Multi-physical system collaborative design method and device, storage medium and electronic equipment
By using a unified analytical mechanics model and multi-objective optimization algorithm, the problem of low efficiency in multi-physics system design is solved, and system-level automated collaborative optimization and global optimal design are achieved, thereby improving design efficiency and reliability.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-16
- Publication Date
- 2026-04-03
AI Technical Summary
Existing technologies suffer from low design efficiency in multiphysics system design, difficulty in early accurate modeling and quantification of interdomain coupling effects, leading to potential performance conflicts and failure risks. Furthermore, cross-domain performance trade-offs that rely on human experience cannot guarantee a system-level global optimal solution.
A unified analytical mechanics model is adopted, and the overall dynamic behavior of the multi-physics system is described by Lagrangian, Hamiltonian or action functional. The interaction between various physical fields is integrated, and a multi-objective optimization function is constructed. The solution and optimization are carried out in combination with physical and engineering constraints to obtain the target collaborative design parameters.
It achieves automated collaborative optimization of multi-physics system design, eliminates redundancy in cross-domain model transformation and data docking, prevents coupling conflicts, improves design efficiency and reliability, and ensures globally optimal design.
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Figure CN121787083A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of physical system design technology, specifically to a multi-physical system collaborative design method, apparatus, storage medium, and electronic device. Background Technology
[0002] In modern engineering technology, the design of complex systems (such as microelectromechanical systems, aerospace propulsion systems, and integrated optoelectronic chips) increasingly relies on accurate modeling and collaborative optimization of the interactions between multiple physical domains. The design of such multiphysics systems is essentially a global optimization problem that requires complex trade-offs between performance indicators and constraints in different physical domains.
[0003] Currently, the industry generally adopts a serial or iterative design approach based on domain-specific tools. This means that engineers from different domains first use independent simulation tools (such as structural finite element method, circuit simulation, and fluid dynamics calculation software) to design and locally optimize the subsystems, and then perform integrated verification through data exchange or coupled simulation.
[0004] However, the current design methodology involves a large amount of model conversion and data integration work, resulting in low design efficiency. Summary of the Invention
[0005] This application provides a method, apparatus, storage medium, and electronic device for collaborative design of multi-physics systems, which can improve the design efficiency of multi-physics systems.
[0006] In a first aspect, embodiments of this application provide a multi-physics system collaborative design method, including: Obtain the design parameters and performance metrics of at least two physical domains in the target system; Based on the design parameters, a unified analytical mechanics model is constructed to describe the overall dynamic behavior of the target system. The unified analytical mechanics model is expressed in Lagrangian, Hamiltonian or action functional and integrates the interactions of at least two of the physical domains. Based on the performance indicators, a multi-objective optimization function is constructed, and the design parameters are used as optimization variables. Combined with preset physical and engineering constraints, the unified analytical mechanics model is solved and optimized to obtain the target collaborative design parameters. The target co-design parameters are output to guide the physical implementation of the target system.
[0007] In the multiphysics system co-design method provided in this application embodiment, the step of constructing a unified analytical mechanics model to describe the overall dynamic behavior of the target system based on the design parameters includes: Define a common set of generalized coordinates for at least two of the physical domains to uniformly describe the state of the target system; Based on the design parameters, determine the energy contribution and / or work contribution of each physical domain under the generalized coordinates; Based on the principles of analytical mechanics, the energy contribution terms and / or work contribution terms are integrated into the total Lagrangian, total Hamiltonian or action functional of the target system. Based on the total Lagrange quantity or the total Hamiltonian, derive the governing equations of the unified analytical mechanics model; or, based on the variational principle, derive the trajectory or governing equations of the unified analytical mechanics model from the action functional.
[0008] In the multi-physics system co-design method provided in this application embodiment, the step of constructing a multi-objective optimization function based on the performance index includes: The performance index is mapped to an objective function that is related to the state of the unified analytical mechanics model; Multiple objective functions are processed using weighted summation, Pareto optimization, or multi-objective decision-making methods to form a multi-objective optimization function.
[0009] In the multi-physics system collaborative design method provided in this application embodiment, the step of solving and optimizing the unified analytical mechanics model by using the design parameters as optimization variables and combining preset physical constraints and engineering constraints to obtain target collaborative design parameters includes: The unified analytical mechanics model under the current design parameters is solved using numerical methods to obtain the response of the target system; Calculate the value of the multi-objective optimization function based on the response, and determine whether the value of the multi-objective optimization function satisfies the preset convergence condition; If satisfied, the current design parameters are determined as the target collaborative design parameters; If not satisfied, the design parameters are iteratively updated based on preset physical constraints, engineering constraints, and optimization algorithms until the convergence condition is met, and the current design parameters are used as the target collaborative design parameters.
