CN104091085B - The cavitation noise feature assessment method being calculated based on screw current pressure fluctuation - Google Patents
The cavitation noise feature assessment method being calculated based on screw current pressure fluctuation Download PDFInfo
- Publication number
- CN104091085B CN104091085B CN201410345592.0A CN201410345592A CN104091085B CN 104091085 B CN104091085 B CN 104091085B CN 201410345592 A CN201410345592 A CN 201410345592A CN 104091085 B CN104091085 B CN 104091085B
- Authority
- CN
- China
- Prior art keywords
- cavitation
- grid
- propeller
- model
- pressure fluctuation
- 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.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 70
- 238000001228 spectrum Methods 0.000 claims abstract description 73
- 230000010349 pulsation Effects 0.000 claims abstract description 23
- 238000004364 calculation method Methods 0.000 claims abstract description 21
- 230000003595 spectral effect Effects 0.000 claims abstract description 17
- 230000009466 transformation Effects 0.000 claims abstract description 16
- 238000000605 extraction Methods 0.000 claims abstract description 9
- 238000012545 processing Methods 0.000 claims abstract description 7
- 239000012071 phase Substances 0.000 claims description 15
- 238000004422 calculation algorithm Methods 0.000 claims description 11
- 238000012937 correction Methods 0.000 claims description 10
- 238000001514 detection method Methods 0.000 claims description 9
- 238000002474 experimental method Methods 0.000 claims description 6
- 238000006243 chemical reaction Methods 0.000 claims description 5
- 241001672694 Citrus reticulata Species 0.000 claims description 3
- 238000005345 coagulation Methods 0.000 claims description 3
- 230000015271 coagulation Effects 0.000 claims description 3
- 238000009833 condensation Methods 0.000 claims description 3
- 230000005494 condensation Effects 0.000 claims description 3
- 230000008878 coupling Effects 0.000 claims description 3
- 238000010168 coupling process Methods 0.000 claims description 3
- 238000005859 coupling reaction Methods 0.000 claims description 3
- 238000009792 diffusion process Methods 0.000 claims description 3
- 230000008676 import Effects 0.000 claims description 3
- 239000007791 liquid phase Substances 0.000 claims description 3
- 238000004519 manufacturing process Methods 0.000 claims description 3
- 238000002156 mixing Methods 0.000 claims description 3
- 238000012986 modification Methods 0.000 claims description 3
- 230000004048 modification Effects 0.000 claims description 3
- 230000035939 shock Effects 0.000 claims description 3
- 230000003068 static effect Effects 0.000 claims description 3
- 230000007704 transition Effects 0.000 claims description 3
- 239000010410 layer Substances 0.000 claims 2
- 241000208340 Araliaceae Species 0.000 claims 1
- 235000005035 Panax pseudoginseng ssp. pseudoginseng Nutrition 0.000 claims 1
- 235000003140 Panax quinquefolius Nutrition 0.000 claims 1
- 235000008434 ginseng Nutrition 0.000 claims 1
- 239000002344 surface layer Substances 0.000 claims 1
- 238000004458 analytical method Methods 0.000 abstract description 8
- 239000012530 fluid Substances 0.000 abstract description 6
- 230000008859 change Effects 0.000 description 9
- 238000005259 measurement Methods 0.000 description 9
- 238000004088 simulation Methods 0.000 description 7
- 239000000284 extract Substances 0.000 description 6
- 238000000354 decomposition reaction Methods 0.000 description 5
- 238000003672 processing method Methods 0.000 description 5
- 238000011160 research Methods 0.000 description 5
- 238000013461 design Methods 0.000 description 4
- 230000000694 effects Effects 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 4
- 238000010606 normalization Methods 0.000 description 3
- 230000000737 periodic effect Effects 0.000 description 3
- 230000009471 action Effects 0.000 description 2
- 238000004141 dimensional analysis Methods 0.000 description 2
- FKLFBQCQQYDUAM-UHFFFAOYSA-N fenpiclonil Chemical compound ClC1=CC=CC(C=2C(=CNC=2)C#N)=C1Cl FKLFBQCQQYDUAM-UHFFFAOYSA-N 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 238000012067 mathematical method Methods 0.000 description 2
- 238000013178 mathematical model Methods 0.000 description 2
- 239000000203 mixture Substances 0.000 description 2
- 230000000644 propagated effect Effects 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 230000001052 transient effect Effects 0.000 description 2
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 2
- 238000007476 Maximum Likelihood Methods 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 210000001367 artery Anatomy 0.000 description 1
- 238000005311 autocorrelation function Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000000205 computational method Methods 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000009977 dual effect Effects 0.000 description 1
- 230000003628 erosive effect Effects 0.000 description 1
- 230000006698 induction Effects 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000001208 nuclear magnetic resonance pulse sequence Methods 0.000 description 1
- 238000012805 post-processing Methods 0.000 description 1
- 230000000541 pulsatile effect Effects 0.000 description 1
- 230000005855 radiation Effects 0.000 description 1
- 238000004064 recycling Methods 0.000 description 1
- 238000000926 separation method Methods 0.000 description 1
- 238000005309 stochastic process Methods 0.000 description 1
- 238000013519 translation Methods 0.000 description 1
- 230000005514 two-phase flow Effects 0.000 description 1
- 210000003462 vein Anatomy 0.000 description 1
- 239000011800 void material Substances 0.000 description 1
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses the cavitation noise feature assessment method calculating based on screw current pressure fluctuation, belong to Acoustic Object feature extraction field.Method is taken based on the special estimation of cavitation noise that screw current pressure fluctuation calculates, comprises the following steps：(1) standby mess generation generate example file after importing calculation procedure, (2) cavitation model and turbulence model set, (3) numerical computations parameter setting, (4) numerical computations, (5) numerical method reliability demonstration and grid determine, (6) the unsteady numerical computations of cavitation wake pressure pulsation, (7) pressure fluctuation signal power spectral transformation and low frequency spectrum lines magnitude extraction, (8) line spectrum feature is estimated and is analyzed.Correlational study achievement in modern fluid mechanics, Bubble dynamics and field of signal processing is introduced the noise characteristic analysis of submarine target by the present invention, embodies multidisciplinary and multi-field intercrossing.
Description
Technical field
The present invention relates to Acoustic Object feature extraction field, specifically, it is related to based on screw current pressure fluctuation meter
The cavitation noise feature assessment method calculated.
Background technology
Propeller noise is one of big noise source of ship three, contains target propeller kind of information and architectural feature, this
A little feature tolerances are strong, have preferable separability, are principal character and the important evidence of identification submarine target.And cavitation is once
Occur, cavitation noise just becomes propeller main noise.These target source noises are due to being disturbed and answered by ambient sea noise
Distortion is produced so that the noise signal feature received by passive sonar is inconspicuous, signal to noise ratio during miscellaneous underwater acoustic channel is propagated
Reduce.Therefore traditional extracts noise characteristic with signal processing method, carries out Underwater Targets Recognition more and more difficult.Dig further
Pick propeller noise substitutive characteristics are Underwater Targets Recognition problems anxious to be resolved.
For using signal processing technology the propeller noise of actual measurement is carried out with the research of feature extraction aspect, external
Person has begun to very early.Just have been proposed for maximum likelihood modulation receiver early in Whalen in 1971.With this skill
The development of art, the time frequency processing method such as higher-order spectrum, AR spectrum, dual spectrum and wavelet analysises, and point shape, chaos, limit cycle and mould
The non-linear processing methods such as state decomposition, all widely attempt in propeller noise feature extraction.In recent years, the scholar such as Li Qihu adopts
With single-frequency component of signal detection method and detecting system performance under theory analysis and Study on Numerical Simulation strong jamming background noise.
