CN104091085A - Cavitation noise feature estimation method based on propeller wake flow pressure fluctuation computing - Google Patents
Cavitation noise feature estimation method based on propeller wake flow pressure fluctuation computing Download PDFInfo
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Abstract
The invention discloses a cavitation noise feature estimation method based on propeller wake flow pressure fluctuation computing and belongs to the field of water sound target feature value extracting. The cavitation noise feature estimation method based on propeller wake flow pressure fluctuation computing comprises the following steps that (1) a standby mesh is generated and is guided into a calculation procedure, and then an example file is generated; (2) a cavitation model and a turbulence model are set; (3) numerical computation parameter setting is carried out; (4) numerical computation is carried out; (5) numerical method reliability demonstration and mesh determining are carried out; (6) cavitation wake flow pressure fluctuation non-constant numerical computation is carried out; (7) pressure fluctuation signal power spectrum conversion and low-frequency line spectrum amplitude value extraction are carried out; and (8) line spectrum feature estimation and analysis are carried out. Related researching results in the fields of modern hydromechanics, vacuole dynamics and signal processing are introduced into underwater target noise feature analysis, and the multidisciplinary and multi-field intersectionality is reflected.
Description
Technical field
The present invention relates to Underwater Acoustic Object feature extraction field, specifically, relate to the cavitation noise feature method of estimation of calculating based on screw current pressure fluctuation.
Background technology
Propeller noise is one of boats and ships three large noise sources, has comprised target thruster kind of information and architectural feature, and these feature tolerances are strong, have good separability, is principal character and the important evidence of identification submarine target.Once and cavitation occurs, cavitation noise just becomes screw propeller overriding noise.These target source noises, owing to being disturbed by ambient sea noise and produce distortion in complicated underwater acoustic channel is propagated, make the received noise signal feature of passive sonar not obvious, and signal to noise ratio (S/N ratio) reduces.Therefore traditionally with signal processing method, extract noise characteristic, carry out Underwater Targets Recognition more and more difficult.Further digging screw oar noise essential characteristic is Underwater Targets Recognition problem anxious to be resolved.
For adopting signal processing technology to carry out the research of feature extraction aspect to the propeller noise of actual measurement, external scholar has started very early.As far back as Whalen in 1971, maximum likelihood modulation receiver has just been proposed.Along with the development of this technology, the time frequency processing methods such as higher-order spectrum, AR spectrum, dual spectrum and wavelet analysis, and the Nonlinear Processing method such as fractal, chaos, limit cycle and mode decomposition are all widely attempted in propeller noise feature extraction.In recent years, single-frequency component of signal detection method and detection system performance under the scholar such as Li Qihu strong jamming ground unrest that adopted theoretical analysis and Study on Numerical Simulation.The Bao Fei of Nanjing University etc. combines Empirical Mode Decomposition method (empirical mode decomposition) and singular value decomposition method (singular value decomposition), extracts the cavitation noise modulation composition of screw propeller from strong jamming ground unrest.Modern signal processing method is extracted the actual measurement noise signal feature under ground unrest, has obtained good effect.But to strong jamming ground unrest, owing to lacking mechanism feature in measured signal, this method adaptive faculty is not higher.
Thus, some scholars have carried out noise characteristic analysis and the method research based on model.Noise modulated envelope is identical shaped as having, equal repetition period of Tao Duchun, random magnitude, the pulse feature stochastic process with block structure is processed.And from the power spectrum density of the ship-radiated noise modulation envelope abundant cadence information relevant with the various physical attributes in naval vessel with extraction autocorrelation function.Jiang Guojian and Lin build the theoretical model of identical people's utilization index decay shape random pulse sequence and analyze ship propeller cavitation erosion, obtain Spectrum of Propeller Cavitation Noise.In recent years, the scholars such as Shi Guangzhi count identification problem for propeller blade, set up cavitation noise signal model.And to the modeling of twin screw ship noise envelope, the structure problem of the two oar target modulation spectrum of research harmonic clan feature, further adopts aspect of model extractive technique, the noise characteristic explication de texte method of research based on Model Matching.Except leaf frequency feature, these models are not considered the parameters such as screw propeller geometric configuration and operating mode, are difficult to embody the mechanism feature of cavitation noise.
Propeller cavitation is the direct sound source of cavitation noise, and propeller cavitation wake flow is the important route of transmission of cavitation noise.Because wake flow is subject to the effect of screw propeller periodic rotary beat, there is periodically pulsing feature.These features have reflected the characteristic informations such as screw propeller operating mode and geometric configuration.Meanwhile, screw propeller rotation beat has obvious Modulation and Amplitude Modulation effect to the cavitation noise of its radiation, and the line spectrum feature of its power spectrum also reflects the screw propeller cadence information of characteristic informations such as comprising screw propeller operating mode and geometric configuration.Therefore, owing to being subject to equally the beat effect of propeller blade, propeller cavitation wake flow and cavitation noise have feature correlation, and its feature has all reflected screw propeller duty parameter and geometric shape parameters.Because the acoustics mechanism research of Propeller Cavitation Noise is at present still far from perfect, so the present invention is a kind of forecasting procedure that cavitation wake flow starts with to set forth its noise characteristic from the origin of cavitation noise.
