Modeling of novel designs for electromagnetic effects

Abstract

To provide a method and system for identifying, with a model-based system engineering tool, an area of interest in a new design where a lightning strike may occur, and modeling a new design including structural features and electromagnetic features.SOLUTION: A method includes generating, with an electromagnetic effects solver tool and a structural solver tool, a design model for the area of interest in the new design, extracting design parameters from the design model, and generating a reduced order model by processing the design parameters, test results and simulation results with a modeling tool. The reduced order model couples the structural features with the electromagnetic features. The test results are determined by tests of known designs. The simulation results are determined by simulations of known models. The method further includes storing the reduced order model in a storage medium that is readable by a statistical modeling tool.SELECTED DRAWING: Figure 3

Description

Technical Field

[0001] The present disclosure relates generally to design modeling, and more particularly to modeling new designs for electromagnetic effects.

Background Art

[0002] Multidisciplinary analysis and optimization are used to evaluate various aircraft architectures and design decisions. Many disciplines have been incorporated into optimization, with the exception of considerations of electromagnetic effects and considerations of coupling between electromagnetic effects and structural performance. Currently, electromagnetic effect analysis is time-consuming and lags behind structural analysis. Accordingly, those skilled in the art continue research and development efforts in the field of modeling designs for electromagnetic effects.

Summary of Invention

[0003] A method of modeling a new design for electromagnetic effects is provided herein. The method includes identifying, in a computer-implemented model-based systems engineering tool, a region of interest within the new design where a lightning strike may potentially occur. The new design includes a plurality of structural features and a plurality of electromagnetic features. The method further comprises: generating, with an electromagnetic effects solver tool and a structural solver tool, a design model for the region of interest within the new design; extracting a plurality of design parameters from the design model; and processing, with a modeling tool, the plurality of design parameters, one or more of a plurality of test results, and one or more of a plurality of simulation results to generate a reduced-order model. The reduced-order model couples the plurality of structural features with the plurality of electromagnetic features. The plurality of test results are determined by testing a plurality of known designs similar to the new design. The plurality of simulation results are determined by simulating a plurality of known models similar to the new design. The method further comprises storing the reduced-order model on a storage medium readable by a statistical modeling tool.

[0004] In one or more examples of this method, the dimensionality reduction model includes one or more of the following: an algebraic model, an ordinary differential equation model, and a partial differential equation model, all of which are fitted to a statistical modeling tool.

[0005] In one or more examples of this method, the electromagnetic influence solver tool and the structural solver tool are numerical modeling tools, respectively.

[0006] In one or more examples of this method, the statistical modeling tool is a multidisciplinary analysis and optimization tool.

[0007] In one or more examples of this method, multiple design parameters include the peak power density in the area of ​​interest due to lightning strikes, the peak current in the area of ​​interest due to lightning strikes, and the peak temperature in the area of ​​interest due to lightning strikes.

[0008] In one or more examples of this method, the multiple design parameters include one or more fatigue margins in the region of interest, one or more stress margins in the region of interest, and one or more damage limits in the region of interest.

[0009] In one or more examples of this method, the generation of a design model includes predicting the power density at the edge of a new design due to a lightning strike based on several design parameters, predicting damage to the new design due to a lightning strike based on several design parameters, and predicting the risk of fire caused by a lightning strike in the new design based on several design parameters.

[0010] In one or more examples, this method involves increasing the weights of one or more simulation results when multiple known models correspond to multiple test results.

[0011] In one or more examples of this method, the novel design forms part of the apparatus.

[0012] In one or more examples of this method, the apparatus is an aircraft.

[0013] A method is provided for modeling a novel design for electromagnetic effects. This method includes storing multiple test results determined by testing multiple known designs similar to the novel design in a memory circuit. The novel design includes multiple structural features and multiple electromagnetic features. This method further includes storing multiple simulation results determined by simulating multiple known models similar to the novel design; extracting multiple design parameters from a design model of the novel design in an area of ​​interest where lightning strikes may occur; generating a reduced-dimensional model that combines multiple structural features with multiple electromagnetic features by processing the multiple design parameters, one or more of the multiple test results, and one or more of the multiple simulation results with a modeling tool; and storing the reduced-dimensional model in a storage medium suitable for use with a statistical modeling tool.

[0014] In one or more examples of this method, the dimensionality reduction model includes one or more of the following: an algebraic model, an ordinary differential equation model, and a partial differential equation model, all of which are fitted to a statistical modeling tool.

[0015] In one or more examples of this method, the statistical modeling tool is a multidisciplinary analysis and optimization tool.

[0016] In one or more examples, this method involves analyzing multiple test results and multiple simulation results among multiple training results, multiple validation results, and multiple test data.

[0017] In one or more examples, this method involves generating a map of the parameter space containing multiple test results and multiple simulation results.

[0018] In one or more examples of this method, the novel design forms part of the aircraft.

[0019] In one or more examples, this method includes using a computer-based model-based systems engineering tool to identify areas of interest in a new design where lightning strikes may occur, and using electromagnetic effects solver tools and structural solver tools to generate design models for the areas of interest in the new design.

