A multi-index screening high-entropy alloy design method for high-temperature bearing performance requirements
By constructing a virtual crystal approximation model of high-entropy alloys and optimizing its structure, the elastic constants and mechanical properties were calculated, thus solving the problem of high experimental costs caused by the large design space of high-entropy alloy composition and realizing efficient screening of high-temperature bearing materials.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- HARBIN INST OF TECH ZHENGZHOU RES INST
- Filing Date
- 2025-08-26
- Publication Date
- 2026-07-03
AI Technical Summary
The vast design space for high-entropy alloys results in huge testing costs, making it difficult to efficiently screen materials suitable for high-temperature bearings.
A multi-index screening method for high-entropy alloys was adopted. A virtual crystal approximation model of high-entropy alloys was constructed using Materials Studio software. Structural optimization and stress-strain relationship fitting were performed, and elastic constants and mechanical properties were calculated to achieve high-throughput screening of ideal alloys.
This significantly reduced experimental costs, enabled the rapid screening of high-entropy alloys suitable for high-temperature bearings, and reduced the workload of synthesis and mechanical property testing.
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Figure CN120805342B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to a multi-index screening design method for high-entropy alloys, belonging to the field of bearing technology. Background Technology
[0002] In high-end equipment fields such as aerospace engines, heavy-duty gas turbines, and nuclear energy, key moving components have long faced the harsh test of continuous high-temperature environments. Taking aero-engine main shaft bearings as an example, their operating temperature has exceeded 500℃ and continues to rise above 800℃. Under extreme conditions, traditional bearing materials suffer from significant technical bottlenecks, including material softening and strength reduction, phase transformation instability and dimensional drift, lubrication system collapse, and a sharp decline in fatigue life. High-entropy alloys, with their unique high-entropy effect, lattice distortion effect, hysteresis diffusion effect, and cocktail effect, show great potential to break through the 900℃ service limit. However, the vast compositional design space of high-entropy alloys leads to huge experimental costs, necessitating breakthroughs through high-throughput computational design technology. Summary of the Invention
[0003] To address the issue of high experimental costs caused by the vast compositional design space of high-entropy alloys, this invention proposes a multi-index screening method for high-entropy alloy design that meets the performance requirements of high-temperature bearings.
[0004] The technical solution adopted by the present invention to solve the above problems is as follows: The steps of the present invention include:
[0005] Step 1: Determine the types of metallic elements contained in the high-entropy alloy material system and the molar ratio of each element;
[0006] Step 2: Determine the crystal system of the high-entropy alloy based on its valence electron concentration, and construct a metallic elemental crystal structure model based on the crystal system of the high-entropy alloy.
[0007] Step 3: Based on the types and molar ratios of metal elements determined in Step 1, modify the Composition of metal atoms in the metal element crystal structure model constructed in Step 2 using the 3D Atomistic window of Materials Studio software to obtain an approximate virtual crystal model of high-entropy alloy.
[0008] Step 4: Import the high-entropy alloy virtual crystal approximation model constructed in Step 3 into the same folder, perform structural optimization on the high-entropy alloy virtual crystal approximation model, and obtain the high-entropy alloy virtual crystal approximation model with the lowest energy.
[0009] Step 5: Based on the crystal structure symmetry of the research object, apply a small deformation to the optimized high-entropy alloy virtual crystal approximation model in Step 4, and perform structural optimization on the deformed model to make it reach a stable state again, and obtain the stress-strain relationship.
[0010] Step 6: Fit the stress-strain relationship obtained in Step 5 using the generalized Hooke's law to obtain the elastic stiffness constant of the high-entropy alloy. and elastic compliance constant ;
[0011] Step 7: Determine the mechanical stability of the high-entropy alloy based on the elastic stiffness coefficient calculated in Step 6;
[0012] Step 8: Calculate the melting point of the high-entropy alloy based on the elastic stiffness coefficient obtained in Step 6. ;
[0013] Step 9: Calculate the Young's modulus of the high-entropy alloy using the VRH criterion based on the elastic constants obtained from the fitting in Step 6. shear modulus bulk modulus Compared to Poisson ;
[0014] Step 10: Based on the shear modulus in Step 9 and bulk modulus Calculating the Vickers hardness of high-entropy alloys According to Young's modulus in step 9 Compared to Poisson Calculate the fracture toughness of high-entropy alloys ;
[0015] Step 11: Based on the mechanical stability and melting point obtained in steps 7, 8, 9, and 10, proceed sequentially. Young's modulus Vickers hardness With fracture toughness High-throughput screening of high-entropy alloys.
