CN114371313B - Neural network-based dragline system time delay compensation method - Google Patents
Neural network-based dragline system time delay compensation method Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 27
- 238000013528 artificial neural network Methods 0.000 title claims abstract description 21
- 238000012360 testing method Methods 0.000 claims abstract description 26
- 239000012530 fluid Substances 0.000 claims abstract description 14
- 230000005284 excitation Effects 0.000 claims abstract description 13
- 238000005259 measurement Methods 0.000 claims abstract description 13
- 238000004088 simulation Methods 0.000 claims abstract description 13
- 238000010219 correlation analysis Methods 0.000 claims description 3
- 238000012795 verification Methods 0.000 claims 1
- 230000003068 static effect Effects 0.000 description 5
- 238000011161 development Methods 0.000 description 2
- 230000007613 environmental effect Effects 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- NAWXUBYGYWOOIX-SFHVURJKSA-N (2s)-2-[[4-[2-(2,4-diaminoquinazolin-6-yl)ethyl]benzoyl]amino]-4-methylidenepentanedioic acid Chemical compound C1=CC2=NC(N)=NC(N)=C2C=C1CCC1=CC=C(C(=O)N[C@@H](CC(=C)C(O)=O)C(O)=O)C=C1 NAWXUBYGYWOOIX-SFHVURJKSA-N 0.000 description 1
- 238000005452 bending Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000000691 measurement method Methods 0.000 description 1
- 230000035939 shock Effects 0.000 description 1
- 230000007704 transition Effects 0.000 description 1
Classifications
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01P—MEASURING LINEAR OR ANGULAR SPEED, ACCELERATION, DECELERATION, OR SHOCK; INDICATING PRESENCE, ABSENCE, OR DIRECTION, OF MOVEMENT
- G01P21/00—Testing or calibrating of apparatus or devices covered by the preceding groups
- G01P21/02—Testing or calibrating of apparatus or devices covered by the preceding groups of speedometers
- G01P21/025—Testing or calibrating of apparatus or devices covered by the preceding groups of speedometers for measuring speed of fluids; for measuring speed of bodies relative to fluids
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01P—MEASURING LINEAR OR ANGULAR SPEED, ACCELERATION, DECELERATION, OR SHOCK; INDICATING PRESENCE, ABSENCE, OR DIRECTION, OF MOVEMENT
- G01P5/00—Measuring speed of fluids, e.g. of air stream; Measuring speed of bodies relative to fluids, e.g. of ship, of aircraft
- G01P5/14—Measuring speed of fluids, e.g. of air stream; Measuring speed of bodies relative to fluids, e.g. of ship, of aircraft by measuring differences of pressure in the fluid
- G01P5/16—Measuring speed of fluids, e.g. of air stream; Measuring speed of bodies relative to fluids, e.g. of ship, of aircraft by measuring differences of pressure in the fluid using Pitot tubes, e.g. Machmeter
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01P—MEASURING LINEAR OR ANGULAR SPEED, ACCELERATION, DECELERATION, OR SHOCK; INDICATING PRESENCE, ABSENCE, OR DIRECTION, OF MOVEMENT
- G01P5/00—Measuring speed of fluids, e.g. of air stream; Measuring speed of bodies relative to fluids, e.g. of ship, of aircraft
- G01P5/14—Measuring speed of fluids, e.g. of air stream; Measuring speed of bodies relative to fluids, e.g. of ship, of aircraft by measuring differences of pressure in the fluid
- G01P5/16—Measuring speed of fluids, e.g. of air stream; Measuring speed of bodies relative to fluids, e.g. of ship, of aircraft by measuring differences of pressure in the fluid using Pitot tubes, e.g. Machmeter
- G01P5/17—Coupling arrangements to the indicating device
- G01P5/175—Coupling arrangements to the indicating device with the determination of Mach number
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
Abstract
The invention discloses a neural network-based dragline pipeline system time delay compensation method, and belongs to the technical field of metering test. The invention combines a cone-pulling pipeline continuity equation and a momentum conservation equation, considers the influence of pipelines with different flow states on gas friction resistance, and obtains delay time by extracting an excitation signal and a response signal; meanwhile, the three-dimensional fluid simulation technology is adopted to realize the correction of wall friction force under different temperatures and pressures in the theoretical model, so that the prediction precision of the model is improved; on the basis, a data set is constructed based on theoretical data, simulation data and experimental measurement data, and a neural network method is adopted to construct high-precision prediction and compensation of the delay time of the towing cone pipeline under the condition of multi-parameter influence. The invention can improve the measurement precision of the pressure delay time of the towing cone pipeline and ensure the high-precision measurement of airspeed in the test flight process.
