Similarity method for predicting self-noise of full-scale model using small-scale model flow excitation
Patent Information
- Application Number
- CN202311267577.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2023-09-28
- Publication Date
- 2026-09-04
- Estimated Expiration
- 2043-09-28
AI Technical Summary
由于实尺度模型造价高，制造周期长，对于水下航器的设计而言，需要一种行之有效的尺度率，以通过测量小尺度模型的流激励自噪声实现对实尺度模型流激自噪声的预报
（1）本发明考虑了流态分布，通过流态控制的手段使得小尺度模型表面湍流边界层压力脉动的分布实现了与实尺度模型的相似，利用本发明得到的实尺度模型流激励自噪声结果可靠。
Smart Images
Figure CN117272863B_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of noise prediction technology, and particularly relates to a method for predicting a real-scale model using self-noise excitation from a small-scale model flow. Specifically, it relates to a method for achieving self-noise prediction of a real-scale model using flow regime control. Background Technology
[0002] When underwater vehicles move at high speeds, pressure pulsations occur within the boundary layer formed on their surface, exciting hull vibrations and generating acoustic radiation. This acoustic radiation is a significant component of the vehicle's self-noise. At high speeds, flow-induced noise is the main component of self-noise, having a substantial impact on the vehicle's internal sonar performance. Due to the high cost and long manufacturing cycle of full-scale models, an effective scale law is needed for underwater vehicle design to predict the flow-induced self-noise of full-scale models by measuring the flow-induced self-noise of small-scale models. Current methods for this problem mostly focus on the similarity of surface flow fields between models of different scales and the similarity law of acoustic radiation from models of different scales under fixed excitation. However, methods for the similarity law of flow-induced self-noise of models under fluid flow environments do not exist. Therefore, this invention proposes a method for predicting the similarity of full-scale models based on the flow-induced self-noise of small-scale models using flow control techniques. This method is of great significance for the design of vehicle hull lines and internal sonar systems. Summary of the Invention
[0003] The purpose of this invention is to solve the problems in the prior art and propose a method for predicting the similarity of a real-scale model by using small-scale model flow to excite self-noise.
[0004] This invention is achieved through the following technical solution: This invention proposes a method for predicting the similarity of a real-scale model using self-noise excitation from a small-scale model flow. The method specifically includes: Step 1: Before measurement, use CFD methods to determine the transition point of the real-scale model based on pressure pulsation and intermittent factors. Step 2: Determine whether to apply a tripwire based on the model classification and requirements. If no tripwire is applied, directly measure the flow excitation self-noise of the small-scale model. If a tripwire is applied, the tripwire size needs to be determined based on the Gibbing Reynolds number and local Reynolds number criteria to ensure that excessive additional pressure fluctuations are not introduced while achieving flow consistency between the small-scale model and the real-scale model. Step 3: Measure the flow excitation self-noise of the processed small-scale model, and obtain the prediction of the flow excitation self-noise of the real-scale model based on the scale law.
[0005] Furthermore, before measurement, the transition position of the real-scale model to be measured is determined: for a flat plate model, the critical Reynolds number method is used; for a rotating body model, it is determined by the position of the intermittent factor between 0 and 1, or by the position of a sudden increase in pressure pulsation, or by applying the potential flow-Thwaites iterative method, using the shape factor... The location is used to determine this.
[0006] Furthermore, for the flat plate model, flow control is achieved by applying tripwires; the application of tripwires should meet the Gibbing Reynolds number requirement. (1) Requirements for local Reynolds numbers: (2) In the formula, For free flow velocity, The flow rate at the top of the tripwire. The height of the tripwire. is the dynamic viscosity coefficient.
[0007] Furthermore, for the rotating body model, it is necessary to determine whether to apply tripwires based on the requirements. Since the rotating body model can use its own pressure gradient for flow control, it can directly scale down the real-scale model without applying tripwires. If more precise control is required, tripwires of appropriate size need to be applied.
[0008] Furthermore, by using the scaled-down small-scale model, the flow-excited self-noise can be obtained through CFD simulation or experimental methods.
[0009] Furthermore, assuming each flow regime is independent and neglecting the development and changes in the internal properties of the flow regime, that is, assuming that each flow regime, once formed, has the ability to maintain its own physical properties unchanged, and that the pressure pulsation intensity varies in different regions within the boundary layer, then the control parameters for the flow-induced self-noise sound pressure inside the bow come from the following three aspects: (1) Source parameter: Incoming flow velocity u fluid density ,frequency f Laminar flow region scale Transition zone scale turbulent region scale ; (2) Bow structure parameters: model scale l structural density elastic modulus e Poisson's ratio Damping ratio ; (3) Sound propagation parameters: density of the sound propagation medium speed of sound Measurement point sound source distance scale z ; Since all factors influencing the acoustic radiation generated by the boundary layer pressure fluctuations on the model excitation have been taken into account, the sound pressure at the measuring point can now be expressed as: (3) Pick , Let l be the fundamental factors, and transform equation (3) into a dimensionless form: (4) For models scaled down, the model scale l structural density Elastic modulus e, Poisson's ratio Damping ratio fluid density Density of the sound propagation medium speed of sound Since all remain unchanged, equation (4) can be simplified to: (5) Therefore, the sound pressure at the measuring points at the bow and fore-aft sections is scaled down. The condition that remains unchanged holds true when the following equation holds: (6) (7) (8) (9) Among them, physical quantities with the scale subscript are scaled physical quantities.
