阅读说明：本技术 基于人工神经网络的可调总压畸变发生器调节指导方法 (Adjustable total pressure distortion generator adjustment guiding method based on artificial neural network ) 是由 王志强 王铭祖 苏毅韩 王英锋 李传鹏 屠宝锋 阮立群 于 2021-07-07 设计创作，主要内容包括：本发明公开了一种基于人工神经网络的可调总压畸变发生器调节指导方法,首先建立人工神经网络,通过实验采集不同状态下可调总压畸变发生器所产生的下游流场总压分布状态作为训练样本,对人工神经网络进行训练预测模型；然后根据预设的下游测点目标总压恢复系数计算下游流场的目标总压分布情况并穷举出可调总压畸变发生器所有可能的状态,并结合当前来流马赫数利用预测模型对每一种状态进行预测,得到和下游流场的目标总压分布情况最接近的解；最后根据最优解对应的状态指导可调总压畸变发生器调节。本发明解决了目前可调式畸变发生器使用中需根据经验反复调整才能获得目标总压畸变的问题,节约实验时间,降低了实验成本。(The invention discloses an adjustable total pressure distortion generator adjustment guiding method based on an artificial neural network, which comprises the steps of firstly establishing the artificial neural network, acquiring downstream flow field total pressure distribution states generated by the adjustable total pressure distortion generator in different states as training samples through experiments, and training a prediction model for the artificial neural network; then, calculating the target total pressure distribution condition of a downstream flow field according to a preset downstream measuring point target total pressure recovery coefficient, exhausting all possible states of the adjustable total pressure distortion generator, and predicting each state by using a prediction model in combination with the current incoming flow Mach number to obtain a solution which is closest to the target total pressure distribution condition of the downstream flow field; and finally, guiding the adjustable total pressure distortion generator to adjust according to the state corresponding to the optimal solution. The invention solves the problem that the target total pressure distortion can be obtained only by repeatedly adjusting according to experience in the use of the current adjustable distortion generator, saves the experiment time and reduces the experiment cost.)

1. The adjustable total pressure distortion generator adjustment guiding method based on the artificial neural network is characterized by comprising the following steps of:

step 1), performing a wind tunnel experiment according to the characteristics of the adjustable distortion generator and a preset measurement method of the total pressure distribution condition of a downstream flow field to obtain the states of different adjustable distortion generators and corresponding downstream total pressure distribution data under different incoming flow Mach numbers, and using the states as training samples of an artificial neural network, wherein the states of the adjustable distortion generators can be used for adjusting the opening and closing angles of hinges in the adjustable distortion generators;

step 2), establishing an artificial neural network, and training the artificial neural network by adopting a training sample to obtain a prediction model capable of calculating the total pressure distribution condition of a downstream flow field according to the opening and closing angle of each hinge of the adjustable distortion generator and the incoming flow Mach number;

step 3), calculating the target total pressure distribution condition of the downstream flow field according to a preset target total pressure recovery coefficient of the downstream measuring point;

step 4), screening the opening and closing angles of each hinge of the adjustable distortion generator according to a preset total pressure recovery coefficient of a target of a downstream measuring point, screening out the hinges which are completely opened and completely closed, and taking the rest hinges as an adjusting set;

step 5), exhausting the opening and closing angles of all hinges in the adjustment set by adopting an exhaustion method according to a preset angle adjustment step threshold value, and arranging and combining all possible states of the adjustable distortion generator by combining the completely opened and completely closed hinges in the adjustable distortion generator to be used as a state set;

step 6), aiming at each state in the state set, combining the current incoming flow Mach number, inputting the current incoming flow Mach number into the artificial neural network prediction model, and obtaining the total pressure distribution condition of the downstream flow field corresponding to each state;

and 7), comparing the target total pressure distribution condition of the downstream flow field with the total pressure distribution condition of the downstream flow field corresponding to each state, screening out the state with the minimum difference between the total pressure distribution condition of the corresponding downstream flow field and the target total pressure distribution condition of the downstream flow field, and adjusting the adjustable distortion generator according to the state.

