CN113324728B - Calibration device and calibration method for wind tunnel balance with mismatched load - Google Patents
Calibration device and calibration method for wind tunnel balance with mismatched load Download PDFInfo
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Abstract
The invention belongs to the technical field of aerodynamic force measurement of aerospace force measurement tests, and particularly relates to a calibration device and a calibration method for a wind tunnel balance with mismatched loads, which comprise the following steps: the device comprises a loading head (1), a first electrothermal film (2), a second electrothermal film (3), a switching device (5), a support rod (6), a third electrothermal film (7), a fourth electrothermal film (8), a calibration frame (9) and a temperature control device (10); the method comprises the following steps of placing a load unmatched balance (4) in a loading head (1), fixing one end of the load unmatched balance (4) at one end of the loading head (1), connecting the other end of the load unmatched balance with a switching device (5), connecting the switching device (5) with a support rod (6), and connecting the support rod (6) with a calibration frame (9); the loading head (1) is covered with a first electric heating film (2) and a second electric heating film (3); the support rod (6) is covered with a third electric heating film (7) and a fourth electric heating film (8).
Description
Technical Field
The invention belongs to the technical field of aerodynamic force measurement of aerospace force measurement tests, and particularly relates to a calibration device and a calibration method for a wind tunnel balance with mismatched load.
Background
The wind tunnel balance is used as a core force measuring device of a wind tunnel test, is a precondition of the wind tunnel force measuring test, and has important influence on the accuracy of wind tunnel force measuring test data. The balance applied to wind tunnel tests of special aerodynamic appearance models such as flat body models and short blunt body models is called a load mismatch wind tunnel balance, and the load of the balance often shows the characteristic of extreme mismatching. Compared with a balance with good load matching performance, the balance with mismatched load often has the characteristics of poor stability and high calibration difficulty, and the reasons mainly include two aspects, namely, the interference among all components of the balance with mismatched load is larger, and the problem of nonlinear interference is easier to occur; secondly, the influence of factors such as temperature gradient and connection rigidity between the balance and the support on the measurement accuracy of the balance with mismatched load cannot be ignored.
In the wind tunnel test, the temperature change of the balance is mainly caused by heat conduction from the model to the balance, heat conduction from the support rod to the balance, and the like. Because the temperature changes of the model and the support rod in the test are different, the heat conduction effect on the balance is different, and the balance has a temperature gradient along the axial direction. In addition, the balance may have a temperature gradient along the longitudinal direction due to the temperature gradient of the windward side and the leeward side of the model and the supporting rod. The existing calibration method usually does not consider the influence of the temperature gradient on the calibration result of the balance, but the influence of the temperature gradient on the measurement accuracy of the balance cannot be ignored aiming at the balance with mismatched load.
In a wind tunnel test, the same model may have various support modes such as tail support, abdomen support, back support and the like, and the balance-support connection rigidity under different support modes is different. However, the existing balance calibration method usually only adopts a tail support mode, and part of balances can also independently design special calibration support rods with larger diameters and better rigidity so as to enhance the stability of a balance-support system in the calibration process.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides a calibration device and a calibration method for a wind tunnel balance with mismatched load, which comprises the following steps: meanwhile, a calibration loading matrix is designed by considering various factors such as load (comprising 3 force loads and 3 moment loads), temperature gradient, balance-support connection rigidity and the like of the wind tunnel balance; the multi-factor calibration work of the load unmatched balance is finished by utilizing a six-degree-of-freedom calibration system, a temperature control device and balance-support switching devices with different specifications; and training the BP neural network model by using the calibration data to obtain a trained balance calibration model with mismatched load.
The invention provides a calibration device for a wind tunnel balance with mismatched load, which comprises: the loading head, the first electrothermal film, the second electrothermal film, the switching device, the support rod, the third electrothermal film, the fourth electrothermal film, the calibration frame and the temperature control device;
placing a load unmatched balance in a loading head, fixing one end of the load unmatched balance at one end of the loading head by using a tensioning screw, connecting the other end of the load unmatched balance with a switching device, connecting the switching device with a support rod, and connecting the support rod with a calibration frame;
the loading head is covered with a first electric heating film and a second electric heating film; the third electric heating film and the fourth electric heating film are covered on the support rod;
the temperature control device is in communication connection with the first electrothermal film, the second electrothermal film, the third electrothermal film and the fourth electrothermal film respectively.
