CN117272815A - Multi-target performance calculation method for vehicle suspension vibration reduction system with knowledge and data fusion - Google Patents
Multi-target performance calculation method for vehicle suspension vibration reduction system with knowledge and data fusion Download PDFInfo
- Publication number
- CN117272815A CN117272815A CN202311251822.2A CN202311251822A CN117272815A CN 117272815 A CN117272815 A CN 117272815A CN 202311251822 A CN202311251822 A CN 202311251822A CN 117272815 A CN117272815 A CN 117272815A
- Authority
- CN
- China
- Prior art keywords
- vehicle suspension
- model
- vibration reduction
- knowledge
- performance
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 239000000725 suspension Substances 0.000 title claims abstract description 72
- 230000009467 reduction Effects 0.000 title claims abstract description 61
- 230000004927 fusion Effects 0.000 title claims abstract description 43
- 238000004364 calculation method Methods 0.000 title description 14
- 238000013016 damping Methods 0.000 claims abstract description 45
- 238000000034 method Methods 0.000 claims abstract description 32
- 238000004458 analytical method Methods 0.000 claims abstract description 23
- 230000002159 abnormal effect Effects 0.000 claims abstract description 19
- 238000004088 simulation Methods 0.000 claims abstract description 18
- 238000012360 testing method Methods 0.000 claims abstract description 9
- 238000011056 performance test Methods 0.000 claims abstract description 4
- 230000008569 process Effects 0.000 claims description 11
- 230000005284 excitation Effects 0.000 claims description 5
- 239000010720 hydraulic oil Substances 0.000 claims description 5
- 230000007613 environmental effect Effects 0.000 claims description 4
- 239000000126 substance Substances 0.000 claims description 4
- 238000012512 characterization method Methods 0.000 claims description 3
- 239000006096 absorbing agent Substances 0.000 description 27
- 239000003921 oil Substances 0.000 description 24
- 230000035939 shock Effects 0.000 description 23
- 239000007788 liquid Substances 0.000 description 6
- 230000001133 acceleration Effects 0.000 description 5
- 239000012530 fluid Substances 0.000 description 4
- 230000006835 compression Effects 0.000 description 3
- 238000007906 compression Methods 0.000 description 3
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 238000011156 evaluation Methods 0.000 description 3
- 230000005855 radiation Effects 0.000 description 3
- 239000007787 solid Substances 0.000 description 3
- 238000012546 transfer Methods 0.000 description 3
- 238000005452 bending Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000006073 displacement reaction Methods 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000007637 random forest analysis Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 238000000638 solvent extraction Methods 0.000 description 2
- NAWXUBYGYWOOIX-SFHVURJKSA-N (2s)-2-[[4-[2-(2,4-diaminoquinazolin-6-yl)ethyl]benzoyl]amino]-4-methylidenepentanedioic acid Chemical compound C1=CC2=NC(N)=NC(N)=C2C=C1CCC1=CC=C(C(=O)N[C@@H](CC(=C)C(O)=O)C(O)=O)C=C1 NAWXUBYGYWOOIX-SFHVURJKSA-N 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 239000011248 coating agent Substances 0.000 description 1
- 238000000576 coating method Methods 0.000 description 1
- 238000003066 decision tree Methods 0.000 description 1
- 230000002950 deficient Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000020169 heat generation Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 230000010355 oscillation Effects 0.000 description 1
- 230000021715 photosynthesis, light harvesting Effects 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
- 238000012549 training Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/28—Design optimisation, verification or simulation using fluid dynamics, e.g. using Navier-Stokes equations or computational fluid dynamics [CFD]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
- G06N20/20—Ensemble learning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computing arrangements using knowledge-based models
- G06N5/01—Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2113/00—Details relating to the application field
- G06F2113/08—Fluids
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/08—Thermal analysis or thermal optimisation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/10—Noise analysis or noise optimisation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/14—Force analysis or force optimisation, e.g. static or dynamic forces
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Evolutionary Computation (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Artificial Intelligence (AREA)
- Computing Systems (AREA)
- Computer Hardware Design (AREA)
- Geometry (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Data Mining & Analysis (AREA)
- Medical Informatics (AREA)
- Fluid Mechanics (AREA)
- Mathematical Optimization (AREA)
- Mathematical Analysis (AREA)
- Pure & Applied Mathematics (AREA)
- Computational Linguistics (AREA)
- Algebra (AREA)
- Vehicle Body Suspensions (AREA)
Abstract
The invention discloses a method for calculating multi-target performance of a vehicle suspension vibration reduction system by fusing knowledge and data, which comprises the following steps of S1, acquiring characteristic parameters of the vehicle suspension vibration reduction system; s2, establishing a key structure characteristic model according to the system characteristic parameters; s3, integrating and constructing a multi-target performance simulation model of the vehicle suspension vibration reduction system with knowledge and data fusion according to the key structure characteristic model; s4, integrating and constructing a multi-target performance simulation model of the vehicle suspension vibration reduction system according to knowledge and data fusion, and constructing a rapid analysis system; s5, verifying and testing a vehicle suspension vibration reduction system according to the rapid analysis system and the multi-objective performance test; the invention also considers the temperature rise performance and abnormal sound resistance performance on the basis of considering the damping performance of the vehicle suspension vibration reduction system, so that the vehicle suspension vibration reduction system can meet various target performance requirements.
