Using measurement data and prediction data for aircraft testing

Abstract

Systems and methods to evaluate aircraft behavior. The systems and methods perform simulation tests using computer-based models to determine predicted behavior of an aircraft. The systems and methods also determine actual behavior of the aircraft using measured data collected during operation of the test aircraft. The systems and methods provide a comparison of the predicted behavior and the actual behavior to provide a more accurate analysis of the aircraft. In some examples, the systems and methods provide real-time flight test optimization, which eliminates and / or reduces the need for irrelevant flight tests.

Description

[0001] Related applications This application claims priority to U.S. Utility Patent Application No. 18 / 410,004, filed January 11, 2024, which is incorporated herein by reference in its entirety. Technical Field

[0002] This disclosure generally relates to the field of aircraft testing, and more specifically, to testing using measurement data sensed during aircraft operation and predictive data using one or more digital models. Background Technology

[0003] An aircraft is tested once or multiple times during its life cycle. For aircraft, testing is typically performed during the design phase before full production. Testing includes one or more test procedures and certification activities to ensure the aircraft's safety and performance. The testing provides a structural assessment of various aspects, including but not limited to wing dexterity, aileron operation, fuselage stress testing, and fatigue testing encountered during takeoff and landing.

[0004] An aircraft test is performed using computer simulation, a computer-based process that uses one or more digital models of the aircraft. This test allows for the analysis of the aircraft in use and exposed to a wide variety of different situations and environments. The simulation determines the predicted performance of the aircraft when exposed to various conditions and environments.

[0005] Other tests use test aircraft constructed according to the aircraft's specifications. Sensors are positioned around the aircraft to detect one or more aspects. The test aircraft provides actual performance data that can be accessed on a real-time basis. The downside is the cost involved in building the test aircraft.

[0006] The current system does not provide a meaningful way to use computer simulation to test both and test aircraft. Testing is done using one or more testing procedures. Summary of the Invention

[0007] One aspect relates to a method for analyzing aircraft design. The method includes: acquiring measurement data detected during the operation of a test aircraft, wherein the test aircraft is constructed according to an aircraft design; acquiring predictive data based on a digital simulation model representing the aircraft design; comparing the measurement data and the predictive data; generating one or more discrepancies between the measurement data and the predictive data; and displaying the measurement data, the predictive data, and the one or more discrepancies via a graphical user interface.

[0008] In another aspect, the measurement data includes physical characteristics that occur during the operation of the test aircraft, and the prediction data includes expected physical characteristics that are anticipated to occur during the operation of the test aircraft.

[0009] In another aspect, the method also includes determining one or more discrepancies between measured and predicted data in real time during the operation of the test aircraft.

[0010] In another aspect, the method also includes: determining when the measurement data was sensed by one or more sensors; applying a timestamp to the measurement data; receiving the timestamped measurement data; generating prediction data using the measurement data and the timestamp; and synchronizing the prediction data and the measurement data.

[0011] In another aspect, the method also includes receiving measurement data from sensors mounted on the test aircraft.

[0012] In another aspect, the method also includes extrapolated prediction data of expected physical properties calculated based on a digital simulation model, wherein the extrapolated prediction data are physical properties that were not sensed by sensors during the operation of the test aircraft.

[0013] In another aspect, the method also includes determining one or more discrepancies between measured and predicted data during ground testing and flight testing of the test aircraft.

[0014] In another aspect, the method also includes determining modifications to the aircraft design based on one or more differences between measured and predicted data.

[0015] In another aspect, the method also includes updating the digital simulation model based on one or more differences between the measured data and the predicted data.

[0016] One aspect relates to a method for analyzing aircraft design. The method includes: receiving measurement data from a test aircraft, wherein the measurement data is captured by one or more sensors on the test aircraft and indicates the actual performance of the test aircraft; calculating predicted data indicating the expected performance of the aircraft design, wherein the predicted data is calculated based on one or more digital simulation models representing the aircraft design; determining one or more differences between the measurement data captured by the one or more sensors and the predicted data based on a comparison of the measurement data and the predicted data; and generating one or more output parameters based on the comparison, wherein each of the one or more output parameters indicates a corresponding difference between the measurement data and the predicted data.

[0017] In another aspect, the method also includes determining modifications to the aircraft design by inputting one or more output parameters into a design modification function of the aircraft design.

