Aircraft cabin explosion cutting safety equivalent experiment test method and system

Abstract

The application provides a kind of aircraft cabin explosion cutting safety equivalent experiment test method and system, method includes based on the single factor simulation test of all structural components in cabin to preset three-dimensional model, determine the characteristic index and influence coefficient of each structural component;According to the characteristic index and influence coefficient, the target component is determined in turn for each structural component, and parameter mapping is carried out to determine a plurality of target simulation modules, to determine the target experimental cabin according to the target simulation module;The dummy is placed in the target experimental cabin based on the preset rule and determines the sensor distribution on the dummy and the target experimental cabin to carry out explosion experiment;Data processing is carried out based on the experimental parameters collected by sensor through the preset model, to determine the correlation between the structure response characteristics and the personnel injury characteristics to evaluate and optimize the cabin.The application solves the problem that there is no system, reliable and economically feasible test method in the prior art.

Description

Technical Field

[0001] This invention relates to the field of aircraft ejection rescue explosion cutting test technology, and in particular to an equivalent test method and system for aircraft cockpit explosion cutting safety. Background Technology

[0002] The rapid development of modern aerospace technology has driven high-performance fighter jets towards high maneuverability, high stealth, and high integration. As a core piece of equipment ensuring pilot safety, the reliability and safety of ejection escape systems directly affect combat effectiveness and the probability of personnel survival. To adapt to the complex cockpit structure and high-speed flight conditions of fighter jets, "cut first, then eject" has become the mainstream escape mode—using a linear shaped charge cutting cable on the canopy or fuselage top to instantly detonate and cut the canopy before ejection, quickly creating an unobstructed escape route. This process requires energy release and structural fracture to be completed within milliseconds. However, the fighter jet cockpit, as a geometrically irregular and spatially limited enclosed cavity, experiences multiple reflections and diffractions from the initial shock wave, high-speed fragments, and high-temperature gases generated by the explosion, along with the instrument panel, seats, and other structures within the cockpit. This creates a complex multi-physics coupling environment, posing not only a multi-dimensional instantaneous risk of injury to the pilot but also potentially affecting the integrity of critical load-bearing structures such as the rear hinge mechanism of the canopy, directly limiting the success rate of ejection escape.

[0003] Currently, the assessment of the safety of fighter jet cockpit explosions in the aviation field mainly relies on three technical approaches: full-scale live-fire tests, pure numerical simulation (CFD / FEA), and existing simplified experiments. Full-scale live-fire tests, by recreating the actual cockpit structure and explosion conditions, can obtain intuitive safety assessment results, but they are extremely costly, highly destructive, and not repeatable. Pure numerical simulations, using computer simulation technology, theoretically analyze the propagation of explosion shock waves, structural response, and the biomechanical characteristics of personnel. They can cover some extreme conditions, but the computational cost is enormous, and the accuracy and reliability of simulations of multiphase flow, turbulence, strong fluid-structure interaction, and dynamic material fracture processes in irregular chambers still need experimental verification, especially in the prediction of biomechanical response, where there is significant uncertainty. Existing simplified experiments reduce experimental complexity and cost by constructing regular containers or flat plate structures and focusing on a single physical quantity. However, most of these are conducted on regular containers or flat plate structures, or only focus on a single physical quantity (such as free-field overpressure), failing to reflect the complex modulation effects of real cockpit geometry on multiple physical fields such as pressure, noise, heat, and acceleration, and also failing to simultaneously assess structural response and personnel injury. Summary of the Invention

[0004] Based on this, the purpose of the present invention is to provide an equivalent experimental test method and system for the safety of aircraft cockpit explosion cutting, aiming to solve the problem that there is a lack of a systematic, reliable and economically feasible equivalent experimental test method for the safety of aircraft cockpit explosion cutting in the prior art.

[0005] A method for conducting an equivalent test of the safety of an aircraft cockpit during explosive cutting, according to an embodiment of the present invention, the method comprising:

[0006] Based on a pre-set 3D model, single-factor simulation tests were conducted on all structural components in the cockpit to determine the characteristic indicators and influence coefficients of each structural component.

[0007] Based on the characteristic indicators and the influence coefficient, each structural component is sequentially screened to determine the target component, and the target component is parameter-mapped to determine multiple target simulation modules, so as to determine the target experimental cabin based on the target simulation modules;

[0008] A pre-set dummy is placed inside the target experimental cabin, and the distribution of the dummy and the sensors on the target experimental cabin is determined based on pre-set rules to conduct an explosion experiment;

[0009] Data processing based on experimental parameters collected by sensors is performed using a preset model to determine the correlation between structural response characteristics and personnel injury characteristics in order to evaluate and optimize the cabin.

[0010] In addition, the aircraft cockpit explosion cutting safety equivalent test method according to the above embodiments of the present invention may also have the following additional technical features:

[0011] Furthermore, the step of conducting single-factor simulation tests on all structural components within the cockpit based on a preset three-dimensional model to determine the characteristic indicators and influence coefficients of each structural component includes:

[0012] The characteristic indicators are determined based on the explosion stress propagation influence coefficient. The characteristic indicators include at least the stress wave reflection contribution, load transfer efficiency, explosion load distribution dominance, and correlation with the target experimental parameters.

[0013] The cockpit was subjected to an overall explosion simulation based on a pre-set 3D model to determine the first simulation parameters. Then, each structural component was removed and a single-factor explosion simulation was performed to determine the second simulation parameters.

[0014] Feature extraction is performed on the first simulation parameters and the second simulation parameters respectively, and the results are compared to determine the feature indicators and influence coefficients of each structural component.

[0015] Furthermore, the step of sequentially screening each structural component to determine the target component based on the characteristic indicators and the influence coefficient includes:

[0016] Based on the aforementioned feature indicators and the first threshold of the corresponding dimension, structural components are screened, and structural components whose feature indicators are greater than the first threshold are identified as the first core components.

