A wind tunnel engine test data acquisition and processing method and system

Abstract

The present application is suitable for the field of wind tunnel test and data processing technology, and provides a wind tunnel engine test data acquisition and processing method and system, the method comprises: obtaining the installation boundary state parameter of the current test experiment, and when it is not in the target parameter interval, generating a control instruction for adjusting the installation boundary state, and executing the control instruction before the test starts, so that the installation boundary state parameter enters the target parameter interval for test experiment. The present application breaks through the processing method of regarding the thrust signal fluctuation as random error in the prior art, realizes the differentiation and utilization of such fluctuations, converts the original experience-dependent installation adjustment process into a quantifiable and predictable control process, reduces the thrust measurement fluctuation from the source, improves the micro-thrust measurement and reconstruction accuracy, and has strong engineering practical value.

Description

Technical Field

[0001] This invention relates to the field of wind tunnel testing and data processing technology, and in particular to a method and system for acquiring and processing wind tunnel engine test data. Background Technology

[0002] Small turbojet engines, due to their small size, light weight, and fast response, are widely used in drones, target drones, and other small aircraft. Wind tunnel testing is a crucial technique in their research and development and performance verification. By testing the engine under controlled flow conditions, key parameters such as thrust characteristics, inlet adaptability, and stability can be obtained. Thrust measurement, as one of the core indicators, typically relies on a thrust measurement platform and thrust sensors to collect real-time data on the engine output.

[0003] Unlike wind tunnel tests of large engines, small turbojet engines are typically mounted on a thrust measurement stand in a wind tunnel using methods such as hangers, cables, and fasteners to adapt to test space limitations and ensure testing flexibility. This mounting method exhibits a certain degree of structural flexibility, and the installation boundary state between the engine and the thrust measurement stand is determined by multiple connection points, making it susceptible to factors such as connection method, preload condition, and structural stiffness.

[0004] In actual wind tunnel tests, especially under asymmetric disturbance conditions such as crosswinds, inlet distortion, or incoming flow pulsations, the aerodynamic characteristics of the incoming flow and the aforementioned installation boundary conditions can couple, resulting in significant noise superimposed on the thrust signal acquired by the thrust sensor. Existing technologies typically process the thrust signal using filtering, signal smoothing, or compensation algorithms to reduce noise and improve measurement accuracy. However, analysis of extensive experimental data reveals that under specific installation boundary conditions and the combined effect of incoming flow disturbances, residual fluctuations within the normal tolerance range still occur in thrust measurements. These fluctuations are not entirely random but exhibit certain regularities, which existing technologies generally fail to distinguish and utilize.

[0005] Furthermore, existing technologies rely heavily on experience to adjust the installation boundary state, lacking quantitative analysis methods based on historical data. This makes it difficult to effectively pre-regulate the system state before the test begins, resulting in non-negligible fluctuations during thrust measurement and affecting the accuracy of small thrust measurements and subsequent reconstruction results. Therefore, how to effectively identify and implement front-end control of thrust signal fluctuations under wind tunnel test conditions, considering the installation boundary characteristics and incoming flow disturbances of a small turbojet engine, has become a pressing technical problem to be solved in this field. Summary of the Invention

[0006] The purpose of this invention is to provide a method and system for acquiring and processing wind tunnel engine test data, aiming to solve the problems mentioned in the background art.

[0007] This invention is implemented as follows: a method for acquiring and processing wind tunnel engine test data, the method comprising: S1. When it is identified that the current experiment to be tested is in a preset disturbance test scenario, a historical reference experiment sample set is obtained, and each sample in the historical reference experiment sample set is consistent with the test conditions of the current experiment to be tested. S2. Analyze the historical reference experimental sample set, determine the installation boundary state parameters between the engine and the thrust measurement platform in the historical test experiments corresponding to each sample, and the thrust signal fluctuation amplitude obtained based on the data collected by the thrust sensor. S3. Based on the correspondence between the installation boundary state parameters and the thrust signal fluctuation amplitude in each historical test experiment, construct a change relationship model, and determine the target parameter range that minimizes the thrust signal fluctuation amplitude according to the change relationship model. S4. Obtain the installation boundary state parameters of the current test experiment, and when they are not in the target parameter range, generate a control command for adjusting the installation boundary state, and execute the control command before the test starts so that the installation boundary state parameters enter the target parameter range before conducting the test experiment.

[0008] As a further limitation of the technical solution of the present invention, the preset disturbance test scenario is a test scenario in which the wind tunnel incoming flow has asymmetric disturbance, including at least one or more of crosswind disturbance, air intake distortion or incoming flow pulsation.

