52 results about "Modelica" patented technology

The invention relates to a nonlinear damping element based on a Modelica language, and relates to the field of mechanical system modeling and simulation. According to the technical scheme, a novel damping element is designed based on the Modelica language, corresponding damping coefficients can be defined according to different movement speeds of the element, and the limitation of the prior art is effectively broken through; specifically, according to the technical scheme, the element types of the Modelica mechanical library are enriched, the blank of nonlinear damping elements in the library is filled up, and the application range of the library is expanded; moreover, simulation requirements of a nonlinear damping system can be accurately matched, so that the modeling process better fits nonlinear characteristics of a damping coefficient in an actual working condition; besides, free switching between non-linear damping and linear damping can be flexibly realized depending on a speed-damping force two-dimensional number table, the adaptability of the element to different modeling scenes is greatly enhanced, and powerful support is provided for precise modeling of a mechanical system based on Modelica.

The invention provides a pipe network model parameter inversion method and device based on an FMU standard and an optimization algorithm, and relates to the technical field of fluid simulation and parameter calibration. A pipe network physical model is established, and a to-be-inverted parameter is defined as an adjustable parameter and compiled and exported as an FMU format file; an FMU file is loaded in an optimization environment, a parameter sample set is generated through uniform random sampling, and a neural network agent model is trained to establish a mapping relation from parameters to system response; and a weighted error loss function is designed, deviation of measured data and simulation output is taken as an optimization target, optimal parameters of the neural network agent model are solved by adopting an optimization algorithm, and the pipe network system is operated according to the optimal parameters. According to the method, the combination of the Modelica modeling advantage and the Python optimization algorithm is realized through the FMU standard; optimization iteration is accelerated through a neural network agent model, and the parameter calibration efficiency is greatly improved; and high-precision automatic inversion of the key parameters of the pipe network is realized.

The invention discloses an LLM (Logical Language Modelica) domain method for Modelica intelligent modeling optimization, and belongs to the field of computer-aided modeling. Structured information is converted and extracted by collecting field data of the new energy automobile; then, based on the RAG technology, intelligent blocking and vectorization processing is carried out on the structured information to form a structured knowledge base, and then a recall test is carried out by calculating a comprehensive weighted score to judge whether the structured knowledge base is available or not; then, the LLM is integrated into an AI-Agent framework, an AI-Agent is formed, and LLM parameters are configured; integrating the structured knowledge base which passes the test into the AI-Agent by utilizing an API (Application Program Interface) and an access protocol; and meanwhile, a specialized cue word template is designed and integrated into the AI-Agent, and a Modelica code generation template and related constraints are standardized. And finally, the user asks a question to the AI-Agent, the LLM works according to a specialized cue word instruction, an API interface is called to access the knowledge base, and Modelica codes which conform to language specifications and can be operated by the user are automatically output through the LLM. According to the invention, end-to-end conversion from unstructured input to high-fidelity model output is realized.

The invention discloses an intelligent system failure analysis method based on fault recognition, and relates to the technical field of intelligent system reliability analysis, and the analysis method comprises the specific steps: employing Modelica to construct a multi-physical domain digital twinborn model, combing historical data, and building a structured fault mode library; developing a fault injection algorithm to acquire data to form a standardized data set; preprocessing data to construct a causal link and a probability flow network; carding task scenes to construct a reliability evaluation model and training the reliability evaluation model; inputting actual parameters to trace faults, generating an optimization plan, and outputting an analysis report and a plan after verification; according to the method, the problem of insufficient fault correlation analysis caused by modeling scale splitting in a traditional method is solved by constructing a multi-physical domain digital twinborn model covering microcosmic, mesoscopic and macroscopic and establishing a structured fault mode library of a standardized feature description system containing three types of faults of hardware, software and environment interaction.

The application relates to a model version automatic conversion method and system for a Modelica language, and the method comprises the following steps: in response to receiving a conversion request for a user model dependency, the conversion request comprising version information of model libraries relied on by the user model before and after conversion; loading at least one version conversion script according to the version information of the model libraries relied on by the user model before and after conversion, the at least one version conversion script being used for recording version conversion information when a current version model library relied on by the user model is upgraded to a target version model library; parsing the at least one version conversion script to obtain at least one corresponding version difference mapping table; and converting the dependency of the user model according to the at least one version difference mapping table, so that the converted user model is compatible with the target version model library. The application can automatically convert the dependency of the user model, adapt to a new version model library, and reduce the complexity of manual operation.

