61 results about "Thermodynamic simulation" patented technology

The technical scheme of the invention relates to the technical field of data processing, in particular to a method for improving the rapid cooling efficiency of a high-temperature graphite furnace, and the method comprises the steps: carrying out the time axis normalization processing based on a cubic spline interpolation algorithm, combining an adaptive Kalman filter, fusing multi-source data, and generating a time-synchronized three-dimensional temperature field reconstruction matrix; the problem of temperature field reconstruction lag caused by insufficient time sequence alignment precision of multi-modal sensing data is solved. And the reinforcement learning network dynamically optimizes the control parameter weight through the residual feature map, preferentially corrects the deviation of the rotating speed of the induced draft fan and the gas injection angle in a high-error region, and feeds back the deviation to the thermodynamic simulation model for online iterative updating of the specific heat capacity parameters of the material, so that model prediction and dynamic calibration of the actually measured thermal field are realized. Through multi-source data alignment, closed-loop feedback and cooperative control architecture, the timeliness and execution precision of cooling path planning are remarkably improved, and the cooling efficiency and equipment reliability of the high-temperature graphite furnace are optimized.

The invention discloses a water-cooling energy consumption adaptive regulation and control method suitable for a data center, and the method comprises the following steps: obtaining a multi-dimensional operation parameter set of the data center in real time, and generating a real-time thermal load density distribution diagram reflecting the heat distribution condition in a machine room; calculating the overall thermal deviation degree and the local hot spot intensity, and solving through a dynamic energy efficiency optimization function to obtain a target system supply and return water temperature difference and a target cooling liquid flow; inputting into a hydraulic and thermodynamic simulation model, and generating an optimal multi-device coordinated regulation instruction set; and executing the multi-device coordinated regulation instruction set, and then performing parameter calibration on the hydraulic and thermodynamic simulation model. The method has the following advantages and effects that the thermal environment of the machine room can be sensed in real time, the energy consumption and heat dissipation requirements can be intelligently balanced, and multiple devices in the cooling system can be cooperatively regulated and controlled, so that the overall energy consumption of the cooling system is optimized while the local overheating risk of the machine room is thoroughly eliminated.

The invention relates to the technical field of data processing, in particular to a data enhancement prediction method for the thermoelastic quality factor of a micro-hemispherical resonator, which comprises the following steps: S1, determining the radius size of the manufactured micro-hemispherical resonator, determining the simulation value range of the radius and thickness of an anchor point of the resonator, and determining the simulation value range of the thickness of the resonator; harmonic oscillators with different geometric boundary dimensions are established through high-temperature blow molding simulation; s2, thermodynamic simulation is carried out on the harmonic oscillator appearance obtained through blow molding simulation, and a harmonic oscillator thermoelastic quality factor data set is established; s3, performing data enhancement on the thermoelastic quality factor data set by adopting a cubic polynomial fitting method; and S4, training the enhanced thermoelastic quality factor data set by using a deep learning model to obtain a harmonic oscillator thermoelastic quality factor prediction model. According to the method, the bottleneck of scarcity of high-precision simulation data is broken through, unification of high precision and high efficiency is realized, the robustness and reliability of a prediction model are improved, and the method has wide engineering applicability and popularization value.

The invention provides a large aqueduct intelligent temperature control system and method based on a digital twinning technology, and relates to the technical field of aqueduct temperature monitoring. A three-dimensional digital model of an aqueduct is constructed, and geometric parameters of the three-dimensional digital model are corrected through three-dimensional point cloud data of the aqueduct, so that the temperature of the aqueduct can be accurately controlled. Thermodynamic simulation calculation is carried out based on the three-dimensional digital model to obtain a simulation result, the aqueduct concrete temperature field simulation result is compared with the collected aqueduct temperature to obtain temperature deviation, parameters of thermodynamic simulation calculation are corrected according to the temperature deviation, and therefore the simulation result is updated, and the accuracy of the simulation result is improved. According to simulation results obtained after geometric parameters of the three-dimensional digital model and parameters obtained after thermodynamic simulation calculation are corrected, the water flow of the corresponding water pipe is controlled for cooling, local targeted cooling is achieved, and the problem that targeted cooling cannot be achieved for too high local hydration heat in the prior art is solved; the method is suitable for intelligent temperature control in the hydration heat process of the large aqueduct.