For propeller cavitation wake flow, there are many scholars to be studied both at home and abroad.The Francesc in Italy's ship model experimental tank laboratory etc. utilizes RANS, LES and BEM method respectively E779A propeller wake field under cavitation and non-cavitation conditions to be carried out to numerical simulation.Sweden Rickard and Goran are based on mixing two-phase flow model, utilize implicit expression LES method and Kunz cavitation model to simulate the dynamic behaviour of E779A screw propeller at Non-uniform Currents cavitation, comparatively successful to the simulation of the flow field structure of Small and Medium Sized and screw propeller tip vortex cavitation.The scholars such as the Ji Bin of Tsing-Hua University utilize Rayleigh – Plessete equation and k-ω Shear Stress Transport (SST) turbulence model to high skewed propeller all the cavitation wake flow of even nonlinear inflow carried out numerical simulation.Sheet cavitation and tip vortex cavitation forecast preferably, and the tail flow field pressure fluctuation characteristic of cavitation induction is simultaneously consistent with propeller shaft frequency leaf frequency feature.Naval engineering university Yang Qiong side carries out analysis and assessment to cavitation model and turbulence model in propeller cavitation simulation, selects to improve Sauer cavitation model and revises SST k-ω turbulence model, and propeller cavitation bucket collection of illustrative plates forecasts with unerring accuracy.To the non-homogeneous influent stream with the large skew back oar of seven leaves, that has analyzed that its cavitation causes pushes away force and moment collapse performance and the impact nascent on blade back tip vortex cavitation, describe cavitation and pushed away pulsation feature, blade cavitation area and the cavitation form of force and moment with the variation of circumferential position, and provided the interval division whether screw propeller in wake occurs blade face sheet cavitation.At present, the research of cavitation wake flow is mainly laid particular emphasis on the numerical forecasting of certain oar matrix cavitation both at home and abroad, research for the characteristic relation aspect between cavitation and screw propeller operating mode and geometric configuration is less, with cavitation wake flow, studies cavitation noise feature still less.
In addition, Chinese Patent Application No. ZL201310538724.7, file also discloses a kind of feature extracting method based on Propeller Cavitation Noise numerical forecasting in nonlinear inflow, and step comprises: first, screw propeller computational fields is carried out to grid division, check mesh quality and define boundary condition; Next, in CFD software, computation model is set, carries out stable state iterative computation and drop down water performance parameter and enter head piece speed verification model accuracy; Then, in CFD software, stable state is calculated to the initial value calculating as unstable state and carry out unstable state iterative computation, and by post processing propeller blade cavitation cycle form and documentary film cavitation area change; Finally, according to single cavity radiated noise theory, by propeller blade cavitation area, calculate propeller cavitation radiated noise, carry out feature extraction.In this application file, method therefor is converted to spherical volume by cavitation zone, and obtains spherical volume radius, then change in radius is brought in spherical single cavitation erosion radiation model, forecasts cavitation noise and feature thereof.Because propeller cavitation and spherical single cavity have a great difference, the accuracy of this translation method awaits further check.Method of the present invention is to utilize the feature correlation between the pressure fluctuation of propeller cavitation wake flow and cavitation noise to carry out estimating noise feature.Specifically, cavitation wake flow pressure fluctuation information contains screw propeller operating mode and geometric shape parameters feature, and cavitation noise also has this attribute.Therefore, they have identical origin relation, are that the rotation of screw propeller causes cavitation wake flow and produces noise, and cavitation noise is also subject to the modulating action of revolving vane simultaneously.The basic reason that cavitation wake flow and noise produce is the rotation of screw propeller in fluid.
Summary of the invention
Propeller Cavitation Noise principal character has:
1. propeller cavitation is the source of cavitation noise, Propeller Cavitation Noise invariably accompany propeller cavitation appearance and occur;
2. propeller cavitation wake flow is not only the sound source of cavitation noise, or the important carrier of cavitation noise propagation;
3. the sound intensity of cavitation noise and void volume variation are closely related, and particularly its volume change in the moment of crumbling and fall is maximum, and its radiated noise is also the strongest;
4. propeller cavitation volume change and position distribution have periodic feature along with the rotation of blade, make cavitation noise also have periodic feature, and can be reflected in its noise spectrum distribution;
5. cavitation wake flow and cavitation noise can be subject to the modulating action that propeller blade rotates beat simultaneously;
6. above-mentioned feature makes the feature of propeller cavitation and its noise have tight essential correlativity;
7. a cavitation noise is generally distributed in low-frequency range, and its frequency spectrum presents line spectrum feature, and the noise that tip vortex cavitation sends is generally distributed in medium-high frequency section, and its frequency spectrum presents continuous feature.
Principle of the present invention is exactly the principal character according to above-mentioned propeller cavitation, theoretical based on stickiness polyphasic flow, utilize modern computational method to build N-S equation to underwater propeller tail flow field, and in conjunction with turbulence model and cavitation model, system of equations is carried out to numerical solution, thereby obtain the underwater propeller blade face relevant information such as pressure fluctuation in vapour phase volume fraction and tail flow field around; The characteristics of low-frequency of the information of flow data that the signal processing method logarithm value such as recycling power spectrum are calculated is extracted and is analyzed; Finally utilize the feature correlation between fluid field pressure pulsation and noise that submarine target propeller noise feature is estimated and judged.Although pressure fluctuation is mechanics parameter and noise acoustic pressure is parameters,acoustic in tail flow field, their physical concept is different, and their features in some aspects, as low frequency spectrum lines characteristics of amplitude distribution, have again some common ground.These common ground essence are to have reflected screw propeller how much and duty parameter feature, they are referred to as to feature correlation here.
The present invention adopts following technical scheme:
The cavitation noise feature method of estimation of calculating based on screw current pressure fluctuation, specifically comprises the following steps:
(1) standby grid generates and imports and generates example file after calculation procedure:
After utilizing professional software to make screw propeller 3-D geometric model, import Grid Generation Software, at grid, divide in software and set up three kinds of alternative grids, the computational fields of these three kinds of alternative grids is identical, the speed frontier distance propeller center that becomes a mandarin is 1D, D is airscrew diameter, downstream pressure outlet frontier distance is 5D, and propeller center is 2.5D to side periphery distance, the grid cell quantity of these three grids according to
multiple increases gradually, to the grid cell size of adjacent boundary in grid in the reasonable transition of frontier point, make its size of mesh opening difference less, finally make the skew of all volume mesh unit in grid all be limited in 0.9, to guarantee the stability of numerical evaluation below;
(2) cavitation model and turbulence model are set:
Adopt full cavitation model and Renormalization Group turbulence model, and its important parameter is revised, the correction of turbulent viscosity coefficient in the correction of transformation ratio parameter in cavitation model and turbulence model is adopted to C language compilation, and recycling macro call (DEFINE_TURBULENT_VISCOSITY etc.) form embeds calculation procedure;
(3) numerical evaluation setting parameter:
Correlation parameter to working condition, boundary condition and numerical algorithm is set;
(4) numerical evaluation:
Because cavitation model adds after RANS equation, the stability of calculating reduces, and easily occurs unusual appearance.Therefore,, in order to make numerical evaluation steadily carry out, adopt step by step computation process step by step, specifically, in screw propeller duty parameter, environmental pressure and inflow velocity can directly be set to operating mode value, and revolution speed of propeller adopts classification to increase, until be increased to predetermined operating mode value; First calculate non-cavitating model Flow Field Distribution, by the time after calculation stability, open again cavitation model; First the parameters such as pressure, density, momentum and vapour phase mark being carried out to single order precision discrete scheme calculates, after calculation stability, again discrete precision is brought up to second order or QUCIK etc., because multiphase flow model, cavitation model and Sliding mesh calculating are larger to computer resource usage, therefore adopt parallel computing to shorten computing time.
