CN113065275A - Online predication method for stagnation point heat flow in flying process of aircraft - Google Patents

Online predication method for stagnation point heat flow in flying process of aircraft Download PDF

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CN113065275A
CN113065275A CN202110246952.1A CN202110246952A CN113065275A CN 113065275 A CN113065275 A CN 113065275A CN 202110246952 A CN202110246952 A CN 202110246952A CN 113065275 A CN113065275 A CN 113065275A
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陈伟芳
沈煊
赵文文
江中正
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Abstract

The invention discloses an on-line predication method for stagnation point heat flow in the flight process of an aircraft. Step 1) before actual flight, one-dimensional heat flow identification is carried out by utilizing temperature data of each measuring point in the structure through a sequential function method, and a neural network three-dimensional correction model is trained by combining real measured heat flow; step 2) simplifying an Fay-Riddell stagnation point heat flow engineering estimation formula, taking actually measured heat flow and inflow conditions of a given trajectory as input sequences, and correcting coefficients in the simplified formula by a least square method to obtain a preliminary stagnation point heat flow prediction formula; and 3) during actual flight, taking the stagnation point identification heat flow updated in real time as new heat flow data, and performing real-time fitting by a least square method to obtain a prediction formula updated in real time for predicting the stagnation point heat flow value in the future within 10 s. The error of the heat flow predicted by the invention is within 15 percent, and the rapidity of engineering estimation is included, so that the real-time and accurate heat flow prediction in the flight process can be realized.