[0010] In the multi-physics system collaborative design method provided in this application embodiment, the iterative update of the design parameters based on preset physical constraints, engineering constraints, and optimization algorithms includes: Based on the physical and engineering constraints, construct the constraint conditions for the design parameters; In each iteration of the optimization algorithm, candidate design parameters are generated based on the value and changing trend of the multi-objective optimization function; The candidate design parameters are substituted into the constraints for verification and processing to generate design parameters for the next iteration.
[0011] In the multi-physics system collaborative design method provided in this application embodiment, the step of substituting the candidate design parameters into the constraint conditions for verification and processing to generate design parameters for the next iteration includes: The candidate design parameters are substituted into the constraints for verification. If satisfied, the candidate design parameters will be used as the design parameters for the next iteration. If the conditions are not met, the candidate design parameters are adjusted through a constraint handling mechanism, or the degree of constraint violation is converted into a penalty term for the multi-objective optimization function, generating adjusted design parameters for the next iteration.
[0012] In the multi-physics system collaborative design method provided in the embodiments of this application, at least two physical fields include any two or more combinations of mechanical, electrical, electromagnetic, thermal, fluid, optical, and acoustic fields.
[0013] Secondly, embodiments of this application provide a multi-physics system collaborative design apparatus, comprising: The acquisition unit is used to acquire design parameters and performance indicators of at least two physical domains in the target system. The building unit is used to build a unified analytical mechanics model to describe the overall dynamic behavior of the target system based on the design parameters. The unified analytical mechanics model is expressed in Lagrangian, Hamiltonian or action functional and integrates the interactions of at least two physical domains. An iterative unit is used to construct a multi-objective optimization function based on the performance index, and to solve and optimize the unified analytical mechanics model by using the design parameters as optimization variables and combining preset physical and engineering constraints to obtain the target collaborative design parameters. The output unit is used to output the target co-design parameters to guide the physical implementation of the target system.
[0014] Thirdly, this application provides a storage medium storing a plurality of instructions adapted for loading by a processor to execute the multi-physics system co-design method described in any of the preceding claims.
[0015] Fourthly, this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the multi-physics system co-design method described in any of the preceding claims.
[0016] In summary, the multiphysics system collaborative design method provided in this application includes: obtaining design parameters and performance indicators of at least two physical domains in a target system; constructing a unified analytical mechanics model to describe the overall dynamic behavior of the target system based on the design parameters, wherein the unified analytical mechanics model is expressed using Lagrangian, Hamiltonian, or action functionals, and integrates the interactions of at least two physical domains; constructing a multi-objective optimization function based on the performance indicators, and solving and optimizing the unified analytical mechanics model using the design parameters as optimization variables, combined with preset physical and engineering constraints, to obtain target collaborative design parameters; and outputting the target collaborative design parameters to guide the physical implementation of the target system. This application embodiment can improve the design efficiency of multiphysics systems. Attached Figure Description
[0017] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0018] Figure 1 This is a schematic diagram illustrating an application scenario of the multi-physics system collaborative design method provided in the embodiments of this application.
[0019] Figure 2 This is a flowchart illustrating the multi-physics system collaborative design method provided in the embodiments of this application.
[0020] Figure 3 This is a schematic diagram of the multiphysics system collaborative design device provided in the embodiments of this application.
[0021] Figure 4 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation
[0022] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.
[0023] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, components, features, and elements with the same names in different embodiments of this application may have the same meaning or different meanings, the specific meaning of which must be determined by its interpretation in that specific embodiment or further in conjunction with the context of that specific embodiment.
[0024] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.
[0025] In the following description, the use of suffixes such as "module," "part," or "unit" to denote elements is solely for the purpose of illustrative purposes and has no specific meaning in itself. Therefore, "module," "part," or "unit" may be used interchangeably.
[0026] In the description of this application, it should be noted that the terms "upper," "lower," "left," "right," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application. In addition, terms such as "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.
[0027] Currently, the industry generally adopts a serial or iterative design approach based on domain-specific tools. This means that engineers from different domains first use independent simulation tools (such as structural finite element method, circuit simulation, and fluid dynamics calculation software) to design and locally optimize the subsystems, and then perform integrated verification through data exchange or coupled simulation.
[0028] However, this domain-isolation design paradigm leads to several prominent technical problems: First, it is difficult to accurately model and quantify the coupling effects between domains in the early design stage, resulting in potential performance conflicts and failure risks being exposed only in the later stages; Second, the trade-offs between cross-domain performance indicators (such as mechanical sensitivity and power consumption, heat dissipation efficiency and structural strength) rely on human experience and time-consuming trial and error, which cannot guarantee obtaining a system-level global optimal solution; Third, the entire process involves a large amount of model conversion and data integration work, resulting in low design efficiency and a long innovation cycle.