The invention relates to the technical field of spacecraft design, in particular to an aircraft information flow analysis, recheck and recalculation system, which comprises an information large graph design module used for automatically extracting design data such as an interface data list and a cable network contact table, generating a static information large graph through data check and analysis, and supporting signal management, layered display and export; the model library construction module is used for constructing a hierarchical information professional model library based on a Modelica language and covering multi-level models from a basic library, a universal library to a model customization library; and the information flow simulation verification analysis module is used for mapping the static information large graph into a simulation model, carrying out dynamic simulation verification on a system network, a communication network and a load network, and realizing multi-dimensional visual display of a result. According to the method, the information system design is rechecked and recalculated from static state to dynamic state and from manual operation to automatic operation, and the design efficiency, accuracy and verification sufficiency are effectively improved.

The invention discloses a Modelica model automatic generation Agent development method based on SysPhS, and belongs to the field of system engineering and AI based on models. Aiming at each core component in the electric vehicle system, extracting each component, a corresponding port and a connection relation through an analysis interface by utilizing a SysMLv2 system model; secondly, aiming at the analyzed component S, retrieving each component model matched with the information of the component S in a Modelica model library through an RAG algorithm; according to the RAG semantic similarity threshold value, sorting is carried out from high to low; selecting the component model with the highest sequence, mapping the component model with the highest sequence to a port consistent with the SysMLv2 system model in connection relation through a SysPhS standard specification, and forming a Modelica system model by using all the component models with the highest sequence and the mapped connection ports; finally, inputting the Modelica system model into a model checking Agent for lexical, syntactic and semantic checking, and if an error prompt exists in a checking result, automatically correcting the Modelica system model and re-checking the Modelica system model; and otherwise, outputting the final Modelica system model. According to the invention, the accuracy, consistency and efficiency of model generation are improved.

This invention belongs to the field of electronic digital data processing and discloses a propulsion modeling method for FPSOs based on Modelica, comprising the following steps: S1: Modeling the propulsion coefficient model of the propeller, including calculating the following parameters: thrust coefficient, thrust torque coefficient, thrust gain coefficient, and thrust torque gain coefficient; S2: Modeling the propeller performance model, including calculating the following parameters: wake coefficient, advance number, thrust loss coefficient, and relative rotational efficiency; S3: Based on the results of steps S1 and S2, outputting the thrust and thrust torque of the propeller, thereby obtaining the propulsion efficiency of the propeller. This method can comprehensively simulate the thrust and torque variation characteristics of the propeller under different operating conditions through a discrete dynamic modeling approach, thus providing accurate simulation support for the dynamic response of FPSO systems in complex marine environments.

The application discloses a component selection method, device and equipment based on Modelica, and the method comprises the following steps: obtaining a component principle model based on multiple system design schemes of a user, generating component model codes of multiple component models based on batch design of the component principle model; determining an arrangement mode included in the system design scheme, generating a system model template according to the arrangement mode; generating system model codes of multiple system models according to the system model template; calling the system model codes to perform simulation, obtaining simulation results of the multiple system models; determining key performance indicator variables of the multiple system models, drawing key performance indicator variable curves of the simulation results according to the key performance indicator variables; and determining an optimal system design scheme according to the key performance indicator variable curves. The application solves the problem that a Modelica-based modeling and simulation software currently lacks rapid verification of multiple system design schemes based on Modelica.

The invention discloses a simulation model code automatic restoration method and device based on deep learning and a medium, and the method comprises the steps: S1, carrying out the preprocessing of an original model file, and forming a structured abstract syntax tree AST and a component dependency graph; error detection and repair code generation are carried out by using an improved LLM model, the error detection stage is realized by an improved CodeBERT, and the repair code stage is generated by adopting a GPT decoder; in the error detection stage, auxiliary information is used for enriching fine adjustment, and a multi-modal fusion mechanism is introduced to integrate serialized code texts and structured auxiliary information into a model training process at the same time, so that the error detection capability of the model is enhanced, and defect positions of deep relationships such as component dependence and equation logic are captured; in the code repairing stage, reinforcement learning fine tuning is used. According to the method, an automatic detection and repair scheme for common errors of Modelica model codes is provided for a user, and the development efficiency and reliability of a complex simulation model in industrial software are improved.

