Method and computer readable medium for analyzing damage tolerance of composite structure
By developing dent depth metamodels and machine learning-based computer simulations, the problem of difficult prediction of composite structure damage tolerance is solved, and faster and more accurate damage tolerance analysis is achieved, reducing testing costs and time.
Patent Information
- Application Number
- CN202410901036.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Priority Date
- 2023-07-07
- Filing Date
- 2024-07-05
- Publication Date
- 2025-01-07
AI Technical Summary
The prior art is difficult to accurately predict the damage tolerance of composite structures, especially under the complexity of composite materials. Digital models are difficult to effectively predict the depth of dents and potential damage, resulting in structural testing reliance on subjective visual inspections and high-cost physics. test.
By obtaining the structural configuration of the composite component and at least one attention metric, the dent depth meta model is developed, the machine learning algorithm is used to train based on historical physical test data sets, the impact energy is estimated, and the design value of the structural configuration is determined through computer-based simulations. Reduce the need for physical testing.
Reduces the development cost of the test physical survey panel, shortens the test time, from months to days or hours, expands the design space, and provides more accurate composite structural damage tolerance analysis.
Smart Images
Figure CN119272599A_ABST
Abstract
Description
Technical Field
[0001] The present disclosure relates to the field of structural analysis, and more particularly to structures made of composite materials for damage tolerance design. Background Art
[0002] Aircraft certification requires demonstration of durability and damage tolerance (DaDT). DaDT refers to the ability of the structure to resist damage growth under cyclic conditions and to maintain adequate strength throughout the life of the aircraft in the presence of damage / defects. Damage tolerance generally refers to the ability of the structure to maintain adequate strength in the presence of defects / damage states that may or may not be easily detectable.
[0003] Current methods for demonstrating damage tolerant structural designs rely on a largely subjective structural testing process that utilizes test articles that exhibit damage that must be determined from a predetermined distance by visual inspection. Due to the inherent uncertainty of visual inspection, a threshold dent depth may be employed to quantify an acceptable initial damage state for demonstrating damage tolerance. While metal-based structures can be analyzed using traditional finite element analysis, the complexity of composite structures limits the ability of numerical models to predict dent depth and potential damage extent within composite structures. Summary of the invention
[0004] A method for analyzing damage tolerance of a composite structure is disclosed herein. The method includes obtaining a structural configuration of a composite component and determining at least one metric of interest for the structural configuration. A dent depth metamodel is developed based on the structural configuration of the composite component, at least the metric of interest, and information obtained from a machine learning algorithm. The at least one metric of interest provides constraints for the dent depth metamodel, and the machine learning algorithm is trained based on a historical physical test data set for a plurality of composite components. The dent depth metamodel for the structural configuration is used to estimate an impact energy to establish a threshold dent depth in the structural configuration. A computer-based simulation is performed with the structural configuration using the impact energy from the dent depth metamodel. At least one design value for the structural configuration is determined based on output data generated from the computer-based simulation.
[0005] In one or more embodiments of the method, developing the dent depth metamodel includes providing greater weight to historical physical test data sets of a plurality of previously analyzed physical composite components based on a similarity of material or configuration between the structural configuration and a corresponding one of the previously analyzed physical composite components.
[0006] In one or more embodiments of the method, the plurality of previously analyzed physical composite components includes composite components from a plurality of different vehicle platforms.
[0007] In one or more embodiments of the method, the historical physical test data set includes a plurality of test data inputs associated with corresponding test data outputs of a plurality of previously analyzed physical composite components.
[0008] In one or more embodiments of the method, the test data input includes at least one of a force-time history, a displacement-time history, an impact energy-time history, an impactor configuration, an indentation depth, material properties, associated boundary conditions, and a composite structure configuration defining a composite layup and geometry.
[0009] In one or more embodiments of the method, the material property includes at least one of a mode 1 strain energy rate, a mode 2 strain energy rate, an interlaminar strength, a stiffness ply property, and a ply strength property.