[0029] Based on this, embodiments of this application provide a multi-physical system collaborative design method, apparatus, storage medium, and electronic device. Specifically, the multi-physical system collaborative design apparatus can be integrated into an electronic device, which can be a server or a terminal, etc. The terminal can include mobile phones, wearable smart devices, tablets, laptops, and personal computers (PCs), etc., as well as other computers and auxiliary devices. The server can be a single server or a server cluster composed of multiple servers, and can be a physical server or a virtual server.
[0030] For example, such as Figure 1 As shown, the electronic device can acquire design parameters and performance indicators of at least two physical domains in the target system; based on the design parameters, a unified analytical mechanics model is constructed to describe the overall dynamic behavior of the target system. The unified analytical mechanics model is expressed in Lagrangian, Hamiltonian, or action functional and integrates the interactions of at least two physical domains; based on the performance indicators, a multi-objective optimization function is constructed, and the unified analytical mechanics model is solved and optimized using the design parameters as optimization variables, combined with preset physical and engineering constraints, to obtain the target co-design parameters; the target co-design parameters are output to guide the physical realization of the target system.
[0031] The technical solutions shown in this application will be described in detail below through specific embodiments. It should be noted that the order of description of the following embodiments is not intended to limit the priority of the embodiments.
[0032] Please see Figure 2 , Figure 2 This is a flowchart illustrating the multi-physics system collaborative design method provided in this application embodiment. The specific flow of the multi-physics system collaborative design method can be as follows: 101. Obtain the design parameters and performance indicators of at least two physical domains in the target system.
[0033] Here, the design parameter refers to the system variable to be optimized, and its value will be determined during the optimization process. This design parameter typically includes the geometric dimensions of each area (such as length, thickness, and gaps), material properties (such as density, elastic modulus, dielectric constant, and thermal conductivity), and excitation conditions (such as voltage, current, and force). In practice, the initial values and possible ranges of these design parameters can be obtained from user input, databases, or prior designs.
[0034] Performance metrics are quantitative targets used to measure and optimize the overall performance of a system. They directly correspond to the key functional requirements of the target system. For example, for an inertial sensor, performance metrics might include bandwidth, sensitivity, resolution, nonlinearity, and noise equivalent power. For an RF filter, performance metrics might include center frequency, insertion loss, bandwidth, out-of-band rejection, and quality factor (Q). For a thermal management system, performance metrics might include maximum temperature rise, thermal resistance, heat dissipation power density, and pumping power consumption.
[0035] It is understood that the multi-physics system co-design method provided in this application is domain-independent and applicable to any combination of physical fields, including but not limited to any two or more of the mechanical, electrical, electromagnetic, thermal, fluid, optical, and acoustic fields. For example, microelectromechanical systems (MEMS) typically involve coupling between mechanical and electrical fields; chip packaging design involves coupling between thermal, mechanical, and electrical fields; and optoelectronic integrated devices involve coupling between optical, electromagnetic, and thermal fields.
[0036] 102. Based on the design parameters, construct a unified analytical mechanics model to describe the overall dynamic behavior of the target system. The unified analytical mechanics model is expressed in the form of a Lagrangian, Hamiltonian, or action functional and integrates the interaction between at least two physical domains.
[0037] In the embodiments of this application, the unified analytical mechanics model adopts the Lagrange quantity, Hamiltonian, or action functional in analytical mechanics as its unified mathematical expression.
[0038] In some embodiments, step 102 may include the following steps: 1021. Define a common set of generalized coordinates for at least two physical domains to uniformly describe the state of the target system.
[0039] Defining generalized coordinates is a crucial step in constructing a unified analytical mechanics model. Its core lies in using a minimal set of independent abstract variables to uniquely describe the instantaneous state of the entire target system. For example, a common set of generalized coordinates {q_i} (where i = 1, 2, ..., N) can be defined for at least two physical domains. These generalized coordinates are domain-independent abstract variables used to uniformly describe the state of the entire target system.
[0040] First, the key physical quantities used to describe the internal state of each physical domain in the target system can be identified. Then, the key physical quantities of each physical domain are abstracted into a set of symbolic generalized coordinates q1, q2, …, q n .
[0041] For example, in an electromechanical system, q_1 can represent the mechanical displacement of the mass block (mechanical degree of freedom), while q_2 can represent the charge on the capacitor plates or the magnetic flux in the inductor (electrical degree of freedom). The derivatives of the generalized coordinates (such as the generalized velocity q_i) or their conjugates (generalized momentum p_i) together constitute the complete state space of the target system.