The invention discloses a Modelica language-based supercritical carbon dioxide Brayton cycle system modeling simulation method, which comprises the following steps of: constructing a system key equipment model according to a mathematical physical equation of equipment characteristics; forming a flow-pressure iterative solution scheme of the system according to the key equipment model and the volume model of the system; all the equipment models are connected according to a design scheme, and equipment structure parameters and simulation iteration initial values are set to form a system simulation model; setting a simulation interval, an output interval and a simulation algorithm on the MWORKS platform; and performing system model inspection and simulation analysis according to the set system simulation model to obtain the operation characteristics of the supercritical carbon dioxide Brayton cycle system. According to the method, rapid reconstruction of a system topological structure, convenient adjustment of equipment parameters and efficient analysis of system characteristics are realized, and reliable data support is provided for engineering design and optimization.

The invention belongs to the technical field of thermal hydraulic simulation software, and particularly relates to a steam turbine modeling method and equipment based on Modelica. According to the method, the thermal-hydraulic process of the steam turbine is efficiently and accurately expressed under the two-fluid six-equation system framework, and coupling simulation of the thermal-hydraulic specialty and the mechanical specialty of the steam turbine is realized, so that the problem of how to ensure that the thermal-hydraulic process of the steam turbine is efficiently and accurately expressed under the two-fluid six-equation system framework is solved; and meanwhile, multi-specialty coupling calculation with a mechanical specialty is realized.

The application relates to the computer field and provides a Modelica model state solidification method, device, equipment, medium and product, wherein the method comprises the following steps: in response to a translation simulation request of a to-be-solidified model, the to-be-solidified model is translated to obtain a model uncertainty representation, the model uncertainty representation comprises translation-selected state variables, initial values of complementary variables, nonlinear iteration variables and iteration initial values of the nonlinear iteration variables, and the complementary variables refer to variables that need to be supplemented with initial values; at least one of the following steps is executed and finally written into a model to solidify the state of the translated model: determining the necessity of the state variables, determining the certainty of the locked initial value, providing the iteration initial value of the nonlinear iteration variable and marking the nonlinear iteration variable by using a manufacturer annotation. The method, device, equipment, medium and product provided by the application eliminate the ambiguity in the model translation process, and realize the high consistency and reliability of cross-platform simulation results.

The invention belongs to the technical field of thermal hydraulic simulation software, and particularly relates to a pilot operated safety valve modeling method and device based on Modelica, and the method comprises the steps: obtaining a modular model library architecture after the pilot operated safety valve is decomposed; constructing a corresponding basic-level model by adopting a modeling mode matched with the model characteristics of the basic-level model; the basic-level models are combined, and two-fluid six-equation modeling is adopted to obtain a component-level model; and building a pilot operated safety valve model by adopting the component-level model, and injecting system parameters into the pilot operated safety valve model. Characteristic analysis is carried out on a modular model library framework obtained after decomposition of the pilot operated safety valve, model combination and system parameter injection are carried out by adopting two fluids and six equations, so that the problem of how to ensure an efficient and accurate numerical calculation result of two-phase flow under a multi-specialty coupling condition is solved; modularization, reusability and expansibility of pilot operated safety valve modeling are achieved so as to meet the requirement of complex engineering application.

The application relates to a kind of Modelica model-oriented incremental compilation method and system, the method comprises: the abstract syntax tree of Modelica model is obtained by keyword matching to the Modelica model text edited and modified;Abstract syntax tree includes the definition and behavior of each component in Modelica model;Determine the changed part of Modelica model editing and modification based on abstract syntax tree and the change of last time;The intermediate representation of the changed part of Modelica model is instantiated;Intermediate representation includes: the argument tree of component and component behavior list;The intermediate representation of the changed part and unchanged part of Modelica model is integrated, and the complete representation of Modelica model is obtained;Through incremental compilation, the modified part of model can be quickly identified and locally compiled, so that the whole model is avoided to be recompiled, and the compiling efficiency is significantly improved.At the same time, incremental compilation can also effectively reduce the output of compiling diagnosis information, so that users can more easily obtain the diagnosis information related to the modified part, and further improve the modeling experience.

