CN113418188A - Double-cyclone combustion instability control method and system - Google Patents
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- F23D14/00—Burners for combustion of a gas, e.g. of a gas stored under pressure as a liquid
- F23D14/20—Non-premix gas burners, i.e. in which gaseous fuel is mixed with combustion air on arrival at the combustion zone
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Abstract
双旋流燃烧不稳定控制方法及系统，先由机器学习算法生成第一代激励函数信号。然后搭建由双旋流燃烧器、燃烧室、供气系统、电磁阀、压力传感器、数据采集卡、控制器以及计算机构成的试验系统。控制器控制电磁阀根据激励函数信号调节内旋流燃料流量。压力传感器采集燃烧室壁面压力并传输给计算机。最后根据压力振荡定义代价函数进行更新，使得机器学习进入第二代。如此循环几代，当机器学习中不再有更小的代价函数产生时，表示算法已经收敛。此时，最小代价函数所对应的激励函数信号即为最优激励函数信号。本发明充分发挥机器学习算法全局寻优的优势，以最大程度地抑制燃烧不稳定，延长航空发动机和燃气轮机的使用寿命。
In the dual-swirl combustion instability control method and system, a first-generation excitation function signal is generated by a machine learning algorithm. Then build a test system consisting of double swirl burner, combustion chamber, air supply system, solenoid valve, pressure sensor, data acquisition card, controller and computer. The controller controls the solenoid valve to adjust the inner swirl fuel flow according to the excitation function signal. The pressure sensor collects the wall pressure of the combustion chamber and transmits it to the computer. Finally, the cost function is defined according to the pressure oscillation and updated, so that the machine learning enters the second generation. This cycle is repeated for several generations. When no smaller cost function is generated in machine learning, it means that the algorithm has converged. At this time, the excitation function signal corresponding to the minimum cost function is the optimal excitation function signal. The invention gives full play to the advantages of the global optimization of the machine learning algorithm, so as to suppress combustion instability to the greatest extent and prolong the service life of aero-engines and gas turbines.
Description
Technical Field
The invention relates to the technical field of double-cyclone combustion instability control, in particular to a double-cyclone combustion instability control method and system.
Background
In recent years, emission requirements for engine pollutants, particularly nitrogen oxides (NOx), have become increasingly stringent. Lean premixed pre-evaporative combustion (LPP) is currently the most likely combustion technology to achieve very low NOx emissions, mostly using a centrally staged dual swirl combustion organization scheme. The lean premixed flame is highly susceptible to turbulence that causes combustion instability, which presents a serious challenge to the application of LPP combustors. Therefore, suppression or prevention of combustion instability from occurring is of great importance in LPP combustor development.
Currently, there are two combustion instability control methods: passive control and active control.
The passive control mainly changes the acoustics and flow field structure of the combustion system by adding fixed structural parts, and realizes unstable control of combustion. Four methods are common, 1) changing the combustion chamber configuration, 2) changing the fuel injection system, 3) adding acoustic liners, and 4) applying helmholtz resonators. However, passive control is often preset before the start, is effective only over a small range of operating conditions, and often fails under off-design conditions or at low frequencies.
The active control means that external active excitation is applied to provide energy for the combustion system so as to destroy the coupling between sound waves and constant heat release when the combustion is unstable, thereby achieving the purpose of inhibiting the combustion from being unstable. The frequency, amplitude and location of the excitation have a significant effect on the control effect of combustion instability. According to the existence of feedback of the sensor, open-loop control and closed-loop control can be divided. The open-loop control only comprises a controller, an actuator and a sensor, and does not relate to a feedback loop, so that the system is simple and is easier to realize. Open loop control is common to four methods, 1) fuel/air perturbation, 2) applying an oscillating signal of fixed frequency and amplitude to regulate the fuel mass flow, 3) directly exciting the shear layer, and 4) injecting a pulsating or steady fuel/air. Among them, applying an oscillation signal to adjust the fuel flow rate can achieve a lower frequency unstable combustion control, and has been receiving attention from researchers. The effectiveness of the periodic equivalence ratio modulation method has been evaluated experimentally on gas turbines. Experiments have found that the periodic equivalence ratio modulation method can reduce the amplitude of the pressure oscillations by one third compared to the natural state. However, the current method of regulating fuel flow by applying oscillation signals and further controlling combustion instability is only to artificially set modulation frequency and amplitude, and global optimization is difficult to achieve.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a method and a system for controlling unstable combustion of double rotational flow, which fully play the advantage of global optimization of a machine learning algorithm in an oscillation signal for regulating fuel flow, so as to inhibit the unstable combustion in an aero-engine and a gas turbine to the maximum extent and prolong the service life of the aero-engine and the gas turbine, and have the characteristics of simple composition, obvious pressure oscillation inhibition effect and the like.