Description
Technical Field
The invention belongs to the technical field of automobile noise, vibration and sound vibration comfortableness, and particularly relates to a multi-target performance calculation method of a vehicle suspension vibration reduction system with knowledge and data fusion.
Background
The shock absorber is used as an important part of an automobile suspension, can restrain the oscillation effect of a spring after damping vibration in the running process of the automobile, and can absorb the impact of a road surface. Plays an important role in the smoothness and the operation stability of the automobile. The shock absorber needs to be adjusted after the design is completed, so that the best matching performance with the whole vehicle is achieved. At present, enterprises mainly rely on the experience of a regulator to operate the vibration damper, the utilization rate of the adjustment data is low, the adjustment direction and the data guidance are lacked, the problems of high adjustment difficulty, long period, high cost and the like exist, and the performance of the vibration damper is difficult to adjust in real time in the production process of the vibration damper.
In the related research data of the shock absorber adjustment disclosed at present, lai Fugang proposes to measure the motion speeds of the shock absorber corresponding to different working conditions by using a strain gauge sensor, so that rapid damping force matching can be performed when the shock absorber is adjusted, a great amount of time spent by a traditional method for estimating the speed of the shock absorber according to experience and then matching the damping force is reduced, but the research is only limited to adjusting the damping characteristics of the shock absorber by a test means so as to meet the performance requirements (DOI: 10.3969/j.issn.1672-9668.2019.12.055). Wang Yanhui A vibration absorber liquid-solid coupling model is built according to vibration absorber liquid characteristics, friction characteristics, gas characteristics and damping characteristics, initial parameters of an AMEPilot model are set according to disassembly test data of a rack and a vibration absorber, valve system parameter sensitivity is analyzed through combined Amesim-iSight full-factor test analysis, and influence of displacement and speed on vibration absorber characteristics and valve system setting rules are studied. Meanwhile, a heat-liquid-solid coupling damper model is established, a heat conduction and heat convection mechanism in the damper is analyzed, thermodynamic phenomena such as related friction heat generation, heat conduction and heat convection in the system are simulated, and the influence rule of temperature on the characteristics of the damper is researched. The method comprehensively considers the speed characteristic and the temperature characteristic of the shock absorber, but the built thermal-liquid-solid coupling model of the shock absorber is complex in calculation process and low in simulation efficiency, and can not evaluate the abnormal sound resistance of the shock absorber. Because the shock absorber can generate impact vibration in the stretching reversing process, thereby exciting noise and influencing the sound quality in the vehicle, defective products caused by the shock absorber occupy a relatively high part in the reverse part, and therefore, the shock absorber cannot be ignored. Du Xitao and Yuan Shimei discloses a method for adjusting a valve system of a shock absorber (CN 202110253063.8), wherein an adjusting coefficient of the valve system of the shock absorber, namely a low-speed idle stroke coefficient and a high-speed throttling coefficient, is introduced, and abnormal noise factors in the adjusting process of the shock absorber are considered by introducing the adjusting coefficient of the valve system of the shock absorber, so that the adjusting efficiency is improved. However, the valve train structure needs to be adjusted after subjective evaluation by an operator, and thus the adjustment threshold value is preset, and the model can only evaluate the abnormal sound resistance of the shock absorber and cannot consider various influences of damping characteristics and temperature characteristics on the shock absorber performance.