As one improvement of the technical scheme, a first temperature measuring point is selected on the upper surface of a transition section of the balance close to one end of a loading head, a second temperature measuring point is selected on the lower surface of the transition section of the balance close to one end of the loading head, a third temperature measuring point is selected on the upper surface of a transition section of the balance close to one end of a supporting rod, and a fourth temperature measuring point is selected on the lower surface of the transition section of the balance close to one end of the supporting rod;
the temperature control device respectively collects the temperatures of the first temperature measuring point, the second temperature measuring point, the third temperature measuring point and the fourth temperature measuring point, and respectively controls the power of the first electrothermal film, the second electrothermal film, the third electrothermal film and the fourth electrothermal film according to the collected temperatures, so that the temperature of the wind tunnel balance with mismatched load is monitored in real time, and different temperature gradient states are simulated.
As one improvement of the technical scheme, the connecting devices are connecting devices with different specifications; by changing the connection device, the distance between the balance axis and the strut axis was changed to simulate different balance-support connection stiffnesses, which are expressed by the parameter Gd:
h is the offset of the axis of the balance and the axis of the support rod; l is the distance from the end face of the balance close to one end of the loading head to the center of the balance; y is max The maximum load is the balance lift in the test;
the maximum load for the balance pitching moment in the test.
The invention also provides a calibration method for the wind tunnel balance with mismatched load, which comprises the following steps:
step 1) uniformly selecting loading points based on a response surface method to obtain a calibration loading matrix M:
wherein, Y N The balance lifting load of the Nth loading point; mz N The balance pitching moment load of the Nth loading point is obtained; x N The balance resistance load of the Nth loading point; mx N The balance rolling moment load is the Nth loading point; z is a linear or branched member N The balance side force load is the Nth loading point; my N The balance yaw moment load of the Nth loading point is;Tqs N the target temperature of the first temperature measuring point of the Nth loading point Tqx N Target temperature, ths, of the second temperature measurement point of the Nth loading point N Is the target temperature, thx, of the third temperature measuring point of the Nth loading point N Is the target temperature, gd, of the fourth temperature measurement point of the Nth loading point N The balance-support connection stiffness parameter is the Nth loading point;
step 2) calibrating each loading point in the calibration matrix M by using a calibration device to obtain the electric signal output of six Wheatstone bridges of the balance corresponding to each loading point: u1, U2, U3, U4, U5 and U6 and 11 parameters corresponding to the loading points form calibration data of the balance;
wherein, U1 represents the electric signal output of a Wheatstone bridge which is pasted on a balance front side lifting force-pitching moment measuring element; u2 represents the electric signal output of a Wheatstone bridge attached to the balance rear side lift force-pitching moment measuring element; u3 represents the electric signal output of a Wheatstone bridge attached to the balance resistance measuring element; u4 represents the electric signal output of a Wheatstone bridge attached to the balance roll torque measuring element; u5 represents the electric signal output of a Wheatstone bridge attached to the balance front side force-yaw moment measuring element; u6 represents the electric signal output of a Wheatstone bridge attached to the balance rear side force-yaw moment measuring element;
step 3) building a BP neural network model, training the BP neural network model by using calibration data of the balance as a training set sample, and obtaining a trained balance calibration model;
step 4) randomly generating any specific number of loading points, and carrying out inspection loading by using a calibration device aiming at the wind tunnel balance with load mismatch to obtain inspection loading data;
step 5) inputting the obtained test loading data into the trained balance calibration model, and outputting the calculated balance load; calculating the comprehensive loading error of each load component of the balance by using the balance load obtained by calculation and the balance load actually loaded on the balance;
if the comprehensive loading error of each load component of the balance is less than 0.3%, the load is not matched with the wind tunnel balance, and the calibration is finished;
and if the comprehensive loading error of one load component of the balance is greater than or equal to 0.3%, repeating the step 3) and the step 5) until the comprehensive loading error of each load component of the balance is less than 0.3%, and finishing calibration.
