CN114414368A - Method for detecting lubricating property of nano alloy particle additive based on molecular dynamics - Google Patents
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- 239000000654 additive Substances 0.000 title claims abstract description 67
- 229910045601 alloy Inorganic materials 0.000 title claims abstract description 59
- 239000000956 alloy Substances 0.000 title claims abstract description 59
- 239000002245 particle Substances 0.000 title claims abstract description 53
- 230000000996 additive effect Effects 0.000 title claims abstract description 51
- 238000000034 method Methods 0.000 title claims abstract description 41
- 230000001050 lubricating effect Effects 0.000 title claims abstract description 30
- 238000000329 molecular dynamics simulation Methods 0.000 title claims abstract description 26
- 238000005461 lubrication Methods 0.000 claims abstract description 33
- 229910052742 iron Inorganic materials 0.000 claims abstract description 22
- 239000010687 lubricating oil Substances 0.000 claims abstract description 21
- 238000004364 calculation method Methods 0.000 claims abstract description 20
- 238000010008 shearing Methods 0.000 claims abstract description 18
- 238000004088 simulation Methods 0.000 claims abstract description 15
- 229910052802 copper Inorganic materials 0.000 claims abstract description 8
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 claims description 37
- IYRDVAUFQZOLSB-UHFFFAOYSA-N copper iron Chemical compound [Fe].[Cu] IYRDVAUFQZOLSB-UHFFFAOYSA-N 0.000 claims description 22
- 239000002105 nanoparticle Substances 0.000 claims description 17
- 230000008569 process Effects 0.000 claims description 17
- 230000003993 interaction Effects 0.000 claims description 14
- RYGMFSIKBFXOCR-UHFFFAOYSA-N Copper Chemical compound [Cu] RYGMFSIKBFXOCR-UHFFFAOYSA-N 0.000 claims description 10
- 229910000640 Fe alloy Inorganic materials 0.000 claims description 10
- 239000000463 material Substances 0.000 claims description 8
- 230000007246 mechanism Effects 0.000 claims description 8
- 239000010949 copper Substances 0.000 claims description 7
- DCAYPVUWAIABOU-UHFFFAOYSA-N hexadecane Chemical compound CCCCCCCCCCCCCCCC DCAYPVUWAIABOU-UHFFFAOYSA-N 0.000 claims description 6
- 230000008859 change Effects 0.000 claims description 4
- 238000004590 computer program Methods 0.000 claims description 4
- 239000013078 crystal Substances 0.000 claims description 4
- 239000000314 lubricant Substances 0.000 claims description 4
- 230000002040 relaxant effect Effects 0.000 claims description 4
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- 230000007613 environmental effect Effects 0.000 claims description 3
- 239000007788 liquid Substances 0.000 claims description 3
- 239000002184 metal Substances 0.000 claims description 3
- 229910052751 metal Inorganic materials 0.000 claims description 3
- 239000000203 mixture Substances 0.000 claims description 3
- 230000000737 periodic effect Effects 0.000 claims description 3
- 230000009467 reduction Effects 0.000 claims description 3
- 238000010586 diagram Methods 0.000 claims description 2
- 230000006870 function Effects 0.000 claims description 2
- 239000003921 oil Substances 0.000 claims description 2
- 229910000859 α-Fe Inorganic materials 0.000 claims description 2
- 230000009471 action Effects 0.000 claims 1
- 238000005299 abrasion Methods 0.000 abstract description 2
- 230000006399 behavior Effects 0.000 abstract description 2
- 230000000007 visual effect Effects 0.000 abstract 1
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- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/26—Oils; Viscous liquids; Paints; Inks
- G01N33/28—Oils, i.e. hydrocarbon liquids
- G01N33/30—Oils, i.e. hydrocarbon liquids for lubricating properties
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Abstract
The invention relates to a method for detecting the lubricating property of a nano alloy particle additive based on molecular dynamics, which is mainly characterized by comprising the following steps of: respectively constructing two boundary lubrication system models containing nano alloy particle additives Cu + Fe and Cu @ Fe with different structures by means of molecular dynamics software; selecting a proper potential function, and simulating the pressurization and shearing behaviors of a boundary lubrication system by utilizing Lammps software programming; and carrying out quantitative calculation and visual expression on the simulation result. The invention aims to quantitatively predict the friction force, the positive pressure, the Von Mises stress, the abrasion loss and the like of a nanometer gap lubricating system from the atomic scale; dynamically displaying the movement and lubrication status of the molecules in real time; the lubricating performance, the anti-wear and anti-wear capacity and the bearing capacity of the lubricating oil containing the nano alloy particle additives with different structures are detected. Provides reliable basis for the practical application of the nano alloy particle additive, and has wide application prospect and guidance value.
