Soft tissue constitutive parameter calibration method in soft tissue-skeleton bird body model for bird strike simulation

Abstract

The invention discloses a soft tissue constitutive parameter calibration method in a soft tissue-skeleton bird body model for bird strike simulation, and the method comprises the construction of a soft tissue-skeleton bird body finite element model and finite element reverse analysis based on flat bird strike. Dividing wing bones, leg bones and head and neck parts in the bones of the bird body into hexahedral unit grids, dividing wishbones, pecking bones, fossil protrusions and ribs into shell units, and dividing soft tissues into SPH particles; further obtaining bone elastic-plastic constitutive parameters through a static compression test, and endowing the bone units with the bone elastic-plastic constitutive parameters; an objective function is set as a mean square error between a flat plate center point pressure-time curve obtained by a real bird body impact flat plate test and corresponding curve data calculated by soft tissue-skeleton bird body impact flat plate finite element simulation, and soft tissue constitutive model parameters are inversely calibrated by adopting a genetic algorithm; and model and material parameter support is provided for bird strike simulation of a high-fidelity aircraft structure.

Description

Technical Field

[0001] This invention relates to the field of bird strike simulation technology for aircraft structures, and in particular to a method for calibrating soft tissue constitutive parameters in a soft tissue-skeleton bird model used for bird strike simulation. Background Technology

[0002] When an aircraft collides with a bird during high-speed flight, it can cause significant structural deformation, potentially leading to catastrophic consequences. Therefore, bird strikes have always been a key consideration in aircraft certification standards within the aviation industry. Accurately assessing bird strike risk is crucial for ensuring aviation safety. With the rapid development of computing and simulation technologies, finite element method (FEM) simulation, by establishing digital models of aircraft structures for bird strikes and simulating the process using computers, has become a primary method for bird strike research, significantly improving research efficiency, reducing research time and costs compared to bird strike testing. However, most current bird strike simulations of aircraft structures treat the bird's body as a continuous, homogeneous fluid or soft matter, neglecting the differences in internal structures such as bones, muscles, and organs. In actual bird strike events, the skeletal structures of large and medium-sized birds exhibit significant mechanical differences compared to soft tissues, which have a substantial impact on the damage to critical components such as engine blades.

[0003] To accurately simulate the mechanical response of a bird's skeleton during impact, the industry has begun to explore the development of soft tissue-skeleton bird models that allow for the separate isolation of the skeleton for simulation studies. However, existing soft tissue-skeleton bird models contain multiple interconnected components such as muscles, organs, blood, and skin, making it impossible to directly separate and measure their constitutive mechanical parameters like skeletal tissue. Since soft tissue mass accounts for a large portion of the bird model's structural mass, the accuracy of its constitutive mechanical parameters directly impacts the computational accuracy of the soft tissue-skeleton bird model during impacts with aircraft structures. Currently, the calibration of bird soft tissue material parameters primarily focuses on parameter inversion from homogeneous hydrodynamic bird models composed entirely of soft tissue. How to calibrate and obtain the soft tissue parameters in a soft tissue-skeleton bird model has become a technical bottleneck affecting the application of soft tissue-skeleton bird models in aerial bird strike research. This patent constructs a finite element model of a bird's skeletal-soft tissue structure using three-dimensional tomography, computer three-dimensional modeling, and mesh generation techniques. Based on this model, the parameters of the soft tissue constitutive model are calibrated by coupling the results of a plate bird strike test with a plate bird strike finite element inverse analysis. This establishes a method for calibrating the constitutive parameters of soft tissue in a soft tissue-skeletal bird model for bird strike simulation.

[0004] In summary, this patent, based on the aforementioned background technology, proposes a novel method for accurately calibrating the soft tissue material parameters of birds. This method combines reverse modeling technology, real bird impact tests on flat plates, and finite element reverse analysis of bird impacts on flat plates. It is expected to bring significant breakthroughs to the study of aviation bird strike problems, providing a more solid theoretical foundation and technical support for bird strike design of aircraft structures, and has important scientific significance and broad application prospects. Summary of the Invention

[0005] This invention addresses the issue that existing bird strike simulation models use homogeneous hydrodynamic bird model material parameters that consider the effects of bone structure. It proposes a novel method for accurately calibrating bird soft tissue material parameters. The core steps of this method are: conducting inverse finite element analysis of a plate bird strike based on a soft tissue-skeleton bird model, and inverting and calibrating the constitutive parameters of the bird soft tissue by fitting the experimental results of the bird strike plate strike with self-consistent fit.

