CN114153173A - Artificial intelligence control system and method for high-speed carrier - Google Patents
Artificial intelligence control system and method for high-speed carrier Download PDFInfo
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- CN114153173A CN114153173A CN202111524268.1A CN202111524268A CN114153173A CN 114153173 A CN114153173 A CN 114153173A CN 202111524268 A CN202111524268 A CN 202111524268A CN 114153173 A CN114153173 A CN 114153173A
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- 239000011159 matrix material Substances 0.000 claims description 15
- 238000005070 sampling Methods 0.000 claims description 13
- 238000005457 optimization Methods 0.000 claims description 10
- 230000008569 process Effects 0.000 claims description 5
- 238000009792 diffusion process Methods 0.000 claims description 3
- 238000012163 sequencing technique Methods 0.000 claims description 3
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- 230000002706 hydrostatic effect Effects 0.000 claims description 2
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
- G05B19/042—Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
- G05B19/0423—Input/output
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/20—Pc systems
- G05B2219/25—Pc structure of the system
- G05B2219/25257—Microcontroller
Abstract
The invention relates to the technical field of drag reduction of high-speed vehicles, in particular to an artificial intelligence control system and method of a high-speed vehicle. The system comprises: the sensing unit is used for acquiring gas path data of the system through the sensing element, judging the tail pressure intensity of the high-speed carrier according to the gas path data, and converting the gas path data into a voltage signal by using the data acquisition system; the execution unit controls the flow structure at the tail part of the high-speed carrier by using the voltage signal; and the control unit is used for controlling the surface pressure intensity of the tail part of the high-speed carrier according to a control target by utilizing a machine learning control algorithm. The control unit controls the execution unit according to the data obtained by the sensing unit, and the jet flow generated by the jet flow exciter effectively changes the wake flow field structure, so that the surface pressure of the tail part of the high-speed carrier is obviously improved, and the pneumatic resistance of the high-speed carrier is greatly reduced.
Description
Technical Field
The invention relates to the technical field of drag reduction of high-speed vehicles, in particular to an artificial intelligence control system and method of a high-speed vehicle.
Background
In recent years, as fuel prices have increased dramatically, research and development of aerodynamic drag reduction technology for high speed vehicles has become more important and urgent. Successful application of high speed vehicle drag reduction technology would make enormous profits to the transportation industry. In an automobile running on an expressway, more than 60% of the running resistance comes from aerodynamic resistance received by the automobile body. The fuel consumption of the automobile can be reduced by about 7 percent when the aerodynamic drag coefficient is reduced by 10 percent. The electricity consumption of the high-speed rail with the speed of 250 kilometers per hour is more than 4800 DEG, while the electricity consumption of the high-speed rail with the speed of 350 kilometers per hour is more than 9600 DEG; when the train runs at 200 kilometers per hour, the air resistance accounts for about 70 percent of the total resistance, and when the harmonious number runs out at the speed of 486.1 kilometers at the high-speed rail of Jinghusu, the pneumatic resistance exceeds 92 percent of the total resistance, and if the speed runs to more than 500 kilometers, more than 95 percent of the total resistance is the pneumatic resistance. The air resistance and the square of the train running speed are in approximate direct proportion, the speed is improved by 2 times, and the air resistance is increased to 4 times. Obviously, reducing the aerodynamic drag experienced by a high speed vehicle, even if the reduction is slight, will save significant operating costs. The reduction of fuel consumption also means the reduction of vehicle exhaust emission, and has important significance for reducing urban air pollution.
The installation of wing plates and the adoption of passive drag reduction technologies such as streamline and the like have achieved certain drag reduction effect. However, as the methods of improving aerodynamic profiles of high-speed vehicles have approached their optimum results, only active drag reduction control techniques have been able to further significantly reduce the aerodynamic drag of high-speed vehicles. In recent years, a great deal of research is carried out on the aerodynamic active drag reduction technology of automobiles by a plurality of scholars and research institutions at home and abroad. But the drag reduction technology is not mature enough and is in the research and exploration stage. Currently, the european automotive industry has identified the following goals: on the premise of not influencing comfort, passenger capacity and safety, the aerodynamic resistance of the automobile is reduced by more than 30%. However, the active resistance reduction technology for the automobile with the high aerodynamic resistance streaming flow field is low in resistance reduction rate, and only reaches 14%.