[0010] In one or more embodiments of the method, the test data output includes at least one of a type of damage caused by the impact, energy absorbed from the impact, and a measured dent depth.
[0011] In one or more embodiments of the method, the structural configuration includes at least one of a skin joined to a reinforcement member, a skin joined to a secondary structure, and a reinforcement structure joined to the secondary structure, wherein the secondary structure includes at least one of a rib and a spar.
[0012] In one or more embodiments of the method, the at least one metric of interest includes dynamic stiffness, energy absorption rate, energy flux into the structural configuration, or initial deflection rate.
[0013] In one or more embodiments of the method, the output data generated from the computer-based simulation includes a determination of the strength of a damaged portion of the structural configuration under at least one of a compressive load, a tensile load, and a shear load.
[0014] In one or more embodiments of the method, the composite component comprises a plurality of individual composite elements that are fixed relative to each other.
[0015] In one or more embodiments of the method, the historical physical test data set includes calculations of damage tolerance criteria based on the physical test data.
[0016] In one or more embodiments of the method, the machine learning algorithm comprises a Bayesian-based algorithm or a physics-informed algorithm.
[0017] Disclosed herein is a non-transitory computer-readable medium embodying programming instructions that, when executed by a processor, are operable to perform a method. The method includes obtaining a structural configuration of a composite component and determining at least one metric of interest for the structural configuration. A dent depth metamodel is developed based on the structural configuration of the composite component, the at least one metric of interest, and information obtained from a machine learning algorithm. The at least one metric of interest provides constraints for the dent depth metamodel, and the machine learning algorithm is trained based on a historical physical test data set for a plurality of composite components. The dent depth metamodel for the structural configuration is used to estimate an impact energy to establish a threshold dent depth in the structural configuration. A computer-based simulation is performed with the structural configuration using the impact energy from the dent depth metamodel. At least one design value for the structural configuration is determined based on output data generated from the computer-based simulation.
[0018] In one or more embodiments, the historical physical test data set includes a plurality of test data inputs associated with respective test data outputs of a plurality of previously analyzed physical composite components, and the historical physical test data set includes a calculation of a damage tolerance criterion based on the physical test data.
[0019] In one or more embodiments, the test data input includes at least one of a force-time history, a displacement-time history, an impact energy-time history, an impactor configuration, an indentation depth, material properties, associated boundary conditions, and a composite structure configuration defining a composite layup and geometry.
[0020] In one or more embodiments, the material property includes at least one of a mode 1 strain energy rate, a mode 2 strain energy rate, an interlaminar strength, a stiffness ply property, and a ply strength property.
[0021] In one or more embodiments, the test data output includes at least one of a type of damage caused by the impact, energy absorbed from the impact, and a measured dent depth.
[0022] In one or more embodiments, the at least one metric of interest includes dynamic stiffness, energy absorption rate, energy flux into the structural configuration, or initial deflection rate.
[0023] Disclosed herein is a non-transitory computer-readable medium embodying programming instructions that, when executed by a processor, are operable to perform a method. The method includes obtaining a structural configuration of a composite component and determining at least one metric of interest for the structural configuration. A dent depth metamodel is developed based on the structural configuration of the composite component, the at least one metric of interest, and information obtained from a machine learning algorithm. The at least one metric of interest provides constraints for the dent depth metamodel, and the machine learning algorithm is trained based on a historical physical test data set for a plurality of composite components, and the historical physical test data set includes calculations of damage tolerance criteria based on the physical test data. The dent depth metamodel for the structural configuration is used to estimate an impact energy to establish a threshold dent depth in the structural configuration. A computer-based simulation is performed with the structural configuration using the impact energy from the dent depth metamodel. At least one design value for the structural configuration is determined based on output data generated from the computer-based simulation. BRIEF DESCRIPTION OF THE DRAWINGS
[0024] Figure 1 An exemplary computer system is shown.
[0025] Figure 2 A method for analyzing the damage tolerance of a composite structure is shown.