Nanjing University Bao Fei etc. is by Empirical Mode Decomposition method (empirical mode decomposition) and singular value decomposition method
(singular value decomposition) combines, and the cavitation noise extracting propeller from strong jamming background noise is adjusted
Make point.Modern signal processing method is extracted to the actual measurement noise signal feature under background noise, achieves good effect
Really.But to strong jamming background noise, due to lacking mechanistic features in measured signal, this method adaptability is less high.
Thus, some scholars have carried out noise characteristic analysis and technique study based on model.Tao Duchun is by noise modulated
As having same shape, equal repetition period, random magnitude, the pulse feature stochastic process with block structure is processed envelope.And
Extract relevant with the various physical attribute in naval vessel from the power spectral density of ship-radiated noise modulation envelope and auto-correlation function
Abundant cadence information.Jiang Guojian and Lin build identical people's utilization index decay shape random pulse sequence theoretical model analyzing warship
Ship propeller cavitation noise, obtains Spectrum of Propeller Cavitation Noise.In recent years, the scholar such as wide intelligence of history is directed to propeller blade number and identifies
Problem, sets up cavitation noise signal model.And twin screw ship noise envelope is modeled, research double oar target modulation spectrum harmonic wave
The structure problem of race's feature, adopts aspect of model extractive technique further, and research is finely divided based on the noise characteristic of Model Matching
Analysis method.Except leaf frequency feature, these models do not account for the parameters such as propeller geometry and operating mode, are difficult to embodiment cavitation and make an uproar
The mechanistic features of sound.
Propeller cavitation is the direct sound source of cavitation noise, and propeller cavitation wake flow is the important propagation of cavitation noise
Approach.Because wake flow is acted on by propeller periodic rotary beat, there is periodically pulsing feature.These features reflect
The characteristic information such as propeller operating mode and geometry.Meanwhile, propeller rotation beat has significantly to the cavitation noise that it radiates
Modulation and Amplitude Modulation acts on, and the line spectrum feature of its power spectrum also reflects including the characteristic information such as propeller operating mode and geometry
Propeller cadence information.Therefore, because being similarly subjected to beat effect, propeller cavitation wake flow and the cavitation noise of propeller blade
There is feature correlation, its feature all reflects propeller duty parameter and geometric shape parameterses.Due to current propeller cavitation
The acoustics study mechanism of noise is still far from perfect, and therefore the present invention is that cavitation wake flow starts with illustrating it from the origin of cavitation noise
A kind of forecasting procedure of noise characteristic.
Many scholars are had to be studied both at home and abroad for propeller cavitation wake flow.Italian ship model experimental tank laboratory
Francesc etc. is carried out to E779A propeller wake field under the conditions of cavitation and non-cavitation respectively using RANS, LES and BEM method
Numerical simulation.Sweden Rickard and Goran is based on mixing Two-phase flow's separation, using implicit expression LES method and Kunz cavitation model mould
The dynamic behaviour in Non-uniform Currents cavitation for the E779A propeller, the flow field structure to Small and Medium Sized and propeller tip whirlpool are intended
The simulation of cavitation is more successful.The scholars such as Tsing-Hua University Ji Bin utilize Rayleigh Plessete equation and k- ω Shear
The cavitation wake flow number to all even nonlinear inflow of high skewed propeller for Stress Transport (SST) turbulence model
Value simulation.Piece cavitation and tip vortex cavitation are preferably forecast, with tail flow field pressure fluctuation characteristic and the propeller of cavitation induction
Axle frequency leaf frequency feature is consistent.Yang Qiong side of naval engineering university is commented in propeller cavitation simulation to cavitation model and turbulence model
Estimate analysis, select to improve Sauer cavitation model and revise SST k- ω turbulence model, accurately forecast propeller cavitation bucket figure
Spectrum.To the non-homogeneous influent stream with seven leaf highly skewed propellers, analyze the thrust that its cavitation causes and moment collapse performance and to leaf
The impact that back of the body tip vortex cavitation is come into being, describe pulsatile characteristics, blade cavitation area and the cavitation form of cavitation thrust and moment with
The change of circumferential position, and give the interval division whether propeller in wake blade face piece cavitation.At present, right both at home and abroad
The research of cavitation wake flow primarily focuses on the numerical forecast of certain oar module cavitation, for cavitation and propeller operating mode and geometric form
The research of the characteristic relation aspect between shape is less, and study cavitation noise feature with cavitation wake flow then less.
In addition, Chinese Patent Application No. ZL201310538724.7, file also disclose that a kind of based in nonlinear inflow
The feature extracting method of Propeller Cavitation Noise numerical forecast, step includes：First, carry out grid to propeller computational fields to draw
Point, check mesh quality and define boundary condition；Next, in CFD software, arranging computation model, carrying out stable state iteration meter
Calculation is dropped down water performance parameter and is entered head piece speed checking model accuracy；Then, in CFD software, stable state is calculated as non-steady
The initial value that state calculates carries out unstable state iterative calculation, and passes through post processing propeller blade cavitation cycle form and documentary film
Cavitation area change；Finally, radiated by propeller blade cavitation areal calculation propeller cavitation according to single cavity radiated noise theory
Noise, carries out feature extraction.In this application file, cavitation zone is converted to spherical volume by method therefor, and obtains spherical volume
Radius, then radius change is brought in spherical single cavitation erosion radiation patterns, to forecast cavitation noise and its feature.Due to spiral
Oar cavitation is very different with spherical single cavity, and the accuracy of this translation method awaits checking further.The side of the present invention
Rule is to estimate noise characteristic using the feature correlation between the pulsation of propeller cavitation wake pressure and cavitation noise.Specifically
For, cavitation wake pressure pulsation information contains propeller operating mode and geometric shape parameterses feature, and cavitation noise also has this
One attribute.Therefore, they have identical origin relation, are that the rotation of propeller leads to cavitation wake flow and produces noise, with
Cavitation noise is also subject to the modulating action of revolving vane.The basic reason of cavitation wake flow and noise generation is propeller in fluid
In rotation.
Content of the invention
Propeller Cavitation Noise principal character has：
1. propeller cavitation is the source of cavitation noise, and Propeller Cavitation Noise invariably accompanies the appearance of propeller cavitation
And occur；
2. propeller cavitation wake flow is not only the sound source of cavitation noise, or the important carrier that cavitation noise is propagated；
3. the sound intensity of cavitation noise and void volume change closely related, particularly its moment of crumbling and fall change in volume
Greatly, its radiated noise is also strong；
4. propeller cavitation change in volume and position distribution has periodic feature with the rotation of blade so that cavitation
Noise also has periodic feature, and can reflect in the distribution of its noise spectrum；
5. cavitation wake flow and cavitation noise can be subject to propeller blade to rotate the modulating action of beat simultaneously；
6. features described above makes propeller cavitation and the feature of its noise have tight essential dependency；
7. a cavitation noise is typically distributed across low-frequency range, and its frequency spectrum assumes line spectrum feature, and the noise that tip vortex cavitation sends
It is typically distributed across medium-high frequency section, its frequency spectrum assumes continuous feature.
The principle of the present invention is exactly the principal character according to above-mentioned propeller cavitation, theoretical based on stickiness multiphase flow, utilizes
Modern computational method builds N-S equation to underwater propeller tail flow field, and combines turbulence model and cavitation model pair
Equation group carries out numerical solution, thus obtaining pressure fluctuation etc. in vapour phase volume fraction and tail flow field around underwater propeller blade face
Relevant information；The characteristics of low-frequency recycling the information of flow data of the signal processing method logarithm value such as power spectrum calculating is extracted
And analysis；Finally using the feature correlation between fluid field pressure pulsation and noise, submarine target propeller noise feature is carried out
Estimate and judge.Although pressure fluctuation be in tail flow field mechanics parameter and noise acoustic pressure is parameters,acoustic, their physical concept
Difference, but their features in some aspects, such as low frequency spectrum lines characteristics of amplitude distribution, there are some common ground again.These common ground
Essence is to reflect propeller geometry and duty parameter feature, here they is referred to as feature correlation.