Description
Technical Field
The invention relates to a time delay compensation method of a dragline pipeline system based on a neural network, and belongs to the field of metering calibration.
Background
Airspeed indicator systems are closely related to the performance and automation level of the aircraft, and not only can provide displays of airspeed and mach numbers directly to pilots, but also can provide desired airspeed information to navigation systems, autopilot systems, and flight recording systems. The cone pulling method has the advantages of high measurement precision, no space limitation and high test flight landing success rate, and is widely applied to airworthiness test flight.
Airspeed of an aircraft is primarily based on measured static and total pressures. Compared with dynamic pressure, static pressure is difficult to obtain a stable and accurate static pressure source due to the fact that the static pressure is easily disturbed by air flow in the measuring process, in addition, the atmospheric environment is complex and changeable, the flight actions of an airplane in maneuvering flight are various, and the difficulty of accurately measuring the static pressure is increased. Because the distance between the cone pulling guide pipe and the pressure sensor is long, the time delay of the cone pulling guide pipe system is serious, and the accuracy of the airspeed system calibration result is greatly influenced.
However, for a dragline system, since it is adapted to a specific model, its length is often on the order of several tens meters or even about hundreds of meters, and in particular, in order to eliminate the influence of the dragline bending on the measurement result, the dragline is often required to be kept in a straight state, which is very difficult for environmental tests of different temperatures and humidities. At present, the influence of the ambient temperature on the delay time of the towing cone pipeline can only be realized through an ambient test cabin, but the size of the ambient test cabin is often smaller, the flat placement of the towing cone pipeline cannot be met, and the influence of the temperature on the delay time can only be obtained through a shorter flat pipe (generally not more than 2m and even shorter).
The aircraft has large time and space spans in the flight process, and the time delay of the towline system is greatly influenced no matter from low latitude to high latitude or from offshore plane to myriad meters high altitude, and the air pressure, temperature, humidity and the like in the flight environment are changed in a large range.
In summary, the accurate measurement of the delay time of the cone-pulling pipeline system under the multiparameter environment condition has too many limited factors to be directly obtained, so that deep analysis on the delay time of the cone-pulling pipeline system is needed, and a delay time compensation method suitable for the specificity of the system is provided, which is a necessary condition for ensuring the validity of cone-pulling measurement data.
Disclosure of Invention
The invention aims to provide a neural network-based dragline pipeline system time delay compensation method so as to realize accurate measurement of airspeed in the aircraft test flight process.