The invention discloses a boiler tube group diagnosis and maintenance method, and relates to the technical field of boiler monitoring, and the method comprises the following steps: building a thermodynamic simulation model, and carrying out reference simulation to obtain a reference system response parameter and a reference thermodynamic parameter; based on the simulation model, multiple times of simulation are carried out by adjusting the operation condition and the fault state, and a tube panel group health evaluation model is constructed by utilizing machine learning; introducing a thermal medium into the target tube group system, collecting actual measurement data, and screening out a problem tube panel group through the health evaluation model; and performing three-dimensional laser scanning and thermal imaging image acquisition on the defective tube panel group, fusing temperature information and space coordinates through coordinate mapping to construct a three-dimensional temperature field data set, positioning an abnormal tube section needing to be maintained according to the three-dimensional temperature field data set, and performing local maintenance. According to the method, step-by-step positioning and targeted maintenance from system-level abnormity to pipe section-level defects can be realized, and the diagnosis accuracy and maintenance efficiency of the boiler pipe group are remarkably improved.

The invention discloses an operation and maintenance management method for a DFMA assembly type machine room, and relates to the technical field of machine room management, and the method comprises the steps: obtaining the type, size, power consumption and environment requirement data of all equipment in the machine room, and building an equipment information database; according to the equipment information database, space utilization rates under different equipment combinations are calculated, and an equipment density distribution diagram is generated; a thermodynamic simulation model is adopted to simulate the heat dissipation performance under the density distribution diagram of the equipment, and temperature field distribution data are obtained; for different equipment types, dividing temperature and humidity control areas, establishing an environment control strategy, and generating an environment control scheme; a machine learning algorithm is adopted, the temperature and humidity change trend is predicted according to historical data, and a control strategy is automatically adjusted; maximization of the space utilization rate of the machine room is achieved, safe and stable operation of equipment is guaranteed, and a scientific basis is provided for future expansion of the machine room.

The invention discloses a method and system for optimizing temperature uniformity of a hearth of a temperature isostatic pressing machine, and the method comprises the steps: arranging a plurality of layers of temperature measurement planes and a temperature monitoring network in the hearth, collecting all-region temperature data in real time, calculating three-dimensional temperature field distribution and uniformity indexes, generating a heater power regulation and control instruction in combination with the indexes, and controlling the temperature uniformity of the hearth of the temperature isostatic pressing machine. And dynamically adjusting the output power of the partitioned independent controllable heater array. According to the method, the whole area of a hearth is covered through multi-layer temperature measurement, temperature uniformity is evaluated through quantitative indexes, prospective power compensation is achieved with the help of a predictive control algorithm, control parameters are optimized through a thermodynamic simulation model, the problems that a traditional temperature isostatic pressing machine is incomplete in temperature monitoring and poor in heating regulation and control pertinence are solved, and the temperature isostatic pressing efficiency is improved. And the temperature uniformity of the hearth is greatly improved, the heating consistency of workpieces is guaranteed, and the device is suitable for a warm isostatic pressing machining scene of high-precision material forming.

The invention discloses a grey wolf algorithm flue gas waste heat control method and system introducing exhaust gas temperature prediction constraint, and the method comprises the steps: firstly building a thermodynamic simulation model of a flue gas waste heat system, collecting the working condition data of a unit in real time, and calibrating the model; under the targets of power supply coal consumption minimization, waste heat recovery rate maximization and the like, an exhaust gas temperature prediction result is used as a dynamic constraint, a grey wolf optimization algorithm is adopted to carry out multi-target optimization on parameters such as the opening degree of a flue gas baffle, the water supply distribution proportion of an economizer and the water flow of an air heater, and multiple regression or a BP neural network model is utilized to predict the exhaust gas temperature. If the predicted value is lower than the safety lower limit, dynamically adjusting the search direction to avoid an infeasible solution; and an optimization result is issued on line through a DCS system, and closed-loop control is realized. Compared with the prior art, on the premise that the safety of the exhaust gas temperature is guaranteed, the waste heat utilization rate is remarkably increased, power supply coal consumption is reduced, the capacity of adapting to different coal qualities and load fluctuations in a self-learning mode is achieved, and the good engineering application prospect is achieved.