[0009] As a further limitation of the technical solution of the present invention, the test conditions of the current test experiment are consistent with those of the historical reference experiment corresponding to the sample and the current test experiment, which are the same as or within the preset consistency range in terms of engine model and thrust level, installation structure form and installation position, wind tunnel inflow parameters and test condition type. The wind tunnel inflow parameters include at least inflow velocity, inflow direction and disturbance type.

[0010] As a further limitation of the technical solution of the present invention, the process of obtaining the installation boundary state parameters specifically includes: obtaining the installation-related forces of each installation connection part between the engine and the thrust measurement platform, wherein the installation connection parts include at least one or more of the following: bracket connection parts, cable connection parts, and fastening connection parts; assigning corresponding weights according to the degree of influence of each installation connection part on the thrust transmission path; and performing a weighted summation of the installation-related forces of each installation connection part to obtain the installation boundary state parameters.

[0011] As a further limitation of the technical solution of the present invention, the thrust signal fluctuation amplitude is a fluctuation characterization quantity obtained by statistical analysis of the thrust sensor output signal within a preset steady-state time window, which is used to reflect the degree of thrust measurement fluctuation caused by changes in the installation boundary state. The fluctuation characterization quantity includes at least one or more of the following: standard deviation, root mean square value, or peak-to-peak value.

[0012] As a further limitation of the technical solution of this embodiment of the invention, step S3 specifically includes: Several samples from the historical reference experimental sample set are arranged in order according to the magnitude of the installation boundary state parameters to obtain a sample sequence; Extract the variation relationship of thrust signal fluctuation amplitude in the sample sequence as the installation boundary state parameters change, and set the variation relationship as a variation relationship model; The change relationship model is analyzed to determine the trend characteristics of the thrust signal fluctuation amplitude as a function of the installation boundary state parameters. Based on the trend characteristics, the parameter range that makes the thrust signal fluctuation amplitude reach a minimum or fall within a local extreme range is identified as the target parameter range.

[0013] As a further limitation of the technical solution of the embodiment of the present invention, the parameter range that makes the thrust signal fluctuation amplitude reach a minimum value or within a local extreme range is the parameter interval near the turning point of the change trend when the change relationship model shows a change trend of first decreasing and then increasing.

[0014] As a further limitation of the technical solution of this embodiment of the invention, step S4 specifically includes: Before the current test begins, obtain the installation boundary state parameters of the current test and determine whether the current installation boundary state parameters are within the target parameter range. The test experiment is performed when the current installation boundary state parameters are within the target parameter range; When the current installation boundary state parameters are not within the target parameter range, an adjustment command is generated based on the deviation between the current installation boundary state parameters and the target parameter range, and the adjustment command is executed before the test begins so that the installation boundary state parameters of the current test experiment enter the target parameter range before the test experiment is performed.

[0015] As a further limitation of the technical solution of this embodiment of the invention, when generating the control command, the process includes: selecting a reference installation boundary state parameter from the target parameter range, wherein the reference installation boundary state parameter is the installation boundary state parameter in the target parameter range that has the highest matching degree with the current experimental conditions to be tested, or the installation boundary state parameter in the target parameter range that has the smallest corresponding thrust signal fluctuation amplitude; and adjusting each parameter in the installation boundary state parameter according to the differences between the current installation boundary state parameter and the reference installation boundary state parameter to generate the control command.

[0016] A wind tunnel engine test data acquisition and processing system, the system comprising: The sample acquisition module is used to acquire a historical reference experiment sample set when the current experiment to be tested is identified as being in a preset perturbation test scenario. Each sample in the historical reference experiment sample set is consistent with the test conditions of the current experiment to be tested. The parameter parsing module is used to parse the historical reference experimental sample set, determine the installation boundary state parameters between the engine and the thrust measurement platform in the historical test experiments corresponding to each sample, and the thrust signal fluctuation amplitude obtained based on the data collected by the thrust sensor. The model building module is used to construct a change relationship model based on the correspondence between the installation boundary state parameters and the thrust signal fluctuation amplitude in each historical test experiment, and to determine the target parameter range that minimizes the thrust signal fluctuation amplitude based on the change relationship model. The control execution module is used to obtain the installation boundary state parameters of the current test experiment, and when they are not in the target parameter range, generate a control command for adjusting the installation boundary state, and execute the control command before the test starts so that the installation boundary state parameters enter the target parameter range before the test experiment is conducted.