[0016] Furthermore, the metallic elements in step 1 include at least five of the following: Ti, Zr, Hf, V, Nb, Ta, Mo, W, and Cr, with each metallic element accounting for a molar ratio in the range of 5% to 35%.
[0017] Furthermore, the formula for calculating the valence electron concentration in step 2 is as follows: ,in c i For the first i The molar ratio of metallic elements, VEC i For the first i The number of valence electrons of an element;
[0018] VEC < 6.84 corresponds to BCC structure, VEC ≥ 8 corresponds to FCC structure, and 6.84 ≤ <8 corresponds to the BCC and FCC biphase structure.
[0019] Furthermore, the parameters for structural optimization in step 4 include: selecting the PBE form of the GGA functional, selecting BFGS as the optimization algorithm, selecting OTFG Norm-conserving as the pseudopotential, and setting the cutoff energy to 1250 eV. k The point size is set to 20×20×20, and the energy convergence tolerance is 5×10. -6 eV, force convergence criterion is 0.01 eV / Å, stress convergence accuracy is 0.02 GPa, and displacement accuracy is 5 × 10⁻⁶. -4 Å, the maximum number of ion step iterations is 200, and the self-consistent field cyclic accuracy is 5 × 10⁻⁶. -7 eV, the maximum number of cycles for a self-consistent field is 200.
[0020] Furthermore, in step 5, the number of small strains is 6, the maximum strain amplitude is 0.3%, and the structural optimization parameters include: the functional is selected as the PBE form in GGA, the pseudopotential is selected as OTFG Norm-conserving, the cutoff energy is set to 1250 eV, the k-point is set to 20×20×20, and the energy convergence tolerance is 2×10⁻⁶. -6 eV, force convergence criterion is 0.006 eV / Å, displacement accuracy is 2 × 10⁻⁶ eV / Å. -4 Å, the maximum number of ion step iterations is 200, and the self-consistent field cyclic accuracy is 5 × 10⁻⁶. -7 eV, the maximum number of cycles for a self-consistent field is 200.
[0021] The beneficial effects of this invention are as follows: Based on a defined element type, molar ratio, and crystal system, and according to a metallic elemental crystal structure model, the invention uses Materials Studio calculation software to dope the metallic elemental model to obtain a high-entropy alloy virtual crystal approximation model. Then, the high-entropy alloy virtual crystal approximation model is structurally optimized to obtain a structurally stable high-entropy alloy virtual crystal approximation model. A small strain is applied to the structurally stable high-entropy alloy virtual crystal approximation model, and geometric optimization is performed again to obtain the stress. Hooke's law is used to fit the stress-strain relationship to obtain the elastic constants. Based on the elastic constants, the mechanical stability and melting point T of the high-entropy alloy are calculated. m Young's modulus E, Vickers hardness H V With fracture toughness K IC Finally, high-throughput screening of high-entropy alloys was performed according to the screening process to obtain the ideal target object.
[0022] This invention uses a first-principles high-throughput method based on a virtual crystal approximation model to calculate the mechanical properties of high-entropy alloys. It can calculate the mechanical properties of multiple high-entropy alloys at once, thereby quickly screening high-entropy alloys with ideal mechanical properties, reducing the heavy workload of synthesis and mechanical property testing experiments, and significantly reducing experimental costs. Attached Figure Description
[0023] Figure 1 This is a flowchart of the high-entropy alloy screening process;
[0024] Figure 2 This is a flowchart of the high-entropy alloy screening process in the embodiment. Detailed Implementation
[0025] Specific implementation method one: as follows Figure 1 and Figure 2 As shown, a multi-index screening design method for high-entropy alloys to meet the performance requirements of high-temperature bearings includes the following steps:
[0026] Step 1: Determine the types of metallic elements contained in the high-entropy alloy material system and the molar ratio of each element;
[0027] Step 2: Determine the crystal system of the high-entropy alloy based on its valence electron concentration, and construct a metallic elemental crystal structure model based on the crystal system of the high-entropy alloy.
