CN113536476A - Optimized preparation method of fluid dynamic pressure lubrication radial sliding bearing - Google Patents
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- CN113536476A CN113536476A CN202110724016.7A CN202110724016A CN113536476A CN 113536476 A CN113536476 A CN 113536476A CN 202110724016 A CN202110724016 A CN 202110724016A CN 113536476 A CN113536476 A CN 113536476A
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- 238000005461 lubrication Methods 0.000 title claims abstract description 48
- 238000002360 preparation method Methods 0.000 title claims abstract description 19
- 239000012530 fluid Substances 0.000 title description 9
- 238000013461 design Methods 0.000 claims abstract description 30
- 238000000034 method Methods 0.000 claims abstract description 23
- 238000005457 optimization Methods 0.000 claims abstract description 11
- 239000007788 liquid Substances 0.000 claims description 8
- 238000010438 heat treatment Methods 0.000 claims description 7
- 239000000314 lubricant Substances 0.000 claims description 6
- 239000003921 oil Substances 0.000 claims description 6
- 230000020169 heat generation Effects 0.000 claims description 4
- 230000003746 surface roughness Effects 0.000 claims description 4
- 230000003749 cleanliness Effects 0.000 claims description 3
- 239000012535 impurity Substances 0.000 claims description 3
- 238000009434 installation Methods 0.000 claims description 3
- 239000010687 lubricating oil Substances 0.000 claims description 3
- 239000002184 metal Substances 0.000 claims description 3
- 230000002093 peripheral effect Effects 0.000 claims description 3
- 231100000817 safety factor Toxicity 0.000 claims description 3
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 3
- 238000005299 abrasion Methods 0.000 claims description 2
- 238000004519 manufacturing process Methods 0.000 claims 1
- 230000000694 effects Effects 0.000 abstract description 6
- 238000010586 diagram Methods 0.000 description 4
- 238000004836 empirical method Methods 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 230000007613 environmental effect Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000010187 selection method Methods 0.000 description 1
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Abstract
A method for optimally preparing a hydrodynamic lubrication radial sliding bearing relates to a sliding bearing preparation method, and the method is a method for optimally preparing the hydrodynamic lubrication radial sliding bearing based on a multi-ecological environment selection strategy and a multi-objective evolutionary algorithm (MeEA), and the multi-objective optimization design of the sliding bearing is realized by adopting the multi-objective evolutionary algorithm based on the multi-ecological environment selection strategy. Firstly, dividing a target space into a plurality of different types of ecological environments in the population evolution process; next, in terms of environment selection, convergence or diversity of the ecological environment is prioritized, and the overall diversity of the population is maintained later. Finally, the method is applied to the optimized design of the hydrodynamic lubrication radial sliding bearing. Through the algorithm optimization, the effect of the hydrodynamic lubrication radial sliding bearing after the optimized design is obvious, and a method basis is provided for improving the performance of the bearing on the actual working condition.
Description
Technical Field
The invention relates to a sliding bearing design method, in particular to an optimized preparation method of a fluid dynamic pressure lubrication radial sliding bearing. The method is a preparation method of fluid dynamic pressure lubrication radial sliding bearing optimization based on multi-ecological environment selection strategy and multi-objective evolutionary algorithm (MeEA).
Background
The hydrodynamic lubrication radial sliding bearing is widely applied to the working condition of high speed and light load due to the characteristics of stability, reliability and no noise. Plain bearings seem to have a simple outer geometry, but different shape parameter settings may have different effects on the bearing performance, such as liquid friction coefficient, heat generation and load-bearing capacity coefficient. The traditional bearing design method is optimized and designed through an empirical method, but the method usually needs a great amount of tests and trial and error. Therefore, it is very important to optimally design the bearing by the multi-objective optimization algorithm.
Plain bearings have a simple outer geometry, but different shape parameter settings have different effects on the bearing performance. The traditional bearing design method is optimized and designed through an empirical method, but the method usually needs a great amount of tests and trial and error.
Disclosure of Invention
The invention aims to provide an optimized preparation method of a hydrodynamic lubrication radial sliding bearing, and provides a preparation method of the hydrodynamic lubrication radial sliding bearing based on a multi-ecological environment selection strategy and a multi-objective evolutionary algorithm. The method has the advantages that the ecological environment is established by analyzing the individual evolution characteristics in the target space, the preference of individual selection in the main body leading environment is taken as the main body, the contradiction between convergence and diversity in the evolution process is relieved to a certain extent, the fluid dynamic pressure lubrication radial sliding bearing is optimized by the method, the effect after design is obvious, and the method basis is provided for improving the performance of the bearing on the actual working condition.