[0020] A system is provided. The system includes a memory circuit and a processor. The memory circuit is configured to store multiple test results and multiple simulation results. The multiple test results are determined by testing multiple known designs similar to the new design. The multiple simulation results are determined by simulating multiple known models similar to the new design. The new design includes multiple structural features and multiple electromagnetic features. The processor is configured to identify regions of interest in the new design where lightning strikes may occur, generate design models for the regions of interest in the new design using electromagnetic influence solver tools and structural solver tools, extract multiple design parameters from the design models, and process the multiple design parameters, one or more of the multiple test results, and one or more of the multiple simulation results using modeling tools to generate a reduced-dimensional model. The reduced-dimensional model combines multiple structural features with multiple electromagnetic features. The processor is further configured to store the reduced-dimensional model in a storage medium readable by statistical modeling tools.

[0021] In one or more examples of the system, the reduced-dimensional model includes one or more of the following: an algebraic model, an ordinary differential equation model, and a partial differential equation model, and the statistical modeling tool is a multidisciplinary analysis and optimization tool.

[0022] In one or more examples of the system, the new design forms part of the aircraft.

[0023] The above-described features and advantages of the present disclosure, as well as other features and advantages, will be readily apparent from the following detailed description of the best mode for carrying out the present disclosure when read in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] [Figure 1] FIG. 1 is a schematic diagram illustrating the context of a system, in accordance with one or more illustrative examples. [Figure 2] FIG. 2 is a schematic diagram of a modeling architecture implemented within a system, in accordance with one or more illustrative examples. [Figure 3] FIG. 3 is a flow diagram of a method for modeling a new design for electromagnetic effects, in accordance with one or more illustrative examples. [Figure 4] FIG. 4 is a flow diagram for generating a design model, in accordance with one or more illustrative examples. [Figure 5] FIG. 5 is a flow diagram for predicting damage, in accordance with one or more illustrative examples. [Figure 6] FIG. 6 is a list of design parameters of a design model, in accordance with one or more illustrative examples. [Figure 7] FIG. 7 is a list of model types, in accordance with one or more illustrative examples. [Figure 8] FIG. 8 is a flow diagram for analyzing test results and simulation results, in accordance with one or more illustrative examples. [Figure 9] FIG. 9 is a schematic diagram of a digital framework, in accordance with one or more illustrative examples. [Figure 10] FIG. 10 is a schematic diagram of joint analysis, in accordance with one or more illustrative examples. [Figure 11] FIG. 11 is a perspective view of a region of interest struck by lightning, in accordance with one or more illustrative examples. DETAILED DESCRIPTION OF EMBODIMENTS FOR CARRYING OUT THE INVENTION

[0025] The examples in this disclosure generally provide techniques for modeling novel designs for electromagnetic effects. Finite element modeling capabilities for electromagnetic effects and structures are combined and integrated into a model-based engineering architecture. A broad database of known models and supplemental test data is accessible within the model-based engineering architecture and can be used to extract key parameters into the database. The database is then fed into modeling tools to create a reduced-dimensional model. The reduced-dimensional model is used by multi-domain analysis and optimization techniques to take into account electromagnetic effects and / or electromagnetic effects combined with structures. Integrating finite element modeling capabilities for electromagnetic effects and structures into a model-based engineering architecture can be useful in aircraft design. Model-based engineering can be used to predict locations where lightning strikes are likely to occur, identify risk areas, predict structural damage, and determine the residual strength of vulnerable components such as fasteners and structural joints.

[0026] Referring to Figure 1, a schematic diagram illustrating the context of system 100 is shown according to one or more exemplary examples. System 100 generally includes a computer 102 having one or more processors 104 (one is shown) and one or more memory circuits 106 (one is shown), and one or more storage media 108 (one is shown).

[0027] System 100 implements a design system. System 100 is capable of operating to assist in the development of novel designs for various machine components and mounting methods that are subject to electromagnetic influences. Machines include ground vehicles, aircraft, spacecraft, ships, and buildings. Electromagnetic influences include lightning strikes, electrostatic discharges, and exposure to high-energy radio waves.

[0028] Computer 102 implements one or more data processing computers. In examples with multiple computers 102, the individual computers 102 are combined to share data, memory space, and processing resources. Computer 102 is operable to store design data and run software tools used to model, simulate, and create new designs.

[0029] Processor 104 implements one or more processors within computer 102. Processor 104 communicates with memory circuit 106 and storage medium 108 to exchange commands and data. Processor 104 is operable to run software tools used to create new designs.