Description

Online predication method for stagnation point heat flow in flying process of aircraft
Technical Field
The invention relates to an on-line predication method for stagnation point heat flow in the flight process of an aircraft, which is suitable for predicating the stagnation point heat flow in the actual flight process of the aircraft.
Background
Hypersonic flight faces serious aerodynamic heating problems. As air is subjected to strong friction and compression, a large amount of kinetic energy is converted into heat energy, so that the temperature of the air around the aircraft is increased sharply, the high temperature affects the structural strength and rigidity of the aircraft, and even causes ablation damage of the outer surface. The design of the thermal protection system is an important support for the rapid development of the hypersonic flight technology, and the research and design of the thermal protection system need a large amount of test data of flight tests. The measurement of key parameters such as temperature, heat flow and the like in the service process of the aircraft is a necessary means for evaluating the service performance of the thermal protection material, verifying a pneumatic thermal model and an algorithm and guiding the thermal protection design. However, for areas with large heat flow density such as stagnation points, a temperature sensor or a heat flow sensor cannot be directly arranged for measurement, and on one hand, due to structural strength reduction caused by structural opening and gap heating, the problems of structural matching such as asynchronous ablation are caused; on the other hand, some sensor body materials cannot bear excessively high thermal loads, and embedding of the sensor brings about discontinuity of wall temperature and circumferential interference, resulting in aerodynamic heat where the heat flow measurement result is not true. Therefore, the aerodynamic heat identification method for inverting the outer surface heat flow and temperature by measuring the temperature of the inner wall of the aircraft structure becomes an important solution for acquiring the aerodynamic heat environment.
The aerodynamic heat identification belongs to a heat conduction inverse problem, and the basic principle is to invert the heat flow time course of an outer wall heating surface by measuring the temperature course of a temperature measuring point on the inner wall of a heat conducting material. At present, a large amount of research is carried out on the inverse problem of heat conduction at home and abroad, and the common method is to select a proper objective function and convert the identification problem into an optimization problem to be solved. Qianworqi researches a one-dimensional surface heat flow identification method by using a sequential function method and a conjugate gradient method respectively, and expands the method to two-dimensional and three-dimensional surface heat flow identification with regular shapes. The physical process of heat conduction has damping property and delaying property, wherein the damping property is represented by that the change of boundary heat flow has great influence on the temperature near the boundary, and the influence of the change of the heat flow is reduced along with the increase of the distance from the boundary; the delayed property is represented by a delayed response of the internal temperature to the boundary heat flow in time. According to the characteristics, the identification of the heat flow by the sequential function method is carried out in a time sequence and gradually advancing mode, namely, a time step factor r is introduced, and the identification of the heat flow at a certain moment depends on temperature measurement values r time steps after the moment. The conjugate gradient method is an iterative regularization method that decomposes an optimization problem into three suitable problems of a heat conduction correction problem, a sensitivity solution, and an accompanying variable solution to solve.
In addition, in the one-dimensional heat conduction inverse problem, Schering adopts a Tikhonov method to research a plurality of quantities of internal heat source intensity, thermal conductivity and boundary conditions for identification, a Bregman distance weighting function is used as a regular term to solve the nonlinear heat conduction inverse problem, a time domain fine algorithm and a space discrete technology are used, nonlinearity of a heat source term is considered, a mathematical model of a transient heat conduction positive/negative problem is established, and a plurality of one-dimensional thermal parameters are combined and identified. The Cui Miao adopts a non-dimensionalization objective equation to perform parameter identification on the heat flow model, but is limited to a known heat flow function form. The Qianworqi considers the ablation retreat of a hypersonic material, utilizes a simplified pyrolysis surface ablation model to research the identification of the heat flow on the one-dimensional ablation surface, and uses the heat flow in the test result analysis of a blunt carbon phenolic material Narmco4028 test piece in a ceramic heating wind tunnel to prove the rationality of the ablation model and the effectiveness of the method. The Zhang smart utilizes simplified one-dimensional and two-dimensional heat transfer models to identify the wall surface heat flow of the hypersonic combustion chamber, and obtains better effect under an axisymmetric model.
The neural network algorithm is widely applied in various fields due to the strong nonlinear fitting capability of the neural network algorithm. The deng and intelligent strong et al study a neural network and a genetic algorithm to solve the inverse problem of heat conduction, under the condition of considering simple heat flow loading, utilize an artificial neural network to approximate the functional relationship between an internal temperature field and unknown surface heat flow or material thermophysical parameters, simultaneously convert the inverse problem into an extremum optimization problem under a proper objective function, and utilize a global optimization method of the genetic algorithm to find the optimal solution of the inverse problem. The least squares method is a traditional parameter estimation method, and obtains the best result or the most probable expression form according to a large number of observations on a certain event. The implementation idea of the hypersonic aeronautical vehicle aerodynamic heat online identification and prediction method based on the neural network and the least square correction is mentioned in an abstract published in the eleventh national fluid mechanics academic conference.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide an on-line predication method for stagnation heat flow in the flight process of an aircraft. On the basis of an online identification model of a neural network three-dimensional correction model, the traditional stagnation point heat flow formula is corrected through least squares, and the inflow condition of a given trajectory and a numerical calculation result are combined, so that the stagnation point heat flow of the aircraft within 10s in the future can be predicted online.
An on-line predication method for stagnation point heat flow in the flight process of an aircraft comprises the following specific steps:
before actual flight, performing one-dimensional heat flow identification by using temperature data of each measuring point in the structure through a sequential function method and training a neural network three-dimensional correction model by combining with real measured heat flow;
simplifying an Fay-Riddell stagnation point heat flow engineering estimation formula, taking actually measured heat flow and inflow conditions of a given trajectory as input sequences, and correcting each coefficient in the simplified formula by a least square method to obtain a preliminary stagnation point heat flow prediction formula;
and (3) during actual flight, substituting the temperature of the measured point as input into the stagnation point heat flow prediction formula to quickly obtain stagnation point identification heat flow, using the stagnation point identification heat flow updated in real time as new heat flow data, and performing real-time fitting by a least square method to obtain a prediction formula updated in real time to predict a stagnation point heat flow value in the future within 10 s.
In the step (2), the Fay-Riddell stagnation point heat flow engineering estimation formula is simplified, and the simplified form is as follows:
Figure BDA0002964430210000031
where A, B, C, D is the coefficient to be fitted, ρ∞Is the density of incoming flow, V∞For the incoming flow velocity, T∞To the temperature of the incoming flow, TWIs the wall temperature, RNIs the curvature radius of the ball head;
taking the logarithm of two sides:
lnqs＝-0.5·lnRN+lnA+B·lnρ∞+C·lnV∞+D·ln(TW-T∞) (4)
and (4) performing least square fitting by taking the formula (4) as a function to be fitted, and updating each coefficient in real time along with the advancing of time.
The invention has the beneficial effects that:
on the basis of the research of the existing online identification model based on the neural network three-dimensional correction model, the correction method provided by the invention finally realizes the online prediction of the stationary point heat flow of the aircraft in 10s in the future by combining a least square method and an engineering estimation formula in consideration of the fact that a single online identification function cannot meet the engineering requirement of the aircraft during actual flight. The heat flow error predicted by the prediction method provided by the invention is within 15%, and the rapidity of engineering estimation is included, so that real-time and accurate heat flow prediction in the flight process can be realized.
Drawings
FIG. 1 is a flow chart of an embodiment of the present invention.
FIG. 2 is a schematic view of a selected model geometry.
FIG. 3 is a graph of neural network training results.
FIG. 4 is a graph of a standing heat flow identification versus error over a period of time.
FIG. 5 is a graph of predicted standing heat flow and error at a future 10 s.
Detailed Description
The invention is further illustrated below.
Firstly, providing an engineering estimation formula of hypersonic semi-circular ball head streaming, under the thermal balance flow of dissociation gas, constant continuous laminar flow with a zero attack angle stationary point and a steady heat transfer heat flow rate:
Figure BDA0002964430210000032
wherein q issIs the stagnation heat flow rate; rho∞Is the incoming flow density; rNIs the curvature radius of the ball head; hw is wall enthalpy; the stagnation point stagnation enthalpy hs is,
Figure BDA0002964430210000033
wherein V∞Is the incoming flow velocity; t is∞Is the incoming flow temperature.
According to this formula, and taking into account the flight conditions given by the subject matter, the heat flow formula is simplified to the following form:
Figure BDA0002964430210000041
wherein A, B, C, D is a coefficient to be fitted, and two sides are logarithmized:
lnqs＝-0.5·lnRN+lnA+B·lnρ∞+C·lnV∞+D·ln(TW-T∞) (4)
wherein T isWIs the wall temperature.
According to the least squares method, for the function the form of the function is as follows:
hθ(x1,x2,...,xn)＝θ0+θ1x1+θ2x2...+θnxn (5)
we note its matrix form as:
Figure BDA0002964430210000042
the loss function is defined as:
Figure BDA0002964430210000043
applying a calculation formula of matrix traces:
Figure BDA0002964430210000044
let the above formula be zero, solve to obtain:
θ＝(XTX)-1XTY (9)
substituting the equation (4) as a function to be fitted into the equations (6) to (9) to construct a matrix:
Figure BDA0002964430210000045
the constructed matrix X is then:
Figure BDA0002964430210000051
the matrix Y is:
Y＝(lnq1 lnq2 ... lnqn)T (12)
from this it can be solved:
θ＝(lnA-0.5lnRN B C D)T (13)
coefficients A, B, C, D can then be found for equation (3) to obtain a new heat flow estimation equation.
The heat flow qs used in the online fitting is the identification heat flow of the preamble time, so the method can simultaneously have high identification precision and rapidity of engineering estimation.
Embodiments of the present invention are described below with reference to the drawings. As shown in fig. 1, before actual flight, a fluid mechanics numerical simulation or experiment method is first used on the ground to obtain a surface heat flow of the projectile body and a temperature field of an inner wall surface or an actual measured heat flow, which are collectively referred to as a heat flow true value. And then, calculating the one-dimensional identification heat flow of the selected temperature measuring point through a one-dimensional identification program, and substituting the one-dimensional identification heat flow and a heat flow truth value into the neural network model for training so as to obtain the trained neural network model. Meanwhile, a heat flow true value of a part of stagnation points and the inflow conditions of a given trajectory are selected, and an original predictive formula is fitted by a least square method.
In actual flight, three-dimensional correction heat flow can be quickly obtained only by taking the temperature of the measuring point as input and performing one-dimensional identification and neural network correction. Meanwhile, the calculated corrected heat flow is used as new heat flow data, and a new prediction formula is fit again to predict the stagnation point heat flow value in a subsequent period of time.
Taking the geometric model shown in fig. 2 as an example, a flow field of the geometric model is subjected to numerical simulation on the premise of giving a trajectory, and a temperature change sequence at a stationary point is calculated to simulate a measured temperature sequence in an actual situation. Then, one-dimensional heat flow identification is performed according to the obtained temperature sequence, and the result and the stagnation point heat flow obtained by numerical simulation are used as input to perform neural network training, wherein the training result is shown in fig. 3. After the training is finished, only the temperature sequence of the measuring points in a period of time needs to be input, and the heat flow identification value in the period of time and the heat flow prediction value in the subsequent 10s can be quickly obtained. As shown in fig. 4 and 5, the error is kept within 15%.
The embodiments in the above description can be further combined or replaced, and the embodiments are only described as preferred examples of the present invention, and do not limit the concept and scope of the present invention, and various changes and modifications made to the technical solution of the present invention by those skilled in the art without departing from the design concept of the present invention belong to the protection scope of the present invention. The scope of the invention is given by the appended claims and any equivalents thereof.

