Method for estimating air flow through aircraft vent, processor readable medium, aircraft vent, system, and aircraft

Abstract

The invention relates to a method for estimating air flow through an aircraft vent, a processor readable medium, an aircraft vent, a system, and an aircraft. In one aspect, the above method is provided in which at least two pressure sensors (103) are positioned on or near a lip (102) of a vent along a pressure measurement extraction line (104), the method comprising a calibration step and a correlation step. The calibration step includes generating a database that associates, for each of the at least two air streams applied to the vent, values of the at least two air stream characteristics with pressure distribution measurements along the measurement extraction line, respectively. The related steps include: measuring a pressure profile along the route (104) while the aircraft is in flight; and correlating the measured pressure profiles associated with the values of the two features with data from a database in order to estimate the air flow when the aircraft is in flight.

Description

Technical Field

[0001] The present invention relates to estimating air flow through aircraft vents and, more particularly, to methods and systems implementing sensors positioned on or near the lip of the vents to estimate such air flow. Background Art

[0002] Aircraft drag is typically determined by evaluating the engine thrust. Engine thrust cannot be measured in flight, but rather is estimated using models, using the air flow through the engine's air intake as a key parameter. This is particularly true during flight testing, where, in order to assess aircraft performance, the engine air flow must be calculated in flight to correctly estimate the engine thrust.

[0003] For other applications, such as cooling or ventilation systems, accurate knowledge of air flow is crucial for system validation, troubleshooting, certification, and predicting the impact on aircraft drag.

[0004] On turbojet engines, the secondary airflow is usually evaluated to determine the average total pressure at the fan nozzle. The airflow is usually derived from the fan nozzle pressure ratio combined with the fan nozzle coefficient, which is determined by wind tunnel testing or ground testing.

[0005] The main limitation of these technologies is that installation and maintenance times are usually extended.

[0006] The techniques mentioned in this section should not be considered to be prior art simply because they are mentioned. Similarly, the issues mentioned in the same section should not be assumed to have been previously identified in the prior art simply because they are mentioned. Summary of the Invention

[0007] Embodiments of the present invention have been developed based on the developer's understanding of the gaps associated with the prior art. Accordingly, the present invention, in various embodiments, includes a method for estimating air flow through an aircraft vent, at least two pressure sensors positioned along a pressure measurement extraction line on or near a lip of the vent, the method comprising:

[0008] A calibration step, the calibration step comprising:

[0009] applying at least two air streams sequentially to the vent, each air stream having at least two characteristics, one of the two characteristics being velocity and the other being an angle of incidence of the air stream relative to the vent, the values ​​of the at least two characteristics of each air stream being known;

[0010] for each of the at least two air flows applied to the vent, measuring a corresponding pressure profile along a measurement extraction line; and

[0011] generating a reference database that relates, for each of at least two air flows applied to the vent, values ​​of at least two characteristics to pressure distribution measurements along the measurement extraction line; and

[0012] A correlating step includes measuring a pressure distribution along a measurement extraction line while the aircraft is in flight; and correlating the measured pressure distribution associated with values ​​of at least two characteristics of air flow through the vent while in flight with data from a reference database to estimate the air flow through the vent while the aircraft is in flight.

[0013] In one implementation of the method, at least one of the at least two air flows of the calibration step is applied to the vent by actual physical testing.

[0014] In another implementation of the method, at least one of the at least two air flows of the calibration step is applied to the vent by computer simulation.

[0015] In another implementation of the method, correlating the measured pressure distribution with data from a reference database comprises extrapolation.

[0016] In another implementation of the method, the calibration step includes: using all or some information from a reference database to feed a neural network and training the neural network by machine learning; and the correlation step includes: feeding the trained neural network with measured pressure distributions associated with values ​​of at least two characteristics of air flow through the vents in flight, the trained model inferring an estimate of the air flow through the vents when the aircraft is in flight.

[0017] The present invention, in various embodiments, further includes a processor-readable medium comprising instructions for executing the above method.

