CN114330035A - High-speed aircraft aerodynamic performance evaluation method - Google Patents
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Abstract
The invention discloses a method for evaluating aerodynamic performance of a high-speed aircraft, which relates to the field of aircraft research, and is based on the superposition effect and modulation effect of an outer-region large-scale turbulence structure on near-wall turbulence pulsation, and according to the generalized Reynolds ratio of speed-temperature pulsation, a model for predicting friction resistance and heat flow pulsation of a high-Reynolds-number wall turbulence is established, so that accurate prediction of the friction resistance and the heat flow pulsation of a compressible high-Reynolds-number turbulence wall based on flow field measurable signals is realized, and accurate evaluation of the aerodynamic performance of the high-speed aircraft is further realized.
Description
Technical Field
The invention relates to the field of aircraft research, in particular to a method for evaluating aerodynamic performance of a high-speed aircraft.
Background
The aerodynamic design and evaluation of the aircraft are important links in the aircraft development process. The aerodynamic design of the aircraft is a process of optimally selecting the layout form and geometric parameters of the aircraft to improve the aerodynamic performance and the flight performance. The aircraft performance evaluation refers to the process of evaluating the performances of the aircraft such as pneumatics, flight control, stealth, structural strength, power and the like through wind tunnel tests, numerical calculation, flight simulation, flight tests and other means in the design and use processes of the aircraft. The two are organic whole, the quality of the aerodynamic design of the aircraft needs to be judged through evaluation, and the innovation of the aerodynamic design can continuously push the development of the evaluation method and means.
High Reynolds number wall turbulence is an important flow problem in the field of aerospace and has a high scientific/engineering value. Outer region with increasing Reynolds number: (
，
At an outer scale, i.e.
) The large-scale motion is enhanced, and large-scale structures (the length is more than 2 times of the thickness of a boundary layer) above a logarithmic region have important influence on wall resistance, heat flow and near-wall turbulence pulsation. The influence rule of the outer region large-scale structure on the near-wall region small-scale (the length is less than 2 times of the boundary layer thickness) turbulence pulsation is deeply known, and the near-wall turbulence pulsation is accurately predicted, so that the method has important significance on the evaluation, optimization and development of the aerodynamic performance of the high-speed aircraft. A great deal of research has been carried out in recent years to reveal the contribution of turbulent pulsations to the mean resistance of the walls and to the mean heat flow. However, accurate prediction of average resistance and heat flow is not sufficientTo completely predict the aerodynamic performance of the aircraft. Researches show that strong shearing events caused by turbulence pulsation on the wall surface can cause local wall surface shearing stress and heat flow pulsation to exceed 3-5 times of the average value, and the structural strength insufficiency and the thermal protection failure of the high-speed aircraft are easily caused. Therefore, the method has important engineering application value for accurately predicting the wall resistance and the heat flow pulsation.
The outer region large scale structure can be aligned with the inner region
，
Dimensionless for the inner scale, i.e. using the viscosity scale) turbulence effects, leaving "footprints" in the near-wall region, a phenomenon known as "stacking effects"; the strength of the small-scale turbulence pulsation of the near-wall area is enhanced in the large-scale high-speed area and weakened in the large-scale low-speed area, the small-scale pulsation of the inner area has strong correlation with the large-scale turbulence pulsation of the outer area, and the nonlinear interaction is called as amplitude modulation effect. Based on the superposition and modulation effects, Marusic et al of the university of Melben proposes a prediction model of the flow direction velocity pulsation in the near-wall region, and proposes a prediction method of the variation trend of the intensity of the wall friction resistance pulsation along with the Reynolds number based on the prediction model, but the model only aims at the incompressible wall turbulence and only qualitatively describes the wall friction resistance pulsation. While for compressible wall turbulence, the near wall predictive model is very limited to study. In compressible turbulence, the density-weighted velocity pulsation intensity is generally considered consistent with the incompressible turbulence results at the same reynolds number, and thus the variation in average density needs to be considered in the predictive model. Helm et al proposed a prediction model of near-wall velocity and temperature pulsation in 2014, which can give better velocity and temperature pulsation strength, wherein the prediction model of near-wall region temperature pulsation is similar to the model of velocity pulsation, however, there is no obvious correlation between the prediction formula and the strong Reynolds ratio, which results in unclear physical meaning of the prediction method and large error.