(5) numerical method reliability demonstration and grid are determined:
To under typical condition, the numerical result of the hydrodynamic parameter of screw propeller oar and cavitation and related experiment result be compared, to verify the reliability of grid independence and the numerical method that adopted; By building three kinds of alternative grids in step 1, according to step 2 and 3 methods, set and carry out numerical evaluation, and hydrodynamic parameter in result of calculation and cavitation are compared, when these results tend towards stability along with the increase of number of grid and be consistent with experimental result, the minimum grid of the selected middle grid cell quantity that satisfies condition is as the selected grid of numerical evaluation below; Otherwise suitably increase number of grid, repeating step 1 restarts;
(6) the non-permanent numerical evaluation of cavitation wake flow pressure fluctuation:
Adopt the selected grid in step 5, the tail flow field of screw propeller is carried out under required working condition to non-permanent numerical evaluation, in calculation procedure, to a certain ad-hoc location in tail flow field (A point), pressure fluctuation detects and preserves it and detects data, data is carried out to nondimensionalization simultaneously;
(7) conversion of pressure fluctuation signal power spectrum and low frequency spectrum lines magnitude extraction:
Adopt signal process in fast fourier transform method stream field the physical quantity such as pressure fluctuation and noise signal data carry out power spectrum conversion, and low frequency spectrum lines amplitude is extracted, low frequency spectrum lines comprises axle frequently, frequently, three times of axles are frequently and leaf frequency for two times of axles; Recycling tail flow field pressure fluctuation characteristic and cavitation noise be by the similarity of blade modulation signature, the feature corresponding relation of foundation from the low frequency spectrum lines amplitude of pressure fluctuation to 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 to one by one to the low frequency spectrum lines amplitude of noise signal power spectrum, as the estimation to cavitation noise signal low frequency spectrum lines characteristics of amplitude distribution, be exactly specifically utilize pressure fluctuation signal power spectrum axle frequently, two times of axles frequently, three times of axles frequently and the amplitude of the low frequency component such as Ye Pin distinguish estimated noise signal axle frequently, two times of axles frequently, the amplitude of the low frequency component such as three times of axle frequencies and Ye Pin.
Further, the non-permanent calculating of cavitation wake flow pressure fluctuation in described step 6 comprises the following steps:
(6-1) import grid selected in step 5 and generate example file;
(6-2) cavitation model and turbulence model are set;
(6-3) numerical evaluation setting parameter;
(6-4) numerical evaluation;
(6-5) pressure fluctuation signal is extracted: to a certain ad-hoc location in tail flow field (A point), pressure fluctuation detects and preserves it and detects data.
The cavitation model of step (6-2) is set with cavitation model and the turbulence model of step 2 respectively with turbulence model setting, the numerical evaluation setting parameter of step (6-3) and the numerical evaluation of (6-4), the numerical evaluation setting parameter of step 3 is identical with the numerical evaluation of step 4.
Further, alternative grid in described step 1 adopts subregion mixed mesh method: screw propeller around flow field regions adopts non-structured grid method to divide, grid is reduced to blade tip gradually by propeller hub, blade tip place surface grids is triangle, grid cell length of side size is about unit, 0.001D ， Jiang Grains place and is about 0.02D; Because cavitation is mainly distributed in Ji Shaowo region, blade face, so this area grid quality requirements is higher.In order to adapt to better Wall-function, at leaf surface, set up boundary layer grid; Adopt structured grid to divide the computational fields of the peripheral regular shape of screw propeller; Based on said method simultaneously different three the computational fields grids of generating mesh unit number as alternative grid; Tip whirlpool area grid is encrypted, blade surface adopts boundary layer grid to improve the forecast precision to tip vortex cavitation simultaneously, tip whirlpool area grid unit size is about 0.001D, boundary layer grid has 4 layers, its adjacent two layers aspect ratio is 1.1, ground floor grid cell height is about 0.001D, makes 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, particularly near screw propeller near zone.Because this area grid quality is most important to the accuracy of propeller cavitation performance and wake flow pressure fluctuation calculating.
Further, full cavitation model setting and the parameter thereof in described step 2 is modified to:
Work as p<p
vtime, steam generation rate is:
Work as p>p
vtime, vapour phase becomes liquid phase, obtains equally steam solidification rate R
c:
Wherein, f
v=α
vρ
v/ ρ
mfor vapour phase massfraction, vaporization coefficient Ce=0.02 and condensation coefficient Cc=0.01 are empirical parameter.
According to dimensional analysis, in transformation ratio (Re and Rc) expression formula, adopt k rather than
under FLUENT software environment, can utilize self-defining function UDF to cavitation model in parameter transformation ratio R
erevise, correction model is called in calculation procedure after adopting C language compilation.
