Quick prediction and dynamic regulation and control method for flow field quality of gas compressor under low Reynolds number

Abstract

The invention discloses a method for rapidly predicting and dynamically regulating and controlling the quality of a flow field of a gas compressor under a low Reynolds number. According to the method, firstly, pressure gradient parameters Ks of a blade suction surface laminar flow separation point under different Reynolds numbers are obtained through high-precision performance testing / numerical calculation, and then experience prediction models of the Ks, an incoming flow Reynolds number Re, a separation point momentum thickness Reynolds number Re theta s, a blade surface separation scale and boundary layer development characteristics are sequentially established; and the influence of the low Reynolds number effect on the separation bubble structure and the boundary layer growth rate is quantified, so that the performance degradation degree of the gas compressor under the low Reynolds number can be quickly predicted. Based on the model, parameters such as excitation frequency of the front edge deformation surface of the blade are optimally configured, periodic pressure disturbance waves are induced to be generated, and dynamic regulation and control of large-scale separation flow under the low Reynolds number are achieved. The prediction speed of the performance variation of the gas compressor under the low Reynolds number is increased, and support is provided for aerodynamic design and performance regulation and control of the high-altitude gas compressor.

Description

Technical Field

[0001] The present invention belongs to the field of aerodynamic thermodynamics of aircraft engine compressors, and relates to large-scale separation flow transition flow inside a compressor at low Reynolds numbers. Specifically, it relates to a method for rapid prediction and dynamic control of compressor flow field quality at low Reynolds numbers, which is used to rapidly evaluate the degree of change of compressor flow field quality at low Reynolds numbers, quantify the influence of low Reynolds number effect on compressor performance degradation, and provide a basis for compressor flow field control and performance improvement at low Reynolds numbers. Background Art

[0002] Lightweight turbofan engines are the main power unit for high-altitude UAVs. In pursuit of higher thrust-to-weight ratio and propulsion efficiency, the bypass ratio of turbofan engines has gradually increased, the core engine structure has become more compact, the blade size has been reduced, and the compressor blade chord length Reynolds number has rapidly decreased. Especially under the high ceiling of UAVs, the compressor inlet Reynolds number has further dropped to 10 5 The Reynolds number is about 1-2 orders of magnitude lower than the ground operating conditions. At this time, the low Reynolds number effect causes the compressor pressure ratio to drop, the efficiency to decay, and the stability margin to degrade sharply, which in turn seriously affects the engine fuel consumption rate and working stability. Therefore, in order to ensure the stable operation of the engine under high-altitude cruise conditions, it is urgent to clarify the flow variation mechanism inside the compressor under low Reynolds number, establish a rapid prediction method for the flow field quality, and then develop an efficient dynamic control strategy.

[0003] When the Reynolds number drops to the critical value (10 5 ), the laminar flow area on the surface of the compressor blades and their end walls expands rapidly. Under the action of the adverse pressure gradient, the laminar boundary layer usually transitions to a turbulent state in the form of separation bubbles. When the separation flow transition occurs, the local turbulent pulsation is accelerated, the loss rises sharply, the boundary layer thickens rapidly, and the significant three-dimensional turbulent mixing and blockage directly affect the compressor efficiency and stable working boundary. Therefore, the separation flow transition process is the essential factor inducing the low Reynolds number effect of the compressor. However, the separation flow transition under low Reynolds number causes the local flow field to present more significant turbulent non-equilibrium / anisotropy. The traditional numerical calculation based on the Reynolds average framework is difficult to accurately predict the degree of performance variation under low Reynolds number. Although the high-precision calculation method based on large eddy simulation or direct numerical simulation can provide more accurate prediction results, the calculation cost is extremely high, and it is difficult to achieve large-scale batch calculation, and it cannot meet the iterative flow field calculation requirements in the aerodynamic design stage. In addition, the test measurement also has problems such as long time consumption and high cost. Moreover, the performance change of the compressor at low Reynolds number is more sensitive to external disturbances. A slight error in the test process may cause the flow field to deviate seriously from the preset state, which will seriously interfere with the quantification of the performance variation of the compressor at low Reynolds number. Therefore, how to improve the timeliness of the prediction of the flow field of the low Reynolds number compressor while ensuring the calculation accuracy is one of the technical problems that need to be solved urgently.

[0004] In terms of flow field control, conventional passive control methods (such as blade leading edge modification, load distribution optimization, end wall non-axisymmetric design, etc.) have limited effects on large-scale separated flows at low Reynolds numbers and cannot take into account different working conditions. Conventional active control technologies (such as synthetic jets, plasma excitation, etc.) have good control potential for large-scale separated flows, but generally have inherent defects such as complex auxiliary structures and high energy consumption, and cannot meet the urgent needs of compressor loss reduction and efficiency improvement at low Reynolds numbers.

[0005] Chinese invention patent application 202410458038.7 discloses a method for analyzing engine performance at high altitude and low Reynolds number. This method is based on a zero-dimensional engine model and a three-dimensional simulation model, combined with design point information, to perform parameter calculations and iterative updates on multiple rotating components (including axial compressors, turbines, etc.) to achieve engine performance evaluation at high altitude and low Reynolds number conditions. However, this method mainly focuses on the overall performance evaluation of the engine, and does not involve laminar separation, transition processes, and turbulent non-equilibrium effects at low Reynolds numbers. It is difficult to accurately reflect the underlying physical mechanism of compressor performance degradation at low Reynolds numbers, so its practical application value is relatively limited.