[0017] Based on the influence coefficient, the second threshold, and the third threshold, the remaining structural components excluding the first core component are screened. Structural components with an influence coefficient greater than the second threshold are identified as the second core component, those with an influence coefficient between the second threshold and the third threshold are optional components, and those with an influence coefficient less than the third threshold are discardable components.

[0018] Wherein, the second threshold is greater than the third threshold, and the target component includes the first core component, the second core component, and the optional component.

[0019] Furthermore, the step of performing parameter mapping on the target component to determine multiple target simulation modules, and then determining the target experimental cockpit based on the target simulation modules, includes:

[0020] Based on the aforementioned feature indicators, the first threshold, and the component type, the first core component and the second core component are divided into different categories of component datasets.

[0021] Based on the preset 3D model, the corresponding first target parameters are determined according to the type of the component dataset, and the first target simulation module is determined by mapping according to the first target parameters;

[0022] The second target parameters of the optional components are determined based on a preset three-dimensional model, and the second target simulation module is determined by mapping according to the second target parameters;

[0023] Using the first target simulation module as the main body and the second target simulation module as an auxiliary, the second target simulation module is adjusted to perform module splicing to determine the target experimental cabin.

[0024] Furthermore, the steps of placing a pre-set dummy inside the target experimental cabin and determining the distribution of sensors on the dummy and the target experimental cabin based on pre-set rules include:

[0025] The stress distribution state is determined based on the first simulation parameters, the distribution location of the first sensor is determined based on the stress distribution state and the sensitive parts of the human body that are prone to injury, and the corresponding sensor type is determined based on the distribution location of the first sensor.

[0026] Based on the stress distribution state, key components with stress concentration coefficients greater than a preset coefficient threshold are identified, and the distribution location and corresponding sensor type of the second sensor are determined based on the key components.

[0027] The first sensor is used to collect physical data of the dummy, and the second sensor is used to collect physical data of the component structure of the target experimental cabin.

[0028] Furthermore, the steps of processing experimental parameters collected by sensors and determining the correlation between structural response characteristics and personnel injury characteristics through a preset model to evaluate and optimize the cabin include:

[0029] The experimental parameters are classified to determine personnel injury data and structural response data, and further subdivided according to the collection location of each type of personnel injury data and structural response data to eliminate redundant data channels that do not exist due to the removal of non-core components;

[0030] The personnel injury data is denoised using a combination of wavelet threshold denoising and adaptive Kalman filtering, and the structural response data is denoised using empirical mode decomposition and intrinsic mode function filtering algorithms.

[0031] Based on the stress load transfer characteristics of the target structure, feature parameters are extracted from the noise-reduced personnel injury data and structural response data, and a correlation model between personnel injury feature parameters and structural response feature parameters is established. The correlation coefficient between the two is calculated using a preset correlation analysis algorithm to evaluate and optimize the cockpit.

[0032] Furthermore, after determining the target experimental cockpit and before conducting the explosion experiment, the key parameters of the target simulation module are iteratively adjusted by comparing the core simulation response parameters of the target experimental cockpit with the benchmark data of the real cockpit explosion test or the high-fidelity simulation verification data, until the equivalent error of the key features meets the preset requirements, so as to ensure that the target experimental cockpit can equivalently reproduce the core physical process of the real cockpit explosion.

[0033] Another objective of this invention is to provide an equivalent experimental testing system for the safety of an aircraft cockpit subjected to explosive cutting, for implementing the aforementioned equivalent experimental testing method for the safety of an aircraft cockpit subjected to explosive cutting. The system includes:

[0034] The simulation testing module is used to perform single-factor simulation tests on all structural components in the cockpit based on a preset 3D model, and to determine the characteristic indicators and influence coefficients of each structural component.

[0035] The target screening module is used to sequentially screen each structural component to determine the target component based on the characteristic indicators and the influence coefficient, and to perform parameter mapping on the target component to determine multiple target simulation modules, so as to determine the target experimental cabin based on the target simulation modules;

[0036] The experimental testing module is used to place a preset dummy inside the target experimental cabin and determine the distribution of sensors on the dummy and the target experimental cabin based on preset rules to conduct an explosion experiment;

[0037] The data analysis module is used to process experimental parameters collected by sensors, determine the correlation between structural response characteristics and personnel injury characteristics through a preset model, and evaluate and optimize the cabin.

[0038] Another objective of this invention is to provide a storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described experimental test method for the safety equivalence of explosive cutting of an aircraft cockpit.

[0039] Another objective of this invention is to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the above-described experimental test method for the safety equivalence of explosive cutting of an aircraft cockpit.

[0040] This invention employs single-factor simulation testing of all structural components within the cockpit based on a pre-defined 3D model. Target components are selected using quantified feature indicators and influence coefficients, and parameter mapping and target experimental cockpit construction are completed. This effectively addresses the problems of insufficient scientific quantitative basis for structural selection and inadequate equivalence of experimental scenarios in existing technologies. By optimizing sensor distribution in conjunction with stress distribution status and stress concentration in sensitive areas of the human body and key structures, it achieves simultaneous acquisition of multi-dimensional physiological data from dummies and physical data from key cockpit structures, overcoming the shortcomings of existing technologies that cannot simultaneously consider multi-physics coupling effects and simultaneous assessment of personnel and structures. Through targeted classification and noise reduction, feature parameter extraction, and correlation model construction, redundant data is eliminated, and the quantitative correlation between structural response and personnel injury is clarified, solving the problems of low data processing efficiency and lack of scientific correlation basis in existing technologies. Furthermore, by comparing and iterating the core parameters of the target experimental cockpit with real benchmark data, the equivalence of the experiment is ensured, significantly reducing the high cost of full-scale live-fire testing and the uncertainty of pure numerical simulation. This provides precise, efficient, and economical experimental support for the safe optimization design of aircraft cockpit explosive cutting systems. Therefore, this invention solves the problem of the lack of a systematic, reliable and economically feasible experimental test method for the safety of explosive cutting of aircraft cockpits in the prior art. Attached Figure Description

[0041] Figure 1 This is a flowchart of the aircraft cockpit explosion cutting safety equivalent test method in the first embodiment of the present invention;

[0042] Figure 2 This is a schematic diagram of the results of the aircraft cockpit explosion cutting safety equivalent test system in the second embodiment of the present invention;

[0043] Figure 3 This is a schematic diagram of the structure of the electronic device in the third embodiment of the present invention;

[0044] The following detailed description, in conjunction with the accompanying drawings, will further illustrate the present invention. Detailed Implementation

[0045] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Several embodiments of the invention are illustrated in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.