[0017] Compared with the prior art, the present invention has the following beneficial effects: This invention addresses the thrust signal fluctuation problem caused by the coupling effect of installation boundary state and incoming flow disturbance in a wind tunnel asymmetric disturbance test scenario for small turbojet engines. It proposes a data-driven control method based on historical reference experimental samples. By modeling and analyzing the relationship between installation boundary state parameters and thrust signal fluctuation amplitude, a target parameter range that minimizes thrust signal fluctuation amplitude is identified. Before the test begins, the current installation boundary state is pre-controlled to bring it into the target parameter range. This invention overcomes the existing approach of treating thrust signal fluctuation as random error, achieving the differentiation and utilization of this type of fluctuation. It transforms the previously experience-based installation adjustment process into a quantifiable and predictable control process, reducing thrust measurement fluctuations at the source and improving the accuracy of small thrust measurement and reconstruction, thus possessing strong engineering practical value. Attached Figure Description

[0018] Figure 1 A flowchart of the method provided in the embodiments of the present invention; Figure 2 This is a flowchart illustrating the process of determining the target parameter range in the method provided in this embodiment of the invention; Figure 3 A flowchart illustrating the generation and execution of control instructions in the method provided in this embodiment of the invention; Figure 4 The application architecture diagram of the system provided in the embodiments of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0020] Figure 1 A flowchart of the method provided by an embodiment of the present invention is shown.

[0021] Specifically, a method for acquiring and processing wind tunnel engine test data includes the following steps: Step S1: When the current experiment to be tested is identified as being in a preset disturbance test scenario, a historical reference experiment sample set is obtained. Each sample in the historical reference experiment sample set is consistent with the test conditions of the current experiment to be tested. The preset disturbance test scenario is a test scenario in which the wind tunnel inflow has asymmetric disturbances, including at least one or more of crosswind disturbances, inlet distortion, or inflow pulsations.

[0022] The test conditions being consistent with the current test experiment specifically refer to the historical reference experiment corresponding to the sample being the same as or within the preset consistency range in terms of engine model and thrust level, installation structure form and installation position, wind tunnel inflow parameters and test condition type. The wind tunnel inflow parameters include at least inflow velocity, inflow direction and disturbance type.

[0023] In this embodiment of the invention, the method described herein is applied to wind tunnel testing of a small turbojet engine. Compared to large engines, small turbojet engines are typically not rigidly mounted as a whole in wind tunnel tests. Instead, they are mounted on a thrust measurement platform using hangers, steel cables, and fastening connections, placing the engine in a suspended or semi-constrained state to achieve thrust testing within a limited space. This mounting method has a certain degree of structural flexibility, and its installation boundary state is determined by multiple connection points, which is significantly different from the rigid mounting method of large engines.

[0024] The research object of this invention is experimental data of thrust signals acquired by thrust sensors. Based on long-term wind tunnel testing experience, those skilled in the art have discovered that, under the aforementioned installation boundary conditions formed by the hanger and steel cables, when there is asymmetric disturbance in the wind tunnel flow, a coupling effect occurs between the installation structure and the aerodynamic disturbance, resulting in significant noise superimposed on the thrust sensor output signal. Although existing technologies possess filtering and compensation methods for high-noise environments, under specific installation boundary conditions and the combined effect of inflow disturbances, the thrust measurement value still exhibits residual fluctuations within the normal tolerance range but with structural regularities. These fluctuations are usually considered random errors and not further differentiated or processed, making effective control before testing difficult, thus limiting further improvements in the accuracy of small thrust reconstruction.

[0025] Furthermore, the inventors discovered that the thrust signal fluctuation amplitude within the normal tolerance range is not completely random, but exhibits differences under different test conditions. Specifically, under similar test conditions, the thrust signal fluctuation amplitude is relatively small in some tests, while it is relatively large in others. Analysis of historical reference test samples revealed a coupling relationship between these differences and the installation boundary state between the engine and the thrust measurement platform. This installation boundary state can be quantitatively characterized by installation boundary state parameters. In other words, under different values ​​of the installation boundary state parameters, the thrust signal fluctuation amplitude exhibits a distribution characteristic with a certain variational pattern.

[0026] However, existing technologies typically only focus on noise reduction or post-compensation of the thrust signal, without identifying the coupling relationship between the installation boundary state parameters and the thrust signal fluctuation amplitude, and without utilizing this coupling relationship to pre-regulate the installation boundary state before the test.

[0027] Based on the above issues, in step S1, it is necessary to first identify whether the current experiment under test is in a preset disturbance test scenario. The preset disturbance test scenario is a test scenario where the wind tunnel inflow exhibits asymmetric disturbances. Specifically, this can be achieved by real-time detection of the inflow parameters using a wind tunnel measurement system. For example, the presence of a lateral component can be determined using an inflow direction sensor, the presence of intake distortion can be determined using pressure measurement point distribution, and the presence of periodic pulsations in the inflow can be determined using time series analysis. When the detection results meet the preset disturbance conditions, it can be determined that the current experiment under test is in the preset disturbance test scenario.

