JP6541104B2 - Observation apparatus and observation method - Google Patents

Description

  The present invention relates to an observation apparatus and an observation method for observing a physical phenomenon.

  For example, in gas turbine combustors, the occurrence of unstable combustion conditions such as combustion oscillations and blowouts is undesirable. Further, in the gas turbine engine, unstable phenomena such as fluttering of the rotary blade, rotational stall and surge may occur in addition to such unstable combustion state. Also, in areas other than gas turbine engines, undesirable and unstable flow conditions of the fluid may occur, for example, in heat exchangers or chemical reactors. Conventionally, it is desirable to detect the occurrence of such unstable physical phenomena.

  Some unstable physical phenomena occur suddenly like blow-off in a gas turbine combustor. Such unstable physical phenomena which occur suddenly are difficult to detect accurately by linear analysis such as frequency analysis. Therefore, conventionally, methods of detecting unstable physical phenomena have been developed using nonlinear analysis. For example, Patent Document 1 discloses a method of measuring flame fluctuations by employing a method used in chaotic time series analysis. In the method described in Patent Document 1, the attractor is reconfigured by using the Tarnes embedding principle, the correlation dimension and the maximum Lyapunov exponent are obtained, and the combustion state is measured from these.

  However, the correlation dimension and the Lyapunov index used in the method described in Patent Document 1 are classical non-linear analysis theory, and in particular, the correlation dimension requires time-series data of a large number of data points. Therefore, the calculation load is high, and the calculation processing can be performed only after acquiring the collected time series data. For this reason, the method described in Patent Document 1 is not suitable for applications that estimate occurrence of physical phenomena with high immediacy, and its application fields are limited.

Further, Patent Document 2 discloses a method of detecting a temporally changing physical quantity, calculating a translational error related to the detected physical quantity, and estimating the occurrence of a specific physical phenomenon based on the calculated translational error. It is done.
The method described in Patent Document 2 can reduce the number of data of physical quantities used for calculation compared to the method described in Patent Document 1, significantly reduces the amount of data, and increases the occurrence of specific physical phenomena. It can be estimated by immediacy. Then, by using the method described in Patent Document 2, the fluctuation of the pressure accompanying the combustion is detected in the gas turbine combustor etc., and the generation of the unstable combustion state is estimated from the pressure fluctuation, whereby the fuel supply amount is obtained. To control the

JP, 2006-138517, A JP, 2013-238365, A

  However, if it is necessary to estimate the physical phenomenon in high immediacy and in a short time, such as control of the fuel supply according to the unstable combustion state in the combustor, by a simpler algorithm In order to speed up the operation, a process with higher responsiveness and stability is required.

  An object of the present invention is to provide an observation device and an observation method capable of observing a physical phenomenon such as higher responsiveness by a method different from the conventional one.

(1) The observation device according to the present invention
A detection unit that detects physical quantities of temporally changing physical phenomena;
A generation unit that sets, as nodes, physical quantities at respective times detected, and temporally generates a complex network in which a plurality of nodes are connected by an edge according to conditions (A) and (B) described below;
An operation unit for obtaining a predetermined feature amount in the complex network;
And an estimation unit configured to estimate a state of a physical phenomenon based on the feature amount.
(A): a node between which is set by the physical quantity detection time is adjacent (B): and the time t _a detected physical quantity y _a, a physical quantity y _b detected at time t _b after time t _a , the node between the physical quantity y _c detected at time t _c between time t _a and the time t _b is in the case which satisfies the following equation, is set by the physical quantity y _a physical quantity y _b

According to this observation apparatus, a complex network of physical quantities of physical phenomena is generated according to a predetermined condition, and the state of physical phenomena is estimated based on the feature quantities of the complex networks. As a result, it becomes possible to estimate the state of the physical phenomenon by a simple algorithm, speeding up the operation, and realizing more responsive processing.
As the feature quantity of the complex network, it is possible to use the average order, the average distance between vertices, the cluster coefficient, the mode value of the order distribution, the frequency of the mode value of the order distribution, and the like. The generation unit may generate an adjacency matrix or an adjacency list for describing a complex network, but the present invention is not limited to this, and data (number of nodes necessary for merely calculating a predetermined feature amount may be used. , The number of edges, etc.) may be acquired, and in this case, the processing as the generation unit may be substantially included in the processing as the calculation unit.

(2) In the complex network, the generation unit preferably limits the range of nodes connected by an edge based on a predetermined rule.
With such a configuration, the amount of calculation can be reduced efficiently, and the speed of calculation can be further improved.

(3) Preferably, the range of the node is set based on the number of times of detection of the physical quantity per fluctuation period of the physical quantity when a predetermined physical phenomenon occurs.
With such a configuration, it is possible to suppress an unintended increase in the number of edges connected to a node when a predetermined physical phenomenon occurs, that is, the order, which occurs when a predetermined physical phenomenon occurs. A difference can be made in the order between when it is not and when. Since the feature quantities of complex networks usually have a high degree of association with the order, adopting the above configuration can be suitably used to estimate the state of a physical phenomenon.

(4) The feature quantity may be an average order of the complex network.
With such a configuration, it is possible to obtain the feature of the network by a very simple operation.

(5) The estimation unit may estimate the state of the physical phenomenon based on comparison between the average order and a predetermined threshold.

