222 results about "Heat balance equation" patented technology

The invention discloses a highway pavement temperature forecasting method based on physical constraints, which comprises the following steps of: collecting and preprocessing meteorological data of all stations in a region, historical observation data of pavement temperature and geographic data, and adding time information to construct time features; building a U-net neural network model, carrying out space field data correction on the multi-station multi-meteorological element forecast data, and outputting corrected meteorological data of each station; constructing a bidirectional LSTM network on the basis of the U-net model, processing time sequence data, and evaluating and optimizing model performance by using a loss function; physical constraints including the time relation of temperature change, seasonal and day and night change characteristics, material thermal characteristics, a thermal balance equation and the like are added into the model, and a model result is optimized; and evaluating the generalization ability and forecasting precision of the model by using a test set, and finely adjusting model parameters. According to the method, the precision and reliability of road surface temperature prediction are remarkably improved, and a reliable basis is provided for traffic management and safety early warning.

The invention relates to the technical field of photovoltaic power generation, and discloses a BAPV-based roof photovoltaic intelligent monitoring system and a dynamic tuning method and device. The method comprises the following steps: scanning a preset BAPV assembly through three-dimensional laser, constructing a surface point cloud model, and establishing a three-dimensional coordinate system comprising BAPV assembly installation base points; acquiring a real-time environment parameter set corresponding to the BAPV component; calculating an optimal working angle of the BAPV component according to the real-time environment parameter set based on an improved particle swarm algorithm; simulating an all-weather shadow movement track according to the optimal working angle based on a Monte Carlo method, and generating a component shielding influence weight matrix; establishing a heat balance equation according to the component shielding influence weight matrix so as to calculate a spacing optimization parameter of the BAPV component; a control instruction is generated according to the spacing optimization parameters, and dynamic tuning of the BAPV assembly is completed according to the control instruction; the control instruction comprises a support rotation angle compensation amount, an inverter parameter adjustment amount and an abnormal component isolation instruction. And the pain points of modeling errors, static optimization limitation, thermal runaway risks and the like are solved.

The invention relates to the technical field of reaction kettle temperature control, and discloses a fine chemical engineering reaction kettle temperature intelligent control method based on self-adaptive search, which comprises the following steps: S1, data acquisition: acquiring operation data of a reaction kettle, including temperature in the kettle, jacket inlet and outlet temperature, fluid flow and valve opening signals; s2, heat balance calculation: establishing a heat balance equation based on the collected data, and calculating reaction heat release and an equivalent heat transfer coefficient; s3, updating the model; s4, predictive control solution is carried out; s5, performing adaptive search; s6, carrying out security constraint; s7, stage identification; and S8, performing control execution. The reaction heat release amount and the equivalent heat transfer coefficient are calculated in real time through linkage of a heat balance calculation module and a model updating module, online parameter identification is achieved through a recursive least square algorithm, a heat model can be dynamically corrected along with working condition changes, it is ensured that a prediction model is consistent with the actual reaction process, and the prediction efficiency is improved. And the temperature prediction precision and the reliability of control response are obviously improved.

The invention discloses an axial flux motor temperature prediction method based on equivalent thermal circuit method calculation, and the method comprises the following steps: 1, motor thermal circuit unit division: decomposing a motor into a stator unit, a rotor unit, an air gap unit and a housing unit, and constructing an equivalent thermal network through a node-branch method; 2, calculating a multi-physics coupling heat source; 3, thermal resistance parameter dynamic calculation and CFD coupling optimization: calculating conduction thermal resistance, convection thermal resistance and contact thermal resistance; and 4, parameter self-calibration based on machine learning: constructing a PINN model according to a physical law and known physical parameters, introducing a Bayesian optimization algorithm on the basis of the PINN model, adjusting and optimizing the parameters of the PINN model by using Bayesian optimization, designing a reinforcement learning algorithm, and carrying out self-calibration on the parameters of the PINN model. Enabling the reinforcement learning algorithm to dynamically adjust parameters according to the real-time operation data of the system and the prediction result of the PINN model; 5, constructing and solving a heat balance equation; and step 6, visualizing and verifying the temperature field.