[0030] The memory circuit 106 implements one or more computer-readable storage devices (e.g., random access memory, read-only memory, magnetic hard drive, solid-state drive, etc.). The memory circuit 106 stores software programs (or tools) executed by the processor 104, as well as design data, simulation results, known models, test results, and related data for one or more known designs. The tools include, but are not limited to, model-based systems (MBS) engineering tools 110, multiple numerical modeling tools 112 (including electromagnetic effects (EME) solver tools 112a and structural solver tools 112b), extraction tools 114, modeling tools 116, and statistical modeling tools 118 (e.g., multi-domain design and optimization (MDAO) tools 118a). In various examples, tools 110-118a may be standard design tools. The design data includes, but is not limited to, design geometry 120, design material properties 122, structural features 124, electromagnetic features 126, design criteria 128, known designs 130, test results 132, known models 134, simulation results 136, weight sets 137, regions of interest in the new design 138, design models 140, and design parameters 142. In various examples, the memory circuit 106 implements volatile memory. In other examples, the memory circuit 106 implements non-volatile (e.g., non-temporary) memory. In yet another example, the memory circuit 106 implements a combination of volatile and non-volatile memory.

[0031] The storage medium 108 implements one or more non-temporary, non-volatile, computer-readable memory devices. The storage medium 108 is operable to store a reduced-dimensional model 144, a novel design 146, and a map of the parameter space 148. The novel design 146 may be a design for part 146a and / or a design for mounting method 146b. In various examples, the storage medium 108 may be outside (as shown in the figure) or inside the computer 102. The reduced-dimensional model 144 includes one or more of the following: an algebraic model 144a, an ordinary differential equation model 144b, and a partial differential equation model 144c, which are adapted for use by the statistical modeling tool 118. The reduced-dimensional model 144 may be suitable for use by the statistical modeling tool 118.

[0032] Referring to Figure 2, a schematic diagram of an exemplary modeling architecture 149 implemented within system 100 is shown according to one or more exemplary examples. The modeling architecture 149 generally includes model-based system engineering tools 110, numerical modeling tools 112 (including electromagnetic influence solver tools 112a and structural solver tools 112b), extraction tools 114, modeling tools 116, and statistical modeling tools 118. The modeling architecture also includes design geometry 120, design material properties 122, structural features 124, electromagnetic features 126, design criteria 128, known designs 130, test results 132, known models 134, simulation results 136, weights 137, regions of interest 138, design models 140, design parameters 142, reduced-dimensionality models 144, new designs 146, and a parameter space map 148.

[0033] Data related to the new design 146, test results of previously tested designs, simulation results of previously modeled designs, and related data are stored in a database and can then be provided to the model-based system engineering tool 110. The model-based system engineering tool 110 uses the received data to determine areas of interest 138 where electromagnetic interference is likely to occur. The electromagnetic influence solver tool 112a and the structural solver tool 112b analyze the data within the areas of interest 138 to create a design model 140. The design model 140 includes a combination of structural and electromagnetic properties. The extraction tool 114 extracts design parameters 142, which are then provided to the modeling tool 116.

[0034] The modeling tool 116 also receives test results 132 and simulation results 136. Based on the design parameters 142, test results 132, and simulation results 136, the modeling tool 116 generates a reduced-dimensional model 144. The reduced-dimensional model 144 is processed by the statistical modeling tool 118. The statistical modeling tool 118 generates a new design 146 and a parameter space map 148. The parameter space map 148 is useful for determining where future models, future simulation data, and / or future test results will be beneficial in supporting other new designs.

[0035] Referring to Figure 3, a flowchart of an embodiment of Method 150 for modeling a novel design for electromagnetic effects is shown according to one or more exemplary examples. Method (or process) 150 is carried out by System 100. Method 150 includes steps 152 to 181, as shown. The order of the steps is shown as a typical example. Other order of steps may be carried out to meet the criteria of a particular application.

[0036] In step 152, the design data can be stored in the memory circuit 106. Additional data can be generated in step 154 ​​by testing a known design 130 and generating test results 132, which are then stored in step 155. Even more data can be generated in step 156 by simulating a known model 134 and generating simulation results 136, which are also stored in the memory circuit 106 in step 157. Data suitable for developing a new design 146 can be transferred to the model-based system engineering tool 110 in step 158.

[0037] In step 160, the model-based systems engineering tool 110 identifies areas of interest 138 within a new design 146. Areas of interest 138 are typically locations where electromagnetic interference (e.g., lightning strikes) is likely to occur. The numerical modeling tool 112 then generates a design model 140 for the areas of interest 138 within the new design 146 in step 162. In step 164, one or more design parameters 142 are extracted from the design model 140 by the extraction tool 114.

[0038] In step 166, the test results 132 and a known model 134 similar in properties to the new design 146 can be transferred to the modeling tool 116. In step 168, one or more weights of the simulation results 136 can be increased before, during, or after the transfer, so that the simulation results 136 are related to the known model 134 similar to the new design 146. In step 170, the test results 132 and the simulation results 136 can be analyzed among one or more training results, one or more validation results, and one or more test datasets.