[0010] The beneficial effects of this invention are: (1) The present invention takes into account the flow distribution. By means of flow control, the distribution of pressure fluctuations in the turbulent boundary layer on the surface of the small-scale model is similar to that of the real-scale model. The flow excitation self-noise results of the real-scale model obtained by using the present invention are reliable.
[0011] (2) With this invention, there is no need to manufacture a real-scale model. The experimental results of the flow excitation self-noise of the small-scale model can be used to predict the real-scale model, which greatly reduces the cost of line optimization and sonar system design. Attached Figure Description
[0012] Figure 1 This is a schematic diagram of a flat plate model (the dotted area on the bottom represents a flat plate with a thickness of 8mm). Figure 2 This is a schematic diagram of a rotating body model (8mm thick). Figure 3 This is a schematic diagram of a tripwire model; Figure 4 The image shows the prediction results of a real-scale flat plate model using the method of this invention. Figure 5 The image shows the prediction results of a real-scale rotating body model using the method of this invention. Detailed Implementation
[0013] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0014] See Figures 1-5 This invention proposes a method for predicting the similarity of a real-scale model using self-noise excitation from a small-scale model flow. The method specifically includes: Step 1: Before measurement, use CFD methods to determine the transition point of the real-scale model based on pressure pulsation and intermittent factors. Step 2: Determine whether to apply a tripwire based on the model classification and requirements. If no tripwire is applied, directly measure the flow excitation self-noise of the small-scale model. If a tripwire is applied, the tripwire size needs to be determined based on the Gibbing Reynolds number and local Reynolds number criteria to ensure that excessive additional pressure fluctuations are not introduced while achieving flow consistency between the small-scale model and the real-scale model. Step 3: Measure the flow excitation self-noise of the processed small-scale model, and obtain the prediction of the flow excitation self-noise of the real-scale model based on the scale law.
[0015] Before measurement, the transition position of the real-scale model to be measured is determined: for a flat plate model, the critical Reynolds number method is used; for a rotating body model, it is determined by the position of the intermittent factor between 0 and 1, or by the position of sudden pressure pulsations, or by applying the potential flow-Thwaites iterative method using the shape factor. The location is used to determine this.
[0016] For the flat plate model, flow control is achieved by applying tripwires; the application of tripwires should meet the Gibbing Reynolds number requirement. (1) Requirements for local Reynolds numbers: (2) In the formula, For free flow velocity, The flow rate at the top of the tripwire. The height of the tripwire. is the dynamic viscosity coefficient. When designing the tripwire, the height of the tripwire must be slightly higher than the critical height to ensure sufficient flow transition, and the height of the rough element must be lower than the local boundary layer thickness to avoid introducing excessive additional resistance.
[0017] For rotating body models, it is necessary to determine whether to apply tripwires based on the requirements. Since rotating body models can use their own pressure gradient for flow control, the full-scale model can be scaled down directly without applying tripwires. However, to achieve more precise control, tripwires of appropriate size need to be applied.
[0018] The flow excitation self-noise is obtained using a scaled-down small-scale model through CFD simulation or experiments. The scale law is then applied to the flow excitation self-noise of the small-scale model to predict the flow excitation self-noise of the real-scale model.
[0019] Assuming each flow regime is independent and neglecting the development and changes in its internal properties—that is, assuming each flow regime, once formed, possesses the ability to maintain its own physical properties—and that the pressure pulsation intensity varies in different regions within the boundary layer, the control parameters for the flow-induced self-noise sound pressure inside the bow come from the following three aspects: (1) Source parameter: Incoming flow velocity u fluid density ,frequency f Laminar flow region scale Transition zone scale turbulent region scale ; (2) Bow structure parameters: model scale l (Including thickness, basic dimensions, etc.), structural density elastic modulus e Poisson's ratio Damping ratio ; (3) Sound propagation parameters: density of the sound propagation medium speed of sound Measurement point sound source distance scale z ; Since all factors influencing the acoustic radiation generated by the boundary layer pressure fluctuations on the model excitation have been taken into account, the sound pressure at the measuring point can now be expressed as: (3) Pick , Let l be the fundamental factors, and transform equation (3) into a dimensionless form: (4) For models scaled down, the model scale l structural density Elastic modulus e, Poisson's ratio Damping ratio fluid density Density of the sound propagation medium speed of sound Since all remain unchanged, equation (4) can be simplified to: (5) Therefore, the sound pressure at the measuring points at the bow and fore-aft sections is scaled down. The condition that remains unchanged holds true when the following equation holds: (6) (7) (8) (9) Among them, physical quantities with the scale subscript are scaled physical quantities.