[0042] 1022. Based on the design parameters, determine the energy contribution and / or work contribution terms of each physical domain in the generalized coordinate system.
[0043] The energy contribution term refers to a scalar function describing the stored energy of the target system, which depends only on the instantaneous state of the system (determined by the generalized coordinates and their time derivative). This energy contribution term may include: Kinetic energy contribution (T): The energy possessed by the target system due to its overall or microscopic motion. Examples include the translational / rotational kinetic energy of a mass, the energy related to the rate of change of the electric field in an electromagnetic field, and the kinetic energy of macroscopic fluid flow. Potential energy contribution (V): The energy stored in a target system due to its configuration, field distribution, or internal state. Examples include elastic deformation potential energy, gravitational potential energy, electrostatic energy, magnetic energy, and thermal energy (internal energy).
[0044] The work contribution term refers to a scalar function or functional that describes the energy input, output, or dissipation through the boundary of the target system, and it is related to the state change process. This work contribution term may include: Work corresponding to a nonconservative generalized force: the energy term contributed by the force exerted by external actions (such as driving force, voltage source, heat source), the work of which is path-dependent. In the Lagrange framework, it often appears directly as a generalized force on the right-hand side of the equation.
[0045] System dissipation term: A function describing the rate at which energy is irreversibly converted into heat or other forms of dissipation. It is usually characterized by the Rayleigh dissipation function (R), which is a quadratic function of the generalized velocity.
[0046] In the embodiments of this application, the interactions between different physical domains are specifically realized through coupling terms. That is, the energy contribution or work contribution term of one physical domain depends on the generalized coordinates of another (or more) physical domains. For example, the functional form of electrostatic energy depends on both mechanical and electrical coordinates, mathematically integrating electromechanical coupling. Fluid damping force may depend on the vibration velocity of the structure (mechanical domain) and fluid properties (fluid domain), and its dissipation function R will simultaneously contain generalized velocity terms related to these physical domains.
[0047] 1023. Based on the principles of analytical mechanics, integrate the energy contribution terms and / or work contribution terms into the total Lagrangian, total Hamiltonian, or action functional of the target system.
[0048] This embodiment integrates the various energy contribution terms and / or work contribution terms into a unified scalar function or functional according to the principles of analytical mechanics, thereby providing a complete dynamic description of the entire multiphysics system.
[0049] The specific construction process of the total Lagrange quantity, total Hamiltonian, and action functional can be found in relevant technologies, and will not be described in detail in this embodiment.
[0050] 1024. Based on the total Lagrange or total Hamiltonian, derive the governing equations of the unified analytical mechanics model; or, based on the variational principle, derive the trajectory or governing equations of the unified analytical mechanics model from the action functional.
[0051] In this embodiment, if a Lagrange quantity is used, a set of coupled second-order differential equations about generalized coordinates (i.e., the governing equations of the unified analytical mechanics model) is derived by applying the Lagrange equations (also known as the Euler-Lagrange equations).
[0052] If Hamiltonian is used, then a set of coupled first-order differential equations (i.e., the governing equations of the unified analytical mechanics model) is derived by applying Hamiltonian canonical equations.
[0053] If the action functional is used, Hamilton's principle (the principle of least action) is applied, that is, the actual trajectory is the path that makes the action S take a stationary value. By using the variational method δS = 0, the Euler-Lagrange equation, which is equivalent to the Lagrange equation, can be directly derived, or it can be used to solve the optimal trajectory problem.
[0054] 103. Construct a multi-objective optimization function based on performance indicators, and use the design parameters as optimization variables. Combine the preset physical and engineering constraints to solve and optimize the unified analytical mechanics model to obtain the target collaborative design parameters.
[0055] This embodiment transforms the design problem into a structured mathematical optimization problem and uses computational tools to automatically find the optimal solution. The goal is to find the target co-design parameters that maximize the overall performance of the target system while satisfying all constraints.
[0056] In some embodiments, performance indicators can first be mapped to objective functions related to the state of a unified analytical mechanics model; then, multiple objective functions can be processed using a weighted summation method, Pareto optimization method, or multi-objective decision method to form a multi-objective optimization function.
[0057] Specifically, each specific performance indicator (such as sensitivity, bandwidth, and power consumption) can be quantified into a mathematical objective function f_j(p). This objective function is associated with the design parameters p through a unified analytical mechanics model: given a set of parameters p, the target system response is obtained by solving the unified analytical mechanics model, and then the objective function f_j(p) corresponding to each performance indicator is calculated based on the response.
[0058] Furthermore, since there are often conflicts between objective functions (for example, increasing sensitivity may lead to increased power consumption), it is necessary to use weighted summation, Pareto optimization, or multi-objective decision-making methods to process these objective functions into a multi-objective optimization function that can be optimized for a single objective or explored the frontier.