[0028] After identifying the aforementioned scenarios, a historical reference experimental sample set is obtained. This historical reference experimental sample set typically originates from data resources accumulated over a long period during wind tunnel testing. This type of data is usually stored and managed in database form, which is a conventional technique in this field. The database contains at least the following underlying data types: thrust signal data collected by thrust sensors, installation boundary-related data (e.g., installation-related forces or pre-tightening status data at various connection points), wind tunnel inflow parameter data (including inflow velocity, inflow direction, and disturbance type), engine operating parameter data (e.g., engine speed, fuel flow rate, etc.), and test condition identification data. The acquisition methods for the above data all belong to existing mature experimental measurement technologies.

[0029] It should be noted that the purpose of screening historical reference experimental samples is to ensure the comparability and effectiveness of the established change relationship model. Since the relationship between installation boundary state parameters and thrust signal fluctuation amplitude is affected by various factors, directly using all historical data for analysis can easily introduce irrelevant variables and reduce model accuracy. Therefore, this invention employs relatively strict screening criteria, selecting samples from the historical database that are consistent with the experimental conditions of the current test, to ensure that the analyzed relationship primarily reflects the influence of the installation boundary state.

[0030] The screening criteria include, but are not limited to: consistent engine model and thrust rating, consistent installation structure and location, similar or identical wind tunnel inflow parameters, and consistent test conditions. Furthermore, additional screening criteria can be added as needed, such as consistent engine operating range, consistent ambient temperature or pressure range, and consistent test time periods.

[0031] The "preset consistency range" is a parameter tolerance interval set based on engineering experience and historical data statistics, used to measure the comparability between different tests. Because wind tunnel tests have a certain degree of variability, it is difficult to achieve complete consistency between different tests. Therefore, while ensuring the consistency of the main influencing factors, some parameters are allowed to vary within a reasonable range, thereby maintaining the representativeness and reliability of the data while ensuring the sample size. This is also the reason why this invention uses "same or within the preset consistency range" rather than complete consistency.

[0032] Furthermore, the wind tunnel engine test data acquisition and processing method also includes the following steps: S2. Analyze the historical reference experimental sample set to determine the installation boundary state parameters between the engine and the thrust measurement platform in the historical test experiments corresponding to each sample, as well as the thrust signal fluctuation amplitude obtained based on the data collected by the thrust sensor.

[0033] The process of obtaining the installation boundary state parameters specifically includes: obtaining the installation-related forces of each installation connection part between the engine and the thrust measurement platform. The installation connection parts include at least one or more of the following: bracket connection parts, cable connection parts, and fastening connection parts. The installation boundary state parameters are obtained by assigning corresponding weights according to the degree of influence of each installation connection part on the thrust transmission path and by weighted summing of the installation-related forces of each installation connection part.

[0034] The thrust signal fluctuation amplitude is a fluctuation characterization quantity obtained by statistically analyzing the thrust sensor output signal within a preset steady-state time window. It is used to reflect the degree of thrust measurement fluctuation caused by changes in the installation boundary state. The fluctuation characterization quantity includes at least one or more of the following: standard deviation, root mean square value, or peak-to-peak value.

[0035] In this embodiment of the invention, the key factors affecting the thrust measurement results are quantified in step S2, including the quantification of the installation boundary state and the extraction of the thrust signal fluctuation amplitude.

[0036] Regarding the installation boundary state parameters, this invention does not simply select parameters from a single connection point for characterization, but rather comprehensively considers the influence of multiple installation connection points between the engine and the thrust measurement platform on the thrust transmission path. Specifically, by acquiring the installation-related forces of each installation connection point and assigning corresponding weights according to the degree of influence of different connection points in the thrust transmission path, the installation-related forces of each installation connection point are weighted and summed to obtain the installation boundary state parameters. The aforementioned installation connection points include at least one or more of the following: bracket connection points, cable connection points, and fastening connection points.

[0037] The influence of different mounting connections on the thrust transmission path can be determined based on their structural connection position between the engine and the thrust measurement platform, the direction of force, the connection stiffness, and their relative relationship to the thrust sensor's measurement axis. Generally, mounting connections located on the main load-bearing path, with higher connection stiffness, a force direction that is consistent with or nearly consistent with the thrust transmission direction, and closer to the thrust sensor's measurement axis have a greater impact on the thrust transmission path.

[0038] It should be noted that in practical applications, the installation boundary state parameters can be calculated based on a combination of multiple installation connection points, or, depending on specific test conditions, only a single or partial connection point that significantly affects the thrust transmission path can be selected for characterization. This invention introduces a weighting mechanism to quantify the influence of different connection points on the overall installation boundary state, thereby more realistically reflecting the comprehensive effect of the installation boundary on thrust measurement.