[0006] In summary, the low Reynolds number effect has a significant impact on the compressor performance, and the existing numerical simulation and measurement methods are difficult to accurately predict complex flow phenomena such as separation and transition under low Reynolds numbers, and cannot accurately quantify the degree of degradation of compressor performance under low Reynolds numbers; traditional flow control methods generally have problems such as complex structure, high energy consumption, and difficulty in balancing multiple working conditions. Therefore, how to quickly predict the degree of change in the compressor flow field quality under high-altitude low Reynolds numbers, and on this basis develop an efficient dynamic control strategy, is a technical problem that needs to be urgently solved in the field of high-altitude light aircraft engine compression system design. Summary of the invention

[0007] 1. Purpose of the invention In order to solve the problem of compressor performance prediction and efficient regulation under low Reynolds number, the present invention focuses on the separation flow transition torsion flow, which is the essential factor inducing the low Reynolds number effect of the compressor, and proposes a laminar separation point pressure gradient parameter. Ks A flow field quality prediction method with the core is used to quickly quantify the degree of compressor performance degradation at low Reynolds numbers. Based on the prediction results of the separation scale, key parameters such as the excitation frequency of the leading edge deformation surface are determined to form a dynamic control scheme for the three-dimensional flow field of the compressor at low Reynolds numbers, providing support for improving the aerodynamic characteristics of the compressor at high altitude and low Reynolds numbers.

[0008] (II) Technical solution The purpose of this invention is to propose a method for quickly predicting the quality of compressor flow field at low Reynolds numbers, and on this basis develop a control strategy based on the leading edge deformation surface to achieve efficient organization and performance improvement of the internal flow field of the compressor at low Reynolds numbers. The key to this invention is to establish a method based on the laminar separation point pressure gradient parameter. K s The separation scale prediction model and leading edge deformation surface control scheme are the core. The main steps are as follows: SS1. Acquisition of low Reynolds number flow field characteristic data: Based on high-precision performance testing or numerical calculation, obtain the flow field characteristic data of the compressor blade surface at different Reynolds numbers, including at least the pressure gradient parameters at the laminar separation point K s , the boundary layer momentum thickness at the laminar separation point Reynolds number Re θs , the distance from the laminar separation point to the leading edge of the blade S sep , separation bubble length L bs , maximum thickness of separation bubble , boundary layer displacement thickness at laminar separation point , turbulent reattachment boundary layer momentum thickness i r and the thickness of the boundary layer momentum at the trailing edge of the blade i T ; SS2. Establishing the Reynolds number Re and K s The association model: The least square method is used to fit the K s Data, build a correlation model between the two K s = f (Re), quantitatively describes the effect of Reynolds number changes on the pressure gradient at the laminar separation point; SS3. Establish the boundary layer momentum thickness Reynolds number Re θs and K s The association model: Based on the Reynolds number Re of the boundary layer momentum thickness at the laminar separation point θs and pressure gradient parameters K s The empirical relationship , establish different Reynolds numbers Re θs and K s The association model, where l θis the Polhausen parameter, and its specific value is determined by least squares fitting; SS4. Establish the geometric scale and K s The association model: Based on the separation bubble length L bs and the boundary layer momentum thickness Reynolds number Re θs The empirical relationship between , and Re θs and K s The empirical relationship between , respectively establish the separation bubble length L bs , maximum thickness of separation bubble and K s The association model: Among them, the parameters C , D , a , b , A 1. B 1 and C The value of 1 was determined by least squares fitting; SS5. Establish boundary layer development characteristics and The association model: The maximum thickness of the separation bubble As the main characterization variable, the momentum thickness of the turbulent reattachment boundary layer is established i r and the thickness of the boundary layer momentum at the trailing edge of the blade i T Maximum thickness of the separation bubble A correlation model is used to quantify the effect of the blade surface separation scale on the growth rate and loss level of the turbulent boundary layer: in, C ax is the axial chord length of the blade, parameter A 2. B 2. C 2. A 3 and B 3 is the empirical coefficient and is determined by least squares fitting; SS6. Flow quality change and performance degradation prediction at low Reynolds numbers: Based on the correlation models established in steps SS2 to SS5, the Reynolds number Re of the working condition to be predicted is input, and the pressure gradient parameters at the laminar separation point are calculated in sequence. K s , the momentum thickness of the boundary layer at the laminar separation point Reynolds number Re θs , separation bubble length L bs and maximum thickness , turbulent reattachment boundary layer momentum thickness i r and the thickness of the boundary layer momentum at the trailing edge of the blade i T , to achieve rapid quantitative prediction of blade surface separation scale and performance degradation degree.