[0059] In this embodiment of the application, the step "using design parameters as optimization variables, and combining preset physical and engineering constraints, to solve and optimize the unified analytical mechanics model to obtain the target collaborative design parameters" may specifically include the following steps: 1041. The unified analytical mechanics model under the current design parameters is solved using numerical methods to obtain the response of the target system.
[0060] For example, numerical methods such as the finite element method, finite volume method, or Runge-Kutta method can be used to solve the unified analytical mechanics model under the current design parameters to obtain the detailed dynamic response of the target system (such as the changes of displacement, velocity, temperature field, current, etc. with time or space).
[0061] 1042. Calculate the value of the multi-objective optimization function based on the response, and determine whether the value of the multi-objective optimization function satisfies the preset convergence condition.
[0062] Specifically, based on the response of the target system, the values of all objective optimizations can be calculated, thus obtaining the value of the multi-objective optimization function. Then, it can be determined whether the value of this multi-objective optimization function satisfies preset convergence conditions (such as the change in the objective function being less than a threshold, or reaching the maximum number of iterations).
[0063] 1043. If satisfied, the current design parameters are determined as the target collaborative design parameters; if not satisfied, the design parameters are iteratively updated based on the preset physical constraints, engineering constraints and optimization algorithms until the convergence condition is met, and the current design parameters are taken as the target collaborative design parameters.
[0064] Understandably, when the value of the multi-objective optimization function meets the preset convergence condition, the current design parameters can be determined as the target collaborative design parameters. When the value of the multi-objective optimization function meets the preset convergence condition, the iterative update phase of the input design parameters can then proceed.
[0065] The optimization algorithm may include, but is not limited to, gradient descent, sequential quadratic programming, genetic algorithm, and particle swarm optimization.
[0066] First, constraints on the design parameters can be constructed based on physical and engineering constraints. These constraints are typically expressed as inequalities or systems of equations concerning the design parameters.
[0067] In each iteration of the optimization algorithm, the algorithm generates candidate design parameters based on the value of the multi-objective optimization function and its changing trend.
[0068] Subsequently, the candidate design parameters are substituted into the constraints for verification and processing to generate design parameters for the next iteration. This process specifically includes: substituting the candidate design parameters into the constraints for verification to determine whether the candidate design parameters satisfy all constraints. If satisfied, the candidate design parameters are used as the design parameters for the next iteration; if not satisfied, the candidate design parameters are adjusted through a constraint processing mechanism, or the degree of constraint violation is converted into a penalty term on the multi-objective optimization function, generating adjusted design parameters for the next iteration.
[0069] Among them, adjusting candidate design parameters through constraint processing mechanism refers to directly adjusting infeasible candidate design parameters to the feasible domain or boundary that meets the constraint conditions through mapping, projection or repair rules, thereby generating adjusted feasible design parameters.
[0070] The process of transforming the degree of constraint violation into a penalty term for the multi-objective optimization function means that, instead of directly modifying the candidate parameters, the degree of constraint violation is quantified and converted into a penalty term, which is then added to the multi-objective optimization function to form an augmented objective function. The optimization algorithm indirectly drives the iterative process to converge towards the feasible region by minimizing this augmented function. In this approach, the candidate design parameters themselves may be used to calculate the value of the augmented objective function.
[0071] The design parameters obtained after the above constraint verification and processing will be used in the next iteration (i.e., return to the step of using numerical methods to solve the unified analytical mechanics model under the current design parameters), and so on, until the preset convergence condition is met.
[0072] In the embodiments of this application, physical constraints are typically derived from physical laws, which are already reflected in the unified analytical mechanics model as part of its intrinsic dynamics. For example, these constraints may be derived from the conservation of energy, momentum, or the Lagrange equations / Hamilton canonical equations themselves. During optimization, physical constraints primarily manifest as the requirement that the governing equations of the unified analytical mechanics model must be satisfied. Therefore, each iterative update of the design parameters, obtaining the response of the target system by solving the unified analytical mechanics model, essentially forces these physical constraints to be satisfied under the current design parameters.
[0073] Engineering constraints stem from external engineering limitations and technical requirements related to actual manufacturing, operation, safety, and cost. Engineering constraints are external limitations imposed on design parameters or system response. They are typically expressed as inequality or equality constraints regarding design parameters and / or the response of the target system. Specific forms include, but are not limited to, geometric constraints (such as upper and lower dimensional limits, minimum clearance), material and strength constraints (such as allowable stress, strain limit, maximum operating temperature), performance constraints (such as minimum resonant frequency, maximum deformation, minimum bandwidth), manufacturing and process constraints (such as minimum machinable feature size, assembly tolerances), and economic constraints (such as total cost budget).