[0018] The present invention, in various embodiments, also includes an aircraft vent, wherein at least two pressure sensors are positioned on or near a lip of the vent along a pressure measurement extraction line, the vent being equipped with a reference database obtained by a calibration step, the calibration step comprising:

[0019] applying at least two air streams sequentially to the vent, each air stream having at least two characteristics, one of the two characteristics being velocity and the other being an angle of incidence of the air stream relative to the vent, the at least two air stream characteristics being known;

[0020] for each of the at least two air flows applied to the vent, measuring a corresponding pressure profile along a measurement extraction line; and

[0021] A reference database is generated that relates values ​​of at least two characteristics to pressure distribution measurements along the measurement extraction line for each of at least two air flows applied to the vent.

[0022] In one implementation of the vent, at least two pressure sensors have a minimal protrusion relative to a lip of the vent.

[0023] In one implementation of the vent, the at least two pressure sensors are ultra-fine pressure sensors embedded in a perforated lip of the vent.

[0024] In one implementation of the vent, at least two pressure sensors are pneumatic type pressure sensors.

[0025] The present invention, in various embodiments, also includes a system comprising a processor configured to execute the above method.

[0026] The present invention, in various embodiments, finally includes an aircraft including a vent as described above, wherein the steps applied to the vent include:

[0027] measuring a pressure distribution along a measurement extraction line while the aircraft is in flight;

[0028] The measured pressure distribution associated with values ​​of at least two characteristics of air flow through the vent in flight is correlated with data from a reference database to estimate the air flow through the vent while the aircraft is in flight. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] For a better understanding of the present invention, reference is made to the following description which must be read in conjunction with the accompanying drawings, in which:

[0030] [ Figure 1a ] depicts a perspective view of an air inlet on an aircraft engine.

[0031] [ Figure 1b ] depicts a detailed stereoscopic view of a vent equipped with a pressure sensor.

[0032] [ Figure 1c ] depicts a pressure sensor positioned along a pressure measurement line according to one embodiment.

[0033] [ Figure 2 ] depicts the pressure values ​​measured at each of the sensors of the pressure measurement circuit for four flight conditions and air flows with known characteristics.

[0034] Figure 3 The steps of the method according to the invention are illustrated.

[0035] [ Figure 4a ] depicts a cross-sectional view of a pressure sensor having a minimal protrusion relative to the lip of the vent.

[0036] [ Figure 4b ] depicts a cross-sectional view of an ultra-fine pressure sensor embedded in the perforated lip of a vent.

[0037] [ Figure 4c ] depicts a cross-sectional view of a conventional pneumatic type pressure sensor.

[0038] [ Figure 5 ] illustrates a computer system that can be used in the present invention.

[0039] [ Figure 6 ] depicts a perspective view of an aircraft equipped with a vent to which the present invention may be applied.

[0040] It should be noted that the drawings are not drawn to scale unless explicitly stated otherwise herein. Finally, identical elements from one drawing to the other have the same reference numerals. DETAILED DESCRIPTION

[0041] In the following, measurement data emitted by a sensor will be understood as a collection of a plurality of measurement data emitted by said sensor.

[0042] In the context of this specification, unless expressly specified otherwise, a "processor" may refer to, but is not limited to, any type of "computer system", "electronic device", "computerized system", "control unit", "monitoring device", "server" and / or any combination thereof related to the reception, storage, processing and / or transmission of data that is suitable for the task in question.

[0043] In the context of this specification, the expression "FPGA" is intended to include field programmable gate array type systems available on the market at the time of filing this patent application, such as the reference Xilinx VU9P or Intel Stratix V, and all equivalent inventions that subsequently become available, regardless of their name, including computer system hardware that can be programmed with software.

[0044] In the context of this specification, a "processor" may include a single dedicated processor, a single shared processor, or multiple individual processors, some of which may be shared. A "processor" may be a general-purpose processor, such as a central processing unit (CPU), a dedicated processor, or a processor implemented in an FPGA. Other conventional and / or custom hardware and software may also be included in a "processor."

[0045] In the context of this specification, unless expressly stated otherwise, the expression "memory" includes random access memory systems available on the market at the time of filing this patent application, and all equivalent inventions that subsequently become available, regardless of their name, including computer system media for storing digital information. An example of such a memory may be a static random access memory (SRAM).