The invention relates to high-voltage line wind vibration analysis, in particular to a high-voltage line wind vibration simulation analysis method, system and device based on the Modelica language and a medium. Constructing a parameterized sub-model of the high-voltage line system based on the physical parameters; packaging each parameterized sub-model into a model assembly by using a Modelica language, integrating the corresponding model assembly based on an actual topological structure of the high-voltage line system, defining an initial state of the system, and establishing a kinetic equation for each cable section to form a high-voltage line system model; acquiring wind vector data of the current environment in real time; inputting the wind vector data of the current environment into the high-voltage line system model for simulation analysis, solving the kinetic equation of each cable section through numerical integration, and calculating the movement speed of each cable section at a specified moment in the future and the position of the midpoint of the cable section. According to the invention, high-precision and high-efficiency multi-physical-field wind vibration simulation can be realized.

The application relates to the field of automobile dynamics simulation, and particularly discloses a Modelica-based automobile single-mass system modeling and adaptive parameter optimization method, which comprises the following steps: determining a simplified module of an automobile single-mass system and a component connection mode according to demand analysis, and building a basic simulation model based on Modelica; performing initial simulation on the basic simulation model to obtain simulation output results under initial parameters; collecting real vehicle measured data, constructing a comprehensive error function, and iteratively optimizing key physical parameters of the basic simulation model by using a parameter optimization engine until a convergence condition is met to obtain optimized parameters; and feeding back the optimized parameters to the basic simulation model. The application combines the advantages of Modelica multi-field physical modeling and data-driven intelligent optimization algorithms, realizes a leap from manual experience parameter adjustment to automatic intelligent optimization, significantly reduces the dependence on accurate initial parameters, and effectively improves the model simulation precision and engineering application efficiency.

The invention provides a gas turbine multi-physical domain simulation platform-based gas compressor non-design point calculation method, which comprises the following steps of: firstly, constructing a gas compressor model through a Modelica language by adopting a SysTides module in a domestic gas turbine multi-system integration platform; then designing a standard physical port of the gas compressor; defining user input parameters and variables required during calculation of non-design points of the compressor model; constructing a key physical equation of the model and developing a characteristic diagram interpolation algorithm with high robustness; performing data processing and storage on the gas compressor characteristic diagram and the gas exhaust characteristic diagram; and finally, solving and calculating the non-design point of the gas compressor. According to the method, the compressor model constructed by adopting the Modelica language in the SysTides module in the domestic gas turbine multi-system integration platform is adopted, so that coupling simulation and response simulation among multiple physical domains are realized, the accuracy of performance prediction of the compressor under multiple working conditions at non-design points is improved, and the method has good model reusability and expansion capability.

The invention belongs to the technical field of thermal hydraulic simulation software, and particularly relates to a steam generator modeling method and system based on Modelica. The method comprises the following steps: obtaining a modular model library architecture after the steam generator system is decomposed, wherein the modular model library architecture comprises a system-level model, a component-level model and a basic-level model; the basic-level model is reused in different component-level models; the basic-level models of the steam generator are combined to obtain component-level models under the model library architecture, and the basic-level models are modeled in the form of Modelica and exist in the form of modular codes; and building a steam generator model library by adopting a component-level model, and injecting system parameters. According to the method, the steam generator model library is built under a two-fluid six-equation system framework, so that the technical problem of how to realize modularization, reusability and expansibility of steam generator modeling on the premise of ensuring an efficient and accurate numerical calculation result of two-phase flow is solved.

The application discloses a kind of based on K8S's elastic simulation Modelica model method, system, equipment and medium, the method includes: Modelica simulation program is packed into Docker image;Simulation task is sent to message queue through browser, simulation manager listens to message queue, when simulation task in message queue is listened to, simulation task is pulled from message queue and is consumed;Simulation manager sends the request of creating the running environment of simulation task to K8S;K8S responds to request, creates running environment based on Docker image, after running environment is successfully created, simulation task is executed;Simulation manager real-time listens to the simulation state of simulation task currently, and according to simulation state, corresponding operation is taken.The application solves the problem that resource cannot be satisfied when online Modelica model simulation service needs indefinite time indefinite quantity to use various resources of cloud computer.