Disclosure of Invention
The invention provides an artificial intelligence control system and method for a high-speed carrier, aiming at successfully reducing the pneumatic resistance of the high-speed carrier by 31 percent and realizing the net energy saving by 15 percent by using the system and the method.
The invention provides an artificial intelligence control system for a high-speed carrier, which comprises:
the sensing unit is used for acquiring gas path data of the system through the sensing element, judging the tail pressure intensity of the high-speed carrier according to the gas path data, and converting the gas path data into a voltage signal by using the data acquisition system;
the execution unit controls the flow structure at the tail part of the high-speed carrier by using the voltage signal;
and the control unit is used for controlling the surface pressure intensity of the tail part of the high-speed carrier according to a control target by utilizing a machine learning control algorithm.
As a further improvement of the invention, the execution unit comprises a jet actuator, and when the jet actuator is electrified, the control unit sets the frequency and the duty ratio of the jet actuator through a control algorithm.
As a further development of the invention, the jet actuator comprises: a first jet actuator arranged on the upper edge of the rear window of the inclined rear back surface of the high-speed vehicle; the second jet flow exciters are arranged at two side edges of a tail window on the inclined rear back of the high-speed vehicle; a third jet actuator disposed at the upper edge of the vertical rear surface of the high speed vehicle; and a fourth jet actuator disposed at the lower edge of the vertical back surface of the high speed vehicle.
As a further improvement of the present invention, the jet nozzles of the first jet actuator, the third jet actuator and the fourth jet actuator are composed of a row of microjet circular holes, and the jet nozzle of the second jet actuator is a slot.
As a further improvement of the invention, the jet nozzle angles of the first jet actuator and the second jet actuator are vertical to the inclined rear back surface of the high-speed vehicle, and the jet nozzle angles of the third jet actuator and the fourth jet actuator form an upward 45-degree angle with the horizontal direction.
As a further improvement of the invention, the jet actuator consists of an actuator cavity and an actuator cover plate, one end of the actuator cavity is connected with an air source, the other end of the actuator cavity is a jet nozzle, the jet nozzle surface of the actuator cavity is flush with the tail surface of the high-speed carrier, the shape of the actuator cavity is a diffusion streamline, and the actuator cover plate is arranged outside the actuator cavity.
As a further improvement of the invention, the execution unit further comprises a flow controller, the flow controller is connected with the jet actuator, and the flow controller controls the flow size of the jet outlet of the jet actuator by adjusting the opening size of the valve.
As a further improvement of the invention, the execution steps of the control unit for setting the jet actuator frequency and the duty ratio through a control algorithm comprise:
a1. initializing to obtain a weight matrix and three initial control functions;
a2. randomly extracting according to the weight to obtain a new control function, wherein the initial sampling probability of each parameter is equal, and recording the initial sampling probability in the layer corresponding to each parameter in the control function;
a3. performing a test by using an initial control function to obtain a corresponding value function J value, and sequencing the obtained J values from small to large, wherein the value function J value is the opposite number of the weighted average value of the pressure coefficients at the tail window and the vertical back surface of the high-speed carrier; and performing steps a4 and a5, respectively;
a4. if the J value is larger than a preset punishment threshold or smaller than an incentive threshold, punishment for reducing the sampling probability or incentive for increasing the sampling probability is carried out on a corresponding layer in the weight matrix, a new weight matrix is obtained, a new control function is randomly obtained according to the new weight matrix, and the step a2 is repeatedly executed; if the J value is not greater than a preset punishment threshold or not less than an award threshold, the operation is not executed;
a5. if the J value corresponding to the new control function is smaller than a preset optimization threshold, the control function replaces the J value to sort a third control function, and then a descending simplex algorithm is used for exploration optimization; and if the J value corresponding to the new control function is larger than the optimization threshold, acquiring the new control function according to the latest weight matrix, and feeding back to the step a2.
a6. And repeating the steps a 2-a 5 until the end condition is met.
As a further improvement of the present invention, the determination criterion that the sensing unit determines the size of the pressure at the tail of the high-speed carrier according to the gas path data is as follows:
the gas path data is measured by a pressure measuring instrument, and the following pressure intensity judgment standard C is obtained by Bernoulli equation and the pressure intensity in the incoming flow directionp：
P in the formula is back gas circuit data (Pa) of the high-speed carrier;
P0-free incoming hydrostatic pressure (Pa) upstream of the high speed vehicle.
The invention also provides an artificial intelligence control method for the high-speed delivery vehicle, which is an execution process of the artificial intelligence control system for the high-speed delivery vehicle.