At present, a prediction method for friction resistance and heat flow pulsation of a compressible wall turbulent flow wall surface, which is accurate in prediction, good in universality and clear in physical significance, is not available, and therefore the aerodynamic performance evaluation result of the existing aircraft based on the friction resistance pulsation and the heat flow pulsation of the wall surface is not accurate.
Disclosure of Invention
The invention aims to improve the accuracy of aerodynamic performance evaluation of a high-speed aircraft.
In order to achieve the above object, the present invention provides a method for evaluating aerodynamic performance of a high-speed aircraft, the method comprising:
constructing an aircraft wall friction resistance pulsation prediction model and an aircraft wall heat flow pulsation prediction model;
obtaining first intermediate quantity data and second intermediate quantity data through numerical simulation or experimental calibration, wherein the first intermediate quantity data comprises: the method comprises the following steps of predicting a relevant modulation coefficient of the friction drag pulsation of the wall surface of the aircraft, predicting a relevant universal signal of the friction drag pulsation of the wall surface of the aircraft, predicting a relevant phase difference of the friction drag pulsation of the wall surface of the aircraft and predicting a relevant linear random estimation kernel function of the friction drag pulsation of the wall surface of the aircraft; the second intermediate quantity data comprises: the method comprises the following steps that a relevant modulation coefficient of prediction of the wall surface heat flow pulsation of the aircraft, a relevant general signal of prediction of the wall surface heat flow pulsation of the aircraft, a relevant phase difference of prediction of the wall surface heat flow pulsation of the aircraft and a linear random prediction kernel function of prediction of the wall surface heat flow pulsation of the aircraft are obtained;
obtaining a flow direction velocity signal and a span direction velocity signal of a first preset size structure in a first area, transforming the flow direction velocity signal and the span direction velocity signal into a spectrum space to obtain a first signal and a second signal,
obtaining a first superposition result of the flow direction speed signal in a second area and a second superposition result of the spread direction speed signal in the second area based on the first signal, the second signal and a linear random prediction kernel function related to aircraft wall friction resistance pulsation prediction;
calculating to obtain the friction pulsation of the wall surface of the aircraft by utilizing the friction pulsation prediction model of the wall surface of the aircraft based on the first superposition result, the second superposition result and the first intermediate quantity data;
calculating and obtaining the wall heat flow pulsation of the aircraft by using the wall heat flow pulsation prediction model of the aircraft based on the first superposition result and the second intermediate quantity data;
and evaluating the aerodynamic performance of the aircraft based on the aircraft wall friction resistance pulsation and the aircraft wall heat flow pulsation.
For convenience in description, the first area is briefly described as an outer area, the first preset size is briefly described as a large size, the second area is briefly described as an inner area, and the first preset size is briefly described as a small size in the description of the invention.
Preferably, the aircraft wall surface friction pulsation prediction model includes:
wherein,
as a variable of the spatial coordinates,
to predict the flow-to-wall shear stress ripple,
to predict the development of wall shear stress pulsations,
for the generic signal of the de-superposition effect,
in order to de-modulate the pervasive signal of the effect,
the modulation coefficients of the flow direction friction pulsation of the second preset-size structure on the wall surface of the aircraft are obtained for the flow direction speed signal and the spreading direction speed signal,
the modulation coefficients of the flow direction speed signal and the spanwise speed signal to the spanwise friction drag pulsation of a second preset size structure on the wall surface of the aircraft,
in order to compensate the phase difference of the flow direction friction pulsation superposition effect to the modulation effect,
for the phase difference of the spanwise superposition effect to the modulation effect,
for the superposition of the streaming velocity signal at the second region,
is a superposition of the spanwise velocity signal at a second region.
The aircraft wall friction pulsation can be accurately predicted and obtained through the aircraft wall friction pulsation prediction model.