[0046] In the context of this specification, the functional steps depicted in the figures may be performed through the use of dedicated hardware as well as hardware capable of executing appropriate software.

[0047] In the context of this specification, unless otherwise expressly specified, the words "first", "second", "third", etc. are used as adjectives merely for the purpose of distinguishing the nouns they accompany from each other, rather than for the purpose of describing a specific relationship between these nouns.

[0048] Implementations of the present invention each have at least one of the above-mentioned objects and / or aspects, but not necessarily all of them.

[0049] Additional and / or alternative features, aspects, and advantages of implementations of the present invention will appear from the following description, drawings, and appended claims.

[0050] The examples and associated conditions described in detail herein are primarily intended to help the reader understand the principles of the present invention, rather than to limit its scope to these specific examples and conditions. It will be understood that those skilled in the art may envision the following various arrangements: although they are not explicitly described or depicted herein, the various arrangements still embody the principles of the present invention and are included within the spirit and scope of the present invention.

[0051] In addition, for ease of understanding, the following description may describe a relatively simplified implementation of the present invention. As understood by those skilled in the art, other implementations of the present invention may have greater complexity.

[0052] In some cases, examples of modifications of the present invention may also be presented. This is done solely to aid understanding and, as such, is not intended to limit the scope of the present invention or to establish limitations thereto. These modifications are not exhaustive, and those skilled in the art may make other modifications while remaining within the scope of the present invention.

[0053] In addition, all statements below relating to the principles, aspects, and implementations of the present invention, as well as specific examples of the present invention, are intended to encompass structural and functional equivalents of the present invention, whether currently known or developed in the future. Thus, for example, it will be understood by those skilled in the art that all block diagrams depict conceptual diagrams of circuit examples incorporating the principles of the present invention. Similarly, it will be well understood that all flow charts, state transition diagrams, pseudocode, etc. depict various processes that can be implemented on a computer-readable medium and thus executed by a computer or processor, regardless of whether such a computer or processor is shown in the figures.

[0054] The functions of the various elements depicted in the figures, including any functional blocks, can be performed using dedicated hardware as well as hardware capable of executing appropriate software. They can also be performed by a processor. In addition, other conventional and / or custom hardware can also be used.

[0055] Software modules, or modules assumed to be software, may be depicted herein as a combination of flow chart elements or other elements indicating the execution of processing steps and / or as a textual description. Such modules may be executed by hardware that is explicitly described or not. Additionally, it should be understood that a "module" may include, for example, but not limited to, computer program logic, computer program instructions, software, a software stack, firmware, hardware circuitry, or a combination of these various elements that provide the desired capabilities.

[0056] That is, several non-limiting examples will now be considered in order to illustrate various implementations of the present invention.

[0057] In one example, and according to Figure 1a 、 Figure 1b and Figure 1c , the present invention is applied to an air inlet 100 of a cooling system of an aircraft engine 101 . At least two pressure sensors 103 located along a pressure measurement extraction line 104 are located on or near a lip 102 of the air inlet 100 .

[0058] The present invention is generally applicable to any air inlet equipped with an aircraft, whether for a propulsion system having a compressor, such as an auxiliary power unit (APU), or a more passive system that uses the energy of an external air flow to cool or ventilate areas or components of the aircraft. The present invention is also applicable to any vent equipped with an aircraft. All such aircraft air inlets and vents are collectively referred to as "vents."

[0059] The present invention proposes a non-intrusive solution for estimating the air flow through a vent equipped with at least two pressure sensors 103 positioned on or near a lip 102 of the vent, such as an air inlet 100, along a pressure measurement extraction line 104. The solution comprises utilizing pressure measurement profile information from the pressure sensors 103 along the pressure measurement extraction line 104 and comprises:

[0060] - calibration step: two air flows with known characteristics (including in particular velocity and angle of incidence) are applied to the vents by actual physical testing (e.g. wind tunnel testing, ground testing, etc.) or by computer simulation (e.g. numerical simulation of fluid flow, etc.); for each air flow, the pressure measurement value from the pressure sensor 103 is recorded along the pressure measurement extraction line 104 so that a reference database can be established;

[0061] - In-flight (in this application, understood as "real" or wind tunnel flight) correlation steps of the aircraft: an air flow with known characteristics (including, in particular, velocity and angle of incidence) is then passed through the vent; pressure measurements from pressure sensor 103 are recorded along pressure measurement extraction circuit 104, and the pressure measurements are correlated with information from a reference database to derive an estimate of the actual air flow through the vent. This correlation step can be performed, for example, by extrapolation, machine learning, etc.