The invention discloses a grey wolf optimization algorithm unit operation optimization method and system with a self-adaptive memory mechanism, and is applied to energy-saving optimization and operation control of a coal-fired power generation unit. The method comprises the following steps: firstly, constructing a thermodynamic simulation model of a unit, and setting a multi-objective optimization function; global optimization is carried out on the target function through a grey wolf optimization algorithm, an adaptive memory mechanism is introduced, a historical working condition memory bank is established, and an optimal solution is extracted from the memory bank in combination with similarity matching to guide population search. During each iteration, the memory bank content is dynamically updated according to the working condition change, the target weight can be adjusted according to the real-time working condition in the optimization process, and the stability and adaptability of the optimization result are improved. And an optimization result is issued to an execution mechanism through the DCS system, so that closed-loop control is realized, and the overall economy and the energy utilization rate of the unit are improved. The method can effectively solve the problems that a traditional optimization method is poor in adaptability, poor in target coordination and the like under complex working conditions, and has high engineering application value.

A method for optimizing process parameters of friction stir welding with synchronous rolling and a related device are provided. A heat source model is applied to a system finite element model, and heat transfer simulation is performed on the welding and cooling processes of friction stir welding. Furthermore, contact relationships and mechanical action relationships between a stirring tool finite element model and a weldment finite element model and between a roller finite element model and the weldment finite element model are applied in the system finite element model. Thermal-mechanical simulation is performed on the welding process, the cooling process and a clamp release process of friction stir welding with synchronous rolling with a temperature field result obtained from heat transfer simulation as an input. Based on the welding stress and welding deformation results obtained from thermal-mechanical simulation, process parameters are adjusted and re-simulated until optimal parameters are obtained.

The invention relates to the technical field of metal smelting, in particular to a metal smelting temperature control method and system. The system comprises a temperature monitoring module, a gas analysis module, a thermodynamic simulation module and a dynamic regulation and control module, the thermodynamic simulation module sets a fitness function according to the Gibbs free energy value, and obtains the optimal temperature and oxygen potential through a particle swarm optimization algorithm; the dynamic regulation and control module is used for converting the theoretical optimal temperature of the thermodynamic model into precise operation, and efficient and stable control over the smelting temperature is achieved. According to the method, the reaction process in the furnace and the current optimal temperature and oxygen potential are accurately controlled on the basis of the Gibbs free energy value and the particle swarm optimization algorithm, the theoretical optimal temperature of a thermodynamic model is converted into precise operation through multi-PID cooperative control, and efficient and stable control over the antimony smelting temperature is achieved; and therefore, the problem that the oxygen-enriched smelting temperature is not accurately controlled is fundamentally solved, the extraction rate of metal is increased, and energy consumption is reduced.

The present invention discloses a method and related device for optimizing additive manufacturing process parameters based on deformation analysis, relating to the field of additive manufacturing technology. The method comprises: constructing a thermo-solid coupling model based on the heterogeneous material parameters, structural characteristic parameters, and preset processing parameters of a target workpiece; constructing a melt channel model based on the preset processing parameters and structural characteristic parameters to generate a structural mesh of the target workpiece; performing temperature field distribution analysis based on the structural mesh in combination with a heat source model; performing thermodynamic simulation of the deposition process based on the structural mesh and combining it with the thermo-solid coupling model to perform elastic-plastic strain analysis; performing stress-deformation analysis of the workpiece during laser additive manufacturing based on elastic-plastic strain components and temperature field distribution data; and optimizing preset processing parameters based on the stress-deformation data in combination with a multi-objective function to perform laser additive manufacturing of the workpiece. The present invention improves the reliability of processing parameter optimization and effectively enhances the quality of laser additive manufacturing of the workpiece.