2. The artificial-based system of claim 1The method for guiding the adjustment of the adjustable total pressure distortion generator of the neural network is characterized in that before the training sample is adopted to train the artificial neural network in the step 2), the numerical value of the training sample needs to be standardized according to min-max, namelyWherein x^*The normalized data is mapped to [0,1 ], x is the original data, min is the minimum value of the sample data, max is the maximum value of the sample data]In the meantime.

3. The adjustable total pressure distortion generator adjustment guiding method based on the artificial neural network as claimed in claim 1, wherein the artificial neural network structure in step 2) is 3 layers, including an input layer, a hidden layer and an output layer;

the number of neurons in the input layer is P-M +1, and M is the number of hinges in the regulation set;

the number of neurons in an output layer is T, and T is the number of downstream total pressure measurement points;

the empirical formula of the number of hidden layer nodes is Q ═ sqrt (P + T) + a, and a is a random integer from 1 to 10;

the hidden layer neuron excitation function adopts a hyperbolic tangent S-shaped function, the output layer excitation function adopts a linear function, a gradient descent training method is adopted, the learning rate of the artificial neural network is set to be a self-adaptive method, and the minimum error of a training target is set to be 0.001.

Recovering coefficient sigma according to total pressure of T measuring points in downstream flow field_iDetermining the number a of hinges that are fully open (angle β) and their position, and the number B of hinges that are fully closed (angle α) and their position, requires an exhaustive number C-M-a-B of hinges and their position, M being the total number of hinges for the adjustable distortion generator. Setting the angle of C hinges needing to be exhausted, changing the angle from alpha to beta once every i degrees, wherein the i degrees are preset angle adjusting step length threshold values, and the hinge change types K are commonPossibility of combining currentAnd sequentially substituting the Mach number of the incoming flow into the artificial neural network prediction model to obtain K groups of results recording total pressure recovery coefficients of the T-position measuring points and corresponding hinge opening and closing conditions, comparing the K total pressure distribution states with the target total pressure distribution state one by one, obtaining the total pressure recovery coefficient distribution state closest to the target through multi-sample inspection, and outputting the opening and closing angles of the M hinges under the state.

Technical Field

The invention relates to the field of aircraft engines, in particular to an adjustable total pressure distortion generator adjustment guiding method based on an artificial neural network.

Background

In the process of actual flight of an airplane, the intake distortion of an engine is usually unavoidable, and an aero-engine almost works under the non-uniform intake condition, so that the intake distortion form and the strength under the real working condition are simulated by an experimental device for researching the influence of the intake distortion on the performance of the engine, and the development process of the aero-engine is essential. The current mature distortion generators such as distortion net, plugboard disturbed flow distortion generators, etc. use the conventional method as follows: firstly, processing an intake total pressure distortion generator according to experience, installing the intake total pressure distortion generator at a certain section of the upstream of an engine inlet so as to generate intake pressure distortion under an experimental working condition on a designed engine inlet interface, measuring the pressure distortion of the engine inlet section in a probe mode and the like after mounting, taking the distortion generator out for secondary processing after measuring the difference between the current intake distortion and the experimental working condition, repeating the steps, and finally obtaining the distortion generator capable of accurately simulating the total pressure distortion condition of an engine inlet flow field through multiple iterations. The recent academic community provides a brand-new total pressure distortion generating device, and the basic structural form of the device is as follows: the vertical-flow-type clamping plates are arranged in parallel at intervals, the clamping plates are provided with an interval channel, hinges with the same size are uniformly distributed in the channel, the hinges are remotely controlled by control assemblies such as push rods and air cylinders, and whether the hinge is opened or closed and the opening and closing angle can be freely adjusted; after the airflow passes through the hinges, flow loss is generated, so that the total pressure is locally reduced, so-called flow field pressure distortion is formed, and as a distortion element, the opening and closing angle of the hinges determines the range and strength of the influence on the downstream airflow. When the hinge is used, the opening and closing angle of each hinge is remotely controlled, and a proper distorted flow field can be generated at the downstream. The adjustable distortion generator overcomes the defects that the prior distortion generator needs to be processed and tested for many times and lacks of variable items, and is a very worthy of recommendation. However, in view of the diversity of changes in the use process of the distortion generator, in the repeated experiment mode adopted in the use process at the present stage, workers need to adjust the hinge opening and closing angles according to own experiences, meanwhile, a measuring element is installed on the downstream target section to monitor the flow field distortion generated by the measuring element, the monitored downstream flow field distortion is compared with the target downstream flow field distortion, the workers adjust the hinge opening and closing angles according to own experiences, and the ideal hinge opening and closing angle of the distortion generator is finally obtained through multiple iterations.

