1732 results about "Thermal coupling" patented technology

The invention discloses an inverter intelligent control method based on a self-adaptive algorithm, and relates to the technical field of inverter intelligent control, and the method comprises the steps: multi-source temperature collection and environment recognition, machine learning-based electric-thermal coupling prediction, self-adaptive inverter parameter optimization, and cooperative thermal management and fault diagnosis self-repairing. According to the high-temperature or low-temperature failure risk of the motor and the inverter under the extreme climate, temperature distribution and load information are obtained in real time, the temperature rise trend is predicted in advance, and the current, voltage and modulation strategy of the inverter are actively adjusted, so that safe derating or torque compensation is achieved; multi-sensor cross validation and observer fusion are carried out when sensors drift or devices are aged, stable operation and efficient energy utilization of the system are kept, and the drivability and the whole vehicle reliability in an extreme environment are remarkably improved; in the process, fault diagnosis and self-repairing can be further improved through online model updating, and the durability and economical efficiency of the electric drive system are improved.

The invention provides a performance optimization and regulation method for a high-temperature sintering furnace, and belongs to the technical field of intelligent regulation, and the performance optimization and regulation method comprises the following steps: arranging sensors in each heating area of the high-temperature sintering furnace, and monitoring the heating areas in real time; constructing a heating area operation state database, comparing the collected real-time data with a preset sintering process parameter range, and analyzing to obtain a performance analysis set of each heating area; establishing a three-dimensional heat conduction model, simulating heat flow distribution of each heating area under different heating powers and temperature settings, determining a thermal coupling effect of each heating area in different stages, and determining a heat transfer coefficient between adjacent heating areas; and an initial strategy of the high-temperature sintering furnace is determined, the initial strategy is optimized based on the auxiliary parameters of all the heating areas, and all the heating areas of the high-temperature sintering furnace are regulated and controlled according to the optimization strategy. The problems of inaccurate temperature control, single parameter analysis, insufficient thermal coupling effect processing and the like in the traditional regulation and control technology are effectively solved.

The invention relates to the technical field of power equipment state monitoring, in particular to a lightning arrester state monitoring method, system and equipment based on multi-physics field coupling and a storage medium. Acquiring current-voltage characteristic parameters, temperature distribution data and mechanical stress data of the lightning arrester, performing electro-thermal-mechanical coupling analysis based on multi-physical field monitoring data, identifying overlapping positions of an electric field distortion area, a temperature abnormal area and a stress concentration area, and determining the overlapping positions as a degradation key area; establishing a correlation response relationship between the leakage current and the temperature and a transfer response relationship between the temperature and the mechanical stress for the deteriorated key area; on the basis of the leakage current change trend of the degradation key area and the coupling influence of the superimposed temperature field and stress field, a degradation feature fusion factor is constructed, the evolution law of the degradation feature fusion factor in the time sequence is analyzed, and the degradation threshold value under the multi-field synergistic effect is determined in combination with the electric-thermal coupling acceleration effect and the thermal-mechanical coupling weakening effect.

The invention relates to the technical field of electric digital data processing, in particular to a display process temperature management optimization system which comprises a data acquisition module, a dynamic modeling module, a cooperative compensation module and an early warning module. The data acquisition module acquires temperature data of a heating area through a multi-type temperature sensor array, and generates a temperature field matrix with a confidence label through multi-source verification; the dynamic modeling module receives the matrix, operates a plurality of machine learning algorithms in parallel to generate a thermal coupling strength distribution diagram, and outputs a region coupling coefficient and a confidence coefficient thermodynamic diagram; the cooperative compensation module calls knowledge base strategies in a layered mode according to the thermodynamic diagram and feeds back early warning signals when abnormity is detected. According to the system, a cross-module closed-loop control mechanism is formed through confidence label driving algorithm weight distribution, confidence thermodynamic diagram control strategy priority, abnormal signal triggering model reconstruction and knowledge base updating, and millisecond-level cooperative suppression of the thermal crosstalk effect of multiple heating areas is achieved.

The invention discloses a soft start control method for intelligent temperature control of a glue injection mold, and particularly relates to the technical field of glue injection molds. Initial temperatures of a plurality of heating areas of the mold are collected, and a temperature distribution model is constructed; calculating a thermal inertia score value based on the thermal response characteristic and the thermal capacity parameter of each region; predicting temperature rise rates of different areas by using the thermal inertia score value, the thermal coupling degree abnormal value and the historical control deviation frequency, and setting differentiated soft start amplitude limiting parameters; temperature feedback is collected in real time in the heating process, a temperature rise rate error sequence is constructed, and the future deviation risk is predicted through the dynamic Bayesian network; when the risk value exceeds the limit, the PWM duty ratio or the conduction angle of the heating area is dynamically corrected, and power output self-adaptive adjustment is achieved; the method has the advantages of being high in predictability, accurate in response and intelligent in control, and is suitable for high-precision heating control application of the complex glue injection mold.