In order to achieve the technical purpose, the technical scheme provided by the invention is as follows:
the invention provides a method for controlling instability of double-swirl combustion, which comprises the following steps:
s1, building a double-cyclone combustion instability control system;
the double-cyclone combustion instability control system comprises a double-cyclone combustion device, an air supply system, an electromagnetic valve, pressure sensors, a data acquisition card, a controller and a computer, wherein the double-cyclone combustion device comprises a double-cyclone combustor and a combustion chamber, the air supply system provides hydrocarbon fuel required by inner cyclone and air required by outer cyclone for the double-cyclone combustor respectively, the electromagnetic valve is installed on a supply pipeline of the hydrocarbon fuel and has a certain distance with an inner cyclone fuel inlet of the double-cyclone combustor, the controller controls the flow of the inner cyclone fuel through the electromagnetic valve so as to realize the modulation of equivalence ratio, a plurality of pressure sensors are installed on the wall surface of the combustion chamber at equal intervals along the axial direction of the combustion chamber, each pressure sensor is connected with the data acquisition card, and pressure data acquired by each pressure sensor is acquired through the data acquisition card and transmitted to the computer.
S2, randomly combining and initializing a plurality of excitation function signals through a machine learning algorithm recorded in a computer in advance to serve as first generation excitation function signals;
s3, enabling a current generation excitation function signal to act on the electromagnetic valve through the controller, and enabling each pressure sensor to measure pressure pulsation, acquire the pressure pulsation through a data acquisition card and transmit the pressure pulsation to a computer;
s4, defining a cost function of unstable combustion control according to pressure pulsation, calculating the size of the cost function corresponding to the current generation excitation function signal, and then performing updating operation on the current generation excitation function signal to generate a new generation excitation function signal;
s5, repeating S3 to S4 until a preset stop condition is reached.
The preset stop conditions are as follows: when the smaller cost function J is not generated in the machine learning algorithm any more, the algorithm is converged, the combustion state in the combustion chamber tends to be stable, and the excitation function signal corresponding to the minimum cost function is the global optimal excitation function signal.
In the double-cyclone combustion instability control system, the equivalent ratio of the inner cyclone of the double-cyclone combustor is 0 and phi0The equivalent ratio of the external rotational flow is always kept phi according to a certain proportion1And is not changed.
In the invention S2, the machine learning algorithm randomly combines and initializes a plurality of excitation function signals according to the form of quasi-harmonic or square wave signals, which is similar to the Monte Carlo method for generating random numbers. The excitation function signal for combustion instability is generally in the form of a harmonic function signal or a square wave function signal, and the harmonic excitation function signal or the square wave excitation function signal should include a main frequency of combustion instability in a natural state.
In the invention S4, the unstable intensity of combustion is estimated through pressure pulsation, the pressure pulsation is obtained by moving average calculation, and the formula is as follows:
s′i＝si(t)-<si(t)>τ
wherein
si(t) is the pressure detected by the ith pressure sensor at time t, i is 1,2, … Ns，NsThe total number of pressure sensors installed in the combustion chamber; τ is the moving average time;
the pressure oscillation in the combustion chamber over a certain time T for each excitation function signal can be expressed as:
in order to stabilize the combustion conditions in the combustion chamber, the corresponding pressure oscillations are reduced, thus defining a cost function for combustion instability control as:
J＝1/K
and calculating the size of a cost function corresponding to the current generation excitation function signal, and then updating the current generation excitation function signal according to a set proportion (such as 10% of copy, 20% of variation and 70% of hybridization) to generate a new generation excitation function signal.