(6) The physical phenomenon may be combustion of fuel in the combustor, and the physical quantity may be pressure in the combustor.
In this case, the state of the physical phenomenon can be estimated from the change in pressure accompanying combustion.

(7) In the above (6), preferably, the estimation unit estimates combustion instability in the combustor.
With such a configuration, the occurrence of combustion instability such as combustion oscillation or blowout can be estimated and utilized for fuel supply control and the like.

(8) It is preferable that the said estimation part estimates generation | occurrence | production of blow-off as instability of the combustion in the said combustor.
In recent years, it has been practiced in the combustor to reduce the generation of carbon dioxide and NOx by diluting the premixed gas, but this dilution increases the possibility of blowout, so It is extremely useful to estimate the occurrence of blowout as in the invention.

(9) The observation method according to the present invention is
A method of observing physical phenomena that a computer executes,
The computer
Obtaining physical quantities of time-varying physical phenomena;
Setting the acquired physical quantity at each time as a node, and generating, over time, a complex network in which a plurality of nodes are connected by an edge according to conditions (A) and (B) shown below;
Determining a predetermined feature amount in the complex network;
Estimating the state of the physical phenomenon based on the feature amount.
(A): a node between which is set by the physical quantity detection time is adjacent (B): and the time t _a detected physical quantity y _a, a physical quantity y _b detected at time t _b after time t _a , the node between the physical quantity y _c detected at time t _c between time t _a and the time t _b is in the case which satisfies the following equation, is set by the physical quantity y _a physical quantity y _b

  According to this observation method, a complex network of physical quantities of physical phenomena is generated according to a predetermined condition, and the state of physical phenomena is estimated based on the feature quantities of the complex networks. As a result, it becomes possible to estimate the state of the physical phenomenon by a simple algorithm, speeding up the operation, and realizing more responsive processing.

  According to the present invention, it is possible to observe responsive physical phenomena.

It is a schematic diagram which shows the structure of the observation apparatus of the combustion state which concerns on embodiment. It is a functional block of a signal analysis device. FIG. 6 is a graph showing an example of a pressure fluctuation signal used to generate a complex network. It is explanatory drawing which shows an example of a general complex network (graph; Graph). It is an explanatory view showing an example of a visible graph. (A) is a figure which shows the result of having connected the vertex (node) by edge (edge) in a visible graph, (b) is a figure which shows the complex network produced | generated from the result. It is a figure which shows an example of the range (eyesight) of the node connected by an edge on visible graph. It is a figure which shows the other example of the range (eyesight) of the node connected by an edge on visible graph. It is a graph for demonstrating the setting of vision. It is a graph which shows the relationship between the average order of an complexity network, and an equivalence ratio. It is a flowchart which shows the procedure of a combustion control process. It is a flowchart which shows the procedure of a secondary fuel flow rate calculation process. It is a graph which shows the result of the performance evaluation test of an observation apparatus. It is a graph which shows the result of the performance evaluation test of an observation apparatus.

Hereinafter, preferred embodiments of the present invention will be described with reference to the attached drawings.
The configuration and operation of an observation device for observing the combustion state of the gas turbine combustor will be described below.

[1. Configuration of observation device]
The observation device according to the present embodiment analyzes the combustion state (physical phenomenon) of a gas turbine model combustor (a combustor simulating a partial element of a gas turbine engine combustor) using feature quantities of a complex network, and the combustion state To estimate. In particular, in the present embodiment, it is estimated that the stable combustion state and the unstable combustion state, that is, the combustion vibration and the occurrence of blow-off can be distinguished. First, the configuration of the observation device will be described.

  FIG. 1 is a schematic view showing a configuration of a combustion state observation device according to the present embodiment. As shown in FIG. 1, the observation device 100 includes a gas turbine model combustor (hereinafter, also simply referred to as a “combustor”) 1, a pressure transducer 2, a control unit 3, a fuel tank 4, and a compressor 5. Is equipped.

The combustor 1 is a premixed combustor (lean premixed combustor) that burns a mixture of fuel and air. The combustor 1 includes a combustion chamber 11, a water cooling chamber 12, an intake portion 13, and a swirler (not shown). The combustor 1 mixes the air supplied from the compressor 5 and the methane supplied from the fuel tank 4 by the intake section 13 to form a premixed gas, and swirls the premixed gas by the swirler. The combustion is performed in the combustion chamber 11.
The combustor 1 may be a combustor of a jet engine or an industrial gas turbine engine.

  A pressure transducer (detection unit) 2 is attached to the combustion chamber 11. The pressure transducer 2 detects pressure fluctuation (physical quantity) at a wall surface position of the combustion chamber 11. The pressure transducer 2 is adapted to output a pressure fluctuation signal which is a voltage signal corresponding to the detected pressure fluctuation.

  An amplifier 21 is connected to the pressure transducer 2, and the pressure fluctuation signal output from the pressure transducer 2 is amplified by the amplifier 21.

The control unit 3 includes a signal analysis device 31 and mass flow controllers 32 and 33. The signal analysis device 31 is configured by installing a computer program for executing the combustion control process described below in a computer, and various functions in the signal analysis device 31 are realized by the computer program.
In addition, the computer which comprises the signal-analysis apparatus 31 has an arithmetic processing unit, a memory | storage part, an input-output device, etc.