[0018] In another aspect, the method also includes extrapolated prediction data of expected physical properties calculated based on a digital simulation model, wherein the extrapolated prediction data are physical properties that were not sensed by sensors during the operation of the test aircraft.

[0019] In another aspect, the method also includes determining one or more discrepancies between measured and predicted data in real time during the operation of the test aircraft.

[0020] In another aspect, the method also includes updating the digital simulation model based on one or more differences between the measured data and the predicted data.

[0021] In another aspect, the method also includes receiving measurement data from the test aircraft during ground testing and during flight testing of the test aircraft.

[0022] In another aspect, the method also includes using timestamps applied to the measurement data to synchronize the measurement data and the prediction data.

[0023] One aspect relates to a system for analyzing aircraft design. The system includes sensors mounted on a test aircraft and configured to detect one or more physical characteristics during operation of the test aircraft. A computing device includes a processing circuitry system and a memory circuitry system, the memory circuitry system including a program 58 that, when executed by the processing circuitry system, causes the computing device to: receive measurement data of one or more physical characteristics detected during operation of the test aircraft from the sensors; calculate predicted data based on a digital simulation model representing the aircraft design; determine one or more differences between the measured data and the predicted data; and update the system based on the one or more differences.

[0024] On another front, system updates include identifying one or more modifications to the aircraft design.

[0025] In another aspect, the processing circuitry is configured to determine extrapolated predictions of expected physical characteristics that were not sensed by sensors during the operation of the test aircraft.

[0026] In another aspect, the display is configured to receive signals from the processing circuitry and display measurement data, prediction data, and one or more discrepancies on the display via a graphical user interface.

[0027] The features, functions and advantages already discussed can be implemented independently in each aspect or combined in other aspects, and further details can be seen in the following description and figures. Attached Figure Description

[0028] Figure 1 It is a schematic diagram of a system with actual test data and predictive simulation data.

[0029] Figure 2 This is a schematic diagram of a system used for storing and processing measurement and forecast data.

[0030] Figure 3 It is a schematic diagram of running a simulation using one or more digital models.

[0031] Figure 4 This is a flowchart of a method for comparing measured data and predicted data.

[0032] Figure 5 This is a flowchart of a method for updating the system based on a definite difference between measured data and predicted data.

[0033] Figure 6 This is a flowchart of a method for generating extrapolated prediction data using digital models.

[0034] Figure 7 It is a graphical user interface that displays the difference between measured data and predicted data.

[0035] Figure 8 This is a flowchart of a method for monitoring the timing of data processing.

[0036] Figure 9 This is a schematic diagram of a computing device.

[0037] Figure 10 This is a schematic diagram of a simulated server. Detailed Implementation

[0038] This application relates to a system and method for evaluating aircraft behavior. The system and method use computer-based models to perform simulation tests to determine the predicted behavior of the aircraft. The system and method also use measurement data collected during the operation of the tested aircraft to determine the actual behavior of the aircraft. The system and method provide a comparison of predicted and actual behavior to provide a more accurate analysis of the aircraft design. In some examples, the system and method provide live flight test optimization, which eliminates and / or reduces the need for extraneous flight tests.

[0039] Figure 1 The functionality of the system and method for analyzing aircraft design 19 is schematically illustrated. Aircraft design 19 includes the engineering specifications of the aircraft. One or more test aircraft 29 are manufactured based on aircraft design 19. In addition, one or more digital models 32 representing the virtual test aircraft are created. System 10 and method test aircraft design 19 based on measurement data 21 obtained from test aircraft 29 and prediction data 31 obtained from digital models 32.

[0040] The actual measurement data 21 is obtained from tests performed using the test aircraft 29. Sensors 22 on the test aircraft 29 sense the physical characteristics experienced during the operation of the test aircraft 20. Various types of sensors 22 are used to collect the measurement data 21. Examples of sensors 22 include, but are not limited to, piezoelectric, capacitive and piezoresistive flexible bending sensors, vibration sensors, force sensors, tachometers, engine temperature sensors, fuel gauges, pressure sensors, altimeters, airspeed sensors, gyroscope sensors, flow sensors, orientation sensors, moment of inertia, aircraft gross weight, flight test air data, flight test aircraft inertial data, and accelerometers.

