37 results about "Thermodynamic equations" patented technology

The invention discloses an intelligent fault diagnosis method fusing state monitoring and a multi-modal large model, and the method specifically comprises the steps: synchronously collecting time sequence data and a space image through a heterogeneous sensor group and monitoring equipment disposed in power grid equipment, and forming original data; based on the original data, a physical constraint feature vector is generated in combination with an equipment thermodynamic equation and a material deformation rule; performing health index prediction through the lightweight LSTM network based on the physical constraint feature vector; when detecting that the health indexes continuously decrease, clustering an HI time sequence curve by adopting a Gaussian mixture model, judging a degradation stage according to a clustering center distance, and obtaining a stage recognition result; and based on finite element simulation parameters, introducing a reinforcement learning model, optimizing the simulation parameters by taking maintenance cost minimization as a target, and outputting a predictive maintenance work order. According to the invention, intelligent fault diagnosis and accurate maintenance of the power grid equipment are realized, the fault processing efficiency and accuracy are improved, and the power failure loss is reduced.

The invention relates to the field of performance testing of road engineering materials, and discloses an emulsified asphalt in-situ cold-recycled mixture high-temperature creep test method which comprises the following steps: preparing a test piece: presetting an old material-emulsified asphalt standard test piece, and embedding a moisture regulation and control device to generate a controllable gradient; multi-field loading: coupling an unsteady-state temperature field, a moisture gradient field and a time-varying attenuation stress field to construct a composite loading environment; synchronously aligning data: synchronously acquiring macro strain and micropore data, and realizing cross-scale alignment through a space-time matrix; performing parameter inversion: inverting nonlinear constitutive parameters by combining macro and micro data, and constraining a solution space by using a thermodynamic equation; and intelligent termination output: triggering a termination mechanism based on a multi-dimensional criterion and outputting damage evolution data. Through multi-field coupling loading, cross-scale data fusion and intelligent criterion cooperation, the problems of traditional test environment distortion, parameter deviation and criterion lag are solved, and accurate analysis of the high-temperature creep behavior of the emulsified asphalt reclaimed material and reliable prediction of the performance degradation law are achieved.

The invention provides a method for detecting a gas production state after thermal runaway of a battery, which comprises the following steps: placing a lithium battery in a closed anti-explosion tank, heating to trigger runaway, constructing a coupling matrix through pressure and temperature data acquired by a dynamic pressure compensation model, and correcting pressure distribution by using a nonlinear thermodynamic equation; analyzing the concentration of gas components by adopting a Fourier transform infrared spectrometer, and calculating the total gas production amount by combining multiple regression analysis; and quantizing energy dissipation based on a second law of thermodynamics to generate an energy release characteristic matrix, mapping the energy release characteristic matrix to a high-dimensional space to generate a risk index, and finally predicting thermal runaway probability distribution through a support vector machine. According to the method, high-precision measurement and analysis of the gas production rate in the thermal runaway process of the battery are realized through dynamic pressure compensation, multi-dimensional gas component analysis and nonlinear thermodynamic modeling, and the testing efficiency and the early warning capability of the battery are improved.

The invention discloses a data center low-carbon control method and system, and relates to the technical field of data centers, and the method comprises the steps: collecting the environment parameters and equipment operation data of a data center in a hot-summer and warm-winter region in real time based on a multi-mode sensor network; constructing a climate-adaptive space-time diagram convolutional network, taking a server rack, a cooling tower and a power distribution cabinet as nodes, taking a cold and hot flow path as a dynamic weighted edge, embedding climate feature vectors, and generating a thermal load prediction map; a dynamic refrigeration strategy and a server load migration strategy are generated by taking a thermodynamic equation and a fluid dynamic model as strategy optimization boundary conditions through a physical constraint reinforcement learning framework and combining a thermal load prediction map; and based on the dynamic refrigeration strategy, the air conditioning system is controlled to start a hybrid refrigeration mode according to the real-time climate type, the water cooling main cycle and indirect evaporative cooling standby loop are synchronously operated, and the cold charging and discharging logic of the chilled water storage system is switched according to the electricity price time period.