[0026] Some embodiments of the present disclosure will now be described by way of example only and with reference to the accompanying drawings. The same reference number represents the same element or the same type of element throughout the drawings. DETAILED DESCRIPTION
[0027] The accompanying drawings and the following description illustrate specific exemplary embodiments of the present disclosure. Those of ordinary skill in the art will be able to design various arrangements that, although not explicitly described or shown herein, embody the principles of the present disclosure and are included within the scope of the present disclosure. In addition, any examples described herein are intended to help understand the principles of the present disclosure and are interpreted as not being limited to such specifically stated examples and conditions. Therefore, the present disclosure is not limited to the specific embodiments or examples described below, but is defined by the claims and their equivalents.
[0028] To keep it simple, Figure 1An example computer system 30 is shown as a single computer module, which can be physically embodied as one or more processing nodes having a non-transitory computer-readable storage medium 34 (i.e., applying sufficient memory) and associated hardware and software (such as, but not limited to, high-speed clocks, timers, input / output circuits, buffer circuits, etc.). The non-transitory computer-readable storage medium 34 may include sufficient read-only memory, such as magnetic or optical memory. Computer-readable code or instructions embodying the methods described below may be executed during operation of the computer system 30. To this end, the computer system 30 may include one or more processors 32, such as logic circuits, application-specific integrated circuits (ASICs), central processing units, microprocessors, and / or other necessary hardware as needed to provide the programming functions described herein.
[0029] The display screen 36 can be connected to the computer readable medium 34 and the processor 32 or can communicate with the computer readable medium 34 and the processor 32 to facilitate the graphical interface of the user of the present disclosure as described below, thereby displaying the structural configuration 38 of the composite component. In one example, the structural configuration 38 for the composite component includes a three-dimensional computer-aided design (CAD) representing the composite component in the digital domain. In the example shown, the structural configuration 38 includes a first composite element 38A (such as a composite skin), a configured structure (e.g., a reinforcement, a rib (ribs), a spars (spars), a frame) or a joint (e.g., a joint, a common joint, a fastener). The first composite element 38A can also be attached to a composite element 38B such as a composite rib or a spars. The structural configuration 38 can also include areas, joints, parts, sub-components, components and assemblies.
[0030] Accurately predicting the dent depth in the structural configuration of a composite part in the digital domain is challenging due to the complexity of the composite structure. This is because the dent depth is affected by many interrelated considerations. One consideration that affects the dent depth in a composite part includes boundary conditions.
[0031] Another consideration that affects dent depth in composite parts includes configuration effects, such as energy absorption or load paths. With configuration effects, larger structures tend to have a more compliant response due to the aforementioned joints, configured attachments, etc., resulting in a greater energy required to achieve dent depth. For this reason, the threshold impact energy observed at the flat coupon level typically used for sizing is much lower than the threshold impact energy observed in configured panels at the component level. This can result in configured panels having greater dent resistance than the coupon-level components used for testing. Because the panels exhibit greater dent resistance than predicted from coupon-level component testing, the panels can be stronger than necessary, which can result in having a heavier component.
[0032] Further considerations that affect the depth of dents in composite parts include the layup or thickness of the materials in the composite part. This increases the complexity of analyzing composite parts because the dent depth is not constant across the layup or even for the thickness. In addition, some applications of composite structures include metal parts within the laminated structure. Therefore, some parts of the structural configuration may involve composite-aluminum-composite, composite-Ti-composite and other arrangements of composites with metals within the overall layup.
[0033] Yet another consideration that affects the depth of dents in composite parts includes the material properties of the composite material. Modern toughened composite materials rely on toughening agents mixed in the matrix. One common toughening agent includes thermoplastics. These materials experience both elastic nonlinearity and damage when subjected to impact, making it difficult to isolate responses that facilitate prediction of dent depth in composite parts that include them.
[0034] Yet another consideration that affects dent depth in composite structures includes the interlayer / interface between plies with composite layups. This interface can be ply to ply, composite layup to composite layup, or other composites to ribs, spars, frames, or stringers using nano reinforcements, adhesives, etc.