Preferably, the aircraft wall heat flow pulsation prediction model is as follows:
wherein,
the prediction result of the heat flow pulsation of the wall surface of the aircraft,
as a variable of the spatial coordinates,
is a universal signal of the pulsation of the heat flow,
the modulation coefficients of the flow direction friction pulsation of the second preset-size structure on the wall surface of the aircraft are obtained for the flow direction speed signal and the spreading direction speed signal,
the modulation coefficients of the flow direction speed signal and the spread direction speed signal to the heat flow pulsation of the structure with the second preset size on the wall surface of the aircraft,
for superposition at a second region of the streaming velocity signal,
in order to compensate the phase difference of the flow direction friction pulsation superposition effect to the modulation effect,
the modulation phase difference is modulated for the heat flow superposition,
the heat flow for the first pre-sized structure is pulsed.
The aircraft wall heat flow pulsation can be accurately predicted and obtained through the aircraft wall heat flow pulsation prediction model.
Preferably, in the method
And
the calculation method is as follows:
wherein,
in order to perform the inverse fourier transform,
in order to flow to the linear random pre-estimated kernel function,
for the span-wise linear random prediction kernel function,
in order to flow to the wavelength(s),
in order to be a fourier transform,
in order to flow to the speed signal,
in order to develop the speed signal in the direction of the speed,
the center position of the first preset size structure of the outer area.
Preferably, in the method, the center of the first region with the first preset size structure is positionedDevice for placing
The calculation method is as follows:
wherein,
in order to obtain the friction reynolds number,
in order to obtain a kinematic viscosity of the composition,
as a result of the total shear stress of the wall,
the density of the wall surface is shown as,
in order to be the viscosity coefficient,
in order to determine the speed of the friction,
in order to be a measure of the viscosity,
is the boundary layer thickness.
Preferably, in the method
The calculation method is as follows:
wherein,
in order to perform the inverse fourier transform,
for the linear random prediction kernel function associated with the heat flow,
in order to flow to the wavelength(s),
in order to be a fourier transform,
the center position of the first preset size structure of the first area,
the temperature pulsation signal is a temperature pulsation signal of a first predetermined size structure at the center of the first area.
Preferably, in the method, the size of the first predetermined dimension is greater than 2 times the thickness of the boundary layer.
Preferably, in the method, the first region is a region with an outer dimension larger than 0.1, and the second region is a region with an inner dimension smaller than 50.
Preferably, the size of the second predetermined dimension in the method is less than or equal to 2 times the thickness of the boundary layer.
One or more technical schemes provided by the invention at least have the following technical effects or advantages:
the traditional prediction model only aims at the velocity pulsation, and only carries out qualitative estimation on the prediction of the wall friction pulsation, the prediction method makes up for the blank, realizes accurate prediction of the wall friction pulsation, can be conveniently popularized to other Mach number flows, and further realizes accurate evaluation of aerodynamic performance of the high-speed aircraft.
The traditional temperature pulsation prediction model and the strong Reynolds ratio are lack of obvious correlation, so that the error of the prediction model is large, the prediction method is based on the generalized Reynolds ratio derivation, the physical significance is clear, and the prediction error is remarkably reduced.
Drawings
The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention;
FIG. 1 is a schematic flow diagram of a method for evaluating aerodynamic performance of a high-speed aircraft;
FIG. 2 is a schematic diagram of joint probability density distribution of large-scale high-speed region condition statistics, in FIG. 2, the solid line is a DNS calculation result, and the dotted line is a prediction result;
FIG. 3 is a schematic diagram of joint probability density distribution of large-scale low-speed region condition statistics, in FIG. 3, the solid line is a DNS calculation result, and the dotted line is a prediction result;
FIG. 4 is a joint probability density distribution of large-scale high-speed region condition statistics, where a solid line in FIG. 4 is a DNS calculation result and a dotted line is a prediction result;
fig. 5 is a joint probability density distribution of the condition statistics of the large-scale low-speed region, in fig. 5, a solid line is a DNS calculation result, and a dotted line is a prediction result.