  FIG. 2 shows functional blocks of the signal analysis device 31. The signal analysis device 31 includes a pressure fluctuation signal input unit 311, a storage unit 312, a complex network generation unit 313, an average order calculation unit 314, a threshold setting unit 315, a blowout occurrence estimation unit 316, a secondary fuel flow rate calculation unit 317, And a function as a secondary fuel flow rate control unit 318.

  The pressure fluctuation signal input unit 311 is for receiving the pressure fluctuation signal output from the pressure transducer 2 and amplified by the amplifier 21. FIG. 3 graphically shows the change over time of the pressure fluctuation signal.

  The storage unit 312 is for storing a pressure fluctuation signal for a predetermined period (for example, 50 msec). The pressure fluctuation in the combustion chamber 11 is detected by the pressure transducer 2 and output as a time-series pressure fluctuation signal. The pressure fluctuation signal fetched by the pressure fluctuation signal input unit 311 is stored in the storage unit 312 in the FIFO format. That is, when a new pressure fluctuation signal is added to the storage unit 312, the old pressure fluctuation signal (the pressure fluctuation signal stored about 50 msec before) is erased. By updating the pressure fluctuation signal of the storage unit 312 as described above, the pressure fluctuation signal of a period of 50 msec is always stored.

The complex network generation unit 313 is for generating a complex network based on the pressure fluctuation signal stored in the storage unit 312.
The average order calculator 314 calculates an average order which is a feature of the complex network.

  Here, the complex network is a generic name of a network in which many components are connected in a complicated manner. FIG. 4 illustrates a graph as an aspect of the complex network. A graph is a mathematical expression method of a network, and is composed of a plurality of nodes and an edge connecting each node. Also, the number of edges coming out of each node is called "order". The order (number of edges) is described in each node shown in FIG. In one network represented by a graph, the average of the number of edges coming out of each node is referred to as "average degree". Therefore, the average degree <k> is expressed by the following equation (1).

Here, n is the number of nodes, and k _i is the order of each node.

For example, in the example shown in FIG.
<K> = (1 + 4 + 2 + 3 + 2) /5=2.4
It becomes.

  The complex network generation unit 313 of the present embodiment generates a complex network from the pressure fluctuation signal as shown in FIG. Then, in order to generate a complex network, the “visible graph” as described below is used.

  FIG. 5 is an explanatory view showing an example of a visible graph. In the visible graph, nodes that look away from obstacles (nodes with line of sight) are connected by edges. The visible graph illustrated in FIG. 5 is configured by a bar graph, and the vertex of each bar graph is set as a node. Then, each node is connected at an edge to a node located to the right of itself.

  For example, when looking from the node n1 of the leftmost bar graph to the nodes n2 to n6 of the right bar graph, the nodes n2 to n5 can be seen without being blocked, but the node n6 and thereafter (right side) All nodes are obscured by the bar graph with node n5 and can not be seen. Therefore, in this case, node n1 is connected to nodes n2 to n5 by the edge and not connected to node n6 and subsequent nodes. A complex network can be generated by connecting a plurality of nodes by edges using a visible graph in this manner.

  In the visible graph, nodes adjacent in the horizontal axis direction are always connected by an edge. Whether or not nodes that are separated by one or more in the horizontal axis direction are connected by an edge is determined by whether or not the following equation (2) is satisfied.

Here, t represents the value of the horizontal axis of the visible graph of FIG. 5, and y represents the value of the vertical axis. The subscript a indicates a node to be a connection source, the subscript b indicates a node to be a connection destination, and the subscript c indicates an intermediate node between the connection source and the connection destination.
Expression (2) is based on the condition that the slope of the line connecting the connection source node and the connection destination node is larger than the slope of the line connecting the connection source node and the intermediate node. If this condition is satisfied, the connection source node and the connection destination node are connected. In FIG. 5, the upward arrow indicates that the inclination is large, the downward arrow indicates that the inclination is small, and the horizontal arrows indicate that the inclination is the same.

  On the lower side of the visible graph in FIG. 5, a connection relationship in the case where the node of the bar graph on the left end is a connection source a and the other nodes are a connection destination b is shown in a table. Intermediate nodes are indicated by c1, c2,. At the left end of this table, "o" and "x" are shown to indicate whether or not connection is possible. That is, when the slope of the line connecting the connection source node and the connection destination node is larger than the slope of the line connecting the connection source node and the intermediate node, both nodes are connected by an edge. If the slope of the line connecting the connection source node and the connection destination node is smaller than the slope of the line connecting the connection source node and the intermediate node, the two nodes are not connected by an edge. Because "X" is attached.

  FIG. 6A shows that each node is connected by an edge according to the determination of the equation (2). The resulting complex network is shown in FIG. 6 (b). In FIG. 6B, a line extending horizontally means an edge connecting adjacent nodes, and a line curving up and down from the line extending horizontally means an edge connecting nodes one or more away from each other. Do.