The invention discloses a CPLD-driven IGBT short-circuit current dynamic monitoring method and system, and relates to the technical field of power electronics, and the method comprises the specific steps of S100 real-time data acquisition, S200 dynamic threshold calculation, S300 short-circuit state judgment, S400 rapid protection execution, and S500 fault data recording and uploading. Accurate and prospective judgment of the short circuit state of the IGBT is achieved, two stress parameters including the direct-current bus voltage and the junction temperature serve as independent variables, when the bus voltage rises, the voltage stress borne by the IGBT in the turn-off period is increased, meanwhile, the current peak at the short circuit moment is more remarkable, early warning is conducted in advance by dynamically lowering the threshold value, and therefore the short circuit state of the IGBT is accurately judged. According to the mechanism, the monitoring system does not simply respond to the occurred overcurrent, but can pre-judge the safety boundary of the device under the current working condition, so that intervention is implemented before the current really reaches a dangerous peak value, and a protection blind area under a severe working condition is effectively avoided.

Disclosed in the present invention is a physics-informed neural network-based thermo-mechanical coupling analysis method for an inertial microsystem, the method comprising: S1, configuring material parameters and boundary conditions of an inertial microsystem, and establishing a thermo-mechanical coupling analysis model; S2, performing electro-thermal coupling analysis to obtain a temperature distribution of the inertial microsystem; S3, performing thermo-mechanical coupling simulation analysis to obtain a thermal stress distribution of the microsystem; S4, predicting temperature fields of the microsystem by means of a physics-informed neural network; S5, using the temperature fields as boundary conditions for mechanical simulation of the microsystem, obtaining mechanical properties such as stress and strain of the microsystem; and S6, performing electromechanical coupling simulation analysis to analyze the impact of structural deformation on various parameters of electrical performance. The present invention improves the solution accuracy of the neural network by means of an improved adaptive weighting strategy, combines a complete polynomial basis function with the neural network, and introduces an expanded basis function to reduce the state dimensionality, thus reducing computational costs and time, achieving accurate prediction of temperature fields of microsystems at multiple moments, and allowing for computation of the performance of microsystems under electro-thermal-mechanical multi-physics coupling.

The invention belongs to the technical field of intelligent home control, and particularly relates to an electric blanket intelligent temperature control system, which analyzes the matching relationship between the heating power and the temperature response in the historical operation cycle of a plurality of independent subareas of an electric blanket, fuses the thermal coupling effect between the subareas, and independently quantifies the dynamic thermal inertia parameter of each subarea. Based on the independent temperature change trend and the real-time temperature deviation of each partition, a control state including a steady state, a transient state or a strong compensation state is decided and maintained for each partition, the dynamic thermal inertia parameter, the real-time temperature deviation and the current control state are integrated, differentiated feed-forward compensation amounts are generated and superposed to the output of a PID controller of the corresponding partition, and the control state of the corresponding partition is calculated. A final power control instruction is generated after amplitude limiting and smooth filtering, temperature response lag caused by thermal inertia is actively predicted and counteracted, precise control is achieved in combination with partition state recognition, and the system response speed and control stability are remarkably improved.

The invention relates to the technical field of cable aging diagnosis, in particular to a cable aging diagnosis monitoring method and system. The method comprises the following steps: acquiring cable operation log data and cable internal structure design data, extracting a cable structure distribution condition in combination with an operation log, and evaluating cable operation state data; electric power sudden demand characteristics are identified, and then a non-uniform thermal coupling unbalance state is determined; based on the thermal coupling unbalance and the operation state data, detecting the coupling failure condition of the cable multilayer structure, and further judging the structural integrity damage condition; deducing a load stability disintegration situation according to a structure damage and insulation strength reduction trend; further detecting the decline of the quality of transmitted electric energy, analyzing the increase degree of abnormal voltage fluctuation, and identifying the fatigue trend of the metal material of the cable; through the above cable aging diagnosis, the cable aging diagnosis data is output. According to the cable aging diagnosis method and device, cable aging diagnosis is diagnosed, so that cable aging diagnosis is more accurate and efficient.