The invention relates to a simulation and optimization method and device for a battery thermal management system, and belongs to the technical field of battery simulation. The method comprises the following steps: constructing a battery thermal simulation model based on physical characteristics of a battery; establishing a heat conduction model and a heat balance equation of the battery, substituting the heat conduction model and the heat balance equation into the battery thermal simulation model, and simulating temperature distribution of the battery under different working conditions by using a numerical solution method to obtain a simulation result; optimizing the design scheme of the battery thermal management system based on the simulation result; verifying the optimized battery thermal management system based on an actual test environment to obtain a verification result so as to determine whether a design scheme of the optimized battery thermal management system is modified or not, and performing thermal management on the battery based on the optimized battery thermal management system under the condition of determining that the design scheme is not modified; the stability and safety of the battery system under the extreme temperature condition can be effectively improved, the service life of the battery is prolonged, and the energy utilization efficiency of the battery is improved.

The invention discloses a roof radiant cooling and independent fresh air dehumidification system mixed operation comprehensive control method, and belongs to the technical field of building environment control and heating ventilation air conditioning. The method comprises the steps that the radiant cooling capacity and the dehumidification capacity of a surface air cooler are calculated through a heat balance equation, and an optimization objective that a top plate radiation system bears 70% sensible heat load and a fresh air system bears 90% humidity load is set; an anti-condensation strategy is triggered by combining dew point temperature monitoring, a control variable is adjusted by taking minimization of energy consumption as a target in a rolling time domain, and it is ensured that the indoor temperature ranges from 23 DEG C to 26 DEG C, the humidity ranges from 40% to 60%, the black ball temperature difference is smaller than or equal to 2 DEG C, and the condensation risk is smaller than or equal to 6%. The method is suitable for energy-saving temperature and humidity regulation and control of large public buildings, and efficient cooperative processing of sensible heat and latent heat loads is achieved.

The invention provides a big data information processing method and system based on deep learning. According to the method, an energy distribution model is generated by combining a building information model with infrared thermal imaging data, and a dynamic thermal balance equation set is constructed according to the model so as to generate three-dimensional temperature field reconstruction data. Then, thermal resistance parameters in the energy distribution model, heat flow abnormal area coordinates in the three-dimensional temperature field reconstruction data and air conditioning unit current data are analyzed through a deep learning algorithm, and space-time coupling characteristics of equipment power consumption and thermal environment parameters are extracted; then, a priority regulation and control chain of the temperature control equipment is formulated, and multi-stage linkage rules of the fresh air exchange rate, illumination dimming and cold release of the cold storage tank are covered; finally, the chilled water pump phase difference, the air conditioner group operation mode and the emergency lighting power limiting value are dynamically adjusted, and power load stabilization is achieved. According to the technical scheme provided by the invention, the data information processing efficiency and stability of the large building can be improved.

The invention discloses a hybrid model driving-based space-time consistency regulation and control method for a central heating system, which comprises the following steps of: performing hydraulic equilibrium analysis on the central heating system, and performing hydraulic analysis on a space structure of a heating pipe network by using a graph theory method based on an energy conservation law, simulating the hydraulic working condition of the official website by using a basic loop analysis method; based on an energy conservation and mass conservation heat balance equation, a heat source and a heat user are indirectly connected through heat exchanger transition, a dynamic physical model of each subsystem is established based on a lumped heat capacity method, a refined model of the heat supply system is established, and the thermal dynamic characteristics of the system are researched and analyzed; based on a Pearson's correlation function, considering thermal inertia of equipment such as a pipe network of the central heating system and a building fence structure, and determining a regulation and control period and time of the regulation and control model; according to the method, by controlling the opening degree of the regulating valve of the heat exchange station, the suitability degree of the room temperature of a heat user is guaranteed.

The invention provides a rotary table bearing-torque motor thermal vibration coupling modeling method, and relates to the technical field of machine tool reliability analysis. Comprising the steps of calculating bearing restoring force, torque and elastic rigidity; giving a dynamic balance equation of the main shaft system; unbalanced magnetic pulling force and unbalanced mass excitation are calculated; a displacement field is solved, and load changes caused by the displacement field are transmitted to the thermal model; the contact force borne by the bearing is updated by considering the load effect related to displacement, and the friction heat generation of the bearing and the heat generation of each part of the motor are calculated; calculating thermal deformation and thermal displacement of the bearing; thermal resistance among nodes is calculated, and a temperature field equilibrium equation is given through a thermal resistance network to achieve rapid thermal-mechanical coupling analysis; the thermal equilibrium equation is solved to calculate temperature response; importing the latest temperature into the kinetic model; lubricating grease viscosity and bearing thermal deformation are updated; and repeating the steps to finally obtain the main shaft displacement field and temperature field response. The method is high in accuracy, high in universality, low in test cost and high in test efficiency.