The invention has the beneficial effects that: the control unit controls the execution unit according to the data obtained by the sensing unit, and the jet flow generated by the jet flow exciter effectively changes the wake flow field structure, so that the surface pressure of the tail part of the high-speed carrier is obviously improved, and the pneumatic resistance of the high-speed carrier is greatly reduced.
Drawings
FIG. 1 is a schematic structural diagram of an artificial intelligence control system of a high-speed vehicle according to an embodiment of the invention;
FIG. 2 is a schematic diagram of the algorithm of the control algorithm in an embodiment of the present invention;
FIG. 3 is a front view of a high speed vehicle aft block diagram in accordance with an embodiment of the present invention;
FIG. 4 is a right side view of a high speed vehicle aft diagrammatic representation in accordance with an embodiment of the present invention;
FIG. 5 is a schematic view of the mounting location of each fluidic actuator in an embodiment of the present invention;
FIG. 6 is a schematic diagram of a fluidic actuator according to an embodiment of the present invention;
FIG. 7 is a schematic view of the mounting location of each fluidic actuator in an extended high speed vehicle high-speed model of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments.
As shown in fig. 1, the present invention provides a high-speed vehicle artificial intelligence control system for actively controlling a system for reducing drag at the tail of a high-speed vehicle, comprising:
the sensing unit is used for acquiring gas path data of the system through the sensing element, judging the tail pressure intensity of the high-speed carrier according to the gas path data, and converting the gas path data into a voltage signal by using the data acquisition system;
the execution unit controls the flow structure at the tail part of the high-speed carrier by using the voltage signal;
and the control unit is used for controlling the surface pressure intensity of the tail part of the high-speed carrier according to a control target by utilizing a machine learning control algorithm.
The execution unit comprises a jet actuator 5 and a flow controller, wherein the flow controller is connected with the jet actuator 5, and the flow controller controls the flow of a jet outlet of the jet actuator 5 by adjusting the opening of a valve.
When the jet actuator 5 is energized, the control unit sets the frequency, duty cycle of the jet actuator 5 by a control algorithm. As shown in fig. 2, the control algorithm includes two parts of weight sampling and simplex optimization. The execution process comprises the following steps:
a1. initializing to obtain a weight matrix and three initial control functions, wherein the initialization process only runs once in the whole execution process;
a2. randomly extracting according to the weight to obtain a new control function, wherein the initial sampling probability of each parameter is equal, and recording the initial sampling probability in the layer corresponding to each parameter in the control function;
a3. performing a test by using an initial control function to obtain a corresponding value function J value, and sequencing the obtained J values from small to large, wherein the value function J value is the opposite number of the weighted average value of the pressure coefficients at the tail window and the vertical back surface of the high-speed carrier; and performing steps a4 and a5, respectively;
a4. if the J value is larger than a preset punishment threshold or smaller than an incentive threshold, punishment for reducing the sampling probability or incentive for increasing the sampling probability is carried out on a corresponding layer in the weight matrix, a new weight matrix is obtained, a new control function is randomly obtained according to the new weight matrix, and the step a2 is repeatedly executed; if the J value is not greater than a preset punishment threshold or not less than an award threshold, the operation is not executed;
a5. if the J value corresponding to the new control function is smaller than a preset optimization threshold, the control function replaces the J value to sort a third control function, and then a descending simplex algorithm is used for exploration optimization; and if the J value corresponding to the new control function is larger than the optimization threshold, acquiring the new control function according to the latest weight matrix, and feeding back to the step a2.
a6. And repeating the steps a 2-a 5 until the end condition is met.
As shown in fig. 1, the frequency, duty cycle and flow rate of the jet actuator 5 are obtained by the control algorithm. In a specific embodiment, a control function b is obtained by a machine learning control algorithm of a control unit, the function controls an execution unit to control the surface pressure of the tail part of the high-speed carrier, and the pressure is judged by a sensing unit.
And the value function J value is an evaluation standard of the control function calculated by the machine learning control algorithm, and the evaluation standard is that the smaller the J value is, the better the control effect is. The cost function J is the inverse of the weighted average of the pressure coefficients at the aft window and vertical rear back of the high speed vehicle. Since the tail window portion has an angle α with the bottom, the pressure term obtained there needs to be multiplied by the corresponding weight sin α. The calculation formula of the value of the cost function J is thus expressed as follows:
wherein C ispA pressure coefficient for each pressure port. m and n are eachAnd the number of the tail window surface and the vertical back surface pressure holes is represented.