[0028] Step 3: Based on the types and molar ratios of metal elements determined in Step 1, modify the Composition of metal atoms in the metal element crystal structure model constructed in Step 2 using the 3D Atomistic window of Materials Studio software to obtain an approximate virtual crystal model of high-entropy alloy.
[0029] Step 4: Import the high-entropy alloy virtual crystal approximation model constructed in Step 3 into the same folder, and perform geometric optimization on the high-entropy alloy virtual crystal approximation model to obtain the high-entropy alloy virtual crystal approximation model with the lowest energy. The parameters for geometric optimization include: the functional is selected as PBE form in GGA, the optimization algorithm is selected as BFGS, the pseudopotential is selected as OTFG Norm-conserving, and the cutoff energy is set to 1250 eV. k The point size is set to 20×20×20, and the energy convergence tolerance is 5×10. -6 eV, force convergence standard is 0.01 eV / Å, stress convergence accuracy is 0.02 GPa, and displacement accuracy is 5 × 10⁻⁶. -4 Å, the maximum number of ion step iterations is 200, and the self-consistent field cyclic accuracy is 5 × 10⁻⁶. -7 eV, the maximum number of cycles in a self-consistent field is 200;
[0030] Step 5: Based on the crystal structure symmetry of the research object, apply small strains to the optimized high-entropy alloy virtual crystal approximation model from Step 4, and perform geometric optimization on the deformed model to bring it back to the energy minimization state. Obtain the stresses corresponding to different strains. The strain quantity is 6, and the maximum strain is 0.3%. The structural optimization parameters include: the functional is selected as the PBE form in GGA, the pseudopotential is selected as OTFG Norm-conserving, and the cutoff energy is set to 1250 eV. k The point size is set to 20×20×20, and the energy convergence tolerance is 2×10. -6 eV, force convergence criterion is 0.006 eV / Å, displacement accuracy is 2×10 -4 Å, the maximum number of ion step iterations is 200, and the self-consistent field cyclic accuracy is 5 × 10⁻⁶. -7 eV, the maximum number of cycles in a self-consistent field is 200;
[0031] Step 6: Fit the stress-strain relationship obtained in Step 5 using the generalized Hooke's law to obtain the elastic constants of the high-entropy alloy;
[0032] Step 7: Based on the calculation of the elastic coefficient in Step 6, determine the mechanical stability of the high-entropy alloy according to formula (1):
[0033] (1),
[0034] In formula (1), , , Both represent elastic stiffness constants;
[0035] Step 8: Based on the calculation of the elastic coefficient in step 6, calculate the melting point T of the high-entropy alloy according to formula (2). m :
[0036] (2),
[0037] Step 9: Based on the elastic coefficient calculation in Step 6, the Young's modulus E, shear modulus G, bulk modulus B, and Poisson's ratio of the high-entropy alloy are calculated using the VRH criterion according to formulas (3) to (10). v ;
[0038] (3),
[0039] (4),
[0040] (5),
[0041] (6),
[0042] (7),
[0043] (8),
[0044] (9),
[0045] (10)
[0046] In formulas (3) to (10), , , , , , Both represent elastic stiffness constants. , , , , , , , , Both represent the elasticity compliance coefficient. , , Indicates bulk modulus. , , Indicates shear modulus;
[0047] Step 10: Based on the shear modulus G and bulk modulus B calculated in step 9, calculate the Vickers hardness H of the high-entropy alloy according to formula (11). V Young's modulus E and Poisson's ratio calculated in step 9 v Based on this, the fracture toughness K of the high-entropy alloy is calculated according to formulas (13) and (14). IC , formula (11) ~ formula (12):
[0048] (11),
[0049] (12)
[0050] (13)
[0051] (14)
[0052] Among them, α0=2GPa, β=0.3, and γ=8.
[0053] Step 11, using Figure 1 The screening process shown is based on the mechanical stability and melting point T obtained in steps 7 to 10, respectively.m Young's modulus E, Vickers hardness H V With fracture toughness K IC High-throughput screening of high-entropy alloys.
[0054] The metal elements include at least five of Ti, Zr, Hf, V, Nb, Ta, Mo, W, and Cr, with each metal element accounting for a molar ratio in the range of 5% to 35%. The high-throughput calculation method for the mechanical properties of high-entropy alloys of the present invention can calculate the mechanical properties of multiple different high-entropy alloys at once.