Disclosure of Invention

The invention aims to solve the technical problem of providing an adjustable total pressure distortion generator adjustment guiding method based on an artificial neural network aiming at the defects involved in the background technology.

The invention adopts the following technical scheme for solving the technical problems:

the adjustable total pressure distortion generator adjustment guiding method based on the artificial neural network comprises the following steps:

step 1), performing a wind tunnel experiment according to the characteristics of the adjustable distortion generator and a preset measurement method of the total pressure distribution condition of a downstream flow field to obtain the states of different adjustable distortion generators and corresponding downstream total pressure distribution data under different incoming flow Mach numbers, and using the states as training samples of an artificial neural network, wherein the states of the adjustable distortion generators can be used for adjusting the opening and closing angles of hinges in the adjustable distortion generators;

step 2), establishing an artificial neural network, and training the artificial neural network by adopting a training sample to obtain a prediction model capable of calculating the total pressure distribution condition of a downstream flow field according to the opening and closing angle of each hinge of the adjustable distortion generator and the incoming flow Mach number;

step 3), calculating the target total pressure distribution condition of the downstream flow field according to a preset target total pressure recovery coefficient of the downstream measuring point;

step 4), screening the opening and closing angles of each hinge of the adjustable distortion generator according to a preset total pressure recovery coefficient of a target of a downstream measuring point, screening out the hinges which are completely opened and completely closed, and taking the rest hinges as an adjusting set;

step 5), exhausting the opening and closing angles of all hinges in the adjustment set by adopting an exhaustion method according to a preset angle adjustment step threshold value, and arranging and combining all possible states of the adjustable distortion generator by combining the completely opened and completely closed hinges in the adjustable distortion generator to be used as a state set;

step 6), aiming at each state in the state set, combining the current incoming flow Mach number, inputting the current incoming flow Mach number into the artificial neural network prediction model, and obtaining the total pressure distribution condition of the downstream flow field corresponding to each state;

and 7), comparing the target total pressure distribution condition of the downstream flow field with the total pressure distribution condition of the downstream flow field corresponding to each state, screening out the state with the minimum difference between the total pressure distribution condition of the corresponding downstream flow field and the target total pressure distribution condition of the downstream flow field, and adjusting the adjustable distortion generator according to the state.

As a further optimization scheme of the adjustable total pressure distortion generator adjustment guiding method based on the artificial neural network, before training the artificial neural network by adopting the training samples in the step 2), the numerical values of the training samples need to be standardized according to min-max, namelyWherein x^*The normalized data is mapped to [0,1 ], x is the original data, min is the minimum value of the sample data, max is the maximum value of the sample data]In the meantime.