The invention relates to the technical field of double-component pouring sealant testing, and discloses a thermal conductivity testing method and system based on a double-component heat-conducting pouring sealant. The method comprises the following steps: classifying base material component proportions and curing conditions of the two-component heat-conducting pouring sealant, and determining a material curing state classification label; loading a corresponding heat conduction reference model, judging the thermal response coverage of the model, and generating correction parameters and updating the model by thermodynamic simulation when the thermal response coverage is insufficient; based on the updated model, applying heat source excitation to the sample under a preset temperature change gradient, and collecting surface dynamic temperature field distribution data; extracting a heat conduction offset position and an abnormal temperature rise node, and generating an abnormal thermal behavior identification group; obtaining temperature change correlation time of the offset position in the identification group, and calculating a thermal response delay interval and a conduction path offset; therefore, the temperature lag time length and the heat flux attenuation ratio of each layered interface of the sample in the continuous thermal cycle period are extracted, a layered thermal resistance response characteristic value is generated, and a reliable basis is provided for evaluating the heat-conducting property of the two-component heat-conducting pouring sealant.

The invention provides a methanol-diesel double direct injection compression ignition engine combustion system design method which comprises the following steps: establishing a methanol-diesel circulating injection quantity function based on a target methanol substitution rate to obtain a methanol circulating injection quantity and a diesel circulating injection quantity, parameters of a combustion system are designed according to the methanol circulating injection amount and the diesel circulating injection amount; a three-dimensional thermodynamic simulation model is constructed based on the design parameters, and a three-dimensional simulation result is obtained through simulation calculation; a combustion heat release rate in a pure diesel oil mode is simulated based on a multi-Weber function, the heat release rate is introduced into one-dimensional thermodynamic simulation software to obtain a pure diesel oil calibration model, and a methanol-diesel oil double direct injection simulation model is established based on the calibration model. And according to the three-dimensional simulation result, carrying out one-dimensional double-fuel-injection performance simulation verification on the methanol-diesel double-direct-injection simulation model. The relation between the small methanol substitution rate and the combustion system is concerned, the heat value of the fuel can be brought into play to a great extent, and the heat efficiency and output power of the engine are improved.

The invention relates to the technical field of power device heat dissipation, and discloses a power device packaging heat dissipation optimization method based on three-dimensional thermal field regulation and control. A power device packaging structure is divided into a heat source area, a heat dissipation substrate area and an interface material area, corresponding models are established after parameters of all the areas are determined, and a nonlinear thermodynamic simulation model is constructed. Thermal field data are obtained through transient thermal field simulation, an optimization objective function with a thermal field uniformity index, maximum temperature rise and thermal resistance as indexes is defined, and heat dissipation substrate parameters and interface material heat conductivity coefficient distribution are optimized through an improved particle swarm optimization algorithm. And performing sensitivity analysis by using an adjoint variable method, and dynamically adjusting model parameters according to whether the uniformity index of the thermal field reaches the standard or not. The method can accurately simulate the thermal field, optimize the heat dissipation performance in a multi-index manner, improve the optimization efficiency, effectively solve the packaging heat dissipation problem of the power device, and improve the performance and reliability of the power device.

The invention discloses an optimal design method and system for a precise mold cooling system, and relates to the technical field of mold design and manufacturing, the method comprises the following steps: obtaining structure parameters of a precise mold and thermophysical parameters of an injection molding material, and constructing a three-dimensional thermodynamic simulation model; performing temperature field dynamic simulation based on the three-dimensional thermodynamic simulation model, and identifying a hot spot distribution area on the surface of a mold cavity; and performing multi-objective optimization according to the hot spot distribution area, performing iterative optimization on topological structure parameters of the cooling channel in the precision mold according to an optimization result, and generating a cooling channel optimization scheme of the precision mold. The technical problems that in the design of a precise mold cooling system, temperature distribution is not uniform, and cooling efficiency is low are solved, and the technical effects that the temperature field is homogenized, the cooling efficiency is improved, the defect rate of injection molding parts is reduced, and the production period is shortened are achieved.