An electrothermal coupling probabilistic load flow calculation method considering time correlation characteristics of an input variable comprises the following steps: introducing an adaptive window width reflection kernel density estimation method to fit wind speed data, and establishing an uncertainty model of the input variable; a Sobol random sequence obeying [0, 1] uniform distribution is generated, a random number sequence considering time correlation is generated by using a position sorting method according to input actual measurement data, and finally a wind speed sequence sample is obtained through a cubic spline interpolation method. Inputting structural parameters of a power system, introducing a heat balance equation, and establishing an electrothermal coupling probabilistic load flow calculation model by being combined with a probabilistic load flow calculation model; and initializing the model, inputting a wind temperature sequence of each scene, and carrying out electrothermal coupling probabilistic load flow calculation by utilizing a Monte Carlo simulation method distribution based on a Sobol sequence. Uncertainty modeling is carried out according to a model output result, the result is evaluated and analyzed, and a conclusion is obtained. The method is of great significance in analyzing the operation condition of the power system and further guaranteeing safe and stable operation of the power system.

The invention relates to the field of intelligent clothing power supply control, in particular to an intelligent clothing power supply control method and system based on temperature feedback. Real-time collected data in a multi-mode sensor are obtained, dynamic baseline calibration is conducted on the real-time collected data, then feature vectors are constructed, fusion calculation is conducted on the human body temperature according to a Pennes biological heat equation, calculation is conducted on the human body initial temperature based on a dynamic heat balance equation, and the system active regulation and control heat flux is obtained. Establishing an intelligent temperature prediction model; and inputting the characteristic data set and the system active regulation and control heat flux into a temperature intelligent prediction model for prediction, and carrying out temperature regulation and control on a power supply control system of the intelligent clothes according to a multi-step temperature prediction result. The precision and efficiency of temperature prediction are significantly improved, and accurate pre-judgment of multi-step temperature is realized.

The invention discloses a heat dissipation control method and device, equipment, a storage medium and a program product, relates to the technical field of server heat dissipation, and can monitor the heat state of a server in real time by obtaining current data of a heat dissipation component. Then, the data are input into a pre-established heat dissipation model, and the model is based on a heat balance equation and a state-space equation of a server and can accurately predict the temperature adjusting amount. Therefore, the system can actively adjust the heat dissipation strategy instead of passively responding to the temperature change, dependence on experimental data is reduced, and complex PID parameter adjustment is avoided. Dynamic control is carried out according to the temperature adjusting quantity, the system can quickly respond to temperature changes, optimize the rotating speed of the fan and reduce energy consumption, meanwhile, the operation stability and reliability of the server are improved, the technical problem of heat dissipation control is solved, and the technical effect of heat dissipation control is achieved.

The invention relates to the technical field of cooling system fault prediction, in particular to a multi-target collaborative liquid cooling system fault prediction and self-healing control method and an energy storage thermal management system.The method comprises the steps that 1, a system health state database is constructed, and data in the database is dynamically updated; step 2, quantizing a parameter change rule based on calculus, constructing a mathematical model of core parameters of the liquid cooling system through a heat balance equation, and quantizing correlation between parameter change and a system state; and step 3, based on the mathematical model in the step 2 and a machine learning algorithm, analyzing the fused data set to realize fault prediction and graded early warning. And 4, according to the fault type in the step 3, by solving a dynamic control equation, actuator adjustment parameters are automatically calculated, and self-healing operation is achieved. According to the method, multi-source data fusion and fault prediction are deeply combined, a self-healing strategy of stage processing and redundant fault tolerance is designed, and high-precision early warning and continuous operation of the system in a fault state are achieved.

