsoc.2019.09.042 ↗
65. Theoretical investigations on mechanical and dynamical properties of MAlB (M=Mo, W) nanolaminated borides at ground-states and elevated temperatures10.1016/j.jallcom.2017.12.206 ↗
66. On improving the phase stability and thermal expansion coefficients of lanthanum cerium oxide solid solutions10.1016/j.scriptamat.2005.12.043 ↗
67. High entropy (Yb0.25Y0.25Lu0.25Er0.25)2SiO5 with strong anisotropy in thermal expansion10.1016/j.jmst.2019.07.022 ↗
68. High-temperature specific heat of quasicrystals and a crystal approximant10.1016/S0921-5093(00)01875-X ↗
69. Theoretical and experimental determination of the major thermo-mechanical properties of RE2SiO5 (RE=Tb, Dy, Ho, Er, Tm, Yb, Lu, and Y) for environmental and thermal barrier coating applications10.1016/j.jeurceramsoc.2015.09.013 ↗
70. Theoretical prediction on mechanical and thermal properties of a promising thermal barrier material: Y4Al2O910.1007/s40145-015-0140-6 ↗
71. Universal elastic anisotropy index10.1103/PhysRevLett.101.055504 ↗
72. Anisotropic elastic and thermal properties of the double perovskite slab–rock salt layer Ln2SrAl2O7 (Ln=La, Nd, Sm, Eu, Gd or Dy) natural superlattice structure10.1016/j.actamat.2012.03.004 ↗
73. Kingery · 1976
74. Investigation into transpiration cooling of blades in high-temperature gasturbines10.1134/S0040601519060090 ↗
75. Multiscale defect-mediated thermophysical properties of high-entropy ferroelastic rare-earth tantalates10.1016/j.ceramint.2023.09.331 ↗
76. Defect-dominated phonon scattering processes and thermal transports of ferroelastic (Sm1-XYbX)TaO4 solid solutions
77. Thermal properties of plasma-sprayed functionally graded thermal barrier coatings10.1016/S0040-6090(00)01024-5 ↗
78. Coupled effects of temperature gradient and oxidation on thermal stress in thermal barrier coating system10.1016/S0020-7683(00)00309-7 ↗
79. An introduction to recursive partitioning: rationale, application, and characteristics of classification and regression trees, bagging, and random forests10.1037/a0016973 ↗
80. Identifying Pb-free perovskites for solar cells by machine learning10.1038/s41524-019-0177-0 ↗
81. Machine learning assisted discovering of new M2X3-type thermoelectric materials10.1007/s12598-021-01911-0 ↗
82. Machine learning models for the lattice thermal conductivity prediction of inorganic materials10.1016/j.commatsci.2019.109155 ↗
83. Characteristics of ferroelastic domains and thermal transport limits in HfO2 alloying YTaO4 ceramics10.1016/j.actamat.2023.118870 ↗
84. Using machine learning to identify factors that govern amorphization of irradiated pyrochlores10.1021/acs.chemmater.6b04666 ↗