The invention provides a double-cyclone combustion instability control system, which comprises a double-cyclone combustion device, an air supply system, an electromagnetic valve, a pressure sensor, a data acquisition card, a controller and a computer, wherein the double-cyclone combustion device comprises a double-cyclone combustor and a combustion chamber,the air supply system provides hydrocarbon fuel required by the inner rotational flow and air required by the outer rotational flow for the double-rotational-flow combustor respectively, the electromagnetic valve is arranged on a supply pipeline of the hydrocarbon fuel and has a certain distance with an inlet of the inner rotational flow fuel of the double-rotational-flow combustor, the controller controls the flow of the inner rotational flow fuel through the electromagnetic valve so as to realize the modulation of the equivalence ratio, and the equivalence ratio of the inner rotational flow is 0 and phi0The equivalent ratio of the external rotational flow is always kept phi according to a certain proportion1The wall surface of the combustion chamber is constantly provided with a plurality of pressure sensors at equal intervals along the axial direction of the combustion chamber, each pressure sensor is connected with a data acquisition card, and pressure pulsation acquired by each pressure sensor is acquired by the data acquisition card and transmitted to a computer; the computer is pre-loaded with a machine learning algorithm, an excitation function signal is generated by operating the machine learning algorithm and acts on the electromagnetic valve through the controller, and the flow of the inner swirling flow fuel is controlled, so that the modulation of the equivalence ratio of the inner swirling flow of the double swirling flow combustor is realized, and the combustion state in the combustion chamber tends to be stable.
The machine learning algorithm firstly generates a first generation of excitation function signals, acts on the electromagnetic valve through the controller, calculates pressure pulsation according to pressure data collected by each pressure sensor, defines a cost function for unstable combustion control according to the pressure pulsation, then performs updating operation according to the size of the cost function corresponding to the current generation of excitation function signals, generates a new generation of excitation function signals, acts on the electromagnetic valve through the controller, and circulates in the way until a preset stop condition is reached. Wherein the preset stop condition is: when the smaller cost function is not generated in the machine learning algorithm any more, the convergence of the machine learning algorithm is shown, the combustion state in the combustion chamber tends to be stable, and the excitation function signal corresponding to the minimum cost function is the global optimal excitation function signal.
Further, each pressure sensor is mounted flush with the inner wall surface of the combustion chamber at the mounting position thereof.
Compared with the prior art, the invention has the advantages that:
the double-cyclone combustion instability control method provided by the invention has high adaptivity. The defect that the passive control is only effective under a small-range working condition and fails under low frequency is avoided.
The invention has more obvious effect of inhibiting combustion instability through the optimal excitation function signal obtained by global optimization in a machine learning algorithm (such as a linear genetic programming algorithm).
The machine learning is a model-free modeling method based on data driving, complex causal relationships do not need to be considered, and the machine learning is applied to combustion instability control and does not need any priori knowledge of combustion and nonlinear dynamics. Therefore, the method can be popularized to a high-dimensional nonlinear combustion instability control system.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments or the prior art of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for the ordinary skill in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a schematic diagram of a dual swirl combustion instability control system in accordance with an embodiment of the present invention;
FIG. 2 is a schematic representation of the operating equivalence ratio for the inner and outer swirlers in an embodiment of the present disclosure;
FIG. 3 is a graph illustrating the suppression of pressure oscillations in accordance with an embodiment of the present invention.
The objects, features, and advantages of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
For the purpose of promoting a clear understanding of the objects, aspects and advantages of the embodiments of the invention, reference will now be made to the drawings and detailed description, wherein there are shown in the drawings and described in detail, various modifications of the embodiments described herein, and other embodiments of the invention will be apparent to those skilled in the art. The exemplary embodiments of the present invention and the description thereof are provided to explain the present invention and not to limit the present invention.
The method for controlling the instability of the double-cyclone combustion in one embodiment of the invention comprises the following steps:
s1, building a double-cyclone combustion instability control system;
referring to fig. 1, the built double-cyclone combustion instability control system comprises a double-cyclone combustion device 1, an air supply system, an electromagnetic valve 2, a pressure sensor 3, a data acquisition card 4, a controller 5 and a computer 6, wherein the double-cyclone combustion device comprises a double-cyclone combustor 101 and a combustion chamber 102, and the air supply system (not shown in fig. 1) provides methane fuel required by inner cyclone and air required by outer cyclone for the double-cyclone combustor respectively.