[0041] Predictive data 31 is obtained by running simulations using one or more digital models 32. Digital models 32 consist of equations defining the functional relationships between the various components in aircraft design 19. Digital models 32 cover regions and / or functions of the aircraft. When the simulation is run, mathematical dynamics form analog quantities of the aircraft's behavior, with the results presented in predictive data 31. Predictive data 31 represents the physical characteristics expected to occur during the operation of the aircraft built according to aircraft design 19. In some examples, predictive data 31 may be output as a graphical image representing dynamic processes in a static or dynamic sequence.

[0042] This analysis identifies discrepancies between the predicted data 31 from the digital model 32 and the measurement data 21 obtained from the sensor 22. These discrepancies may indicate one or more problems, such as, but not limited to, issues with the aircraft design 19, the digital model 32, the sensor 22, and the testing protocol.

[0043] In some examples, test aircraft 29 and digital model 32 are complete versions of the aircraft. In some examples, test aircraft 29 is flyable, and many tests are performed during flight testing. In other examples, one or both of test aircraft 29 and digital model 32 are one or more limited sections of the aircraft. For example, test aircraft 29 may be just the landing gear assembly or just the wings of the aircraft. Similarly, digital model 32 may represent the entire aircraft or just one or more smaller sections of the aircraft (e.g., just the landing gear assembly).

[0044] Measurement data 21 and prediction data 31 include various physical characteristics of the aircraft that occur during operation. Examples include, but are not limited to, wing bending, fuselage pressure, landing gear tire pressure, wheel temperatures, brake temperature, brake energy, stress on tail components, aircraft coefficients (Cl, Cd, Cy, Cr, Cm, Cn), predicted surface orientation, braking torque, and prediction parameters not monitored in flight test data.

[0045] Tests of test aircraft 29 can occur under various conditions and environments. Tests may include one or more ground tests and flight tests. In some examples, the analysis of measurement data 21 and predicted data 31 occurs on a real-time basis. This provides comparative results or other analyses to be performed while the test is still ongoing. In a specific example, this occurs during a test flight. Changes can be made based on the results of comparing data 21 and 31 while test aircraft 29 is still operational. Previous systems had delays in returning predicted data 31 and therefore required additional test flights or other uses of test aircraft 29, which could be time-consuming and costly. Another advantage of real-time results is that the same test conditions can be used for subsequent tests, providing more accurate results (as opposed to subsequent tests conducted at different times under potentially different conditions).

[0046] In some examples, one or more aspects of system 10 are modified based on the results of determining the differences between data 21 and 31. In some examples, these modifications include updating the digital model 32 in light of the differences from actual performance data. The updated model 32 provides more accurate predictive data 31 during future testing. The accurate digital model 32 can also provide extrapolated predictive data 31 for calculating data 21 that does not have a corresponding measurement data 21. For example, measurement data 21 may include the amount of wing flex and the amount of force applied to the wing during flex. Predictive data 31 can be calculated using the digital model 32 to determine the movement of flight control components (e.g., ailerons) when the wing flexes.

[0047] Figure 2 This is a functional block diagram illustrating the architecture of an exemplary system 10 that tests aircraft design 19 using measurement data 21 and prediction data 31. Figure 2As shown, system 10 includes one or more IP networks 61 that communicatively interconnect sensors 22, computing devices 40, 50, database server 65, and simulation server 30. IP network 61 is a communication network comprising multiple computers and other devices (e.g., routers, gateway nodes, etc.). The computers and devices in IP network 61 are configured to send and receive data packets between endpoints (such as simulation server 30) and computing devices 40, 50 using the Internet Protocol (IP). Generally, IP networks and their operation are well known to those skilled in the art; however, it should be understood that… Figure 2 The IP network 61 shown may include one or more public networks (e.g., the Internet) and / or private networks.

[0048] Database server 65 is a computer or similar device that uses a database application to store and maintain measurement data 21 and prediction data 31. Database server 65 includes a memory circuitry configured to contain programming instructions for maintaining data 21, 31 by a processing circuitry. Database server 65 is configured to retain timestamps on the measurement data such that prediction data 31 will be output and timestamped with the measurement data timestamps. This provides a clear indication of the lag in prediction data compared to measurement data 21. In some examples, display device 51 is configured to align the timestamps of measurement data 21, which indicates the “now” aircraft status, with the prediction data timestamps, which, depending on the complexity of the model, may lag slightly from the “now” but are still available and accurate to when the aircraft is being measured. In some examples, if one or more models 32 take significantly too long to compute predictions, the user will know this by observing that the prediction data is no longer available in a near real-time window of the graphical user interface (GUI). Measurement data 21 can be received as raw data from sensor 22 and / or from computing device 40 that processes raw data, and then sent to database server 65.