[0006] Specifically, the technical solution provided by this invention is as follows:

[0007] A method for calibrating soft tissue constitutive parameters in a soft tissue-skeleton bird model for bird strike simulation includes the following steps:

[0008] Step 1: Construction of the finite element model of the bird's soft tissue-skeleton body

[0009] (1.1) Select a real bird body as the object, and reconstruct and generate a bird body soft tissue-skeleton geometric model through three-dimensional tomographic scanning technology and computer three-dimensional modeling technology;

[0010] (1.2) Using the finite element mesh generation technology, the geometric model of bird soft tissue-skeleton in step (1.1) is geometrically cleaned and simplified, and the bird soft tissue is divided into SPH particles. The wing bones, leg bones and head and neck of the bird are divided into hexahedral element meshes, and the furcula, scapula, keel and ribs of the bird are divided into shell elements.

[0011] (1.3) Set the soft tissues of the bird to the bones and the bones to the bones to be in bonded contact;

[0012] (1.4) Select the elastoplastic mechanical constitutive model of the bone material in step (1.2) and obtain the elastic modulus, yield stress and yield strain, tangent modulus, failure stress and failure strain parameters of different parts of the bone through quasi-static compression test.

[0013] (1.5) Select the constitutive model of the soft tissue material in step (1.2) as the hydrodynamic constitutive model;

[0014] Step 2: Finite element reverse analysis based on flat plate bird strike

[0015] (2.1) Using finite element software, import the soft tissue-skeleton bird body finite element model obtained in step one into the finite element software, and construct a flat plate bird impact finite element model based on the soft tissue-skeleton bird body model with reference to the real bird impact test, so as to obtain the simulation result data of flat plate bird impact.

[0016] (2.2) Determine the parameters in the state equation of the bird's soft tissue hydrodynamic model, use them as variables for inverse inversion analysis, and set the initial value range of the variables;

[0017] (2.3) The mean square error between the actual bird impact test data and the simulated bird impact data obtained in step (2.1) is used as the objective function of the inverse analysis;

[0018] (2.4) The genetic method is used to couple the finite element simulation of the flat plate bird collision in step (2.1) to optimize the key parameter variables of the state equation of the bird's soft tissue in step (2.2). When the objective function value is within the allowable range, the current key parameter value of the state equation is output as the calibration result. If the objective function value under the current material parameters exceeds the maximum allowable value, the parameter value is readjusted within the parameter value range and the simulation is repeated. Until the objective function value is within the allowable range, the current key parameter value of the state equation is output as the calibration result.

[0019] Preferably, step (1.2) specifically includes the following:

[0020] Based on the geometric structure of the bird's body, the internal skeletal structure is divided into three parts: spine, wing bones, and leg bones. Small features are removed and the structure is divided into hexahedral element meshes. The furcula is simplified into a slat model with a large spatial curvature and moderate width, the keel bone is simplified into a flat plate model with a small spatial curvature and large width, the ribs are simplified into a slat model with a small width and large spatial curvature, and the beak bone is simplified into a flat plate model with a moderate spatial curvature and moderate width. The mid-surfaces of each of these models are extracted and divided into shell elements. The constructed bird skeletons are then bound to contact to ensure the realism and accuracy of the simulation results.

[0021] Preferably, the objective function in step (2.3) is defined as:

[0022]

[0023] In the above formula, num represents the number of experimental curves involved in the calibration; n is the number of data points for each experimental curve, that is, the number of data pairs on each curve. This represents the predicted pressure value calculated for the i-th data point; Let be the experimental measurement value corresponding to the i-th data point; when fitting the state equation parameters, the objective function is used as the fitness function. The smaller the fitness function, the higher the similarity between the experimental curve and the calculated curve, and the more accurate the corresponding model parameter set.

[0024] Preferably, the simulation results of bird strike on a flat plate in step (2.3) are stress-time curve data in the normal direction of the center point region of the flat plate, which can be divided into four stages: (a) initial impact stage, (b) attenuation stage, (c) steady state stage, and (d) termination stage; the experimental and simulation data are mainly compared and analyzed to determine the average relative error of the first three stages of bird impact.