[0009] The second invention object of the present invention is to provide a method for dynamically controlling large-scale separation flow on the surface of a compressor blade at a low Reynolds number, wherein a periodically vibrating deformation surface is arranged near the leading edge of the compressor blade, and a pressure disturbance wave is induced by the periodic vibration of the deformation surface to affect the velocity profile distribution of the downstream boundary layer, thereby realizing dynamic control of the separation flow characteristics on the surface of the compressor blade at a low Reynolds number. The dynamic control method comprises at least the following steps when implemented: SS1. Design and layout of deformation surfaces A deformation surface is arranged near the leading edge of the compressor blade, and the deformation surface performs sinusoidal periodic vibration along the normal direction of the wall surface, and the vibration amplitude A The same order of magnitude as the local boundary layer thickness, the excitation frequency f According to the separation bubble length L bs and the leading edge disturbance propagation speed V 1. It should be ensured that at least there is a pressure disturbance wave induced by the deformation surface inside the separation shear layer at any time to achieve the best control effect; SS2. Dynamic optimization of the excitation frequency of the deformed surface Based on the above-mentioned method for rapid prediction of compressor flow field quality under low Reynolds number of the present invention, the distance from the laminar separation point to the leading edge of the blade under the current working condition is obtained. S sep and separation bubble length L bs , combined with the leading edge disturbance propagation speed V 1 and the induced eddy transport velocity in the large-scale separation zone V 2 , dynamically optimize the excitation frequency of the deformation surface f satisfy: This allows the pressure disturbance wave to continue to act on the separation shear layer, thereby suppressing large-scale separation flow; SS3. Performance feedback and adjustment of dynamic control effects Based on the above-mentioned rapid prediction method of compressor flow field quality under low Reynolds number, the flow field characteristics on the compressor blade surface are quickly predicted, including at least the distance from the laminar separation point to the leading edge of the blade. S sep , separation bubble length L bs , maximum thickness of separation bubble , turbulent reattachment boundary layer momentum thickness i r and the thickness of the boundary layer momentum at the trailing edge of the blade i T , real-time feedback of the effect of deformation surface vibration parameter adjustment: when the separation bubble length L bs When it increases, increase the excitation frequency f To speed up the propagation of disturbance; when the maximum thickness of the separation bubble When it increases, the amplitude increases A To enhance the disturbance intensity; by dynamically adjusting the excitation frequency f and amplitude A The intensity of the pressure disturbance wave is optimized until continuous and efficient suppression of large-scale separation flows under extreme conditions is achieved.

[0010] (III) Technical Effect Compared with the prior art, the method for rapid prediction and dynamic control of compressor flow field quality under low Reynolds number of the present invention has the following beneficial and significant technical effects: (1) The method for rapid prediction of compressor flow field quality at low Reynolds numbers proposed in this invention has clear physical meaning and can quickly guide the design of control schemes. Pressure gradient parameter at laminar separation point K s It is the fundamental factor that determines the separation bubble structure, boundary layer growth rate and overall loss / performance variation. Based on the above physical essence, the present invention constructs a K s As the core variable, the separation scale prediction model is established. K s The Reynolds number of the incoming flow Re and the Reynolds number of the momentum thickness at the separation point Re θs The quantitative relationship between the separation scale of the blade surface and the boundary layer development characteristics can be quickly quantified to achieve the degree of compressor performance degradation at low Reynolds numbers, providing a direct theoretical basis for flow field control at low Reynolds numbers.

[0011] (2) The present invention also proposes an efficient flow field control method based on the leading edge dynamic deformation surface based on the separation scale prediction model under low Reynolds number, and establishes the minimum excitation frequency criterion to ensure that the pressure disturbance wave can continue to act on the separation shear layer. The deformation surface vibrates periodically along the local normal to generate pressure disturbance waves, making the boundary layer velocity profile fuller and significantly enhancing the ability to resist adverse pressure gradients, thereby suppressing large-scale separation. In addition, the deformation surface can also take into account the control effects of different working conditions, and has significant advantages in reducing flow field losses and increasing efficiency in a wide airspace range. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 It is a flow chart of the implementation of the method for rapid prediction of compressor flow field quality under low Reynolds number of the present invention; Figure 2 It is a schematic diagram of the correlation model between the momentum thickness Reynolds number and the pressure gradient parameter at the laminar separation point; Figure 3 Schematic diagram of the correlation model between the separation bubble length and the pressure gradient parameter at the laminar separation point; Figure 4 Schematic diagram of the correlation model between separation bubble thickness and laminar separation point pressure gradient parameter; Figure 5 is the momentum thickness of the trailing edge boundary layer i T Maximum thickness of the separation bubble d B Schematic diagram of the association model; Figure 6 It is a schematic diagram of the regulation of the leading edge dynamic deformation surface of the present invention. DETAILED DESCRIPTION

[0013] In order to make the purpose, technical scheme and advantages of the implementation of the present invention clearer, the technical scheme in the embodiment of the present invention will be described in more detail below in conjunction with the drawings in the embodiment of the present invention. The described embodiments are part of the embodiments of the present invention, not all of the embodiments, and the described embodiments are exemplary and are intended to be used to explain the present invention, and cannot be understood as limiting the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0014] Example 1: Rapid prediction method In order to solve the problem of compressor performance prediction and efficient regulation under low Reynolds number, the present invention focuses on the separation flow transition torsion flow, which is the essential factor inducing the low Reynolds number effect of the compressor, and proposes a laminar separation point pressure gradient parameter. KsA flow field quality prediction method with the core is used to quickly quantify the degree of compressor performance degradation at low Reynolds numbers. Based on the prediction results of the separation scale, key parameters such as the excitation frequency of the leading edge deformation surface are determined to form a dynamic control scheme for the three-dimensional flow field of the compressor at low Reynolds numbers, providing support for improving the aerodynamic characteristics of the compressor at high altitude and low Reynolds numbers.