The invention discloses a method, a system, equipment and a medium for predicting thermal power plant energy consumption indexes by using yield, and belongs to the technical field of thermal power plant energy management. The method comprises the following steps: obtaining thermal power plant steam turbine performance test data, and determining parameter types of the steam turbine performance test data; establishing a heat balance equation set based on the first law of thermodynamics according to the parameter type; obtaining thermal power plant yield plan data; performing calculation processing on the heat balance equation set and the yield plan data to obtain thermal power plant operation parameters; and calculating a thermal power plant energy consumption index according to the thermal power plant operation parameters, wherein the thermal power plant energy consumption index is used for making a fuel purchase plan and optimizing a unit operation mode. According to the method, the accuracy and adaptability of energy consumption index prediction are improved, the calculation process is simplified, the prediction time is shortened, a scientific basis is provided for making a fuel purchase plan and optimizing a unit operation mode, and the economic benefits of a thermal power plant are improved.

The invention provides an indoor temperature prediction method, an indoor temperature prediction device, air conditioning equipment and a storage medium. The indoor temperature prediction method comprises the steps that an indoor temperature sequence and an outdoor temperature sequence of a current time period, an air conditioning equipment cooling capacity output value at the current moment and the current date and time are obtained; and the target indoor temperature of the next moment is calculated through a temperature prediction model according to the indoor temperature sequence and the outdoor temperature sequence of the current time period and the cooling capacity output value of the air conditioning equipment at the current moment, and the temperature prediction model is constructed through a heat balance equation and obtained through historical temperature data training. Thus, the temperature prediction model is obtained by constructing and training the heat balance equation representing the heat change, and the heat balance equation is only established according to the basic rule of the physical heat transfer theory, so that the temperature prediction model does not depend on detailed understanding of the target building during modeling; the automation degree and reusability of the indoor temperature prediction method are improved, and the prediction accuracy is improved.

The invention discloses a switch cabinet temperature rise prediction method and device, equipment and a storage medium, and relates to the field of power transmission and transformation devices and equipment.The method comprises the steps that first heating power meeting a preset heating stable condition is determined based on the comprehensive heat dissipation coefficient of a target switch cabinet conductor, and the relation between temperature rise and power-on time is obtained in combination with a preset heat balance equation; constructing a first temperature rise prediction model; second heating power corresponding to the alternating current is determined, a temperature rise and current relation is established according to the first heating power, the second heating power and the heat dissipation coefficient, and a second model is constructed; circuit resistance is calculated through conductor resistance and contact resistance, the relation between temperature rise and contact resistance is determined by combining the two models, and a third model is constructed. And determining the three models as target temperature rise prediction models, establishing a target three-dimensional model based on the switch cabinet structure for verification, and performing temperature rise prediction after verification is passed. Therefore, the temperature rise risk of the switch cabinet can be accurately predicted.

The invention belongs to the technical field of flight simulation training equipment, particularly relates to an intelligent temperature control system for a flight simulator cabin, and solves the problem that the requirements of flight simulator temperature control on multi-dimensional physiological perception fusion, multi-target collaborative optimization and rapid personalized adaptation and active safety protection of new pilots cannot be met in the prior art. According to the method, a multi-modal sensing layer processes multi-channel skin physiological data, physical environment data and pilot electrocardiosignals, a human body heat balance equation for blood flow correction is constructed, and physiological feature vectors including predicted core body temperature, predicted average votes and predicted dissatisfaction percentage are generated; the decision-making layer generates a temperature control instruction and a confidence evaluation value through a meta-learning model, a reward function is in a four-dimensional dynamic weighting form, and the weight is adjusted through an IF-THEN rule according to the state; and the dynamic execution layer adopts a PID closed loop to adjust the partition temperature and triggers response in a grading manner. Accurate and personalized temperature control is achieved, and the training quality and safety are guaranteed.

The invention discloses a cold-chain logistics energy consumption optimization method and system based on digital twinning, relates to the technical field of cold-chain logistics, and provides the following scheme that the method comprises the steps that an environment data distribution field in a cold-chain carrier and space coordinates of goods units are obtained and synchronized to a virtual body instantiated based on an existing digital twinning platform, and the space coordinates of the goods units are obtained; local environment information vectors are extracted from the environment data distribution field according to the space coordinates of all the goods units, and the equivalent heat exchange flux value of the surface of each goods unit is calculated through a heat balance equation in combination with the physical parameters of all the goods units. According to the method, the digital twin virtual body is constructed, the continuous environment field is synthesized, and the metamorphic reaction rate field is iteratively calculated in combination with the thermal penetration-metamorphic dynamics coupling model, so that the defects that a traditional monitoring system depends on scattered temperature and humidity point data, lacks the three-dimensional environment field modeling capability and cannot quantitatively predict the metamorphic rate are overcome; the full-dimension continuous reconstruction of the environment field in the cold chain carrier and the dynamic quantitative prediction of the corruption rate of the goods are realized.