Description
Technical Field
The invention belongs to the technical field of molecular dynamics simulation, and particularly relates to a method for detecting lubricating property of a nano alloy particle additive based on molecular dynamics.
Background
In recent years, metal nanoparticles have drawn attention from researchers due to their wide variety and excellent properties. Based on the properties of metal nanoparticles in reducing energy dissipation and material interface damage, they can be used as ideal lubricant additives. A great deal of experimental research and simulation analysis are carried out on the friction performance of different types of metal nanoparticle additives, but the analysis on the influence of the structure of the nano alloy particle additive on the friction performance is not sufficient.
Copper nanoparticles are widely used as solid lubricating materials due to their good ductility, low hardness, low shear strength, and good anti-friction and anti-wear properties. The iron nanoparticles have strong magnetic and catalytic properties. However, the copper-iron alloy can further enhance corrosion resistance, thermal conductivity, wear resistance and wear reduction, and surface repairability, which are largely determined by its structure.
The molecular dynamics simulation can simulate the motion of a molecular system through Newton mechanics, and can be used for simulating the physicochemical properties of the system under extreme conditions such as high temperature and high pressure. Quantitatively calculating the density, stress, abrasion loss and the like of the lubricating system from the atomic scale; dynamically and timely displaying the lubrication condition and the bearing capacity of the lubricating molecules; the lubricating performance and the anti-wear and anti-wear capability of the lubricating oil containing the nano alloy particle additives with different structures are explored.
At present, the friction characteristics of the alloy structure nanoparticle additive are not deeply discussed in the field of molecular dynamics simulation, and particularly the influence of different copper-iron alloy structures on the friction performance of a lubricating system is involved. Therefore, the influence of the copper-iron alloy structure on the friction performance is revealed from the molecular level, and the method is very important for analyzing the influence rule of the anti-wear and anti-wear characteristics and the bearing capacity of the additive.
Disclosure of Invention
In view of the above, the invention aims to provide a method for detecting the lubricating property of a nano alloy particle additive based on molecular dynamics, which can observe the change of a system in a shearing process from an atomic scale, effectively analyze the influence of the copper-iron nano alloy particle additive on the surface friction and wear of a contact area of a friction pair under different structures, and explore the wear resistance behavior, the bearing capacity and the low friction mechanism of the copper-iron nano alloy particle additive.
In order to achieve the purpose, the invention adopts the following technical scheme:
a method for detecting the lubricating property of a nano alloy particle additive based on molecular dynamics comprises the following steps:
step S1, respectively constructing a lubricating oil model and a metallic iron rough wall surface model in a crystal direction, and acquiring an atom coordinate file; respectively constructing two alloy nanoparticle additive models with different structures, and assembling a rough wall model, a lubricating oil model and a nanoparticle additive model into two groups of alloy nanoparticle additive boundary lubricating models with different structures;
s2, simulating the pressurizing and shearing process of the boundary lubrication model containing the alloy nano-particle additive based on a preset potential function, and setting basic simulation parameters to obtain a simulation result;
step S3: based on the simulation result, the boundary lubrication model containing the nano alloy additive is subjected to image display, the wear-resistant and antifriction mechanism of the wall surface in the shearing process is observed, the data file is processed and drawn, and the wear and stress conditions of the lubrication system are analyzed.