[0049] Simulation server 30 is a computer or similar device configured to maintain digital model 32. In operation, simulation server 30 receives measurement data 21 from database server 65 via IP network 61 and performs simulations during testing based on this data to obtain prediction data 31. Simulation server 30 then outputs the prediction data 31 to database server 65 for storage via IP network 61.

[0050] Computing device 50 is configured to obtain measurement data 21 and prediction data 31 from database server 65 via IP network 61, and perform analysis to identify differences between the prediction data 31 and measurement data 21. In some examples, the results are output to a display 51 operatively connected to computing device 50. For example, the output may be displayed on a graphical user interface (GUI) shown on display 51. According to this embodiment, the GUI may include one or more graphical components (e.g., icons, menus, etc.) configured to provide the user with the ability to interact with the GUI to convey necessary information to the user.

[0051] Figure 3 A schematic representation of the processing performed by simulation server 30 according to some examples is shown. As previously described, simulation server 30 obtains data from database server 65 and performs simulations to generate predicted data 31. Figure 3 As shown, the data model 32 on which the simulation is performed includes an input model 32a, one or more system models 32b, and an output model 32c. In some examples, the input model 32a is configured to receive measurement data 21 from the database server 65. In some examples, the measurement data 21 is received from the database server 65 and appropriately formatted for direct use during the simulation. In some examples, the input model 32a is configured to map data from the database server 65 for use by the simulation server 32.

[0052] One or more system models 32b are used to perform one or more simulations to generate predictive data 31. As described above, the predictive data 31 includes values ​​representing predicted behaviors or outcomes of the aircraft's operation. One or more digital models 32b represent different parts and functions of the aircraft. Each system model 32b is configured to organize data related to the aircraft's operation and further standardize how that data relates to other data. In some examples, one or more digital models 32b include data representing the functions of the entire aircraft. In other examples, one or more digital models 32b include data representing one or more finite segments or functions of the aircraft. Examples of such finite segments or functions include, but are not limited to, the fuselage, wings, cockpit, engines, tail assembly, and landing gear.

[0053] Output model 32c outputs the predicted data 31 to database server 65. In some examples, output model 32c includes a mapping function that formats the predicted data 31 output by system model 32b according to the format of database server 65. As described above, database server 65 receives the predicted data 31 from simulation server 30 and stores the predicted data 31 in memory.

[0054] The computing device 50 is configured to use measurement data 21 and prediction data 31 to obtain and perform calculations. Figure 4 A method 109 for comparing data 21 and 31 performed by computing device 50 is illustrated. The method includes obtaining measurement data 21 measured during operation of test aircraft 29 (box 100). The method also includes obtaining prediction data 31 based on a digital model 32 representing the aircraft design (box 102). Computing device 50 compares the measurement data and the prediction data (box 104). One or more differences between the measurement data 21 and the prediction data 31 are determined (box 106). This information is displayed on display device 51 (box 108).

[0055] In some examples, one or more aspects of system 10 are updated based on the determined differences between measured data 21 and predicted data 31. Figure 5 A method 159 for modifying system 10 is illustrated. This method includes obtaining measurement data 21 and predicted data 31 (box 150). The differences between the measurement data 21 and the predicted data 31 are determined (box 152). Based on these differences, one or more aspects of system 10 are updated (box 154).

[0056] In some examples, changes are made when the difference exceeds a predetermined amount (such as a predetermined percentage difference between data 21 and 31). Changes may be made to one or more components of system 10. In some examples, changes are made to aircraft design 19 when anticipated and actual operation indicate a problem. In one specific example, predicted data 31 indicates a specific flex on the wing under predetermined conditions, but measured data 21 indicates a larger amount of flex. Additionally or alternatively, one or more of the digital models 32 are updated to more accurately match the results detected by actual measured data 21. Additionally or alternatively, one or more of the sensors 22 are identified as problematic and replaced.