The invention provides a rolling pass parameter intelligent inversion and dynamic correction method based on machine learning, and the method comprises the steps: outputting a deformation resistance Kf value according to the obtained current rolling temperature and strain rate; based on the deformation resistance Kf value, predicted values of the total rolling force and the rolling moment are obtained; establishing a transient heat balance equation, and outputting a final rolling temperature predicted value; by taking the minimization of the pass number and production time and the minimization of the total energy consumption as targets, an optimal rolling schedule is generated and decided; and the corresponding model parameters are optimized in batches by regularly utilizing historical data, so that short-term self-adaption and long-term optimization control of the rolling process is realized. According to the method, various feasible rolling schemes are automatically generated by utilizing an optimization algorithm, so that an operator can select the optimal scheme according to actual conditions; by comparing a predicted value with an actual value in real time, current model parameters are quickly adjusted, and accumulated production data are regularly utilized to comprehensively optimize the model, so that long-term stable operation and accurate prediction of the system are ensured.

According to the intelligent plunger pump temperature control method and system based on model predictive control, a thermodynamic model predictive control algorithm is introduced, key parameters such as the oil temperature of a plunger pump body, the water temperature of a water tank and the environment temperature are brought into a unified calculation model, and cooperative closed-loop control over a heating plate, a semiconductor chilling plate and a circulating pump is achieved. By constructing the heat balance equation of the lumped heat capacity system and solving the rolling optimization problem in the limited time domain, the heat load change of the plunger pump can be accurately predicted and adjusted in advance, the oil temperature is always stabilized in the preset target interval, and the temperature control precision is remarkably improved; meanwhile, mutual exclusion constraints of heating and refrigerating power are set, and weights are dynamically adjusted and optimized according to working conditions, so that meaningless consumption of energy is effectively avoided, and the operation cost of equipment is reduced; in addition, according to the control method, preheating, cooling and energy-saving modes can be automatically recognized, so that the plunger pump is rapidly started in the severe cold environment and stably operates in the high-temperature environment.

The invention discloses an electrothermal coupling load flow calculation method and system, and the method comprises the steps: building a power transmission line load flow balance model of a target power system, and solving the power transmission line load flow balance model of the target power system, and obtaining a first calculation result; updating the power transmission line temperature and the branch resistance; and the first calculation result is corrected again according to the updated power transmission line temperature and branch resistance, and loop iteration is carried out. And meanwhile, the interaction between the power flow change of the power system and the temperature of the system can be clearly displayed. According to the method, the system temperature is calculated through a heat balance equation, the electrical parameters of the system are corrected, the temperature iterative calculation process is outside a power flow iterative loop to balance convergence and accuracy, and therefore completely-coupled electric heating power flow calculation is simplified. Due to decoupling of the heat balance equation and the power flow equation, a traditional power flow calculation structure is reserved, so that a power flow equation calculation part possibly adopts a more efficient and quicker power flow algorithm in the future, and a good application prospect is shown.

The invention discloses a heat management and control parameter dynamic optimization method and system for a liquid-cooled lithium iron phosphate battery, and relates to the field of battery heat management and control parameter dynamic optimization, and the method comprises the steps: building a carrier heat state offline training database based on a carrier state, a heat state, a cooling system and environment parameters, and carrying out the data cleaning and alignment; based on a LightGBM model, constructing a general thermal state prediction basic model of the carrier; verifying whether the thermal state of the predicted carrier is reliable or not by combining an actual monitoring result; dynamically optimizing the carrier general thermal state prediction basic model based on an online gradient descent algorithm; dynamically optimizing the flow velocity of the cooling liquid based on a heat balance equation; a sensor set is arranged and used for collecting current state parameters and thermal state actual data in real time. The method has the advantages that data driving and a physical model are fused, the heat management and control parameters are dynamically optimized through online learning, and the heat management precision, adaptability and safety of the liquid-cooled lithium iron phosphate battery are remarkably improved.