The purpose of the invention is realized by the following technical scheme:
an optimized preparation method of a hydrodynamic lubrication radial sliding bearing, which comprises the following steps: :
step 1: establishing an optimized design model (objective function) of the hydrodynamic lubrication radial sliding bearing;
step 2: basic theoretical description of MeEA;
and step 3: optimizing the hydrodynamic lubrication radial sliding bearing by adopting a multi-objective evolutionary algorithm of a multi-ecological environment selection strategy to obtain a group of solution sets;
and 4, step 4: the optimized results are compared with the traditional design scheme.
In the method for optimally preparing the hydrodynamic lubrication radial sliding bearing, the step 1 of optimally designing the hydrodynamic lubrication radial sliding bearing comprises three parts: designing variables, objective functions and constraint conditions;
(1) design variables
The main parameter of hydrodynamic lubrication of radial sliding bearings is the width to diameter ratio
Relative gap between them
And dynamic viscosity of lubricants
Wherein
And
respectively showing the width and diameter of the bearing; bearing design variables are as follows:
(2) objective function
The hydrodynamic lubrication radial sliding bearing has three important performance indexes, namely a liquid friction coefficient, a heating value and a bearing capacity coefficient; in order to obtain the best bearing performance, the optimization aims are the minimum heat generation, the minimum friction coefficient and the maximum bearing capacity; the three objective functions are as follows:
wherein
Is the angular velocity of the journal;
is the average specific pressure of the bearing;
is the ratio coefficient of bearing width to diameter; when in use
When the temperature of the water is higher than the set temperature,
otherwise
；
Is the working load of the bearing;
is the peripheral speed of the journal;
(3) constraint conditions
In order to avoid bearing abrasion caused by metal contact between raceways in the bearing, the influences of friction surface roughness, minimum oil film thickness, elasticity and thermal deformation of a shaft and the bearing, cleanliness of a lubricant, the size of impurities and the like must be considered; thus, the first constraint is as follows:
wherein
Taking into account the safety factors of geometric errors, installation errors, journal deformation, etc., and are usually taken as
；
And
surface roughness of the journal and bearing bore, respectively;
the width-diameter ratio of the bearing is required to meet the design requirement as follows:
the specific pressure should satisfy the following limiting conditions:
the relative play of the bearing can affect the bearing capacity, minimum oil film thickness, power consumption and bearing temperature rise; the relative play should satisfy the constraint:
the viscosity of the lubricating oil is constrained as follows:
in the optimized preparation method of the fluid dynamic pressure lubrication radial sliding bearing, the idea of the MeEA algorithm in the step 2 is to divide a target space into different types of ecological environments: establishing an ecological environment by analyzing the individual evolution characteristics in the target space, and taking the ecological environment as a main body to dominate the preference of individual selection in the environment;
first, the center of the local ecological environment is adopted as the solution with obvious trend, and the solution is called as the prominent solution
(ii) a Then looking for the outstanding solution
The adjacent individuals are taken as neighbors; finally, the ecological environment is classified in consideration of the complexity of the target space environment.
In the optimized preparation method of the fluid dynamic pressure lubrication radial sliding bearing, the ecological environments are classified in the step 2;
this classification includes the aggressive environment: the prior generation of outstanding solutions has one Euclidean distance between two solutions in the ecological environment with the current generation of outstanding solutions as the center
An average Euclidean distance from the salient solution larger than the domain to the current-generation salient solution; the aggressive ecosphere is characterized by the target space being able to generate valid individuals with a greater probability; relatively ineffective individuals are individuals whose evolution in the space is in a relatively stagnant state;
lazy environment:
less than the average Euclidean distance; the lazy environment is characterized in that the evolution of individuals in the target space is in a relative stagnation state;
the new environment is as follows: the previous generation of outstanding solutions are not explored in the ecological environment taking the outstanding solutions as the center; the new environment is characterized by a new space area which can be explored, and has great value for maintaining diversity;
crowded environment: searching more than two (including two) previous generation outstanding solutions in the area with the current generation outstanding solution as the ecological environment, and defining the environment as a crowded environment; the crowded environment is characterized in that the target space is overcrowded by individuals, so that two outstanding solutions are closely located when the outstanding solutions are searched.