The invention relates to a method for machine learning aided design of a structural alloy for a fourth-generation thorium-based molten salt reactor, and belongs to the technical field of artificial intelligence auxiliary material design. The method comprises the steps that S1, an alloy component machine learning model for phase composition prediction is established, specifically, based on known alloy sample data, the sample data comprise alloy element content and corresponding phase composition information, and the machine learning model used for predicting alloy phase composition is established; s2, screening an initial alloy component space through phase composition constraint conditions; s3, secondary screening of alloy components based on thermodynamic simulation; s4, alloy sample preparation and solid solution homogenization treatment; s5, carrying out a mechanical property test; and S6, experimental result feedback and machine learning model closed-loop updating are carried out. According to the method, the artificial intelligence technology and the traditional alloy design theory are organically fused, an efficient and reliable new path is provided for research and development of key structural materials for the fourth-generation thorium-based molten salt reactor, and the important scientific research value and the engineering application prospect are achieved.

The application discloses a method for eliminating thermal cracks in nickel-based alloy additive repair, which comprises the following steps: firstly, quantitatively analyzing elements contained in the nickel-based alloy, and simulating a solidification curve based on thermodynamics according to the elements; secondly, obtaining a CSI-fs curve based on the solidification curve; thirdly, designing a heat treatment system through thermodynamic simulation, and performing composition homogenization treatment on the nickel-based alloy; and finally, performing electron beam additive repair. Results show that the nickel-based alloy treated by the method does not produce cracks during the additive repair, i.e., the thermal cracks are eliminated, the connection is stable, and the crack-free additive repair is successfully realized.

The invention provides a thermodynamic simulation method and device for a composite structure and electronic equipment, and relates to the technical field of composite structure simulation, and the thermodynamic simulation method for the composite structure comprises the following steps: carrying out proton irradiation simulation on a mesoscale model according to a preset proton irradiation condition, primary recoil atomic data of indium tin oxide and primary recoil atomic data of polyimide are obtained; performing cascade collision simulation according to the indium tin oxide primary recoil atomic data and the polyimide primary recoil atomic data to obtain an indium tin oxide material defect rate and a polyimide material defect rate; combining and modeling defect models respectively constructed according to the indium tin oxide material defect rate and the polyimide material defect rate to obtain a composite structure defect model; and performing energy transmission simulation on the composite structure defect model to obtain a temperature gradient curve. The accuracy of thermodynamic simulation of the composite structure can be improved.

The invention discloses an additive manufacturing process parameter optimization method based on deformation analysis and a related device, and relates to the technical field of additive manufacturing, the method comprises the following steps: constructing a thermosetting coupling model based on heterogeneous material parameters, structural characteristic parameters and preset machining process parameters of a target workpiece; based on the preset machining process parameters and the structural characteristic parameters, constructing a melting channel model to generate a structural body grid of the target workpiece; performing temperature field distribution analysis based on the structural body grid in combination with the heat source model; thermodynamic simulation of the deposition process is carried out based on the structure body grid, and elastic-plastic strain analysis is carried out in combination with a thermosetting coupling model; stress deformation analysis of workpiece laser additive manufacturing is conducted on the basis of the elastic-plastic strain component and the temperature field distribution data; and the preset machining process parameters are optimized based on the stress deformation data in combination with the multi-objective function so that laser additive manufacturing of the workpiece can be conducted. According to the method, the reliability of parameter optimization of the machining process is improved, and the laser additive manufacturing quality of the workpiece is effectively improved.

The present invention discloses a non-linear finite element modeling method for creep analysis of rubber bearings, including: establishing a non-linear finite element model of a rubber bearing and performing stiffness and damping simulation calculations; conducting stiffness and damping tests on the rubber bearing and performing static calibration analysis on the non-linear finite element model of the rubber bearing according to the test results; when the static calibration analysis meets the first preset requirement, based on the non-linear finite element model of the rubber bearing that meets the preset requirement, establishing a thermosetting coupling non-linear model of the rubber bearing and performing steady-state and transient thermodynamics simulation analyses; conducting stress and temperature tests on the rubber bearing and performing dynamic calibration analysis on the non-linear finite element model of the rubber bearing according to the test results; when the dynamic calibration analysis meets the second preset requirement, setting boundary conditions and load conditions and performing creep simulation calculations on the rubber bearing. The present invention improves the creep simulation accuracy of rubber bearings and the practicability and operability of finite element simulation for creep calculation of rubber bearings.