[0015] The method for rapidly predicting the compressor flow field quality under low Reynolds number proposed in the embodiment of the present invention is used to rapidly predict the compressor flow field quality under low Reynolds number conditions, quantify the impact of the low Reynolds number effect on the compressor performance degradation, and provide a theoretical basis for the dynamic control of the compressor. Figure 1 As shown, the main steps are as follows: SS1. Acquisition of low Reynolds number flow field characteristic data: Based on high-precision performance testing or numerical calculation, obtain the flow field characteristic data of the compressor blade surface at different Reynolds numbers, including at least the pressure gradient parameters at the laminar separation point K s , the boundary layer momentum thickness at the laminar separation point Reynolds number Re θs , the distance from the laminar separation point to the leading edge of the blade S sep , separation bubble length L bs , maximum thickness of separation bubble , boundary layer displacement thickness at laminar separation point , turbulent reattachment boundary layer momentum thickness i r and the thickness of the boundary layer momentum at the trailing edge of the blade i T wait.

[0016] Preferably, the high-precision performance test can be: measuring the surface load distribution and boundary layer velocity profile of the compressor blades through wall static pressure holes, hot wire anemometers, laser Doppler velocimeters or particle image velocimetry, and obtaining the pressure gradient parameters and boundary layer integral parameters at the laminar separation point on the compressor blade surface.

[0017] Numerical calculation can be: using large eddy simulation (LES) or direct numerical simulation (DNS) method, by setting different inlet and outlet conditions to change the incoming flow Reynolds number, high-precision numerical analysis of the compressor flow field, the near-wall grid scale meets y + <1, the time step advances to meet the CFL number less than 1, so as to capture the highly unsteady flow characteristics such as separation and transition under low Reynolds number conditions, and obtain the pressure gradient parameters, boundary layer parameters and separation bubble structure information at the laminar separation point on the compressor blade surface.

[0018] In the embodiment of the present invention, the pressure gradient parameter at the laminar separation point is Ks It is used to characterize the strength of the pressure gradient at the separation point, which determines the disturbance amplification rate and transition trigger position in the separation shear layer, and thus affects the separation scale and loss level. The algorithm formula is: ,in v is the kinematic viscosity coefficient, U is the flow velocity on the blade surface, x is the blade flow direction coordinate position, dU / dx is the velocity gradient of the blade surface; by obtaining the velocity distribution of the blade surface U ( x ), calculated using numerical difference method K s .

[0019] In the embodiment of the present invention, the Reynolds number of the boundary layer momentum thickness Re θs The acquisition and calculation include: obtaining the boundary layer velocity profile at the laminar separation point on the compressor blade surface through high-precision performance testing or numerical calculation, based on the algorithm formula Integrate to calculate the boundary layer momentum thickness θs ,in U is the flow velocity on the blade surface, is the mainstream velocity at the laminar separation point, is the mainstream density, d is the boundary layer thickness, n is the normal coordinate of the wall; then based on the obtained boundary layer momentum thickness θs Calculating the Momentum Thickness Reynolds Number .

[0020] SS2. Establish the Reynolds number Re and K s The association model: Based on the pressure gradient parameters at the laminar separation point on the blade surface under different Reynolds numbers Re K s Data, using the least squares method to establish different Reynolds numbers K s Correlation model with Reynolds number K s = f (Re).

[0021] As a preference, establish the pressure gradient parameter K s Model associated with Reynolds number Re K s = f (Re) includes: first, obtaining the pressure gradient parameters at the laminar separation point under different Reynolds numbers Re in turn K s; Then the nonlinear least squares regression method is used to fit K s The functional relationship between and Re is established, and cross-validation techniques are used in the fitting process to evaluate the generalization ability of the model and optimize the fitting parameters.

[0022] SS3. Establish the boundary layer momentum thickness Reynolds number Re θs and K s The association model: Based on Polhausen parameters l θ ,Establish K s The momentum thickness of the boundary layer at the laminar separation point Re θs Using the same least squares method, we can determine l θ Specific value.

[0023] Specifically, based on the Reynolds number Re of the boundary layer momentum thickness at the laminar separation point θs and pressure gradient parameters K s The empirical relationship , establish different Reynolds numbers Re θs and K s The association model, where l θ is the Polhausen parameter, and its specific value is determined by least squares fitting.

[0024] As a preference, l θ The specific value of is determined by the following steps: First, the Reynolds number Re of the boundary layer momentum thickness at the laminar separation point under different Reynolds numbers is obtained. θs and pressure gradient parameters K s ; Then, the Polhausen parameters under different Reynolds numbers were determined using the least squares method , thus we get Re θs and K s The relationship model between K s = f (Re θs ).

[0025] SS4. Establish the geometric scale and K s The association model: The separation bubble structure (length and thickness) has a decisive influence on the boundary layer growth rate and loss level at low Reynolds numbers, and is also an intuitive representation of the degree of performance variation / degradation at low Reynolds numbers. L bs With Re θs The following empirical relationship is satisfied: In the above formula, S sep is the distance from the leading edge of the blade to the laminar separation point. Based on the least squares method, the parameters are determined C , D After that, we further deduce L bs and K s The association model is: by L bs / S sep As an intermediate variable, the maximum relative thickness of the separation bubble can be obtained and K s The association model of is the displacement thickness of the boundary layer at the laminar separation point.