The present invention adopts the following technical scheme that：
The cavitation noise feature assessment method being calculated based on screw current pressure fluctuation, specifically includes following steps：
(1) standby mess generation generate example file after importing calculation procedure：
Made using professional software and after propeller 3-D geometric model, import Grid Generation Software, soft in stress and strain model
Three kinds of alternative grids are set up, the computational fields of these three alternative grids are identical, the speed frontier distance propeller center that becomes a mandarin is in part
1D, D are airscrew diameter, and downstream pressure outlet border distance is 5D, and propeller center to side periphery distance is 2.5D, and this three
The grid cell quantity of individual grid according toMultiple is gradually increased, to the grid cell size of adjacent boundary in grid on side
Point reasonable transition in boundary's, so as to size of mesh opening difference is less, finally makes the skew of all volume mesh units in grid be defined in
Within 0.9, to ensure the stability of numerical computations below；
(2) cavitation model and turbulence model set：
Using full-cavitation model and Renormalization Group turbulence model, and its important parameter is modified, in cavitation model
In the correction of transformation ratio parameter and turbulence model, the correction of turbulent viscosity coefficient is adopted and is shown a C language, and recycles macro-call
(DEFINE_TURBULENT_VISCOSITY etc.) form embeds calculation procedure；
(3) numerical computations parameter setting：
The relevant parameter of working condition, boundary condition and numerical algorithm is set；
(4) numerical computations：
Because cavitation model adds after RANS equation, the stability of calculating reduces, and unusual appearance easily.Therefore, it is
Numerical computations can be made steadily to carry out, using calculating process step by step step by step, specifically, in propeller duty parameter,
Ambient pressure and inflow velocity can directly be set to operating mode value, and revolution speed of propeller is increased using classification, pre- until increasing to
Determine operating mode value；First calculate non-cavitating model Flow Field Distribution, after calculating is stable, opens cavitation model again；First to pressure, density,
The parameter such as momentum and vapour phase fraction carries out single order precision discrete scheme and calculates, and calculates after stablizing, more discrete precision is brought up to two
Rank or QUCIK etc., because the calculating of multiphase flow model, cavitation model and Sliding mesh is larger to computer resource usage, therefore adopt
Shorten the calculating time with parallel computing.
(5) numerical method reliability demonstration and grid determine：
By numerical result and the related experiment result to the hydrodynamic parameter of propeller oar and cavitation under typical condition
It is compared, to verify the reliability of grid independence and adopted numerical method；By built in step 1 three kinds of alternative grids by
Set according to step 2 and 3 methods and carried out numerical computations, and hydrodynamic parameter in result of calculation and cavitation are compared,
When these results tend towards stability with the increase of number of grid and be consistent with experimental result, then select and meet grid in condition
The minimum grid of element number is as the selected grid of following numerical computations；Otherwise suitably increase number of grid, repeat step 1 weight
Newly start；
(6) the unsteady numerical computations of cavitation wake pressure pulsation：
Using the selected grid in step 5, unsteady numerical value is carried out under required working condition to the tail flow field of propeller
Calculate, in calculation procedure to tail flow field in the pressure fluctuation of a certain ad-hoc location (A point) detect and preserve its detection data, simultaneously
Nondimensionalization is carried out to data；
(7) pressure fluctuation signal power spectral transformation and low frequency spectrum lines magnitude extraction：
Using the physical quantitys such as pressure fluctuation in fast fourier transform method stream field in signal processing and noise signal number
According to carrying out power spectral transformation, and low frequency spectrum lines amplitude is extracted, low frequency spectrum lines include axle frequency, two times of axles frequencies, three times axle frequency
With leaf frequency；Recycle tail flow field pressure fluctuation characteristic and cavitation noise by the similarity of blade modulation signature, set up from pressure arteries and veins
Dynamic low frequency spectrum lines amplitude is to the feature corresponding relation of the low frequency spectrum lines amplitude of noise；
(8) line spectrum feature is estimated and is analyzed：
Step 7 medium and low frequency line spectrum amplitude is corresponded the low frequency spectrum lines amplitude of noise power spectrum, as to sky
Change the estimation of noise signal low frequency spectrum lines characteristics of amplitude distribution, be exactly specifically the axle using pressure fluctuation signal power spectrum
Frequently, the amplitude of the low frequency component such as two times of axles frequency, three times axle frequency and Ye Pin come distinguish estimated noise signal axle frequency, two times of axles frequently, three
The amplitude of times low frequency component such as axle frequency and Ye Pin.
Further, the cavitation wake pressure pulsation unsteady computation in described step 6 comprises the following steps：
(6-1) the mess generation example file selected in steps for importing 5；
(6-2) cavitation model and turbulence model set；
(6-3) numerical computations parameter setting；
(6-4) numerical computations；
(6-5) pressure fluctuation signal is extracted：Ad-hoc location a certain in tail flow field (A point) pressure fluctuation is detected and preserves it
Detection data.
The cavitation model of step (6-2) and turbulence model set, the numerical computations parameter setting of step (6-3) and (6-4)
Numerical computations set with the cavitation model of step 2 and turbulence model respectively, the numerical computations parameter setting of step 3 and step 4
Numerical computations identical.
Further, the alternative grid in described step 1 adopts subregion mixed mesh method：Propeller week
Enclosing flow field regions adopts unstrctured grid method to divide, and grid is gradually reduced to blade tip by propeller hub, and at blade tip, surface grids are triangle
Shape, grid cell length of side size is about 0.001D, and at oar, unit is about 0.02D；Because cavitation is mainly distributed on blade face and the tip
Whirlpool region, therefore this area grid prescription are higher.In order to better adapt to Wall-function, set up boundary region in leaf surface
Grid；Divide the computational fields of propeller periphery regular shape using structured grid；Grid cell is generated based on said method simultaneously
Number three computational fields grid alternately grids of difference；Tip whirlpool area grid is encrypted, blade surface adopts border simultaneously
Layer grid is about 0.001D to improve the forecast precision to tip vortex cavitation, tip whirlpool area grid unit size, and body fitted anisotropic mesh is common
There are 4 layers, its adjacent two layers aspect ratio is 1.1, ground floor grid cell highly about 0.001D is so that dimensionless group 20<y+<
300.In addition, the grid cell quantity difference of three alternative grids is mainly reflected in screw current in its card inner region, special
It is not proximate to propeller near zone.Because this area grid quality calculates to propeller cavitation performance and wake pressure pulsation
Accuracy most important.
Further, the full-cavitation model in described step 2 set and its parameters revision as：
Work as p<pvWhen, steam production is：
Work as p>pvWhen, vapour phase becomes liquid phase, is similarly obtained steam coagulation rate Rc：
Wherein, fv=αvρv/ρmFor vapour phase mass fraction, vaporize coefficient Ce=0.02 and condensation coefficient Cc=0.01 be through
Test parameter.
According to dimensional analysis in transformation ratio (Re and Rc) expression formula adopt k rather thanUnder FLUENT software environment
Available self-defining function UDF is to parameter transformation ratio R in cavitation modeleIt is modified, correction model is adopted and adjusted after showing a C language
Enter calculation procedure.