Detailed Description
In order that the above objects, features and advantages of the present invention can be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings. It should be noted that the embodiments of the present invention and features of the embodiments may be combined with each other without conflicting with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those specifically described and thus the scope of the present invention is not limited by the specific embodiments disclosed below.
Example one
Referring to fig. 1, fig. 1 is a schematic flow chart of a method for evaluating aerodynamic performance of a high-speed aircraft, according to an embodiment of the present invention, the method includes:
constructing an aircraft wall friction resistance pulsation prediction model and an aircraft wall heat flow pulsation prediction model;
obtaining first intermediate quantity data and second intermediate quantity data through numerical simulation or experimental calibration, wherein the first intermediate quantity data comprises: the method comprises the following steps of predicting a relevant modulation coefficient of the friction drag pulsation of the wall surface of the aircraft, predicting a relevant universal signal of the friction drag pulsation of the wall surface of the aircraft, predicting a relevant phase difference of the friction drag pulsation of the wall surface of the aircraft and predicting a relevant linear random estimation kernel function of the friction drag pulsation of the wall surface of the aircraft; the second intermediate quantity data comprises: the method comprises the following steps that a relevant modulation coefficient of prediction of the wall surface heat flow pulsation of the aircraft, a relevant general signal of prediction of the wall surface heat flow pulsation of the aircraft, a relevant phase difference of prediction of the wall surface heat flow pulsation of the aircraft and a linear random prediction kernel function of prediction of the wall surface heat flow pulsation of the aircraft are obtained;
obtaining a flow direction velocity signal and a span direction velocity signal of a first preset size structure in a first area, transforming the flow direction velocity signal and the span direction velocity signal into a spectrum space to obtain a first signal and a second signal,
obtaining a first superposition result of the flow direction speed signal in a second area and a second superposition result of the spread direction speed signal in the second area based on the first signal, the second signal and a linear random prediction kernel function related to aircraft wall friction resistance pulsation prediction;
calculating to obtain the friction pulsation of the wall surface of the aircraft by utilizing the friction pulsation prediction model of the wall surface of the aircraft based on the first superposition result, the second superposition result and the first intermediate quantity data;
calculating and obtaining the wall heat flow pulsation of the aircraft by using the wall heat flow pulsation prediction model of the aircraft based on the first superposition result and the second intermediate quantity data;
and evaluating the aerodynamic performance of the aircraft based on the aircraft wall friction resistance pulsation and the aircraft wall heat flow pulsation.
The method adopts the existing technical means to realize the evaluation of the aerodynamic performance of the aircraft based on the wall friction pulsation of the aircraft and the wall heat flow pulsation of the aircraft, for example, the aerodynamic performance of the aircraft can be evaluated by adopting the local distribution of the wall friction and the heat flow pulsation, the extremely high friction and the high heat flow events which are easy to cause structural damage and the failure of a heat protection device, and the like.
The method is described in detail below with reference to specific steps:
from the near-wall (i.e., inner zone) velocity pulsations, wall friction pulsations are predicted:
firstly, the total shear stress of the wall surface is determined
Wall surface density
And coefficient of viscosity
The friction speed can be defined
And a viscosity scale
：
Definition of Friction Reynolds number
，
Kinematic viscosity:
superscript for variables dimensionless by viscosity scale "+"means.
The basic idea of the prediction model is to consider the superposition of a pervasive signal which is independent of the Reynolds number of an inner region in the near-wall turbulence pulsation and an outer region large-scale signal, wherein the outer region large-scale signal has an amplitude modulation effect on the near-wall small-scale pulsation. Thus, following the velocity ripple, the wall friction ripple prediction model can be written in the form (the following physical quantities are dimensionless by a viscosity scale defined by wall mean shear stress, viscosity coefficient and density):
wherein the subscript (.) x And (·) z Respectively representing the flow direction and the spreading direction;
for space coordinate variables, left side of equation
、
For wall shear stress pulsation in flow and spread directionThe predicted outcome of (a "wall" refers to a solid surface); the two terms on the right side of the equation represent the modulating effect and the superposition effect of the outer region large scale structure on the near wall small scale pulsation, respectively ("near wall" i.e., inner region turbulence). In the first item, the first item is,
and
for a generic signal that de-superimposes and modulates effects,
and
in addition to the superposition of the outer large-scale signal at the near wall,
and
the modulation coefficients corresponding to the flow direction and the span direction respectively,
and
the phase difference of the modulation is the sum-pair of the flow direction and the spread direction, respectively.