Further, the step S1 is specifically:
constructing a model of the upper wall surface of a convex peak and the lower wall surface of a groove of the metal iron by using Lammps software, and selecting the lattice constant of alpha-Fe as
Crystal orientation of [100 ]](ii) a Respectively selecting two structures of nanoparticle additives: cu + Fe, i.e. the random mixture of copper and iron in each particle, and Cu @ Fe, i.e. the core of each particle is iron, and the shell is coated with a layer of copper; selecting n-hexadecane as base oil, constructing a lubricating oil model by using an Amorphous Cell module in Materials Studio software, and optimizing the space structure of the constructed model by a Forcite module;
establishing a lubrication model of an upper wall surface, an oil film and a lower wall surface, and assembling the wall surface model and the lubricating oil model into a boundary lubrication model by using a Build module in Materials Studio software;
and programming in Lammps to assemble the rough wall model, the lubricating oil model and the nano particle additive model into two groups of boundary lubricating models containing nano alloy particle additives with different structures, and finally exporting the boundary lubricating models into a data folder which can be identified by Lammps.
Furthermore, the upper wall surface and the lower wall surface are respectively divided into 3 layers, wherein the outer layer is a rigid layer for applying boundary conditions, the middle layer is a constant temperature layer for providing environmental influence factors, and the inner layer is a Newton deformation layer for extracting mechanical properties.
Further, the step S2 is specifically:
step S21: setting boundary conditions of a model, setting periodic boundary conditions in the x direction and the y direction, and setting contractive boundary conditions in the z direction;
step S22: describing interactions between lubricant molecules based on a combined atomic force field; the interaction between copper-iron alloy atoms and between the copper-iron alloy atoms and an iron wall surface adopts eam/alloy potential; the interaction of the solid-liquid interface includes the interaction between iron atoms and lubricating oil molecules, and the interaction between copper atoms and lubricating oil molecules adopts Lennard-Jones (L-J) potential;
s23, relaxing the system by using a Nose-Hoover hot bath method under the condition of a regular ensemble to enable an initial model of the lubrication system to reach an equilibrium state;
step S24, in the pressurizing stage, the regular ensemble during relaxation is released, the temperature of the constant temperature layer is set, the lower wall rigid layer is fixed, and the load is applied to the upper wall rigid layer, so that the system reaches a stable state;
step S25, in the shearing stage, the pressure is kept unchanged, and meanwhile, the two rigid layers move along the positive direction and the negative direction of the x respectively at the same speed;
and step S26, data processing, namely performing molecular dynamics simulation calculation on the written in file by using Lammps software, counting a calculation result, and outputting atom coordinate information and calculation data of set parameters in the simulation process.
Further, in step S22, specifically, the step includes:
in the formula, EnonbondIs the L-J potential between atoms, i represents the ith atom, J represents the jth atom, εijAs a characteristic value of energy, σijIs the molecular characteristic length, r is the distance between atoms;
further calculation of σ by Lorentz-Bertholt binding ruleijAnd εijThe calculation formulas are respectively
And
further, the shearing movement distance in the shearing stage needs to ensure that the nano alloy particle additive and the upper and lower rough wall surfaces fully act.
Further, the step S3 is specifically: adopting Ovito software to visually express the output data file, processing and drawing the data file by using Origin software, and comparing the wear resistance and the friction reduction capability of the nano alloy particle additives with two different structures according to the changes of the wear loss and the friction force in the shearing process; comparing the bearing capacity of the nano alloy particle additives with two different structures according to the change of positive pressure and Von Mises stress in the shearing process; and analyzing the antifriction and antiwear mechanisms of the nano alloy particle additives with two different structures according to a shear dynamic diagram in the wall surface movement process.