[0046] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0047] Example 1

[0048] Please see Figure 1 The figure shows the aircraft cockpit explosion cutting safety equivalent test method in the first embodiment of the present invention, which specifically includes steps S01-S04.

[0049] S01, based on the preset three-dimensional model, conduct single-factor simulation tests on all structural components in the cockpit to determine the characteristic indicators and influence coefficients of each structural component.

[0050] Specifically, the characteristic indicators are determined based on the explosion stress propagation influence coefficient. These characteristic indicators include at least the stress wave reflection contribution, load transfer efficiency, explosion load distribution dominance, and correlation with target experimental parameters. A first simulation parameter is determined by performing an overall explosion simulation of the cabin based on a preset 3D model. Then, individual structural components are removed, and single-factor explosion simulations are performed to determine the second simulation parameter. Features are extracted from the first and second simulation parameters and compared to determine the characteristic indicators and influence coefficients of each structural component. In practical implementation, by clarifying the core characteristic indicators, the correlation logic between structural components and explosion stress propagation is established. Furthermore, the single-factor simulation test, through a comparison of overall and individual component removal, accurately quantifies the influence of each component on the explosion load distribution. The calculated influence coefficients can be directly used for component classification and screening. This addresses the pain point of lacking quantitative standards for structural screening in existing technologies, ensuring that subsequent experimental cabin construction can focus on core influencing components, guaranteeing experimental equivalence, and providing data support for simplifying experimental structures and reducing costs. In addition, it provides a scientific and quantitative basis for the selection of cockpit structural components, avoiding the problems of traditional selection relying on experience and being highly subjective, and ensuring that the selected components can accurately reflect the core influencing factors of explosion stress propagation.

[0051] S02, based on the characteristic indicators and the influence coefficient, each structural component is sequentially screened to determine the target component, and the target component is parameter-mapped to determine multiple target simulation modules, so as to determine the target experimental cabin based on the target simulation modules.

[0052] Specifically, structural components are screened based on the aforementioned feature indicators and a first threshold corresponding to each dimension. Structural components with any of the aforementioned feature indicators exceeding the first threshold are identified as first core components. Other structural components, excluding the first core components, are screened based on the influence coefficient, a second threshold, and a third threshold. Structural components with an influence coefficient exceeding the second threshold are identified as second core components. Components with influence coefficients between the second and third thresholds are considered optional components, and those with influence coefficients less than the third threshold are considered removable components. The second threshold is greater than the third threshold, and the target components include the first core component, the second core component, and the optional components. In practical implementation, the first threshold screens components that play a dominant role in the propagation of explosive stress, ensuring that the core experimental logic is not missing. The second and third thresholds further refine the priority of non-core components. Optional components reserve space for experimental optimization, while removable components directly reduce the complexity of the experimental chamber construction. This avoids the high cost and complexity of full-size replication, while preventing experimental distortion caused by over-simplification, and resolving the contradiction of insufficient equivalence or excessive cost in existing simplified experiments. This allows for the hierarchical classification and screening of structural components, retaining key components crucial for explosion safety assessment while eliminating irrelevant and redundant components, thus balancing experimental accuracy with cost control.

[0053] Furthermore, based on the characteristic indicators, the first threshold, and the component type, the first core component and the second core component are divided into different categories of component datasets; based on a preset 3D model, corresponding first target parameters are determined according to the type of the component dataset, and a first target simulation module is determined by mapping according to the first target parameters; based on the preset 3D model, second target parameters of the optional components are determined, and a second target simulation module is determined by mapping according to the second target parameters; the second target simulation module is adjusted with the first target simulation module as the main body and the second target simulation module as an auxiliary module to perform module splicing to determine the target experimental cabin. In specific implementation, the dataset is divided according to component type, making the simulation module design more targeted; the mapping of the first target parameter ensures the equivalence of the core components, and the mapping of the second target parameter provides flexible adjustment space for optional components; the module splicing method realizes the rapid construction and parameter iteration of the experimental cabin. This solves the problem of the fixed structure of traditional experimental cabins, which is difficult to adapt to different models or working conditions. The modular design not only ensures the equivalent reproduction of the core physical process, but also improves the versatility and scalability of the experimental system, and reduces the repeated construction cost of multi-condition experiments. This allows the selected core components to be transformed into a modular experimental structure that is feasible and adjustable, ensuring that the experimental cabin can accurately reproduce the core explosion physical environment of the real cockpit.

[0054] S03, a preset dummy is placed inside the target experimental cabin, and the distribution of the dummy and the sensors on the target experimental cabin is determined based on preset rules to conduct an explosion experiment.