Further, RNG k-ε turbulence model and parameter thereof in described step 2 are modified to: Renormalization Group turbulence model is RNG k-ε turbulence model, Renormalization Group turbulence model RNG k-ε is the model that the mathematical method of transient state N-S Renormalization Group (Renormalization Group is called for short RNG) for equation is derived.It,, by embody the impact of small scale in Large Scale Motion item and correction viscosity item, is removed from governing equation and make these small scale kinematic systems.Its k equation and ε equation are respectively:
In formula, Turbulent Kinetic dissipative shock wave (Turbulent Dissipation Rate)
the a reciprocal of effective turbulent prandtl number of Turbulent Kinetic k and dissipative shock wave ε
k=a
ε=1.39; Model parameter C
1 ε=1.47, C
2 ε=1.68; Viscosity coefficient is μ=μ
t+ μ
m, μ
mfor mixed flow coefficient of viscosity; Revise turbulent viscosity coefficient μ
t=[ρ
v+ α
l 10(ρ
l-ρ
v)] C
μk
2/ ε, C
μ=0.085 is more suitable for the non-permanent two-phase simulated flow of high reynolds number, thereby can simulate better propeller cavitation.
Further, the numerical evaluation setting parameter of described step 3, comprises that the correlation parameter of working condition, boundary condition and numerical algorithm is set;
Working condition is mainly set screw propeller rotational speed, and environmental pressure and inflow velocity value, determine screw propeller dimensionless group, i.e. advance coefficient (J) and cavitation number (σ
n); For boundary condition, set, speed inlet boundary adopts inflow velocity value, and far field boundary condition adopts inflow velocity to set, and the top hole pressure at downstream pressure outlet interface is set to static pressure; Parameter setting in numerical algorithm: it is discrete that in governing equation, convective term adopts Second-order Up-wind form, diffusion term adopts Using Second-Order Central Difference form discrete, velocity pressure coupling adopts the SIMPLE algorithm that is applicable to non-structured grid, uses pointwise Gauss-Seidel iterative discrete equation; Utilize the convergence of algebraic multigrid speed-up computation, for non-permanent calculating, adopt Sliding mesh computing technique, improve the accuracy of calculating.Adopt second order accuracy discrete scheme, for the stability that guarantees that second order calculates, sub-relaxation factor is suitably reduced, the isoparametric sub-relaxation factor of pressure, momentum, vapour phase mark, Turbulent Kinetic, turbulence dissipation rate and turbulent flow stickiness is set as respectively: 0.25,0.6,0.2,0.7,0.7,0.9, mass conservation continuity (continuity) residual error convergence is three rank, and in equation, other physical quantity residual error convergence is quadravalence.
Further, the ad-hoc location A point of described step 6 is positioned at screw current radially r=0.5R and axial x=2R place, according to dimension conversion principle, adopts formula
carry out nondimensionalization, the total pressure pulsation value that wherein Δ P is numerical result, ρ is fluid-mixing density, n is revolution speed of propeller, D position airscrew diameter; In non-permanent calculating, TIME STEP time step is set as T=0.0125T
p, T
pfor screw propeller swing circle, it is 30T that data relate to time span
p.
Method of the present invention can realize in general general CFD fluid calculation software (CFX, FLUENT etc.), and grid is divided softwares such as can adopting GAMBIT and realized.First the screw propeller digital model of primary design is imported to grid and divide software, and carry out grid division according to the method in the present invention.Grid model forms the numerical example file after importing computing platform, and in example file, numerical parameter is set, and carries out numerical evaluation, and pressure fluctuation signal is outputed to text according to design conditions.In MATLAB software, write power spectrum density conversion program, realize signal conversion to frequency domain by time domain, and finally extract low frequency spectrum lines amplitude parameter.In addition, the present invention also utilizes autoexec to carry out parallel numerical calculating in operating system platform.
Beneficial effect:
(1) the present invention introduces correlative study achievement in modern fluid mechanics, Bubble dynamics and signal process field the noise characteristic analysis of submarine target, embodies multidisciplinary and multi-field intercrossing.
(2) the current impact due to strong jamming ground unrest and complicated underwater acoustic channel, only from detection noise, extract the requirement that target signature is difficult to meet Underwater Targets Recognition technology, so the present invention have significant application value and application prospect in Underwater Acoustic Object identification field.
(3) because being subject to effect and radiated noise that propeller blade rotates beat, tail flow field is subject to equally leaf modulating action frequently, in tail flow field, pressure fluctuation is closely related with the feature of sound pressure signal slowly varying component, the features such as power spectrum density low frequency spectrum lines amplitude distribution of the two have correlativity, and the present invention utilizes the correlativity of Field Characteristics parameter and flow noise characteristic parameter to carry out feature estimation to Propeller Cavitation Noise.
(4) correction of the inventive method to cavitation model and turbulence model correlation parameter, sets up blade surface boundary layer and in tip vortex cavitation region, grid is carried out to fine processing.By compare (the seeing Fig. 5) with experiment, these innovative approachs have significantly improved the forecast precision of tip vortex cavitation, solve preferably a difficult problem in cavitation numerical forecasting.Numerical evaluation for next step cavitation wake flow pressure fluctuation provides strong assurance simultaneously.
(5) utilize definite grid to the non-permanent numerical evaluation of the cavitation wake flow of screw propeller E779A and E779B, and extract its wake flow pressure fluctuation signal, again pressure fluctuation is carried out to the distribution characteristics that power spectrum conversion obtains low frequency spectrum lines amplitude, the low frequency spectrum lines of this distribution and actual measurement noise is distributed and compared (seeing Fig. 6 and Fig. 7), verified the feature correlation of the two.Finally just can utilize this feature correlation by pressure fluctuation numerical evaluation, noise characteristic under other working conditions to be estimated, thereby provide important directive property to be worth to carry out Underwater Target Classification recognition technology with propeller noise feature.
Accompanying drawing explanation
Fig. 1 (a) is E779A screw propeller geometric model;
Fig. 1 (b) is E779B screw propeller geometric model;
Fig. 2 is the schematic diagram of full runner screw propeller computational fields hybrid grid of the present invention;
Fig. 3 (a) is the structural representation of boundary layer of the present invention grid;
Fig. 3 (b) is the enlarged drawing at A place in Fig. 3 (a);
Fig. 4 (a) is method flow diagram of the present invention;
Fig. 4 (b) is the non-permanent numerical evaluation process flow diagram of cavitation wake flow of the present invention pressure fluctuation;
Fig. 5 is E779A oar cavitation numerical value forecast result and experimental result;
Fig. 6 is that the E779B oar that is rotated counterclockwise under nonlinear inflow condition is in diverse location cavitation numerical value forecast result and experimental result constantly;
Fig. 7 is the normalized measurement noise power spectrum of E779A screw propeller and pressure fluctuation numerical evaluation power spectrum signal;
The normalized power spectral density low frequency spectrum lines amplitude of the E779B propeller cavitation wake flow pressure fluctuation that Fig. 8 (a) is n=15rps and measurement noise signal;
The normalized power spectral density low frequency spectrum lines amplitude of the E779B propeller cavitation wake flow pressure fluctuation that Fig. 8 (b) is n=20rps and measurement noise signal;
The normalized power spectral density low frequency spectrum lines amplitude of the E779B propeller cavitation wake flow pressure fluctuation that Fig. 8 (c) is n=25rps and measurement noise signal.