The invention belongs to the technical field of additive manufacturing, and particularly discloses an additive manufacturing residual stress in-situ monitoring method based on finite element simulation and three-dimensional shape measurement. According to the method, thermodynamic simulation is conducted on the additive manufacturing process through a finite element method, simulation deformation and simulation residual stress of all points on the surface of a workpiece are obtained, and a deformation frequency histogram and a residual stress frequency histogram of the surface of the workpiece are further obtained; and based on a deep learning model, training and learning a mapping relation between the deformation frequency histogram and the residual stress frequency histogram. In the actual additive manufacturing process, the deformation amount of the surface of the workpiece is measured in real time, the deformation amount frequency histogram of the surface of the workpiece is further obtained, the deformation amount frequency histogram is input into the pre-trained deep learning model, and then the residual stress histogram of the workpiece at the moment is obtained. According to the scheme, high-precision in-situ detection of the workpiece in the additive manufacturing process is achieved.

The present invention discloses a method for accurate milling simulation that updates crystal orientation; the simulation method comprises the following steps: 1. Performing crystal orientation detection on the machined surface of the workpiece before milling to obtain crystal orientation data before milling. 2. Establishing a model and assigning material parameters. 3. Cutting simulation to obtain the crystal orientation of the workpiece after cutting. The crystal orientation is updated during the plastic deformation stage and the damage stage of the cutting simulation. The present invention introduces crystal orientation data into thermodynamic simulation, which can more accurately reflect the microstructure and crystal orientation of the material, and achieve high-precision modeling of thin-walled parts milling, thereby improving the reliability of the simulation results, so that the cutting simulation can more effectively guide the milling process. In addition, in the calculation of crystal orientation distribution, the present invention updates the crystal orientation by calculating the deformation gradient matrix under consideration of deformation, and the computational complexity is low.

The present invention addresses to a real-time flow assurance occurrence monitoring system with the aim of reducing oil production losses caused by occurrences in the flow assurance area, which uses a web tool to integrate results of simulations of multiphase flow and thermodynamic simulators with production data and specific correlations, developed to monitor the formation of deposits or blockages in subsea lines. This tool has the specific objective of monitoring the formation of hydrates, paraffins and emulsions in real time and other occurrences that may cause a production shutdown. Based on this information, it issues an alert of the occurrence in the control rooms of the operational units, supporting decision-making on operational procedures that must be carried out to avoid the loss of oil production. It further records the operations carried out to mitigate occurrences. All data is structured in tables and graphs and can be exported to other systems.

The invention relates to the field of material evaluation, and discloses a system and method for rapidly evaluating the carbon sequestration capability of recycled concrete micropowder, and the method comprises the following steps: S1, creating and naming a new project: opening thermodynamic simulation calculation software developed based on a Gibbs energy minimization principle during chemical reaction equilibrium, and establishing a new project; selecting and creating a new reaction item at the lower left corner of the guide interface, and naming the new item and writing related remarks on a defined page of the new item; s2, selecting matter states appearing in the cement hydration process: on the basis of entering the software in the step S1 and creating a new project, setting a cement simulation reaction environment; by means of GEMS thermodynamic simulation software, hydration product data under different CO input amounts are rapidly obtained in batches, the CO absorption amount is quantified according to the calcite generation amount, rapid and accurate evaluation of the recycled concrete micro-powder carbon sequestration capacity is achieved, and the problems that a traditional experiment method is long in time consumption and large in resource waste are solved.

The present invention discloses a method and system for mechanical-thermal coupling simulation and structural optimization of ultra-complex structures. The method comprises: generating parameters and data structures required for subsequent simulation and optimization for a given model and design domain; constructing a background coarse grid, seed points and radii, modeling a porous structure based on a Voronoi structure, and precalculating its stress field; calculating a density field, performing thermodynamic simulation on the generated porous structure based on the density field, the background coarse grid, and a shape function; querying the stress matrix corresponding to each seed point based on the obtained stress field, performing eigendecomposition and extraction on the stress matrix, and obtaining a weight matrix corresponding to the seed point; using a topology optimization method, updating control parameters through a GCMMA method based on sensitivity, thereby updating the porous structure and optimizing material distribution, and continuously optimizing until the change in control parameters is less than a threshold. The method can quickly and effectively solve the topology optimization problem of anisotropic porous structures under multi-physics field conditions.