[0074] In the optimization process, engineering constraints are constructed as the constraints of the optimization algorithm, forming a constrained mathematical optimization problem together with the multi-objective optimization function. The optimization algorithm must find the design parameters that optimize the objective function within the "feasible region" that satisfies (or approximates satisfies through penalty functions, etc.), thereby ensuring the engineering feasibility of the design scheme.
[0075] In summary, physical constraints are automatically and accurately satisfied through the construction and solution of a unified analytical mechanics model; while engineering constraints, as boundary conditions, are verified and processed during the optimization iteration process. The synergistic effect of both ensures that the target system corresponding to the final target co-design parameters conforms to both fundamental physical laws and meets all practical engineering requirements.
[0076] 104. Output target co-design parameters to guide the physical implementation of the target system.
[0077] The target collaborative design parameters, in the form of structured data files or engineering reports, clearly provide the optimal configuration values of the target system in various physical domains (such as geometry, materials, electrical, thermal, etc.).
[0078] The target collaborative design parameters can be used to guide subsequent physical implementation: First, they drive CAD / CAE tools to automatically generate detailed 3D models and engineering drawings, and support high-fidelity verification; second, they are converted into instruction codes that can be recognized by manufacturing equipment (such as CNC machine tools, 3D printers, and lithography machines), directly guiding production, processing, and assembly; third, they provide benchmarks for the testing and performance calibration of physical prototypes, thereby forming a complete technical closed loop from virtual optimization to physical manufacturing and then to feedback improvement, ensuring that design performance is accurately and efficiently transformed into product effectiveness.
[0079] In summary, the multiphysics system collaborative design method provided in this application includes: obtaining design parameters and performance indicators of at least two physical domains in the target system; constructing a unified analytical mechanics model based on the design parameters to describe the overall dynamic behavior of the target system, wherein the unified analytical mechanics model is expressed using Lagrangian, Hamiltonian, or action functionals, and integrates the interactions of at least two physical domains; constructing a multi-objective optimization function based on the performance indicators, and solving and optimizing the unified analytical mechanics model using the design parameters as optimization variables, combined with preset physical and engineering constraints, to obtain the target collaborative design parameters; and outputting the target collaborative design parameters to guide the physical realization of the target system. This application, by constructing a unified analytical mechanics model, transforms the fragmented process of traditional multiphysics system design—characterized by domains, sequential iterations, and heavy reliance on manual trial and error—into an automated collaborative optimization process based on a single mathematical framework. The embodiments of this application inherently integrate the interactions between various physical domains during the modeling stage and automatically find the optimal solution through a multi-objective optimization algorithm. This eliminates the redundant overhead of cross-domain model conversion and data docking in one go, makes the performance trade-off decision-making process intelligent, and fundamentally prevents disruptive rework caused by later coupling conflicts. It achieves global optimal design at the system level, significantly improving design efficiency and reliability.
[0080] To facilitate better implementation of the multi-physics system collaborative design method provided in this application, this application also provides a multi-physics system collaborative design apparatus. The meanings of the terms used are the same as in the multi-physics system collaborative design method described above, and specific implementation details can be found in the descriptions within the method embodiments.
[0081] Please see Figure 3 , Figure 3 This is a schematic diagram of the structure of a multi-physics system collaborative design device provided in an embodiment of this application. The multi-physics system collaborative design device may include an acquisition unit 201, a construction unit 202, an iteration unit 203, and an output unit 204. Acquisition unit 201 is used to acquire design parameters and performance indicators of at least two physical domains in the target system; Building unit 202 is used to build a unified analytical mechanics model to describe the overall dynamic behavior of the target system based on design parameters. The unified analytical mechanics model is expressed in Lagrangian, Hamiltonian or action functional and integrates the interaction of at least two physical domains. Iteration unit 203 constructs a multi-objective optimization function based on performance indicators, and uses design parameters as optimization variables. Combined with preset physical and engineering constraints, it solves and optimizes the unified analytical mechanics model to obtain the target collaborative design parameters. Output unit 204 is used to output target co-design parameters to guide the physical implementation of the target system.
[0082] For specific implementation methods of each of the above units, please refer to the embodiments of the multi-physics system collaborative design method described above, which will not be repeated here.