[0035] There has been (and still can be) considerable work across the industry to improve damage tolerant design via ply-to-ply, or composite layup-to-composite layup, or other composite-to-rib / spar / frame / stringer interface modifications using some form of nano reinforcement / adhesive / etc. This can impact the inner mold line (IML) or outer mold line (OML) simulations, and even impact the layup area.
[0036] Due to the above considerations regarding the complexity of composite structures, an exemplary testing practice is to develop physical screening specimens to determine the threshold impact energy that will produce a threshold dent depth. This method involves testing multiple different impact energies to converge to a threshold impact energy for each impact location. This method is used to determine the threshold impact energy for subsequent application to a specimen, component, or sub-component in which damage tolerance is demonstrated. Although tools exist for demonstrating damage tolerance, as changes occur, the industry is limited by the requirement to perform impact investigation testing for each configuration. This method results in significant costs to build and test composite components for damage tolerance.
[0037] One feature of the present disclosure is to provide an analytical method for determining damage tolerance of a composite structure without building and testing physical hardware. The analytical method utilizes a dent depth metamodel known by a historical physical test data set through a machine learning (ML) algorithm to estimate the impact energy for dent depth in a structural configuration 38 of a simulated composite component. The disclosed method can reduce the development costs associated with testing physical investigation panels and enable expansion of the design space by evaluating structural configurations of composite components in the digital domain on a faster time frame. For example, the method disclosed herein can reduce testing time from months to days or even hours.
[0038] Figure 2 An exemplary method 100 of analyzing damage tolerance of a structural configuration 38 of a composite component is shown. The method 100 includes receiving a structural configuration 38 of a composite component (block 102). In one example, the structural configuration 38 defines a composite component having a single stiffener level and is represented in a three-dimensional digital domain.
[0039] The structural configuration 38 may identify at least one composite material forming the structural configuration 38 and at least one composite element joined relative to another composite element. For example, the structural configuration 38 may include composite elements such as at least one of a skin joined to a reinforcement member, a skin joined to a secondary structure, and a reinforcement structure joined to a secondary structure, wherein the secondary structure includes at least one of a rib and a spar. The composite elements represented in the structural configuration 38 may be bonded, bonded, adhered, or attached relative to each other in the structural configuration 38.
[0040] The method 100 then determines at least one metric of interest for the structural configuration 38 at block 104. The at least one metric of interest may include dynamic stiffness, energy absorption rate, energy flux into the composite component, or initial deflection rate. In one example, the at least one metric of interest may be selected for evaluation by a user of the computer system 30. Alternatively, the computer system 30 may pre-select the at least one metric of interest for evaluation.
[0041] The method 100 may then develop a dent depth metamodel (block 106) based on the structural configuration (block 102), at least one metric of interest (block 104), and information obtained from a machine learning algorithm (block 110). The at least one metric of interest (block 104) provides constraints for the development of the dent depth metamodel at block 106.
[0042] The machine learning algorithm at block 110 is provided with information for development of the dent depth metamodel at block 106, and the machine learning algorithm is trained. The machine learning algorithm may be pre-trained based on the historical physical test data set from block 108 as an input training data set. In addition to the physical test data, the historical physical test data set from block 108 includes calculations of damage tolerance criteria based on the physical test data. The calculations may be performed by the computer system 30 at block 108, or the calculations may be performed before the method 100 obtains the historical physical test data set at block 108. The damage tolerance criteria may be based on at least one of calculations (theoretical), design curves (theoretical, semi-empirical, empirical), such as manual calculations, and relevant design criteria / criteria (e.g., safety margins) that drive the configuration of the structure.
[0043] Alternatively, a machine learning algorithm may be trained on a historical physical test data set from block 108 in conjunction with the development of a dent depth metamodel for a specific structural configuration from block 102. In one example, the machine learning algorithm from block 110 is a Bayesian based algorithm. However, other types of machine learning algorithms may be used, such as physics-informed machine learning algorithms.