Further, the RNG k- ε turbulence model in described step 2 and its parameters revision are：Renormalization Group turbulent flow
Model is RNG k- ε turbulence model, and Renormalization Group turbulence model RNG k- ε is to transient state N-S equation Renormalization Group
The model that the mathematical method of (Renormalization Group, abbreviation RNG) is derived.It passes through in Large Scale Motion item
Embody the impact of little yardstick with revising in viscosity item, and remove from governing equation with making these little yardstick motor systems.Its k side
Journey and ε equation are respectively：
In formula, dissipation turbulent kinetic energy (Turbulent Dissipation Rate)Rapid
The a reciprocal of effective turbulent prandtl number of energy of flow k and dissipative shock wave εk=aε=1.39；Model parameter C1ε=1.47, C2ε=
1.68；Viscosity coefficient is μ=μt+μm, μmFor mixed flow coefficient of viscosity；Modification turbulent viscosity coefficient μt=[ρv+αl 10(ρl-ρv)]Cμ
k2/ ε, Cμ=0.085 unsteady two-phase simulated flow being more suitable for high reynolds number, such that it is able to more preferable simulation propeller cavitation.
Further, the numerical computations parameter setting of described step 3, including working condition, boundary condition and numerical value
The relevant parameter of algorithm sets；
Working condition mainly sets propeller rotary speed, ambient pressure and inflow velocity value, determines propeller dimensionless
Parameter, i.e. advance coefficient (J) and cavitation number (σn)；Boundary condition is set, speed entrance boundary adopts inflow velocity value, far
Field boundary condition adopts inflow velocity to set, and the outlet pressure of downstream pressure exit interface is set to static pressure；In numerical algorithm
Parameter setting：In governing equation, convective term adopts Second-order Up-wind form discrete, and diffusion term adopts Second-Order Central Difference form discrete,
Velocity pressure coupling adopts the SIMPLE algorithm being suitable for unstrctured grid, using the discrete side of pointwise Gauss-Seidel iterative
Journey；Using the convergence of algebraic multigrid speed-up computation, Sliding mesh computing technique is adopted for unsteady computation, improve calculating
Accuracy.Using second order accuracy discrete scheme, in order to ensure the stability that second order calculates, under-relaxation factor is suitably reduced, pressure
Power, momentum, vapour phase fraction, Turbulent Kinetic, turbulence dissipation rate and the isoparametric under-relaxation factor of turbulent flow stickiness are respectively set as：
0.25th, 0.6,0.2,0.7,0.7,0.9, conservation of mass seriality (continuity) residual error convergence is three ranks, in equation
Other physical quantity residual error convergence are quadravalence.
Further, the ad-hoc location A point of described step 6 is located at screw current radial direction r=0.5R and axial x=
At 2R, according to dimension conversion principle, using formulaCarry out nondimensionalization, wherein Δ P is numerical result
Total pressure pulsation value, ρ be fluid-mixing density, n be revolution speed of propeller, D position airscrew diameter；TIME in unsteady computation
STEP time step is set as T=0.0125TP, TPFor propeller swing circle, it is 30T that data is related to time spanP.
The method of the present invention can be realized in typically general CFD fluid calculation software (CFX, FLUENT etc.), grid
Division can adopt the softwares such as GAMBIT to realize.First the propeller mathematical model of Preliminary design is imported stress and strain model software,
And carry out stress and strain model according to the method in the present invention.Grid model forms the numerical example file after importing calculating platform, and
In example file, numerical parameter is set, carries out numerical computations according to design conditions, and pressure fluctuation signal is exported text literary composition
Part.Write power spectral density conversion program in MATLAB software, realize the conversion by time domain to frequency domain for the signal, and finally extract
Low frequency spectrum lines magnitude parameters.Additionally, the present invention also carries out parallel numerical calculating using autoexec in operating system platform.
Beneficial effect：
(1) correlational study achievement in modern fluid mechanics, Bubble dynamics and field of signal processing is introduced water by the present invention
The noise characteristic analysis of lower target, embodies multidisciplinary and multi-field intercrossing.
(2) at present due to the impact of strong jamming background noise and complicated underwater acoustic channel, only extract target from detection noise
Feature is difficult to meet the requirement of Underwater Targets Recognition technology, and the therefore present invention has important application valency in Acoustic Object identification field
Value and application prospect.
(3) because tail flow field is subject to the effect of propeller blade rotation beat and radiated noise to be similarly subjected to leaf frequency modulation system
Effect, in tail flow field, pressure fluctuation is closely related with the feature of sound pressure signal slowly varying component, the power spectral density low frequency wire of the two
The features such as spectrum amplitude Distribution value have dependency, and the present invention is exactly related to flow noise characteristic parameter using Field Characteristics parameter
Property to carry out feature assessment to Propeller Cavitation Noise.
(4) correction to cavitation model and turbulence model relevant parameter for the inventive method, set up blade surface boundary region and
In tip vortex cavitation region, fine processing is carried out to grid.By with experiment compare (see Fig. 5), these corrective measures significantly improve
The forecast precision of tip vortex cavitation, preferably solves one of a cavitation numerical forecast difficult problem.It is next step cavitation wake flow simultaneously
The numerical computations of pressure fluctuation provide strong guarantee.
(5) the unsteady numerical computations of cavitation wake flow to propeller E779A and E779B using the grid determining, and extract
Its wake pressure fluctuating signal, then the distribution characteristicss that power spectral transformation obtains low frequency spectrum lines amplitude are carried out to pressure fluctuation, by this
The low frequency spectrum lines distribution of one distribution and actual measurement noise is compared (see Fig. 6 and Fig. 7), demonstrates the feature correlation of the two.?
Just noise characteristic under other working conditions can be estimated by pressure fluctuation numerical computations using this feature correlation eventually
Meter, thus provide important directivity to be worth to carrying out Underwater Target Classification technology of identification with propeller noise feature.
Brief description
Fig. 1 (a) is E779A propeller geometric model；
Fig. 1 (b) is E779B propeller geometric model；
Fig. 2 is the schematic diagram of the full runner propeller computational fields hybrid grid of the present invention；
Fig. 3 (a) is the structural representation of the body fitted anisotropic mesh of the present invention；
Fig. 3 (b) is the enlarged drawing at A in Fig. 3 (a)；
Fig. 4 (a) is method of the present invention flow chart；
Fig. 4 (b) is the cavitation wake pressure unsteady numerical computations flow chart of pulsation of the present invention；
Fig. 5 is E779A oar cavitation numerical value forecast result and experimental result；
Fig. 6 be nonlinear inflow under the conditions of rotate counterclockwise E779B oar the diverse location moment cavitation numerical forecast
Result and experimental result；
Fig. 7 is E779A propeller normalized measurement noise power spectrum and pressure fluctuation numerical computations power spectrum signal；
Fig. 8 (a) is the E779B propeller cavitation wake pressure pulsation of n=15rps and the normalization work(of measurement noise signal
Rate spectrum density low frequency spectrum lines amplitude；
Fig. 8 (b) is the E779B propeller cavitation wake pressure pulsation of n=20rps and the normalization work(of measurement noise signal
Rate spectrum density low frequency spectrum lines amplitude；
Fig. 8 (c) is the E779B propeller cavitation wake pressure pulsation of n=25rps and the normalization work(of measurement noise signal
Rate spectrum density low frequency spectrum lines amplitude.
Specific embodiment
The present invention is further detailed explanation with reference to the accompanying drawings and examples.