  By the above procedure, a complex network can be generated using a visible graph. Then, the complex network generation unit 313 in the present embodiment generates a complex network in the same procedure as described above by using the graph of the pressure fluctuation signal illustrated in FIG. 3 as a visible graph. The pressure fluctuation signal illustrated in FIG. 3 is time-series data, and the complex network generation unit 313 can generate a complex network over time each time one or a plurality of pressure fluctuation signals are acquired. In general, in a complex network, a connection relation of a plurality of nodes is described by an adjacency matrix or an adjacency list, but the complex network generation unit 313 of this embodiment does not generate the adjacency matrix or the adjacency list in a clear form. In addition, data of the number of nodes and the order used in the average order calculator 314 described below is acquired. Therefore, high-speed arithmetic processing is possible.

  The average order calculator 314 shown in FIG. 2 calculates the average order <k> according to the above-mentioned equation (1) for the complex network of pressure fluctuation signals generated using the equation (2). In this embodiment, the calculation of the average order is repeated every 50 msec to perform observation with high immediacy.

When a complex network is generated using a visible graph, for example, the number of edges becomes enormous when a high value occurs suddenly in the time-series data or when the variation becomes very gentle, and the average order <k> also increases accordingly.
For example, as shown in FIG. 7A, if there is only one high value, such as an outlier, in the time series data, the line of sight between the node and other nodes is improved. The order of is significantly higher.
In addition, as shown in FIG. 8A, when time series data fluctuates in a concave manner gently with a low frequency cycle, the visibility of nodes improves, and the overall order becomes remarkably high.

  In the present embodiment, the concept of “visual acuity” is used to limit the line of sight from each node such that the order is not significantly high in any of the above cases, and the order is high only in a specific frequency band. It has been introduced.

FIG.7 (b) has shown the state which introduced the concept of "visual acuity" to the same visible graph as Fig.7 (a). The eyesight is a numerical value that represents the range of nodes connected by an edge, and is represented by "N _vis ". In the example shown in FIG. 7 (b), N _vis = 3. In this case, nodes from the connection source node to three nodes ahead (right side) are set as connection destination nodes, and only nodes having line of sight are connected by edges. Therefore, the degree of each node is 3 at most. In the example shown in FIG. 7A, since no restriction due to visual acuity is given, N _vis = inf (infinity; infinity) is set.

FIG. 8B shows a state in which the concept of “visual acuity” is introduced to the same visible graph as FIG. 8A. The visual acuity is N _vis = 3.
As is apparent from FIGS. 7 and 8, by introducing the concept of “visual acuity”, the average value is obtained when a suddenly high value occurs or when a lower frequency condition than a specific frequency band occurs. It is possible to prevent the order from rising extremely.

The specific vision setting is performed by the following equation (3).
Here, n is a natural number, f _s is a sampling frequency (Hz), and f is a frequency to be detected (Hz).

The sampling frequency f _s is the number of data of the pressure fluctuation signal acquired in one second. The frequency to be detected is the frequency to set the average order to the highest. Therefore, in the equation (3), the value of f _s / f is the number of data of the pressure fluctuation signal acquired in one cycle of the frequency to be detected as shown in FIG. 9A. Then, a value obtained by approximating the value of f _s / f by a power of 2 is defined as the visual acuity N _vis . If the visual acuity N _vis is set using equation (3), the average order does not decrease if the pressure fluctuation signal fluctuates at a frequency higher than the frequency to be detected (see FIG. 9 (b)). When the pressure fluctuation signal fluctuates at a low frequency, the average order is lowered (a rise in the average order is suppressed) (see FIG. 9C). In the equation (3), the visual acuity N _vis is approximated by a power of 2 because the peak frequency confirmed by the combustion oscillation is not always constant. It should be noted that the approximation of the visual acuity N _vis may adopt another form other than the power of two.

  Unstable combustion states in the combustion chamber 11 include combustion vibration and blow-off. FIG. 10 is a graph illustrating the relationship between the average order of the complex network and the equivalence ratio. In FIG. 10, a region where the equivalence ratio is smaller than about 0.51 is a region where blow-out tends to occur (hereinafter, also referred to as “blow-out generation region”), and a region where the equivalence ratio is about 0.61 or more is This is a region where combustion vibration is likely to occur (hereinafter, also referred to as “combustion vibration generation region”). Between the blowout generation region and the combustion vibration generation region is a region where the combustion state is stable, and it is desirable to control the fuel supply amount so as to be maintained in this region. Therefore, the area is referred to as an area to be controlled (hereinafter, also referred to as “control area”).

The pressure in the combustion chamber 11 fluctuates stably in a relatively low frequency band in the combustion vibration generation region, and higher frequency fluctuation occurs in the control region. Furthermore, in the blowout region, more complex fluctuations occur at higher frequencies than this. Therefore, in the present embodiment, when introducing the visual acuity N _vis as described above, the frequency of the region is set as the frequency f to be detected so that the average order in the combustion vibration generation region becomes high. By setting in this manner, the average order in the combustion vibration generation area can be increased, and the average order in the area other than the combustion vibration generation area can be reduced.

  In the example shown in FIG. 10, the average order is high in the combustion area generation region where the equivalence ratio is about 0.61 or more, and the average order is low in the control region where the equivalence ratio is smaller than about 0.61. It can be seen that the distinction is made. In addition, in the control region, it is understood that as the equivalence ratio decreases, the average order also decreases with a large change.