The patent is different from the paper and the patent in that a vehicle suspension vibration reduction system model with knowledge and data fusion is established, the most basic damping characteristic of the vibration reduction system is considered, and meanwhile, the temperature characteristic and abnormal sound resistance characteristic of the vibration reduction system are also considered. And a rapid adjustment system is built based on the Appdesigner and the fusion model to realize joint simulation, so that rapid prediction of the vehicle suspension vibration reduction system performance is realized rapidly and efficiently.
Disclosure of Invention
The invention aims to solve the problem that the performance prediction of a vehicle suspension vibration reduction system is single in the prior art, and covers damping performance, temperature rise performance and abnormal sound resistance, and simultaneously provides a multi-target performance calculation method of the vehicle suspension vibration reduction system with knowledge and data fusion, so that the calculation efficiency and accuracy of a prediction model are improved.
The technical scheme of the invention is as follows:
the method for calculating the multi-target performance of the vehicle suspension vibration reduction system by fusing knowledge and data comprises the following steps:
s1, acquiring characteristic parameters of a vehicle suspension vibration reduction system;
s2, establishing a key structure characteristic model according to the system characteristic parameters;
s3, integrating and constructing a multi-target performance simulation model of the vehicle suspension vibration reduction system with knowledge and data fusion according to the key structure characteristic model;
s4, integrating and constructing a multi-target performance simulation model of the vehicle suspension vibration reduction system according to knowledge and data fusion, and constructing a rapid analysis system;
s5, verifying and testing the vehicle suspension vibration reduction system according to the rapid analysis system and the multi-target performance test.
Preferably, the characteristic parameters of the vehicle suspension damping system in step S1 include: physical and chemical properties, excitation properties, compensation cavity gas properties, environmental properties and structural dimensions of hydraulic oil.
Preferably, step S2 comprises the sub-steps of:
s21, establishing a throttle plate group deflection characteristic knowledge model;
s22, establishing a throttle valve plate constant through hole throttle characteristic data driving model;
s23, establishing an AMEsim parameterized model of the vehicle suspension vibration reduction system.
Preferably, the stiffness characteristic of the throttle plate group in step S21 is characterized by establishing a knowledge model of the plate deformation process.
Preferably, the throttle valve plate constant orifice throttle characteristic in step S22 is predicted by creating a data driving model.
Preferably, step S23 comprises the sub-steps of:
substep S231: the AMEsim parameterized model is established through AMEsim parameterized software;
substep S232: and selecting an element model corresponding to the structure for characterization.
Preferably, step S3 comprises the following sub-steps:
s31, integrating and constructing a vehicle suspension vibration reduction system model with knowledge and data fusion;
and S32, simulating and calculating the multi-target performance of the vehicle suspension vibration reduction system based on the fusion model.
Preferably, the multi-objective performance in step S32 includes: damping performance, temperature rise performance and abnormal sound resistance.
Preferably, the rapid analysis system in step S4 is built using an Appdesigner and associated with the fusion model in step S3 to achieve rapid prediction of multi-target performance of the vehicle suspension damping system.
Preferably, in step S5, performance simulation is performed on the fusion model by using the rapid analysis system in step S4, and test verification is performed on damping performance, temperature rise performance and abnormal sound resistance of the vehicle suspension vibration reduction system respectively, so as to verify the confidence of the fusion model in step S3.
The multi-target performance calculation method of the vehicle suspension vibration reduction system based on knowledge and data fusion has the following beneficial effects:
1. according to the invention, the mathematical model and the knowledge driving model are introduced to build the vehicle suspension vibration damping system fusion model with knowledge and data fused, so that the problems of insufficient equivalent precision and low finite element simulation efficiency of the traditional parameterized model are reasonably avoided.
2. The invention also considers the temperature rise performance and abnormal sound resistance performance on the basis of considering the damping performance of the vehicle suspension vibration damping system, so that the vehicle suspension vibration damping system can meet the requirements of various performances.
Drawings
FIG. 1 is a flowchart of a method for calculating the multi-objective performance of a vehicle suspension vibration damping system and an analysis system with knowledge and data fusion.
FIG. 2 shows a throttle plate assembly according to the present invention.
FIG. 3 is a model of the AMEsim parameterized element of the throttle system of the present invention.