As an improvement of the above technical solution, the BP neural network model includes: the device comprises an input layer, a first hidden layer, a second hidden layer and an output layer;
the number of the neurons of the input layer is 11, and the neurons respectively represent the electric signal output of 6 Wheatstone bridges of the balance: u1, U2, U3, U4, U5, U6,4 balance temperature parameters: tqs, tqx, ths, thx, and a stiffness parameter Gd;
the number of the neurons of the first hidden layer is 11;
the number of the neurons of the second hidden layer is 17;
the number of neurons in the output layer was 6, representing 6 load components of the balance: lift Y, pitching moment Mz, resistance X, rolling moment Mx, lateral force Z and yawing moment My;
the method comprises the steps of taking the electric signal output of 6 Wheatstone bridges, 4 balance temperature parameters and 1 rigidity parameter corresponding to each loading point as input, propagating along the BP neural network in the forward direction, training a parameter matrix of the BP neural network, outputting values of 6 load components, comparing a calculated value with a real load value of the corresponding loading point, calculating an error, propagating along the BP neural network in the reverse direction, adjusting the parameter matrix of the BP neural network in the reverse direction, and outputting a trained balance calibration model after continuous iteration.
As an improvement of the above technical solution, the stop condition of the BP neural network model is that the number of training times is 2000.
As an improvement of the above technical solution, in the step 5), a calculation formula of a comprehensive loading error of each load component of the balance is as follows:
wherein, W i The comprehensive loading error of the ith load component of the balance; p im calculated value The calculated value of the mth load point of the ith load component of the balance is obtained; p im truth value The true value of the mth load point of the ith load component of the balance is obtained; p imax K is the calibrated maximum value of the ith load component of the balance, and k is the number of load points.
Compared with the prior art, the invention has the beneficial effects that:
aiming at the characteristic that the load is not matched with the balance, influence factors such as the load, the temperature gradient, the balance-support connection rigidity and the like are considered simultaneously in the calibration process, so that the balance calibration is more comprehensive and fine; the adaptability of the balance calibration model in the wind tunnel test is enhanced by simulating various influence factors of the load mismatch balance in the wind tunnel test more comprehensively, and the measurement accuracy of the load mismatch balance is improved.
Drawings
FIG. 1 is a schematic diagram of a calibration apparatus for a load mismatch wind tunnel balance according to the present invention;
FIG. 2 is a schematic diagram of four temperature measurement points selected for a calibration method for a load mismatch wind tunnel balance according to the present invention;
fig. 3 is a schematic illustration of the adapter device of fig. 1 according to the invention for a calibration device for a load-mismatched wind tunnel balance.
Reference numerals are as follows:
1. loading head 2 and first electrothermal film
3. Second electric heating film 4, load mismatch balance
5. Switching device 6, branch
7. A third electrothermal film 8 and a fourth electrothermal film
9. Calibration piece 10 and temperature control device
11. Tensioning screw
21. A first temperature measuring point 31 and a second temperature measuring point
71. A third temperature measuring point 81 and a fourth temperature measuring point
Detailed Description
The invention will now be further described with reference to the accompanying drawings and examples.
As shown in fig. 1, the present invention provides a calibration device for a load mismatch wind tunnel balance, comprising: the device comprises a loading head 1, a first electrothermal film 2, a second electrothermal film 3, a switching device 5, a support rod 6, a third electrothermal film 7, a fourth electrothermal film 8, a calibration frame 9 and a temperature control device 10;
placing a load unmatched balance 4 in a loading head 1, fixing one end of the load unmatched balance 4 at one end of the loading head 1 by using a tensioning screw 11, connecting the other end of the load unmatched balance with a switching device 5, connecting the switching device 5 with a support rod 6, and connecting the support rod 6 with a calibration frame 9;
the loading head 1 is covered with a first electrothermal film 2 and a second electrothermal film 3; the support rod 6 is covered with a third electric heating film 7 and a fourth electric heating film 8;
the temperature control device 10 is respectively connected with the first electrothermal film 2, the second electrothermal film 3, the third electrothermal film 7 and the fourth electrothermal film 8 in a communication way.