[0083] In summary, the multiphysics system collaborative design apparatus provided in this application can acquire design parameters and performance indicators of at least two physical domains in the target system through the acquisition unit 201; the construction unit 202 constructs a unified analytical mechanics model to describe the overall dynamic behavior of the target system based on the design parameters. The unified analytical mechanics model is expressed using Lagrangian, Hamiltonian, or action functionals and integrates the interactions of at least two physical domains; the iteration unit 203 constructs a multi-objective optimization function based on the performance indicators, and solves and optimizes the unified analytical mechanics model using the design parameters as optimization variables, combined with preset physical and engineering constraints, to obtain the target collaborative design parameters; and the output unit 204 outputs the target collaborative design parameters to guide the physical realization of the target system. This application embodiment transforms the fragmented process of traditional multiphysics system design—characterized by domain-specific, sequential iterations and heavily reliant on manual trial and error—into an automated collaborative optimization process based on a single mathematical framework by constructing a unified analytical mechanics model. The embodiments of this application inherently integrate the interactions between various physical domains during the modeling stage and automatically find the optimal solution through a multi-objective optimization algorithm. This eliminates the redundant overhead of cross-domain model conversion and data docking in one go, makes the performance trade-off decision-making process intelligent, and fundamentally prevents disruptive rework caused by later coupling conflicts. It achieves global optimal design at the system level, significantly improving design efficiency and reliability.
[0084] This application also provides an electronic device that may integrate the multi-physics system collaborative design device of this application, such as... Figure 4 As shown, it illustrates a structural schematic diagram of the electronic device involved in the embodiments of this application, specifically: The electronic device may include components such as a processor 301 with one or more processing cores and a memory 302 with one or more computer-readable storage media. Those skilled in the art will understand that... Figure 4 The electronic device structure shown does not constitute a limitation on the electronic device and may include more or fewer components than shown, or combine certain components, or have different component arrangements. Wherein: The processor 301 is the control center of the electronic device. It connects various parts of the electronic device via various interfaces and lines. By running or executing software programs stored in the memory 302 and / or this application, and by calling data stored in the memory 302, it performs various functions and processes data, thereby providing overall monitoring of the electronic device. Optionally, the processor 301 may include one or more processing cores; preferably, the processor 301 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operation of the storage medium, user interface, and application programs, while the modem processor mainly handles wireless communication. It is understood that the modem processor may not be integrated into the processor 301.
[0085] The memory 302 can be used to store software programs and this application. The processor 301 executes various functional applications and data processing by running the software programs and this application stored in the memory 302. The memory 302 may mainly include a program storage area and a data storage area. The program storage area may store applications required for operating the storage medium and at least one function; the data storage area may store data created based on the use of the electronic device. In addition, the memory 302 may include high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the memory 302 may also include a memory controller to provide the processor 301 with access to the memory 302.
[0086] Although not shown, the electronic device may also include a display unit, an input unit, and a power supply, etc., which will not be described in detail here. Specifically, in this embodiment, the processor 301 in the electronic device loads the executable files corresponding to the processes of one or more application programs into the memory 302 according to the following instructions, and the processor 301 runs the application programs stored in the memory 302 to realize various functions, as follows: Obtain the design parameters and performance metrics of at least two physical domains in the target system; Based on the design parameters, a unified analytical mechanics model is constructed to describe the overall dynamic behavior of the target system. The unified analytical mechanics model is expressed in Lagrangian, Hamiltonian or action functional and integrates the interaction of at least two physical domains. A multi-objective optimization function is constructed based on performance indicators, and the design parameters are used as optimization variables. Combined with preset physical and engineering constraints, the unified analytical mechanics model is solved and optimized to obtain the target collaborative design parameters. Output target co-design parameters to guide the physical implementation of the target system.
[0087] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be performed by instructions, or by instructions controlling related hardware. These instructions can be stored in a computer-readable storage medium and loaded and executed by a processor.
[0088] Therefore, embodiments of this application provide a storage medium storing a plurality of instructions that can be loaded by a processor to execute steps in any of the methods provided in embodiments of this application. For example, the instructions can execute the following steps: Obtain the design parameters and performance metrics of at least two physical domains in the target system; Based on the design parameters, a unified analytical mechanics model is constructed to describe the overall dynamic behavior of the target system. The unified analytical mechanics model is expressed in Lagrangian, Hamiltonian or action functional and integrates the interaction of at least two physical domains. A multi-objective optimization function is constructed based on performance indicators, and the design parameters are used as optimization variables. Combined with preset physical and engineering constraints, the unified analytical mechanics model is solved and optimized to obtain the target collaborative design parameters. Output target co-design parameters to guide the physical implementation of the target system.
[0089] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.
[0090] The storage medium may include: read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.
[0091] Since the instructions stored in the storage medium can execute the steps of any method provided in the embodiments of this application, the beneficial effects that any method provided in the embodiments of this application can achieve can be realized. For details, please refer to the previous embodiments, which will not be repeated here.