FIG. 4 is a block diagram of a constant orifice throttle characteristic data driven predictive model architecture in accordance with the present invention.
FIG. 5 is a knowledge and data fusion model of a vehicle suspension damping system of the present invention.
FIG. 6 is a diagram of a rapid analysis system interface according to the present invention.
Detailed Description
Embodiments of the present invention are further described below with reference to the accompanying drawings.
The invention provides a method for calculating multi-target performance of a vehicle suspension vibration reduction system by fusing knowledge and data, which is shown in fig. 1 and comprises the following steps:
s1, acquiring characteristic parameters of a vehicle suspension vibration reduction system;
the method for acquiring necessary characteristic parameters for constructing the multi-target performance model according to the vehicle suspension vibration reduction system comprises the following steps: excitation parameters, physical and chemical parameters, environmental parameters, structural dimensions (cylinder parameters, compensation chamber parameters, piston rod parameters, valve train parameters).
S2, establishing a key structure characteristic model;
the throttle valve system of the vehicle suspension damping system is generally a disc-shaped valve plate, and meanwhile, the valve plate at the uppermost layer is generally provided with an abnormal structure through hole. In order to effectively avoid the problem of low efficiency of calculating deflection characteristics of the throttle plate group through finite element simulation, a knowledge model is introduced to describe the deflection characteristics of the throttle plate group; in order to effectively solve the problem that the precision of the throttling characteristic of the equivalent extraordinary through hole of the pure parameterized element model is insufficient, a data driving model is introduced to describe the throttling characteristic of the ordinary through hole of the throttle plate.
The method specifically comprises the following steps of:
s21, establishing a throttle plate group deflection characteristic knowledge model;
as shown in FIG. 2, the stiffness element model in the AMEsim parameterized element model is only suitable for the spring stiffness equivalent of the flat push valve, and the error of the calculation kernel is larger when calculating the deflection characteristic of the butterfly valve with deflection. The patent introduces a knowledge model, and builds the knowledge model of the valve plate deformation process for characterization based on the elastic mechanics theory. The original rigidity element model in AMEsim is shown in FIG. 3, and a knowledge model is adopted to replace the rigidity element model in AMEsim.
The throttle valve plate group structure in the vehicle suspension vibration reduction system consists of a throttle valve plate, a gasket valve plate and a nut. The nut fixes the gasket valve plate and the throttle valve plate with a certain torque, and the throttle valve plate has a certain deflection.
The structure uses the center of the valve plate as a pole to establish a polar coordinate system, and as the structure and the load are z-axis symmetry, the elastic valve plate bending deformation curved surface differential equation can be obtained according to the elastic mechanics principle as shown in formulas (1) - (2):
wherein: e is the elastic modulus of the valve plate; μ is poisson's ratio; r is (r) a The radius of the inner circle of the valve plate; r is (r) b The outer circle radius of the valve plate; r is the polar diameter, r E [ r ] a ,r b ]The method comprises the steps of carrying out a first treatment on the surface of the f is the bending deformation of the valve plate at the polar diameter r; and delta is the thickness of the valve plate, and when a plurality of valve plates are overlapped, delta is calculated according to the equivalent thickness.
The general solution of equation (1) is represented by equation (3):
wherein: c (C) 1 、C 2 、C 3 And C 4 To be tied toThe number is determined by the boundary conditions of the valve plate.
S22, building a throttle valve plate constant through hole throttle characteristic data driving model;
in order to ensure the circulation of hydraulic oil of the shock absorber in a low-speed state, a throttle valve plate is provided with an abnormal through hole, and the normal through hole element model in AMEsim can only calculate the throttle characteristic of a hole with a regular cross-section shape, so that the error of a calculation core is larger when the throttle valve plate is calculated to be in the abnormal through hole. Usually, a finite element analysis method is adopted to calculate the flow-throttling pressure curve of the very-structure through hole, but the equivalent mode is low in efficiency, and the patent introduces a data driving model to predict the flow-throttling pressure curve of the very-structure through hole of the throttling valve plate so as to characterize the throttling characteristic of the throttle valve plate. The original model of the normal-through-hole throttling characteristic element in AMEsim is shown in FIG. 2, and the data driving model is adopted to replace the model of the normal-through-hole throttling characteristic element in AMEsim.