[0025] Preferably, the method further includes step (2.5) setting the population size and maximum number of generations using the genetic algorithm, which specifically includes the following:

[0026] Selection operator: A tournament selection strategy is adopted, randomly selecting a certain number of individuals from the current population for comparison. Individuals with higher fitness are more likely to be selected to participate in subsequent operations; Crossover operator: A mixed crossover strategy is adopted, exchanging gene fragments between parent gene carriers, so that offspring inherit some genes from both parents; Mutation operator: A polynomial mutation strategy is adopted.

[0027] The formula is:

[0028]

[0029] P i P represents the original or current value of the parameter of the i-th Grüneisen state equation before mutation; i,max With P i,min These represent the maximum and minimum values ​​allowed for the i-th parameter in physics or the model, respectively; β represents the distribution exponent in the polynomial variation; u represents a random number uniformly sampled from the interval [0,1]; P i′ This represents the new value of the i-th parameter after polynomial mutation; it sets the crossover probability parameter and the mutation probability parameter.

[0030] The advantages of this invention compared to the prior art are:

[0031] This invention provides a method for calibrating the structural parameters of a bird's body using a "soft tissue-skeleton" model for bird strike simulations. By constructing a finite element model of a bird's body with soft tissue-skeleton features, and utilizing real bird impact test results on a flat plate and inverse analysis of bird impacts on a flat plate, the method achieves accurate calibration of the constitutive parameters of the bird's soft tissue. This method offers the following significant advantages: Improved simulation accuracy. By introducing a skeletal-soft tissue structure bird model, it overcomes the limitations of traditional homogeneous hydrodynamic models, more realistically reflecting the mechanical action of the skeleton on the impacting structure during bird impact, significantly improving the accuracy of bird strike simulations; Precise calibration method for constitutive parameters of soft tissue materials. Based on a global optimization framework using a genetic algorithm, it can efficiently search for optimal solutions within a large parameter space. By quantifying the deviation between simulation and experiment through an objective function, it achieves accurate calibration of the state equation parameters, providing reliable parameter support for the constitutive model of bird's soft tissue. Attached Figure Description

[0032] Figure 1 This is a schematic diagram of the bird soft tissue material parameter calibration process in this invention;

[0033] Figure 2 This is a schematic diagram of the soft tissue-skeleton bird body structure model in this invention;

[0034] Figure 3 This is a mesh diagram of the finite element model of a bird impacting a flat plate in this invention;

[0035] Figure 4 This is a detailed flowchart of the parameter calibration based on the combination of numerical simulation and genetic algorithm in this invention;

[0036] Figure 5 This is a comparison chart of pressure-time curves before and after calibration in this invention. Detailed Implementation

[0037] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0038] This embodiment is based on a method for calibrating the constitutive parameters of soft tissue in a soft tissue-skeleton bird model used for bird strike simulation, which includes the following steps:

[0039] Step 1: Construction of the finite element model of the bird's soft tissue-skeleton body

[0040] (1.1) Select a real bird body as the object, and reconstruct and generate a bird body soft tissue-skeleton geometric model through three-dimensional tomographic scanning technology and computer three-dimensional modeling technology;

[0041] (1.2) Using the finite element mesh generation technology, the geometric model of bird soft tissue-skeleton in step (1.1) is geometrically cleaned and simplified, and the bird soft tissue is divided into SPH particles. The wing bones, leg bones and head and neck of the bird are divided into hexahedral solid element meshes, and the furcula, scapula, keel and ribs of the bird are divided into shell elements.

[0042] Specifically:

[0043] This step divides the internal skeletal structure of the bird into three parts: spine, wing bones, and leg bones, based on the geometric structure of the bird's external shape. Small features are removed and the structure is divided into hexahedral solid element meshes. The furcula is simplified into a slat model with a large spatial curvature and moderate width, the keel bone is simplified into a flat plate model with a small spatial curvature and large width, the ribs are simplified into a slat model with a small width and large spatial curvature, and the beak bone is simplified into a flat plate model with a moderate spatial curvature and moderate width. The mid-surfaces of each of these models are extracted and divided into SHELL elements. Binding contacts are set between the various bird skeletons to ensure the realism and accuracy of the simulation results.