The first embodiment is as follows:
as shown in fig. 3 to 5, the actuator unit according to the invention comprises, according to its arrangement position, four fluidic actuators 5, respectively: a first jet actuator 1 arranged on the upper edge of the rear window of the inclined rear surface of the high-speed vehicle; the second jet flow exciters 2 are arranged at two side edges of a tail window on the inclined back of the high-speed vehicle; a third fluidic actuator 3 arranged at the upper edge of the vertical rear face of the high-speed vehicle; a fourth fluidic actuator 4 arranged at the lower edge of the vertical back surface of the high speed vehicle. The air source is connected to the four jet actuators through the flow controllers, the steady jet is ejected from the jet nozzles of the jet actuators 5, and the jet outlet speeds of the four jet actuators 5 are all adjusted through the flow controllers.
The jet directions generated by the four jet actuators 5 form a specific included angle with the horizontal direction, so that the wake structure is effectively changed to obtain the maximum drag reduction rate. In this embodiment, the jet nozzle angles of the first jet actuator 1 and the second jet actuator 2 are perpendicular to the inclined rear back surface of the high-speed vehicle, and the jet nozzle angles of the third jet actuator 3 and the fourth jet actuator 4 form an upward 45 ° angle with the horizontal direction, and the jet angle can be adjusted according to specific conditions during specific implementation.
As shown in fig. 6, the jet actuator 5 is composed of an actuator cavity 7 whose jet opening surface is flush with the rear surface of the high-speed vehicle, and an actuator cover plate 6 which is disposed outside the actuator cavity 7. One end of the exciter cavity 7 is a threaded hole which is positioned at the right center and is used for installing a pneumatic connector and connecting the pneumatic connector to an air source. The other end of the exciter cavity 7 is provided with a jet nozzle of the jet exciter 5. The jet nozzles of the first jet actuator 1, the third jet actuator 3 and the fourth jet actuator 4 are all composed of a row of micro-jet circular holes, and the jet nozzle of the second jet actuator 2 is a slot. For all jet actuators 5, the jet nozzle face of the actuator cavity 7 is flush with the high speed vehicle aft surface. The exciter cavity 7 is in the shape of a diffusion streamline and has the function of guiding jet flow entering the cavity to be uniformly dispersed and sprayed out from a jet flow nozzle.
In this embodiment, the control system of the present invention is used to perform active drag reduction control by jointly using the first jet actuator 1, the second jet actuator 2, the third jet actuator 3, and the fourth jet actuator 4. The gas source is connected to four actuators, respectively, via four different flow controllers. The jet velocities generated by the different actuators are regulated by the four flow controllers, respectively. The combined control can simultaneously control the tail window and the flow structure at the vertical rear back surface of the high-speed carrier, and reduce the aerodynamic resistance of the high-speed carrier. The combined control can realize the pneumatic drag reduction of a high-speed vehicle by 31 percent, and simultaneously obtain the net energy saving of the drag reduction control by up to 15 percent.
Example two:
as shown in fig. 7, on the basis of the first embodiment, each jet actuator 5 is installed on a high-speed vehicle high-speed rail, and since the tail wake flow structure of the high-speed rail is very similar to that of an automobile during operation, the arrangement position and the jet injection angle of the jet actuator 5 in the present embodiment can also be applied to active drag reduction of the high-speed rail. Further, the optimal combined use mode of the jet actuators 5 and the jet injection angle of the automobile in the embodiment are used as references, and the jet injection angle of the high-speed rail tail jet actuator can be changed according to the situation in practical application, so that the requirement on the drag reduction amount is met.
Based on the system, the artificial intelligence control method for the high-speed vehicle is generated by utilizing the system when the system is used and executed.
The invention adopts a method of generating steady jet flow at the tail part of a high-speed carrier based on a jet flow exciter, and changes the wake flow structure of the high-speed carrier by adjusting the jet flow speed and the jet flow angle, thereby realizing the increase of the surface pressure at the tail part of the high-speed carrier and further reducing the aerodynamic resistance of the high-speed carrier.
The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and it is not intended that the invention be limited to these specific details. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.
Claims (10)
1. An artificial intelligence control system for a high speed vehicle, comprising:
the sensing unit is used for acquiring gas path data of the system through the sensing element, judging the tail pressure intensity of the high-speed carrier according to the gas path data, and converting the gas path data into a voltage signal by using the data acquisition system;
the execution unit controls the flow structure at the tail part of the high-speed carrier by using the voltage signal;
and the control unit is used for controlling the surface pressure intensity of the tail part of the high-speed carrier according to a control target by utilizing a machine learning control algorithm.