[0055] Example
[0056] Example 1
[0057] Step (1) Select seven metallic elements, Zr, Hf, V, Nb, Ta, Mo and W, as components of the high-entropy alloy. The same proportion of each element constitutes an equimolar high-entropy alloy.
[0058] Step (2): The valence electron concentration of the high-entropy alloy is greater than 8, indicating a BCC structure. Construct a metallic BCC structure elemental crystal model;
[0059] Step (3) Modify the Composition of metal atoms in the metal element crystal structure model constructed in step (2) using the 3D Atomistic window of Materials Studio software to obtain the approximate virtual crystal model of 21 high-entropy alloy;
[0060] Step (4) Import the high-entropy alloy virtual crystal approximation model constructed in step (3) into the same folder, and perform geometric optimization on the high-entropy alloy virtual crystal approximation model to obtain the high-entropy alloy virtual crystal approximation model with the lowest energy. The parameters for geometric optimization include: the functional is selected as PBE form in GGA, the optimization algorithm is selected as BFGS, the pseudopotential is selected as OTFG Norm-conserving, and the cutoff energy is set to 1250 eV. k The point size is set to 20×20×20, and the energy convergence tolerance is 5×10. -6 eV, force convergence standard is 0.01 eV / Å, stress convergence accuracy is 0.02 GPa, and displacement accuracy is 5 × 10⁻⁶. -4 Å, the maximum number of ion step iterations is 200, and the self-consistent field cyclic accuracy is 5 × 10⁻⁶. -7 eV, the maximum number of cycles in a self-consistent field is 200;
[0061] Step (5) Based on the crystal structure symmetry of the research object, apply a small strain to the optimized high-entropy alloy virtual crystal approximation model in step (4), and perform geometric optimization on the deformed model to make it reach the energy minimization state again, obtaining the stress corresponding to different strains. Among them, the strain number is 6, the maximum strain is 0.3%, and the structural optimization parameters include: the functional is selected as the PBE form in GGA, the pseudopotential is selected as OTFG Norm-conserving, and the cutoff energy is set to 1250 eV. k The point size is set to 20×20×20, and the energy convergence tolerance is 2×10. -6 eV, force convergence criterion is 0.006 eV / Å, displacement accuracy is 2×10 -4 Å, the maximum number of ion step iterations is 200, and the self-consistent field cyclic accuracy is 5 × 10⁻⁶. -7 eV, the maximum number of cycles in a self-consistent field is 200;
[0062] Step (6) uses the generalized Hooke's law to fit the stress-strain relationship obtained in step (5) to obtain the elastic constants of the high-entropy alloy;
[0063] Step (7) Calculate the mechanical properties of the high-entropy alloy according to steps (7) to (10) of the specific implementation method. Finally, screen the high-entropy alloy according to step (11) of the specific implementation method to obtain 5 high-entropy alloys with excellent performance, as shown in Table 1.
[0064] Table 1 Screening results of Example 1
[0065]
[0066] Example 2:
[0067] Step (1) Select eight metallic elements, Ti, Zr, Hf, V, Nb, Ta, Mo, and W, as components of the high-entropy alloy. The same proportion of each element constitutes an equimolar high-entropy alloy.
[0068] Step (2): The valence electron concentration of the high-entropy alloy is greater than 8, indicating a BCC structure. Construct a metallic BCC structure elemental crystal model;
[0069] Step (3) The Composition of metal atoms in the metal element crystal structure model constructed in step (2) is modified using the 3D Atomistic window of Materials Studio software to obtain the approximate virtual crystal model of 56 high-entropy alloy;
[0070] Step (4) According to the specific implementation steps (4) to (11), the high entropy alloys are structurally optimized, elastic constants are calculated, mechanical properties are calculated and high-throughput screening is performed. Finally, eight high entropy alloys are obtained, as shown in Table 2.
[0071] Table 2 Screening Results of Example 2
[0072]
[0073] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent substitutions, and improvements made to the above embodiments without departing from the scope of the present invention, based on the technical essence of the present invention and within the spirit and principles of the present invention, shall still fall within the protection scope of the present invention.