Embodiment
As described in Fig. 4 (a), based on the cavitation noise feature assessment method of screw current pressure fluctuation calculating, concrete bag
Include following steps：
(1) standby mess generation generate example file after importing calculation procedure：
Made using professional software and after propeller 3-D geometric model, import Grid Generation Software, such as Fig. 1 (a) Fig. 1
Shown in (b), it is E778A and E779B model propeller, stress and strain model software is set up three kinds of alternative grids, these three are alternative
The computational fields of grid are identical, and speed becomes a mandarin frontier distance propeller center for 1D, and D is airscrew diameter, and downstream pressure exports side
Boundary's distance is 5D, and propeller center to side periphery distance is 2.5D, the grid cell quantity of these three grids according toTimes
Number is gradually increased, to the grid cell size of adjacent boundary in grid in the reasonable transition of boundary point so as to size of mesh opening difference relatively
Little, within finally making the skew of all volume mesh units in grid be defined in 0.9, steady with guarantee numerical computations below
Qualitative；
Grid adopts subregion mixed mesh method (as shown in Figure 2)：Around propeller, flow field regions adopt non-knot
Structure grid method divides, and grid is gradually reduced to blade tip by propeller hub, and at blade tip, surface grids are triangle, grid cell length of side size
It is about 0.001D, at oar, unit is about 0.02D；Because cavitation is mainly distributed on blade face and tip whirlpool region, therefore this region
Mesh quality requires higher.In order to better adapt to Wall-function, set up body fitted anisotropic mesh (as shown in Figure 3) in blade surface；
Divide the computational fields of propeller periphery regular shape using structured grid；Number of meshes is generated based on said method simultaneously different
Three computational fields grid alternately grids；Tip whirlpool area grid is encrypted, blade surface adopts body fitted anisotropic mesh simultaneously
To improve the forecast precision to tip vortex cavitation, tip whirlpool area grid unit size is about 0.001D, and body fitted anisotropic mesh has 4 layers,
Its adjacent two layers aspect ratio is 1.1, and ground floor grid cell highly about 0.001D is so that dimensionless group 20<y+<300.Separately
Outward, the grid cell quantity difference of three alternative grids is mainly reflected in screw current in its card inner region, particularly leans on
Nearly propeller near zone.Because it is accurate that this area grid quality calculates to propeller cavitation performance and wake pressure pulsation
Property is most important.
(2) cavitation model and turbulence model set：
Using full-cavitation model and Renormalization Group turbulence model, and its important parameter is modified, in cavitation model
In the correction of transformation ratio parameter and turbulence model, the correction of turbulent viscosity coefficient is adopted and is shown a C language, and recycles macro-call
(DEFINE_TURBULENT_VISCOSITY etc.) form embeds calculation procedure；
Full-cavitation model set and its parameters revision as：
Work as p<pvWhen, steam production is：
Work as p>pvWhen, vapour phase becomes liquid phase, is similarly obtained steam coagulation rate Rc：
Wherein, fv=αvρv/ρmFor vapour phase mass fraction, vaporize coefficient Ce=0.02 and condensation coefficient Cc=0.01 be through
Test parameter；
According to dimensional analysis in transformation ratio (Re and Rc) expression formula adopt k rather thanUnder FLUENT software environment
Available self-defining function UDF is to parameter transformation ratio R in cavitation modeleIt is modified, correction model is adopted and adjusted after showing a C language
Enter calculation procedure.
Renormalization Group turbulence model is RNG k- ε turbulence model, and Renormalization Group turbulence model RNG k- ε is to transient state N-
The model that S equation is derived with the mathematical method of Renormalization Group (Renormalization Group, abbreviation RNG).It passes through
The impact of little yardstick is embodied in Large Scale Motion item and correction viscosity item, and with making these little yardstick motor systems from controlling party
Remove in journey.Its k equation and ε equation are respectively：
In formula, dissipation turbulent kinetic energy (Turbulent Dissipation Rate)Rapid
The a reciprocal of effective turbulent prandtl number of energy of flow k and dissipative shock wave εk=aε=1.39；Model parameter C1ε=1.47, C2ε=
1.68；Viscosity coefficient is μ=μt+μm, μmFor mixed flow coefficient of viscosity；Modification turbulent viscosity coefficient μt=[ρv+αl 10(ρl-ρv)]Cμ
k2/ ε, Cμ=0.085 unsteady two-phase simulated flow being more suitable for high reynolds number, such that it is able to more preferable simulation propeller cavitation.
(3) numerical computations parameter setting：
The relevant parameter of working condition, boundary condition and numerical algorithm is set；Working condition mainly sets spiral
Oar rotary speed, ambient pressure and inflow velocity value, determine propeller dimensionless group, i.e. advance coefficient (J) and cavitation number
(σn)；Boundary condition is set, speed entrance boundary adopts inflow velocity value, far field boundary condition is set using inflow velocity
Fixed, the outlet pressure of downstream pressure exit interface is set to static pressure；Parameter setting in numerical algorithm：Convective term in governing equation
Discrete using Second-order Up-wind form, diffusion term adopts Second-Order Central Difference form discrete, and velocity pressure coupling is using suitable non-knot
The SIMPLE algorithm of network forming lattice, using pointwise Gauss-Seidel iterative discrete equation；Accelerated using algebraic multigrid
Calculate convergence, Sliding mesh computing technique is adopted for unsteady computation, improve the accuracy calculating.Discrete using second order accuracy
Form, in order to ensure the stability that second order calculates, under-relaxation factor is suitably reduced, and pressure, momentum, vapour phase fraction, turbulent flow are moved
Energy, turbulence dissipation rate and the isoparametric under-relaxation factor of turbulent flow stickiness are respectively set as：0.25th, 0.6,0.2,0.7,0.7,0.9,
Conservation of mass seriality (continuity) residual error convergence is three ranks, and in equation, other physical quantity residual error convergence are four
Rank.
(4) numerical computations：
Because cavitation model adds after RANS equation, the stability of calculating reduces, and unusual appearance easily.Therefore, it is
Numerical computations can be made steadily to carry out, using calculating process step by step step by step, specifically, in propeller duty parameter,
Ambient pressure and inflow velocity can directly be set to operating mode value, and revolution speed of propeller is increased using classification, pre- until increasing to
Determine operating mode value；First calculate non-cavitating model Flow Field Distribution, after calculating is stable, opens cavitation model again；First to pressure, density,
The parameter such as momentum and vapour phase fraction carries out single order precision discrete scheme and calculates, and calculates after stablizing, more discrete precision is brought up to two
Rank or QUCIK etc., because the calculating of multiphase flow model, cavitation model and Sliding mesh is larger to computer resource usage, therefore adopt
Shorten the calculating time with parallel computing.
(5) numerical method reliability demonstration and grid determine：
By numerical result and the related experiment result to the hydrodynamic parameter of propeller oar and cavitation under typical condition
It is compared, to verify the reliability of grid independence and adopted numerical method；By built in step 1 three kinds of alternative grids by
Set according to step 2 and 3 methods and carried out numerical computations, and hydrodynamic parameter in result of calculation and cavitation are compared,
When these results tend towards stability with the increase of number of grid and be consistent with experimental result, then select and meet grid in condition
The minimum grid of element number is as the selected grid of following numerical computations；Otherwise suitably increase number of grid, repeat step 1 weight
Newly start, Fig. 5 is E779A oar cavitation numerical value forecast result and experimental result, Fig. 6 is rotate counterclockwise under the conditions of nonlinear inflow
E779B oar in the cavitation numerical value forecast result of diverse location and experimental result, the result of Fig. 5 and Fig. 6 shows to make in the present invention
Method is relatively good to the value of forecasting of propeller cavitation；
(6) the unsteady numerical computations of cavitation wake pressure pulsation：
Using the selected grid in step 5, unsteady numerical value is carried out under required working condition to the tail flow field of propeller
Calculate, in calculation procedure to tail flow field in the pressure fluctuation of a certain ad-hoc location (A point) detect and preserve its detection data, simultaneously
Nondimensionalization is carried out to data；
Ad-hoc location A point is located at screw current radial direction r=0.5R and axial x=2R, according to dimension conversion principle, adopts
Use formulaCarry out nondimensionalization, wherein Δ P is the total pressure pulsation value of numerical result, ρ is mixed flow
Body density, n is revolution speed of propeller, D position airscrew diameter；In unsteady computation, TIME STEP time step is set as T=
0.0125TP, TPFor propeller swing circle, it is 30T that data is related to time spanP.