[0020] The self-noise measurement point is in the center of the model. The model scaling ratio and the distance scale of the sound source at the measurement point have the same scaling ratio. Therefore, it is necessary to control the flow velocity to be equal (Equation (6)) and the flow state to be similar (Equation (9)). The measured sound pressure spectrum is scaled down according to the model scale ratio (Equation (7)).
[0021] In practical applications, when scaling a rotating body model, interference occurs internally. Therefore, the root mean square sound pressure of the entire acoustic cavity is usually used to replace the sound pressure at the measurement point. In this case, the sound propagation distance is not considered. z Instead, it is based on the model scale. l The equivalent acoustic cavity size will not need to meet the requirements of equation (8) at this time.
[0022] Simulation results ( Figure 4 and Figure 5 The results show that, for both the flat plate model and the rotating body model, the mean square acoustic pressure spectrum prediction results of the real-scale model using the method of this invention with the small-scale model are quite similar in terms of spectral trend, peak frequency, and peak amplitude, proving the effectiveness of the method.
[0023] This invention proposes a method for predicting the flow excitation self-noise of a real-scale model using the flow excitation self-noise of a small-scale model. The method derives a scaling factor for model scaling considering flow regime distribution using the Π theorem. By applying this scaling factor to the flow excitation self-noise of the small-scale model, the flow excitation self-noise of the real-scale model can be predicted. Furthermore, to achieve flow regime control, different flow regime control methods are provided for flat plate models and rotating body models, and a method for determining the transition position of the real-scale model is also given. Compared to other common scaling methods, this method is based on practical applications and considers the influence of flow regime distribution on acoustic similarity. Applying this method to the prediction of flow excitation self-noise of a large-scale model using a small-scale model is simple and yields reliable results.
Claims
1. A method for predicting the similarity of a real-scale model using self-noise excitation from a small-scale model flow, characterized in that, The method is specifically as follows: Step 1: Before measurement, use CFD methods to determine the transition point of the real-scale model based on pressure pulsation and intermittent factors. Step 2: Determine whether to apply tripwires based on model classification and requirements. The model classification includes flat plate models and rotating body models. For rotating body models, no tripwires are applied, and the flow excitation self-noise of the small-scale model is directly measured. For flat plate models, if tripwires are applied, the tripwire size needs to be determined according to the Gibbing Reynolds number and local Reynolds number criteria to ensure that excessive additional pressure fluctuations are not introduced while achieving consistency between the flow state of the small-scale model and the real-scale model. Step 3: Measure the flow excitation self-noise of the processed small-scale model, and obtain the prediction of the flow excitation self-noise of the real-scale model based on the scale law. Assuming each flow regime is independent and neglecting the development and changes in its internal properties—that is, assuming each flow regime, once formed, possesses the ability to maintain its own physical properties—and that the pressure pulsation intensity varies in different regions within the boundary layer, the control parameters for the flow-induced self-noise sound pressure inside the bow come from the following three aspects: (1) Source parameter: Incoming flow velocity u fluid density ,frequency f Laminar flow region scale Transition zone scale turbulent region scale ; (2) Bow structure parameters: model scale l structural density elastic modulus e Poisson's ratio Damping ratio ; (3) Sound propagation parameters: density of the sound propagation medium speed of sound Measurement point sound source distance scale z ; Since all factors influencing the acoustic radiation generated by the boundary layer pressure fluctuations on the model excitation have been taken into account, the sound pressure at the measuring point can now be expressed as: (3) Pick , Let l be the fundamental factors, and transform equation (3) into a dimensionless form: (4) For models scaled down, the model scale l structural density Elastic modulus e, Poisson's ratio Damping ratio fluid density Density of the sound propagation medium speed of sound Since all remain unchanged, equation (4) can be simplified to: (5) Therefore, the sound pressure at the measuring points at the bow and fore-aft sections is scaled down. The condition that remains unchanged holds true when the following equation holds: (6) (7) (8) (9) Among them, physical quantities with the scale subscript are scaled physical quantities.
2. The method according to claim 1, characterized in that: Before measurement, the transition position of the real-scale model to be measured is determined: for a flat plate model, the critical Reynolds number method is used; for a rotating body model, it is determined by the position of the intermittent factor between 0 and 1, or by the position of sudden pressure pulsations, or by applying the potential flow-Thwaites iterative method using the shape factor. The location is used to determine this.
3. The method according to claim 2, characterized in that: For the flat plate model, flow control is achieved by applying tripwires; the application of tripwires should meet the Gibbing Reynolds number requirement. (1) and the requirements for local Reynolds numbers: (2) In the formula, For free flow velocity, The flow rate at the top of the tripwire. The height of the tripwire. is the dynamic viscosity coefficient.
4. The method according to claim 2, characterized in that: For rotating body models, it is necessary to determine whether to apply tripwires based on the requirements. Since rotating body models can use their own pressure gradient for flow control, the full-scale model can be scaled down directly without applying tripwires. However, to achieve more precise control, tripwires of appropriate size need to be applied.
5. The method according to claim 4, characterized in that: The flow-excited self-noise is obtained by using a scaled-down small-scale model through CFD simulation or experimental methods.