The patent introduces a data driving model, builds a multi-output random forest model and predicts the flow-throttling pressure curve of the normal through hole. The 20 sections were taken at equal intervals along the length of the orifice using a plane, and the area of the section was calculated as: a is that 1 、A 2 、A 3 …A 19 、A 20 . In the multi-output random forest model, the length L and the cross section area A of a constant through hole are used 1 ～A 20 As an input parameter; the flow-throttling pressure curve of the normal through hole is taken as an output parameter, and the model architecture is shown in figure 3. Wherein, the data set W is assumed to have N sample data, i.e., d= { (x) (1) ,y (1) ),...,(x (N) ,y (N) ) -a }; wherein the feature set has p dimensions, x (l) ＝{x 1 (l) ,...,x j (l) ,...x p (l) -a }; the target set has q dimensions, y (l) ＝(y 1 (l) ,...,y i (l) ,...,y q (l) ). The optimal partitioning is chosen based on the sum of the square errors of the variables, dividing the data set into two subsets. Assuming that a value s corresponding to the jth feature vector is selected as a dividing variable and a dividing point, the training set is divided into two subsets represented by formulas (4) to (5):
R 1 (j,s)＝{x∣x(j)≤s} (4)
R 2 (j,s)＝{x∣x(j)＞s} (5)
the requirements satisfy formulas (6) to (8):
traversing the variable j to obtain j and s which minimize the above formula, namely, an optimal dividing variable and an optimal dividing point, and obtaining an output value represented by a formula (9):
the partitioning process is repeated until the stop condition is satisfied, the input set is partitioned into M regions, and the decision tree is generated as represented by formula (10):
s23, establishing an AMEsim parameterized element model of the vehicle suspension vibration reduction system (except for a knowledge model and a data driving model in the steps S21 and S22).
The AMEsin parameterized element is used to build a vehicle suspension damping system model (except the knowledge model and the data driving model in the steps S21 and S22), wherein the model comprises an excitation element model, a cylinder element model and a compensation cavity element model.
S3, integrating building and multi-objective performance simulation calculation of a vehicle suspension vibration reduction system model with knowledge and data fusion;
and (3) fusing and integrating the models established in the steps S21 to S23 to form a vehicle suspension vibration reduction system model with knowledge and data fused. On the basis, damping performance, temperature performance and abnormal sound resistance are calculated.
The method specifically comprises the following steps of:
s31, integrating and constructing a vehicle suspension vibration reduction system model with knowledge and data fusion.
And (3) integrating the knowledge driving model of the deflection characteristics of the throttle plate group and the data driving model of the throttle plate constant through hole throttle characteristics established in the steps S21 to S22 with the AMEsim parameterized element model in the step S23 to form a vehicle suspension vibration reduction system model with knowledge and data fusion, as shown in figure 5.
S32, simulating and calculating the multi-target performance of the vehicle suspension vibration reduction system based on the fusion model.
The multi-objective performance simulation calculation of the vehicle suspension damping system in step S32 includes: damping performance, temperature rise performance and abnormal sound resistance. The damping performance is damping force of the vibration reduction system at different running speeds; the temperature rise performance is the condition that the temperature of the outer wall of the cylinder barrel changes along with time in the working process of the vibration reduction system; the abnormal sound resistance is the vibration acceleration of the top end part of the piston connecting rod of the vibration reduction system under different working conditions.
In the process of restoring and compressing reciprocating motion of the vehicle suspension vibration reduction system, hydraulic oil flows through each throttling component to form damping force, damping performance simulation analysis is carried out through evaluation of damping force under different running speeds, and F in formulas (11) - (12) fy And F ys Respectively representing the restoring stroke and compression stroke damping forces; abnormal sound resistance performance simulation analysis is evaluated by link end vibration acceleration, a in formulas (11) - (12) fy And a ys The restoring stroke and compression stroke link end vibration accelerations are shown, respectively.
m d a fy ＝F f +F hydrd +F air -F fy (11)
m d a ys ＝F f -F hydrd -F air -F ys (12)
Wherein: m is m d The mass of the piston connecting rod is; x is x d Is the displacement of the piston connecting rod; f (F) f The friction force is applied to the piston connecting rod; f (F) hydrv Is subjected to hydraulic pressure by a piston connecting rod, F hydrd ＝p rec A rec -p compr A compr ，p rec The pressure of the oil in the cavity is uniformly distributed for the restoration of the shock absorber, A rec Is the oil liquid acting area of the upper surface of the piston, p compr The pressure of the oil liquid in the compression cavity of the shock absorber is uniformly distributed, A compr The oil liquid acting area is the lower surface of the piston; f (F) air Is subjected to the atmospheric force of the connecting rod, F air ＝p air A rod ，p air Is at standard atmospheric pressure, A rod Is the cross-sectional area of the connecting rod.