Selecting a first temperature measuring point 21 on the upper surface of a transition section of the balance close to one end of the loading head, selecting a second temperature measuring point 31 on the lower surface of the transition section of the balance close to one end of the loading head, selecting a third temperature measuring point 71 on the upper surface of the transition section of the balance close to one end of the support rod, and selecting a fourth temperature measuring point 81 on the lower surface of the transition section of the balance close to one end of the support rod;
the temperature control device 10 collects temperatures of the first temperature measuring point 21, the second temperature measuring point 31, the third temperature measuring point 71 and the fourth temperature measuring point 81 respectively, and controls power of the first electric heating film 2, the second electric heating film 3, the third electric heating film 7 and the fourth electric heating film 8 according to the collected temperatures respectively, so that the temperature of the wind tunnel balance with load not matched is monitored in real time, and different temperature gradient states are simulated.
Wherein, the connecting device 5 is a connecting device with different specifications; by changing the connection device, the distance between the balance axis and the strut axis was changed to simulate different balance-support connection stiffness, which is expressed by the parameter Gd:
h is the offset of the balance axis and the support rod axis; l is the distance from the end face of the balance close to one end of the loading head to the center of the balance; y is max The maximum load is the balance lift in the test; mz max The maximum load for the balance pitching moment in the test.
The invention also provides a calibration method for the wind tunnel balance with mismatched load, which comprises the following steps:
step 1) uniformly selecting loading points based on a response surface method to obtain a calibration loading matrix M:
wherein Y is N The balance lifting load of the Nth loading point; mz N The balance pitching moment load of the Nth loading point is obtained; x N The balance resistance load of the Nth loading point; mx N The balance rolling moment load is the Nth loading point; z N The balance side force load is the Nth loading point; my N The balance yaw moment load is the Nth loading point; tqs N Is the target temperature of the Nth loading point first temperature measuring point 21, tqx N Target temperature, ths, for the Nth load point second temperature measurement point 31 N The target temperature, thx, of the third temperature measuring point 71 of the Nth loading point N Is the target temperature, gd, of the Nth load point fourth temperature measurement point 81 N The balance-support connection stiffness parameter is the Nth loading point;
step 2) calibrating each loading point in the calibration matrix M by using a calibration device to obtain the electric signal output of six Wheatstone bridges of the balance corresponding to each loading point: u1, U2, U3, U4, U5 and U6 and 11 parameters corresponding to the loading points form calibration data of the balance;
wherein U1 represents the electric signal output of a Wheatstone bridge which is pasted on a balance front side lifting force-pitching moment measuring element; u2 represents the electric signal output of a Wheatstone bridge attached to the balance rear side lift force-pitching moment measuring element; u3 represents the electric signal output of a Wheatstone bridge attached to the balance resistance measuring element; u4 represents the electric signal output of a Wheatstone bridge attached to the balance roll torque measuring element; u5 represents the electric signal output of a Wheatstone bridge attached to the balance front side force-yaw moment measuring element; u6 represents the electrical signal output of the wheatstone bridge attached to the rear side force-yaw moment measuring element of the balance.
Step 3) building a BP neural network model, training the BP neural network model by using calibration data of the balance as a training set sample, and obtaining a trained balance calibration model;
step 4) randomly generating any specific number of loading points, and carrying out inspection loading by using a calibration device aiming at the wind tunnel balance with load mismatch to obtain inspection loading data;
step 5) inputting the obtained inspection loading data into the trained balance calibration model, and outputting the calculated balance load; calculating the comprehensive loading error of each load component of the balance by using the balance load obtained by calculation and the balance load actually loaded on the balance;
if the comprehensive loading error of each load component of the balance is less than 0.3%, the load is not matched with the wind tunnel balance, and the calibration is finished;
and if the comprehensive loading error of one load component of the balance is greater than or equal to 0.3%, repeating the step 3) and the step 5) until the comprehensive loading error of each load component of the balance is less than 0.3%, and finishing calibration.
Wherein the BP neural network model comprises: the device comprises an input layer, a first hidden layer, a second hidden layer and an output layer;
the number of the neurons of the input layer is 11, and the neurons respectively represent the electric signal output of 6 Wheatstone bridges of the balance: u1, U2, U3, U4, U5, U6,4 balance temperature parameters: tqs, tqx, ths, thx, and a stiffness parameter Gd;
the number of the neurons of the first hidden layer is 11;
the number of the neurons of the second hidden layer is 17;
the number of neurons in the output layer was 6, representing 6 load components of the balance: lift Y, pitching moment Mz, resistance X, rolling moment Mx, lateral force Z and yawing moment My;
the method comprises the steps of taking the electric signal output of 6 Wheatstone bridges, 4 balance temperature parameters and 1 rigidity parameter corresponding to each loading point as input, propagating along the BP neural network in the forward direction, training a parameter matrix of the BP neural network, outputting values of 6 load components, comparing a calculated value with a real load value of the corresponding loading point, calculating an error, propagating along the BP neural network in the reverse direction, adjusting the parameter matrix of the BP neural network in the reverse direction, and outputting a trained balance calibration model after continuous iteration.