Embodiment
Below in conjunction with drawings and Examples, the present invention is further detailed explanation.
Embodiment
As described in Fig. 4 (a), the cavitation noise feature method of estimation of calculating based on screw current pressure fluctuation, specifically comprises the following steps:
(1) standby grid generates and imports and generates example file after calculation procedure:
After utilizing professional software to make screw propeller 3-D geometric model, import Grid Generation Software, as shown in Fig. 1 (a) Fig. 1 (b), for E778A and E779B model propeller, at grid, divide in software and set up three kinds of alternative grids, the computational fields of these three kinds of alternative grids is identical, the speed frontier distance propeller center that becomes a mandarin is 1D, D is airscrew diameter, downstream pressure outlet frontier distance is 5D, propeller center to side periphery distance is 2.5D, the grid cell quantity of these three grids according to
multiple increases gradually, to the grid cell size of adjacent boundary in grid in the reasonable transition of frontier point, make its size of mesh opening difference less, finally make the skew of all volume mesh unit in grid all be limited in 0.9, to guarantee the stability of numerical evaluation below;
Grid adopts subregion mixed mesh method (as shown in Figure 2): screw propeller around flow field regions adopts non-structured grid method to divide, grid is reduced to blade tip gradually by propeller hub, blade tip place surface grids is triangle, grid cell length of side size is about unit, 0.001D ， Jiang Grains place and is about 0.02D; Because cavitation is mainly distributed in Ji Shaowo region, blade face, so this area grid quality requirements is higher.In order to adapt to better Wall-function, at blade surface, set up boundary layer grid (as shown in Figure 3); Adopt structured grid to divide the computational fields of the peripheral regular shape of screw propeller; Based on said method simultaneously different three the computational fields grids of generating mesh unit number as alternative grid; Tip whirlpool area grid is encrypted, blade surface adopts boundary layer grid to improve the forecast precision to tip vortex cavitation simultaneously, tip whirlpool area grid unit size is about 0.001D, boundary layer grid has 4 layers, its adjacent two layers aspect ratio is 1.1, ground floor grid cell height is about 0.001D, makes 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, particularly near screw propeller near zone.Because this area grid quality is most important to the accuracy of propeller cavitation performance and wake flow pressure fluctuation calculating.
(2) cavitation model and turbulence model are set:
Adopt full cavitation model and Renormalization Group turbulence model, and its important parameter is revised, the correction of turbulent viscosity coefficient in the correction of transformation ratio parameter in cavitation model and turbulence model is adopted to C language compilation, and recycling macro call (DEFINE_TURBULENT_VISCOSITY etc.) form embeds calculation procedure;
Full cavitation model sets and parameter is modified to:
Work as p<p
vtime, steam generation rate is:
Work as p>p
vtime, vapour phase becomes liquid phase, obtains equally steam solidification rate R
c:
Wherein, f
v=α
vρ
v/ ρ
mfor vapour phase massfraction, vaporization coefficient Ce=0.02 and condensation coefficient Cc=0.01 are empirical parameter;
According to dimensional analysis, in transformation ratio (Re and Rc) expression formula, adopt k rather than
under FLUENT software environment, can utilize self-defining function UDF to cavitation model in parameter transformation ratio R
erevise, correction model is called in calculation procedure after adopting C language compilation.
Renormalization Group turbulence model is RNG k-ε turbulence model, and Renormalization Group turbulence model RNG k-ε is the model that the mathematical method of transient state N-S Renormalization Group (Renormalization Group is called for short RNG) for equation is derived.It,, by embody the impact of small scale in Large Scale Motion item and correction viscosity item, is removed from governing equation and make these small scale kinematic systems.Its k equation and ε equation are respectively:
In formula, Turbulent Kinetic dissipative shock wave (Turbulent Dissipation Rate)
the a reciprocal of effective turbulent prandtl number of Turbulent Kinetic k and dissipative shock wave ε
k=a
ε=1.39; Model parameter C
1 ε=1.47, C
2 ε=1.68; Viscosity coefficient is μ=μ
t+ μ
m, μ
mfor mixed flow coefficient of viscosity; Revise turbulent viscosity coefficient μ
t=[ρ
v+ α
l 10(ρ
l-ρ
v)] C
μk
2/ ε, C
μ=0.085 is more suitable for the non-permanent two-phase simulated flow of high reynolds number, thereby can simulate better propeller cavitation.
(3) numerical evaluation setting parameter:
Correlation parameter to working condition, boundary condition and numerical algorithm is set; Working condition is mainly set screw propeller rotational speed, and environmental pressure and inflow velocity value, determine screw propeller dimensionless group, i.e. advance coefficient (J) and cavitation number (σ
n); For boundary condition, set, speed inlet boundary adopts inflow velocity value, and far field boundary condition adopts inflow velocity to set, and the top hole pressure at downstream pressure outlet interface is set to static pressure; Parameter setting in numerical algorithm: it is discrete that in governing equation, convective term adopts Second-order Up-wind form, diffusion term adopts Using Second-Order Central Difference form discrete, velocity pressure coupling adopts the SIMPLE algorithm that is applicable to non-structured grid, uses pointwise Gauss-Seidel iterative discrete equation; Utilize the convergence of algebraic multigrid speed-up computation, for non-permanent calculating, adopt Sliding mesh computing technique, improve the accuracy of calculating.Adopt second order accuracy discrete scheme, for the stability that guarantees that second order calculates, sub-relaxation factor is suitably reduced, the isoparametric sub-relaxation factor of pressure, momentum, vapour phase mark, Turbulent Kinetic, turbulence dissipation rate and turbulent flow stickiness is set as respectively: 0.25,0.6,0.2,0.7,0.7,0.9, mass conservation continuity (continuity) residual error convergence is three rank, and in equation, other physical quantity residual error convergence is quadravalence.