In the optimized preparation method of the fluid dynamic pressure lubrication radial sliding bearing, the ecological environments are classified in the step 2; selecting individuals in an ecological environment is considered firstly in environment selection; respectively considering different ecological environment characteristics, and keeping different numbers of individuals in the ecological environment to enter the next generation; then, selecting other non-dominant solutions according to cosine similarity between individuals so as to maintain diversity of pareto frontiers in the target space; different from a method considering diversity of the whole space or convergence of a single individual, the proposed environment selection mode preferentially performs offspring selection by taking a local target space as a unit; and when the non-dominant solutions in the population are smaller than the population number, the non-dominant solutions are preferably considered to enter the next generation, and some dominant solutions are selected to maintain diversity.
According to the core idea of the MeEA, firstly, a target space is divided into a plurality of different types of ecological environments; secondly, in the aspect of environment selection, the convergence or diversity of the ecological environment is considered preferentially, and the overall diversity of the population is maintained later; the contradiction between convergence and diversity is relieved through the steps.
In the step 3, a multi-objective evolutionary algorithm of a multi-ecological environment selection strategy is adopted to optimize three performance indexes of the hydrodynamic lubrication radial sliding bearing, namely a liquid friction coefficient, a heating value and a bearing capacity coefficient; the algorithm steps are as shown in a flow chart.
The invention has the advantages and effects that:
the invention provides a preparation method for optimizing a hydrodynamic pressure lubrication radial sliding bearing by a multi-objective evolutionary algorithm based on a multi-ecological environment selection strategy. The ecological environment is established by analyzing the evolution characteristics of the individuals in the target space, and the ecological environment is taken as a main body to dominate the preference of individual selection in the environment. And the contradiction between convergence and diversity in the evolution process is relieved to a certain extent. The method optimizes the hydrodynamic lubrication radial sliding bearing, and verifies that the method can effectively improve the performance of the bearing.
The invention establishes an ecological environment by analyzing the individual evolution characteristics in the target space and takes the ecological environment as a main body to dominate the preference of individual selection in the environment, thereby relieving the contradiction between convergence and diversity. The algorithm is used for carrying out multi-objective optimization design on the hydrodynamic lubrication radial sliding bearing, and the service performance of the bearing under the actual working condition is improved.
Drawings
FIG. 1 is an illustrative view of the ecological environment of the present invention;
FIG. 2 is a diagram of a new environment of the present invention;
FIG. 3 is an active environment diagram of the present invention;
FIG. 4 is a diagram of a crowded environment of the present invention;
FIG. 5 is a diagram of a lazy environment of the present invention;
FIG. 6 is a pareto front view of an optimized hydrodynamic lubrication radial slide bearing of the present invention;
FIG. 7 is a flow chart of the algorithm steps of the present invention.
Detailed Description
The present invention will be described in detail with reference to the embodiments shown in the drawings.
The invention relates to a preparation method of fluid dynamic pressure lubrication radial sliding bearing optimization based on a multi-objective optimization algorithm, which comprises the following steps:
step 1: establishing an optimized design model (objective function) of the hydrodynamic lubrication radial sliding bearing;
step 2: basic theoretical description of MeEA;
and step 3: optimizing the hydrodynamic lubrication radial sliding bearing by adopting a multi-objective evolutionary algorithm of a multi-ecological environment selection strategy to obtain a group of solution sets;
and 4, step 4: comparing the optimized result with the traditional design scheme;
step 1.1: the optimized design of the hydrodynamic lubrication radial sliding bearing comprises three parts: variables, objective functions and constraints are designed.
(1) And designing variables.
The main parameter of hydrodynamic lubrication of radial sliding bearings is the width to diameter ratio
Relative gap between them
And dynamic viscosity of lubricants
Wherein
And
respectively, the width and diameter of the bearing. Bearing design variables are as follows:
(2) an objective function.
The hydrodynamic lubrication radial sliding bearing has three important performance indexes, namely a liquid friction coefficient, a heating value and a bearing capacity coefficient. To obtain optimum bearing performance, minimum heat generation, minimum coefficient of friction and maximum bearing capacity are optimization objectives. The three objective functions are as follows:
wherein
Is the angular velocity of the journal;
is the average specific pressure of the bearing;
is the ratio coefficient of the bearing width to the diameter. When in use
When the temperature of the water is higher than the set temperature,
otherwise
；
Is the working load of the bearing;
is the peripheral speed of the journal.