The invention relates to the technical field of detection circuits suitable for various electricity utilization information acquisition terminals, and discloses a detection circuit method suitable for various electricity utilization information acquisition terminals. The method comprises the following steps: setting an environment temperature, calibrating a shunt resistor and a thermistor, constructing a three-order resistance-temperature mapping relation, and calculating thermal resistance and thermal response characteristics to realize accurate modeling; a thermistor is attached to a shunt resistor, heat-conducting silicone grease is added to form a thermal coupling structure, voltage is collected through a constant current source and a differential amplifier, a resistance value is converted into temperature in real time, instantaneous current is inversely calculated based on a heat balance equation, then energy integration and grade judgment is completed, and various electrical parameter indexes are output. According to the method, modeling, collection, conversion and inversion processes are integrated, a complete thermal-electric detection link is constructed, a traditional current sensor is not needed, and the method has the advantages of being compact in structure, low in cost, flexible in deployment and the like and is suitable for a low-power-consumption distributed terminal.

The invention provides an overhead transmission conductor radiation coefficient evaluation method and system, and the method comprises the steps: A1, obtaining the operation data and environment data of a transmission conductor in a preset time period, and enabling the operation data to comprise a conductor temperature collection value; a2, establishing a heat balance equation according to the operation data and the environment data; and A3, solving the transient differential equation based on the established heat balance equation to obtain a conductor temperature calculation value, and obtaining a radiation coefficient evaluation value of the transmission conductor based on the conductor temperature acquisition value and the conductor temperature calculation value. Based on a heat balance equation of the conductor, an optimization solving model of the conductor radiation coefficient is established, so that accurate solving of the transmission conductor radiation coefficient is realized, and the problem that the current transmission conductor radiation coefficient is difficult to effectively and accurately obtain is solved. The accuracy of the evaluation result of the thermal stability limit of the power transmission line and the power supply reliability of a power system are improved.

The invention provides a temperature monitoring method and device in a power transmission line, and relates to the technical field of power transmission lines. The method comprises the following steps: acquiring steady-state temperature data corresponding to a power transmission line in response to a temperature monitoring operation for the power transmission line; according to the steady-state temperature data, constructing a first power transmission line temperature calculation model through a heat balance equation; through a wire through-flow test, if first accuracy corresponding to the first power transmission line temperature calculation model does not meet preset accuracy according to first temperature data calculated by the first power transmission line temperature calculation model, obtaining the influence degree of a plurality of error influence factors on the temperature calculation model; according to the influence degree of the plurality of error influence factors on the temperature calculation model, performing model improvement on the first power transmission line temperature calculation model to obtain an improved second power transmission line temperature calculation model; and performing temperature monitoring operation on the power transmission line in real time through the second power transmission line temperature calculation model if it is confirmed that the second accuracy of the second power transmission line temperature calculation model meets the preset accuracy according to second temperature data calculated by the second power transmission line temperature calculation model through a wire through-flow test. According to the invention, the problems of low monitoring efficiency and low monitoring accuracy caused by analyzing the influence degree of error influence factors on the temperature parameters in the wire by only depending on manual routing inspection which needs to consume a large amount of manpower and material resources to cover the whole network and cannot accurately combine the influence factors in multiple aspects are solved.

The invention discloses a fan blade water drop three-dimensional freezing coefficient calculation method and system, and relates to the technical field of wind power generation blade icing simulation, and the method comprises the steps: drawing a surface grid for a target region, and obtaining related data; solving an external flow field of the three-dimensional blade based on related data to obtain airflow and water drop speed distribution, and further calculating a collision coefficient of water drops on the surface of the blade; and in combination with environmental parameters, the supercooling water yield of each unit is calculated, and various heat energy changes of water drops in collision, capture and freezing processes are deeply solved. Accurately solving a water drop freezing coefficient of each unit on the surface of the fan blade by establishing a heat balance equation; carrying out rationality correction on the calculated freezing coefficient; according to the method, the water drop freezing coefficient is calculated through the mass balance and the heat balance of the water drops in the three-dimensional surface domain, convenience is provided for simulation and further updating of the icing form of the fan blade, the icing prediction precision can be effectively improved, the fan design is optimized, and a powerful basis is provided for the anti-icing technology.