[0055] Specifically, the stress distribution state is determined based on the first simulation parameters. The distribution locations of the first sensor are then determined based on this stress distribution state and the sensitive areas of human injury, and the corresponding sensor types are determined based on the distribution locations of the first sensor. Key components with stress concentration coefficients greater than a preset threshold are identified based on the stress distribution state, and the distribution locations and corresponding sensor types of the second sensor are determined based on these key components. The first sensor is used to collect physical data from the dummy, and the second sensor is used to collect physical data from the component structures of the target experimental cabin. In practical implementation, the stress distribution state provided by the first simulation parameters clearly identifies high-risk areas for personnel injury and areas of structural stress concentration. Combined with the screening of sensitive areas of human injury and key structures, the sensor placement becomes more targeted, and the sensor types match the collection requirements to ensure data accuracy. This addresses the shortcomings of existing technologies that cannot simultaneously monitor personnel and structures, achieving comprehensive and accurate collection of multi-physics field data, providing a high-quality data foundation for subsequent correlation analysis, and improving the scientific rigor and reliability of safety assessments. It also enables precise sensor placement, ensuring the simultaneous collection of key data on personnel injury and structural response, avoiding data redundancy or missing key information due to blind placement.

[0056] S04. Based on the experimental parameters collected by the sensors, data processing is performed to determine the correlation between structural response characteristics and personnel injury characteristics through a preset model in order to evaluate and optimize the cabin.

[0057] Specifically, the experimental parameters are categorized into personnel injury data and structural response data. Further subdivision is performed based on the collection location of each type of personnel injury data and structural response data to eliminate redundant data channels that are no longer present due to the removal of non-core components. The personnel injury data is denoised using a combination of wavelet threshold denoising and adaptive Kalman filtering, while the structural response data is denoised using empirical mode decomposition and intrinsic mode function (EMF) filtering algorithms. Based on the stress load transfer characteristics of the target structure, feature parameters are extracted from the denoised personnel injury data and structural response data, and a correlation model between personnel injury feature parameters and structural response feature parameters is established. The correlation coefficient between the two is calculated using a preset correlation analysis algorithm to evaluate and optimize the cockpit. In practical implementation, data classification and redundancy removal reduce processing complexity and improve data processing efficiency; targeted denoising algorithms ensure the purity of different types of data and avoid noise interference with key features; feature parameter extraction and correlation model construction establish the coupling relationship between structure and personnel, and quantify the correlation coefficient to clarify the influence weight. This approach addresses the issues of low data processing efficiency and a disconnect between data and assessment needs. Through precise noise reduction and correlation analysis, it extracts core value information from massive datasets, providing quantitative guidance for optimizing cockpit cutting system parameters and improving structural design, thereby enhancing the safety of ejection escape systems. By achieving efficient processing and in-depth analysis of experimental data, it clarifies the quantitative correlation between structural response and personnel injury, providing a scientific basis for cockpit safety optimization.

[0058] Furthermore, after determining the target experimental cockpit and before conducting the explosion experiment, the core simulation response parameters of the target experimental cockpit are compared with the benchmark data of the real cockpit explosion test or high-fidelity simulation verification data. The key parameters of the target simulation module are iteratively adjusted until the equivalent error of the key features meets the preset requirements, ensuring that the target experimental cockpit equivalently reproduces the core physical processes of the real cockpit explosion. In specific implementation, differences between the experimental cockpit and the real cockpit are identified by comparing with real benchmark data or high-fidelity simulation data; iterative adjustments to the module parameters achieve error convergence, enabling the experimental cockpit to accurately reproduce the core physical processes such as the propagation of the explosion shock wave, structural response, and personnel injury. This solves the problem of insufficient verification of equivalence in existing simplified experiments, ensures the reliability of the experimental system through closed-loop iteration, provides high confidence in subsequent multi-condition experimental data, provides reliable experimental support for engineering design optimization, and bridges the technical gap between pure numerical simulation and full-scale experiments. By verifying the equivalence of the target experimental cockpit, the distortion of experimental results due to structural simplification is avoided, ensuring the credibility of the experimental data and its engineering application value.

[0059] As an example, and not a limitation, in some alternative embodiments, the experimental testing steps for a specific type of cabin, based on the above, are now described as follows:

[0060] 1. Target compartment feature analysis, selective screening of core components, and parametric design of equivalent experimental compartment

[0061] 1.1 Target compartment feature analysis and selective screening of core components

[0062] Analyzing the 3D model of a real aircraft cockpit, and based on the principle of "prioritizing key influencing factors of explosion stress propagation," a selective and purposeful screening of core components was completed. The specific screening process and judgment criteria are as follows:

[0063] (1) Construction of screening index system: The core screening indexes include ① stress wave reflection contribution, ② load transfer efficiency, ③ explosion load distribution dominance, and ④ correlation with target experimental parameters (personnel injury / structural response). The weights of each index are set to 35%, 25%, 20%, and 20%, respectively.

[0064] (2) Quantitative evaluation of indicators: The numerical simulation pre-analysis method is used to conduct single-factor simulation tests on all structural components in the actual cockpit - that is, retain the target component alone, remove other components, apply standard explosive load (such as 1MPa initial overpressure, 10ms pulse duration), and calculate the influence coefficient of each component on the explosive stress distribution in the key areas (personnel seating area, structural hinge area) of the cockpit respectively. Influence coefficient = (stress peak when retaining the component - stress peak when removing the component) / stress peak when retaining the component;

[0065] (3) Screening threshold determination: The impact coefficient ≥30% is set as the core must-select component, 10%-30% is the optional optimization component, and <10% is the component that can be eliminated; through this screening process, the categories of core components to be retained and the judgment criteria are finally determined:

[0066] ① Primary primary reflector (required): The bottom plate, main sidewalls, and the area of ​​the top cover to be cut all have a stress wave reflection contribution of ≥45%, and are the core carriers that dominate the distribution of stress field inside the cabin. Their effective projected area, average curvature, and spatial normal must be recorded.

[0067] ② Level 2 large obstacle (mandatory): The instrument panel in front of the pilot and the ejection seat back behind him. Their stress load transmission efficiency to the passenger area is ≥38%, which directly affects the distribution of impact load on the personnel. Their characteristic dimensions, spatial coordinates (X,Y,Z) and main reflector tilt angle must be recorded.

[0068] ③ Level III critical cavities (mandatory): Seat-side wall gap and footrest area depression. Their dominance in local stress concentration is ≥32%, and they are prone to stress superposition effect. Their opening angle θ and depth D need to be recorded.