[0039] The installation boundary state parameters obtained through the above method have the following advantages: Firstly, they transform the installation boundary state, which is originally difficult to quantify directly, into a calculable parameter form, facilitating data modeling and analysis. Secondly, by considering the differentiated influence of different connection parts, they improve the accuracy and specificity of parameter characterization, thus providing a foundation for establishing the relationship between installation boundary state parameters and thrust signal fluctuation amplitude. Existing technologies typically only control or empirically adjust single installation parameters, without uniformly quantifying the combined influence of multiple installation connection parts.

[0040] The thrust signal fluctuation amplitude is a fluctuation characterization quantity obtained through statistical analysis of the thrust signal acquired by the thrust sensor within a preset steady-state time window. Specifically, after necessary denoising processing of the original thrust signal, the fluctuation degree of the thrust signal can be quantitatively described by calculating statistical quantities such as standard deviation, root mean square value, or peak-to-peak value. The above processing method is a commonly used data processing technique in this field, used to reflect the fluctuation characteristics during the thrust measurement process. In this invention, by using the thrust signal fluctuation amplitude as a characterization quantity, it can reflect the impact of changes in the installation boundary state on thrust measurement, providing a data foundation for subsequently establishing the relationship between the two.

[0041] Furthermore, the wind tunnel engine test data acquisition and processing method also includes the following steps: Step S3: Based on the correspondence between the installation boundary state parameters and the thrust signal fluctuation amplitude in each historical test experiment, construct a change relationship model, and determine the target parameter range that minimizes the thrust signal fluctuation amplitude according to the change relationship model.

[0042] Specifically, Figure 2 A flowchart for determining the target parameter range is shown.

[0043] The specific steps involved in constructing a variation relationship model based on the correspondence between the installation boundary state parameters and the thrust signal fluctuation amplitude from various historical test experiments, and determining the target parameter range that minimizes the thrust signal fluctuation amplitude according to the variation relationship model, include the following steps: Step S31: Arrange several samples from the historical reference experimental sample set in order according to the magnitude of the installation boundary state parameters to obtain a sample sequence; Step S32: Extract the variation relationship of the thrust signal fluctuation amplitude in the sample sequence as the installation boundary state parameters change, and set the variation relationship as a variation relationship model; Step S33: Analyze the change relationship model to determine the trend characteristics of the thrust signal fluctuation amplitude as a function of the installation boundary state parameters, and identify the parameter range that makes the thrust signal fluctuation amplitude reach a minimum or fall within a local extreme range based on the trend characteristics, as the target parameter range.

[0044] The parameter range that makes the thrust signal fluctuation amplitude reach a minimum or within a local extreme range is the parameter interval near the turning point of the change trend when the change relationship model shows a change trend of first decreasing and then increasing.

[0045] In this embodiment of the invention, the core of step S3 lies in establishing a quantitative relationship between the installation boundary state parameters and the thrust signal fluctuation amplitude based on historical reference experimental sample data, and identifying the parameter range that minimizes the thrust signal fluctuation amplitude, thereby providing a basis for subsequent installation boundary state control before the experiment. This step directly corresponds to the core research content of the aforementioned findings, namely, that there is a coupling relationship between the thrust signal fluctuation amplitude and the installation boundary state parameters, and that this relationship is not random but has a certain pattern of change. By modeling and analyzing this pattern, superior and inferior states can be further distinguished from the "fluctuations within the normal tolerance range".

[0046] Specifically, in step S31, several samples from the historical reference experimental sample set are arranged in order according to the magnitude of the installation boundary state parameters to obtain a sample sequence. This step can be achieved by numerically sorting the installation boundary state parameters, for example, by using ascending or descending order. Since the installation boundary state parameters are scalar parameters obtained through weighted summation, they can be directly used as the sorting basis. Through this sorting process, the originally discretely distributed sample data can be transformed into a data sequence with monotonic parameter sequence characteristics, providing a foundation for subsequent extraction of change relationships.

[0047] In step S32, the variation relationship of thrust signal fluctuation amplitude with changes in installation boundary state parameters in the sample sequence is extracted, and this variation relationship is set as a variation relationship model. Specifically, the installation boundary state parameters in the sample sequence can be used as independent variables, and the corresponding thrust signal fluctuation amplitudes as dependent variables, forming discrete data point pairs. Subsequently, commonly used data fitting or smoothing methods in the art can be used to process the discrete data, such as polynomial fitting, moving average, piecewise linear fitting, or spline interpolation, to obtain continuous or quasi-continuous variation relationship curves, thereby constructing the variation relationship model. The above methods are all mature data processing techniques and can be implemented in actual systems through data processing modules.