A system for detecting the lubrication performance of a nano-alloy particle additive based on molecular dynamics, comprising a processor, a memory, and a computer program stored on the memory, wherein the processor, when executing the computer program, specifically performs the steps of the method for detecting the lubrication performance according to any one of claims 1-7.
Compared with the prior art, the invention has the following beneficial effects:
1. the invention can observe the change of the system in the shearing process from the atomic scale, effectively analyze the influence of the copper-iron nano alloy particle additive under different structures on the surface friction and wear of the contact area of the friction pair, and explore the wear resistance, the bearing capacity and the low friction mechanism of the copper-iron nano alloy particle additive;
2. the invention successfully completes the simulation of the boundary lubrication system of the copper-iron nano alloy particle additive by utilizing the molecular dynamics simulation and the synergistic antifriction property of the copper-iron nano alloy particle additive, proves that the mechanism of realizing wear resistance and antifriction by using the copper-iron nano alloy particles as the lubrication additive is analyzed by the feasibility of using the copper-iron nano alloy particles as the lubrication additive, and provides a theoretical basis for the practical application of the copper-iron alloy additives with different structures.
Drawings
FIG. 1 is a flow chart of a method of an embodiment of the present invention.
FIG. 2 is a model of the additive for copper-iron nano alloy particles with different structures according to the invention.
FIG. 3 is a boundary lubrication model of a Cu @ Fe nanoparticle-containing additive in an example of the present invention.
Fig. 4 is a graph of the amount of wear of different lubrication systems in an embodiment of the present invention.
FIG. 5 is a graph of friction versus positive pressure over time for various lubrication systems in an embodiment of the present invention. FIG. 6 is a graph of the shear effect of various lubrication systems simulated by molecular dynamics in an embodiment of the present invention.
FIG. 7 is a Von Mises stress profile for different lubrication systems in an embodiment of the present invention.
Detailed Description
The invention is further explained below with reference to the drawings and the embodiments.
Referring to fig. 1, the invention provides a method for detecting lubricating property of a nano alloy particle additive based on molecular dynamics, wherein an external load is set to be 300Mpa, a wall surface temperature is set to be 298.15K, and a shearing speed is set to be 5m/s, so that an actual working condition is simulated, and the method comprises the following specific steps:
step S1, establishing a molecular dynamics simulation model, namely, establishing a lubricating oil model and a rough wall model of metal iron by using Materials Studio software, wherein the size of the model is
The method comprises the steps of selecting n-hexadecane as base oil, constructing a lubricating oil model by using an Amorphous Cell module in Materials Studio software, and optimizing the space structure of the constructed model by a Forcite module. The copper-iron nano alloy particle additive model is respectively established by using Lammps as shown in figure 2, and the structure is as follows: cu + Fe, i.e. the random mixture of copper and iron in each particle, and Cu @ Fe, i.e. the core of each particle is iron, a layer of copper is wrapped by a shell, and the number of the nanoparticle additives contained in different lubricating systems is 1.
And step S2, combining the wall model, the lubricating oil model and the copper-iron nano alloy particle additive model into a boundary lubrication model containing the copper-iron nano alloy particle additive by using Lammps software, as shown in figure 3. The upper and lower wall surfaces are divided into 3 layers, the outer layer is a rigid layer for applying boundary conditions, the middle layer is a constant temperature layer for providing environmental influence factors, and the inner layer is a Newton deformation layer for extracting mechanical properties.
Step S21: the boundary conditions of the model are set, periodic boundary conditions are set in the x and y directions, and contractive boundary conditions are set in the z direction.