The invention relates to the technical field of gas turbine combined cycle systems, in particular to a load prediction method and system for a gas turbine combined cycle system based on a nonlinear regression model.The method comprises the steps that historical data of the cycle system are obtained, and the historical data comprise operation parameters, heat efficiency and system loads; based on the thermodynamic equation and the operation parameters, calculating to obtain the theoretical efficiency of the circulating system; using the operation parameters, the thermal efficiency and the theoretical efficiency to form a historical feature set; and predicting the load of the gas turbine combined cycle system. According to the method, a feature set containing real-time state monitoring, actual performance evaluation and physical limit constraint is constructed, irreversible loss in system operation is quantified, physical constraint conditions are provided for prediction, overfitting or working condition extrapolation failure caused by lack of physical significance of a pure data driving model is avoided, and prediction accuracy is improved.

The invention discloses a die temperature control method, electronic equipment and a storage medium, and the method comprises the steps: obtaining an infrared image of a die-casting die and the real-time temperature data of an internal pre-buried thermocouple, fusing the infrared image and the thermocouple data, generating a three-dimensional correction temperature field containing the surface and internal temperature of the die, and storing the three-dimensional correction temperature field in the die-casting die; when the three-dimensional correction temperature field is abnormal, an adjustment value of a target adjustment parameter is output through a parameter prediction model, thermodynamic equation residual constraint is introduced into a loss function when the parameter prediction model is trained, the adjustment value is sent to a mold temperature control system to be executed, a new temperature field is collected in the next production cycle, and if the deviation between the new temperature field and the target temperature field exceeds a threshold value, the mold temperature control system is started. And if so, incrementally adjusting the parameters through a PID algorithm. By means of the die temperature control method, the die temperature of the die-casting model in the die-casting process can be controlled, and the speed and precision of die temperature control can be improved.

The invention discloses a temperature prediction control method and system for materials at an outlet of a loosening and dampening machine. The method comprises the steps of establishing a thermodynamic equation model, collecting and preprocessing data, optimizing and verifying model parameters, predicting and controlling the temperature in real time and the like, and comprises the following steps of: splitting a prediction model into a quantifiable linear part and a nonlinear residual term which is difficult to directly measure through correlation analysis and time delay evaluation on system input variables; and the interpretability of a physical model and the nonlinear fitting capability of machine learning are fully utilized, so that high-precision and low-calculation-complexity temperature prediction control is realized.

The invention provides a kiln temperature prediction method based on fusion of a space-time Transform and a physical information neural network (PINN), and belongs to the technical field of industrial kiln intelligent control. The method comprises the steps that historical combustion parameters of a burner are collected, time evolution and space coupling are modeled through a space-time coupling Transform model, a time attention mechanism captures combustion parameter time sequence dependence, and a prediction result is obtained; a space attention mechanism models a heat coupling effect among multiple burners, then a PINN framework is introduced, an oxygen-enriched combustion thermodynamic equation is used as an explicit constraint to be embedded into a training process, it is ensured that prediction accords with energy conservation and heat transfer rules through a physical loss function, learnable physical parameters are adopted to realize parameter inversion so as to automatically find real combustion characteristics, and the prediction accuracy is improved. Therefore, deep learning fitting ability and physical law priori knowledge are fused, and accuracy, interpretability and generalization ability of kiln temperature prediction are improved.

A system and method for chemical process simulation integrates thermodynamic models into machine learning frameworks to enhance predictive accuracy and optimize chemical separation processes. The system employs a variety of methods, such as using a physics- informed neural network (PINN)-like approach and methods that embed thermodynamic equations directly into the architecture, ensuring adherence to physical laws such as conservation of energy and mass. Additionally, experimental data, virtual sampling points derived from thermodynamic models, and auxiliary predictions capturing interdependencies between physical and chemical variables are combined into an augmented dataset for training. A hybrid loss function balances contributions from experimental data and thermodynamic insights, dynamically adjusted via a scaling hyperparameter. The system predicts target properties, such as adsorption efficiency, under diverse operating conditions while maintaining compliance with thermodynamic constraints.