[0039] In step 172, a modeling tool 116 (e.g., a machine learning tool, a neural network tool, an artificial intelligence tool, etc.) generates a reduced-dimensional model 144. The modeling tool 116 is trained by simulation results 136 and test results 132 from existing models and designs that are similar to the new design under consideration. The modeling tool 116 can extend the design parameters 142 based on the training. The reduced-dimensional model 144 is then stored in a storage medium 108 in step 174. The reduced-dimensional model 144 may be an ordered model. The format of the reduced-dimensional model 144 may be configured to be supplied to a statistical modeling tool 118 used in method 150. The statistical modeling tool 118 generates a new design 146 in step 176. The new design 146 is stored in a storage medium 108 in step 178. The parameter space map 148 can also be generated by the statistical modeling tool 118 in step 180 and stored in the storage medium 108 in step 181.

[0040] In various cases, Method 150 may be performed as a single cycle. The resulting reduced-dimensional model 144 and / or novel design 146 may then be evaluated. Where appropriate, Method 150 may be repeated one or more times until the intended result is obtained.

[0041] Referring to Figure 4, a detailed flowchart of an embodiment of step 162 for generating a design model is shown according to one or more exemplary examples. Step 162 generally includes steps 182, 184, 186, and 188, as shown in the figure. The order of the steps is shown as a representative example. Other order of steps may be implemented to meet the criteria of a particular application.

[0042] In step 182, the numerical modeling tool 112 predicts the power density 183 at the edge of the new design 146 due to electromagnetic interference (e.g., lightning strike) based on the design parameter 142. In step 184, the numerical modeling tool 112 predicts the fire risk 185 caused by electromagnetic interference in the new design 146, based on the design parameter 142. In step 186, the numerical modeling tool 112 similarly predicts the damage 187 to the new design 146 due to electromagnetic interference, based on the design parameter 142. The power density 183, fire risk 185, and damage 187 are stored as part of the design model 140 in step 188.

[0043] Referring to Figure 5, a detailed flowchart of an embodiment of step 186 for predicting damage is shown according to one or more exemplary examples. Step 186 generally includes steps 190, 192, 194, 196, and 198, as shown in the figure. The order of the steps is shown as a representative example. Other order of steps may be implemented to meet the criteria of a particular application.

[0044] In step 190, ablation 191 due to electromagnetic interference (e.g., lightning strike) can be predicted by the numerical modeling tool 112. Interply delamination 193 due to electromagnetic interference can be predicted by the numerical modeling tool 112 in step 192. In step 194, intraply delamination 195 due to electromagnetic interference can be predicted. Punch-through 197 caused by electromagnetic interference can be predicted in step 196. In step 198, ablation 191, interply delamination 193, intraply delamination 195, and punch-through 197 can be stored as part of the design model 140.

[0045] Referring to Figure 6, a list of exemplary design parameters extracted from design model 140 is shown according to one or more exemplary examples. Design parameters 142 include peak power density 210, peak current 220, peak current density 224, peak temperature 226, peak energy 228, peak energy density 230, peak pressure 232, peak voltage 234, peak electric field 236, and / or peak magnetic field 238. Furthermore, design parameters 142 include material phase 240, linear conductivity 242, nonlinear conductivity 244, relative permittivity 246, and / or relative permeability 248. Additional design parameters 142 include specific heat 250, thermal conductivity 252, coefficient of thermal expansion 254, fatigue margin 256, stress margin 258, damage limit 260, static margin 262, and / or stability limit 264.

[0046] Referring to Figure 7, a list of known model types is shown according to one or more exemplary examples. Known models 134 include one or more of the anisotropic material model 270, the phase change material model 272, the decomposition model 274, the electrothermal model 276, and the electrochemical model 278.

[0047] Referring to Figure 8, a flowchart of an embodiment of step 170 for analyzing test results 132 and simulation results 136 is shown according to one or more exemplary examples. Step 170 generally includes steps 280 and 288. The order of the steps is shown as a typical example. Other order of steps may be implemented to meet the criteria of a particular application.

[0048] In step 280, the test results 132 and simulation results 136 may be split among the training results 282, the validation results 284, and the test data 286. Each of the training results 282, the validation results 284, and the test data 286 may then be provided to the modeling tool 116 in step 288.

[0049] Referring to Figure 9, schematic diagrams of exemplary digital frameworks 290 for predicting performance are shown according to one or more exemplary examples. The digital frameworks 290 can use model-based system engineering tools 110 to predict areas of interest 138 where lightning strikes 90 are likely to occur, identify risk areas, predict structural damage, and determine residual strength. As shown in the figure, lightning 90 may strike equipment 304 which forms part of an aircraft 300. Equipment 304 has several parts 304a to 304b, and the lightning 90 strikes along the edge 308 of equipment 304.

[0050] The area of ​​interest 138 is further subjected to a “hotspot” analysis 320 using the electromagnetic influence solver tool 112a and the structural solver tool 112b. For example, the hotspot analysis 320 may relate to fasteners 310a to 310b that connect parts 304a to 304b of the apparatus 304 at the joint. The hotspot analysis 320 determines tolerances, fatigue margins 256, stress margins 258, power density 183, peak currents 220, peak temperatures 226, damage 187, as well as other parameters of the apparatus 304, the mounting method of the apparatus 304, and / or the aircraft 300.