Returning to FIG. 2, the threshold setting unit 315 is for setting the threshold <k> _thre of the average order for determining whether or not blowout occurs. In the present embodiment, the threshold value <k> _thre is stored in a hard disk of a computer or the like, and the threshold value setting unit 315 reads out the stored threshold value <k> _thre and stores it in the RAM as a setting value. . The threshold setting unit 315 may receive information indicating the threshold <k> _thre from the user and store the threshold <k> _thre as a setting value in the storage unit, or may be provided from other software. The threshold information may be received and stored as a set value in the storage unit.

The blowout occurrence estimation unit 316 is for estimating the occurrence of blowout on the basis of the average degree <k> calculated by the average order calculation unit 314. As shown in FIG. 10, the average order <k> decreases as the combustion state in the combustion chamber 11 approaches the blowout occurrence region. Therefore, the blowout occurrence estimation unit 316 compares the average order <k> calculated by the average order calculation unit 314 with the threshold <k> _thre set by the threshold setting unit 315, and calculates the average order <k>. When it is smaller than a threshold <k> _thre , it is estimated that blowout occurs.

  The secondary fuel flow rate calculation unit 317 is for calculating the secondary fuel flow rate based on the estimation result by the blowout occurrence estimation unit 316. As described later, the fuel supply passage extending from the fuel tank 4 is branched midway, one of which is a main fuel supply passage and the other of which is a secondary fuel supply passage (see FIG. 1). The secondary fuel flow rate calculation unit 317 calculates the flow rate of the secondary fuel supplied from the secondary fuel supply path.

In the control region shown in FIG. 10, it is estimated that the blowout is closer as the average degree <k> is smaller. Blow-off is a phenomenon that occurs when the equivalence ratio of the premixed gas is small, and in order to eliminate the occurrence of blow-off, it is necessary to increase the equivalence ratio. Therefore, the secondary fuel flow rate calculation unit 317 increases the secondary fuel flow rate as the difference between the average degree <k> and the threshold value <k> _thre increases. Specifically, the amount of change in the secondary fuel flow rate is calculated according to the following equation (4), and a value obtained by changing only the amount of change with respect to the secondary fuel flow rate at that point is Determined as a target value.

ΔQ _{CH4, secondary} is the change in secondary fuel flow rate for 50 ms, α is a proportionality constant, <k> is the average order obtained from the data of the pressure fluctuation signal for 50 ms, <k> _thre is the average order Is the threshold of The proportional constant α can be set, for example, to α = 0.01.

  The secondary fuel flow rate control unit 318 is for controlling the secondary fuel flow rate so as to approach the target value of the secondary fuel flow rate determined by the secondary fuel flow rate calculation unit 317. The secondary fuel flow rate control unit 318 outputs the information indicating the target value to the mass flow controller 33 (see FIG. 1) provided in the middle of the secondary fuel supply path.

  The mass flow controller 32 is provided in the middle of the main fuel supply path. The mass flow controller 32 is connected to the signal analysis device 31, and controls the main fuel flow rate in the main fuel supply path in accordance with the control signal output from the signal analysis device 31.

  The mass flow controller 33 is provided in the middle of the secondary fuel supply path. The mass flow controller 33 is connected to the signal analysis device 31, and controls the secondary fuel flow rate to approach the target value according to the control signal (information indicating the target value) output from the signal analysis device 31.

  The fuel tank 4 contains methane which is a fuel. A fuel supply path extends from the fuel tank 4 and is connected to the intake section 13 of the gas turbine model combustor 1.

  The compressor 5 supplies air containing oxygen, which is an oxidant. An air supply path extends from the compressor 5 and is connected to the intake section 13 of the gas turbine model combustor 1.

  A mass flow controller 51 is provided in the middle of the air supply path (see FIG. 1). The mass flow controller 51 controls the flow rate of air supplied to the gas turbine model combustor 1 in accordance with the given set value.

[2. Operation of observation equipment]
Hereinafter, the operation of the observation device 100 will be described.
Each of the mass flow controllers 32 and 33 controls the main fuel flow rate and the secondary fuel flow rate according to the instruction of the signal analysis device 31, whereby methane is supplied from the fuel tank 4. Further, air is supplied from the compressor 5, and the amount of supply is controlled by the mass flow controller 51.

  The methane supplied from the fuel tank 4 and the air supplied from the compressor 5 are taken into the intake section 13. The intake unit 13 mixes fuel and air to generate an air-fuel mixture. The generated mixture is swirled by the swirler and introduced into the combustion chamber 11 to burn the mixture.

  The pressure transducer 2 detects pressure fluctuations in the combustion chamber. The pressure fluctuation signal output from the pressure transducer 2 is amplified by an amplifier 21 and provided to a signal analysis device 31.

  The signal analysis device 31 executes a combustion control process, and outputs a control signal indicating the target value of the secondary fuel flow rate obtained thereby to the mass flow controller 33. The mass flow controller 33 controls the secondary fuel flow rate so as to approach the target value. Thus, the combustion state of the gas turbine model combustor 1 is feedback-controlled.

  Hereinafter, the combustion control process by the signal analysis device 31 will be described. FIG. 11 is a flowchart showing the procedure of the combustion control process. First, acquisition of a pressure fluctuation signal is performed (step S1). In this process, the pressure fluctuation signal input unit 311 stores the input pressure fluctuation signal in the storage unit 312. Here, the storage unit 312 stores time-series data of pressure fluctuation signals for 50 msec. For example, when the sampling frequency of the pressure fluctuation signal is 5 kHz, 250 time-series data are stored in the storage unit 312.