[0048] In step S33, the variation relationship model is analyzed to determine the trend characteristics of the thrust signal fluctuation amplitude as a function of the installation boundary state parameters, and the target parameter range is identified based on these trend characteristics. Specifically, this can be achieved by performing extreme value analysis or trend analysis on the variation relationship model. For example, the increasing or decreasing relationship can be determined by calculating the first-order trend of the curve, the local minimum point can be determined by identifying the turning point from a decreasing trend to an increasing trend, or a continuous parameter range with low fluctuation amplitude can be identified by setting a threshold range. When the variation relationship model shows a trend of first decreasing and then increasing, a parameter range near the turning point of the trend can be taken as the target parameter range.

[0049] Through the processes described in steps S31 to S33, this invention extracts the intrinsic variation law between the installation boundary state parameters and the thrust signal fluctuation amplitude from historical reference experimental samples, and further determines the target parameter range that minimizes the thrust signal fluctuation amplitude. This process not only transforms the original experience-based installation adjustment process into a data-driven quantitative analysis process, but also provides a clear basis for the pre-regulation of the installation boundary state in step S4, thereby guiding the system state to a more optimal region before the start of the experiment, reducing fluctuations during thrust measurement, and improving the accuracy of small thrust measurement and reconstruction.

[0050] Furthermore, the wind tunnel engine test data acquisition and processing method also includes the following steps: Step S4: Obtain the installation boundary state parameters of the current test experiment, and when they are not within the target parameter range, generate a control command for adjusting the installation boundary state, and execute the control command before the test starts so that the installation boundary state parameters enter the target parameter range before conducting the test experiment.

[0051] Specifically, Figure 3 The flowchart illustrating the generation and execution of control commands is shown.

[0052] The process of acquiring the installation boundary state parameters of the current test experiment, generating adjustment instructions to adjust the installation boundary state when the parameters are not within the target parameter range, and executing the adjustment instructions before the test begins to bring the installation boundary state parameters into the target parameter range before conducting the test experiment specifically includes the following steps: Step S41: Before the current test experiment begins, obtain the installation boundary state parameters of the current test experiment, and determine whether the current installation boundary state parameters are within the target parameter range. Step S42: When the current installation boundary state parameters are within the target parameter range, perform a test experiment; Step S43: When the current installation boundary state parameter is not within the target parameter range, generate a control command based on the deviation between the current installation boundary state parameter and the target parameter range, and execute the control command before the test starts so that the installation boundary state parameter of the current test experiment enters the target parameter range before the test experiment is performed.

[0053] When generating the control command, the process includes: selecting a reference installation boundary state parameter from the target parameter range, wherein the reference installation boundary state parameter is the installation boundary state parameter within the target parameter range that has the highest matching degree with the current experimental conditions to be tested, or the installation boundary state parameter within the target parameter range that has the smallest corresponding thrust signal fluctuation amplitude; and adjusting each parameter in the installation boundary state parameter according to the differences between the current installation boundary state parameter and the reference installation boundary state parameter to generate the control command.

[0054] In this embodiment of the invention, step S4 is an execution step for actual control based on the target parameter range obtained in step S3, which is used to pre-adjust the installation boundary state before the start of the test, so that the system is in a state with a better thrust signal fluctuation amplitude.

[0055] Specifically, in step S41, before the current test begins, the installation boundary state parameters of the current test are acquired, and it is determined whether they are within the target parameter range. The installation boundary state parameters can be obtained by collecting and weighting the installation-related forces at each installation connection point. For example, the actual force state of each connection point can be obtained using a force sensor, cable tension meter, torque measuring tool, or preload measuring device, and calculated using preset weights. Subsequently, the calculated installation boundary state parameters are compared with the target parameter range. This comparison process can be implemented using simple range judgment logic, i.e., determining whether the parameter is within the upper or lower limit of the target parameter range.

[0056] In step S42, when the installation boundary state parameters of the current test experiment are within the target parameter range, it indicates that the current installation state has met the condition of optimal thrust signal fluctuation amplitude. At this point, no additional adjustments are needed, and the test experiment can be executed directly. This step essentially confirms the current installation state to avoid unnecessary adjustments and improve test efficiency.

[0057] In step S43, when the current installation boundary state parameter is not within the target parameter range, a control command needs to be generated and executed. Specifically, a reference installation boundary state parameter is first selected from the target parameter range. This reference parameter can be the installation boundary state parameter within the target parameter range that best matches the current experimental conditions, or the installation boundary state parameter within the target parameter range that corresponds to the smallest thrust signal fluctuation amplitude. Then, the current installation boundary state parameter is compared with the reference parameter, the differences in each parameter are calculated, and the direction and magnitude of adjustment required for each installation connection part are determined based on these differences. For example, the installation-related forces are adjusted by adjusting the bracket connection position, adjusting the cable tension, or changing the preload of the fastening connection part, thereby generating a specific control command. The control command can be output in numerical form or as an operational instruction, and the corresponding adjustment can be completed manually or by an actuator. After adjustment, the installation boundary state parameter is acquired again and judged until it enters the target parameter range before the test experiment is executed.