[0026] Through the above steps, the mathematical modeling of the separation scale (separation bubble length + thickness) of the blade surface under different Reynolds numbers can be completed.

[0027] Preferably, the separation bubble geometry is K s The parameters in the correlation model are determined by the following steps: First, the pressure gradient parameters at the laminar separation point under different low Reynolds number conditions are obtained. K s , boundary layer momentum thickness Reynolds number Re θs , the distance from the laminar separation point to the leading edge of the blade S sep , separation bubble length L bs , maximum thickness of separation bubble , boundary layer displacement thickness , Secondly, based on the algorithm formula , , The least squares method is used to fit and determine the parameters C , D , a ,b , A 1. B 1 and C The specific value of 1.

[0028] SS5. Establish boundary layer development characteristics and The association model: To characterize the effect of blade surface separation scale on loss / performance, the turbulent reattachment boundary layer momentum thickness is established next i r and the momentum thickness of the boundary layer at the trailing edge i T Maximum thickness of the separation bubble The empirical prediction model is as follows: Maximum thickness of separation bubble It directly determines the growth rate of the turbulent boundary layer. The larger the value, the faster the turbulent boundary layer grows downstream of the separation bubble. i r and i T Rapidly increases, inducing strong flow blockage and wake mixing, and deteriorating the aerodynamic performance of the blade. As a preferred option, the momentum thickness of the turbulent reattachment boundary layer i r and the thickness of the boundary layer momentum at the trailing edge of the blade i T Maximum thickness of the separation bubble In the correlation model, each empirical coefficient is determined by the following steps: First, the momentum thickness of the turbulent reattachment boundary layer under different low Reynolds number conditions is obtained. i r , Boundary layer momentum thickness at the trailing edge of the blade i T , maximum thickness of separation bubble ; Secondly, the least squares method is used to fit and determine the empirical coefficients A 2. B 2. C 2. A 3 and B The specific value of 3.

[0029] SS6. Flow quality change and performance degradation prediction at low Reynolds numbers: Based on the correlation models established in steps SS2 to SS5, the Reynolds number Re of the working condition to be predicted is input, and the pressure gradient parameters at the laminar separation point are calculated in sequence. K s , the momentum thickness of the boundary layer at the laminar separation point Reynolds number Re θs , separation bubble lengthL bs and maximum thickness , turbulent reattachment boundary layer momentum thickness i r and the thickness of the boundary layer momentum at the trailing edge of the blade i T , to achieve rapid quantitative prediction of blade surface separation scale and performance degradation degree.

[0030] As a preferred method, the separation bubble length at different Reynolds numbers obtained based on the correlation model L bs and maximum thickness Quantify the variability of flow blockage in compressors at low Reynolds numbers; Momentum thickness of turbulent reattachment boundary layer based on correlation model i r and the thickness of the boundary layer momentum at the trailing edge of the blade i T , quantify the variation of the three-dimensional mixing loss of the compressor at low Reynolds numbers, thereby evaluating the impact of the low Reynolds number effect on the compressor efficiency and stability boundary, and providing theoretical support for compressor flow control and performance improvement at low Reynolds numbers.

[0031] Example 2: Dynamic Control Method

[0032] As described in Example 1, the present invention is based on the laminar separation point pressure gradient parameter K s A rapid prediction method for the compressor flow field quality at low Reynolds numbers was constructed, which achieved quantitative characterization of the separation bubble structure and boundary layer growth rate, and quantified the impact of the low Reynolds number effect on the compressor performance degradation. On this basis, in order to further improve the aerodynamic performance of the compressor at low Reynolds numbers, this embodiment 2 further proposes a dynamic control method based on the leading edge deformation surface, which induces pressure disturbance waves to affect the downstream boundary layer velocity profile distribution by setting a periodically vibrating deformation surface at the leading edge of the blade, enhancing the ability to resist adverse pressure gradients, thereby effectively eliminating large-scale flow separation inside the compressor at low Reynolds numbers and achieving loss reduction and efficiency improvement.

[0033] Specifically, since the separation scale at low Reynolds numbers determines the loss level, it is also the main object and goal of flow regulation. Setting a periodically vibrating deformation surface near the leading edge and inducing pressure disturbance waves to affect the velocity profile distribution of the downstream boundary layer is expected to improve the separation scale and loss level. For the deformation surface, the excitation frequency is a key factor affecting the separation regulation effect. Under ideal regulation conditions, the presence of pressure disturbance waves must be continuously felt in the separation shear layer at any time. Therefore, the excitation frequency of the leading edge deformation surface should satisfy: in, V1 and V 2 are the leading edge disturbance propagation velocity and the induced eddy transport velocity in the large-scale separation zone, respectively. Through the above separation scale prediction model, the deformation surface excitation frequency, the most critical parameter in the dynamic control scheme of the flow field, can be quickly determined, providing basic support for achieving efficient organization of the flow field and reducing losses and increasing efficiency under low Reynolds numbers.