[0062] In one example, when the present invention is implemented by a manufacturer of a system including an air vent, which manufacturer sells the system as a supplier to an aircraft manufacturer, the present invention only includes the calibration step. In this case, the supplier delivers the information generated by the calibration step (i.e., the reference database) along with the sold system, thereby enabling the aircraft manufacturer to perform the relevant steps in-flight.

[0063] When an aircraft is in flight, a number of measurements (angle of attack (Alpha), angle of sideslip (Beta), Mach number (Mach), pitch angle (Pitch), revolutions per minute (RPM), etc.) are available and known. These measurements characterize the airflow through the vents equipped on the aircraft, which are typically positioned at a certain angle relative to the flight path. These same characteristics can be simulated during the calibration step by the characteristics of at least two air flows applied to the vents.

[0064] Thus, during the calibration step, at least two "flight situations" are defined, each associated with at least two characteristics of the air flow applied to the vents and simulating the same two characteristics of an actual flight. For each flight situation in the calibration mode, the pressure measurement provided by the pressure sensor 103 is recorded along the pressure measurement extraction line 104.

[0065] Thus, at the end of the calibration step, the following Table 1 can be established (as an example of an implementation of a reference database), where, in a simplified example:

[0066] Four flight situations C1-C4 were simulated;

[0067] The pressure measurement extraction line is equipped with six pressure sensors positioned along the line at the abscissas X1-X6;

[0068] Each flight situation C j Corresponding to four flight characteristics Car I The four corresponding values ​​of ValCar ij ;

[0069] For each flight situation C j , measure and record the six corresponding horizontal coordinate values ​​X k Pressure value at: ValPres jk .

[0070] (Table 1)

[0071]

[0072]

[0073] Figure 2 A graph corresponding to the information from Table 1 is depicted. The horizontal axis corresponds to the horizontal axis along the vent, with six corresponding values ​​X1-X6. The vertical axis corresponds to the corresponding pressure measurement for each of the six sensors located at the horizontal coordinates X1-X6. A value is indicated for each of the four flight conditions C1-C4. In addition, a value ValCar for each of the four characteristics Car1-Car4 corresponds to each flight condition. Thus, the information is presented in the flight characteristic value ValCar. ij and the pressure value ValPres along the pressure measurement extraction line 104 jk The relationship between them.

[0074] Each of the flight situations C1-C4 may correspond to an actual physical test or a computer simulation.

[0075] In one example, the calibration step includes using all or some of the information from Table 1 to feed a model, such as a neural network, and train it through machine learning.

[0076] The correlation step specifically involves measuring the pressure distribution along the measurement extraction line associated with known flight characteristics (angle of attack (Alpha), sideslip angle (Beta), Mach number (Mach), pitch angle (Pitch), revolutions per minute (RPM), etc.) while the aircraft is in flight, and then correlating the pressure distribution measured while the aircraft is in flight, associated with these known flight characteristics, with the data and values ​​from Table 1, in order to estimate the air flow through the vents while the aircraft is in flight. Those skilled in the art will know how to perform this correlation step, for example, by extrapolation or by using the aforementioned trained model in an inference mode.

[0077] Figure 3 The method according to the present invention is illustrated in step 301. The method includes sequentially applying at least two air streams to a vent (100), each air stream having at least two known characteristics, one of which is velocity and another of which is an angle of incidence of the air stream relative to the vent, and at least two pressure sensors (103) positioned on or near a lip (102) of the vent along a pressure measurement extraction line (104).

[0078] In step 302 , the method further includes, for each of the at least two air flows applied to the vent, measuring a corresponding pressure profile along a measurement extraction line.