[0058] Through steps S41 to S43 described above, this invention achieves a closed-loop control process based on historical data analysis results. Specifically, before the test begins, the installation boundary state is detected, judged, and adjusted to guide the system state to a region with optimal thrust signal fluctuation amplitude. This process no longer relies on coarse adjustments based on experience but instead uses data-driven fine-grained control, thereby effectively reducing thrust measurement fluctuations caused by the coupling between the installation boundary and incoming flow disturbances.

[0059] Step S4 transforms the analysis results obtained in steps S1 to S3 into actionable control actions, achieving a closed-loop connection from "data analysis" to "engineering application." Its significance lies in pre-suppressing potential thrust measurement fluctuations before the experiment begins, ensuring the thrust signal is in an optimal state during the acquisition phase. This reduces the burden of subsequent data processing from the outset, improving the accuracy and stability of thrust measurement and reconstruction results.

[0060] In summary, this invention analyzes historical reference experimental samples to identify the variation patterns between installation boundary state parameters and thrust signal fluctuation amplitude. Before the experiment, the current installation boundary state is adjusted to within the target parameter range, thereby achieving active control of thrust signal fluctuations. This method effectively solves the aforementioned technical problem: under specific installation boundary conditions and the combined effect of incoming flow disturbances, thrust measurements, although within the normal tolerance range, exhibit structural fluctuations that are difficult to distinguish. This invention not only distinguishes the quality of such fluctuations but also allows for targeted adjustment before the experiment, significantly improving the accuracy of small thrust measurement and reconstruction.

[0061] This invention has promising applications, particularly in wind tunnel testing of small turbojet engines. It can also be extended to other power system tests employing flexible mounting structures, such as small propulsion system testing and UAV power system verification. This invention is effective in applications requiring high-precision thrust measurement.

[0062] Furthermore, Figure 4 An application architecture diagram of the system provided in an embodiment of the present invention is shown.

[0063] In another preferred embodiment of the present invention, a wind tunnel engine test data acquisition and processing system includes: The sample acquisition module 100 is used to acquire a historical reference experiment sample set when the current experiment to be tested is identified as being in a preset disturbance test scenario. Each sample in the historical reference experiment sample set is consistent with the test conditions of the current experiment to be tested.

[0064] Furthermore, the wind tunnel engine test data acquisition and processing system also includes: The parameter parsing module 200 is used to parse the historical reference experimental sample set, determine the installation boundary state parameters between the engine and the thrust measurement platform in the historical test experiment corresponding to each sample, and the thrust signal fluctuation amplitude obtained based on the data collected by the thrust sensor.

[0065] Furthermore, the wind tunnel engine test data acquisition and processing system also includes: The model building module 300 is used to build a change relationship model based on the correspondence between the installation boundary state parameters and the thrust signal fluctuation amplitude in each historical test experiment, and to determine the target parameter range that minimizes the thrust signal fluctuation amplitude based on the change relationship model.

[0066] Furthermore, the wind tunnel engine test data acquisition and processing system also includes: The control execution module 400 is used to acquire the installation boundary state parameters of the current test experiment, and when the parameters are not within the target parameter range, generate a control command for adjusting the installation boundary state, and execute the control command before the test starts so that the installation boundary state parameters enter the target parameter range before the test experiment is conducted.

[0067] It should be understood that although the steps in the flowcharts of the various embodiments of the present invention are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the various embodiments may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps.

[0068] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0069] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0070] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

[0071] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for acquiring and processing wind tunnel engine test data, characterized in that, The method includes: S1. When it is identified that the current experiment to be tested is in a preset disturbance test scenario, a historical reference experiment sample set is obtained, and each sample in the historical reference experiment sample set is consistent with the test conditions of the current experiment to be tested. S2. Analyze the historical reference experimental sample set, determine the installation boundary state parameters between the engine and the thrust measurement platform in the historical test experiments corresponding to each sample, and the thrust signal fluctuation amplitude obtained based on the data collected by the thrust sensor. S3. Based on the correspondence between the installation boundary state parameters and the thrust signal fluctuation amplitude in each historical test experiment, construct a change relationship model, and determine the target parameter range that minimizes the thrust signal fluctuation amplitude according to the change relationship model. S4. Obtain the installation boundary state parameters of the current test experiment, and when they are not in the target parameter range, generate a control command for adjusting the installation boundary state, and execute the control command before the test starts so that the installation boundary state parameters enter the target parameter range before conducting the test experiment.