Wherein the stop condition of the BP neural network model is that the training times are 2000 times.
In the step 5), a calculation formula of the comprehensive loading error of each load component of the balance is as follows:
wherein, W i The comprehensive loading error of the ith load component of the balance; p im calculated value Calculating the mth load point of the ith load component of the balance; p im truth value The true value of the mth load point of the ith load component of the balance is obtained; p imax And k is the calibrated maximum value of the ith load component of the balance, and k is the number of loading points.
Example 1.
A calibration method for a wind tunnel balance with mismatched load comprises the following specific steps:
firstly, uniformly selecting loading points based on a response surface method to obtain a calibration loading matrix:
the first 6 parameters are 6 loads (lift force Y, pitching moment Mz, resistance X, rolling moment Mx, lateral force Z and yawing moment My) of the balance, the 7 th to 10 th parameters represent parameters (Tqs, tqx, ths and Thx) of the temperature gradient, and the 11 th parameter represents a balance-support connection rigidity parameter (Gd).
For the convenience of calibration, the calibration loading matrix is rewritten into a table form, as shown in the following table, while loading points with the same stiffness parameters are arranged together.
Secondly, the loading head 1 is covered with a first electrothermal film 2 and a second electrothermal film 3; the support rod 6 is covered with a third electric heating film 7 and a fourth electric heating film 8. The method comprises the steps of selecting a first temperature measuring point 21 on the upper surface of a transition section of the balance close to one end of a loading head for measuring a temperature parameter Tqs, selecting a second temperature measuring point 31 on the lower surface of the transition section of the balance close to one end of the loading head for measuring a temperature parameter Tqx, selecting a third temperature measuring point 71 on the upper surface of the transition section of the balance close to one end of a supporting rod for measuring a temperature parameter Ths, selecting a fourth temperature measuring point 81 on the lower surface of the transition section of the balance close to one end of the supporting rod for measuring a temperature parameter Thx, and setting the positions of the temperature measuring points as shown in FIG. 2.
Thirdly, the corresponding adapter 5 is selected according to the Gd parameters. The calculation formula of the stiffness parameter is as follows:
wherein h is the offset of the axis of the balance and the axis of the support rod, L is the distance from the large end of the front cone of the balance to the design center of the balance, and Y is max Maximum load of the balance lift in the test, mz max The maximum load for the balance pitching moment in the test. The adapter stiffness parameter as shown in fig. 3 is 4.5 x 10 -6 And when the axis of the balance is coaxial with the axis of the support rod, the rigidity parameter is 0.
Fourthly, the load unmatched balance 4 is placed in the loading head 1, one end of the load unmatched balance 4 is fixed to one end of the loading head 1 through a tensioning screw 11, the other end of the load unmatched balance is connected with a switching device 5, the switching device 5 is connected with a support rod 6, and the support rod 6 is connected with a calibration frame 9.
And fifthly, completing balance calibration work under the same rigidity condition according to a balance calibration loading meter. Aiming at each loading point, the method specifically comprises the following steps:
A. the six-freedom-degree calibration frame is utilized to finish the loading of the first 6 force and moment loads in the calibration loading table
B. And monitoring the temperature of the balance by using a temperature control system, and acquiring the electric signals output by six Wheatstone bridges of the balance when the temperatures of the first temperature measuring point 21, the second temperature measuring point 31, the third temperature measuring point 71 and the fourth temperature measuring point 81 reach the parameter indexes from item 7 to item 10 in the calibration loading table simultaneously.
C. And C, repeating the step A and the step B to complete the loading of all loading points with the same balance-strut connection rigidity parameters.
And sixthly, selecting a new switching device according to the balance calibration load meter, and repeating the fourth step to the fifth step. And (4) until the calibration of all the loading points is completed, and complete calibration data is obtained.