The large-scale shear stress pulsation is determined by an outer region large-scale speed pulsation signal and is given by a spectrum space linear random estimation method:
wherein,
and
respectively, the kernel functions of linear random estimation of flow direction and span direction, and the independent variable is the wavelength of flow direction
The central position of the outer zone large-scale structure is taken as
. The meaning expressed by the above formula is: utilizing Fourier transform (FFT) to flow the outer region to the velocity signal in large scale
And spanwise velocity signal
Transformation to spectral space
And
multiplying by linear random prediction kernel function
And
then through inverse Fourier transform
Transformation to physical space.
In the prediction model, an outer region large-scale signal is an input signal; other parameters need to be calibrated by high-precision numerical simulation or experimental data.
Based on the generalized Reynolds ratio, the near-wall heat flow pulsation is obtained by prediction:
to better understand the pulsation of the physical quantity, the reynolds average and Favre average are first defined. A general physical quantity
Reynolds average of
Corresponding turbulence pulsation is
Namely:
having a Favre average of
Corresponding turbulence pulsation is
Namely:
defining root mean square
(or referred to as pulse intensity):
the generalized Reynolds simulation indicates a strong correlation between velocity and temperature pulsation in a compressible wall turbulence, and it is easy to judge that there is also a strong correlation between wall friction and heat flow. Gaviglio-based proposed modificationAdvancing Reynolds analogous relation (GSRA), i.e. velocity and temperature pulsation intensity
And
the relation between:
wherein
And
to determine the reynolds average velocity and temperature profile,
the ratio of specific heat is shown as the ratio,
the local reynolds number is the local reynolds number,
is the average total temperature distribution. Further assume velocity pulsation
And temperature pulsation
The correlation can be directly carried out by the above formula, which is derived as follows:
in the formula
To be fixedSpecific heat under pressure. Equation (11) can also be written as:
equal simultaneous derivation of both sides to obtain wall friction pulsation
And heat flow pulsation
The association of (1):
suppose large-scale friction pulsation
And heat flow pulsation
Satisfy strong reynolds analog relation GSRA:
from the quasi-stationary, constant, quasi-uniform assumption, the following small-scale pulsations are proposed (subscript:)
) Correction of (2):
wherein,
is a small-scale friction drag pulsation, and the friction drag pulsation is small,
is a large scale flow direction velocity pulsation. For a general physical quantity
，
。
Given the strong dependence of large-scale velocity and temperature pulsations, the large-scale heat flow pulsations are expressed as:
writing the small-scale heat flow pulsation as universal signal and modulation coefficient
In the form of:
wherein the function
Is defined as:
substituting equation (16) into equation (18), suppose
As a constant, we obtain:
substituting (17) to obtain:
further assume that the generic signal of heat flow pulsation can be written as:
the small scale heat flow pulsation can be written as:
the large-scale speed in the above formula is replaced by the wall friction pulsation, and the wall average heat flow is used for non-dimensionalization, so that the prediction model of the wall heat flow pulsation is as follows:
wherein the large-scale heat flow pulsation is obtained by a linear random estimation method of a spectrum space:
modulation factor
Universal signal
Phase difference of superposition pair modulation
And linear random pre-estimated kernel function
Given by direct numerical simulation or experimental calibration.
The method is based on the superposition effect and modulation effect of the outer region large-scale turbulence structure on the near-wall turbulence pulsation and the generalized Reynolds ratio of speed-temperature pulsation, obtains a high Reynolds number wall turbulence wall friction resistance and heat flow pulsation prediction model, and realizes accurate evaluation of aerodynamic performance of the high-speed aircraft based on the 2 models.