Step S22: the combined atomic force field (TraPPE-UA) is useful for describing interactions between lubricating oil molecules; the interaction between copper-iron alloy atoms and between the copper-iron alloy atoms and an iron wall surface adopts eam/alloy potential; interactions at the solid-liquid interface include interactions between iron atoms and lubricant molecules, copper atoms and lubricant molecules using the Lennard-Jones (L-J) potential:
Further calculation of σ by Lorentz-Bertholt binding ruleijAnd εijThe calculation formulas are respectively
And
and step S23, under the condition of a regular ensemble, relaxing the system by using a Nose-Hoover hot bath method so as to enable the initial model of the lubrication system to reach an equilibrium state.
And step S24, in the pressurizing stage, releasing the regular ensemble during relaxation, setting the temperature of the constant temperature layer, fixing the lower wall rigid layer, and applying load to the upper wall rigid layer to enable the system to reach a stable state. And (3) relaxing the system by using a Nose-Hoover hot bath method at the temperature of 298.5K for 200ps, and setting the temperature damping coefficient to be 200 fs. In this embodiment, in the Lammps simulation process, 2fs is selected for reducing the simulation time, so that the calculation efficiency is improved, and effective analysis data can be obtained.
Step S25, shear stage, keeping the pressure of the pressure stage constant, and making the two rigid layers move along the positive direction and the negative direction of the x axis at the speed of 5m/S respectively, and the shear distance is taken
And step S26, data processing, namely performing molecular dynamics simulation calculation on the written in file by using Lammps software, counting a calculation result, and outputting atom coordinate information and calculation data of set parameters in the simulation process.
In this example, the wear of the different lubrication systems was analyzed, and the amount of wear was represented by counting the number of iron atoms that moved the substrate by at least one lattice distance, as shown in fig. 4, and the amount of wear of each lubrication system was calculated.
The positive pressure and friction of the lubricating system containing the nano alloy particle additive mainly comprise the acting force of a lubricating oil film on a free deformation layer, the acting force between two free deformation layers when a rough peak is contacted and the acting force of the nano alloy particle additive on the free deformation layer.
And performing molecular dynamics simulation calculation on the written program file by utilizing Lammps software, and counting the calculation result to obtain an output file of the positive pressure and the friction force of the simulation process and the calculation result.
And step S3, processing data of the output files of the positive pressure and the friction force, drawing by Oringin software, comparing the friction force and the positive pressure of different lubricating systems as shown in figure 5, and analyzing the corresponding antifriction performance and the bearing capacity.
In the present embodiment, the data information in step S26 is extracted, and the output dump file is visually expressed by Ovito software, as shown in fig. 6. The main lubrication mechanism of the nano-alloy particle additive in different lubrication systems was observed.
In this embodiment, the Von Mises stress of the contact area between the friction pairs is visualized by Ovito software. The maximum value of the Von Mises stress and the occurrence position of the Von Mises stress in different lubrication systems can be obtained from the graph shown in fig. 7, and the load-bearing capacity is further verified.
The above description is only a preferred embodiment of the present invention, and all equivalent changes and modifications made in accordance with the claims of the present invention should be covered by the present invention.
Claims (8)
1. A method for detecting the lubricating property of a nano alloy particle additive based on molecular dynamics is characterized by comprising the following steps:
step S1, respectively constructing a lubricating oil model and a metallic iron rough wall surface model in a crystal direction, and acquiring an atom coordinate file; respectively constructing two alloy nanoparticle additive models with different structures, and assembling a rough wall model, a lubricating oil model and a nanoparticle additive model into two groups of alloy nanoparticle additive boundary lubricating models with different structures;
s2, simulating the pressurizing and shearing process of the boundary lubrication model containing the alloy nano-particle additive based on a preset potential function, and setting basic simulation parameters to obtain a simulation result;
step S3: based on the simulation result, the boundary lubrication model containing the nano alloy additive is subjected to image display, the wear-resistant and antifriction mechanism of the wall surface in the shearing process is observed, the data file is processed and drawn, and the wear and stress conditions of the lubrication system are analyzed.