Next, complex network generation processing is executed (step S2). In the complex network generation process, a visible graph is generated from the time-series data of the pressure fluctuation signal read from the storage unit 312, and a complex network consisting of nodes and edges is generated according to the above-described equation (2). In addition, when connecting nodes by edges, the visual acuity N _vis determined by the above-mentioned equation (3) is taken into consideration.

  Next, calculation processing of the average order in the complex network is executed (step S3). In this calculation process, the number of nodes of the complex network generated in the previous step S2 and the number of edges (order) connected to each node are acquired, and the average order is calculated by the above-described equation (1).

Next, secondary fuel flow rate calculation processing is executed (step S4). FIG. 12 is a flowchart showing the procedure of secondary fuel flow rate calculation processing. First, in the secondary fuel flow rate calculation process, a threshold value <k> _thre of the average degree <k> is set (step S41). In this process, the threshold setting unit 315 reads the threshold value <k> _thre from the hard disk or the like and stores the read value in the RAM to set the threshold value <k> _thre . The threshold value <k> can be set to, for example, <k> _thre = 8.87 in the example shown in FIG.

Next, the secondary fuel flow rate Q _{CH4, secondary} at that time is calculated (step S42). In this process, the secondary fuel flow rate calculation unit 317 calculates the secondary fuel flow rate Q _{CH4, secondary} at that time using the setting value of the mass flow controller 33 and the like.

Next, the change amount ΔQ _{CH4, secondary} of _{the secondary} fuel flow rate is calculated (step S43). In this process, the blowout estimation unit 316 estimates the occurrence of _blowout by comparing the average degree <k> with the threshold <k> _thre, and the average degree <k> with the threshold <k> _thre The amount of change ΔQ _{CH4, secondary} of the _secondary fuel flow rate according to the difference is calculated according to the above-mentioned equation (4).

Next, the change amount ΔQ _{CH4, secondary} is reflected in the current secondary fuel flow rate Q _{CH4, secondary} . That is, as the target value of the secondary fuel flow rate, and the secondary fuel flow rate _{Q CH4, secondary-,} by adding a secondary fuel flow variation _{Delta] Q CH4, secondary-,} secondary fuel flow _{Q CH4, secondary-} target value Is calculated (step S44). After this process, the process is returned to the call address of the secondary fuel flow rate calculation process in the main routine.

  Referring back to FIG. 11, next, secondary fuel flow rate control processing is executed (step S5). In this process, the secondary fuel flow rate control unit 318 transmits a control signal indicating the target value calculated in step S4 to the mass flow controller 33, thereby controlling the secondary fuel flow rate so as to approach the target value.

  When the process of step S5 ends, it is determined whether the combustion control process is ended (step S6). In this process, it is determined that the combustion control process is to be ended when a predetermined ending condition is satisfied, for example, when the user instructs the signal analysis device 31 to stop the operation. If it is determined that the combustion control process is not to be ended (NO in step S6), the process is returned to step S1, whereby the process of steps S1 to S6 is repeatedly performed every 50 msec. On the other hand, when it is determined in step S6 that the combustion control process is to be ended (YES in step S6), the combustion control process is ended.

  As described above, the combustion control process is repeatedly performed every 50 msec. That is, as shown in FIG. 3, a complex network is generated from the pressure fluctuation signal for a period of 50 msec, and the average order <k> is calculated. Therefore, the combustion state of the gas turbine model combustor 1 is controlled every 50 msec.

[3. Evaluation test result]
The inventors of the present application carried out a performance evaluation test of the observation device 100 according to the present embodiment. 13 and 14 are graphs showing the results of this performance evaluation test.
In the performance evaluation test shown in FIG. 13, as shown in the upper graph, the flow rate of the main fuel is reduced from an initial value of about 8 L / min to about 6 L / min at a constant rate, and thereafter the flow rate of about 6 L / min Maintained. Since the blowout limit is at a total fuel flow rate of about 6.5 L / min, the main fuel flow rate is changed until the limit is exceeded.

  In the observation device 100 according to the present embodiment, feedback control is applied when the main fuel flow rate reaches about 6.7 L / min, and the secondary fuel supply is started as shown in the middle graph. As a result, as shown in the lower graph, the equivalence ratio was maintained at a value above about 0.48, which is the blowout occurrence limit.

  In the performance evaluation test shown in FIG. 14, as shown in the upper graph, the flow rate of the main fuel is decreased from an initial value of about 8 L / min to about 6 L / min at a constant rate, and thereafter constant to about 8 L / min. Increased by the ratio of Also in this case, since the blowout occurrence limit is when the total fuel flow rate is about 6.5 L / min, the main fuel flow rate is changed until it falls below this limit.

  In the observation device 100 according to the present embodiment, feedback control is applied when the main fuel flow rate reaches about 6.9 L / min, and secondary fuel supply is started as shown in the middle graph. Then, while the main fuel flow rate is decreasing, the secondary fuel flow rate continues to increase, and when the main fuel flow rate starts to decrease and then increases, the secondary fuel flow rate starts to increase and then decreases. As a result, as shown in the lower graph, the equivalence ratio was maintained at a value above about 0.48, which is the blowout occurrence limit. When the main fuel flow rate recovered to the original value (about 8 L / min), the secondary fuel supply was stopped.