The invention aims at realizing the following technical scheme: the invention discloses a neural network-based dragline pipeline system time delay compensation method, which comprises the following steps:
step one, constructing a drag taper pipeline delay time theoretical model:
based on the assumption of compressible gas medium, respectively establishing a continuity equation of the towing cone pipeline as shown in a formula (1) and a momentum conservation equation as shown in a formula (2):
wherein ρ is the density of the gaseous medium, u is the gas flow velocity, p is the pressure of the gaseous medium, D is the diameter of the dragline pipe, f c Is a compressible friction factor;
the pressure drop Δp in the trawl pipe is shown in formula (3):
obtaining a pressure signal at the tail end of the cone-pulling pipeline through an excitation signal in a given form, and further obtaining delay time of the cone-pulling pipeline;
step two, acquiring delay time test data of the towing cone pipeline:
measuring delay time of the drag taper pipe system under different lengths, different diameters and different step pressures based on the step pressure generating device in a laboratory environment;
the step pressure generating device and the environment test cabin are combined to measure the delay time of the straight cone dragging pipeline meeting the requirement of the test cabin size under different environment temperatures;
step three, constructing a fluid simulation model of the drag taper pipeline:
modeling the trawl pipe by using fluid simulation software, meshing the trawl pipe model, determining a turbulence model and a time step to eliminate the influence of the mesh number on a measurement result, and obtaining a reference model by adopting the test data obtained in the step two and taking the delay time as an evaluation index;
step four, compressible Friction factor f c Is obtained and corrected:
determination of compressible Friction factor f by means of a secondary development function of fluid simulation software c ；
Based on the real length of the towing cone pipe, the delay time of the towing cone pipe under different temperatures and different step pressures is obtained through simulation, and the compressible friction factor f is calculated c Correcting;
step five, acquiring theoretical data of delay time of the cone dragging pipeline:
determining the delay time of the towing cone pipeline under different towing cone pipeline structural parameters, different gas attribute parameters and different excitation signal parameters based on the corrected towing cone pipeline theoretical model in the step four;
the towing cone pipeline structure parameters comprise: the diameter of the cone-pulling pipeline, the length of the cone-pulling pipeline and the roughness of the cone-pulling pipeline;
the gas attribute parameters include: gas pressure and aerodynamic viscosity coefficient;
the excitation signal parameters include: step pressure;
step six, constructing a data set based on the delay time of the cone towing pipeline of the step pressure generating device based on the test data obtained in the step two, the simulation data obtained in the step four and the theoretical data obtained in the step five;
step seven, based on the structural parameters of the dragline, the gas attribute parameters, the excitation signal parameters and Re numbers closely related to the fluid state, simultaneously carrying out correlation analysis by combining the dragline delay time tau, extracting core parameters closely related to the dragline delay time tau, reducing the number of input parameters, and constructing a model of the dragline delay time tau by combining a neural network method as shown in a formula (4):
τ＝f(D,L,ε,p,η,Re) (4)
wherein, drag the taper pipeline structural parameter and include: the diameter D of the towing cone pipe, the length L of the towing cone pipe and the roughness epsilon of the towing cone pipe; the gas attribute parameters include: gas pressure p and dynamic viscosity coefficient η;
further, by adjusting model parameters of the neural network, an error of the model is ensured to be less than 3%, as shown in the formula (5):
(τ ex -τ)/τ ex ≤3％ (5)
wherein τ ex For a desired dragline delay time.
The beneficial effects are that:
1. the invention discloses a neural network-based time delay compensation method for a dragline pipeline system, which is characterized in that viscous resistance in a dragline pipeline theoretical model is corrected by fluid simulation of a dragline pipeline, a dragline pipeline delay time data set based on structural parameters, environmental parameters and the like of the dragline pipeline is quickly constructed by combining experimental test data, theoretical data and simulation analysis data, and delay time is modeled based on the neural network, so that accurate measurement and compensation of the dragline pipeline delay time in a test flight process are realized.
Drawings
FIG. 1 is a flow chart of a method for compensating time delay of a dragline pipeline system based on a neural network according to the present invention;
FIG. 2 is a graph of a shock tube-based dragline system delay time measurement method employed in the present invention;
FIG. 3 is a neural network-based modeling method of delay time of a dragline pipeline system of the present invention.
Detailed Description
For a better description of the objects and advantages of the present invention, the following description will be given with reference to the accompanying drawings and examples.