2. The high speed vehicle artificial intelligence control system of claim 1 wherein the execution unit includes a jet actuator, the control unit setting a frequency, duty cycle of the jet actuator by a control algorithm when the jet actuator is energized.
3. The high speed vehicle artificial intelligence control system of claim 2, wherein the jet actuator comprises: a first jet actuator arranged on the upper edge of the rear window of the inclined rear back surface of the high-speed vehicle; the second jet flow exciters are arranged at two side edges of a tail window on the inclined rear back of the high-speed vehicle; a third jet actuator disposed at the upper edge of the vertical rear surface of the high speed vehicle; and a fourth jet actuator disposed at the lower edge of the vertical back surface of the high speed vehicle.
4. The high speed vehicle artificial intelligence control system of claim 3 wherein the jet ports of the first, third and fourth jet actuators are comprised of an array of microjet orifices and the jet port of the second jet actuator is a slot.
5. The high speed vehicle artificial intelligence control system of claim 3 wherein the jet nozzle angles of the first and second jet actuators are perpendicular to the inclined rear face of the high speed vehicle and the jet nozzle angles of the third and fourth jet actuators are angled 45 ° upward from horizontal.
6. The artificial intelligence control system for the high-speed vehicle of claim 2, wherein the jet actuator is composed of an actuator cavity and an actuator cover plate, one end of the actuator cavity is connected with an air source, the other end of the actuator cavity is a jet nozzle, a jet nozzle surface of the actuator cavity is flush with the tail surface of the high-speed vehicle, the actuator cavity is in a diffusion streamline shape, and the actuator cover plate is arranged outside the actuator cavity.
7. The high speed vehicle artificial intelligence control system of claim 2, wherein the execution unit further comprises a flow controller, the flow controller is connected with a jet actuator, and the flow controller controls the magnitude of the jet outlet flow of the jet actuator by adjusting the magnitude of the valve opening.
8. The high speed vehicle artificial intelligence control system of claim 2, wherein the execution step of the control unit setting the jet actuator frequency, duty cycle by a control algorithm comprises:
a1. initializing to obtain a weight matrix and three initial control functions;
a2. randomly extracting according to the weight to obtain a new control function, wherein the initial sampling probability of each parameter is equal, and recording the initial sampling probability in the layer corresponding to each parameter in the control function;
a3. performing a test by using an initial control function to obtain a corresponding value function J value, and sequencing the obtained J values from small to large, wherein the value function J value is the opposite number of the weighted average value of the pressure coefficients at the tail window and the vertical back surface of the high-speed carrier; and performing steps a4 and a5, respectively;
a4. if the J value is larger than a preset punishment threshold or smaller than an incentive threshold, punishment for reducing the sampling probability or incentive for increasing the sampling probability is carried out on a corresponding layer in the weight matrix, a new weight matrix is obtained, a new control function is randomly obtained according to the new weight matrix, and the step a2 is repeatedly executed; if the J value is not greater than a preset punishment threshold or not less than an award threshold, the operation is not executed;
a5. if the J value corresponding to the new control function is smaller than a preset optimization threshold, the control function replaces the J value to sort a third control function, and then a descending simplex algorithm is used for exploration optimization; and if the J value corresponding to the new control function is larger than the optimization threshold, acquiring the new control function according to the latest weight matrix, and feeding back to the step a2.
a6. And repeating the steps a 2-a 5 until the end condition is met.
9. The artificial intelligence control system for the high-speed vehicle of claim 1, wherein the sensing unit determines the pressure at the tail of the high-speed vehicle according to the gas path data according to the following criteria:
the gas path data is measured by a pressure measuring instrument, and the following pressure intensity judgment standard C is obtained by Bernoulli equation and the pressure intensity in the incoming flow directionp：
P in the formula is back gas circuit data (Pa) of the high-speed carrier;
P0-free incoming hydrostatic pressure (Pa) upstream of the high speed vehicle.
10. A high-speed vehicle artificial intelligence control method, characterized in that the implementation process of the high-speed vehicle artificial intelligence control system of any one of claims 1 to 9 is carried out.
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2021
- 2021-12-14 CN CN202111524268.1A patent/CN114153173A/en active Pending
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