[0051] Based on the results of the hotspot analysis 320, a structural performance analysis 322 can be performed. The structural performance analysis 322 generally determines the prediction of ignition sources and containment failures from mechanical forces.

[0052] Referring to Figure 10, schematic diagrams of embodiments of joint analysis 340 are shown according to one or more exemplary examples. Joint analysis 340 can be used to evaluate joints in a new design 146. The analysis involves gathering a list of known joint types 342 from known designs 130 and / or known models 134. Parameters 344 related to the known joint types 342 can be gathered from known designs 130 and / or known models 134. Parameters 344 may include, but are not limited to, fastener diameter, ply thickness, number of fasteners, secondary containment criteria, connectivity enhancement, final lightning load, and economic lightning damage load. Other parameters 344 may be used to meet design criteria for a particular application.

[0053] Known joint types 342 and parameters 344 can be processed by the numerical modeling tool 112 to generate a design model 140. The design model 140 may include electromagnetic joint tolerances 346 and structural joint tolerances 348. A model interface / connection 350 between the electromagnetic joint tolerances 346 and the structural joint tolerances 348 may be included in the design model 140. The model interface / connection 350 may include, but is not limited to, pressure, temperature, thermally affected zones, and containment breaches.

[0054] For electromagnetic effects, numerical modeling tool 112 is used to predict the power density at the edges of the composite material panel, and the results are compared to values ​​set to cause edge glow. Furthermore, an electromagnetic effects / structure coupling model can be used to predict when the fastener / structure interface will dissipate a sufficient amount of energy due to contact resistance, resulting in a breach of containment and the release of a potential fire hazard. This design can then be compared to known designs with secondary containment features such as cap seals.

[0055] Referring to Figure 11, a perspective view of the area of ​​interest 138 struck by lightning 90 is shown according to one or more exemplary examples. The device 304 has visible surface damage 360 ​​where the device 304 was struck by lightning 90. The lightning 90 may create subsurface damage 362 around the visible surface damage 360. Heat from the lightning 90 may spread beyond the subsurface damage 362 to a wider area 364 of the device 304.

[0056] An example of this disclosure generates a database of executed models, including test results from existing designs and simulation results from existing models. For a new design, the database can be searched for similar existing designs and / or similar existing models so that background information is provided. The new design and background information are processed in parallel by both the electromagnetic influence solver tool 112a and the structural solver tool 112b to create a design model 140. Design parameters 142 are extracted from the design model 140 and subsequently fed into a modeling tool 116. The modeling tool 116 may be a machine learning tool, a neural network tool, an artificial intelligence tool, etc.

[0057] The extraction tool 114 and the modeling tool 116 can convert the design model 140 into a reduced-dimensional model 144. In various examples, the reduced-dimensional model 144 can define algebraic equations for a new design 146. The coefficients of the algebraic equations can be optimized by the modeling tool 116. Additional weights can be given to the known model 134 corresponding to experimentally known test results 132.

[0058] The reduced-dimensional model 144 is transferred to the statistical modeling tool 118, which can optimize the layout of the apparatus 304 and / or aircraft 300. The statistical modeling tool 118 generates a parameter space map 148 to indicate where the database is sparse, suggest building a new model, and further test known designs.

[0059] The disclosed techniques help evaluate electromagnetic influences and structural tolerances. These techniques are integrated into the model-based engineering digital thread, making it easier to capture simultaneous and cross-disciplinary influences. The model-based engineering environment can also generate a comprehensive database that stores simulation results, as well as existing and future test results, to support, validate, and supplement finite element modeling. The database of test data and models is then used to extract details of new designs. Extraction is performed manually or by model query techniques and can be applied to other models of finite element models, computer-aided designs, and experimental tests. The collection of parameters generally increases and evolves over time.

[0060] The parameter database is periodically fed into modeling tools 116 (e.g., machine learning tools, neural network tools, artificial intelligence tools, etc.) to create and optimize reduced-dimensional models (e.g., algebraic equations, ordinary differential equations, and / or partial differential equations). The parameter database can be divided into training, validation, and test data, which may be 50%, 40%, and 10%, respectively. Models validated by testing are given additional weights. A parameter space map 148 also indicates requirements for future models to be built. Finally, the reduced-dimensional model 144 can be used in the statistical modeling tool 118 to weight the overall architecture of the new design 146, taking into account electromagnetic influences and electromagnetic influence / structural coupling performance.

[0061] This disclosure can take many different forms. Representative examples of this disclosure are shown in the drawings, and these examples are provided as illustrations of the disclosed principles and are described in detail herein with the understanding that they are not limitations on the broader aspects of this disclosure. To that extent, elements and limitations described, for example, in the sections of the abstract, background, overview, and detailed description but not expressly stated in the claims should not be incorporated into the claims, either individually or collectively, by implication, inference or otherwise.