(4) numerical evaluation:
Because cavitation model adds after RANS equation, the stability of calculating reduces, and easily occurs unusual appearance.Therefore,, in order to make numerical evaluation steadily carry out, adopt step by step computation process step by step, specifically, in screw propeller duty parameter, environmental pressure and inflow velocity can directly be set to operating mode value, and revolution speed of propeller adopts classification to increase, until be increased to predetermined operating mode value; First calculate non-cavitating model Flow Field Distribution, by the time after calculation stability, open again cavitation model; First the parameters such as pressure, density, momentum and vapour phase mark being carried out to single order precision discrete scheme calculates, after calculation stability, again discrete precision is brought up to second order or QUCIK etc., because multiphase flow model, cavitation model and Sliding mesh calculating are larger to computer resource usage, therefore adopt parallel computing to shorten computing time.
(5) numerical method reliability demonstration and grid are determined:
To under typical condition, the numerical result of the hydrodynamic parameter of screw propeller oar and cavitation and related experiment result be compared, to verify the reliability of grid independence and the numerical method that adopted; By building three kinds of alternative grids in step 1, according to step 2 and 3 methods, set and carry out numerical evaluation, and hydrodynamic parameter in result of calculation and cavitation are compared, when these results tend towards stability along with the increase of number of grid and be consistent with experimental result, the minimum grid of the selected middle grid cell quantity that satisfies condition is as the selected grid of numerical evaluation below; Otherwise suitably increase number of grid, repeating step 1 restarts, Fig. 5 is E779A oar cavitation numerical value forecast result and experimental result, Fig. 6 be the E779B oar that is rotated counterclockwise under nonlinear inflow condition in cavitation numerical value forecast result and the experimental result of diverse location, the result of Fig. 5 and Fig. 6 shows that the method for using in the present invention is relatively good to the value of forecasting of propeller cavitation;
(6) the non-permanent numerical evaluation of cavitation wake flow pressure fluctuation:
Adopt the selected grid in step 5, the tail flow field of screw propeller is carried out under required working condition to non-permanent numerical evaluation, in calculation procedure, to a certain ad-hoc location in tail flow field (A point), pressure fluctuation detects and preserves it and detects data, data is carried out to nondimensionalization simultaneously;
Ad-hoc location A point is positioned at screw current radially r=0.5R and axial x=2R place, according to dimension conversion principle, adopts formula
carry out nondimensionalization, the total pressure pulsation value that wherein Δ P is numerical result, ρ is fluid-mixing density, n is revolution speed of propeller, D position airscrew diameter; In non-permanent calculating, TIME STEP time step is set as T=0.0125T
p, T
pfor screw propeller swing circle, it is 30T that data relate to time span
p.
(7) conversion of pressure fluctuation signal power spectrum and low frequency spectrum lines magnitude extraction:
Adopt signal process in fast fourier transform method stream field the physical quantity such as pressure fluctuation and noise signal data carry out power spectrum conversion, and to low frequency spectrum lines (axle frequently, two times of axles frequently, three times of axles frequently and leaf frequency) amplitude extracts; Recycling tail flow field pressure fluctuation characteristic and cavitation noise be by the similarity of blade modulation signature, the feature corresponding relation of foundation from the low frequency spectrum lines amplitude of pressure fluctuation to 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 to one by one to the low frequency spectrum lines amplitude of noise signal power spectrum, as the estimation to cavitation noise signal low frequency spectrum lines characteristics of amplitude distribution.In Fig. 7 (a) and (b) for operating mode under E779A screw propeller uniform inflow is J=0.88, the normalized power spectral density of the pressure fluctuation of cavitation wake flow and cavitation noise during n=25rps.By Fig. 7 (a) and contrast (b), find that pressure fluctuation signal normalized power spectral density and measurement noise pressure signal power spectrum have some common features: (1) take line spectrum as main at 10-100Hz low-band signal, and consistent with propeller blade number and rotary speed parameter value.Wherein, leaf frequently line spectrum (100Hz) peak value is the highest, and axle frequently line spectrum (25Hz) peak value takes second place, and is then 75Hz and 50Hz line spectrum.Except 75Hz signal intensity is lower, the Noise line spectra peak change feature in other and Fig. 7 (a) is basic identical.(2) show abundant line spectrum feature the Mid Frequency of 100-1000Hz is same, line spectrum take axle frequently and leaf frequency multiplication frequently as main, and continuous spectrum spectral line also starts decline, the spectrum deformationization in these and Fig. 7 (a) is basically identical.But in numerical result, the line spectrum amplitude of this frequency field is less compared with the middle experiment value of Fig. 7 (a), and frequency resolution is lower.(3) power spectrum amplitude range is 10
0to 10
-9between, basically identical with the experimental data in Fig. 7 (a).In the above, common trait shows that E779A oar mould, under uniform inflow condition, exists similar feature between pressure fluctuation and noise, and they have feature correlation.
Fig. 8 (a), (b) and (c) be the pressure fluctuation of cavitation wake flow and the normalized power spectral density low frequency spectrum lines amplitude contrast of measuring noise signal under E779B screw propeller nonlinear inflow.The low frequency spectrum lines amplitude that Fig. 7 shows pressure fluctuation and noise is 15,20 with during 25rps rotating speed, the axle of the two frequently, two times of axles frequently and three times of axles amplitude variation tendencies are basic identical frequently.And leaf is frequently when 15rps rotating speed, the two differs maximum, and during 20rps rotating speed, the two differs and reduces, basic identical during 25rps rotating speed.This shows that revolution speed of propeller is 25rps, and cavitation obviously occurs, and cavitation noise becomes overriding noise, now the two leaf amplitude Characteristics correlativity reinforcement frequently.And when rotating speed is lower (15rps), non-cavitating occurs, now neighbourhood noise becomes main sound source, so the two leaf frequency feature correlation weakens.This shows to occur when cavitation, and when cavitation noise becomes screw propeller overriding noise, the accuracy of this method is significantly improved.