2. The wind tunnel engine test data acquisition and processing method according to claim 1, characterized in that, The preset disturbance test scenario is a test scenario in which the wind tunnel incoming flow has asymmetric disturbances, including at least one or more of crosswind disturbances, air intake distortion, or incoming flow pulsation.

3. The method for acquiring and processing wind tunnel engine test data according to claim 1, characterized in that, The test conditions being consistent with the current test experiment specifically refer to the historical reference experiment corresponding to the sample being the same as or within the preset consistency range in terms of engine model and thrust level, installation structure form and installation position, wind tunnel inflow parameters and test condition type. The wind tunnel inflow parameters include at least inflow velocity, inflow direction and disturbance type.

4. The method for acquiring and processing wind tunnel engine test data according to claim 1, characterized in that, The process of obtaining the installation boundary state parameters specifically includes: obtaining the installation-related forces of each installation connection part between the engine and the thrust measurement platform. The installation connection parts include at least one or more of the following: bracket connection parts, cable connection parts, and fastening connection parts. The installation boundary state parameters are obtained by assigning corresponding weights according to the degree of influence of each installation connection part on the thrust transmission path and by weighted summing of the installation-related forces of each installation connection part.

5. The method for acquiring and processing wind tunnel engine test data according to claim 1, characterized in that, The thrust signal fluctuation amplitude is a fluctuation characterization quantity obtained by statistically analyzing the thrust sensor output signal within a preset steady-state time window. It is used to reflect the degree of thrust measurement fluctuation caused by changes in the installation boundary state. The fluctuation characterization quantity includes at least one or more of the following: standard deviation, root mean square value, or peak-to-peak value.

6. The method for acquiring and processing wind tunnel engine test data according to claim 1, characterized in that, Step S3 specifically includes: Several samples from the historical reference experimental sample set are arranged in order according to the magnitude of the installation boundary state parameters to obtain a sample sequence; Extract the variation relationship of thrust signal fluctuation amplitude in the sample sequence as the installation boundary state parameters change, and set the variation relationship as a variation relationship model; The change relationship model is analyzed to determine the trend characteristics of the thrust signal fluctuation amplitude as a function of the installation boundary state parameters. Based on the trend characteristics, the parameter range that makes the thrust signal fluctuation amplitude reach a minimum or fall within a local extreme range is identified as the target parameter range.

7. The method for acquiring and processing wind tunnel engine test data according to claim 6, characterized in that, The parameter range that makes the thrust signal fluctuation amplitude reach a minimum or within a local extreme range is the parameter interval near the turning point of the change trend when the change relationship model shows a change trend of first decreasing and then increasing.

8. The method for acquiring and processing wind tunnel engine test data according to claim 4, characterized in that, Step S4 specifically includes: Before the current test begins, obtain the installation boundary state parameters of the current test and determine whether the current installation boundary state parameters are within the target parameter range. The test experiment is performed when the current installation boundary state parameters are within the target parameter range; When the current installation boundary state parameters are not within the target parameter range, an adjustment command is generated based on the deviation between the current installation boundary state parameters and the target parameter range, and the adjustment command is executed before the test begins so that the installation boundary state parameters of the current test experiment enter the target parameter range before the test experiment is performed.

9. The method for acquiring and processing wind tunnel engine test data according to claim 8, characterized in that, When generating the control command, the process includes: selecting a reference installation boundary state parameter from the target parameter range, wherein the reference installation boundary state parameter is the installation boundary state parameter within the target parameter range that has the highest matching degree with the current experimental conditions to be tested, or the installation boundary state parameter within the target parameter range that has the smallest corresponding thrust signal fluctuation amplitude; and adjusting each parameter in the installation boundary state parameter according to the differences between the current installation boundary state parameter and the reference installation boundary state parameter to generate the control command.

10. A wind tunnel engine test data acquisition and processing system, characterized in that, The system includes: The sample acquisition module is used to acquire a historical reference experiment sample set when the current experiment to be tested is identified as being in a preset perturbation test scenario. Each sample in the historical reference experiment sample set is consistent with the test conditions of the current experiment to be tested. The parameter parsing module is used to parse the historical reference experimental sample set, determine the installation boundary state parameters between the engine and the thrust measurement platform in the historical test experiments corresponding to each sample, and the thrust signal fluctuation amplitude obtained based on the data collected by the thrust sensor. The model building module is used to construct a change relationship model based on the correspondence between the installation boundary state parameters and the thrust signal fluctuation amplitude in each historical test experiment, and to determine the target parameter range that minimizes the thrust signal fluctuation amplitude based on the change relationship model. The control execution module is used to obtain the installation boundary state parameters of the current test experiment, and when they are not in the target parameter range, generate a control command for adjusting the installation boundary state, and execute the control command before the test starts so that the installation boundary state parameters enter the target parameter range before the test experiment is conducted.

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