[0069] ④ Removable components: interior trim, non-load-bearing pipelines, small auxiliary supports, etc., all have an impact coefficient of <8%. Removing them will not affect the core distribution law of explosive stress, and can reduce the construction cost and complexity of the experimental cabin.

[0070] 1.2 Parametric Design of Equivalent Experimental Chamber

[0071] Based on the principle of "equivalence between principal stress wave propagation path and key load interface," the parameters of the core components determined after screening are mapped to the design drawings of a modular equivalent experimental chamber. The main body of this experimental chamber is a regular or semi-regular cavity (such as a rectangular cross-section long chamber), and the core screened components are reproduced internally through detachable and adjustable standardized feature modules.

[0072] ① Primary main reflector simulation module: It adopts a replaceable plate structure. Based on the selected projection area, curvature and normal parameters, it realizes the equivalent reproduction of the main reflector of different cabins through modular splicing. The plate material is consistent with the material of the corresponding part of the real cabin (such as aviation aluminum alloy 7075).

[0073] ② Level 2 Large Obstacle Simulation Module: Includes an adjustable tilt instrument panel simulation board (tilt adjustment range 0-60°, accuracy ±1°) and a movable seat back simulation board (movement range 0-300mm, positioning accuracy ±2mm), which can be adjusted by parameters to match the spatial attitude of obstacles in the cockpit of different aircraft models.

[0074] ③ Three-level critical cavity simulation module: adopts a parameterized concave angle module, the opening angle θ can be steplessly adjusted in the range of 15°-120°, and the depth D can be adjusted in the range of 50mm-200mm. Precise positioning is achieved through threaded transmission to reproduce the key cavity characteristics obtained by screening.

[0075] 2. Construction of a multi-dimensional comprehensive testing system, optimization of sensor layout, and determination of acquisition parameters.

[0076] 2.1 Construction of the Core Architecture of the Comprehensive Testing System

[0077] A comprehensive testing system integrating personnel injury assessment and structural response monitoring was constructed. The system adopts a layered architecture design: a perception layer (sensor array), a transmission layer (high-speed data acquisition bus), and a processing layer (real-time data preprocessing module) to ensure the integrity and timeliness of data acquisition under explosion impact.

[0078] 2.2 Sensor Layout Optimization and Acquisition Parameter Determination

[0079] A standard test dummy conforming to the target pilot's percentile body type (e.g., 50% male) was used and fixed in a simulated seat. Based on the principle of "priority of injury-sensitive areas + stress load gradient distribution," the sensor layout for key physiological areas was optimized. The specific layout scheme, selection parameters, and data acquisition parameters were determined based on the following:

[0080] (1) Head unit:

[0081] ① Placement locations: top of the head, forehead, and back of the head (all of which are highly sensitive areas for traumatic brain injury; according to the human injury biomechanical model, the stress load in these areas is directly related to the risk of brain contusion and skull fracture).

[0082] ② Sensor selection: High-frequency pressure sensor (range 0-1MPa, response frequency >100kHz);

[0083] ③ Determine the acquisition parameters: The sampling frequency is set to 500kHz (based on the rising edge characteristics of the explosion shock wave to ensure that the pressure peak change at the 10μs level is captured), and the acquisition duration is set to 50ms (to cover the entire cycle of the explosion shock wave and the subsequent stress decay stage).

[0084] ④ Additional setup: Fast-response thin-film thermocouples (K-type, response time <1ms) with a sampling frequency of 100kHz are installed on the inside of the face / helmet simulator to synchronously collect instantaneous temperature rise data accompanying the explosion impact, and to assist in assessing the risk of secondary injury to personnel.

[0085] (2) Ear unit:

[0086] ① Location: At the entrance of both external auditory canals (this location is the direct point of impact of ear pressure and is directly related to the risk of tympanic membrane damage and inner ear concussion).

[0087] ② Sensor selection: Composite sensor probe (integrating a miniature pressure sensor and a high sound pressure level noise sensor, pressure range 0-1MPa, noise range 140-200dB);

[0088] ③ Determine the acquisition parameters: pressure sampling frequency 500kHz, noise sampling frequency 200kHz (matching the high-frequency spectrum range of impulse noise to ensure complete capture of noise signals from 10Hz to 20kHz), and acquisition duration 50ms.

[0089] (3) Thoracic unit:

[0090] ① Placement: The anterior chest heart projection area and the bilateral lung projection area (using a 3×3 grid array with a grid spacing of 20mm, covering the surface projection areas corresponding to the major internal organs in the chest. Based on the simulation results of chest cavity stress distribution, this area is the area with the highest stress concentration and risk of injury); the corresponding back pressure sensor is placed at the back position (to form an anterior-posterior pressure difference monitoring).

[0091] ② Sensor selection: High-sensitivity pressure sensor (range 0-0.5MPa, accuracy ±0.5%FS);

[0092] ③ Determine the acquisition parameters: sampling frequency 300kHz, acquisition duration 50ms, focusing on capturing the dynamic change curve of intrathoracic pressure and the peak value of the pressure difference between the front and back.

[0093] (4) Neck biomechanical unit:

[0094] ① Positioning: Simulate the position of the C1-C3 segment of the cervical spine at the back of the dummy's neck (this segment is a high-risk area for cervical spine injuries, and the angular acceleration generated by the backward tilting of the head can easily lead to fractures or dislocations in this segment).

[0095] ② Sensor selection: Triaxial high g-force accelerometer (range ±500g, accuracy ±1g);

[0096] ③ Determination of acquisition parameters: Sampling frequency 1000kHz (to match the dynamic response characteristics of rapid cervical spine movement and ensure the capture of millisecond-level angular acceleration peaks), measurement axes strictly aligned with the human anatomical coordinate system (X-axis along the longitudinal direction of the cervical spine, Y-axis horizontal left and right, Z-axis vertical up and down), acquisition parameters include triaxial linear acceleration and triaxial angular acceleration.