Example 1:
as shown in fig. 1, the method for compensating the time delay of the dragline pipeline system disclosed in the embodiment includes the following specific implementation steps:
step one, constructing a drag taper pipeline delay time theoretical model:
assuming that a gas medium in the dragline pipe is compressible gas, constructing a corresponding continuity equation and a momentum conservation equation based on the dragline pipe of the dragline pipe system, wherein the continuity equation is as follows:
where ρ is the density of the gaseous medium and u is the gas flow velocity;
in order to more accurately describe the evolution condition of the gas pressure in the cone-pulling pipeline in the gas pressure delay process of the cone-pulling pipeline system, the friction force generated on the wall surface of the cone-pulling pipeline must be considered, and a momentum conservation equation comprising the friction force is as follows:
wherein ρ is the density of the gaseous medium, u is the gas flow velocity, p is the pressure of the gaseous medium, D is the diameter of the dragline pipe, f c Is a compressible friction factor;
the pressure drop Δp in the trawl pipe is shown in formula (3):
obtaining a pressure signal at the tail end of the cone-pulling pipeline through an excitation signal in a given form, and further obtaining delay time of the cone-pulling pipeline;
further, the implementation is also based on the gas in the trawl pipe satisfying the ideal gas state equation, as shown in formula (4): the pressure p and density p in the cone pipe at any time satisfies:
p＝ρR g T (4)
wherein p is the pressure of the gaseous medium in the towing cone pipeline, ρ is the density of the gaseous medium, R g Is a gas constant;
from this, a compressible friction factor f is obtained c The darcy friction factor f with the incompressible stream satisfies the relationship shown in formula (5):
wherein, ma is Mach number, and gamma is specific heat ratio;
further, ma is derived by formula (6):
further, when turbulent flow is present in the trawl pipe, the darcy friction factor f is derived by formula (6):
wherein epsilon is the absolute roughness of the wall surface of the cone-pulling pipeline, and Re is the flowing Reynolds number;
further, re is derived by formula (8):
wherein eta is the dynamic viscosity coefficient of the gaseous medium;
when the gas medium in the cone-pulling pipeline is converted from laminar flow to turbulent flow, the Darcy friction factor f is obtained through a difference value;
layer flow directionCritical value Re of turbulent transition process Re c,min Re and Re c,max As shown in formula (9) and formula (10), respectively:
when the flow in the trawl pipe is laminar, the darcy friction factor f is as shown in formula (11):
step two, acquiring delay time test data of the towing cone pipeline:
in a laboratory environment, measuring delay times of the drag-cone pipeline system at different lengths, different diameters and different step pressures based on the step pressure generating device as shown in fig. 2;
the step pressure generating device and the environment test cabin are combined to measure the delay time of the straight cone dragging pipeline meeting the requirement of the test cabin size under different environment temperatures;
step three, constructing a fluid simulation model of the drag taper pipeline:
modeling the trawl pipe by using fluid simulation software, and meshing the trawl pipe model; determining a turbulence model and a time step to eliminate the influence of the grid quantity on a measurement result, and adopting the test data obtained in the second step to obtain a reference model by taking the delay time as an evaluation index;
step four, compressible Friction factor f c Is obtained and corrected:
determination of compressible Friction factor f by means of a secondary development function of fluid simulation software c ；
Based on the real pipe length, different temperatures and different step pressures are obtained through simulationDelay time of lower towing cone pipeline, relative to compressible friction factor f c Correcting;
step five, acquiring theoretical data of delay time of the cone dragging pipeline:
determining the delay time of the towing cone pipeline under different towing cone pipeline structural parameters, different gas attribute parameters and different excitation signal parameters based on the corrected towing cone pipeline theoretical model in the step four;
the towing cone pipeline structure parameters comprise: the diameter of the cone-pulling pipeline, the length of the cone-pulling pipeline and the roughness of the cone-pulling pipeline;
the gas attribute parameters include: gas pressure and aerodynamic viscosity coefficient;
the excitation signal parameters include: step pressure;
step six, constructing a data set based on the delay time of the cone towing pipeline of the step pressure generating device based on the test data obtained in the step two, the simulation data obtained in the step four and the theoretical data obtained in the step five;
step seven, based on the structural parameters of the dragline, the gas attribute parameters, the excitation signal parameters and Re numbers closely related to the fluid state, simultaneously carrying out correlation analysis by combining the dragline delay time tau, extracting core parameters closely related to the dragline delay time tau, reducing the number of input parameters, and constructing a model of the dragline delay time tau by combining a neural network method as shown in a formula (4):
τ＝f(D,L,ε,p,η,Re) (4)
wherein, drag the taper pipeline structural parameter and include: the diameter D of the towing cone pipe, the length L of the towing cone pipe and the roughness epsilon of the towing cone pipe; the gas attribute parameters include: gas pressure p and dynamic viscosity coefficient η;
further, by adjusting model parameters of the neural network, an error of the model is ensured to be less than 3%, as shown in the formula (5):
(τ ex -τ)/τ ex ≤3％ (5)
wherein τ ex For the intended towlineDelay time.