As shown in Fig. 4 (b), the non-permanent calculating of cavitation wake flow pressure fluctuation in described step 6 comprises the following steps:
(6-1) import grid selected in step 5 and generate example file;
(6-2) cavitation model and turbulence model are set;
(6-3) numerical evaluation setting parameter;
(6-4) numerical evaluation;
(6-5) pressure fluctuation signal is extracted: to a certain ad-hoc location in tail flow field (A point), pressure fluctuation detects and preserves it and detects data.
The cavitation model of step (6-2) is set with cavitation model and the turbulence model of step 2 respectively with turbulence model setting, the numerical evaluation setting parameter of step (6-3) and the numerical evaluation of (6-4), the numerical evaluation setting parameter of step 3 is identical with the numerical evaluation of step 4.
Method of the present invention can realize in general general CFD fluid calculation software (CFX, FLUENT etc.), and grid is divided softwares such as can adopting GAMBIT and realized.First the screw propeller digital model of primary design is imported to grid and divide software, and carry out grid division according to the method in the present invention.Grid model forms the numerical example file after importing computing platform, and in example file, numerical parameter is set, and carries out numerical evaluation, and pressure fluctuation signal is outputed to text according to design conditions.In MATLAB software, write power spectrum density conversion program, realize signal conversion to frequency domain by time domain, and finally extract low frequency spectrum lines amplitude parameter.In addition, the present invention also utilizes autoexec to carry out parallel numerical calculating in operating system platform.
Claims (9)
1. the cavitation noise feature of calculating based on screw current pressure fluctuation is estimated access method, it is characterized in that: comprise the following steps:
(1) standby grid generates and imports and generates example file after calculation procedure:
After utilizing professional software to make screw propeller 3-D geometric model, import Grid Generation Software, at grid, divide in software and set up three kinds of alternative grids, the computational fields of these three kinds of alternative grids is identical, the speed frontier distance propeller center that becomes a mandarin is 1D, D is airscrew diameter, downstream pressure outlet frontier distance is 5D, and propeller center is 2.5D to side periphery distance, the grid cell quantity of these three grids according to
multiple increases gradually, to the grid cell size of adjacent boundary in grid, in the reasonable transition of frontier point, makes the skew of all volume mesh unit in grid all be limited in 0.9;
(2) cavitation model and turbulence model are set:
Adopt full cavitation model and Renormalization Group turbulence model, and its important parameter is revised;
(3) numerical evaluation setting parameter:
Correlation parameter to working condition, boundary condition and numerical algorithm is set;
(4) numerical evaluation:
Adopt step by step computation process step by step, in screw propeller duty parameter, environmental pressure and inflow velocity can directly be set to operating mode value, and revolution speed of propeller adopts classification to increase, until be increased to predetermined operating mode value; First calculate non-cavitating model Flow Field Distribution, by the time after calculation stability, open again cavitation model; First the parameters such as pressure, density, momentum and vapour phase mark are carried out to single order precision discrete scheme and calculate, after calculation stability, more discrete precision is brought up to second order or QUCIK etc., and adopt parallel computing to calculate;
(5) numerical method reliability demonstration and grid are determined:
To under typical condition, the numerical result of the hydrodynamic parameter of screw propeller oar and cavitation and related experiment result be compared, to verify the reliability of grid independence and the numerical method that adopted; And in logarithm value result of calculation, hydrodynamic parameter and cavitation compare, when result tends towards stability along with the increase of number of grid and be consistent with experimental result, the minimum grid of the selected middle grid cell quantity that satisfies condition is as the selected grid of numerical evaluation below; Otherwise suitably increase number of grid, repeating step 1 restarts;
(6) the non-permanent numerical evaluation of cavitation wake flow pressure fluctuation:
Adopt the selected grid in step 5, the tail flow field of screw propeller is carried out under required working condition to non-permanent numerical evaluation, in calculation procedure, to a certain ad-hoc location in tail flow field (A point), pressure fluctuation detects and preserves it and detects data, data is carried out to nondimensionalization simultaneously;
(7) conversion of pressure fluctuation signal power spectrum and low frequency spectrum lines magnitude extraction:
Adopt signal process in fast fourier transform method stream field the physical quantity such as pressure fluctuation and noise signal data carry out power spectrum conversion, and low frequency spectrum lines amplitude is extracted; Recycling tail flow field pressure fluctuation characteristic and cavitation noise be by the similarity of blade modulation signature, the feature corresponding relation of foundation from the low frequency spectrum lines amplitude of pressure fluctuation to 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 to one by one to the low frequency spectrum lines amplitude of noise signal power spectrum, as the estimation to cavitation noise signal low frequency spectrum lines characteristics of amplitude distribution.
2. the cavitation noise feature method of estimation of calculating based on screw current pressure fluctuation as claimed in claim 1, is characterized in that: the non-permanent calculating of cavitation wake flow pressure fluctuation in described step 6 comprises the following steps:
(6-1) the selected grid importing in step 5 generates example file;
(6-2) cavitation model and turbulence model are set;
(6-3) numerical evaluation setting parameter;
(6-4) numerical evaluation;
(6-5) pressure fluctuation signal is extracted: to a certain ad-hoc location in tail flow field (A point), pressure fluctuation detects and preserves it and detects data.
3. the cavitation noise feature method of estimation of calculating based on screw current pressure fluctuation as claimed in claim 1, is characterized in that: the alternative grid in described step 1 adopts subregion mixed mesh method:
Screw propeller around flow field regions adopts non-structured grid method to divide, and grid is reduced to blade tip gradually by propeller hub, and blade tip place surface grids is triangle, and grid cell length of side size is about unit, 0.001D ， Jiang Grains place and is about 0.02D; At blade surface, set up boundary layer grid; Adopt structured grid to divide the computational fields of the peripheral regular shape of screw propeller; Tip whirlpool area grid is encrypted, blade surface adopts boundary layer grid to improve the forecast precision to tip vortex cavitation simultaneously, tip whirlpool area grid unit size is about 0.001D, boundary layer grid has 4 layers, its adjacent two layers aspect ratio is 1.1, ground floor grid cell height is about 0.001D, makes dimensionless group 20<y
+<300.