As a further optimization scheme of the adjustable total pressure distortion generator adjustment guiding method based on the artificial neural network, the artificial neural network in the step 2) has a structure of 3 layers, wherein the artificial neural network comprises an input layer, a hidden layer and an output layer;

the number of neurons in the input layer is P-M +1, and M is the number of hinges in the regulation set;

the number of neurons in an output layer is T, and T is the number of downstream total pressure measurement points;

the empirical formula of the number of hidden layer nodes is Q ═ sqrt (P + T) + a, and a is a random integer from 1 to 10;

the hidden layer neuron excitation function adopts a hyperbolic tangent S-shaped function, the output layer excitation function adopts a linear function, a gradient descent training method is adopted, the learning rate of the artificial neural network is set to be a self-adaptive method, and the minimum error of a training target is set to be 0.001.

Compared with the prior art, the invention adopting the technical scheme has the following technical effects:

1. the total pressure distortion generated by the distortion generator is predicted through the artificial neural network, so that the total pressure partial states of a downstream flow field under different distortion generator forms can be rapidly obtained, research personnel can conveniently research the performance of the distortion generator, and the distortion generator can be further optimized in subsequent work;

2. the distortion generator guidance program saves the process that a large amount of expenditure time is spent to design and modify the distortion generator to obtain the target total pressure distribution state before the traditional intake distortion experiment, and the adjustment state of the distortion generator can be obtained when the intake distortion experiment of the engine is designed, so that the experiment efficiency is greatly improved, and the cost is saved;

3. compared with the traditional numerical calculation means, the artificial neural network prediction model constructed by taking the experimental result as the sample is more accurate.

Drawings

FIG. 1 is a flow chart of an adjustable total pressure distortion generator adjustment guidance method based on an artificial neural network;

FIG. 2 is a specific structure of an artificial neural network used in the present invention;

FIG. 3(a), FIG. 3(b), FIG. 3(c) and FIG. 3(d) are respectively a schematic diagram of the central symmetry of the hinge position, a schematic diagram of the horizontal symmetry of the hinge position, a schematic diagram of the vertical symmetry of the hinge position and a schematic diagram of the symmetry of the measuring point position;

FIG. 4 is a flow chart of an exhaustive algorithm for directing the generation of test sets in a program.

Detailed Description

The technical scheme of the invention is further explained in detail by combining the attached drawings:

the present invention may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art. In the drawings, components are exaggerated for clarity.

The invention aims to construct a model for predicting the total pressure distribution of a downstream flow field of a distortion generator through the regulation state of the distortion generator by an artificial neural network, construct an efficient guiding program by combining the characteristics of rapid calculation of the model and an exhaustive algorithm, provide a feasible distortion generator regulation scheme when the total pressure distribution state of a downstream target is obtained, and guide the reasonable use of the distortion generator. The method can be used for rapidly predicting a feasible adjusting scheme according to a required target distortion flow field, guiding the reasonable adjustment of each hinge of the adjustable distortion generator, rapidly forming the required distortion flow field and fully playing the advantages of the distortion generator.

Referring to fig. 1, the invention discloses an adjustable total pressure distortion generator adjustment guiding method based on an artificial neural network, comprising the following steps:

step 1), performing a wind tunnel experiment according to the characteristics of the adjustable distortion generator and a preset measurement method of the total pressure distribution condition of a downstream flow field to obtain the states of different adjustable distortion generators and corresponding downstream total pressure distribution data under different incoming flow Mach numbers, and using the states as training samples of an artificial neural network, wherein the states of the adjustable distortion generators can be used for adjusting the opening and closing angles of hinges in the adjustable distortion generators;

step 2), establishing an artificial neural network, and training the artificial neural network by adopting a training sample to obtain a prediction model capable of calculating the total pressure distribution condition of a downstream flow field according to the opening and closing angle of each hinge of the adjustable distortion generator and the incoming flow Mach number;

before the training of the artificial neural network by adopting the training samples, the numerical values of the training samples need to be standardized according to min-maxWherein x^*The normalized data is mapped to [0,1 ], x is the original data, min is the minimum value of the sample data, max is the maximum value of the sample data]To (c) to (d);

the artificial neural network structure is 3 layers and comprises an input layer, a hidden layer and an output layer;