[0097] (5) Spinal load interface unit:

[0098] ① Placement location: The central area of ​​the contact surface between the dummy's pelvis and the seat (this location is the main interface for the transmission of axial loads on the spine, and the load data is directly related to the risk of lumbar spine injury).

[0099] ② Sensor selection: Six-component force sensor (range X / Y / Z axis ±50kN, torque ±5kN·m, accuracy ±0.3%FS) or high-sensitivity seat base acceleration sensor (range ±200g, response frequency >50kHz).

[0100] ③ Determination of acquisition parameters: sampling frequency of 500kHz for the six-component force sensor, sampling frequency of 1000kHz for the accelerometer sensor, and acquisition duration of 50ms.

[0101] (6) Structural response monitoring subsystem:

[0102] ① Location: The simulated rear hinge mechanism, the welded joint between the cabin and the top cover, the seat mounting base and other key load-bearing structures inside the experimental chamber (based on the results of structural mechanics simulation, key parts with stress concentration coefficient ≥1.5 were selected, and these parts are high-risk areas for structural failure under explosive impact).

[0103] ② Sensor selection: Resistance strain gauge (BE120-3AA series, sensitivity coefficient 2.10±0.02, grid length 3mm);

[0104] ③ Arrangement method: The semi-bridging method is adopted, and the strain gauges are pasted along the direction of the principal stress of the structure. Three strain gauges are arranged in each key part (distributed at 120°) to ensure comprehensive capture of the three-dimensional stress state.

[0105] ④ Determination of acquisition parameters: Sampling frequency 500kHz, acquisition duration 50ms, acquisition parameters include dynamic strain peak value, strain rate, strain-time curve.

[0106] 3. Differentiated Data Processing and Experimental Result Analysis

[0107] For the differentiated data collected after screening core components using this method (compared to the full data collected by existing technologies through proportional replication), a proprietary processing flow of "layered noise reduction - feature extraction - multi-dimensional coupling analysis" is adopted. The specific steps are as follows:

[0108] 3.1 Data Layering and Noise Reduction Processing

[0109] (1) Hierarchical classification: The collected data is divided into two categories: “personnel injury” (head pressure, neck acceleration, chest pressure, etc.) and “structural response” (strain of key parts, structural acceleration, etc.). Each category is further subdivided into data subsets according to the collection site, and redundant data channels that do not exist due to the removal of non-core components are eliminated (compared to the existing technology, the number of data channels is reduced by 40%-60%, reducing the processing complexity).

[0110] (2) Targeted noise reduction algorithm:

[0111] ① Personnel injury data: A combined algorithm of "wavelet threshold denoising + adaptive Kalman filtering" is adopted. The wavelet basis is selected as db4, the decomposition level is 5, and the threshold is calculated as λ=σ√(2lnN) (σ is the noise standard deviation, N is the data length). The Kalman filter state equation is set as X(k)=AX(k-1)+B u(k-1)+W(k), and the observation equation is set as Z(k)=HX(k)+V(k). This effectively filters out high-frequency electromagnetic interference and mechanical vibration noise accompanied by explosion impact. The signal-to-noise ratio of the data is improved by ≥25dB ​​after denoising. Wherein, X(k) is the system state vector at time k, X(k 1) is k The system state vector at time 1, A is the state transition matrix, B is the input matrix, and u(k) is the input matrix. 1) is k The control input vector at time 1, W(k) is the process noise vector, Z(k) is the observation vector at time k, H is the observation matrix, and V(k) is the observation noise vector at time k.

[0112] ② Structural response data: The "Empirical Mode Decomposition (EMD) - Intrinsic Mode Function (IMF) Screening" algorithm is used to decompose the data into 8th-12th order IMF components. The effective IMF components related to the structural stress response are screened out by the correlation coefficient method (correlation coefficient threshold ≥ 0.8), and the noise-dominated components are removed to achieve accurate noise reduction of structural strain data.

[0113] 3.2 Extraction of Differentiated Feature Parameters

[0114] Based on the stress load transfer characteristics of the core components after screening, specific feature parameters are extracted, which differs from the generalized feature extraction of full data in existing technologies. Specifically, these include:

[0115] (1) Characteristic parameters of personal injury:

[0116] ① Head: Peak pressure of each part, pressure rise rate (dP / dt), pressure duration (duration of overpressure ≥ 0.1MPa), and head temperature rise rate;

[0117] ② Neck: Peak value of triaxial angular acceleration, rate of change of angular acceleration, peak value of linear acceleration and duration of C1-C3 segments;

[0118] ③ Chest: Peak pressure difference between front and back, pressure peak distribution uniformity coefficient (maximum pressure value / average pressure value), and thoracic pressure loading rate.

[0119] (2) Structural response characteristic parameters:

[0120] ① Peak strain, peak strain rate, and strain delay time (the difference between the arrival time of the explosion shock wave and the time when the peak strain occurs) of key components.

[0121] ② Structural stress concentration factor (stress value corresponding to strain peak / material yield stress), structural plastic deformation (calculated by integrating the strain-time curve).

[0122] 3.3 Multidimensional Coupling Analysis and Result Output

[0123] (1) Personnel-structure coupling analysis: Establish a correlation model between personnel injury characteristic parameters and structural response characteristic parameters, calculate the correlation coefficient between the two through Pearson correlation analysis (threshold ≥ 0.7 is strong correlation), clarify the influence weight of structural stress distribution on personnel injury, for example: the coupling relationship between the peak strain of the rear hinge mechanism and the peak neck angular acceleration, and provide a collaborative basis for cabin structure optimization and personnel protection design;

[0124] (2) Equivalence verification analysis: The processed experimental data is compared with the core data of the real cockpit explosion test (obtained by proportional replication experiment through existing technology), and the equivalence error (|experimental value - real value| / real value × 100%) is calculated. The equivalence error of the core characteristic parameters (such as the peak pressure of the head and the peak strain of the key structure) is required to be ≤10% to verify the equivalence of this method.