2. The method for detecting the lubricating property of the nano alloy particle additive based on molecular dynamics as claimed in claim 1, wherein the step S1 is specifically as follows:
constructing a model of the upper wall surface of a convex peak and the lower wall surface of a groove of the metal iron by using Lammps software, and selecting the lattice constant of alpha-Fe as
Crystal orientation of [100 ]](ii) a Respectively selecting two structures of nanoparticle additives: cu + Fe, i.e. the random mixture of copper and iron in each particle, and Cu @ Fe, i.e. the core of each particle is iron, and the shell is coated with a layer of copper; selecting n-hexadecane as base oil, constructing a lubricating oil model by using an Amorphous Cell module in Materials Studio software, and optimizing the space structure of the constructed model by a Forcite module;
establishing a lubrication model of an upper wall surface, an oil film and a lower wall surface, and assembling the wall surface model and the lubricating oil model into a boundary lubrication model by using a Build module in Materials Studio software;
and programming in Lammps to assemble the rough wall model, the lubricating oil model and the nano particle additive model into two groups of boundary lubricating models containing nano alloy particle additives with different structures, and finally exporting the boundary lubricating models into a data folder which can be identified by Lammps.
3. The method for detecting the lubricating property of the nano alloy particle additive based on the molecular dynamics as claimed in claim 2, wherein the upper and lower wall surfaces are respectively divided into 3 layers, the outer layer is a rigid layer for applying boundary conditions, the middle layer is a constant temperature layer for providing environmental influence factors, and the inner layer is a Newtonian deformation layer for extracting mechanical properties.
4. The method for detecting the lubricating property of the nano alloy particle additive based on molecular dynamics as claimed in claim 1, wherein the step S2 is specifically as follows:
step S21: setting boundary conditions of a model, setting periodic boundary conditions in the x direction and the y direction, and setting contractive boundary conditions in the z direction;
step S22: describing interactions between lubricant molecules based on a combined atomic force field; the interaction between copper-iron alloy atoms and between the copper-iron alloy atoms and an iron wall surface adopts eam/alloy potential; the interaction of the solid-liquid interface includes the interaction between iron atoms and lubricating oil molecules, and the interaction between copper atoms and lubricating oil molecules adopts Lennard-Jones (L-J) potential;
s23, relaxing the system by using a Nose-Hoover hot bath method under the condition of a regular ensemble to enable an initial model of the lubrication system to reach an equilibrium state;
step S24, in the pressurizing stage, the regular ensemble during relaxation is released, the temperature of the constant temperature layer is set, the lower wall rigid layer is fixed, and the load is applied to the upper wall rigid layer, so that the system reaches a stable state;
step S25, in the shearing stage, the pressure is kept unchanged, and meanwhile, the two rigid layers move along the positive direction and the negative direction of the x respectively at the same speed;
and step S26, data processing, namely performing molecular dynamics simulation calculation on the written in file by using Lammps software, counting a calculation result, and outputting atom coordinate information and calculation data of set parameters in the simulation process.
5. The method for detecting the lubricating property of the nano alloy particle additive based on molecular dynamics as claimed in claim 4, wherein the step S22 is specifically as follows:
in the formula, EnonbondIs the L-J potential between atoms, i represents the ith atom, J represents the jth atom, εijAs a characteristic value of energy, σijIs the molecular characteristic length, r is the distance between atoms;
further calculation of σ by Lorentz-Bertholt binding ruleijAnd εijThe calculation formulas are respectively
And
6. the method for detecting the lubricating property of the nano alloy particle additive based on the molecular dynamics as claimed in claim 4, wherein the shearing movement distance in the shearing stage is required to ensure the sufficient action between the nano alloy particle additive and the upper and lower rough wall surfaces.