4. the cavitation noise feature method of estimation of calculating based on screw current pressure fluctuation as claimed in claim 1, is characterized in that: the full cavitation model in described step 2 sets and parameter is modified to:
Work as p<p
vtime, steam generation rate is:
Work as p>p
vtime, vapour phase becomes liquid phase, obtains equally steam solidification rate R
c:
Wherein, f
v=α
vρ
v/ ρ
mfor vapour phase massfraction, vaporization coefficient Ce=0.02 and condensation coefficient Cc=0.01 are empirical parameter.
5. the cavitation noise feature method of estimation of calculating based on screw current pressure fluctuation as claimed in claim 1, it is characterized in that: RNG k-ε turbulence model and parameter thereof in described step 2 are modified to: Renormalization Group turbulence model is RNG k-ε turbulence model, k equation and the ε equation of RNG k-ε turbulence model are respectively:
In formula, Turbulent Kinetic dissipative shock wave (Turbulent Dissipation Rate)
the a reciprocal of effective turbulent prandtl number of Turbulent Kinetic k and dissipative shock wave ε
k=a
ε=1.39; Model parameter C
1 ε=1.47, C
2 ε=1.68; Viscosity coefficient is μ=μ
t+ μ
m, μ
mfor mixed flow coefficient of viscosity; Revise turbulent viscosity coefficient μ
t=[ρ
v+ α
l 10(ρ
l-ρ
v)] C
μk
2/ ε, C
μ=0.085.
6. the cavitation noise feature method of estimation of calculating based on screw current pressure fluctuation as claimed in claim 1, is characterized in that: the numerical evaluation setting parameter of described step 3, comprises that the correlation parameter of working condition, boundary condition and numerical algorithm is set;
Working condition is mainly set screw propeller rotational speed, and environmental pressure and inflow velocity value, determine screw propeller dimensionless group, i.e. advance coefficient (J) and cavitation number (σ
n); For boundary condition, set, speed inlet boundary adopts inflow velocity value, and far field boundary condition adopts inflow velocity to set, and the top hole pressure at downstream pressure outlet interface is set to static pressure; Parameter setting in numerical algorithm: it is discrete that in Na Wei-Stokes (N-S) equation, convective term adopts Second-order Up-wind form, diffusion term adopts Using Second-Order Central Difference form discrete, velocity pressure coupling adopts the SIMPLE algorithm that is applicable to non-structured grid, uses pointwise Gauss-Seidel iterative discrete equation; Utilize the convergence of algebraic multigrid speed-up computation, for non-permanent calculating, adopt Sliding mesh computing technique, adopt second order accuracy discrete scheme, for the stability that guarantees that second order calculates, sub-relaxation factor is suitably reduced, the isoparametric sub-relaxation factor of pressure, momentum, vapour phase mark, Turbulent Kinetic, turbulence dissipation rate and turbulent flow stickiness is set as respectively: 0.25,0.6,0.2,0.7,0.7,0.9, mass conservation continuity (continuity) residual error convergence is three rank, and in equation, other physical quantity residual error convergence is quadravalence.
7. the cavitation noise feature method of estimation of calculating based on screw current pressure fluctuation as claimed in claim 1, it is characterized in that: the ad-hoc location A point of described step 6 is positioned at screw current radially r=0.5R and axial x=2R place, according to dimension conversion principle, adopt formula
carry out nondimensionalization, the total pressure pulsation value that wherein Δ P is numerical result, ρ is fluid-mixing density, n is revolution speed of propeller, D position airscrew diameter; In non-permanent calculating, TIME STEP time step is set as T=0.0125T
p, T
pfor screw propeller swing circle, it is 30T that data relate to time span
p.
8. the cavitation noise feature method of estimation of calculating based on screw current pressure fluctuation as claimed in claim 1, is characterized in that: the low frequency spectrum lines in the power spectrum conversion in described step 7 comprises axle frequently, and frequently, three times of axles frequently and leaf frequency for two times of axles.
9. the cavitation noise feature method of estimation of calculating based on screw current pressure fluctuation as described in claim 1 or 4 or 5, it is characterized in that: the correction of turbulent viscosity coefficient in the correction of transformation ratio parameter in cavitation model and turbulence model is adopted to C language compilation, and recycling macro call (DEFINE_TURBULENT_VISCOSITY etc.) form embeds calculation procedure.
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Cited By (24)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
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| 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 |
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| CN110738115A (en) * | 2019-09-12 | 2020-01-31 | 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 |
| CN111160207A (en) * | 2019-12-24 | 2020-05-15 | 浙江大学 | Paddle number feature extraction method based on radiation noise modulation |
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| CN112284681A (en) * | 2020-10-20 | 2021-01-29 | 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 |
| CN113139306A (en) * | 2020-01-17 | 2021-07-20 | 哈尔滨工业大学 | Numerical prediction method for cavitation noise of composite propeller |
| 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 |
| CN113643679A (en) * | 2021-10-14 | 2021-11-12 | 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 |
| CN114169203A (en) * | 2021-12-09 | 2022-03-11 | 西安交通大学 | Two-phase flow total-hidden numerical method for transient safety analysis of nuclear power plant |
| CN114611214A (en) * | 2022-03-15 | 2022-06-10 | 中国舰船研究设计中心 | Open water performance simulation method based on propeller efficient modeling |
| CN114879718A (en) * | 2022-07-12 | 2022-08-09 | 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 |
|---|
| 方世良 等: "船舶螺旋桨辐射噪声与尾流场的相关性及特征分析方法", 《声学技术》 * |
| 朱志峰 等: "基于非结构网格RAND方法螺旋浆空化研究", 《海洋工程》 * |
| 朱志峰 等: "船舶螺旋桨黏性空化流场数值方法", 《东南大学学报》 * |
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