Claims (2)

1. An on-line predication method for stagnation point heat flow in the flight process of an aircraft is characterized by comprising the following specific steps:
step 1) before actual flight, one-dimensional heat flow identification is carried out by utilizing temperature data of each measuring point in the structure through a sequential function method, and a neural network three-dimensional correction model is trained by combining real measured heat flow;
step 2) simplifying an Fay-Riddell stagnation point heat flow engineering estimation formula, taking actually measured heat flow and inflow conditions of a given trajectory as input sequences, and correcting coefficients in the simplified formula by a least square method to obtain a preliminary stagnation point heat flow prediction formula;
and 3) during actual flight, substituting the temperature of the measured point as input into the stagnation point heat flow prediction formula to quickly obtain stagnation point identification heat flow, using the stagnation point identification heat flow updated in real time as new heat flow data, and performing real-time fitting by a least square method to obtain a prediction formula updated in real time to predict a stagnation point heat flow value in the future within 10 s.
2. The method for on-line prediction of stagnation heat flow during flight of an aircraft according to claim 1, characterized in that the Fay-Riddell stagnation heat flow engineering estimation formula is simplified in step 2) and has the form:
Figure FDA0002964430200000011
where A, B, C, D is the coefficient to be fitted, ρ∞Is the density of incoming flow, V∞For the incoming flow velocity, T∞To the temperature of the incoming flow, TWIs the wall temperature, RNIs the curvature radius of the ball head;
taking the logarithm of two sides:
ln qs＝-0.5·ln RN+ln A+B·lnρ∞+C·ln V∞+D·ln(TW-T∞) (4)
and (4) performing least square fitting by taking the formula (4) as a function to be fitted, and updating each coefficient in real time along with the advancing of time.
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