The construction idea of the prediction model of the friction resistance and the heat flow pulsation of the middle and high Reynolds number wall turbulence wall surface which is invalid is as follows:
the wall friction resistance and the heat flow are considered to be composed of large-scale and small-scale signals; the large scale has a modulation effect on the small scale signal, and the small scale pervasive signal without the modulation effect has nothing to do with Reynolds number and Mach number. Therefore, the wall friction can be written as a superposition of the small-scale pulse of the large-scale modulation and the large-scale signal. Further, according to the strong correlation between the speed and the temperature pulsation, a prediction model of the wall surface heat flow pulsation is derived.
Example two
The second embodiment of the present invention will be described in detail with reference to specific examples:
in application, the modulation coefficient, the phase difference of modulation pair superposition, the linear correlation kernel function and the like in the model need to be directly numerically simulated and calibrated by the medium Reynolds number/the high Reynolds number, and the model can be suitable for other parameters after being calibrated.
When in use, the input signals are as follows: the outer zone large-scale velocity pulsation can be given by low-resolution numerical simulation or experimental measurement; outputting a signal: wall friction and heat flow pulsation signals.
The accuracy of the prediction result of the method is verified through joint Probability Density (PDF), and the parameters are calculated as follows: the incoming flow Mach number is 3.0, the total incoming flow temperature is 300K, the wall surface temperature is 420K, and the Reynolds number is 42000. As shown in fig. 2-5. The result in the graph shows that the statistical results of PDF (Portable document Format) and Direct Numerical Simulation (DNS) given by the prediction method are well matched, the prediction method can accurately reflect the superposition and modulation effects of the large-scale pulsation of the outer region on the near-wall turbulence, and the accuracy of the prediction method is proved.
FIG. 2 is a schematic diagram of joint probability density distribution of large-scale high-speed region condition statistics, in FIG. 2, the solid line is a DNS calculation result, and the dotted line is a prediction result; fig. 3 is a schematic diagram of joint probability density distribution of large-scale low-speed region condition statistics, in fig. 3, a solid line is a DNS calculation result, and a dotted line is a prediction result.
FIG. 4 is a joint probability density distribution of large-scale high-speed region condition statistics, where a solid line in FIG. 4 is a DNS calculation result and a dotted line is a prediction result; fig. 5 is a joint probability density distribution of the condition statistics of the large-scale low-speed region, in fig. 5, a solid line is a DNS calculation result, and a dotted line is a prediction result.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (9)
1. A method for evaluating aerodynamic performance of a high-speed aircraft, the method comprising:
constructing an aircraft wall friction resistance pulsation prediction model and an aircraft wall heat flow pulsation prediction model;
obtaining first intermediate quantity data and second intermediate quantity data through numerical simulation or experimental calibration, wherein the first intermediate quantity data comprises: the method comprises the following steps of predicting a relevant modulation coefficient of the friction drag pulsation of the wall surface of the aircraft, predicting a relevant universal signal of the friction drag pulsation of the wall surface of the aircraft, predicting a relevant phase difference of the friction drag pulsation of the wall surface of the aircraft and predicting a relevant linear random estimation kernel function of the friction drag pulsation of the wall surface of the aircraft; the second intermediate quantity data comprises: the method comprises the following steps that a relevant modulation coefficient of prediction of the wall surface heat flow pulsation of the aircraft, a relevant general signal of prediction of the wall surface heat flow pulsation of the aircraft, a relevant phase difference of prediction of the wall surface heat flow pulsation of the aircraft and a linear random prediction kernel function of prediction of the wall surface heat flow pulsation of the aircraft are obtained;
obtaining a flow direction velocity signal and a span direction velocity signal of a first preset size structure in a first area, transforming the flow direction velocity signal and the span direction velocity signal into a spectrum space to obtain a first signal and a second signal,
obtaining a first superposition result of the flow direction speed signal in a second area and a second superposition result of the spread direction speed signal in the second area based on the first signal, the second signal and a linear random prediction kernel function related to aircraft wall friction resistance pulsation prediction;
calculating to obtain the friction pulsation of the wall surface of the aircraft by utilizing the friction pulsation prediction model of the wall surface of the aircraft based on the first superposition result, the second superposition result and the first intermediate quantity data;
calculating and obtaining the wall heat flow pulsation of the aircraft by using the wall heat flow pulsation prediction model of the aircraft based on the first superposition result and the second intermediate quantity data;
and evaluating the aerodynamic performance of the aircraft based on the aircraft wall friction resistance pulsation and the aircraft wall heat flow pulsation.