[0079] In step 303 , the method further includes generating a database that relates known characteristics of each of the at least two air flows applied to the vent to a pressure distribution along the measurement extraction line.

[0080] In step 304 , the method further includes measuring a pressure distribution along the measurement extraction line while the aircraft is in flight.

[0081] In step 305 , the method further includes correlating pressure distributions associated with known flight characteristics measured while the aircraft is in flight with data from a database to estimate air flow through the vents while the aircraft is in flight.

[0082] In one example, the correlating step includes feeding data from a pressure distribution measured while the aircraft is in flight, associated with known flight characteristics, to a model trained by machine learning during a calibration step, the trained model inferring an estimate of air flow through the vents while the aircraft is in flight.

[0083] In one example, a measurement extraction circuit 104 comprising 400 points, ie, 400 pressure sensors 103, and 24 flight situations C1-C4 each associated with 4 features Car1-Car4 are used. 24The calibration step is performed using the data from the training neural network. During the in-flight correlation step, the trained neural network estimates the air flow through the vent 100 with an accuracy of 0.5%.

[0084] The present invention is implemented, for example, in Figure 4a 、 Figure 4b and Figure 4c When the pressure sensor 103 is positioned on or near the lip 102 , the pressure sensor 103 must be able to assess the local static pressure along the pressure measurement extraction line 104 . Figure 4a The illustration shows the case with minimal protrusion relative to the lip 102 , for example by an ultra-fine pressure sensor 103 , for example of the micro-electromechanical system (MEMS) type. Figure 4b The diagram shows an ultra-fine pressure sensor 103 embedded in the perforated lip 102. Figure 4c The diagram shows the case of a conventional pneumatic type pressure sensor 103. Thus, the sensor 103 suitable for use with the present invention may be more or less flush with the surface of the lip 102; those skilled in the art will know how to adjust the calibration steps accordingly.

[0085] Figure 5 The diagram shows a computer system that can be used in the present invention, for example, to perform the calibration steps or related steps described above. As will be understood by those skilled in the art, such a computer system can be implemented using any other suitable hardware, software and / or firmware or a combination thereof, and can be a single physical entity, or several separate physical entities with distributed functionality.

[0086] Computer system 500 may include various hardware components, including one or more single-core or multi-core processors collectively represented by processor 501, memory 503, and input / output interface 504. In this case, processor 501 may or may not be included in an FPGA. Computer system 500 may be a "ready-to-use" general-purpose computer system. Computer system 500 may also be distributed across multiple systems. Computer system 500 may also be specifically designed to implement the present invention. Those skilled in the art will appreciate that many variations of implementing computer system 500 are contemplated.

[0087] Communications between the various components of the computer system 500 may be enabled by one or more internal and / or external buses 505 (e.g., a PCI bus, a Universal Serial Bus, a "FireWire" IEEE 1394 bus, a SCSI bus, a Serial ATA bus, an ARINC bus, etc.) that electronically couple the various hardware components.

[0088] The input / output interface 504 can enable networking capabilities such as wired or wireless access. For example, the input / output interface 504 can include a network interface, such as but not limited to a network port, a network connector, a network interface controller, etc. For those skilled in the art of the present invention, many examples of how the network interface can be implemented will become apparent.

[0089] The memory 503 may store code instructions 508, such as those forming part of, for example, a library, an application, etc., which may be loaded into the memory 503 and executed by the processor 501, for example, to implement the calibration steps or related steps according to the present invention. The memory 503 may also store a database 509, such as the database generated in the calibration steps described above. It will be understood by those skilled in the art that the database 509, the code instructions 508, and generally the memory 503 may also be physically located outside the computer system 500 and remain within the scope of the present invention.

[0090] Input / output interface 504 may enable computer system 500 to communicate with other processors via connection 510. This may be the case, for example, if the calibration steps described above are implemented in computer system 500, while the correlation steps described above are implemented in a processor external to computer system 500, such as on an aircraft.