(7) pressure fluctuation signal power spectral transformation and low frequency spectrum lines magnitude extraction：
Using the physical quantitys such as pressure fluctuation in fast fourier transform method stream field in signal processing and noise signal number
According to carrying out power spectral transformation, and low frequency spectrum lines (axle frequency, two times of axles frequencies, three times axle frequency and leaf frequency) amplitude is extracted；Profit again
With tail flow field pressure fluctuation characteristic with cavitation noise by the similarity of blade modulation signature, set up the low frequency spectrum lines from pressure fluctuation
Amplitude is to the feature corresponding relation of the low frequency spectrum lines amplitude of noise；
(8) line spectrum feature is estimated and is analyzed：
Step 7 medium and low frequency line spectrum amplitude is corresponded the low frequency spectrum lines amplitude of noise power spectrum, as to sky
Change the estimation of noise signal low frequency spectrum lines characteristics of amplitude distribution.In Fig. 7, (a) and (b) is operating mode under E779A propeller uniform inflow
For J=0.88, the normalized power spectral density of the pulsation of n=25rps cavitation wake pressure and cavitation noise.By Fig. 7 (a) and
B the contrast of () finds that pressure fluctuation signal normalized power spectral density has some common with measurement noise pressure signal power spectrum
Feature：(1) in 10-100Hz low-band signal based on line spectrum and consistent with propeller blade number and rotary speed parameter value.Wherein,
Leaf frequency line spectrum (100Hz) peak value highest, axle frequency line spectrum (25Hz) peak value takes second place, followed by 75Hz and 50Hz line spectrum.Except 75Hz
Signal intensity is relatively low, other essentially identical with the Noise line spectra peak change feature in Fig. 7 (a).(2) in 100-1000Hz
Frequency range is same to show abundant line spectrum feature, and line spectrum is based on the frequency multiplication of axle frequency and leaf frequency, and continuous spectrum spectral line also begins to decline,
These are basically identical with the spectrum deformationization in Fig. 7 (a).But the line spectrum amplitude of this frequency field is compared with Fig. 7 (a) in numerical result
Experiment value is less, and frequency resolution is relatively low.(3) power spectrum amplitude range is 100To 10-9Between, with the experiment in Fig. 7 (a)
Data is basically identical.In the above, common trait shows E779A oar mould under the conditions of uniform inflow, pressure fluctuation and noise it
Between there is similar feature, that is, they have feature correlation.
Fig. 8 (a), (b) and (c) are the pulsation of cavitation wake pressure and measurement noise letter under E779B propeller nonlinear inflow
Number normalized power spectral density low frequency spectrum lines amplitude contrast.Fig. 7 show the low frequency spectrum lines amplitude of pressure fluctuation and noise 15,
During 20 and 25rps rotating speed, the axle frequency of the two, two times of axle frequencies and three times axle frequency amplitude variation tendency are essentially identical.And leaf frequency exists
During 15rps rotating speed, the two difference is maximum, and both during 20rps rotating speed, difference reduces, essentially identical during 25rps rotating speed.This shows spiral shell
Rotation oar rotating speed is 25rps, and cavitation substantially occurs, and cavitation noise becomes main noise, now the two leaf frequency amplitude Characteristics dependency
Strengthen.And when rotating speed is relatively low (15rps), non-cavitating occurs, now environment noise becomes main sound source, and therefore the two leaf frequency is special
Levy dependency to weaken.This shows to occur when cavitation, and when cavitation noise becomes propeller main noise, the accuracy of this method obtains
Significantly improve.
As shown in Fig. 4 (b), the cavitation wake pressure pulsation unsteady computation in described step 6 comprises the following steps：
(6-1) the mess generation example file selected in steps for importing 5；
(6-2) cavitation model and turbulence model set；
(6-3) numerical computations parameter setting；
(6-4) numerical computations；
(6-5) pressure fluctuation signal is extracted：Ad-hoc location a certain in tail flow field (A point) pressure fluctuation is detected and preserves it
Detection data.
The cavitation model of step (6-2) and turbulence model set, the numerical computations parameter setting of step (6-3) and (6-4)
Numerical computations set with the cavitation model of step 2 and turbulence model respectively, the numerical computations parameter setting of step 3 and step 4
Numerical computations identical.
The method of the present invention can be realized in typically general CFD fluid calculation software (CFX, FLUENT etc.), grid
Division can adopt the softwares such as GAMBIT to realize.First the propeller mathematical model of Preliminary design is imported stress and strain model software,
And carry out stress and strain model according to the method in the present invention.Grid model forms the numerical example file after importing calculating platform, and
In example file, numerical parameter is set, carries out numerical computations according to design conditions, and pressure fluctuation signal is exported text literary composition
Part.Write power spectral density conversion program in MATLAB software, realize the conversion by time domain to frequency domain for the signal, and finally extract
Low frequency spectrum lines magnitude parameters.Additionally, the present invention also carries out parallel numerical calculating using autoexec in operating system platform.
Claims (9)
1. based on screw current pressure fluctuation calculate cavitation noise feature assessment method it is characterised in that：Walk including following
Suddenly：
(1) standby mess generation generate example file after importing calculation procedure：
Made using professional software and import Grid Generation Software after propeller 3-D geometric model, in stress and strain model software
Set up three kinds of alternative grids, the computational fields of these three alternative grids are identical, speed becomes a mandarin frontier distance propeller center for 1D, D
For airscrew diameter, downstream pressure outlet border distance is 5D, and propeller center to side periphery distance is 2.5D, these three nets
The grid cell quantity of lattice according toMultiple is gradually increased, and the grid cell size of adjacent boundary in grid is closed in boundary point
Reason transition is so that within grid, the skew of all volume mesh units is defined in 0.9；
(2) cavitation model and turbulence model set：
Using full-cavitation model and Renormalization Group turbulence model, and its important parameter is modified；
(3) numerical computations parameter setting：
The relevant parameter of working condition, boundary condition and numerical algorithm is set；
(4) numerical computations：
Using calculating process step by step step by step, in propeller duty parameter, ambient pressure and inflow velocity can directly set
Surely arrive operating mode value, and revolution speed of propeller is increased using classification, until increasing to predetermined operating mode value；First calculate non-cavitating model flow field
Distribution, opens cavitation model after calculating is stable again；First pressure, density, momentum and vapour phase fraction parameter are carried out with single order essence
Degree discrete scheme calculates, and calculates after stablizing, more discrete precision is brought up to second order or QUCIK, and is come using parallel computing
Calculated；
(5) numerical method reliability demonstration and grid determine：
Under typical condition, the hydrodynamic parameter of propeller oar and the numerical result of cavitation will be carried out with related experiment result
Relatively, to verify the reliability of grid independence and adopted numerical method；And in logarithm value result of calculation hydrodynamic parameter and
Cavitation is compared, and when result tends towards stability with the increase of number of grid and be consistent with experimental result, then selectes and meets
In condition, the minimum grid of grid cell quantity is as the selected grid of following numerical computations；Otherwise suitably increase number of grid,
Repeat step 1 restarts；
(6) the unsteady numerical computations of cavitation wake pressure pulsation：
Using the selected grid in step 5, under required working condition, unsteady numerical computations are carried out to the tail flow field of propeller,
In calculation procedure to tail flow field in a certain ad-hoc location A point pressure pulsation detection preserve its detection data, simultaneously to data
Carry out nondimensionalization；
(7) pressure fluctuation signal power spectral transformation and low frequency spectrum lines magnitude extraction：
Carried out using pressure fluctuation physical quantity in fast fourier transform method stream field in signal processing and noise signal data
Power spectral transformation, and low frequency spectrum lines amplitude is extracted；Recycle tail flow field pressure fluctuation characteristic and cavitation noise by blade
The similarity of modulation signature, sets up the corresponding pass of feature of the low frequency spectrum lines amplitude from the low frequency spectrum lines amplitude of pressure fluctuation to noise
System；
(8) line spectrum feature is estimated and is analyzed：
Step 7 medium and low frequency line spectrum amplitude is corresponded the low frequency spectrum lines amplitude of noise power spectrum, make an uproar as to cavitation
The estimation of acoustical signal low frequency spectrum lines characteristics of amplitude distribution.