And seventhly, training the BP neural network by taking 6 bridge outputs, 4 temperature parameters and 1 rigidity parameter of the balance as inputs and 3 force and 3 moment values of the corresponding balance as true values.
The BP neural network comprises an input layer, a first hidden layer, a second hidden layer and an output layer, wherein the input layer is provided with 11 neurons, the first hidden layer is provided with 11 neurons, the second hidden layer is provided with 17 neurons, and the output layer is provided with 6 neurons.
And (5) stopping BP neural network training for 2000 times, and storing a balance calibration model.
And eighthly, randomly generating a certain number of loading points, repeating the second step to the sixth step, finishing the balance test loading, and obtaining test loading data.
And ninthly, inputting the test loading data into a balance calibration model for calculation to obtain calculated values of 6 loads, and substituting the calculated values and the true values into the following formula to calculate the comprehensive loading error of the 6 load components of the balance.
Wherein, W i For the combined loading error of the ith load component of the balance, P im calculated value Calculated for the mth load component of the balance at the mth load point, P im truth value Is the true value, P, of the mth load point of the ith load component of the balance imax And k is the calibrated maximum value of the ith load component of the balance, and k is the number of loading points.
The comprehensive loading error of each load component of the calibration balance is less than 0.3%, the test requirement is met, the test condition is met, and the calibration is completed.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention and are not limited. Although the present invention has been described in detail with reference to the embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (6)
1. A calibration device for a load mismatch wind tunnel balance, comprising: the device comprises a loading head (1), a first electrothermal film (2), a second electrothermal film (3), a switching device (5), a support rod (6), a third electrothermal film (7), a fourth electrothermal film (8), a calibration frame (9) and a temperature control device (10);
the load unmatched balance (4) is placed in the loading head (1), one end of the load unmatched balance (4) is fixed to one end of the loading head (1) through a tensioning screw (11), the other end of the load unmatched balance is connected with a switching device (5), the switching device (5) is connected with a support rod (6), and the support rod (6) is connected with a calibration frame (9);
the loading head (1) is covered with a first electric heating film (2) and a second electric heating film (3); the support rod (6) is covered with a third electric heating film (7) and a fourth electric heating film (8);
the temperature control device (10) is respectively in communication connection with the first electrothermal film (2), the second electrothermal film (3), the third electrothermal film (7) and the fourth electrothermal film (8);
the switching device (5) is a connecting device with different specifications; by changing the connection device, the distance between the balance axis and the strut axis was changed to simulate different balance-support connection stiffnesses, which are expressed by the parameter Gd:
h is the offset of the balance axis and the support rod axis; l is the distance from the end face of the balance close to one end of the loading head to the center of the balance; y is max The maximum load is the balance lift in the test; mz max The maximum load for the balance pitching moment in the test.
2. The calibration device for the wind tunnel balance with the mismatch of load according to claim 1, wherein a first temperature measuring point (21) is selected on the upper surface of the transition section of the balance near one end of the loading head, a second temperature measuring point (31) is selected on the lower surface of the transition section of the balance near one end of the loading head, a third temperature measuring point (71) is selected on the upper surface of the transition section of the balance near one end of the supporting rod, and a fourth temperature measuring point (81) is selected on the lower surface of the transition section of the balance near one end of the supporting rod;
the temperature control device (10) respectively collects the temperatures of the first temperature measuring point (21), the second temperature measuring point (31), the third temperature measuring point (71) and the fourth temperature measuring point (81), and respectively controls the power of the first electrothermal film (2), the second electrothermal film (3), the third electrothermal film (7) and the fourth electrothermal film (8) according to the collected temperatures, so that the temperature of the wind tunnel balance with mismatched load is monitored in real time, and different temperature gradient states are simulated.