[0062] For the purposes of the detailed description of this invention, unless otherwise specified, the singular form includes the plural form and vice versa. The words “and” and “or” shall be both conjunctive and disjunctive. The words “any” and “all” shall both mean “any and all,” and the words “including,” “containing,” “equipped,” and “possess” shall each mean “including without limitation.” Furthermore, in this specification, approximate words such as “about,” “almost,” “substantially,” “approximately,” and “generally” may be used to mean “at,” “near,” “approximately,” “within 0–5%,” “within tolerance,” or other logical combinations thereof. Referring to the drawings, similar reference numbers refer to similar components.

[0063] Furthermore, this disclosure includes examples provided in the following clauses.

[0064] Clause 1. A method (150) for modeling a novel design (146) for electromagnetic effects, Identifying (160) areas of interest (138) within a novel design (146) where lightning (90) may occur, using a model-based system engineering tool (110) run on a computer (102), wherein the novel design (146) includes a number of structural features (124) and a number of electromagnetic features (126), Using the electromagnetic influence solver tool (112a) and the structural solver tool (112b), generate a design model (140) for a region of interest (138) within a new design (146) (162), Extracting multiple design parameters (142) from the design model (140) (164), The process involves generating a reduced-dimensional model (144) (172) by processing one or more of several design parameters (142), several test results (132), and one or more of several simulation results (136) using a modeling tool (116), The dimensionality reduction model (144) combines multiple structural features (124) with multiple electromagnetic features (126), Multiple test results (132) were determined by testing (154) of multiple known designs (130) similar to the new design (146). Multiple simulation results (136) are determined by simulations (156) of multiple known models (134) similar to the new design (146), generating (172), and A method (150) including storing a reduced-dimensional model (144) in a storage medium (108) readable by a statistical modeling tool (118) (174).

[0065] Clause 2. The method (150) according to Clause 1, wherein the reduced-dimensional model (144) includes one or more of the following: an algebraic model (144a), an ordinary differential equation model (144b), and a partial differential equation model (144c), which are fitted to a statistical modeling tool (118).

[0066] Clause 3. The method according to Clause 1 or 2 (150), wherein the electromagnetic influence solver tool (112a) and the structural solver tool (112b) are numerical modeling tools (112), respectively.

[0067] Clause 4. A method (150) according to any of Clauses 1 to 3, wherein the statistical modeling tool (118) is a multidisciplinary analysis and optimization tool (118a).

[0068] Clause 5. A method (150) according to any of Clauses 1 to 4, wherein multiple design parameters (142) include the peak power density (210) in the area of ​​interest (138) due to a lightning strike (90), the peak current (220) in the area of ​​interest (138) due to a lightning strike (90), and the peak temperature (226) in the area of ​​interest (138) due to a lightning strike (90).

[0069] Clause 6. A method (150) according to any of Clauses 1 to 5, wherein multiple design parameters (142) include one or more fatigue margins (256) within the region of interest (138), one or more stress margins (258) within the region of interest (138), and one or more damage limits (260) within the region of interest (138).

[0070] Clause 7. The generation (162) of the design model (140) Based on multiple design parameters (142), predict the power density (183) at the edge (308) of a new design (146) due to a lightning strike (90) (182). Based on multiple design parameters (142), predict (187) damage (146) to a new design (146) due to a lightning strike (90), and A method (150) according to any of clauses 1 to 6, including predicting (185) the risk of fire (90) caused by a new design (146) due to a lightning strike (90) based on several design parameters (142).

[0071] Clause 8. A method (150) according to any of Clauses 1 to 7, further comprising increasing (168) one or more weights (137) of multiple simulation results (136) when multiple known models (134) correspond to multiple test results (132).

[0072] Clause 9. A new design (146) forms part of an apparatus (304) by any method (150) according to any of Clauses 1 to 8.

[0073] Clause 10. The method (150) according to Clause 9, wherein the device (304) is an aircraft (300).

[0074] Article 11. A method (150) for modeling a novel design (146) for electromagnetic effects, The memory circuit (106) stores (155) multiple test results (132) determined by testing (154) multiple known designs (130) similar to a new design (146), wherein the new design (146) includes multiple structural features (124) and multiple electromagnetic features (126). Storing multiple simulation results (136) determined by simulations (156) of multiple known models (134) similar to the new design (146), (157) Extracting multiple design parameters (142) (164) from a design model (140) of a new design (146) in an area of ​​interest (138) where lightning strikes (90) may occur, By processing one or more of multiple design parameters (142), multiple test results (132), and one or more of multiple simulation results (136) with a modeling tool (116), a reduced-dimensional model (144) is generated, which combines multiple structural features (124) with multiple electromagnetic features (126) (172), and A method (150) comprising storing a reduced-dimensional model (144) in a storage medium (108) suitable for use by a statistical modeling tool (118) (174).

[0075] Clause 12. The method (150) according to Clause 11, wherein the reduced-dimensional model (144) includes one or more of the following: an algebraic model (144a), an ordinary differential equation model (144b), and a partial differential equation model (144c), which are fitted to a statistical modeling tool (118).

[0076] Clause 13. A method (150) according to Clause 11 or 12, wherein the statistical modeling tool (118) is a multidisciplinary analysis and optimization tool (118a).