For the temperature rise performance simulation calculation of the vehicle suspension vibration reduction system, based on the analysis of damping performance, other forms of energy dissipation are ignored, and the damping force acting is finally converted into heat energy, wherein one part of the heat energy causes the temperature of the vibration reduction system to rise, and the other part of the heat energy is dissipated with external heat exchange, and the heat energy is represented by a formula (13):
W＝c s m s ΔT s +c o m o ΔT o +Q (13)
wherein: c s ，m s ，c o ，m o Respectively representing the specific heat capacity and the mass of the internal structure and the specific heat capacity and the mass of oil liquid; t (T) s And T o The average temperature of the internal structure and the average temperature of the oil liquid are respectively.
The heat flow is transferred inside the shock absorber equivalently as one-dimensional radial heat transfer, and the heat transfer modes comprise conduction, convection and heat radiation. The internal flow forced convection heat exchange is carried out between the oil in the working cylinder of the vibration reduction system and the inner wall of the working cylinder, and the heat flow rate is calculated by adopting a formula (14).
Wherein: l is the length of a circular tube; r is (r) 1 And r 2 Respectively representing the inner diameter and the outer diameter of the circular tube; lambda is the thermal conductivity of the material，T 1 And T 2 The temperatures of the inner wall surface and the outer wall surface are shown, respectively.
The thermal convection process comprises convection of oil in the working cylinder and the inner wall of the working cylinder, convection of oil in the oil storage cylinder and the outer wall of the working cylinder, convection of oil in the oil storage cylinder and the inner wall of the oil storage cylinder, and convection of the outer wall of the oil storage cylinder and the environment. The convection of the inner wall of the working cylinder and the inner wall of the oil storage cylinder with oil belongs to forced convection in a pipe, and the number of Knoop is calculated by adopting formulas (15) to (16).
Wherein: n (N) ul Is the laminar noose number; n (N) ut Is the number of flow noose; re is the Reynolds number; p (P) r Is the Plantt number; d is the pipe diameter; l is the length of the heat exchange area tube; μ is the absolute viscosity of the oil at the fluid temperature; mu (mu) s Is the absolute viscosity of oil at the temperature of the pipe wall.
The convection of the oil in the oil storage cylinder and the outer wall of the working cylinder and the convection of the outer wall of the oil storage cylinder and the environment are forced convection outside the pipe, and the number of the Knoop is calculated by adopting a formula (17).
Nu＝c(Re n Pr 0.33 ) (17)
Wherein: the coefficient c is a function of n and Re.
After calculation of the noose number, the convective heat transfer coefficient is represented by equation (18):
wherein: λ is the thermal conductivity of the fluid; c d For the dimension of the geometric feature of the reaction flow field, for the fluid in the tube, the feature dimension refers to the tube inner diameter in the vertical flow direction; the characteristic dimension refers to the outside diameter of the pipe as the fluid flows around the cylinder.
The radiation heat exchange quantity of the outer wall of the oil storage cylinder of the vibration reduction system and the unit time of the environment is represented by a formula (19):
q＝εSσ(T w -T g ) (19)
wherein: epsilon is the radiation factor, which is related to the coating material of the outer wall; s is the area of the outer wall of the oil storage cylinder, sigma Boltzmann constant, T w And T g The temperature of the outer wall surface of the oil storage cylinder and the temperature of the environment are respectively.
S4, establishing a rapid analysis system;
the setup of the rapid analysis system interface based on programming software Appdesigner is shown in fig. 6. The input parameters comprise physical and chemical characteristic parameters, excitation characteristic parameters, compensation cavity gas characteristic parameters, environment characteristic parameters and structural dimensions of the hydraulic oil of the vehicle suspension vibration reduction system in the step S1, a callback function is used for associating a parameter endowing module in the rapid analysis system with the fusion model, and the parameters of the fusion model are modified through the rapid analysis system. And a callback function is used for associating a function drawing module in the rapid analysis system with the fusion model so as to receive performance data generated by the operation of the fusion model, and a damping performance curve, a temperature rise performance curve and a vibration acceleration curve are drawn in the rapid analysis system, so that efficient and accurate calculation of the vehicle suspension vibration reduction system performance simulation of knowledge data fusion is realized.