3. A method for calibrating a load-mismatched wind tunnel balance, which is implemented on the basis of the calibration apparatus for a load-mismatched wind tunnel balance according to any one of claims 1 to 2, comprising:
step 1) uniformly selecting loading points based on a response surface method to obtain a calibration loading matrix M:
wherein Y is N The balance lifting load of the Nth loading point; mz N The balance pitching moment load of the Nth loading point is obtained; x N The balance resistance load of the Nth loading point; mx N The balance rolling moment load is the Nth loading point; z N The balance side force load is the Nth loading point; my N The balance yaw moment load is the Nth loading point; tqs N Is the target temperature of the Nth loading point first temperature measuring point (21), tqx N Target temperature, ths, of the second temperature measuring point (31) of the Nth loading point N Is the target temperature, thx, of the third temperature measuring point (71) of the Nth loading point N Is a target temperature of the fourth temperature measuring point (81) of the Nth loading point, gd N The balance-support connection stiffness parameter is the Nth loading point;
step 2) calibrating each loading point in the calibration matrix M by using a calibration device to obtain the electric signal output of six Wheatstone bridges of the balance corresponding to each loading point: u1, U2, U3, U4, U5 and U6 and 11 parameters corresponding to the loading points form calibration data of the balance;
wherein, U1 represents the electric signal output of a Wheatstone bridge pasted on the lifting force-pitching moment measuring element at the front side of the balance; u2 represents the electric signal output of a Wheatstone bridge which is pasted on the lifting force-pitching moment measuring element at the rear side of the balance; u3 represents the electric signal output of a Wheatstone bridge attached to the balance resistance measuring element; u4 represents the electric signal output of a Wheatstone bridge attached to the balance roll torque measuring element; u5 represents the electric signal output of a Wheatstone bridge attached to the balance front side force-yaw moment measuring element; u6 represents the electric signal output of a Wheatstone bridge attached to the balance rear side force-yaw moment measuring element;
step 3) building a BP neural network model, training the BP neural network model by using calibration data of the balance as a training set sample, and obtaining a trained balance calibration model;
step 4) randomly generating any specific number of loading points, and carrying out inspection loading by using a calibration device aiming at the wind tunnel balance with load mismatch to obtain inspection loading data;
step 5) inputting the obtained inspection loading data into the trained balance calibration model, and outputting the calculated balance load; calculating the comprehensive loading error of each load component of the balance by using the balance load obtained by calculation and the balance load actually loaded on the balance;
if the comprehensive loading error of each load component of the balance is less than 0.3%, the load is not matched with the wind tunnel balance, and the calibration is finished;
and if the comprehensive loading error of one load component of the balance is greater than or equal to 0.3%, repeating the step 3) and the step 5) until the comprehensive loading error of each load component of the balance is less than 0.3%, and finishing calibration.
4. The method for calibrating a load mismatch wind tunnel balance according to claim 3, wherein said BP neural network model comprises: the device comprises an input layer, a first hidden layer, a second hidden layer and an output layer;
the number of the neurons of the input layer is 11, and the neurons respectively represent the electric signal output of 6 Wheatstone bridges of the balance: u1, U2, U3, U4, U5, U6,4 balance temperature parameters: tqs, tqx, ths, thx, and a stiffness parameter Gd;
the number of the neurons of the first hidden layer is 11;
the number of the neurons of the second hidden layer is 17;
the number of neurons in the output layer was 6, representing 6 load components of the balance: lift Y, pitching moment Mz, resistance X, rolling moment Mx, lateral force Z and yawing moment My;
the method comprises the steps of taking the electric signal output of 6 Wheatstone bridges, 4 balance temperature parameters and 1 rigidity parameter corresponding to each loading point as input, propagating along the BP neural network in the forward direction, training a parameter matrix of the BP neural network, outputting values of 6 load components, comparing a calculated value with a real load value of the corresponding loading point, calculating an error, propagating along the BP neural network in the reverse direction, adjusting the parameter matrix of the BP neural network in the reverse direction, and outputting a trained balance calibration model after continuous iteration.
5. The method for calibrating a load mismatch wind tunnel balance according to claim 3, wherein the stop condition of the BP neural network model is 2000 training times.
6. The calibration method for the load mismatch wind tunnel balance according to claim 3, wherein in the step 5), the calculation formula of the comprehensive loading error of each load component of the balance is as follows:
wherein, W i The comprehensive loading error of the ith load component of the balance; p im calculated value Calculating the mth load point of the ith load component of the balance; p im truth value The true value of the mth load point of the ith load component of the balance is shown; p imax A calibration maximum for the ith load component of the balance; k is the number of load points.
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| CN115406617B (en) * | 2022-11-02 | 2022-12-27 | 中国航空工业集团公司沈阳空气动力研究所 | Wind tunnel loading detection device and method for large-load-ratio balance |
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