[0044] (1.3) Set the soft tissues of the bird to the bones and the bones to the bones to be in bonded contact;

[0045] (1.4) Select the elastoplastic mechanical constitutive model of the bone material in step (1.2) and obtain the elastic modulus, yield stress and yield strain, tangent modulus, failure stress and failure strain parameters of different parts of the bone through quasi-static compression test.

[0046] (1.5) Select the constitutive model of the soft tissue material in step (1.2) as the hydrodynamic constitutive model;

[0047] Step 2: Finite element reverse analysis based on flat plate bird strike

[0048] (2.1) Using finite element software, import the soft tissue-skeleton bird body finite element model obtained in step one into the finite element software, and construct a flat plate bird impact finite element model based on the soft tissue-skeleton bird body model with reference to the real bird impact test, so as to obtain the simulation result data of flat plate bird impact.

[0049] (2.2) Determine the parameters in the state equation of the bird's soft tissue hydrodynamic model, use them as variables for inverse inversion analysis, and set the initial value range of the variables;

[0050] (2.3) The mean square error between the actual bird impact test data and the simulated bird impact data obtained in step (2.1) is used as the objective function of the inverse analysis;

[0051] Specifically, the objective function is defined as:

[0052]

[0053] In the above formula, num represents the number of experimental curves involved in the calibration; n is the number of data points for each experimental curve, that is, the number of data pairs on each curve. This represents the predicted pressure value calculated for the i-th data point; Let be the experimental measurement value corresponding to the i-th data point; when fitting the parameters of the state equation, the objective function is used as the fitness function. The smaller the fitness function, the higher the similarity between the experimental curve and the calculated curve, and the more accurate the corresponding model parameter set.

[0054] Specifically, in this step, the simulation results of bird strike on a flat plate are the stress-time curve data in the normal direction of the center point region of the flat plate, which can be divided into four stages: (a) initial impact stage, (b) attenuation stage, (c) steady state stage, and (d) termination stage; the experimental and simulation data are mainly compared and analyzed to determine the average relative error of the first three stages of bird impact.

[0055] (2.4) The genetic method is used to couple the finite element simulation of the flat plate bird collision in step (2.1) to optimize the key parameter variables of the state equation of the bird's soft tissue in step (2.2). When the objective function value is within the allowable range, the current key parameter value of the state equation is output as the calibration result. If the objective function value under the current material parameters exceeds the maximum allowable value, the parameter value is readjusted within the parameter value range and the simulation is repeated. Until the objective function value is within the allowable range, the current key parameter value of the state equation is output as the calibration result.

[0056] (2.5) The genetic algorithm sets the population size and maximum number of generations, which specifically includes the following:

[0057] Selection operator: A tournament selection strategy is adopted, randomly selecting a certain number of individuals from the current population for comparison. Individuals with higher fitness are more likely to be selected to participate in subsequent operations; Crossover operator: A mixed crossover strategy is adopted, exchanging gene fragments between parent gene carriers, so that offspring inherit some genes from both parents; Mutation operator: A polynomial mutation strategy is adopted.

[0058] The formula is:

[0059]

[0060] P i P represents the original or current value of the parameter of the i-th Grüneisen state equation before mutation; i,max With P i,min These represent the maximum and minimum values ​​allowed for the i-th parameter in physics or the model, respectively; β represents the distribution exponent in the polynomial variation; u represents a random number uniformly sampled from the interval [0,1]; Pi′ This represents the new value of the i-th parameter after polynomial mutation; it sets the crossover probability parameter and the mutation probability parameter.

[0061] Example 1:

[0062] As a specific example, this example selects the CT scan data of mallards collected by the Hedayati team as the basic data source. This dataset, through high-resolution tomography technology, fully preserves the detailed information of the biological tissue structure of birds.

[0063] In this embodiment, a bird simulation model of a mallard with a total mass of 820g was constructed using CATIA 3D modeling software. The model strictly follows anatomical features and distinguishes between soft tissue and skeletal structures through differentiated material property settings: the legs, torso, and wings adopt the MAT-NULL(009) material constitutive model in LSDYNA, and the Grüneisen equation of state is used as the flexible property of real biological tissues; while the skeletal parts such as wing bones, ribs, and spine are constructed with a 3D model of bird structure based on the physiological characteristics of bird skeletons.