[0125] (3) Quantitative Analysis of Advantages: Comparing the experimental costs (material costs, processing costs, assembly costs) and experimental efficiency (design cycle, construction cycle, testing cycle) of this method with existing proportional replication methods, the following quantitative results are obtained: The experimental cabin construction cost of this method is reduced by 50%-70% (due to the elimination of non-core components, the amount of materials used is reduced by 40%-60%, and the processing procedures are simplified by 30%-50%), the total experimental cycle is shortened by 40%-60% (the design cycle is shortened by 50%-70%, and the construction cycle is shortened by 30%-50%), and the data processing efficiency is improved by 50%-80% (due to the reduction of data channels by 40%-60%, and the time spent on noise reduction and feature extraction is shortened). It should be noted that the above examples are for the purpose of understanding this scheme, and are not intended to limit the specific implementation parameters and steps of this scheme.

[0126] In summary, the aircraft cockpit explosion cutting safety equivalent test method in the above embodiments of the present invention effectively solves the problems of insufficient scientific quantitative basis for structural selection and inadequate equivalence of experimental scenarios in the prior art by conducting single-factor simulation tests on all structural components in the cockpit based on a preset three-dimensional model, selecting target components and completing parameter mapping and target experimental cockpit construction based on quantified characteristic indicators and influence coefficients; by combining stress distribution state with the stress concentration of sensitive parts of the human body and key structures to optimize sensor distribution, it realizes the synchronous acquisition of multi-dimensional physiological data of the dummy and physical data of key cockpit structures, making up for the shortcomings of the prior art. Existing technologies cannot simultaneously address the coupling effects of multiphysics fields and the simultaneous assessment of personnel and structures. This invention, through targeted classification and noise reduction, feature parameter extraction, and correlation model construction, eliminates redundant data and clarifies the quantitative correlation between structural response and personnel injury. This solves the problems of low data processing efficiency and lack of scientific correlation in assessment found in existing technologies. Furthermore, by comparing and iteratively examining the core parameters of the target experimental cockpit with real baseline data, the equivalence of the experiment is ensured, significantly reducing the high cost of full-scale live-fire testing and the uncertainties of pure numerical simulation. This provides precise, efficient, and economical experimental support for the safety optimization design of aircraft cockpit explosive cutting systems. Therefore, this invention solves the problem of the lack of a systematic, reliable, and economically feasible experimental testing method for the safety equivalence of aircraft cockpit explosive cutting in existing technologies.

[0127] Example 2

[0128] Please see Figure 2 The diagram shown is a structural block diagram of the aircraft cockpit explosion cutting safety equivalent experimental test system proposed in the second embodiment of the present invention. The aircraft cockpit explosion cutting safety equivalent experimental test system 200 includes: a simulation test module 21, a target selection module 22, an experimental test module 23, and a data analysis module 24, wherein:

[0129] Simulation test module 21 is used to perform single-factor simulation tests on all structural components in the cockpit based on a preset three-dimensional model, and to determine the characteristic indicators and influence coefficients of each structural component.

[0130] The target screening module 22 is used to sequentially screen each structural component to determine the target component based on the characteristic index and the influence coefficient, and to perform parameter mapping on the target component to determine multiple target simulation modules, so as to determine the target experimental cabin based on the target simulation modules;

[0131] Experimental testing module 23 is used to place a preset dummy inside the target experimental cabin and determine the distribution of sensors on the dummy and the target experimental cabin based on preset rules to conduct an explosion experiment;

[0132] The data analysis module 24 is used to process experimental parameters collected by sensors, determine the correlation between structural response characteristics and personnel injury characteristics through a preset model, and evaluate and optimize the cabin.

[0133] Example 3

[0134] In another aspect, the present invention also proposes an electronic device, please refer to [link to relevant documentation]. Figure 3 The diagram shows an electronic device according to the third embodiment of the present invention, including a memory 20, a processor 10, and a computer program 30 stored in the memory and executable on the processor. When the processor 10 executes the computer program 30, it implements the equivalent experimental test method for the safety of the aircraft cockpit explosion cutting as described above.

[0135] In some embodiments, the processor 10 may be a central processing unit (CPU), controller, microcontroller, microprocessor or other data processing chip, used to run program code stored in memory 20 or process data, such as executing access restriction programs.

[0136] The memory 20 includes at least one type of readable storage medium, such as flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 20 can be an internal storage unit of an electronic device, such as the hard disk of the electronic device. In other embodiments, the memory 20 can also be an external storage device of the electronic device, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. Furthermore, the memory 20 can include both internal and external storage units of the electronic device. The memory 20 can be used not only to store application software and various types of data of the electronic device, but also to temporarily store data that has been output or will be output.

[0137] It should be pointed out that, Figure 3 The structure shown does not constitute a limitation on the electronic device. In other embodiments, the electronic device may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0138] This invention also proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned experimental test method for the safety equivalence of explosive cutting of an aircraft cockpit.

[0139] Those skilled in the art will understand that the logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can mean any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0140] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0141] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0142] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0143] The above embodiments merely illustrate several implementation methods of the present invention, and their descriptions are relatively specific and detailed, but 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 all fall within the protection scope of the present invention. Therefore, the protection scope of this patent should be determined by the appended claims.