[0057] System 10 provides a digital model 32 to provide accurate predictive data 31 indicative of the aircraft's behavior. In some examples, the digital model 32 also provides extrapolation to determine behavior for which there is no corresponding measurement data 21. For example, the measurement data 21 includes sensed data indicating tire pressure in one or more tires in the landing gear during operation of the test aircraft 29. This measurement data 21 can be compared with the corresponding predictive data 31. When the comparison is accurate, such that the difference does not exceed a threshold, the digital model 32 is instructed to accurately predict the aircraft's behavior. The digital model 32 can then be used to determine additional aspects regarding behaviors not detected by the sensors 22 on the test aircraft 29.

[0058] Figure 6A method 209 for determining extrapolated prediction data 31 is illustrated. Measurement data 21 and prediction data 31 are obtained (box 200), and the difference is determined (box 202). One or more digital models 32 are updated based on the difference (box 204). In some examples, digital model 32 is updated when the difference exceeds a predetermined amount. This process can continue for various periods until digital model 32 provides accurate predictive behavior, indicated by the difference between data 21 and 31 falling below a predetermined threshold. After determining that digital model 32 is accurate, one or more of the digital models 32 are used to calculate extrapolated prediction data 31 not sensed by sensor 22 (box 206).

[0059] The output of calculations performed by computing device 50 can be in various formats. Examples include data tables, graphs, charts, and dynamic models. In some examples, the output is displayed on display device 51. Figure 7 This includes a display device 51 configured to display a GUI 52. The GUI 52 displayed on the display device 51 is configured to display various information and data (from which the operator can better understand data 21, 31), as well as comparisons between measured data 21 and predicted data 31. For example, a network operator uses the GUI 52 to select and / or modify images. Figure 7 An example of GUI 52 is shown, which displays the measured data 21 and the predicted data 31 in a manner that highlights the difference 39 between the measured data 21 and the predicted data 31. In some examples, GUI 52 highlights one or more differences 39.

[0060] System 10 is configured to process data 21, 31 in a timely manner. In some examples, processing data 21, 31 in real time allows for additional testing (if necessary) using an operational test aircraft 29 or a simulator.

[0061] In some examples, system 10 ensures that the predicted data 31 is time-aligned with the sensed measurement data 21. System 10 has a timestamp when sensor 22 senses the signal. Measurement data 21 and the timestamp are passed to simulation server 30, which runs the simulation and calculates the predicted data 31. System 10 is configured to monitor the time difference between the time when sensor 22 senses measurement data 21 and the time when the corresponding predicted data 31 is calculated. The predicted data 31 is calculated using the timestamp. This provides time synchronization between measurement data 21 and predicted data 31. Synchronization provides the ability to observe what the simulation generates at a given time (assuming the test aircraft encounters the same conditions at that time). The lag between the time when measurement data 21 is sensed and the time when the simulation generates predicted data 31 is determined. If the lag exceeds a predetermined amount, system 10 indicates an error message, which may be sent to a remote node and / or displayed on display 51.

[0062] In some examples, the lag is determined by the simulation server 30 after the prediction data 31 is calculated. Additionally or alternatively, the lag is determined by the database server 65 when the prediction data 31 is sent from the simulation server 30.

[0063] In some examples, the analysis of data 21 and 31 is performed in real time. Real time includes the actual time when the testing and / or simulated operation of test aircraft 29 occurs. Figure 8 A method for timing the monitoring data processing is illustrated. This method includes determining the time when sensor 22 senses measurement data 21 (box 300). In some examples, the data is timestamped by sensor 22. In other examples, the data is timestamped by computing device 40 or database server 65. Measurement data 21 is sent to simulation server 30, which then generates predicted data 31 (box 302). In some examples, measurement data 21 is sent directly from sensor 22 or computing device 40 to simulation server 30. In other examples, measurement data 21 is initially sent to database server 65 and then obtained by simulation server 30. Predicted data 31 is timestamped with the measurement data timestamp (box 304).

[0064] Measurement data 21 and predicted data 31 are output to the user (box 306). The output can include various methods, including but not limited to display on display device 51 and various graphs and charts. In some examples, the output includes data 21 and 31 aligned with timestamps, which highlights the lag in the generation of predicted data 31 (box 308). When a lag is determined in predicted data 31, an error message is generated and sent to one or more nodes (box 309). The lag threshold can vary, with examples including but not limited to 1 second and 10 seconds. In some examples, data 21 and 31 are still valuable even if the lag exceeds the threshold because the data can be analyzed after the condition has ended.