S5, verifying and testing the multi-target performance test of the vehicle suspension vibration reduction system.
Installing a vehicle suspension vibration reduction system on an MTS test bench, and collecting damping forces at different running speeds to verify damping characteristics; collecting the outer cylinder temperatures at different running speeds and different environmental temperatures to verify the temperature rise performance; and collecting the vibration acceleration of the end part of the connecting rod at different running speeds to verify the abnormal sound resistance. And (3) verifying the confidence level of the vehicle suspension vibration reduction system model with the knowledge and data fusion established in the step (S3) through the evaluation index.
Those of ordinary skill in the art will recognize that the embodiments described herein are for the purpose of aiding the reader in understanding the principles of the present invention and should be understood that the scope of the invention is not limited to such specific statements and embodiments. Those of ordinary skill in the art can make various other specific modifications and combinations from the teachings of the present disclosure without departing from the spirit thereof, and such modifications and combinations remain within the scope of the present disclosure.
Claims (10)
1. The method for calculating the multi-target performance of the vehicle suspension vibration reduction system by fusing knowledge and data is characterized by comprising the following steps of:
s1, acquiring characteristic parameters of a vehicle suspension vibration reduction system;
s2, establishing a key structure characteristic model according to the system characteristic parameters;
s3, integrating and constructing a multi-target performance simulation model of the vehicle suspension vibration reduction system with knowledge and data fusion according to the key structure characteristic model;
s4, integrating and constructing a multi-target performance simulation model of the vehicle suspension vibration reduction system according to knowledge and data fusion, and constructing a rapid analysis system;
s5, verifying and testing the vehicle suspension vibration reduction system according to the rapid analysis system and the multi-target performance test.
2. The method for calculating the multi-objective performance of the vehicle suspension vibration reduction system according to claim 1, wherein the characteristic parameters of the vehicle suspension vibration reduction system in step S1 include: physical and chemical properties, excitation properties, compensation cavity gas properties, environmental properties and structural dimensions of hydraulic oil.
3. The method for calculating the multi-target performance of the vehicle suspension damping system with knowledge and data fusion according to claim 1, wherein said step S2 comprises the sub-steps of:
s21, establishing a throttle plate group deflection characteristic knowledge model;
s22, establishing a throttle valve plate constant through hole throttle characteristic data driving model;
s23, establishing an AMEsim parameterized model of the vehicle suspension vibration reduction system.
4. The method for calculating the multi-objective performance of the suspension damping system of the vehicle with knowledge and data fusion according to claim 3, wherein the stiffness characteristic of the throttle plate group in the step S21 is characterized by establishing a knowledge model of the deformation process of the throttle plate.
5. The method for calculating the multi-objective performance of the suspension damping system of the vehicle with knowledge and data fusion according to claim 3, wherein the throttle plate constant orifice throttle characteristic in the step S22 is predicted by establishing a data driving model.
6. A method for calculating the multi-target performance of a vehicle suspension damping system with knowledge and data fusion according to claim 3, wherein said step S23 comprises the sub-steps of:
substep S231: the AMEsim parameterized model is established through AMEsim parameterized software;
substep S232: and selecting an element model corresponding to the structure for characterization.
7. The method for calculating the multi-objective performance of the suspension vibration damping system of the vehicle with knowledge and data fusion according to claim 1, wherein said step S3 comprises the following sub-steps:
s31, integrating and constructing a vehicle suspension vibration reduction system model with knowledge and data fusion;
and S32, simulating and calculating the multi-target performance of the vehicle suspension vibration reduction system based on the fusion model.
8. The method for calculating the multi-objective performance of the suspension damping system for the vehicle with knowledge and data fusion according to claim 7, wherein the multi-objective performance in the step S32 comprises: damping performance, temperature rise performance and abnormal sound resistance.
9. The method for calculating the multi-objective performance of the vehicle suspension damping system by combining knowledge and data according to claim 1, wherein the rapid analysis system in the step S4 is established by using an Appdesigner and is associated with the fusion model in the step S3, so as to realize rapid prediction of the multi-objective performance of the vehicle suspension damping system.