[0092] The foregoing has provided a detailed description of the multi-physics system collaborative design method, apparatus, storage medium, and electronic device provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.
Claims
1. A multi-physics system collaborative design method, characterized in that, include: Obtain the design parameters and performance metrics of at least two physical domains in the target system; Based on the design parameters, a unified analytical mechanics model is constructed to describe the overall dynamic behavior of the target system. The unified analytical mechanics model is expressed in Lagrangian, Hamiltonian or action functional and integrates the interactions of at least two of the physical domains. Based on the performance indicators, a multi-objective optimization function is constructed, and the design parameters are used as optimization variables. Combined with preset physical and engineering constraints, the unified analytical mechanics model is solved and optimized to obtain the target collaborative design parameters. The target co-design parameters are output to guide the physical implementation of the target system.
2. The multi-physics system collaborative design method as described in claim 1, characterized in that, The construction of a unified analytical mechanics model based on the design parameters to describe the overall dynamic behavior of the target system includes: Define a common set of generalized coordinates for at least two of the physical domains to uniformly describe the state of the target system; Based on the design parameters, determine the energy contribution and / or work contribution of each physical domain under the generalized coordinates; Based on the principles of analytical mechanics, the energy contribution terms and / or work contribution terms are integrated into the total Lagrangian, total Hamiltonian or action functional of the target system. Based on the total Lagrange quantity or the total Hamiltonian, derive the governing equations of the unified analytical mechanics model; or, based on the variational principle, derive the trajectory or governing equations of the unified analytical mechanics model from the action functional.
3. The multi-physics system collaborative design method as described in claim 1, characterized in that, The construction of the multi-objective optimization function based on the performance index includes: The performance index is mapped to an objective function that is related to the state of the unified analytical mechanics model; Multiple objective functions are processed using weighted summation, Pareto optimization, or multi-objective decision-making methods to form a multi-objective optimization function.
4. The multi-physics system collaborative design method as described in claim 1, characterized in that, The unified analytical mechanics model is solved and optimized using the design parameters as optimization variables, combined with preset physical and engineering constraints, to obtain the target collaborative design parameters, including: The unified analytical mechanics model under the current design parameters is solved using numerical methods to obtain the response of the target system; Calculate the value of the multi-objective optimization function based on the response, and determine whether the value of the multi-objective optimization function satisfies the preset convergence condition; If satisfied, the current design parameters are determined as the target collaborative design parameters; If not satisfied, the design parameters are iteratively updated based on preset physical constraints, engineering constraints, and optimization algorithms until the convergence condition is met, and the current design parameters are used as the target collaborative design parameters.
5. The multi-physics system collaborative design method as described in claim 4, characterized in that, The iterative update of the design parameters based on preset physical constraints, engineering constraints, and optimization algorithms includes: Based on the physical and engineering constraints, construct the constraint conditions for the design parameters; In each iteration of the optimization algorithm, candidate design parameters are generated based on the value and changing trend of the multi-objective optimization function; The candidate design parameters are substituted into the constraints for verification and processing to generate design parameters for the next iteration.
6. The multi-physics system collaborative design method as described in claim 5, characterized in that, The step of substituting the candidate design parameters into the constraints for verification and processing to generate design parameters for the next iteration includes: The candidate design parameters are substituted into the constraints for verification. If satisfied, the candidate design parameters will be used as the design parameters for the next iteration. If the conditions are not met, the candidate design parameters are adjusted through a constraint handling mechanism, or the degree of constraint violation is converted into a penalty term for the multi-objective optimization function, generating adjusted design parameters for the next iteration.
7. The multiphysics system collaborative design method as described in any one of claims 1-6, characterized in that, At least two physical fields include any two or more combinations of the mechanical, electrical, electromagnetic, thermal, fluid, optical, and acoustic fields.
8. A multi-physics system collaborative design device, characterized in that, include: The acquisition unit is used to acquire design parameters and performance indicators of at least two physical domains in the target system. The building unit is used to build a unified analytical mechanics model to describe the overall dynamic behavior of the target system based on the design parameters. The unified analytical mechanics model is expressed in Lagrangian, Hamiltonian or action functional and integrates the interactions of at least two physical domains. An iterative unit is used to construct a multi-objective optimization function based on the performance index, and to solve and optimize the unified analytical mechanics model by using the design parameters as optimization variables and combining preset physical and engineering constraints to obtain the target collaborative design parameters. The output unit is used to output the target co-design parameters to guide the physical implementation of the target system.
9. A storage medium, characterized in that, The storage medium stores multiple instructions, which are adapted for loading by a processor to execute the multiphysics system co-design method according to any one of claims 1-7.
10. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the multiphysics system co-design method as described in any one of claims 1-7.