7. The method for detecting the lubricating property of the nano alloy particle additive based on molecular dynamics as claimed in claim 1, wherein the step S3 is specifically as follows: adopting Ovito software to visually express the output data file, processing and drawing the data file by using Origin software, and comparing the wear resistance and the friction reduction capability of the nano alloy particle additives with two different structures according to the changes of the wear loss and the friction force in the shearing process; comparing the bearing capacity of the nano alloy particle additives with two different structures according to the change of positive pressure and Von Mises stress in the shearing process; and analyzing the antifriction and antiwear mechanisms of the nano alloy particle additives with two different structures according to a shear dynamic diagram in the wall surface movement process.
8. A system for testing the lubricity of a nano-alloy particle additive based on molecular dynamics, comprising a processor, a memory, and a computer program stored on the memory, wherein the processor, when executing the computer program, performs the steps of the method for testing the lubricity of a nano-alloy particle additive according to any one of claims 1-7.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101338244A (en) * | 2007-01-03 | 2009-01-07 | 雅富顿公司 | Nanoparticle additives and lubricant formulations containing the nanoparticle additives |
| CN102768700A (en) * | 2011-03-09 | 2012-11-07 | 通用汽车环球科技运作有限责任公司 | Systems and methods for computationally developing manufacturable and durable cast components |
| CN103745046A (en) * | 2013-12-26 | 2014-04-23 | 武汉轻工大学 | Method for simulating and predicting lubricating base oil antifriction performance according to molecular dynamics |
| US20180223208A1 (en) * | 2017-02-09 | 2018-08-09 | Uchicago Argonne, Llc | Low friction wear resistant graphene films |
| CN108559580A (en) * | 2018-06-14 | 2018-09-21 | 张永宁 | A kind of energy-saving soft alloy atom state film lubrication oil additive |
| CN113380333A (en) * | 2021-07-09 | 2021-09-10 | 福州大学 | Analysis method for friction reduction and wear resistance of nanoparticle additive in lubricating oil |
| CN113392571A (en) * | 2021-06-17 | 2021-09-14 | 湘潭大学 | Method for simulating structural characteristics of interlayer graphene oxide/binary ionic liquid lubricating film through molecular dynamics |
| CN113506597A (en) * | 2021-07-09 | 2021-10-15 | 福州大学 | Method for analyzing adsorption performance of organic friction reducer based on molecular dynamics |
-
2022
- 2022-01-11 CN CN202210026183.9A patent/CN114414368A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101338244A (en) * | 2007-01-03 | 2009-01-07 | 雅富顿公司 | Nanoparticle additives and lubricant formulations containing the nanoparticle additives |
| CN102768700A (en) * | 2011-03-09 | 2012-11-07 | 通用汽车环球科技运作有限责任公司 | Systems and methods for computationally developing manufacturable and durable cast components |
| CN103745046A (en) * | 2013-12-26 | 2014-04-23 | 武汉轻工大学 | Method for simulating and predicting lubricating base oil antifriction performance according to molecular dynamics |
| US20180223208A1 (en) * | 2017-02-09 | 2018-08-09 | Uchicago Argonne, Llc | Low friction wear resistant graphene films |
| CN108559580A (en) * | 2018-06-14 | 2018-09-21 | 张永宁 | A kind of energy-saving soft alloy atom state film lubrication oil additive |
| CN113392571A (en) * | 2021-06-17 | 2021-09-14 | 湘潭大学 | Method for simulating structural characteristics of interlayer graphene oxide/binary ionic liquid lubricating film through molecular dynamics |
| CN113380333A (en) * | 2021-07-09 | 2021-09-10 | 福州大学 | Analysis method for friction reduction and wear resistance of nanoparticle additive in lubricating oil |
| CN113506597A (en) * | 2021-07-09 | 2021-10-15 | 福州大学 | Method for analyzing adsorption performance of organic friction reducer based on molecular dynamics |
Non-Patent Citations (1)
| Title |
|---|
| 刘芳 等: "纳米润滑油添加剂抗磨减摩分子动力学模拟研究", 润滑与密封, vol. 35, no. 04, pages 94 - 96 * |
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