While the foregoing is directed to embodiments of the present invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof, and the scope thereof is determined by the claims that follow.
Claims (2)
1. A neural network-based dragline system time delay compensation method is characterized in that: the method comprises the following steps:
step one, constructing a drag taper pipeline delay time theoretical model;
based on the definition of compressible gas medium, respectively establishing a continuity equation of the towing cone pipeline as shown in a formula (1) and a momentum conservation equation as shown in a formula (2):
wherein ρ is the density of the gaseous medium, u is the gas flow velocity, p is the pressure of the gaseous medium, and D is the diameter of the dragline pipe;
the pressure drop Δp in the trawl pipe is shown in formula (3):
obtaining a pressure signal at the tail end of the cone-pulling pipeline through an excitation signal in a given form, and further obtaining delay time of the cone-pulling pipeline;
step two, acquiring delay time test data of the cone dragging pipeline;
measuring delay time of the drag taper pipe system under different lengths, different diameters and different step pressures based on the step pressure generating device in a laboratory environment;
the step pressure generating device and the environment test cabin are combined to measure the delay time of the straight cone dragging pipeline meeting the requirement of the test cabin size under different environment temperatures;
step three, constructing a fluid simulation model of the drag taper pipeline;
modeling the trawl pipe by using fluid simulation software, meshing the trawl pipe model, determining a turbulence model and a time step to eliminate the influence of the mesh number on a measurement result, and obtaining a reference model by using the test data obtained in the step two and using the delay time as a verification index;
step four, compressible Friction factor f c Is obtained and corrected;
determination of compressible Friction factor f by fluid simulation c ；
Based on the real length of the towing cone pipe, the delay time of the towing cone pipe under different temperatures and different step pressures is obtained through simulation, and the compressible friction factor f is calculated c Correcting;
step five, acquiring theoretical data of delay time of the cone dragging pipeline;
determining the delay time of the towing cone pipeline under different towing cone pipeline structural parameters, different gas attribute parameters and different excitation signal parameters based on the corrected towing cone pipeline theoretical model in the step four;
the towing cone pipeline structure parameters comprise: the diameter of the cone-pulling pipeline, the length of the cone-pulling pipeline and the roughness of the cone-pulling pipeline;
the gas attribute parameters include: gas pressure and aerodynamic viscosity coefficient;
the excitation signal parameters include: step pressure;
step six, constructing a data set based on the delay time of the cone towing pipeline of the step pressure generating device based on the test data obtained in the step two, the simulation data obtained in the step four and the theoretical data obtained in the step five;
step seven, based on the pipeline structure parameters, the gas attribute parameters, the excitation signal parameters and the dimensionless parameters of the Reynolds number Re of the flow closely related to the fluid state, simultaneously carrying out correlation analysis by combining the delay time tau of the dragline, extracting the core parameters closely related to the delay time tau of the dragline, reducing the number of input parameters, and constructing a model of the delay time tau of the dragline by combining a neural network method as shown in a formula (4):
τ＝f(D,L,ε,p,η,Re) (4)
wherein, pipeline structural parameters include: pipe diameter D, pipe length L, and pipe roughness ε; the gas attribute parameters include: gas pressure p and dynamic viscosity coefficient η;
by adjusting model parameters of the neural network, the error of the model is ensured to be smaller than preset precision, namely, the time delay compensation of the dragline pipeline system is realized based on the neural network, and the measurement precision of airspeed in the aircraft test flight process is improved.
2. The method for compensating for time delay of a dragline pipeline system based on a neural network as claimed in claim 1, wherein: in the seventh step, by adjusting the model parameters of the neural network, the error of the model is ensured to be less than 3%, as shown in the formula (5):
(τ ex -τ)/τ ex less than or equal to 3 percent (5), wherein tau ex For a desired dragline delay time.
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