[0034] More specifically, the method for dynamically controlling large-scale separation flow on the surface of a compressor blade at a low Reynolds number provided in this embodiment comprises at least the following steps when implemented: SS1. Design and arrangement of deformation surfaces A deformation surface is arranged near the leading edge of the compressor blade, and the deformation surface performs sinusoidal periodic vibration along the normal direction of the wall surface, and the vibration amplitude A The same order of magnitude as the local boundary layer thickness, the excitation frequency f According to the separation bubble length L bs and the leading edge disturbance propagation speed V 1. It should be ensured that at least there is a pressure disturbance wave induced by the deformation surface inside the separation shear layer at any time to achieve the best control effect; SS2. Dynamic optimization of the excitation frequency of the deformed surface Based on the above-mentioned method for rapid prediction of compressor flow field quality under low Reynolds number of the present invention, the distance from the laminar separation point to the leading edge of the blade under the current working condition is obtained. S sep and separation bubble length L bs , combined with the leading edge disturbance propagation speed V 1 and the induced eddy transport velocity in the large-scale separation zone V 2 , dynamically optimize the excitation frequency of the deformation surface f satisfy: This allows the pressure disturbance wave to continue to act on the separation shear layer, thereby suppressing large-scale separation flow; SS3. Performance feedback and adjustment of dynamic control effects Based on the above-mentioned method for rapid prediction of compressor flow field quality under low Reynolds number of the present invention, the flow field characteristics on the surface of the compressor blade are rapidly predicted, including at least the distance from the laminar separation point to the leading edge of the blade. S sep , separation bubble length L bs , maximum thickness of separation bubble , turbulent reattachment boundary layer momentum thickness ir and the thickness of the boundary layer momentum at the trailing edge of the blade i T , real-time feedback of the effect of deformation surface vibration parameter adjustment: when the separation bubble length L bs When it increases, increase the excitation frequency f To speed up the propagation of disturbance; when the maximum thickness of the separation bubble When it increases, the amplitude increases A To enhance the disturbance intensity; by dynamically adjusting the excitation frequency f and amplitude A The intensity of the pressure disturbance wave is optimized until continuous and efficient suppression of large-scale separation flows under extreme conditions is achieved.

[0035] Example 3: Application Example Taking a high subsonic compressor blade as an example, at a low Reynolds number of 1.5×10 5 ~4.5×10 5 Separation scale prediction and efficient control can be carried out within a certain range. Figure 2 The momentum thickness Reynolds number of the laminar separation point on the blade suction surface under different working conditions is given and pressure gradient parameters K s The Polhausen parameter Take -0.093. When the Reynolds number increases from 4.5×10 5 Reduced to 1.5×10 5 hour, K s (Absolute value) increased by 2.1 times.

[0036] Figure 3-Figure 4 The separation scales (separation bubble length) of the blade surface under different Reynolds number conditions are given respectively. L bs and maximum thickness )and K s Based on the least squares method, the association model is determined Medium model parameters a =13972.6, b =0.716. Compared with the Reynolds number of 4.5×10 5 Working condition, Reynolds number 1.5×10 5 The separation bubble length increases by 122% and the maximum thickness increases by 147%. The above results show that as the Reynolds number decreases, the laminar separation point does not change much ( S sep about 30% of the axial chord length), but the adverse pressure gradient at the laminar separation point is enhanced ( K sThe absolute value increases), resulting in a sharp increase in the length and maximum thickness of the separation bubble.

[0037] As the Reynolds number decreases, the separation bubble on the blade surface grows rapidly, and the eddy dynamics process and turbulent mixing in the separation shear layer are enhanced, which has a more significant impact on the boundary layer growth rate and loss level. Figure 5 The momentum thickness of the boundary layer at the trailing edge of the blade suction surface under different working conditions is given Maximum thickness of the separation bubble As you can see, Almost Linear growth. When the Reynolds number increases from 4.5×10 5 Reduced to 1.5×10 5 hour, The increase was 185%, and the corresponding blade loss increased by 241%, and the performance declined sharply. Therefore, the separation bubble size (length and maximum thickness) can be used as an intuitive variable to characterize the performance variation / degradation degree at low Reynolds numbers. The above process quantifies the impact of the low Reynolds number effect on the separation bubble structure and boundary layer growth rate, and realizes the rapid prediction of the compressor performance degradation degree at low Reynolds numbers.

[0038] Figure 6 A schematic diagram of the control of the dynamic deformation surface of the leading edge under low Reynolds number is given. Near the leading edge, the deformation surface performs sinusoidal periodic vibration along the wall normal. A is the amplitude (usually of the same order of magnitude as the local boundary layer thickness), f is the vibration frequency. Combined with the proposed separation zone scale prediction model, the excitation frequency of the leading edge dynamic deformation surface can be obtained, and the control scheme design can be completed. In a vibration cycle, when the profile bulges, the airflow accelerates, and a transient low-pressure area is formed locally; when the profile shrinks, the airflow decelerates, and a transient high-pressure area appears locally. Therefore, pressure disturbance waves will be periodically generated downstream of the dynamic deformation surface. The pressure disturbance wave moves at a speed of V 1 propagates downstream, making the local boundary layer velocity profile fuller and the ability to resist adverse pressure gradient greatly enhanced, thereby achieving efficient regulation of large-scale separated flow. In addition, it should be noted that when the deformation surface excitation source is removed, the flow field automatically transforms into a non-regulated state. Therefore, this dynamic regulation scheme can take into account the regulation effects of different working conditions and has significant advantages in reducing losses and increasing efficiency of flow fields in a wide airspace range.