(3) A constraint condition.
In order to avoid bearing wear caused by metal contact between raceways in the bearing, it is necessary to consider the effects of frictional surface roughness, minimum oil film thickness, elasticity and thermal deformation of the shaft and the bearing, cleanliness of the lubricant, the size of impurities, and the like. Thus, the first constraint is as follows:
wherein
Taking into account the safety factors of geometric errors, installation errors, journal deformation, etc., and are usually taken as
；
And
the surface roughness of the journal and bearing bore, respectively.
The width-diameter ratio of the bearing is required to meet the design requirement as follows:
the specific pressure should satisfy the following limiting conditions:
the relative play of the bearings affects the bearing capacity, minimum oil film thickness, power consumption and bearing temperature rise. The relative play should satisfy the constraint:
the viscosity of the lubricating oil is constrained as follows:
step 1.2: the idea of the MeEA algorithm is to divide the target space into different types of ecoenvironments: the ecological environment is established by analyzing the evolution characteristics of individuals in the target space, and the ecological environment is taken as a main body and dominates the preference of individual selection in the environment.
In the present invention, the center of the local ecological environment is first adopted as the solution with a clear tendency, which is called a prominent solution. Neighboring individuals of the highlighted solution are then sought as neighbors. Finally, the ecological environment is classified in consideration of the complexity of the target space environment.
The active environment is as follows: the previous generation of the outstanding solutions appear in an ecological environment with the current generation of the outstanding solutions as the center, and the Euclidean distance between the two solutions is larger than the average Euclidean distance from the outstanding solutions of the field to the current generation of the outstanding solutions. A positive ecological environment is characterized by the target space being able to produce effective individuals with a greater probability. Relatively ineffective individuals are individuals whose evolution in this space is in a relatively stagnant state.
Lazy environment:
less than the average euclidean distance. Lazy environments are characterized by individual evolutions in the target space being in a relatively stagnant state.
The new environment is as follows: the previous generation of the outstanding solutions was not explored in the ecological environment centered on the outstanding solutions. The new environment is characterized by new space regions which can be explored, and has great value for diversity maintenance.
Crowded environment: if more than two (including two) previous generation outstanding solutions are searched in the area with the current generation outstanding solution as the ecological environment, the environment is defined as a crowded environment. The crowded environment is characterized in that the target space is overcrowded by individuals, so that two outstanding solutions are closely located when the outstanding solutions are searched.
Selection of individuals in an ecological environment is first considered in the environmental selection. Different ecological environment characteristics are considered respectively, and different numbers of individuals in the ecological environment are kept to enter the next generation. Other non-dominant solutions are then selected based on cosine similarity between individuals to maintain diversity of pareto fronts in the target space. Unlike methods that consider the diversity of the entire space or the convergence of individual individuals, the proposed environment selection method preferentially performs offspring selection in units of local target spaces. And when the non-dominant solutions in the population are smaller than the population number, the non-dominant solutions are preferably considered to enter the next generation, and some dominant solutions are selected to maintain diversity.
The core idea of MeEA is: firstly, dividing a target space into a plurality of different types of ecological environments; next, in terms of environment selection, convergence or diversity of the ecological environment is prioritized, and the overall diversity of the population is maintained later. The contradiction between convergence and diversity is relieved through the steps.
Step 1.3: the invention optimizes three performance indexes of the hydrodynamic pressure lubrication radial sliding bearing, namely the liquid friction coefficient, the heating value and the bearing capacity coefficient, by adopting a multi-objective evolutionary algorithm of a multi-ecological environment selection strategy.
The invention optimizes three performance indexes of the hydrodynamic pressure lubrication radial sliding bearing, namely the liquid friction coefficient, the heating value and the bearing capacity coefficient, by adopting a multi-objective evolutionary algorithm of a multi-ecological environment selection strategy.
The known working load is 35 kN and the diameter of the shaft is
And a speed of
. Get
，
，
，
，
. The number of populations was set at 500 and the number of evaluations was 500000.