The application discloses a kind of mechanism-data hybrid urban sewage treatment simulation model based on Modelica's construction method, comprising: six kinds of unit equipment models of primary sedimentation tank, nitrification tank, denitrification tank, secondary sedimentation tank, 2 flow-diverter and 2 flow-combiner are constructed;According to actual working condition, the simulation model of urban sewage treatment system is constructed, and water quality and flow are output;Determine the model mechanism parameters that need to be adjusted in the system simulation model as input;Optimization module is constructed, with simulation result and actual data deviation as evaluation index, and the optimal parameter is output;Parameter identification correction is carried out by combining calculation of the optimization module and the system simulation model, so as to construct the simulation model of urban sewage treatment system.The present application is based on Modelica modeling, which overcomes the technical problems of traditional modeling method, such as incompleteness, causality, difficulty in modification and reuse, thereby improving the accuracy and reusability of the urban sewage treatment simulation model.

This invention relates to the field of intelligent manufacturing, specifically to a multidisciplinary motion control modeling method for large component attitude adjustment and alignment systems. It combines the theories of multiple disciplines and fields involved in large component attitude adjustment and alignment systems with the Modelica technology system for the construction and simulation analysis of motion control models for these systems. This invention eliminates the need for pre-defining the input and output variables of components and the order of equation solving during the modeling process. During system solution, it automatically derives the causal relationships between variables based on mathematical equations. It has a wide range of applications, as this invention, based on the Modelica language, achieves integrated modeling and simulation across multiple disciplines and fields, including mechanics and control, overcoming the limitations of cross-platform modeling and simulation across different disciplines.

The application discloses a kind of based on Modelica's whole vehicle bus system construction method, device and equipment, the method includes: establishing controller model, controlled object model and extensible bus interface model, controller model, controlled object model and extensible bus interface model are based on Modelica language and write;According to the attribute of controller model, controlled object model and extensible bus interface model, determine the first extensible bus interface model matched with controller model, and determine the second extensible bus interface model matched with controlled object model;According to connection instruction, between extensible bus interface model is connected to complete the construction of whole vehicle bus model;After constructing whole vehicle bus model, whole vehicle bus model is verified and tested;When verification and test pass, then determine that whole vehicle bus model construction succeeds.

The application provides a multi-objective conflict coordination method for model mapping configuration optimization, relates to the technical field of model-based system engineering, and focuses on a personalized configuration extension mechanism in a SysML to Modelica automatic mapping process, and especially realizes an innovation breakthrough in conflict detection and optimization coordination mechanism between personalized configuration and basic configuration. The application flexibly defines special mapping relationships from SysML elements to Modelica elements through configuration files (such as XML, JSON, YAML and the like), the configuration content can be directly embedded into multiple sub-processes such as model analysis, type mapping, attribute relationship and behavior constraint, and the standardized interface is deeply integrated, so that the customization and fine-grained capability of automatic mapping are improved.

The application relates to a Modelica-oriented distributed simulation method, which comprises the following steps: encapsulating a dynamic library into a component which can be used on a Modelica platform; establishing a simulation model, compiling to generate a simulation program; generating a configuration file and simulation data, and packing the configuration file, the dynamic library and the simulation data; a master node requests task scheduling; after receiving the task scheduling request, a distributed coordinator recommends a computing node; and the computing node returns the execution result to the master node after executing the distributed computing task. The application realizes Modelica-oriented distributed simulation calculation, is suitable for simulation calculation of large and complex simulation models, reduces the processing task time of the master node by distributing simulation calculation tasks of the master node to the computing node, improves the simulation efficiency, and realizes the light weight of the computing node by installing an execution container capable of running an executable program on the distributed computing node, taking the dynamic library and running data as the distributed scheduling unit.