[0039] Through the above embodiments, the purpose of the present invention is fully and effectively achieved. Those skilled in the art can understand that the present invention includes but is not limited to the contents described in the drawings and the above specific embodiments. Although the present invention has been described with respect to the most practical and preferred embodiments currently considered, it should be understood that the present invention is not limited to the disclosed embodiments, and any modification that does not deviate from the functional and structural principles of the present invention will be included in the scope of the claims.

Claims

1. A method for rapid prediction of compressor flow field quality at low Reynolds number, characterized in that: include: SS1. Based on high-precision performance testing or numerical calculation, obtain the flow field characteristic data of the compressor blade surface at different Reynolds numbers, including at least the pressure gradient parameters at the laminar separation point K s , the boundary layer momentum thickness at the laminar separation point Reynolds number Re θs , the distance from the laminar separation point to the leading edge of the blade S sep , separation bubble length L bs , maximum thickness of separation bubble , boundary layer displacement thickness at laminar separation point , turbulent reattachment boundary layer momentum thickness θ r and the thickness of the boundary layer momentum at the trailing edge of the blade θ T ; SS2. Least square method is used to fit the Reynolds number Re K s Data, build a correlation model between the two K s = f (Re), quantitatively describes the effect of Reynolds number changes on the pressure gradient at the laminar separation point; SS3. Based on the Reynolds number Re of the boundary layer momentum thickness at the laminar separation point θs and pressure gradient parameters K s The empirical relationship , establish different Reynolds numbers Re θs and K s The association model, where λ θ is the Polhausen parameter, and its specific value is determined by least squares fitting; SS4. Based on separation bubble length L bs and the boundary layer momentum thickness Reynolds number Re θs The empirical relationship between , and Re θs and K s The empirical relationship between , respectively establish the separation bubble length L bs , maximum thickness of separation bubble and K s The association model: Among them, the parameters C , D , a , b , A 1. B 1 and C The value of 1 was determined by least squares fitting; SS5. Maximum thickness of separation bubble As the main characterization variable, the momentum thickness of the turbulent reattachment boundary layer is established θ r and the thickness of the boundary layer momentum at the trailing edge of the blade θ T and The association model: in, C ax is the axial chord length of the blade, parameter A 2. B 2. C 2. A 3 and B 3 is the empirical coefficient and is determined by least squares fitting; SS6. Based on the correlation models established in steps SS2 to SS5, input the Reynolds number Re of the working condition to be predicted, and calculate the pressure gradient parameters at the laminar separation point in sequence K s , the momentum thickness of the boundary layer at the laminar separation point Reynolds number Re θs , separation bubble length L bs and maximum thickness , turbulent reattachment boundary layer momentum thickness θ r and the thickness of the boundary layer momentum at the trailing edge of the blade θ T , to achieve rapid quantitative prediction of compressor blade surface separation scale and performance degradation degree.

2. The method for rapid prediction of compressor flow field quality at low Reynolds number according to claim 1 is characterized in that: In the above step SS1, the high-precision performance test measures the load distribution on the compressor blade surface and the boundary layer velocity profile by means of a wall static pressure hole, a hot wire anemometer, a laser Doppler velocimeter or a particle image velocimeter, and obtains the pressure gradient parameters and the boundary layer integral parameters at the laminar separation point on the compressor blade surface; the numerical calculation adopts a large eddy simulation or a direct numerical simulation method, and the incoming flow Reynolds number is changed by setting different inlet and outlet conditions, and the compressor flow field is subjected to high-precision numerical analysis, and the near-wall grid scale meets y + <1, the time step advances to meet the CFL number less than 1, so as to capture the highly unsteady flow characteristics such as separation and transition under low Reynolds number conditions, and obtain the pressure gradient parameters, boundary layer parameters and separation bubble structure information at the laminar separation point on the compressor blade surface.

3. The method for rapid prediction of compressor flow field quality at low Reynolds number according to claim 1 is characterized in that: In the above step SS1, the pressure gradient parameter at the laminar separation point is K s It is used to characterize the strength of the flow pressure gradient at this location. The algorithm formula is: ,in v is the kinematic viscosity coefficient, U is the flow velocity on the blade surface, x is the blade flow direction coordinate position, Ud / dx is the velocity gradient of the blade surface; by obtaining the velocity distribution of the blade surface U ( x ), the pressure gradient parameters are calculated using the numerical difference method K s .

4. The method for rapid prediction of compressor flow field quality at low Reynolds number according to claim 1, characterized in that: In the above step SS1, the boundary layer momentum thickness Reynolds number Re θs The acquisition and calculation include: firstly, obtaining the boundary layer velocity profile at the laminar separation point on the compressor blade surface through high-precision performance testing or numerical calculation, and then based on the algorithm formula Integrate to calculate the boundary layer momentum thickness θs ,in U is the flow velocity on the blade surface, is the mainstream velocity at the laminar separation point, is the mainstream density, δ is the boundary layer thickness, n is the normal coordinate of the wall surface; then based on the obtained boundary layer momentum thickness θs Calculating the Momentum Thickness Reynolds Number .