2. The high-speed aircraft aerodynamic performance evaluation method according to claim 1, wherein the aircraft wall friction pulsation prediction model comprises:
wherein,
as a variable of the spatial coordinates,
to predict the flow-to-wall shear stress ripple,
to predict the development of wall shear stress pulsations,
for the generic signal of the de-superposition effect,
in order to de-modulate the pervasive signal of the effect,
the modulation coefficients of the flow direction friction pulsation of the second preset-size structure on the wall surface of the aircraft are obtained for the flow direction speed signal and the spreading direction speed signal,
the modulation coefficients of the flow direction speed signal and the spanwise speed signal to the spanwise friction drag pulsation of a second preset size structure on the wall surface of the aircraft,
in order to compensate the phase difference of the flow direction friction pulsation superposition effect to the modulation effect,
for the phase difference of the spanwise superposition effect to the modulation effect,
for the superposition of the streaming velocity signal at the second region,
is a superposition of the spanwise velocity signal at a second region.
3. The high-speed aircraft aerodynamic performance evaluation method according to claim 1, wherein the aircraft wall heat flow pulsation prediction model is:
wherein,
the prediction result of the heat flow pulsation of the wall surface of the aircraft,
as a variable of the spatial coordinates,
is a universal signal of the pulsation of the heat flow,
the modulation coefficients of the flow direction friction pulsation of the second preset-size structure on the wall surface of the aircraft are obtained for the flow direction speed signal and the spreading direction speed signal,
the modulation coefficients of the flow direction speed signal and the spread direction speed signal to the heat flow pulsation of the structure with the second preset size on the wall surface of the aircraft,
for superposition at a second region of the streaming velocity signal,
in order to compensate the phase difference of the flow direction friction pulsation superposition effect to the modulation effect,
the modulation phase difference is modulated for the heat flow superposition,
the heat flow for the first pre-sized structure is pulsed.
4. The high-speed aircraft aerodynamic performance evaluation method according to claim 2,
and
the calculation method is as follows:
wherein,
in order to perform the inverse fourier transform,
in order to flow to the linear random pre-estimated kernel function,
for the span-wise linear random prediction kernel function,
in order to flow to the wavelength(s),
in order to be a fourier transform,
in order to flow to the speed signal,
in order to develop the speed signal in the direction of the speed,
the center position of the first preset size structure of the first area is shown.
5. The method of claim 4, wherein the first region is centered on a first predetermined size structure
The calculation method is as follows:
wherein,
in order to obtain the friction reynolds number,
in order to obtain a kinematic viscosity of the composition,
as a result of the total shear stress of the wall,
the density of the wall surface is shown as,
in order to be the viscosity coefficient,
in order to determine the speed of the friction,
in order to be a measure of the viscosity,
is the boundary layer thickness.
6. The high-speed aircraft aerodynamic performance evaluation method according to claim 3,
the calculation method is as follows:
wherein,
in order to perform the inverse fourier transform,
for the linear random prediction kernel function associated with the heat flow,
in order to flow to the wavelength(s),
in order to be a fourier transform,
the center position of the first preset size structure of the first area,
the temperature pulsation signal is a temperature pulsation signal of a first predetermined size structure at the center of the first area.
7. The high-speed aircraft aerodynamic performance evaluation method according to claim 1, wherein the first predetermined dimension is greater than 2 boundary layer thicknesses in size.
8. The high-speed aircraft aerodynamic performance evaluation method of claim 1, wherein the first region is a region having an outer dimension greater than 0.1 and the second region is a region having an inner dimension less than 50.
9. The high-speed aircraft aerodynamic performance evaluation method according to claim 2, characterized in that the second predetermined dimension is less than or equal to 2 boundary layer thicknesses in size.
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