[0044] The historical physical test data set (block 108) of the composite component includes many different configurations of the composite component with different material properties. In one example, the historical physical test data set obtained at block 108 was generated from the development of multiple product assemblies (such as aircraft platforms, land vehicle platforms, or other mostly composite platforms) and was not specifically generated for evaluation of the structural configuration 38. The historical physical test data set may also include data collected over many years, where the composite components have different complexities and material structures.
[0045] The historical physical test data set (block 108) may include a set of physical test input data associated with a corresponding set of physical test data output data for each physical test performed for different configurations of the composite part. The set of physical test input data may include at least one of force-time history, displacement-time history, impact energy-time history, impactor configuration, dent depth, material properties, relevant boundary conditions, relevant environmental conditions (e.g., heat and moisture), and composite structure configuration including layup and geometry of the composite material. Material properties from the physical test input data may include at least one of mode 1 strain energy release rate, mode 2 strain energy release rate, interlaminar strength, stiffness ply properties, and ply strength properties.
[0046] The set of physical testing output data may include at least one of a type of damage caused by impacts during the physical testing recorded as input data, energy absorbed by impacts during the physical testing, and a dent depth in the composite component caused by the physical testing.
[0047] In one example, the historical physical test data set from block 108 can be divided into three main categories of composite component components, where each category includes the physical test input data and physical test output data identified above. The first or broadest category of composite components from the historical physical test data set (block 108) includes physical test data regarding material property evaluation. This type of physical test data may include information about material screening and selection, material specification development, or allowable development. The second or intermediate type of physical test data includes physical test data regarding design value development. This type of physical test data may include information about sub-component testing and structural element testing. The third or highest type of physical test data regarding the complexity of the composite component includes physical test data with analytical verification. This type of physical test data may include information about full-scale testing and component testing.
[0048] During the development of the dent depth metamodel at block 106, a greater weight may be provided to a historical physical test data set of the plurality of composite parts that has a greater similarity in at least one of material and configuration between the structural configuration 38 of the composite part and a corresponding one of the plurality of composite parts from the historical physical testing. The machine learning algorithm may be pre-trained based on the historical physical test data set, or it may be trained in conjunction with the development of the dent depth metamodel. Furthermore, the machine learning algorithm may be trained on the computer system 30 or on a remote computer system 40 having a greater amount of computing power than the computer system 30. Furthermore, the historical physical test data set may be stored in a computer-readable medium 34 on the remote computer system 40 or on the computer system 30.
[0049] The method 100 then utilizes the dent depth metamodel developed for the structural configuration 38 from block 106 to estimate the impact energy required to establish a threshold dent depth in the structural configuration (block 112). In one example, the threshold dent depth corresponds to a dent depth of 0.01 inches in the surface of the structural configuration 38. In another example, the threshold dent depth corresponds to a dent depth of 0.02 inches in the structural configuration 38.
[0050] With the impact energy required for the threshold dent depth established in the structural configuration of the composite component, the method 100 may perform a computer-based simulation (block 114) with the structural configuration 38 using the estimated impact energy from block 112. The computer-based simulation generates output data including a determination of the strength of a damaged portion of the structural configuration under at least one of a compressive load, a tension load, and a shear load. The computer-based simulation may include a low velocity impact (LVI) simulation, a high velocity impact (HVI) simulation, an outer mold line (OML), an inner mold line (IML), or other type of impact simulation of the structural configuration.
[0051] Additionally, the computer-based simulation includes input and output data sets similar to the information obtained from the historical physical test data set at block 108. Results from block 114 may be recursively provided back to block 110 to improve the machine learning model as the accuracy of the simulation increases, thereby providing a larger training data set for the machine learning algorithm.
[0052] The method 100 may then use the information from the simulation to determine design values for the structural configuration 38. The determination of design values at block 116 allows for significant savings through digital engineering, as opposed to physical testing of component parts of varying degrees of complexity.
[0053] The following terms provide Figure 2 An exemplary configuration of a system and method of an exemplary method 100 for analyzing damage tolerance of a composite structure.