The invention discloses a magnetic suspension bearing temperature rise rapid prediction method based on an equivalent thermal network, and relates to the technical field of prediction, and the method comprises the steps: dispersing a thermal network into typical thermal network units through geometric features and a heat flow direction, dividing different thermal nodes, and connecting the thermal network units with adjacent solid thermal nodes through equivalent convective thermal resistance. The method has the advantages that the rapid analysis of the temperature rise is realized, the calculation complexity of modeling and solving is reduced through a nodal thermal network structure, the time consumed by iterative solving is shortened to the second level, the efficiency is improved by more than thousand times, and the method is suitable for large-scale popularization and application. The real-time accurate prediction requirement in engineering is met, anisotropic thermal conductance modeling is carried out, and thermal gradient distortion caused by dimension reduction of a traditional one-dimensional / two-dimensional model is avoided through independent assignment of axial, radial and circumferential thermal conductance parameters and accurate characterization of heat conduction direction anisotropy and multidirectional heat flow coupling of laminated materials such as silicon steel sheets.

The invention aims to provide a compressor disc flow thermal coupling temperature calculation method. The compressor disc flow thermal coupling temperature calculation method comprises the following steps: acquiring a flow analysis result of fluid in a compressor disc cavity; the compressor disc cavity is divided into a first cavity body and a second cavity body according to the analysis result; simplifying the temperature in the first cavity into a cavity temperature node, and obtaining the temperature of the cavity temperature node and the temperature of a wall surface node of the first cavity; and the temperature in the second cavity is simplified into a heat flow line which exchanges heat with the wall face of the compressor disc cavity, a heat balance equation of the heat flow line is constructed, and the temperature of a node of the wall face of the second cavity is obtained according to the heat balance equation. The method can improve the heat analysis precision of the compressor disc.

The invention belongs to the technical field of power transmission line monitoring and dynamic capacity increasing. A power transmission line dynamic current-carrying capacity prediction and capacity increase method based on double-model adaptive fusion comprises the following steps: data acquisition and preprocessing: acquiring historical weather and current-carrying capacity data, and cleaning and standardizing the historical weather and current-carrying capacity data; weather-driven prediction: optimizing a kernel extreme learning machine through an artificial egret optimization algorithm to predict future meteorological parameters, and substituting the future meteorological parameters into a heat balance equation to calculate a first current-carrying capacity predicted value; performing data-driven prediction, optimizing variational mode decomposition by adopting a dynamic perception and accurate capture algorithm, and directly predicting a second current-carrying capacity predicted value in combination with a long-short-term memory network; in the fusion step, weights are dynamically distributed according to errors of the two models on the verification set, and self-adaptive weighted fusion is carried out to obtain a final point prediction value; and a probability modeling step: fitting probability distribution based on historical errors, and constructing a confidence interval. According to the invention, the precision and reliability of current-carrying capacity prediction are improved, and dynamic capacity increasing and safe operation of the power transmission line are realized.

The invention discloses a wet clutch sliding friction state prediction method based on friction pair fluid-solid heat behavior calculation and a vehicle, and the method comprises the steps: carrying out the iterative calculation of the internal fluid-solid pressure, oil film thickness and rough contact area of a friction pair based on the structure and working condition parameters of a wet clutch through a fluid-solid bearing equation, and representing the fluid-solid coupling behavior of a friction gap; a heat-source-input-free heat balance equation and a contact temperature continuity equation are established in combination with physical property parameters, the sliding friction interface heat flow dynamic distribution proportion is calculated at the same time, and a wet clutch temperature field prediction model is established by superposing heat conduction characteristics; through a non-dominated sorting genetic algorithm, clutch dry friction test data and a rough torque model considering radial temperature difference are fused, a rough friction coefficient-temperature fitting equation is solved, and a temperature field prediction model is combined to achieve updating of rough friction coefficients under different thermal load characteristics; and rough torque and viscous torque are respectively solved by integrating fluid-solid heat behaviors of the wet clutch, and the rough torque and the viscous torque and the temperature field jointly represent the sliding friction state of the wet clutch.