This invention discloses a carbon dioxide Carnot battery design method, system, device, and storage medium, comprising: determining multiple carbon dioxide Carnot battery configurations to be selected and establishing a steady-state thermodynamic model of the carbon dioxide Carnot battery configuration; performing thermodynamic simulations by varying system operating parameters to obtain the system electro-electric efficiency of the carbon dioxide Carnot battery under different system operating parameters; plotting the system electro-electric efficiency results obtained from the thermodynamic simulation into a graph with the system high-temperature heat storage tank temperature as the abscissa and the system pressure ratio as the ordinate, dividing the coordinate plane into different temperature ranges according to the high-temperature heat storage tank temperature, marking the recommended carbon dioxide Carnot battery configurations in different temperature ranges, and obtaining a carbon dioxide Carnot battery configuration selection graph; and reading the recommended high-efficiency system configuration category, system efficiency, and system pressure ratio from the carbon dioxide Carnot battery configuration selection graph.

The invention discloses a method for reducing steel ladle top slag crusting, which belongs to the technical field of ferrous metallurgy and comprises the following steps: S1, performing thermodynamic simulation on components of steel ladle top slag to obtain the components of the steel ladle top slag, and identifying crusting characteristics; s2, optimizing slag system components of steel ladle top slag; and S3, the white slag retention time and the ladle temperature after RH outbound in the ladle process are controlled. According to the method for reducing the crusting of the steel ladle top slag, on the premise that the cost is not increased, TiO2 is used for replacing traditional CaF2 to serve as a fluidity regulator, meanwhile, a high-carbon magnesia carbon brick refractory material is used in a matched mode, the complete melting temperature of the steel ladle top slag is effectively reduced to 1350-1450 DEG C, the viscosity of the molten slag is controlled, the crusting phenomenon is restrained, and therefore the steel ladle top slag crusting is reduced. And thermodynamic simulation and field experiment verification are combined, so that the steel ladle slag turning efficiency and the refining operation stability can be improved, and the method is suitable for a converter + RH composite refining process.

The invention discloses a dynamic priority scheduling method and system based on multi-heat-source cooperative heat supply in a greenhouse, and the method comprises the following steps: S1, collecting three-dimensional environment parameters in the greenhouse in real time through a multi-mode sensor array, the three-dimensional environment parameters comprising temperature field distribution, humidity gradient change and photosynthetically active radiation intensity; s2, carrying out real-time preprocessing on operation parameters of each heat source by adopting an edge computing node, wherein the operation parameters comprise instantaneous power fluctuation characteristics, a unit time energy consumption curve, a fault prediction state and dynamic residual life; and S3, establishing a crop growth demand-environmental parameter mapping model, and dynamically generating a target heat supply demand map according to a three-dimensional thermodynamic simulation result of temperature field distribution in combination with crop variety specific parameters. According to the method and the system, efficient collaborative scheduling of multiple heat sources in the greenhouse can be realized, the energy utilization efficiency is improved, and the crop growth environment is accurately regulated and controlled.

This invention relates to the field of steel experimental curve analysis, specifically to a method for analyzing stress-strain curves, comprising the following steps: conducting a compression deformation simulation experiment using a thermodynamic simulation testing machine and collecting experimental data; plotting a first stress-strain curve and performing high-order polynomial fitting to determine the segmentation points of the first stress-strain curve; dividing the first stress-strain curve based on the segmentation points, removing portions of the segmented curve that meet the rejection criteria, and plotting a second stress-strain curve; fitting the second stress-strain curve, recording the second fitting coefficient and comparing it with a fitting coefficient threshold, and repeating the rejection and refitting strategy until a stress-strain curve with satisfactory accuracy is obtained. This invention significantly improves the smoothness of the stress-strain curve and the accuracy of feature point recognition by using segmented fitting and dynamic threshold rejection of interfering data, providing a reliable basis for the analysis of the thermal deformation behavior of metallic materials. It has the advantages of simple operation, strong adaptability, and high engineering application value.