[0054] Item 1: A method for analyzing damage tolerance of a composite structure, the method comprising: obtaining a structural configuration of a composite component; determining at least one metric of interest for the structural configuration; developing a dent depth metamodel based on the structural configuration of the composite component, the at least one metric of interest, and information obtained from a machine learning algorithm, wherein the at least one metric of interest provides constraints for the dent depth metamodel, and the machine learning algorithm is trained based on a historical physical test data set of multiple composite components; estimating an impact energy using the dent depth metamodel for the structural configuration to establish a threshold dent depth in the structural configuration; performing a computer-based simulation with the structural configuration using the impact energy from the dent depth metamodel; and determining at least one design value for the structural configuration based on output data generated from the computer-based simulation.
[0055] Clause 2: The method of clause 1, wherein developing the dent depth metamodel comprises providing greater weight to historical physical test data sets of a plurality of previously analyzed physical composite components based on a similarity of material or configuration between the structural configuration and a corresponding one of the previously analyzed physical composite components.
[0056] Clause 3: The method of any of Clauses 1-2, wherein the plurality of previously analyzed physical composite components comprises composite components from a plurality of different vehicle platforms.
[0057] Clause 4: The method of any of clauses 1 to 3, wherein the historical physical test data set includes a plurality of test data inputs associated with respective test data outputs of a plurality of previously analyzed physical composite components.
[0058] Clause 5: A method according to any of clauses 1 to 4, wherein the test data input includes at least one of a force-time history, a displacement-time history, an impact energy-time history, an impactor configuration, an indentation depth, material properties, associated boundary conditions, and a composite structure configuration defining a composite layup and geometry.
[0059] Clause 6: The method of any one of clauses 4 to 5, wherein the material property comprises at least one of a mode 1 strain energy rate, a mode 2 strain energy rate, an interlaminar strength, a stiffness ply property, and a ply strength property.
[0060] Clause 7: The method of any of clauses 1 to 6, wherein the test data output includes at least one of a type of damage caused by the impact, energy absorbed from the impact, and a measured dent depth.
[0061] Clause 8: A method according to any one of clauses 1 to 7, wherein the structural configuration includes at least one of a skin joined to a reinforcement member, a skin joined to a secondary structure, and a reinforcement structure joined to the secondary structure, wherein the secondary structure includes at least one of a rib and a spar.
[0062] Clause 9: The method of any of clauses 1 to 8, wherein the at least one metric of interest comprises dynamic stiffness, energy absorption rate, energy flux into the structural configuration, or initial deflection rate.
[0063] Clause 10: The method of any of clauses 1 to 9, wherein the output data generated from the computer-based simulation includes a determination of the strength of a damaged portion of the structural configuration under at least one of a compressive load, a tensile load, and a shear load.
[0064] Clause 11: The method of any of clauses 1 to 10, wherein the composite component comprises a plurality of individual composite elements fixed relative to one another.
[0065] Clause 12: The method of any of clauses 1 to 11, wherein the historical physical test data set includes calculations of damage tolerance criteria based on the physical test data.
[0066] Clause 13: A method according to any one of clauses 1 to 12, wherein the machine learning algorithm comprises a Bayesian-based algorithm or a physically-informed algorithm.
[0067] Clause 14: A non-transitory computer-readable medium embodying programming instructions that, when executed by a processor, are operable to perform a method, the method comprising: obtaining a structural configuration of a composite component; determining at least one metric of interest for the structural configuration; developing a dent depth metamodel based on the structural configuration of the composite component, the at least one metric of interest, and information obtained from a machine learning algorithm, wherein the at least one metric of interest provides constraints for the dent depth metamodel, and the machine learning algorithm is trained based on a historical physical test data set of multiple composite components; estimating an impact energy using the dent depth metamodel for the structural configuration to establish a threshold dent depth in the structural configuration; performing a computer-based simulation with the structural configuration using the impact energy from the dent depth metamodel; and determining at least one design value for the structural configuration based on output data generated from the computer-based simulation.