[0091] Figure 6 The aircraft 600 depicted in FIG. 6 includes the vent 100 to which the present invention is applied in order to estimate the air flow through the vent during flight. The aircraft 600 also includes an onboard computer system 500 in which, for example, the above-described steps can be implemented.

[0092] Although the above implementation has been described and depicted with reference to specific steps performed in a specific order, it will be understood that these steps may be combined, subdivided, or reordered without departing from the teachings of the present disclosure. At least some of the steps may be performed in parallel or serially. Therefore, the order and grouping of the steps do not constitute a limitation of the present invention.

[0093] It will be apparent to those skilled in the art that modifications and improvements may be made to the above-described implementations of the present invention. The above description is provided by way of example only and is not intended to be limiting. Therefore, the scope of the present invention is limited only by the scope of the following claims.

Claims

1. A method for estimating air flow through an aircraft vent (100), at least two pressure sensors (103) being positioned on or near a lip (102) of the vent along a pressure measurement extraction line (104), the method comprising: A calibration step, the calibration step comprising: i. applying (301) at least two air streams sequentially to the vent, each air stream having at least two characteristics, one of the two characteristics being velocity and the other being an angle of incidence of the air stream relative to the vent, the values ​​of the at least two characteristics of each air stream being known; ii. for each of the at least two air flows applied to the vent, measuring (302) a corresponding pressure profile along a measurement extraction line; and iii. generating (303) a reference database that relates values ​​of the at least two characteristics to pressure distribution measurements along a measurement extraction line for each of the at least two air flows applied to the vent; and Related steps, the related steps include: i. measuring (304) the pressure distribution along the measurement extraction line when the aircraft is in flight; and ii. correlating (305) the measured pressure distribution associated with the values ​​of the at least two characteristics of air flow through the vent in flight with data from the reference database to estimate the air flow through the vent when the aircraft is in flight.

2. The method according to claim 1, wherein At least one of the at least two air streams of the calibration step is applied to the vent (100) by actual physical testing.

3. The method according to claim 1 or 2, wherein: At least one of the at least two air flows of the calibration step is applied to the vent (100) by computer simulation.

4. The method according to claim 1 , wherein: Correlating the measured pressure distribution with data from the reference database (305) includes extrapolation.

5. The method according to claim 1 , wherein The calibration steps include: Using all or some of the information from the reference database to feed a neural network and train the neural network through machine learning; and the related steps include: A trained neural network is fed with measured pressure distributions associated with values ​​of the at least two characteristics of air flow through the vents in flight, and the trained model infers an estimate of air flow through the vents while the aircraft is in flight.

6. A processor-readable medium comprising instructions for executing the method according to one of claims 1 to 5.

7. An aircraft vent (100) having at least two pressure sensors (103) positioned on or near a lip (102) of the vent along a pressure measurement extraction line (104), the vent being provided with a reference database obtained by a calibration step comprising: applying (301) at least two air streams sequentially to the vent, each air stream having at least two characteristics, one of the two characteristics being velocity and the other being an angle of incidence of the air stream relative to the vent, the at least two air stream characteristics being known; For each of the at least two air flows applied to the vent, measuring (302) a corresponding pressure profile along a measurement extraction line; and A reference database is generated (303) that relates values ​​of the at least two characteristics to pressure distribution measurements along a measurement extraction line for each of the at least two air flows applied to the vent.

8. The vent according to claim 7, wherein: The at least two pressure sensors (103) have a minimal protrusion relative to the lip (102) of the vent.

9. The vent according to claim 7, wherein: The at least two pressure sensors (103) are ultra-fine pressure sensors embedded in the perforated lip (102) of the vent.

10. The vent according to claim 7, wherein: The at least two pressure sensors (103) are pneumatic type pressure sensors.

11. A system comprising a processor configured to perform the method according to one of claims 1 to 5.

12. An aircraft (600) comprising a vent (100) according to one of claims 7 to 10, the steps being applied to the vent, the steps comprising: measuring (304) a pressure distribution along a measurement extraction line while the aircraft is in flight; and The measured pressure distribution associated with the values ​​of the at least two characteristics of air flow through the vent in flight is correlated (305) with data from the reference database to estimate the air flow through the vent when the aircraft is in flight.

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