[0077] Clause 14. A method according to any of Clauses 11 to 13 (150), further comprising analyzing multiple test results (132) and multiple simulation results (136) among multiple training results (282), multiple validation results (284), and multiple test data (286) (170).

[0078] Clause 15. A method (150) according to any of Clauses 11 to 14, further comprising (180) generating a map (148) of the parameter space in which multiple test results (132) and multiple simulation results (136) exist.

[0079] Clause 16. A new design (146) forms part of an aircraft (300) by any means (150) in accordance with any of Clauses 11 to 15.

[0080] Clause 17. Using a model-based systems engineering tool (110) run on a computer (102), identify (138) areas of interest (146) in a new design (160) where lightning (90) may occur, and A method (150) according to any of clauses 11 to 16, further comprising generating a design model (140) for a region of interest (138) within a new design (146) using an electromagnetic influence solver tool (112a) and a structural solver tool (112b) (162).

[0081] Clause 18. A memory circuit (106) configured to store multiple test results (132) and multiple simulation results (136), Multiple test results (132) are determined by testing (154) multiple known designs (130) that are similar to the new design (146). Multiple simulation results (136) are determined by simulating (156) multiple known models (134) that are similar to the new design (146). The new design (146) includes a memory circuit (106) comprising multiple structural features (124) and multiple electromagnetic features (126), and A processor (104), Identify areas of interest (138) within a new design (146) where lightning strikes (90) may occur (160), The electromagnetic influence solver tool (112a) and the structural solver tool (112b) generate a design model (140) for the region of interest (138) within the new design (146) (162), Multiple design parameters (142) are extracted from the design model (140) (164), By processing one or more of the multiple design parameters (142), multiple test results (132), and one or more of the multiple simulation results (136) with a modeling tool (116), a reduced-dimensional model (144) is generated (172) in which multiple structural features (124) are combined with multiple electromagnetic features (126), A system (100) comprising a processor (104) configured to store (174) a reduced-dimensional model (144) in a storage medium (108) readable by a statistical modeling tool (118).

[0082] Clause 19. The reduced-dimensional model (144) includes one or more of the following: an algebraic model (144a), an ordinary differential equation model (144b), and a partial differential equation model (144c) that are fitted to a statistical modeling tool (118). Statistical modeling tools are multidisciplinary analysis and optimization tools, as per the System (100) of Clause 18.

[0083] Clause 20. A new design (146) forming part of an aircraft (300) system (100) under Clause 18 or 19.

[0084] While detailed descriptions and drawings or figures support and illustrate this disclosure, the scope of this disclosure is defined solely by the claims. Although several best forms and other examples for carrying out the claimed disclosure have been described in detail, various alternative designs and alternative examples exist for carrying out the disclosure set forth in the attached claims. Furthermore, the features of the examples shown in the drawings or the various examples referred to herein should not necessarily be understood as independent examples of each other. Rather, each feature described in one example can be combined with one or more other desired features from other examples, resulting in other examples not described in words or by reference to the drawings. Such other examples therefore fall within the scope of the attached claims.

Claims

1. A method (150) for modeling a novel design (146) for electromagnetic effects, Identifying (138) an area of ​​interest (138) within the novel design (146) where lightning (90) may occur, wherein the novel design (146) includes a plurality of structural features (124) and a plurality of electromagnetic features (126), using a model-based systems engineering tool (110) run on a computer (102) (160), The electromagnetic influence solver tool (112a) and the structural solver tool (112b) generate a design model (140) for the region of interest (138) within the new design (146) (162), Extracting multiple design parameters (142) from the aforementioned design model (140) (164), The process involves using a modeling tool (116) to process the aforementioned plurality of design parameters (142), one or more of the plurality of test results (132) determined by testing (154) of a plurality of known designs (130) similar to the new design (146), and one or more of the plurality of simulation results (136) determined by simulation (156) of a plurality of known models (134) similar to the new design (146), thereby generating a reduced-dimensional model (144) in which the plurality of structural features (124) are combined with the plurality of electromagnetic features (126) (172), and The reduced-dimensional model (144) is stored (174) in a storage medium (108) readable by a statistical modeling tool (118). A method including (150).

2. The method according to claim 1 (150), wherein the reduced-dimensionality model (144) includes one or more of the following: an algebraic model (144a), an ordinary differential equation model (144b), and a partial differential equation model (144c), which are fitted to the statistical modeling tool (118).

3. The method according to claim 1 or 2 (150), wherein the electromagnetic influence solver tool (112a) and the structural solver tool (112b) are each numerical modeling tools (112).

4. The method according to claim 1 or 2 (150), wherein the statistical modeling tool (118) is a multi-field analysis and optimization tool (118a).

5. The method according to claim 1 or 2 (150), wherein the plurality of design parameters (142) include the peak power density (210) in the area of ​​interest (138) due to the lightning strike (90), the peak current (220) in the area of ​​interest (138) due to the lightning strike (90), and the peak temperature (226) in the area of ​​interest (138) due to the lightning strike (90).