[0064] Morphological comparison verified that the model's geometric dimensions are highly consistent with those of a real mallard, with a wingspan accurate to 40cm and a body length of 34cm, as shown in the attached image. Figure 2 As shown;

[0065] Meanwhile, in order to obtain reliable biomechanical parameters, the standardized quasi-static compression test scheme designed by Luo et al. and the mechanical parameters of each segment of the chicken leg bone were finally measured to provide input parameters for subsequent bird strike dynamics simulation.

[0066] The specific parameters are shown in the table below;

[0067] Table 1 Bird skeletal parameters

[0068]

[0069] This embodiment refers to the classic Wilbeck experimental setup and constructs a bird strike simulation model of a rigid circular plate. The model is designed to strictly match the dimensions of common structural components in aerospace engineering. A rigid circular plate with a diameter of 0.6m and a thickness of 0.06m is constructed to simulate the mechanical response of key parts such as aircraft skin under bird strike scenarios.

[0070] In this embodiment, a Lagrangian hexahedral mesh is used to discretize the plate during the model meshing stage. This mesh form can effectively capture the stress and strain distribution during the structural deformation process. By repeatedly adjusting the mesh density, a high-quality mesh model containing 17,798 elements is finally generated, ensuring a balance between computational accuracy and efficiency.

[0071] Regarding the definition of material properties, based on the characteristics of commonly used metallic materials in aerospace, the flat plate is defined as a linear elastic material with a density of 7800 kg / m³. 3 Elastic modulus 2.07×10 11 The parameters Pa and Poisson's ratio of 0.3 are used to accurately reproduce the mechanical properties of the metal structure. Figure 3 This is a finite element model of a bird hitting a flat plate.

[0072] In the bird strike dynamics simulation, the mallard bird model constructed above was given an impact velocity of 116 m / s, causing it to strike the center of the plate perpendicularly. This velocity parameter references the typical bird strike velocity conditions during the cruise phase of a civil airliner. At the same time, fixed constraints were applied around the plate to simulate the mechanical conditions of fixed boundaries in an actual structure. In terms of the contact algorithm, a contact algorithm was used between the bird and the plate to realistically reflect the frictional behavior during the impact process.

[0073] The bird's internal skeleton and soft tissues are bound together by node-to-surface (CONTACT-TIED-NODES-TO-SURFACE) constraints, ensuring that different materials move in tandem during collision deformation, thus fully demonstrating the mechanical response characteristics of biological tissues under impact loads.

[0074] Genetic algorithms can efficiently solve unconstrained and constrained nonlinear optimization problems by simulating the natural selection mechanism in biological evolution. Figure 4 A flowchart of the parameter optimization process based on the combination of numerical simulation and genetic algorithm in this study is presented.

[0075] In this embodiment, the five parameters C, S1, S2, S3, and GAMMA in the Grüneise equation of state for bird soft tissue materials are set as calibration variables. The root mean square error (RMSE) is used as the objective function to quantify the deviation between the simulation curve and the experimental measurement data. The genetic algorithm continuously evolves and optimizes the population during the iteration process, gradually approaching the optimal solution, thereby achieving precise calibration of the material parameters.

[0076] The objective function is defined as shown in the formula:

[0077]

[0078] In the formula, num represents the number of experimental curves involved in the calibration; n is the number of data points for each experimental curve, i.e., the number of pressure-time data pairs on each curve. This represents the predicted pressure value calculated for the i-th data point; Let be the experimentally measured pressure value corresponding to the i-th data point. When fitting the parameters of the state equation, the objective function is used as the fitness function. The smaller the fitness function, the higher the similarity between the experimental curve and the calculated curve, and correspondingly, the more accurate the model parameter set.

[0079] In the selection phase, this embodiment adopts a tournament selection strategy, which randomly selects a certain number of individuals from the current population for comparison; individuals with higher fitness are more likely to be selected to participate in subsequent crossover and mutation operations, thereby accelerating the convergence of the algorithm by prioritizing the retention of excellent individuals; in the Grünesen state equation, a mixed crossover strategy is adopted, as shown in the following formula.