The invention belongs to the field of marine seismic exploration, and particularly relates to a non-ideal gas gun wavelet simulation method based on a unified bubble theory, which comprises the following steps: acquiring sea environment parameters; designing air gun working parameters and an air gun array; calculating the bubble radius, the bubble volume, the bubble pressure, the bubble heat loss rate, the gas release rate, the bubble volume change rate and the bubble temperature change rate at each moment according to the sea area environment parameters, the air gun working parameters and the air gun array; and calculating a pressure signal generated at any point in the seawater by bubble oscillation at each moment. According to the method, a non-ideal gas thermodynamic state equation is introduced on the basis of a traditional bubble dynamic model, and an evolution mechanism of the whole process of generation, expansion, contraction and the like of bubbles is considered in a unified manner on the basis of a unified bubble theory, so that sound wave wavelet signals generated by an air gun in an actual working environment are simulated more accurately; and the accuracy and adaptability of neutron wave modeling and inversion in seismic exploration are improved.

The present invention discloses a method for testing the kinetics of high-pressure ethylene homopolymerization or copolymerization reactions, comprising measuring the temperature and pressure inside a reactor during a blank experiment and during the ethylene homopolymerization or copolymerization reaction to be tested; obtaining a correlation equation for the change in compression work pressure to the change in system temperature based on the blank experiment data; processing the data of the homopolymerization or copolymerization reaction to be tested using the correlation equation to eliminate the effect of pressure on the reaction temperature; and fitting the kinetic parameters of the ethylene homopolymerization or copolymerization reaction based on the processed data. The present invention provides that the effect of pressure on the reaction temperature can be effectively eliminated through precise temperature and pressure monitoring and the application of the thermodynamic equation of state, and that the reaction kinetic parameters can still be accurately fitted under operating conditions with large pressure fluctuations. The method is simple to operate and has accurate measurement, providing important technical support for the optimization and control of high-pressure polymerization processes.

The application discloses a die temperature control method, an electronic device and a storage medium. The die temperature control method comprises the following steps: acquiring an infrared image of a die casting die and real-time temperature data of an internally embedded thermocouple, fusing the infrared image and the thermocouple data, generating a three-dimensional corrected temperature field containing the surface and internal temperature of the die, when the three-dimensional corrected temperature field is abnormal, outputting an adjustment value of a target adjustment parameter through a parameter prediction model, wherein the parameter prediction model introduces a thermodynamic equation residual constraint in a loss function during training, sending the adjustment value to a die temperature control system for execution, collecting a new temperature field in the next production cycle, and if the deviation from the target temperature field exceeds a threshold value, incrementally adjusting the parameter through a PID algorithm. Through the die temperature control method, the die temperature of the die casting model in die casting can be controlled, and the speed and accuracy of die temperature control can be improved.

The invention discloses a natural gas consumption and generating capacity prediction system, which comprises a data acquisition unit used for acquiring a natural gas calorific value, an environment temperature, a generating curve and a heat supply amount; the data preprocessing unit is used for denoising the time sequence data in the data acquisition unit by adopting wavelet transform; the multi-modal data fusion modeling unit is used for constructing a reference model based on a thermodynamic equation; the dynamic adjusting unit adopts an FTRL-Proximal online optimizer to update model parameters in real time, and meanwhile, failure features are automatically eliminated through L1 regularization; the hybrid prediction unit inputs the natural gas heat value, the environment temperature, the power generation curve and the heat supply quantity processed by the data preprocessing unit into a thermodynamic equation to construct a reference model, and predicts daily power generation capacity and natural gas usage amount in real time; and the man-machine interaction unit is used for screening the automatically generated next-day power generation curve and marking an uncertain time period, a user adjusts key nodes, and a compliance curve is re-fitted through constraint optimization.

A kind of topology-driven thermodynamic cycle system steady-state operation data acquisition method;The thermodynamic cycle system steady-state operation data acquisition method in the present application is to obtain the topology structure corresponding to various devices in the constructed thermodynamic cycle system, establish a topology standard information library after analyzing and identifying the corresponding topology structure, establish the corresponding constraint equation according to the topology standard information library, obtain the output system steady-state operation data through the calculation of the corresponding constraint equation, the process of obtaining the topology structure corresponding to various devices in the constructed thermodynamic cycle system is to obtain the topology structure corresponding to the constructed thermodynamic cycle system, determine the composition structure and connection relationship corresponding to the topology structure, and identify the composition structure and connection relationship corresponding to the topology structure;The present application analyzes the system topology structure and automatically constructs the system thermodynamic equation, realizes the unified modeling and solving of the thermodynamic cycle system, so as to improve the generality and standard corresponding automation of system modeling.

The invention discloses a multi-modal fusion and reasoning enhanced boiler four-tube intelligent monitoring method and system, and the method comprises the steps: collecting unstructured asynchronous event flow data through a sensor group disposed on the inner wall of a boiler, carrying out the processing of the asynchronous event flow data, generating a sparse pulse feature tensor, and carrying out the processing of the sparse pulse feature tensor; the sparse pulse feature tensor is mapped to a high-dimensional potential space, the future physical state of the boiler system is dynamically predicted in the high-dimensional potential space, the physical conservation law constraint is introduced in the training process of an industrial world model, it is ensured that prediction of the future physical state conforms to hydromechanics and thermodynamic equations, and the prediction accuracy is improved. By predicting a set of states in a physical state parameter set, a future physical state is comprehensively predicted, so that an artificial intelligence system conforms to the pre-judgment capability of a physical law and obtains key dynamic details for multiple physical states, a perception-decision-action closed loop is realized, and the boiler monitoring efficiency is greatly improved.

The present invention relates to the field of grain temperature technology and discloses a thermodynamically based grain pile temperature calibration method and system. This method aims to address the high cost and poor accuracy of existing grain pile temperature detection methods. The solution primarily includes: installing a sensor matrix in the grain pile and obtaining the temperature of each sensor-detected point in the sensor matrix; discretizing the space where the grain pile resides into grain pile grids, determining the temperature of each grain pile grid based on the temperature detected by each sensor, and generating a temperature distribution field representing the temperature distribution; constructing a thermodynamic equation for each grain pile grid, discretizing the thermodynamic equation to generate a discrete equation, and converting the discrete equation into a linear equation system in matrix form; solving the linear equation system using the temperature of each sensor-detected point as a boundary condition, and calibrating the temperature based on the solution results. This method reduces cost and improves accuracy, making it particularly suitable for granaries.

The invention discloses a quantitative prediction method for an icing load of a power transmission line, and the method comprises the following steps: collecting and fusing multi-source meteorological data: employing a dual-polarization radar to invert the particle size distribution of liquid drops through differential reflectivity, calculating the content of supercooled water through a differential phase, and obtaining an atmosphere liquid water path and a vertical temperature profile; according to the measured wind speed, wind direction and wind shear index, an environment parameter field is constructed, and icing early warning is triggered; conducting wire state parameter real-time sensing: monitoring conducting wire three-axis amplitude and galloping frequency, obtaining conducting wire surface temperature gradient, quantifying damping effect of conducting wire galloping on collision efficiency and providing boundary conditions for a thermodynamic equation; mixed phase state dynamic discrimination: constructing a phase state discrimination matrix based on a combination rule of a temperature interval and droplet particle size distribution; dynamic collision efficiency correction: calculating the dynamic collision efficiency by adopting an unsteady state collision formula; thermodynamics-fluid mechanics coupling calculation is carried out; and ice coating load accumulation and output: calculating the ice coating thickness, mass and mechanical load.

The invention relates to the technical field of industrial process control and low-temperature engineering, in particular to a phase change anti-freezing control method based on state equation constraint, and overcomes the defects of response lag and non-intrinsic safety of traditional temperature feedback control under a cryogenic transient working condition. The method comprises the steps that the pressure of a refrigerant containing cavity is collected in real time, and the unmeasurable hysteresis freezing risk is mapped into a saturation temperature boundary capable of being instantaneously observed through a thermodynamic state equation (EOS); and constructing a freezing margin index, and when the index is in contact with a safety threshold value, triggering the hard constraint override logic with the highest priority, and forcibly adjusting the gas phase resistance so as to increase the phase change backpressure. According to the method, the sound velocity transfer characteristic of pressure waves is utilized, a physical anti-freezing barrier is established within the millisecond level, relying on postmortem detection and melting remediation in the prior art is not needed, and zero-lag prevention and multivariable decoupling control in the cryogenic heat exchange process are achieved.

The invention relates to the field of compressed air energy storage, in particular to a method, equipment and medium for determining the deflation rate of an underground gas storage in the overhaul period, and the method comprises the following steps: S1, determining calculation parameters, and writing a thermodynamic equation in software; s2, setting a deflation rate mouth and a single iterative calculation time period, substituting calculation parameters and mouth into a thermodynamic equation for iterative calculation, and outputting final lining stress-strain parameters; s3, adjusting the mouth, substituting the mouth into the thermodynamic equation, and repeating the step S2 to obtain multiple groups of final lining stress-strain parameters; s4, generating a fitting curve graph according to the mouth and the corresponding final lining stress-strain parameters; and S5, determining the deflation rate mouth according to the fitting curve graph. According to the method, the thermodynamic equation can be incorporated into software, the fitting curve is generated through loop iterative calculation, the deflation rate can be comprehensively determined according to the fitting curve, the maintenance efficiency can be improved, and a scientific reference basis is provided for determination of the deflation rate.

The application discloses a kind of data center low-carbon control method and system, it is related to data center technical field, the method includes: based on multimodal sensor network real-time collection summer hot winter warm region data center's environmental parameter and equipment operation data;Climate adaptive spatiotemporal graph convolution network is constructed, with server rack, cooling tower, switchgear as node, cold and hot flow path is dynamic weighted edge, embedding climate feature vector, generates heat load prediction atlas;Through physical constraint reinforcement learning framework, thermodynamics equation and fluid dynamics model are regarded as strategy optimization boundary condition, combined with heat load prediction atlas, generate dynamic refrigeration strategy and server load migration strategy;Based on dynamic refrigeration strategy, control air conditioning system according to real-time climate type starts mixed refrigeration mode, water-cooling main cycle and indirect evaporative cooling standby loop are synchronously operated, and according to electricity price time period, the logic of filling and discharging cold of water storage refrigeration system is switched.

The present invention relates to the field of natural gas hydrate freeze-blocking prediction and provides a method for predicting and evaluating natural gas hydrate freeze-blocking in wellbores. The method comprises: obtaining real-time environmental data in the wellbore, substituting the real-time environmental data into a set of thermodynamic equations to determine the natural gas hydrate generation rate, and simulating the generation rate to obtain a simulated amount of natural gas hydrate generation; obtaining historical environmental data and historical natural gas hydrate generation data collected by sensors to construct sample vector data; training a neural network model with the sample vector data, and then substituting the real-time environmental data into the data to obtain a modeled amount of natural gas hydrate generation; and determining predicted amounts of natural gas hydrate generation and predicted blockage rates at multiple locations and times in the wellbore based on the simulated and predicted generation amounts. The method combines the physical constraints of the generation process of the thermodynamic equations with the predictive capabilities of the neural network model for unknown situations, making the predicted values ​​more accurate and reliable.

The invention relates to the technical field of industrial signal processing, solves the technical problems of transient stage data distortion and unreliable differential characteristics caused by sparse sampling of a sensor in a photovoltaic boron diffusion process, and particularly relates to a data processing method for photovoltaic boron diffusion process parameters. Comprising the following steps: S1, collecting original time sequence data of a multi-source sensor in a photovoltaic boron diffusion process, constructing a data matrix, calculating a composite gradient amplitude based on the data matrix, dividing the composite gradient amplitude into a transient stage and a steady stage, and constructing a physical constraint equation set; and S2, positioning a temperature inflection point position in a transient stage, and optimizing and reconstructing an exponential basis function of a transient stage temperature curve through a physical constraint equation. The method comprises the following steps: pre-judging a temperature inflection point in a transient stage, constructing an index basis function, and optimizing parameters through a thermodynamic equation; the bottleneck of interpolation distortion can be broken through, the real gradient of the abrupt change region is restored under sparse sampling, and the problem of distortion process dynamic characteristics of a traditional method is solved.

The application discloses a kind of polymer stress-aging sensitivity characterization methods under multi-field combined action, belong to dielectric material aging evaluation technical field.The method includes by constructing the molecular chain fracture energy barrier reduction model based on transition state theory, obtain theoretical energy barrier reduction threshold and aging rate constant, calculate to obtain the dynamic life index under different aging time;Based on the polymer sample after aging, by thermogravimetric analysis and Coats-Redfern method constructs thermal decomposition kinetics model, and the activation energy under different aging states is calculated;By comparing and analyzing the change law of life index and activation energy, the comprehensive sensitivity evaluation under the electric-thermal coupling effect is carried out;The physical self-consistency of model is verified by introducing entropy compensation effect thermodynamic equation.The application realizes the comprehensive quantitative characterization of polymer stress-aging sensitivity under electric-thermal combined action, with the advantages of clear physical meaning, can simultaneously characterize electric and thermal stress, clear mechanism explanation and the like.

The invention relates to the cross technical field of deep learning and meteorological monitoring, in particular to an extreme weather intelligent monitoring and early warning method and system based on deep learning, and the method comprises the steps: collecting and processing data, and constructing a meteorological state tensor; inputting the tensor into a space-time Transform architecture with a meta-learning capability, and extracting cross-scale meteorological features by capturing long-range correlation through an encoder and integrating a space-time convolution gating cycle unit through a decoder; an adversarial training mechanism is introduced, and a numerical weather forecast mode is used as a judgment reference to optimize features; establishing a federal learning model updating mechanism to realize distributed optimization; and inputting the predicted trajectory into a power grid digital twin system, solving an equipment thermodynamic equation through a physical information neural network, and feeding back to a feature extraction process to form a closed loop. According to the method, the problems of low identification accuracy and poor early warning timeliness caused by multi-source heterogeneity and strong time sequence nonlinearity of extreme meteorological data are solved.

The application discloses a fire-fighting effect evaluation method under digital modeling, and relates to the technical field of fire-fighting evaluation and public safety management. In a fire-fighting layout digital model, a parallel simulation calculation task is established for each preset fire scene, a dynamic fire development thermodynamic equation and a smoke diffusion calculation model are introduced, a flame heat spreading and diffusion process, a three-dimensional diffusion process of toxic smoke, an automatic starting response process of fire-fighting facilities after reaching a threshold value and an escape and evacuation process of people in a building are independently simulated under multi-level fire conditions, and dynamic simulation characteristic data under each fire scene is batch output. The application constructs a set of physical level general and interactive digital modeling and simulation framework. By introducing a bottom fluid partial differential equation and a thermodynamic model, the application can seamlessly adapt to modern buildings of any heterogeneous structure and complex floor without the need of developing a special model for each evaluation task.

This invention discloses a big data-based intelligent heating management method and system, belonging to the field of heating management technology. The method includes: abstracting the heating system into a directed graph structure and constructing an adjacency matrix that integrates physical coupling and adaptive learning; collecting system operation data to construct node feature matrices and edge feature matrices; constructing a spatiotemporal graph neural network (PI-STGNN) that integrates thermodynamic equation constraints, outputting the predicted state values ​​of each node in the pipeline network; and based on the prediction results, employing a centralized training-distributed execution multi-agent reinforcement learning framework, with system energy efficiency and user comfort as optimization objectives, outputting the optimal control action. This invention combines graph neural networks, physical information constraints, and multi-agent reinforcement learning to achieve intelligent and precise control of the heating system, effectively improving heating quality and energy utilization efficiency.