2. the cavitation noise feature assessment method being calculated based on screw current pressure fluctuation as claimed in claim 1, it is special
Levy and be：Cavitation wake pressure pulsation unsteady computation in described step 6 comprises the following steps：
(6-1) the selected mess generation example file in steps for importing 5；
(6-2) cavitation model and turbulence model set；
(6-3) numerical computations parameter setting；
(6-4) numerical computations；
(6-5) pressure fluctuation signal is extracted：To ad-hoc location A point pressure pulsation detection a certain in tail flow field and preserve its detect number
According to.
3. the cavitation noise feature assessment method being calculated based on screw current pressure fluctuation as claimed in claim 1, it is special
Levy and be：Alternative grid in described step 1 adopts subregion mixed mesh method：
Around propeller, flow field regions adopt unstrctured grid method to divide, and grid is gradually reduced to blade tip by propeller hub, at blade tip
Surface grids are triangle, and grid cell length of side size is 0.001D, and at oar, unit is 0.02D；Set up border in blade surface
Layer grid；Divide the computational fields of propeller periphery regular shape using structured grid；Tip whirlpool area grid is encrypted, simultaneously
Blade surface adopts the body fitted anisotropic mesh to improve the forecast precision to tip vortex cavitation, tip whirlpool area grid unit size to be
0.001D, body fitted anisotropic mesh has 4 layers, and its adjacent two layers aspect ratio is 1.1, and ground floor grid cell height is 0.001D, makes
Obtain dimensionless group 20<y+<300.
4. the cavitation noise feature assessment method being calculated based on screw current pressure fluctuation as claimed in claim 1, it is special
Levy and be：Full-cavitation model in described step 2 set and its parameters revision as：
Work as p<pvWhen, steam production is：
Work as p>pvWhen, vapour phase becomes liquid phase, is similarly obtained steam coagulation rate Rc：
Wherein, fv=αvρv/ρmFor vapour phase mass fraction, vaporize coefficient Ce=0.02 and condensation coefficient Cc=0.01 and join for experience
Number.
5. the cavitation noise feature assessment method being calculated based on screw current pressure fluctuation as claimed in claim 1, it is special
Levy and be：RNG k- ε turbulence model in described step 2 and its parameters revision are：Renormalization Group turbulence model is RNG
K- ε turbulence model, the k equation of RNG k- ε turbulence model and ε equation are respectively：
In formula, dissipation turbulent kinetic energy (Turbulent Dissipation Rate)Turbulent Kinetic
The a reciprocal of effective turbulent prandtl number of k and dissipative shock wave εk=aε=1.39；Model parameter C1ε=1.47, C2ε=1.68；Glutinous
Property coefficient is μ=μt+μm, μmFor mixed flow coefficient of viscosity；Modification turbulent viscosity coefficient μt=[ρv+αl 10(ρl-ρv)]Cμk2/ ε, Cμ
=0.085.
6. the cavitation noise feature assessment method being calculated based on screw current pressure fluctuation as claimed in claim 1, it is special
Levy and be：The numerical computations parameter setting of described step 3, including the correlation ginseng of working condition, boundary condition and numerical algorithm
Number sets；
Working condition sets propeller rotary speed, ambient pressure and inflow velocity value, determines propeller dimensionless group, that is, enters
Fast coefficient (J) and cavitation number (σn)；Boundary condition is set, speed entrance boundary adopts inflow velocity value, far field boundary bar
Part adopts inflow velocity to set, and the outlet pressure of downstream pressure exit interface is set to static pressure；Parameter setting in numerical algorithm：
Receive dimension Stokes (N-S) equation in convective term adopt Second-order Up-wind form discrete, diffusion term adopt Second-Order Central Difference lattice
Formula is discrete, and velocity pressure coupling, using the SIMPLE algorithm being suitable for unstrctured grid, is asked using pointwise Gauss-Seidel iteration
Solution discrete equation；Using the convergence of algebraic multigrid speed-up computation, Sliding mesh computing technique is adopted for unsteady computation, adopts
Use second order accuracy discrete scheme, in order to ensure the stability that second order calculates, under-relaxation factor is suitably reduced, pressure, momentum, vapour
Phase fraction, the under-relaxation factor of Turbulent Kinetic, turbulence dissipation rate and turbulent flow stickiness parameter are respectively set as：0.25、0.6、0.2、
0.7th, 0.7,0.9, conservation of mass seriality (continuity) residual error convergence is three ranks, other physical quantity residual errors in equation
Convergence is quadravalence.
7. the cavitation noise feature assessment method being calculated based on screw current pressure fluctuation as claimed in claim 1, it is special
Levy and be：The ad-hoc location A point of described step 6 is located at screw current radial direction r=0.5R and axial x=2R, according to amount
Guiding principle conversion principle, using formulaCarry out nondimensionalization, wherein Δ P is the total pressure pulsation of numerical result
Value, ρ is fluid-mixing density, and n is revolution speed of propeller, D position airscrew diameter；TIME STEP time step in unsteady computation
It is set as T=0.0125TP, TPFor propeller swing circle, it is 30T that data is related to time spanP.
8. the cavitation noise feature assessment method being calculated based on screw current pressure fluctuation as claimed in claim 1, it is special
Levy and be：The low frequency spectrum lines in power spectral transformation in described step 7 include axle frequency, two times of axle frequencies, three times axle frequency and leaf frequency.