[0080]

[0081] P i P represents the original or current value of the parameter of the i-th Grüneisen state equation before mutation; i,max With P i,min These represent the maximum and minimum values ​​allowed for the i-th parameter in physics or the model, respectively; β represents the distribution exponent in the polynomial variation; u represents a random number uniformly sampled from the interval [0,1]; P i′ This represents the new value of the i-th parameter after polynomial mutation.

[0082] This embodiment's strategy involves exchanging gene fragments between parental gene vectors, allowing offspring to inherit parts of both parents' genes simultaneously, thus preserving paternal characteristics. This method helps to efficiently transfer information between different parents, avoiding bias towards one parent; it also allows for more precise control of the offspring generation process, ensuring the rationality and stability of parameter values.

[0083] The domain of the design variables (Gruneisen equation of state parameters) and the optimization variables are specified at the beginning: the algorithm population size is set to 20, the maximum number of generations is set to 100, the crossover probability is set to 0.9, and the mutation probability is set to 0.7; the optimization range of the Gruneisen equation of state is shown in the table.

[0084] Table 2. Calibration range of Grüneisen's equations of state

[0085]

[0086] The table below shows the Grünesen equation of state parameters obtained through genetic algorithm calibration, which closely match the experimental data of Wilbeck's real bird impact plate.

[0087] Table 3. Parameters of Grüneisen's state equations after calibration.

[0088]

[0089] Appendix Figure 5 The pressure-time curves (divided into four stages) before and after calibration were compared with Wilbeck's experimental data; this allows for a direct assessment of the degree of agreement between the calibration results and the experimental data. In the initial impact stage, the calibrated pressure-time curves showed excellent agreement with the experimental data; furthermore, the magnitude and timing of the pressure peaks were also very similar. However, before calibration, the calculated pressure peak trend did not completely match the experimental data. In the pressure decay and steady-state stages, the calibrated numerical simulation curves and the experimental curves maintained similar fluctuation trends. In the pressure termination stage, both curves exhibited similar downward trends.

[0090] As the formula shows, the average percentage error between the numerical results and the first three peak points of the experimental data after initial calibration is 3.3%, while the average percentage error between the initial stage and the experimental data before inversion is 19.03%.

[0091]

[0092] The parameter inversion calibration based on the genetic algorithm makes the numerical simulation results highly consistent with the Wilbeck bird strike plate experiment in terms of both overall trend and local peak characteristics; thus, the calibrated model has higher accuracy and reliability in bird strike simulation analysis.

[0093] In summary, this invention, through its scientifically and rationally designed steps, achieves accurate identification of bird soft tissue parameters, providing advanced technical means for aerial bird strike research and possessing significant scientific and engineering application value.

[0094] The present invention and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present invention; the actual structure is not limited thereto. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the invention, such designs should fall within the protection scope of the present invention.

Claims

1. A method for calibrating soft tissue constitutive parameters in a soft tissue-skeleton bird model for bird strike simulation, characterized in that, This includes conducting inverse finite element analysis of bird impact on a flat plate based on a soft tissue-skeleton bird model, and inverting and calibrating the constitutive parameters of the bird's soft tissue by fitting the experimental test results of bird impact on a flat plate in a self-consistent manner. The specific steps include the following: Step 1: Construction of the finite element model of the bird's soft tissue-skeleton body (1.1) Select a real bird body as the object, and reconstruct and generate a bird body soft tissue-skeleton geometric model through three-dimensional tomographic scanning technology and computer three-dimensional modeling technology; (1.2) Using the finite element mesh generation technology, the geometric model of bird soft tissue-skeleton in step (1.1) is geometrically cleaned and simplified, and the bird soft tissue is divided into SPH particles. The wing bones, leg bones and head and neck of the bird are divided into hexahedral element meshes, and the furcula, scapula, keel and ribs of the bird are divided into shell elements. (1.3) Set the soft tissues of the bird to the bones and the bones to the bones to be in bonded contact; (1.4) Select the elastoplastic mechanical constitutive model of the bone material in step (1.2) and obtain the elastic modulus, yield stress and yield strain, tangent modulus, failure stress and failure strain parameters of different parts of the bone through quasi-static compression test. (1.5) Select the constitutive model of the soft tissue material in step (1.2) as the hydrodynamic constitutive model; Step 2: Finite element reverse analysis based on flat plate bird strike (2.1) Using finite element software, import the soft tissue-skeleton bird body finite element model obtained in step one into the finite element software, and construct a flat plate bird impact finite element model based on the soft tissue-skeleton bird body model with reference to the real bird impact test, so as to obtain the simulation result data of flat plate bird impact. (2.2) Determine the parameters in the state equation of the bird's soft tissue hydrodynamic model, use them as variables for inverse inversion analysis, and set the initial value range of the variables; (2.3) The mean square error between the actual bird impact test data and the simulated bird impact data obtained in step (2.1) is used as the objective function of the inverse analysis; (2.4) The genetic method is used to couple the finite element simulation of the flat plate bird collision in step (2.1) to optimize the key parameter variables of the state equation of the bird's soft tissue in step (2.2). When the objective function value is within the allowable range, the current key parameter value of the state equation is output as the calibration result. If the objective function value under the current material parameters exceeds the maximum allowable value, the parameter values ​​should be adjusted within the parameter range and the simulation should be repeated. Until the objective function value is within the allowable range, output the current key parameter values ​​of the state equation as the calibration result.