[0065] Figure 2 System 10 with a distributed architecture is shown, where different components perform different processes. In other examples, one or more of the components may perform multiple functions. For example, a single computing device processes raw sensor data and also performs comparative calculations using measurement data 21 and prediction data 31. In another example, simulation server 30 and database server 65 are incorporated into a single component. In a specific example, system 10 includes a computing device that receives measurement data 21 and performs each step of the process, and stores measurement data 21 and prediction data 31.

[0066] A schematic diagram of computing device 50 is shown in Figure 9 As shown in the diagram, computing device 50 includes a communication circuitry system 53, a processing circuitry system 54, and a memory 56 configured to store a computer program 58 thereon. The communication circuitry system 53 includes the hardware required for communication with one or more components in system 10. In this regard, the communication circuitry system 53 may include a network interface circuit (NIC) (e.g., Ethernet or a similar interface). In some embodiments, computing device 50 may be configured for wireless communication. In these cases, the communication circuitry system 53 will include a radio frequency (RF) circuitry required for transmitting and receiving signals through a wireless communication channel. Therefore, computing device 50 may also be coupled to one or more antennas (not shown). However, in other embodiments, computing device 50 is configured to communicate via a wired interface.

[0067] The processing circuitry 54 includes one or more microprocessors, hardware, firmware, or combinations thereof that control the overall operation of the computing device 50. According to this disclosure, the processing circuitry 54 can be configured by software to perform one or more of the methods described herein (including those respectively in…). Figure 4 , Figure 5 and Figure 6(Any of methods 109, 159, 209 seen in the document) and various other calculations disclosed herein.

[0068] Memory 56 includes both volatile and non-volatile memory for storing computer program code and data required for the operation of the processing circuitry system 54. Memory 56 may include any tangible, non-transitory computer-readable storage medium for storing data, including electronic, magnetic, optical, electromagnetic, or semiconductor data storage devices. Memory 56 stores a computer program 58, which includes executable instructions configuring the processing circuitry system 54 to perform one or more of the methods described herein. In this respect, the computer program 58 may include one or more code modules corresponding to the aforementioned devices or units. Furthermore, according to this disclosure, the computer program 58 may include instructions and data for one or both of the database server 65 and the simulation server 30.

[0069] Regardless, computer program instructions and configuration information are stored in non-volatile memory (such as ROM, erasable programmable read-only memory (EPROM), or flash memory). Temporary data generated during operation may be stored in volatile memory (such as random access memory (RAM)). In some embodiments, computer program 58 for configuring the processing circuitry system 54 as described herein may be stored in removable memory (such as portable optical disc, portable digital video disc, or other removable media). Computer program 58 may also be embodied in a carrier (such as electronic signals, optical signals, radio signals, or computer-readable storage media).

[0070] Those skilled in the art will also understand that the embodiments herein also include corresponding computer programs. The computer program includes instructions that, when executed on at least one processor of the device, cause the device to perform any of the corresponding processes described above. In this respect, the computer program may include one or more code modules corresponding to the aforementioned device or unit.

[0071] Embodiments of this disclosure also include a carrier containing such a computer program 58. The carrier may include one of electronic signals, optical signals, radio signals, or a computer-readable storage medium.

[0072] In this regard, embodiments herein also include a computer program product stored on a non-transitory computer-readable (storage or recording) medium and including instructions that, when executed by a processor of the device, cause the device to perform as described above.

[0073] The embodiments also include a computer program product comprising program code portions for performing the steps of any of the embodiments herein when executed on computing device 50. The computer program product may be stored on a computer-readable recording medium.

[0074] In some embodiments, some or all of the functions described herein may be provided by a processing circuitry system 54 that executes instructions stored in memory, which in some embodiments may be a computer program product in the form of a non-transitory computer-readable storage medium. In alternative embodiments, some or all of the functions may be provided by the processing circuitry system without executing instructions stored on separate or discrete device-readable storage media (e.g., in a hard-wired manner). In any of these particular embodiments, the processing circuitry system may be configured to perform the described functions regardless of whether instructions stored on a non-transitory computer-readable storage medium are executed. The benefits provided by such functions are not limited to the individual processing circuitry system 54 or other components of the computing device 50, but are enjoyed by the computing device 50 as a whole and / or generally by the end user and wireless network.