TABLE 1 comparison of optimized design results with conventional design results
The present invention selects the point in the middle of pareto as a design parameter. As can be seen from Table 1, the bearing performance of the optimized design of the invention by adopting the multi-objective evolutionary algorithm of the multi-ecological environment selection strategy is improved. Compared with the conventional design, the friction coefficient is reduced by 12.5%, the heat productivity is reduced by 7.7%, and the bearing capacity is improved by 232.7%. The optimization scheme has greater practical value in engineering practice.
Claims (7)
1. An optimized preparation method of a hydrodynamic lubrication radial sliding bearing is characterized by comprising the following steps: :
step 1: establishing an optimized design model (objective function) of the hydrodynamic lubrication radial sliding bearing;
step 2: basic theoretical description of MeEA;
and step 3: optimizing the hydrodynamic lubrication radial sliding bearing by adopting a multi-objective evolutionary algorithm of a multi-ecological environment selection strategy to obtain a group of solution sets;
and 4, step 4: the optimized results are compared with the traditional design scheme.
2. The optimized manufacturing method of a hydrodynamic lubrication radial sliding bearing according to claim 1, wherein the optimized design of the hydrodynamic lubrication radial sliding bearing in step 1 comprises three parts: designing variables, objective functions and constraint conditions;
(1) design variables
The main parameter of hydrodynamic lubrication of radial sliding bearings is the width to diameter ratio
Relative gap between them
And dynamic viscosity of lubricants
Wherein
And
respectively showing the width and diameter of the bearing; bearing design variables are as follows:
(2) objective function
The hydrodynamic lubrication radial sliding bearing has three important performance indexes, namely a liquid friction coefficient, a heating value and a bearing capacity coefficient; in order to obtain the best bearing performance, the optimization aims are the minimum heat generation, the minimum friction coefficient and the maximum bearing capacity; the three objective functions are as follows:
wherein
Is the angular velocity of the journal;
is the average specific pressure of the bearing;
is the ratio coefficient of bearing width to diameter; when in use
When the temperature of the water is higher than the set temperature,
otherwise
；
Is the working load of the bearing;
is the peripheral speed of the journal;
(3) constraint conditions
In order to avoid bearing abrasion caused by metal contact between raceways in the bearing, the influences of friction surface roughness, minimum oil film thickness, elasticity and thermal deformation of a shaft and the bearing, cleanliness of a lubricant, the size of impurities and the like must be considered; thus, the first constraint is as follows:
wherein
Taking into account the safety factors of geometric errors, installation errors, journal deformation, etc., and are usually taken as
；
And
surface roughness of the journal and bearing bore, respectively;
the width-diameter ratio of the bearing is required to meet the design requirement as follows:
the specific pressure should satisfy the following limiting conditions:
the relative play of the bearing can affect the bearing capacity, minimum oil film thickness, power consumption and bearing temperature rise; the relative play should satisfy the constraint:
the viscosity of the lubricating oil is constrained as follows:
3. the optimized preparation method of a hydrodynamic lubrication radial sliding bearing according to claim 1, characterized in that the idea of the step 2MeEA algorithm is to divide the target space into different types of ecoenvironments: establishing an ecological environment by analyzing the individual evolution characteristics in the target space, and taking the ecological environment as a main body to dominate the preference of individual selection in the environment;
first, the center of the local ecological environment is adopted as the solution with obvious trend, and the solution is called as the prominent solution
(ii) a Then looking for the outstanding solution
The adjacent individuals are taken as neighbors; finally, the ecological environment is classified in consideration of the complexity of the target space environment.
4. The optimized preparation method of a hydrodynamic lubrication radial sliding bearing according to claim 1, wherein the ecological environment is classified in step 2;
this classification includes the aggressive environment: the prior generation of outstanding solutions has one Euclidean distance between two solutions in the ecological environment with the current generation of outstanding solutions as the center
An average Euclidean distance from the salient solution larger than the domain to the current-generation salient solution; the aggressive ecosphere is characterized by the target space being able to generate valid individuals with a greater probability; relatively ineffective individuals are individuals whose evolution in the space is in a relatively stagnant state;
lazy environment:
less than the average Euclidean distance; the lazy environment is characterized in that the evolution of individuals in the target space is in a relative stagnation state;
the new environment is as follows: the previous generation of outstanding solutions are not explored in the ecological environment taking the outstanding solutions as the center; the new environment is characterized by a new space area which can be explored, and has great value for maintaining diversity;
crowded environment: searching more than two (including two) previous generation outstanding solutions in the area with the current generation outstanding solution as the ecological environment, and defining the environment as a crowded environment; the crowded environment is characterized in that the target space is overcrowded by individuals, so that two outstanding solutions are closely located when the outstanding solutions are searched.