10. The method for calculating the multi-objective performance of the vehicle suspension vibration reduction system based on knowledge and data fusion according to claim 1, wherein the step S5 is characterized in that the rapid analysis system in the step S4 is used for performing performance simulation on the fusion model, and the damping performance, the temperature rise performance and the abnormal sound resistance of the vehicle suspension vibration reduction system are respectively tested and verified to verify the confidence of the fusion model in the step S3.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311251822.2A CN117272815A (en) | 2023-09-26 | 2023-09-26 | Multi-target performance calculation method for vehicle suspension vibration reduction system with knowledge and data fusion |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311251822.2A CN117272815A (en) | 2023-09-26 | 2023-09-26 | Multi-target performance calculation method for vehicle suspension vibration reduction system with knowledge and data fusion |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117272815A true CN117272815A (en) | 2023-12-22 |
Family
ID=89204097
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311251822.2A Pending CN117272815A (en) | 2023-09-26 | 2023-09-26 | Multi-target performance calculation method for vehicle suspension vibration reduction system with knowledge and data fusion |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117272815A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117681856A (en) * | 2024-02-04 | 2024-03-12 | 西南交通大学 | Energy management control method based on whole vehicle torque demand and electric quantity state |
-
2023
- 2023-09-26 CN CN202311251822.2A patent/CN117272815A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117681856A (en) * | 2024-02-04 | 2024-03-12 | 西南交通大学 | Energy management control method based on whole vehicle torque demand and electric quantity state |
| CN117681856B (en) * | 2024-02-04 | 2024-05-07 | 西南交通大学 | Energy management control method based on whole vehicle torque demand and electric quantity state |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Simms et al. | The influence of damper properties on vehicle dynamic behaviour | |
| CN117272815A (en) | Multi-target performance calculation method for vehicle suspension vibration reduction system with knowledge and data fusion | |
| CN101458205A (en) | Fixation joint part dynamics parameter identification method for machine tool | |
| US5900542A (en) | Method and apparatus for modelling a tire for use with a vehicle spindle-coupled simulator | |
| Chang et al. | Dynamic model of an air spring and integration into a vehicle dynamics model | |
| CN111507017B (en) | Dynamics modeling method of oil-gas separation type buffer | |
| CN109902357B (en) | Method for analyzing dynamic response sensitivity of complex-variant differential smooth nonlinear structure | |
| Ma et al. | Finite element modeling of straight pipeline with partially attached viscoelastic damping patch based on variable thickness laminated element | |
| CN107301257A (en) | A kind of Semi-active mount parameter identification method | |
| CN109186967B (en) | A kind of rubber shock absorber performance prediction and selection method based on BP neural network | |
| Jiang et al. | Design of a BP neural network PID controller for an air suspension system by considering the stiffness of rubber bellows | |
| Xu et al. | Semi-active control of a new quasi-zero stiffness air suspension for commercial vehicles based on event-triggered H∞ dynamic output feedback | |
| JP7310641B2 (en) | Model generation method | |
| CN117195406A (en) | Vehicle inertial-energy hydro-pneumatic suspension structure and parameter determination method | |
| Sehovic et al. | Experimental determination of non-linear characteristics of the passenger vehicle suspension system | |
| Morettini et al. | Experimental diagnosis of cavitation for a hydraulic monotube shock absorber | |
| CN114216634B (en) | Online monitoring and evaluating method for vibration damping performance of floating slab track | |
| CN113158297B (en) | Hydraulic arc steel gate parameter load identification method | |
| Benson et al. | A theoretical and experimental investigation of a gas dynamic model for a single stage reciprocating compressor with intake and delivery pipe systems | |
| Gao et al. | Programmed load spectrum for fatigue bench test of a vehicle body | |
| Carrera Akutain et al. | A parametric damper model validated on a track | |
| CN111209694B (en) | Structure identification method for rigidity and axial force of truss structure | |
| Castellani et al. | Numerical and experimental dynamics of a monotube shock absorber | |
| Xu et al. | 3D TFSI FEA Simulation and Analysis of Thermodynamic Characteristics of a Gas-pressurized Hydraulic Shock Absorber | |
| CN112434369A (en) | Structural load sensing method based on machine learning |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |