18 results about "Thermodynamic equations" patented technology

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.

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 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 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 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.

The present application provides a kiln temperature prediction method based on the fusion of space-time Transformer and physical information neural network (PINN), belonging to the technical field of intelligent control of industrial kiln. The method collects historical combustion parameters of the burner, models time evolution and space coupling through a space-time coupling Transformer model, captures the time sequence dependence of the combustion parameters through a time attention mechanism, models the heat coupling effect between multiple burners through a space attention mechanism, then introduces a PINN framework and embeds the oxygen-enriched combustion thermodynamic equation as an explicit constraint into the training process, ensures that the prediction conforms to the energy conservation and heat transfer law through a physical loss function, and realizes parameter inversion through learnable physical parameters to automatically discover the true combustion characteristics, thereby fusing the deep learning fitting capability and the prior knowledge of physical law, and improving the accuracy, interpretability and generalization ability of kiln temperature prediction.

The invention discloses a simulation model establishment method of a vehicle power system and a related device, and relates to the technical field of simulation, and the method comprises the steps: constructing a part simulation unit and a sub-simulation unit of the part simulation unit according to the connection relation between parts of a part topological structure of the vehicle power system; a two-layer structure is constructed based on a topological structure and heat interaction, heat interaction is refined into a component simulation unit, and the accuracy degree of determining the local temperature through a simulation model is improved. And constructing a first mapping relation and a second mapping relation to realize configuration of performance parameters of the component simulation unit and the sub-simulation unit at different temperatures. And constructing a thermodynamic equation of the sub-simulation unit. The temperature of the sub-simulation unit is determined based on the thermodynamic equation, the accurate temperature change condition of the simulation unit and the sub-simulation unit is obtained, refined simulation of the heat transfer process of the vehicle power system is achieved, and technical support is provided for researching the local overheating problem.

The application discloses a kind of lithium ion battery distributed thermal process sensor fault estimation method, comprising: using two-dimensional partial differential equation to describe rectangular lithium ion battery distributed thermodynamic equation;Distributed thermal model of large size lithium ion battery is constructed;Using Chebyshev-Galerkin method, distributed thermodynamic equation and distributed thermal model are decomposed into reduced order model described by standard state space equation in time domain;State variable in reduced order model is constructed using sensor measurement output and a Hurwitz matrix, and enhanced reduced order model is constructed;Enhanced adaptive observer and error state space equation are constructed using enhanced reduced order model;Based on error state space equation, the fault strength of temperature sensor that fails is estimated using fast adaptive algorithm.The application can effectively estimate sensor fault, including time-invariant and time-varying fault, and single sensor and multiple sensor fault.

The invention discloses a thermo-sensitive load orderly recovery method and system considering a rebound effect, and the method comprises the steps: obtaining a load thermal characteristic parameter of each load, and collecting the indoor temperature and outdoor temperature of each load in real time; based on the constructed thermodynamic equation, equivalent heat capacity and an equivalent heat conductivity coefficient are obtained, and a polymerization heat gap of the load is calculated; obtaining a future load prediction curve based on the total load, the weather forecast data and the aggregation heat gap within the predetermined time length; a multi-objective optimization function is constructed by considering a load peak-valley difference, a user comfort level and a power grid security constraint, and an optimal recovery scheme is solved; target recovery load power is calculated, and theoretical recovery load power is determined and corrected in combination with the actual load of the power grid; and carrying out load recovery based on a corrected result. According to the method, dynamic thermal characteristic modeling and multi-target optimization control are introduced, so that ordered recovery and rebound effect suppression of the temperature-sensitive load are realized on the premise of considering the safety of the power grid and the comfort level of the user.

The application provides a simulation verification method for the ejection property of a micro supercharging device, which is designed by combining the integrated application principle of gunpowder, a thermodynamic equation and a physical motion equation, obtaining simulated speed and acceleration, and comparing the simulated speed and acceleration with the initial speed and maximum overload measured in the actual test process to complete the simulation verification of the ejection property. The simulation of the application has high authenticity, can efficiently solve the high-overload safety hazard during the ejection of the barrel, and provides a good adjustment basis for the ejection parameters.

The invention discloses a design method for an exhaust diffuser of a high-altitude test bed. The design method comprises the following steps that firstly, the inlet area and the outlet area of the exhaust diffuser are solved based on a one-dimensional thermodynamic equation; 2, based on an existing high-altitude test bed, establishing an equal-proportion two-dimensional simulation model, and verifying the reliability of the simulation model; 3, the reflux quantity at an inlet of the exhaust diffuser is obtained; and 4, solving the distance between the inlet of the exhaust diffuser and the outlet of the exhaust nozzle and the diffusion angle when the injection coefficient is maximum on the premise of meeting the constraint conditions through a multi-task deep learning model. The advantages of a one-dimensional thermodynamic equation and numerical simulation are combined, and the exhaust diffuser at the front end of the exhaust system is designed for a high-altitude test bed of an existing exhaust system.