5. The optimized preparation method of a hydrodynamic lubrication radial sliding bearing according to claim 4, wherein the ecological environment is classified in step 2; selecting individuals in an ecological environment is considered firstly in environment selection; respectively considering different ecological environment characteristics, and keeping different numbers of individuals in the ecological environment to enter the next generation; then, selecting other non-dominant solutions according to cosine similarity between individuals so as to maintain diversity of pareto frontiers in the target space; different from a method considering diversity of the whole space or convergence of a single individual, the proposed environment selection mode preferentially performs offspring selection by taking a local target space as a unit; and when the non-dominant solutions in the population are smaller than the population number, the non-dominant solutions are preferably considered to enter the next generation, and some dominant solutions are selected to maintain diversity.
6. The optimized preparation method of the hydrodynamic lubrication radial sliding bearing according to the claim 1, characterized in that the core idea of MeEA is to divide the target space into several different types of ecological environments; secondly, in the aspect of environment selection, the convergence or diversity of the ecological environment is considered preferentially, and the overall diversity of the population is maintained later; the contradiction between convergence and diversity is relieved through the steps.
7. The optimized preparation method of the hydrodynamic lubrication radial sliding bearing according to claim 1, wherein the step 3 adopts a multi-objective evolutionary algorithm of a multi-ecological environment selection strategy to optimize three performance indexes of the hydrodynamic lubrication radial sliding bearing, namely a liquid friction coefficient, a heating value and a bearing capacity coefficient; the algorithm steps are as shown in a flow chart.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5356225A (en) * | 1992-09-11 | 1994-10-18 | Hanes Charles E | Air bearing system with large stiffness and minimal vibration charcteristics |
| US20060051000A1 (en) * | 2003-01-09 | 2006-03-09 | Izhak Etsion | Laser surface textured fluid-film bearing |
| CN103246759A (en) * | 2013-01-15 | 2013-08-14 | 河海大学常州校区 | Optimum design method of sliding bearing |
| CN104091028A (en) * | 2014-07-18 | 2014-10-08 | 湖大海捷(湖南)工程技术研究有限公司 | Multi-objective optimization design method of spiral oil wedge bearing |
| CN105205222A (en) * | 2015-08-27 | 2015-12-30 | 洛阳轴研科技股份有限公司 | Multi-objective optimization design method and system of rolling bearing |
| US20160213896A1 (en) * | 2013-08-30 | 2016-07-28 | Indiana University Research & Technology Corporation | Hydrodynamic method and apparatus for delivering fluids to kidney tissues |
-
2021
- 2021-06-29 CN CN202110724016.7A patent/CN113536476B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5356225A (en) * | 1992-09-11 | 1994-10-18 | Hanes Charles E | Air bearing system with large stiffness and minimal vibration charcteristics |
| US20060051000A1 (en) * | 2003-01-09 | 2006-03-09 | Izhak Etsion | Laser surface textured fluid-film bearing |
| CN103246759A (en) * | 2013-01-15 | 2013-08-14 | 河海大学常州校区 | Optimum design method of sliding bearing |
| US20160213896A1 (en) * | 2013-08-30 | 2016-07-28 | Indiana University Research & Technology Corporation | Hydrodynamic method and apparatus for delivering fluids to kidney tissues |
| CN104091028A (en) * | 2014-07-18 | 2014-10-08 | 湖大海捷(湖南)工程技术研究有限公司 | Multi-objective optimization design method of spiral oil wedge bearing |
| CN105205222A (en) * | 2015-08-27 | 2015-12-30 | 洛阳轴研科技股份有限公司 | Multi-objective optimization design method and system of rolling bearing |
Non-Patent Citations (2)
| Title |
|---|
| ZEINIZADEH, A等: "Hydrodynamic pressures in contraction joints including waterstops on seismic response of high arch dams", 《STRCTURES》, vol. 14, pages 1 - 14 * |
| 王亚辉等: "基于分解机制的多目标蝙蝠算法", 《农业机械学报》, vol. 46, no. 4, pages 316 - 323 * |
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