the number of neurons in the input layer is P-M +1, and M is the number of hinges in the regulation set;

the number of neurons in an output layer is T, and T is the number of downstream total pressure measurement points;

the empirical formula of the number of hidden layer nodes is Q ═ sqrt (P + T) + a, and a is a random integer from 1 to 10;

the hidden layer neuron excitation function adopts a hyperbolic tangent S-shaped function, the output layer excitation function adopts a linear function, a gradient descent training method is adopted, the learning rate of the artificial neural network is set to be a self-adaptive method, and the minimum error of a training target is set to be 0.001;

step 3), calculating the target total pressure distribution condition of the downstream flow field according to a preset target total pressure recovery coefficient of the downstream measuring point;

step 4), screening the opening and closing angles of each hinge of the adjustable distortion generator according to a preset total pressure recovery coefficient of a target of a downstream measuring point, screening out the hinges which are completely opened and completely closed, and taking the rest hinges as an adjusting set;

step 5), exhausting the opening and closing angles of all hinges in the adjustment set by adopting an exhaustion method according to a preset angle adjustment step threshold value, and arranging and combining all possible states of the adjustable distortion generator by combining the completely opened and completely closed hinges in the adjustable distortion generator to be used as a state set;

step 6), aiming at each state in the state set, combining the current incoming flow Mach number, inputting the current incoming flow Mach number into the artificial neural network prediction model, and obtaining the total pressure distribution condition of the downstream flow field corresponding to each state;

and 7), comparing the target total pressure distribution condition of the downstream flow field with the total pressure distribution condition of the downstream flow field corresponding to each state, screening out the state with the minimum difference between the total pressure distribution condition of the corresponding downstream flow field and the target total pressure distribution condition of the downstream flow field, and adjusting the adjustable distortion generator according to the state.

Recovering coefficient sigma according to total pressure of T measuring points in downstream flow field_iDetermining the number a of hinges that are fully open (angle β) and their position, and the number B of hinges that are fully closed (angle α) and their position, requires an exhaustive number C-M-a-B of hinges and their position, M being the total number of hinges for the adjustable distortion generator. Setting the angle of C hinges needing to be exhausted, changing the angle from alpha to beta once every i degrees, wherein the i degrees are preset angle adjusting step length threshold values, and the hinge change types K are commonThe method comprises the steps of obtaining K groups of results recording total pressure recovery coefficients of a T-position measuring point and corresponding hinge opening and closing conditions by sequentially substituting a current incoming flow Mach number into an artificial neural network prediction model, comparing K total pressure distribution states with a target total pressure distribution state one by one, obtaining a total pressure recovery coefficient distribution state closest to a target through multi-sample detection, and outputting opening and closing angles of M hinges under the state.

The wind tunnel experiment of the influence of the distortion generator on the total pressure distribution of the downstream flow field comprises the following specific steps:

the experimental platform is a subsonic air suction type wind tunnel experimental platform, air flow is sucked from the atmosphere through a horn mouth, the total pressure of incoming flow is atmospheric pressure, the air flow is subjected to disturbance reduction through a stabilizing section and then flows through a distortion generator model, and then sequentially passes through a measuring section, an expanding section and an electric valve and finally enters an inlet of a gas compressor. Arranging static pressure measuring points at the upstream 1.5-time pipe diameter of the distortion generator, measuring the Mach number of incoming flow, arranging a total pressure distribution measuring section at the downstream 1-time pipe diameter, and determining the total pressure distribution condition of the section by measuring the total pressure value of the measuring points at 41 positions on the section. The 41 measuring points comprise a section circle center measuring point, the other 40 measuring points are uniformly divided into 8 groups along the circumferential direction, and each group is arranged along the radial direction according to the center of an isotorus. In the experiment, the flow of the wind tunnel is controlled by an electric switch valve, so that the incoming flow Mach number is controlled.