2. The method for calibrating soft tissue constitutive parameters in a soft tissue-skeleton bird model for bird strike simulation according to claim 1, characterized in that, Step (1.2) specifically includes the following: Based on the geometric structure of the bird's body, the internal skeletal structure is divided into three parts: spine, wing bones, and leg bones. Small features are removed and the structure is divided into hexahedral element meshes. The furcula is simplified into a slat model with a large spatial curvature and moderate width, the keel bone is simplified into a flat plate model with a small spatial curvature and large width, the ribs are simplified into a slat model with a small width and large spatial curvature, and the beak bone is simplified into a flat plate model with a moderate spatial curvature and moderate width. The mid-surfaces of each of these models are extracted and divided into shell elements. Binding contacts are set between the various bird skeletons to ensure the realism and accuracy of the simulation results.

3. The method for calibrating soft tissue constitutive parameters in a soft tissue-skeleton bird model for bird strike simulation according to claim 1, characterized in that, The objective function in step (2.3) is defined as follows: In the above formula, num represents the number of experimental curves involved in the calibration; n is the number of data points for each experimental curve, that is, the number of data pairs on each curve. This represents the predicted pressure value calculated for the i-th data point; Let be the experimental measurement value corresponding to the i-th data point; when fitting the state equation parameters, the objective function is used as the fitness function. The smaller the fitness function, the higher the similarity between the experimental curve and the calculated curve, and the more accurate the corresponding model parameter set.

4. The method for calibrating soft tissue constitutive parameters in a soft tissue-skeleton bird model for bird strike simulation according to claim 1, characterized in that, The simulation results of bird strike on a flat plate in step (2.3) are the stress-time curve data in the normal direction of the center point region of the flat plate, which can be divided into four stages: (a) initial impact stage, (b) attenuation stage, (c) steady state stage, and (d) termination stage; the experimental and simulation data are mainly compared and analyzed to determine the average relative error of the first three stages of bird impact.

5. The method for calibrating soft tissue constitutive parameters in a soft tissue-skeleton bird model for bird strike simulation according to claim 1, characterized in that, It also includes step (2.5) setting the population size and maximum number of generations in the genetic algorithm, which specifically includes the following: Selection operator: A tournament selection strategy is adopted, randomly selecting a certain number of individuals from the current population for comparison. Individuals with higher fitness are more likely to be selected to participate in subsequent operations; Crossover operator: A mixed crossover strategy is adopted, exchanging gene fragments between parent gene carriers, so that offspring inherit some genes from both parents; Mutation operator: A polynomial mutation strategy is adopted. The formula is: P i P represents the original or current value of the parameter of the i-th Grüneisen state equation before mutation; i,max With P i,min These represent the maximum and minimum values ​​allowed for the i-th parameter in physics or the model, respectively. β represents the distribution exponent in the polynomial variation; u represents a random number obtained by uniform sampling from the interval [0,1]; P i′ This represents the new value of the i-th parameter after polynomial mutation; it sets the crossover probability parameter and the mutation probability parameter.

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