[0068] Clause 15: The computer-readable medium of clause 14, wherein the historical physical test data set includes a plurality of test data inputs associated with respective test data outputs of a plurality of previously analyzed physical composite components, and wherein the historical physical test data set includes a calculation of a damage tolerance criterion based on the physical test data.
[0069] Clause 16: A computer readable medium according to any of clauses 14 to 15, wherein the test data input includes at least one of a force-time history, a displacement-time history, an impact energy-time history, an impactor configuration, an indentation depth, material properties, associated boundary conditions, and a composite structure configuration defining a composite layup and geometry.
[0070] Clause 17: The computer readable medium of any of clauses 14 to 16, wherein the material property comprises at least one of a mode 1 strain energy rate, a mode 2 strain energy rate, an interlaminar strength, a stiffness ply property, and a ply strength property.
[0071] Clause 18: The computer-readable medium of any of clauses 14 to 17, wherein the test data output includes at least one of a type of damage caused by the impact, energy absorbed from the impact, and a measured dent depth.
[0072] Clause 19: The computer readable medium of any of clauses 14 to 18, wherein the at least one metric of interest comprises dynamic stiffness, energy absorption rate, energy flux into the structural configuration, or initial deflection rate.
[0073] Item 20: A non-transitory computer-readable medium embodying programming instructions that, when executed by a processor, are operable to perform a method, the method comprising: obtaining a structural configuration of a composite component; determining at least one metric of interest for the structural configuration; developing a dent depth metamodel based on the structural configuration of the composite component, at least one metric of interest, and information obtained from a machine learning algorithm, wherein the at least one metric of interest provides constraints for the dent depth metamodel, the machine learning algorithm is trained based on a historical physical test data set for a plurality of composite components, and the historical physical test data set includes calculations of damage tolerance criteria based on the physical test data; estimating an impact energy using the dent depth metamodel for the structural configuration to establish a threshold dent depth in the structural configuration; performing a computer-based simulation with the structural configuration using the impact energy from the dent depth metamodel; and determining at least one design value for the structural configuration based on output data generated from the computer-based simulation.
[0074] Although different embodiments have been described, the description is intended to be exemplary rather than restrictive. It will be clear to those skilled in the art that more embodiments and implementations within the scope of the embodiments are possible. Unless otherwise specified, any feature of any embodiment may be used in combination with any other feature or element in any other embodiment or replace any other feature or element. Therefore, the embodiments are not limited except according to the attached claims and their equivalents. In addition, various modifications and changes may be made within the scope of the attached claims.
Claims
1. A method for analyzing damage tolerance of a composite structure, the method comprising: obtaining a structural configuration of the composite component; determining at least one metric of interest for the structural configuration; developing a dent depth metamodel based on the structural configuration of the composite component, the at least one metric of interest, and information obtained from a machine learning algorithm, wherein the at least one metric of interest provides constraints for the dent depth metamodel, and the machine learning algorithm is trained based on a plurality of historical physical test data sets of the composite components; estimating an impact energy using the dent depth metamodel for the structural configuration to establish a threshold dent depth in the structural configuration; performing a computer-based simulation with the structural configuration using the impact energy from the dent depth metamodel; and At least one design value for the structural configuration is determined based on output data generated from the computer-based simulation.
2. The method according to claim 1, wherein: Developing the dent depth metamodel includes providing greater weight to the historical physical test data sets of a plurality of previously analyzed physical composite components based on a similarity of material or configuration between the structural configuration and a corresponding one of the plurality of previously analyzed physical composite components.
3. The method according to claim 2, wherein: The plurality of previously analyzed physical composite components include composite components from a plurality of different vehicle platforms.
4. The method according to claim 1, wherein: The historical physical test data set includes a plurality of test data inputs associated with corresponding test data outputs of a plurality of previously analyzed physical composite components.
5. The method according to claim 4, wherein: The test data input includes at least one of a force-time history, a displacement-time history, an impact energy-time history, an impactor configuration, an indentation depth, material properties, associated boundary conditions, and a composite structure configuration defining a composite layup and geometry.