The pressure values measured in the experiment are noted as P_i ^*(i is 1 … 41), in order to facilitate later comparison and data normalization, the total pressure value obtained by each point measurement is converted into the total pressure recovery coefficient sigma during experimental data processing_iWherein The total pressure of the incoming flow is the environmental pressure during the experiment.

In the experimental data processing, as shown in fig. 3(a), 3(b), 3(c) and 3(d), each blank space in fig. 3(a) represents a hinge position, and each origin point in fig. 3(d) represents a total pressure measuring point, and it can be known from the figure that spatial positions of hinge arrangement and total pressure measuring point arrangement have horizontal symmetry, vertical symmetry and central symmetry, so that when hinges at three different positions are opened at the same angle, three corresponding groups of artificial neural network samples only need to be subjected to one experiment, and the experimental data at the corresponding positions are exchanged to obtain the experimental data.

The experimental data normalization adopts min-max standardizationWherein x^*Is normalized numberAccording to the method, x is original data, min is the minimum value of sample data, max is the maximum value of the sample data, and the normalized data is mapped to [0,1 ]]In the meantime.

As shown in fig. 2, the specific structure of the artificial neural network is as follows:

X_iand (i ═ 1 … … n) is an input layer neuron subjected to preprocessing, namely data normalization, and the artificial neural network used in the invention has 109 input layer neurons in total, and respectively represents the hinge opening and closing angle at 108 positions and the mach number of 1 incoming flow.

Y_iAnd (i-1 … … s) is output layer neurons subjected to data normalization, and the artificial neural network used in the invention has 41 output layer neurons which respectively represent the total pressure recovery coefficient values of 41 measuring points on the section at one-time pipe diameter position downstream of the distortion generator.

P represents the number of hidden layer nodes, and in the artificial neural network used in the present invention, P is 22.

Hidden layer output model is O_j＝f(∑W_ij×X_i-θ_j)

The output layer has an output model of Y_k＝f(∑T_jk×O_j-θ_k)

Where f represents the excitation function and theta represents the threshold. In the invention, the hidden layer neuron excitation function adopts sigmoid, and the output layer excitation function adopts linear function.

The self-learning function of the artificial neural network is delta W_ij(n+1)＝η×φ_i×O_j+α×w_ij(n), where η is a learning factor, φ represents a calculation error of a node, O represents an output value of the node, and α represents a momentum factor.

And (4) drawing a target total pressure recovery coefficient cloud picture according to a target total pressure recovery coefficient program of a preset downstream measuring point. And removing the hinges which are completely opened and closed in the adjustable total pressure distortion generator according to the target total pressure recovery coefficient cloud picture, exhausting the rest hinges according to a preset angle adjusting step threshold value, and obtaining all possible states of the adjustable distortion generator as a test set.

And inputting each state in the test set and the current incoming flow Mach number into a finished artificial neural network prediction model for prediction to generate a result data set.

As shown in figure 4, the test set in the invention is obtained by adopting an exhaustive algorithm, the complete closing angle of the hinge is 0 degree, the complete opening angle is 120 degrees, C hinges needing to guide the opening and closing angle are respectively adjusted from 0 degree to 120 degrees at intervals of 5 degrees to obtain 25^ C different schemes, and the artificial neural network is used for respectively predicting and obtaining the results with the same quantity.

And (3) for ensuring the reliability of the obtained result, adopting two methods for parallel inspection, preferably inspecting that the error between each value and the target value is less than 1%, if the error is not consistent with the target value, kicking out a result data set, and taking the result with the minimum variance of the target value as the optimal solution from the rest results.

And tracing to obtain the opening and closing angle of each hinge corresponding to the optimal solution and outputting the opening and closing angle, namely finishing the guidance of adjusting the hinges of the distortion generator.

It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

The above-mentioned embodiments, objects, technical solutions and advantages of the present invention are further described in detail, it should be understood that the above-mentioned embodiments are only illustrative of the present invention and are not intended to limit the present invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the protection scope of the present invention.