In this type of combustor 1, in order to reduce the generation of carbon dioxide and NO _X, it is desirable to as much as possible dilute the premixed gas. However, if the premixed gas is too lean, blow-out will occur, and a stable combustion state can not be maintained. The above evaluation test results show that the observation apparatus 100 according to the present embodiment maintains the low equivalent ratio near the blowout occurrence limit while avoiding the occurrence of blowout. As described above, according to the observation device 100 according to the present embodiment, stable lean premixed combustion can be maintained.

In the present embodiment, as shown in FIG. 10, the average order <k> largely changes between about 6 and about 14 with the change of the equivalence ratio in the control area, and the change even when approaching the blowout occurrence area The quantity remains large. Therefore, by setting the value of the average degree <k> in the vicinity of the blowout occurrence region as the threshold value <k> _thre , the occurrence of blowout can be stably estimated.

  Further, as in the example shown in FIG. 10, it can be seen that the value of the average degree <k> significantly changes in the vicinity of the boundary between the combustion vibration generation region and the control region. It is also possible to estimate whether combustion oscillation is occurring or not by using such an average degree <k>.

  The observation device 100 according to the present embodiment generates a complex network using a visible graph based on time series data of pressure fluctuation signals, and generates combustion instability according to the average order <k>, which is a feature of this complex network. It is estimated. Therefore, the operation can be performed by a very simple algorithm. Therefore, the calculation speed can be increased, and the combustion state and the like of the combustor can be observed with high immediacy and high responsiveness. In addition, the signal analyzer 31 is not required to have a high capability.

  Moreover, the observation apparatus 100 of this embodiment can reduce the setting of the parameter used for calculation. Specifically, in the technique described in Patent Document 2, the number of setting parameters is seven, but in the present embodiment, substantially only two parameters of the number of time series data and visual acuity are set. Parameter setting is easy.

  In addition, the concept of “vision” was introduced when creating a complex network, and the range of nodes connected by edges was appropriately limited, so the effects of sudden fluctuations in time series data and low frequency fluctuations Thus, it is possible to reduce the influence of and efficiently reduce the amount of calculation and further speed up the calculation.

  Moreover, according to the observation apparatus 100 which concerns on this embodiment, when it is estimated that blow-out will generate | occur | produce, it is set as the structure which controls the secondary fuel flow volume, and avoids generation | occurrence | production of blow-off. As a result, the occurrence of blow-off can be suppressed, and a stable combustion state can be maintained in the gas turbine model combustor 1.

  Further, according to the observation device 100 according to the present embodiment, the pressure fluctuation signal stored in the storage unit 312 is updated based on the newly input pressure fluctuation signal, whereby the pressure fluctuation signal for 50 msec is constantly stored. Thus, the average order is calculated every 50 msec using the pressure fluctuation signal for 50 msec stored in the storage unit 312. Therefore, the observation of the combustion state can be continuously performed with high immediacy, and the control of the secondary fuel flow rate can also be appropriately performed.

[4. Other Embodiments]
In the above-mentioned embodiment, although the composition which observes the combustion state in a gas turbine model burner was described, it is not limited to this. For example, it may be a combustor mounted on a jet engine or an industrial gas turbine engine. In addition, with regard to time-series measurement signals of distortion of rotor blades (fans and compressors) of gas turbine engine and pressure fluctuation before and after rotor blades, feature quantities of complex networks are calculated, and flutter, rotational stall, It can also be configured to estimate the occurrence of a surge. Furthermore, for industrial plants, complex network feature values also for unstable pressure fluctuation signals of gas-liquid two-phase flow in evaporator tubes and temperature fluctuation signals of fluid in pipe junction where two fluids with different temperature differences are mixed It is also possible to calculate the occurrence of other physical phenomena, such as health monitoring of breakage of the evaporation tube of the boiler or breakage of the piping section due to thermal striping.

  Further, in the above-described embodiment, the configuration is described in which the occurrence of blow-off is estimated based on the feature value of the complex network, and the combustion state is controlled to avoid the occurrence of blow-off. It is not a thing. For example, the occurrence of blow-off may be estimated based on the feature amount of the complex network, but the combustion state may not be controlled. The same applies to the configuration for estimating the occurrence of other physical phenomena.

  Moreover, in the embodiment mentioned above, although the structure which estimates generation | occurrence | production of blow-off as combustion instability in a gas turbine model combustor was described, it is not limited to this. The generation of the combustion vibration may be estimated based on the feature amount of the complex network. Further, in this case, the main fuel flow rate or the secondary fuel flow rate may be controlled based on the feature value of the complex network in order to avoid the occurrence of combustion oscillation.

  Further, in the above-described embodiment, the configuration for controlling the secondary fuel flow rate to control the combustion state so as to avoid the occurrence of blowout has been described, but the present invention is not limited to this. By controlling the main fuel flow rate based on the feature value of the complex network, the occurrence of blow-off may be avoided, or by controlling the air flow rate, the occurrence of blow-off may be avoided. Good. Also, instead of separately providing the main fuel supply path and the secondary fuel supply path, only one fuel supply path is provided, and the fuel flow rate in this fuel supply path is controlled based on the feature value of the complex network. It is also possible to make a configuration to avoid the occurrence of blowout.