Claims
1. A multi-index screening method for high-entropy alloy design to meet the performance requirements of high-temperature bearings, characterized in that, The aforementioned multi-index screening high-entropy alloy design method for high-temperature bearing performance requirements is achieved through the following steps: Step 1: Determine the types of metallic elements contained in the high-entropy alloy material system and the molar ratio of each element; Step 2: Determine the crystal system of the high-entropy alloy based on its valence electron concentration, and construct a metallic elemental crystal structure model based on the crystal system of the high-entropy alloy. Step 3: Based on the types and molar ratios of metal elements determined in Step 1, modify the Composition of metal atoms in the metal element crystal structure model constructed in Step 2 using the 3D Atomistic window of Materials Studio software to obtain an approximate virtual crystal model of high-entropy alloy. Step 4: Import the high-entropy alloy virtual crystal approximation model constructed in Step 3 into the same folder, perform structural optimization on the high-entropy alloy virtual crystal approximation model, and obtain the high-entropy alloy virtual crystal approximation model with the lowest energy. Step 5: Based on the crystal structure symmetry of the research object, apply a small deformation to the optimized high-entropy alloy virtual crystal approximation model in Step 4, and perform structural optimization on the deformed model to make it reach a stable state again, and obtain the stress-strain relationship. Step 6: Fit the stress-strain relationship obtained in Step 5 using the generalized Hooke's law to obtain the elastic stiffness constant of the high-entropy alloy. and elastic compliance constant ; Step 7: Determine the mechanical stability of the high-entropy alloy based on the elastic stiffness coefficient calculated in Step 6; Step 8: Calculate the melting point of the high-entropy alloy based on the elastic stiffness coefficient obtained in Step 6. ; Step 9: Calculate the Young's modulus of the high-entropy alloy using the VRH criterion based on the elastic constants obtained from the fitting in Step 6. shear modulus bulk modulus Compared to Poisson ; Step 10: Based on the shear modulus in Step 9 and bulk modulus Calculating the Vickers hardness of high-entropy alloys According to Young's modulus in step 9 Compared to Poisson Calculate the fracture toughness of high-entropy alloys ; Step 11: Based on the mechanical stability and melting point obtained in steps 7, 8, 9, and 10, proceed sequentially. Young's modulus Vickers hardness With fracture toughness High-throughput screening of high-entropy alloys.
2. The multi-index screening high-entropy alloy design method for high-temperature bearing performance requirements according to claim 1, characterized in that, The metallic elements in step 1 include at least 5 of the following: Ti, Zr, Hf, V, Nb, Ta, Mo, W, and Cr, with each metallic element accounting for a molar ratio in the range of 5% to 35%.
3. The multi-index screening high-entropy alloy design method for high-temperature bearing performance requirements according to claim 1, characterized in that, The formula for calculating valence electron concentration in step 2 is as follows: ,in c i For the first i The molar ratio of metallic elements, VEC i For the first i The number of valence electrons of an element; VEC < 6.84 corresponds to BCC structure, VEC ≥ 8 corresponds to FCC structure, and 6.84 ≤ < 8 corresponds to the BCC and FCC biphase structure.
4. The multi-index screening high-entropy alloy design method for high-temperature bearing performance requirements according to claim 1, characterized in that, The parameters for structural optimization in step 4 include: the functional is selected as the PBE form in GGA, the optimization algorithm is selected as BFGS, the pseudopotential is selected as OTFG Norm-conserving, and the cutoff energy is set to 1250 eV. k The point size is set to 20×20×20, and the energy convergence tolerance is 5×10. -6 eV, force convergence criterion is 0.01 eV / Å, stress convergence accuracy is 0.02 GPa, and displacement accuracy is 5 × 10⁻⁶. -4 Å, the maximum number of ion step iterations is 200, and the self-consistent field cyclic accuracy is 5 × 10⁻⁶. -7 eV, the maximum number of cycles for a self-consistent field is 200.
5. The multi-index screening high-entropy alloy design method for high-temperature bearing performance requirements according to claim 1, characterized in that, The number of small strains in step 5 is 6, the maximum strain amplitude is 0.3%, and the structural optimization parameters include: the functional is selected as the PBE form in GGA, the pseudopotential is selected as OTFG Norm-conserving, the cutoff energy is set to 1250 eV, the k-point is set to 20×20×20, and the energy convergence tolerance is 2×10. -6 eV, force convergence criterion is 0.006 eV / Å, displacement accuracy is 2 × 10⁻⁶ eV / Å. -4 Å, the maximum number of ion step iterations is 200, and the self-consistent field cyclic accuracy is 5 × 10⁻⁶. -7 eV, the maximum number of cycles for a self-consistent field is 200.