Claims

1. A method for conducting an equivalent experimental test of the safety of an aircraft cockpit during explosive cutting, characterized in that, The method includes: Based on a pre-set 3D model, single-factor simulation tests were conducted on all structural components in the cockpit to determine the characteristic indicators and influence coefficients of each structural component. Based on the characteristic indicators and the influence coefficient, each structural component is sequentially screened to determine the target component, and the target component is parameter-mapped to determine multiple target simulation modules, so as to determine the target experimental cabin based on the target simulation modules; A pre-set dummy is placed inside the target experimental cabin, and the distribution of the dummy and the sensors on the target experimental cabin is determined based on pre-set rules to conduct an explosion experiment; Based on the experimental parameters collected by the sensors, data processing is performed and a preset model is used to determine the correlation between structural response characteristics and personnel injury characteristics in order to evaluate and optimize the cabin. The step of sequentially screening and determining the target component based on the characteristic indicators and the influence coefficient includes: Based on the aforementioned feature indicators and the first threshold of the corresponding dimension, structural components are screened, and structural components whose feature indicators are greater than the first threshold are identified as the first core components. Based on the influence coefficient, the second threshold, and the third threshold, the remaining structural components excluding the first core component are screened. Structural components with an influence coefficient greater than the second threshold are identified as the second core component, those with an influence coefficient between the second threshold and the third threshold are optional components, and those with an influence coefficient less than the third threshold are discardable components. Wherein, the second threshold is greater than the third threshold, and the target component includes the first core component, the second core component, and the optional component; The steps for evaluating and optimizing the cockpit include processing experimental parameters collected by sensors, determining the correlation between structural response characteristics and personnel injury characteristics through a preset model, and then performing data processing based on these parameters. The experimental parameters are classified to determine personnel injury data and structural response data, and further subdivided according to the collection location of each type of personnel injury data and structural response data to eliminate redundant data channels that do not exist due to the removal of non-core components; The personnel injury data is denoised using a combination of wavelet threshold denoising and adaptive Kalman filtering, and the structural response data is denoised using empirical mode decomposition and intrinsic mode function filtering algorithms. Based on the stress load transfer characteristics of the target structure, feature parameters are extracted from the noise-reduced personnel injury data and structural response data, and a correlation model between personnel injury feature parameters and structural response feature parameters is established. The correlation coefficient between the two is calculated by a preset correlation analysis algorithm to evaluate and optimize the cockpit. After determining the target experimental cockpit and before conducting the explosion experiment, the key parameters of the target simulation module are iteratively adjusted by comparing the core simulation response parameters of the target experimental cockpit with the benchmark data of the real cockpit explosion test or the high-fidelity simulation verification data, until the equivalent error of the key features meets the preset requirements, so as to ensure that the target experimental cockpit can equivalently reproduce the core physical process of the real cockpit explosion.

2. The equivalent experimental test method for the safety of aircraft cockpit explosion cutting according to claim 1, characterized in that, The step of conducting single-factor simulation tests on all structural components within the cockpit based on a preset three-dimensional model to determine the characteristic indicators and influence coefficients of each structural component includes: The characteristic indicators are determined based on the explosion stress propagation influence coefficient. The characteristic indicators include at least the stress wave reflection contribution, load transfer efficiency, explosion load distribution dominance, and correlation with the target experimental parameters. The cockpit was subjected to an overall explosion simulation based on a pre-set 3D model to determine the first simulation parameters. Then, each structural component was removed and a single-factor explosion simulation was performed to determine the second simulation parameters. Feature extraction is performed on the first simulation parameters and the second simulation parameters respectively, and the results are compared to determine the feature indicators and influence coefficients of each structural component.

3. The equivalent experimental test method for the safety of aircraft cockpit explosion cutting according to claim 1, characterized in that, The steps of performing parameter mapping on the target component to determine multiple target simulation modules, and determining the target experimental cockpit based on the target simulation modules, include: Based on the aforementioned feature indicators, the first threshold, and the component type, the first core component and the second core component are divided into different categories of component datasets. Based on the preset 3D model, the corresponding first target parameters are determined according to the type of the component dataset, and the first target simulation module is determined by mapping according to the first target parameters; The second target parameters of the optional components are determined based on a preset three-dimensional model, and the second target simulation module is determined by mapping according to the second target parameters; Using the first target simulation module as the main body and the second target simulation module as an auxiliary, the second target simulation module is adjusted to perform module splicing to determine the target experimental cabin.

4. The equivalent experimental test method for the safety of aircraft cockpit explosion cutting according to claim 2, characterized in that, The steps of placing a pre-set dummy inside the target experimental cabin and determining the distribution of sensors on the dummy and the target experimental cabin based on pre-set rules include: The stress distribution state is determined based on the first simulation parameters, the distribution location of the first sensor is determined based on the stress distribution state and the sensitive parts of the human body that are prone to injury, and the corresponding sensor type is determined based on the distribution location of the first sensor. Based on the stress distribution state, key components with stress concentration coefficients greater than a preset coefficient threshold are identified, and the distribution location and corresponding sensor type of the second sensor are determined based on the key components. The first sensor is used to collect physical data of the dummy, and the second sensor is used to collect physical data of the component structure of the target experimental cabin.

5. A test system for equivalent experimental safety of explosive cutting of an aircraft cockpit, characterized in that, The system for implementing the aircraft cockpit explosion-cutting safety equivalent experimental test method according to any one of claims 1 to 4, the system comprising: The simulation testing module is used to perform single-factor simulation tests on all structural components in the cockpit based on a preset 3D model, and to determine the characteristic indicators and influence coefficients of each structural component. The target screening module is used to sequentially screen each structural component to determine the target component based on the characteristic indicators and the influence coefficient, and to perform parameter mapping on the target component to determine multiple target simulation modules, so as to determine the target experimental cabin based on the target simulation modules; The experimental testing module is used to place a preset dummy inside the target experimental cabin and determine the distribution of sensors on the dummy and the target experimental cabin based on preset rules to conduct an explosion experiment; The data analysis module is used to process experimental parameters collected by sensors, determine the correlation between structural response characteristics and personnel injury characteristics through a preset model, and evaluate and optimize the cabin.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the experimental test method for the safety equivalence of explosive cutting of the aircraft cockpit as described in any one of claims 1 to 4.

7. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the aircraft cockpit explosion cutting safety equivalent experimental test method as described in any one of claims 1-4.

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