6. The method (150) according to claim 1 or 2, wherein the plurality of design parameters (142) include one or more fatigue margins (256) within the region of interest (138), one or more stress margins (258) within the region of interest (138), and one or more damage limits (260) within the region of interest (138).

7. Generating the aforementioned design model (140) (162) Based on the aforementioned multiple design parameters (142), predict (182) the power density (183) at the edge (308) of the new design (146) due to the lightning strike (90), Based on the plurality of design parameters (142), predict (186) the damage (187) to the new design (146) caused by the lightning strike (90), and Based on the aforementioned multiple design parameters (142), predict (184) the risk of fire (185) caused by the lightning strike (90) resulting in the new design (146), The method according to claim 1 or 2 (150), including the method according to claim 1 or 2.

8. The method according to claim 1 or 2 (150), further comprising increasing the weight (137) of one or more of the simulation results (136) when the plurality of known models (134) correspond to the plurality of test results (132) (168).

9. The method according to claim 1 or 2 (150), wherein the novel design (146) forms part of the apparatus (304).

10. The method according to claim 9 (150), wherein the device (304) is an aircraft (300).

11. A method (150) for modeling a novel design (146) for electromagnetic effects, Multiple test results (132) determined by testing (154) of multiple known designs (130) similar to the new design (146), wherein the new design (146) includes multiple structural features (124) and multiple electromagnetic features (126), and the multiple test results (132) are stored in a memory circuit (106) (155). Storing (157) multiple simulation results (136) determined by simulations (156) of multiple known models (134) similar to the aforementioned new design (146), Extracting multiple design parameters (142) (164) from the design model (140) of the new design (146) in the area of ​​interest (138) where lightning strikes (90) may occur, By processing one or more of the aforementioned multiple design parameters (142), the aforementioned multiple test results (132), and one or more of the aforementioned multiple simulation results (136) with a modeling tool (116), a reduced-dimensional model (144) is generated, wherein the reduced-dimensional model (144) is formed by combining a plurality of structural features (124) with a plurality of electromagnetic features (126) (172), and The reduced-dimensional model (144) is stored (174) in a storage medium (108) suitable for use with a statistical modeling tool (118). A method including (150).

12. The method according to claim 11 (150), wherein the reduced-dimensionality model (144) includes one or more of the following: an algebraic model (144a), an ordinary differential equation model (144b), and a partial differential equation model (144c), which are fitted to the statistical modeling tool (118).

13. The method according to claim 11 or 12 (150), wherein the statistical modeling tool (118) is a multi-field analysis and optimization tool (118a).

14. The method according to claim 11 or 12 (150), further comprising analyzing the plurality of test results (132) and the plurality of simulation results (136) among a plurality of training results (282), a plurality of verification results (284), and a plurality of test data (286) (170).

15. The method according to claim 11 or 12 (150), further comprising generating a map (148) of the parameter space in which the plurality of test results (132) and the plurality of simulation results (136) reside (180).

16. The method according to claim 11 or 12 (150), wherein the novel design (146) forms part of an aircraft (300).

17. Identifying the area of ​​interest (138) within the new design (146) where the lightning strike (90) may occur using a model-based system engineering tool (110) run on a computer (102) (160), and The electromagnetic influence solver tool (112a) and the structural solver tool (112b) generate the design model (140) for the region of interest (138) within the new design (146) (162), The method according to claim 11 or 12, further comprising (150).

18. A memory circuit (106) configured to store multiple test results (132) and multiple simulation results (136), The aforementioned multiple test results (132) are determined by testing (154) multiple known designs (130) that are similar to the new design (146). The aforementioned multiple simulation results (136) are determined by simulating (156) multiple known models (134) similar to the novel design (146), The aforementioned new design (146) includes a plurality of structural features (124) and a plurality of electromagnetic features (126), Memory circuit (106), and A processor (104), Identifying (160) areas of interest (138) within the new design (146) where lightning strikes (90) are likely to occur, The electromagnetic influence solver tool (112a) and the structural solver tool (112b) generate a design model (140) for the region of interest (138) within the new design (146) (162), Extracting multiple design parameters (142) from the aforementioned design model (140) (164), By processing one or more of the aforementioned multiple design parameters (142), the aforementioned multiple test results (132), and one or more of the aforementioned multiple simulation results (136) with a modeling tool (116), a reduced-dimensional model (144) is generated, wherein the aforementioned multiple structural features (124) are combined with the aforementioned multiple electromagnetic features (126) (172), The reduced-dimensional model (144) is stored (174) in a storage medium (108) readable by a statistical modeling tool (118), A processor (104) configured to perform the following: A system (100) equipped with [this feature].

19. The reduced-dimensional model (144) includes one or more of the following: an algebraic model (144a), an ordinary differential equation model (144b), and a partial differential equation model (144c), which are compatible with the statistical modeling tool (118). The system (100) according to claim 18, wherein the statistical modeling tool is a multi-field analysis and optimization tool.

20. The system (100) according to claim 18 or 19, wherein the novel design (146) forms part of an aircraft (300).

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