  In the above-described embodiment, the pressure fluctuation signal for 50 msec is stored in the storage unit, and while the pressure fluctuation signal in the storage unit is updated, the feature quantity is repeatedly calculated every 50 msec. It is not limited to this. For example, a pressure fluctuation signal of a period other than 50 msec, such as 100 msec, may be stored in the storage unit, and the feature amount may be calculated using this.

  In addition, the pressure fluctuation signal for a period of 50 msec or more (for example, 1 sec) is stored in the storage unit, and the pressure fluctuation signal for the latest predetermined period (for example, 50 msec) is used for calculating the feature amount. Good.

  Further, instead of repeatedly calculating the feature amount every 50 msec, for example, the feature amount may be repeatedly calculated every period other than 50 msec, such as 10 msec. However, in order to appropriately control the combustion state with high immediacy, the feature quantity is calculated in a cycle of 10 μsec to 300 msec according to the characteristic time of the phenomenon to be controlled, and the combustion state is controlled. Is preferred.

  The feature quantity of the complex network is not limited to the average order, but may be an average distance between vertices, a cluster coefficient, a mode of the order distribution, a frequency of a mode of the order distribution, or the like. However, since the average order can be calculated only by acquiring the number of edges connected to each node, there is an advantage that calculation load is smaller than other feature quantities and calculation can be performed at higher speed.

  The average inter-vertex distance is an average value of the minimum number of edges from the node to the node. The average vertex distance can be reduced in variation by applying the visual acuity of the above embodiment. The cluster coefficient is the number of edges between three nodes adjacent to each other divided by the maximum possible number of edges between the three nodes. Cluster coefficients have properties similar to the average order in that they increase as nodes are closely connected. The mode value of the order distribution is the order that is the mode among the orders that are the number of edges connected to the node. The frequency of the mode value of the order distribution is the frequency at which the order having the mode value appears. When time series data fluctuates in a sine wave, the mode value of the order distribution depends on the number of data for one wavelength of the sine wave, and the frequency becomes remarkably high, and there is a property that it becomes a biased order distribution . Thus, the order distribution may change significantly more than the average order. The mode value of the degree distribution and the frequency thereof can be applied to time series data and the like obtained from the Lorentz equation.

  In the above-described embodiment, the signal analysis device 31 is realized by a computer, but the present invention is not limited to this. For example, hardware capable of executing the combustion control process may be configured by an ASIC, an FPGA, or the like, and this may be used as a signal analysis device. Further, instead of configuring the signal analysis device 31 by one computer, the combustion control process may be executed by distributed processing by a plurality of computers to form a distributed system including a plurality of computers.

1: Gas turbine model combustor 2: Pressure transducer (detector)
3: Control unit 31: Signal analysis device 100: Observation device 313: Complex network generation unit 314: Average order calculation unit 316: Blow-out occurrence estimation unit N _vis : Visual acuity

Claims (9)

A detection unit that detects physical quantities of temporally changing physical phenomena;
A generation unit that sets, as nodes, physical quantities at respective times detected, and temporally generates a complex network in which a plurality of nodes are connected by an edge according to conditions (A) and (B) described below;
An operation unit for obtaining a predetermined feature amount in the complex network;
An estimation unit configured to estimate a state of a physical phenomenon based on the feature amount.
(A): a node between which is set by the physical quantity detection time is adjacent (B): and the time t _a detected physical quantity y _a, a physical quantity y _b detected at time t _b after time t _a , the node between the physical quantity y _c detected at time t _c between time t _a and the time t _b is in the case which satisfies the following equation, is set by the physical quantity y _a physical quantity y _b
  The observation device according to claim 1, wherein the generation unit restricts the range of nodes connected by an edge based on a predetermined rule in the complex network.   The observation device according to claim 2, wherein the range of the node is set based on the number of times of detection of physical quantity per fluctuation period of physical quantity when a predetermined physical phenomenon occurs.   The observation device according to any one of claims 1 to 3, wherein the feature quantity is an average order of the complex network.   The observation device according to claim 4, wherein the estimation unit estimates a state of a physical phenomenon based on a comparison between the average order and a predetermined threshold.   The observation device according to any one of claims 1 to 5, wherein the physical phenomenon is combustion of fuel in a combustor, and the physical quantity is a pressure in the combustor.   The observation device according to claim 6, wherein the estimation unit estimates occurrence of combustion instability in the combustor.   The observation device according to claim 7, wherein the estimation unit estimates occurrence of blow-out as instability of combustion in the combustor. A method of observing physical phenomena that a computer executes,
The computer
Obtaining physical quantities of time-varying physical phenomena;
Setting the acquired physical quantity at each time as a node, and generating, over time, a complex network in which a plurality of nodes are connected by an edge according to conditions (A) and (B) shown below;
Determining a predetermined feature amount in the complex network;
Estimating the state of a physical phenomenon based on the feature amount.
(A): a node between which is set by the physical quantity detection time is adjacent (B): and the time t _a detected physical quantity y _a, a physical quantity y _b detected at time t _b after time t _a , the node between the physical quantity y _c detected at time t _c between time t _a and the time t _b is in the case which satisfies the following equation, is set by the physical quantity y _a physical quantity y _b