[0075] The computing device 50, simulation server 30, and database server 65 are configured to further process data using the methods disclosed above. These components include corresponding communication circuitry 53, processing circuitry 54, and memory 56 to process data and execute the methods. One or more computer programs are stored in memory, providing instructions for the processing circuitry of the components to perform the functions disclosed herein.

[0076] The simulation server 30 is configured to process the measurement data 21 using the digital model 32 and output the corresponding prediction data 31. Figure 10 An example of a simulation server 30 is schematically shown, which includes a communication circuitry 33, a processing circuitry 34, and a memory 36 configured to store a model 32 of a computer program 38 thereon. The communication circuitry 33 includes the hardware required for communication with one or more components in system 10. The communication circuitry 33 is configured to provide one or more wireless communications or via a wired interface. The processing circuitry 34 includes one or more microprocessors, hardware, firmware, or combinations thereof that control the overall operation of the simulation server 30. According to this disclosure, the processing circuitry 34 can be software-configured to perform one or more of the methods described herein.

[0077] Memory 36 includes both volatile and non-volatile memory for storing computer program code and data required for the operation of the processing circuitry system 34. Memory 36 may include any tangible, non-transitory computer-readable storage medium for storing data, including electronic, magnetic, optical, electromagnetic, or semiconductor data storage devices. Memory 36 stores a computer program 38, which includes executable instructions configuring the processing circuitry system 34 to execute one or more of the methods described herein.

[0078] Of course, the invention may be practiced in ways other than those specifically set forth herein without departing from its essential characteristics. These embodiments should be considered illustrative rather than restrictive in all respects, and all changes within the meaning and equivalence of the appended claims are intended to be included therein.

Claims

1. A method for analyzing aircraft design, the method comprising: Receive measurement data from the test aircraft, wherein the measurement data is captured by one or more sensors on the test aircraft and indicates the actual performance of the test aircraft; Calculate predictive data indicating the expected performance of the aircraft design, wherein the predictive data is calculated based on one or more digital simulation models representing the aircraft design; Based on the comparison between the measured data and the predicted data, one or more differences between the measured data and the predicted data captured by the one or more sensors are determined; as well as One or more output parameters are generated based on the comparison, each of which indicates a corresponding difference between the measured data and the predicted data.

2. The method of claim 1, further comprising determining modifications to the aircraft design by inputting the one or more output parameters into a design modification function of the aircraft design.

3. The method of claim 1, further comprising calculating extrapolated prediction data of expected physical characteristics based on the digital simulation model, wherein the extrapolated prediction data are physical characteristics that were not sensed by the sensors during operation of the test aircraft.

4. The method of claim 2, further comprising determining, in real time, the one or more differences between the measurement data and the predicted data during operation of the test aircraft.

5. The method of claim 2, further comprising updating the digital simulation model based on the one or more differences between the measurement data and the prediction data.

6. The method of claim 2, further comprising receiving measurement data from the test aircraft during ground testing and flight testing of the test aircraft.

7. The method of claim 2, further comprising using a timestamp applied to the measurement data to synchronize the measurement data and the prediction data.

8. A system for analyzing aircraft design, the system comprising: The sensor is mounted on the test aircraft and is configured to detect one or more physical characteristics during the operation of the test aircraft; Computing device, comprising: Processing circuit system; and A memory circuit system including a program that, when executed by the processing circuit system, causes the computing device to: Receive measurement data of one or more physical characteristics detected during operation of the test aircraft from the sensor; Predictive data is calculated based on a digital simulation model representing the design of the aircraft. Determine one or more differences between the measured data and the predicted data; and Based on the one or more differences, generate one or more output parameters and update the system.

9. The system of claim 8, wherein the update to the system comprises determining one or more modifications to the aircraft design.

10. The system of claim 8, wherein the processing circuitry is configured to determine extrapolated prediction data of expected physical characteristics not sensed by the sensors during the operation of the test aircraft.

11. The system of claim 8, further comprising a display configured to receive signals from the processing circuitry system and to display the measurement data, the prediction data, and the one or more differences on the display via a graphical user interface.

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