9. the cavitation noise feature assessment side being calculated based on screw current pressure fluctuation as described in claim 1 or 4 or 5
Method it is characterised in that：Correction to turbulent viscosity coefficient in the correction of transformation ratio parameter in cavitation model and turbulence model adopts C
Language is write, and recycles macro-call DEFINE_TURBULENT_VISCOSITY form to embed calculation procedure.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410345592.0A CN104091085B (en) | 2014-07-18 | 2014-07-18 | The cavitation noise feature assessment method being calculated based on screw current pressure fluctuation |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410345592.0A CN104091085B (en) | 2014-07-18 | 2014-07-18 | The cavitation noise feature assessment method being calculated based on screw current pressure fluctuation |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN104091085A CN104091085A (en) | 2014-10-08 |
| CN104091085B true CN104091085B (en) | 2017-03-08 |
Family
ID=51638801
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201410345592.0A Active CN104091085B (en) | 2014-07-18 | 2014-07-18 | The cavitation noise feature assessment method being calculated based on screw current pressure fluctuation |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104091085B (en) |
Families Citing this family (24)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104834829B (en) * | 2015-06-02 | 2017-10-13 | 中国航天空气动力技术研究院 | Fluctuation pressure Numerical Predicting Method |
| CN106207719B (en) * | 2016-09-08 | 2022-03-29 | 成都微深科技有限公司 | Structural laser with support cylinder |
| CN106934134B (en) * | 2017-03-01 | 2019-04-26 | 兰州理工大学 | A method of the unsteady intensity of pump cavitation is centrifuged for characterizing |
| CN107203656A (en) * | 2017-04-19 | 2017-09-26 | 西安电子科技大学 | A kind of potential flows analogy method based on quadravalence Compact Difference Scheme |
| CN107444673B (en) * | 2017-07-12 | 2019-12-03 | 南京航空航天大学 | Noise reduction test platform is controlled based on being synchronised for small propeller fixed-wing unmanned plane |
| CN109285561B (en) * | 2018-09-06 | 2022-08-19 | 东南大学 | Ship propeller cavitation noise modulation spectrum feature fidelity enhancement method based on self-adaptive window length |
| CN109992903B (en) * | 2019-04-08 | 2022-11-29 | 中国核动力研究设计院 | Method for calculating pump-induced pulsating pressure load acting on heat transfer tube of steam generator |
| CN110304226B (en) * | 2019-07-02 | 2021-05-28 | 中国人民解放军海军工程大学 | Method for reducing hydrodynamic noise of propeller by introducing jet flow in front of propeller |
| CN110489902B (en) * | 2019-08-26 | 2022-07-29 | 安徽工业大学 | Multi-element statistical modeling method for fine characteristics of cavitation wake flow of propeller |
| CN110738115B (en) * | 2019-09-12 | 2022-11-15 | 浙江大学 | Propeller cavitation degree identification method based on pulse frequency characteristic pattern identification |
| CN110765867A (en) * | 2019-09-18 | 2020-02-07 | 山东建筑大学 | Method for extracting low-frequency characteristics of radiation noise of propeller |
| CN110683014B (en) * | 2019-10-29 | 2022-04-01 | 中国船舶工业集团公司第七0八研究所 | Excitation load loading method of water jet propeller |
| CN111160207B (en) * | 2019-12-24 | 2023-08-15 | 浙江大学 | Paddle number feature extraction method based on radiation noise modulation |
| CN113139306A (en) * | 2020-01-17 | 2021-07-20 | 哈尔滨工业大学 | Numerical prediction method for cavitation noise of composite propeller |
| CN111597602B (en) * | 2020-03-26 | 2022-11-29 | 哈尔滨工业大学（深圳） | High-rise building structure efficient analysis method based on AMGPCG algorithm |
| CN111751105B (en) * | 2020-04-28 | 2022-08-05 | 浙江工业大学 | Regulating valve cavitation diagnosis method based on vibration data power spectrum |
| CN112067283A (en) * | 2020-09-16 | 2020-12-11 | 浙江工业大学 | Regulating valve cavitation diagnosis system based on sound power spectrum and diagnosis method thereof |
| CN112230205B (en) * | 2020-10-16 | 2023-07-07 | 哈尔滨工程大学 | Performance evaluation method of underwater target recognition system by utilizing ship radiation noise simulation signals |
| CN112284681B (en) * | 2020-10-20 | 2022-10-11 | 中国船舶工业集团公司第七0八研究所 | Method for simulating wake field for cavitation water drum paddle mold test |
| CN113392601A (en) * | 2021-07-12 | 2021-09-14 | 东南大学 | Geometric feature analysis method of hydrodynamic noise line spectrum of cylinder-like shell |
| CN113643679B (en) * | 2021-10-14 | 2021-12-31 | 中国空气动力研究与发展中心低速空气动力研究所 | Rotor wing and tail rotor aerodynamic noise separation method based on cascade filter |
| CN114169203B (en) * | 2021-12-09 | 2024-01-16 | 西安交通大学 | Two-phase flow total hidden numerical method for transient safety analysis of nuclear power plant |
| CN114611214B (en) * | 2022-03-15 | 2024-05-07 | 中国舰船研究设计中心 | Open water performance simulation method based on propeller efficient modeling |
| CN114879718B (en) * | 2022-07-12 | 2022-09-13 | 南京理工大学 | Method for controlling an aircraft with a grid rudder |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20120080835A (en) * | 2011-01-10 | 2012-07-18 | 서울대학교산학협력단 | The prediction method of propeller-induced hull pressure fluctuation using the noise source inversion |
-
2014
- 2014-07-18 CN CN201410345592.0A patent/CN104091085B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20120080835A (en) * | 2011-01-10 | 2012-07-18 | 서울대학교산학협력단 | The prediction method of propeller-induced hull pressure fluctuation using the noise source inversion |
Non-Patent Citations (3)
| Title |
|---|
| 基于非结构网格RAND方法螺旋浆空化研究;朱志峰 等;《海洋工程》;20091130;第27卷(第4期);第103-120页 * |
| 船舶螺旋桨辐射噪声与尾流场的相关性及特征分析方法;方世良 等;《声学技术》;20120229;第31卷(第1期);第37-41页 * |
| 船舶螺旋桨黏性空化流场数值方法;朱志峰 等;《东南大学学报》;20101130;第40卷(第6期);第1146-1151页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN104091085A (en) | 2014-10-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104091085B (en) | The cavitation noise feature assessment method being calculated based on screw current pressure fluctuation | |
| Liu et al. | Data-driven modal decomposition of transient cavitating flow | |
| Constantinescu et al. | Numerical investigations of flow over a sphere in the subcritical and supercritical regimes | |
| Hsiao et al. | Scaling of tip vortex cavitation inception noise with a bubble dynamics model accounting for nuclei size distribution | |
| Li et al. | Assessment of cavitation erosion with a URANS method | |
| Cao et al. | RANS-VOF solver for solitary wave run-up on a circular cylinder | |
| Posa et al. | Hydroacoustic analysis of a marine propeller using large-eddy simulation and acoustic analogy | |
| Wang et al. | LES investigation of cavitation harmonic tone around a Delft twist-11 hydrofoil | |
| Hu et al. | Tip vortex prediction for contra-rotating propeller using large eddy simulation | |
| Sun et al. | Numerical investigation of the effects of free surface on tip-leakage vortex cavitation behaviors over a NACA0009 hydrofoil | |
| Alfonsi et al. | Large-eddy simulation of turbulent natural-bed flow | |
| Yin et al. | Numerical investigation of unsteady cavitation around a twisted hydrofoil | |
| Posa et al. | Momentum recovery downstream of an axial-flow hydrokinetic turbine | |
| Wang et al. | Modal analysis of tip vortex cavitation with an insight on how vortex roll-up enhances its instability | |
| CN110489902A (en) | A kind of propeller cavitation wake flow fine-feature multivariate statistics modeling method | |
| Zhu | Characteristic correlation between propellers cavitating wake and cavitation noise | |
| Shi et al. | Broadband force spectra of a pump-jet propulsor subjected to inflow turbulence: Comparison with ducted propeller and propeller | |
| Zhao et al. | Wavelet-based multiresolution analysis of cavitation-induced loading instabilities under phase effect in a waterjet pump | |
| Posa | Anisotropy of turbulence at the core of the tip and hub vortices shed by a marine propeller | |
| Wang et al. | The dynamic characteristics in the wake systems of a propeller operating under different loading conditions | |
| Yu et al. | Analysis of dipole noise level characteristics of NACA0015 hydrofoil under different working conditions | |
| Yang et al. | Scale effects on propeller cavitating hydrodynamic and hydroacoustic performances with non-uniform inflow | |
| Klapwijk et al. | Exciting a cavitating tip vortex with synthetic inflow turbulence: A CFD analysis of vortex kinematics, dynamics and sound generation | |
| Du et al. | A study of the collapse speed of bubble clusters | |
| CN110968971A (en) | Real-scale ship sonar air guide sleeve cavitation numerical prediction method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20170808 Address after: 243000 Anhui city of Ma'anshan province Jiuhua Mountain Rain Road No. 1369 youth e-commerce Industrial Park Building 9 402-403 Patentee after: ANHUI FCAR ELECTRONIC TECHNOLOGY Co.,Ltd. Address before: 243032 Anhui new East District Ma'anshan City, Ma Xianglu School of electrical and Information Engineering Anhui University of Technology Patentee before: Anhui University of Technology |
|
| TR01 | Transfer of patent right | ||
| PE01 | Entry into force of the registration of the contract for pledge of patent right |
Denomination of invention: A Method for Estimating the Characteristics of Cavitation Noise Based on the Calculation of Pressure Fluctuations in the wake of a Propeller Effective date of registration: 20231205 Granted publication date: 20170308 Pledgee: Ma'anshan branch of Bank of China Ltd. Pledgor: ANHUI FCAR ELECTRONIC TECHNOLOGY Co.,Ltd. Registration number: Y2023980069496 |
|
| PE01 | Entry into force of the registration of the contract for pledge of patent right |