The invention relates to the technical field of chemical process simulation, in particular to a gas-liquid flash calculation method and device, a medium and a product. The method comprises the following steps: acquiring initial parameters of a multi-component system, performing composition estimation on the multi-component system based on a preset thermodynamic equation, determining a liquid phase fugacity coefficient and a gas phase fugacity coefficient of each component, performing lower limit constraint on the fugacity coefficients, and determining a phase equilibrium constant of each component based on the constrained fugacity coefficients. The method comprises the following steps: constructing a gas phase fraction solving equation through a phase equilibrium constant and a feeding molar fraction, and determining a target expression corresponding to the gas phase fraction solving equation based on a relative size between a current gas phase fraction and a preset threshold value, so as to iteratively carry out mixed solving on the target expression to update the gas phase fraction until the gas phase fraction meets a preset convergence condition, the gas-liquid flash calculation result representing that the current phase composition of the multi-component system meets the gas-liquid two-phase equilibrium condition is obtained, so that the efficiency and the accuracy of gas-liquid flash calculation are improved.

The application discloses a method and device for determining main control factors of mechanical properties of non-homogeneous dolomite, relates to the technical field of rock mechanics, and aims to improve the accuracy and efficiency of determining main control factors of mechanical properties of dolomite. The main technical scheme of the application is as follows: the discrete element method is used to obtain the particle contact force distribution according to the contact information between particles in the microstructure model of dolomite; the numerical method is used to simulate the flow of pore water in the pore network structure of the microstructure model of dolomite, and the pore pressure distribution is obtained; the thermodynamic equation is used to determine the temperature change effect according to temperature data; the multi-physical field coupling model is constructed according to the particle contact force distribution, the pore pressure distribution and the temperature change effect; if the calculation complexity of the multi-physical field coupling model exceeds a preset threshold, the multi-physical field coupling model is simplified to obtain a simplified model; and the multi-physical field coupling model and the simplified model are used to determine the main control factors of the mechanical properties of dolomite under different environmental conditions.

The application discloses a supercritical carbon dioxide cycle system of a laser fusion reactor and a design method thereof, and is used for establishing a high-efficiency power cycle system suitable for a large temperature difference of a multi-heat source of a laser fusion reactor primary loop; the cycle system comprises a heat exchanger connected with heat sources of different temperature ranges, a turbine, a regenerator, a compressor and a cooling device; a high-temperature heat exchanger and a low-temperature heat exchanger are arranged respectively and used for leading out heat of a high-temperature lithium-lead alloy loop and a low-temperature supercritical carbon dioxide loop in the laser fusion reactor; the design method firstly establishes a multi-stage thermodynamic model based on thermodynamic system parameters and a specific configuration of the cycle system; then, the thermodynamic equations of each device are calculated to obtain a cycle system thermal efficiency and thermodynamic parameters of each device; finally, a particle swarm algorithm is used for optimization. The application provides two cycle systems suitable for a large temperature difference of a laser fusion reactor primary loop and a design method for realizing optimal system cycle thermal efficiency.

The invention discloses an icing prediction model construction method of a power transmission line and an icing monitoring method and system, and relates to the technical field of power transmission line safety monitoring, and the technical scheme is characterized in that historical icing data and corresponding historical monitoring data of the power transmission line are acquired; constructing a physical information neural network model; wherein the physical information neural network model takes time, spatial position and environmental parameters as input, and takes icing thickness, conductor temperature, conductor strain and risk level as output; taking residual terms of a thermodynamic equation and a mechanical equation which are constructed in advance and used for describing the wire icing process as a physical constraint loss function of the physical information neural network model, and embedding the residual terms into a training loss function of the physical information neural network model; and inputting the historical icing data and the corresponding historical monitoring data into the physical information neural network model for training, and when a training loss function is converged, obtaining a trained icing prediction model.