The electromagnetic valve 2 is installed on the supply line of the hydrocarbon fuel with a certain distance from the swirling fuel inlet of the dual swirling burner 101, and the distance between the electromagnetic valve 2 and the swirling fuel inlet of the dual swirling burner 101 is 3cm in this embodiment.
The computer 6 is pre-loaded with a machine learning algorithm, an excitation function signal is generated by operating the machine learning algorithm, and the controller controls the flow of the inner cyclone fuel through the electromagnetic valve according to the excitation function signal generated by the machine learning algorithm, so that the modulation of the equivalence ratio of the inner cyclone of the double-cyclone burner is realized. In the embodiment, the inner swirl equivalence ratio is changed between 0 and 0.5 according to a certain proportion (determined by an excitation function), and the outer swirl equivalence ratio is kept constant at 0.4 all the time.
A plurality of PCB pressure sensors are arranged on the wall surface of the combustion chamber at equal intervals along the axial direction of the combustion chamber, and each pressure sensor is flush mounted with the inner wall surface of the combustion chamber at the mounting position. Each pressure sensor is connected with a data acquisition card, and pressure data acquired by each pressure sensor is acquired by the data acquisition card and transmitted to a computer.
And S2, randomly combining and initializing a plurality of excitation function signals b (K) (h) through a machine learning algorithm recorded in advance in a computer to serve as first generation excitation function signals.
Due to the open-loop control, the excitation function signal h of multiple frequencies is only related to the time t. In the embodiment, the form of the excitation function signal h is a harmonic function signal, and the excitation function signal h includes a main frequency of combustion instability in a natural state. The method is characterized in that the method randomly combines and initializes 50 excitation function signals through a machine learning algorithm and conducts pretesting, and each excitation function signal can generate effective excitation.
And S3, acting the current generation excitation function signal on the electromagnetic valve through the controller, measuring pressure pulsation by each pressure sensor, collecting the pressure pulsation through a data acquisition card and transmitting the pressure pulsation to the computer.
And S4, defining a cost function of unstable combustion control according to the pressure pulsation, calculating the size of the cost function corresponding to the current generation excitation function signal, and updating the current generation excitation function signal according to the corresponding size of the cost function and a set proportion to generate a new generation excitation function signal.
The cost function is used for evaluating the cost corresponding to the excitation function signal. The strength of combustion instability can be estimated through pressure pulsation in the process of controlling the double-swirl combustion instability. The pressure pulsation is obtained by a moving average calculation, and the formula is as follows:
s′i＝si(t)-<si(t)>τ
wherein
S herei(t) is the pressure detected by the ith pressure sensor at time t, i is 1,2, … Ns，NsThe total number of pressure sensors installed in the combustion chamber. τ is the moving average time, and in the embodiment, 3 times of the combustion instability characteristic period in the natural state of the double-swirl combustion instability control system is selected as the moving average time.
The pressure oscillation in the combustion chamber over a certain time T for each excitation function signal can be expressed as:
t-10 s was chosen here as the time range for the pressure measurement during the experiment for each excitation function signal. It is sufficient to eliminate accidental errors of pressure and to guarantee convergence of K.
The main purpose of the double-swirl combustion instability control system is to stabilize the combustion state in the combustion chamber, and the corresponding pressure oscillation is reduced. Therefore, the cost function for the unsteady control of the double swirl combustion is defined as:
J＝1/K
and (3) obtaining the size of the cost function corresponding to each excitation function signal according to a definition formula of the cost function of the double-swirl combustion unstable control, and performing updating operations such as copying, mutation or/and hybridization according to the size of the cost function corresponding to each excitation function signal by a machine learning algorithm according to a certain proportion to generate a new generation of excitation function.
S5, repeating S3 to S4 until a preset stop condition is reached.
The preset stop conditions are as follows: when the smaller cost function J is not generated in the machine learning algorithm any more, the algorithm is converged, the combustion state in the combustion chamber tends to be stable, and the excitation function signal corresponding to the minimum cost function is the global optimal excitation function signal.
In this embodiment, when the machine learning algorithm proceeds to generation 4, more than 50% of the excitation function signal achieves about a 60% reduction in pressure oscillations. At this time, the excitation function signal corresponding to the minimum cost function is the global optimal excitation function signal, which can achieve a pressure amplitude reduction of about 62%, as shown in fig. 3.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
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