The invention discloses a complex air conditioning system fault simulation method and system based on a Modelica language, and relates to the technical field of air conditioners. Comprising the following steps: establishing a plurality of general component models of the air conditioning system based on a Modelica language, adding corresponding connectors to establish a plurality of key equipment models, and connecting the key equipment models to form a Modelica dynamic simulation model of the air conditioning system; the Modelica dynamic simulation model of the air conditioning system is set; the Modelica dynamic simulation model of the air conditioning system is configured, and model calibration and verification are carried out; and performing fault injection, and outputting and generating fault simulation output data of the whole set of air conditioning system at different levels of'component-equipment-system 'in a system level and a specific fault scene. Complex dynamic behaviors of an actual air conditioning system can be truly reflected, dynamic simulation of system fault characteristics is achieved, high-quality whole set of air conditioning system fault operation data in a system-level and specific fault scene is generated, and key data support is provided for research and development and performance evaluation of fault detection and diagnosis algorithms.

The invention provides a Modelica language-based rotational inertia dynamic adjustment method and system, and relates to the field of engineering modeling and simulation. The method comprises the following steps: S1, acquiring an external torque; s2, the rotational inertia at the current moment is substituted into the inertia moment relational expression, and an angular acceleration solving equation is established; s3, calculating the angular acceleration at the current moment based on an angular acceleration solving equation; s4, according to the angular acceleration at the current moment, the corresponding angular velocity and angular displacement are obtained, and system response under the dynamic rotational inertia condition is obtained; and the steps S1 to S4 are repeated, and the dynamic rotational inertia and the corresponding system response are obtained. The method and the device are used in the rotational inertia dynamic adjustment process based on the Modelica language, and the technical problem that the dynamic rotational inertia cannot be directly modeled in the prior art is solved.

The application discloses a project multi-stage-oriented building energy system carbon emission simulation method and system, which carries out dynamic simulation of energy consumption and carbon emission of building subsystems, energy consumption subsystems and renewable energy supply subsystems in four stages of planning and preliminary design, construction drawing design, construction installation and commissioning and operation and maintenance of a construction project. The application can establish building energy system models with different degrees of precision in a Modelica simulation environment according to the granularity of input data at different stages of the project cycle; through the construction of a component model library and the establishment of a dynamic updating mechanism of the system simulation model, the construction project multi-stage data source is connected, and the current operation carbon emission calculation tool is supplemented in the aspect of assisting multi-professional low-carbon design; based on the FMI standard, a three-dimensional building heat transfer model and a device control model are coupled, the building operation and regulation strategy can be flexibly constructed for a real operation and maintenance scene, and the problem that the current operation carbon emission calculation tool is difficult to reflect the actual operation condition of the system is solved.

The invention discloses an RAG knowledge base construction method oriented to intelligent generation of a Modelica model. The RAG knowledge base construction method is used for constructing an electric vehicle system model. The method comprises the steps that a knowledge document of the electric vehicle field is acquired based on data of an electric vehicle physical system, the knowledge document is segmented, each segmented paragraph comprises a complete information unit, and the information units are stored as structured field knowledge; the method comprises the following steps: organizing documents according to component categories of an electric vehicle physical system, partitioning each document, obtaining text embedding vectors after partitioning, and storing the text embedding vectors into a vector database; the method comprises the following steps: selecting an RAG framework and a large language model, building a model generation agent, importing a Modelica language specification knowledge base and an electric vehicle field knowledge base, retrieving knowledge fragments from the knowledge bases by using the RAG framework for user questions in a question and answer form, and generating a model. Based on the method, efficient and accurate generation of the Modelica model of the electric vehicle assembly, the whole vehicle and the control system can be realized.

The invention relates to the field of automobile dynamics simulation, and particularly discloses a Modelica-based automobile single mass system modeling and adaptive parameter optimization method, which comprises the following steps of: analyzing and determining a simplified module and a component connection mode of an automobile single mass system according to requirements, and building a basic simulation model based on Modelica; performing initial simulation on the basic simulation model to obtain a simulation output result under the initial parameters; actual measurement data of a real vehicle are collected, a comprehensive error function is constructed, a parameter optimization engine is adopted to carry out iterative optimization on key physical parameters of the basic simulation model until convergence conditions are met, and optimized parameters are obtained; and feeding back the optimized parameters to the basic simulation model. According to the method, the advantages of Modelica multi-field physical modeling and a data-driven intelligent optimization algorithm are fused, the crossing from manual experience parameter adjustment to automatic intelligent optimization is realized, the dependence on accurate initial parameters is remarkably reduced, and the model simulation precision and the engineering application efficiency are effectively improved.