6. The method according to claim 5, wherein: The material property includes at least one of a mode 1 strain energy rate, a mode 2 strain energy rate, an interlaminar strength, a stiffness ply property, and a ply strength property.
7. The method according to claim 5, wherein: The test data output includes at least one of a type of damage caused by the impact, energy absorbed from the impact, and a measured dent depth.
8. The method according to claim 1, wherein: The structural arrangement includes at least one of a skin joined to a reinforcement member, a skin joined to a secondary structure, and a reinforcement structure joined to the secondary structure, wherein the secondary structure includes at least one of a rib and a spar.
9. The method according to claim 1, wherein: The at least one metric of interest includes dynamic stiffness, energy absorption rate, energy flux into the structural configuration, or initial deflection rate.
10. The method according to claim 1, wherein: The output data generated from the computer-based simulation includes a determination of a strength of a damaged portion of the structural configuration under at least one of a compressive load, a tensile load, and a shear load.
11. The method according to claim 10, wherein: The composite component comprises a plurality of individual composite elements which are fixed relative to one another.
12. The method according to claim 11, wherein: The historical physical test data set includes calculations of damage tolerance criteria based on the physical test data.
13. The method according to claim 1, wherein: The machine learning algorithms include Bayesian-based algorithms or physics-informed algorithms.
14. A non-transitory computer readable medium embodying programming instructions, the programming instructions being operable when executed by a processor to perform a method, the method comprising: obtaining a structural configuration of the composite component; determining at least one metric of interest for the structural configuration; developing a dent depth metamodel based on the structural configuration of the composite component, the at least one metric of interest, and information obtained from a machine learning algorithm, wherein the at least one metric of interest provides constraints for the dent depth metamodel, and the machine learning algorithm is trained based on a plurality of historical physical test data sets of the composite components; estimating an impact energy using the dent depth metamodel for the structural configuration to establish a threshold dent depth in the structural configuration; performing a computer-based simulation with the structural configuration using the impact energy from the dent depth metamodel; and At least one design value for the structural configuration is determined based on output data generated from the computer-based simulation.
15. The computer-readable medium of claim 14, wherein: The historical physical test data set includes a plurality of test data inputs associated with respective test data outputs of a plurality of previously analyzed physical composite components, and the historical physical test data set includes a calculation of a damage tolerance criterion based on the physical test data.
16. The computer-readable medium of claim 15, wherein: The test data input includes at least one of a force-time history, a displacement-time history, an impact energy-time history, an impactor configuration, an indentation depth, material properties, associated boundary conditions, and a composite structure configuration defining a composite layup and geometry.
17. The computer-readable medium of claim 16, wherein: The material property includes at least one of a mode 1 strain energy rate, a mode 2 strain energy rate, an interlaminar strength, a stiffness ply property, and a ply strength property.
18. The computer-readable medium of claim 16, wherein: The test data output includes at least one of a type of damage caused by the impact, energy absorbed from the impact, and a measured dent depth.
19. The computer-readable medium of claim 14, wherein: The at least one metric of interest includes dynamic stiffness, energy absorption rate, energy flux into the structural configuration, or initial deflection rate.
20. A non-transitory computer readable medium embodying programming instructions, the programming instructions being operable when executed by a processor to perform a method comprising: obtaining a structural configuration of the composite component; determining at least one metric of interest for the structural configuration; developing a dent depth metamodel based on the structural configuration of the composite component, the at least one metric of interest, and information obtained from a machine learning algorithm, wherein the at least one metric of interest provides constraints for the dent depth metamodel, the machine learning algorithm being trained based on a historical physical test dataset of a plurality of the composite components, and the historical physical test dataset including calculations of damage tolerance criteria based on physical test data; estimating an impact energy using the dent depth metamodel for the structural configuration to establish a threshold dent depth in the structural configuration; performing a computer-based simulation with the structural configuration using the impact energy from the dent depth metamodel; and At least one design value for the structural configuration is determined based on output data generated from the computer-based simulation.