5. The method for rapid prediction of compressor flow field quality at low Reynolds number according to claim 1, characterized in that: In the above step SS2, the pressure gradient parameter is established K s Model associated with Reynolds number Re K s = f (Re) includes: first, obtaining the pressure gradient parameters at the laminar separation point under different Reynolds numbers Re in turn K s ; Then the nonlinear least squares regression method is used to fit K s The functional relationship between and Re is established, and cross-validation techniques are used in the fitting process to evaluate the generalization ability of the model and optimize the fitting parameters.

6. The method for rapid prediction of compressor flow field quality at low Reynolds number according to claim 1, characterized in that: In the above step SS3, the Polhausen parameter λ θ The specific value of is determined by the following steps: First, the Reynolds number Re of the boundary layer momentum thickness at the laminar separation point under different Reynolds numbers is obtained. θs and pressure gradient parameters K s ; Then, the Polhausen parameters under different Reynolds numbers were determined using the least squares method , thus we get Re θs and K s The relationship model between K s = f (Re θs ).

7. The method for rapid prediction of compressor flow field quality at low Reynolds number according to claim 1, characterized in that: In the above step SS4, the geometrical dimensions of the separation bubble are K s The parameters in the correlation model are determined by the following steps: First, the pressure gradient parameters at the laminar separation point under different Reynolds number conditions are obtained. K s , boundary layer momentum thickness Reynolds number Re θs , the distance from the laminar separation point to the leading edge of the blade S sep , separation bubble length L bs , maximum thickness of separation bubble , boundary layer displacement thickness , Secondly, based on the algorithm formula , , The least squares method is used to fit and determine the parameters C , D , a , b , A 1. B 1 and C The specific value of 1.

8. The method for rapid prediction of compressor flow field quality at low Reynolds number according to claim 1, characterized in that: In the above step SS5, the momentum thickness of the turbulent reattachment boundary layer is θ r and the thickness of the boundary layer momentum at the trailing edge of the blade θ T Maximum thickness of the separation bubble In the correlation model, each empirical coefficient is determined by the following steps: First, the momentum thickness of the turbulent reattachment boundary layer under different Reynolds number conditions is obtained. θ r , Boundary layer momentum thickness at the trailing edge of the blade θ T , maximum thickness of separation bubble ; Secondly, the least squares method is used to fit and determine the empirical coefficients A 2. B 2. C 2. A 3 and B The specific value of 3.

9. The method for rapid prediction of compressor flow field quality at low Reynolds number according to claim 1, characterized in that: In the above step SS6, the separation bubble length at different Reynolds numbers obtained based on the correlation model is L bs and maximum thickness , quantify the variability of flow blockage inside the compressor at low Reynolds numbers; Momentum thickness of turbulent reattachment boundary layer based on correlation model θ r and the thickness of the boundary layer momentum at the trailing edge of the blade θ T , quantify the variation of the three-dimensional mixing loss of the compressor at low Reynolds numbers, thereby evaluating the impact of the low Reynolds number effect on the compressor efficiency and stability boundary, and providing theoretical support for compressor flow control and performance improvement at low Reynolds numbers.

10. A method for dynamically controlling large-scale separation flow on the surface of compressor blades at low Reynolds numbers, characterized in that: The dynamic control method comprises at least the following steps when implemented: SS1. Design and layout of deformation surfaces A deformation surface is arranged near the leading edge of the compressor blade, and the deformation surface performs sinusoidal periodic vibration along the normal direction of the wall surface, and the vibration amplitude A The same order of magnitude as the local boundary layer thickness, the excitation frequency f According to the separation bubble length L bs and the leading edge disturbance propagation speed V 1. It should be ensured that at least there is a pressure disturbance wave induced by the deformation surface inside the separation shear layer at any time to achieve the best control effect; SS2. Dynamic optimization of the excitation frequency of the deformed surface Based on the method for rapid prediction of compressor flow field quality under low Reynolds number as described in any one of claims 1 to 9, the distance from the laminar separation point to the leading edge of the blade under the current working condition is obtained. S sep and separation bubble length L bs , combined with the leading edge disturbance propagation speed V 1 and the induced eddy transport velocity in the large-scale separation zone V 2 , dynamically optimize the excitation frequency f satisfy: This allows the pressure disturbance wave to continue to act on the separation shear layer, thereby suppressing large-scale separation flow; SS3. Performance feedback and adjustment of dynamic control effects Based on the method for rapid prediction of compressor flow field quality under low Reynolds number as described in any one of claims 1 to 9, the flow field characteristics on the surface of the compressor blade are rapidly predicted, including at least the distance from the laminar separation point to the leading edge of the blade. S sep , separation bubble length L bs , maximum thickness of separation bubble , turbulent reattachment boundary layer momentum thickness θ r and the thickness of the boundary layer momentum at the trailing edge of the blade θ T , real-time feedback of the effect of deformation surface vibration parameter adjustment: when the separation bubble length L bs When it increases, increase the excitation frequency f To speed up the propagation of disturbance; when the maximum thickness of the separation bubble When it increases, the amplitude increases A To enhance the disturbance intensity; by dynamically adjusting the excitation frequency f and amplitude A The intensity of the pressure disturbance wave is optimized until continuous and efficient suppression of large-scale separation flows under extreme conditions is achieved.

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