The invention provides a fused salt heat storage coupled compressed air energy storage cooperative power generation system and a control method thereof.The fused salt heat storage coupled compressed air energy storage cooperative power generation system comprises a data acquisition module, a multi-target optimization decision algorithm based on dynamic programming, an energy distribution module, a dynamic adjustment module and a safety protection module; a multi-objective optimization decision algorithm based on dynamic programming automatically switches system working modes, an energy distribution module optimizes fused salt and air flow of a high-temperature fused salt air heat exchanger, and a dynamic adjusting module adjusts fused salt pump flow, compressor rotating speed and valve opening in real time. And the safety protection module is used for monitoring and preventing over-temperature, fused salt solidification and pipeline corrosion. The problems that in the prior art, a fused salt heat storage type photo-thermal power generation system is insufficient in heat storage capacity under the extreme weather condition, a compressed air energy storage system depends on fossil fuel, the afterburning efficiency is low, and the energy loss is caused due to the low system coupling degree can be effectively solved.

The invention discloses a pressure storage and heat storage coupled regenerative heat pump power storage system and a regulation and control method thereof. The system is composed of an electric-power generation integrated unit, an energy storage compression and energy release expansion integrated unit, an energy storage expansion and energy release compression integrated unit, a high-temperature heat exchanger, a backheating heat exchanger, a cold energy absorption heat exchanger, a heat storage and cold storage storage tank, a high-pressure gas storage tank and the like. And an energy storage / energy release loop, a heat storage / heat release loop, a cold storage / cold release loop and a pressure heat coupling loop are formed by connecting pipelines. In the energy storage mode, the compressor unit is driven by electric energy to conduct multi-stage compression, and compression heat and expansion cold are stored in the high-temperature storage tank and the low-temperature storage tank correspondingly. In the energy release mode, the heat storage medium and the cold storage medium are sequentially released to supply heat or cold to the circulating working medium, and the expansion unit is driven to drive the generator to generate electricity. According to the method, bidirectional operation of energy storage and energy release is achieved through valve switching and circulation control, and power output and load change are adjusted within a second level through a pressure-heat coupling mechanism.

The invention discloses a multivariable self-adaptive collaborative optimization control system and method for a high-alkali coal boiler, and relates to the technical field of high-alkali coal boiler optimization control, the system is based on real-time operation data acquisition and multi-working-condition state recognition, and dynamic evaluation and collaborative adjustment of combustion heat transfer and physical coupling effects are achieved; by collecting a first data set, a second data set and a third data set, a high-alkali coal quality and load mapping relation is constructed, and a multi-working-condition state label is output; based on the label, the multivariable cooperative adjustment module calculates a combustion heat transfer coupling coefficient and performs efficiency judgment; the physical coupling effect evaluation module evaluates the boiler internal structure risk; and the dynamic control optimization module integrates multiple indexes to realize control response optimization. According to the system, through threshold value judgment and strategy feedback, the combustion efficiency of the high-alkali coal boiler is improved, structural safety guarantee and dynamic regulation and control optimization are achieved, and the operation stability and economical efficiency of the high-alkali coal boiler are remarkably improved.

The invention relates to the technical field of electrical digital data processing, and discloses a train ice melting simulation optimization method of an electromagnetic thermal coupling model fused with a deep learning method, which comprises the following steps of: inputting a geometric representation tensor and a physical working condition parameter vector containing an electromagnetic excitation frequency and a reference environment temperature into a feature mapping neural network; outputting a dual-channel space source item tensor containing basic heat source power density and a heat source to temperature change sensitivity distribution matrix through nonlinear convolution operation; constructing a heat conduction discrete numerical value evolution operator configured with an active item linear correction interface; time stepping operation is executed according to the heat conduction time scale, a basic heat source is corrected in real time through the Hadamard product of a sensitivity distribution matrix and temperature deviation, and an operator is substituted for solution. On the premise that electromagnetic-thermal nonlinear coupling characteristics are reserved, decoupling of the time scale is achieved, and the calculation efficiency in high-frequency physical field simulation is effectively improved.

The invention relates to a boiler flue gas temperature dynamic compensation method based on CFD-reinforcement learning, and belongs to the technical field of boiler operation control. The method comprises the steps that boiler operation parameters and flue gas temperature data are collected in real time, a multi-scale coupling three-dimensional CFD model is constructed, a macroscopic gas-solid two-phase flow-combustion reaction-radiation heat transfer coupling algorithm and a microcosmic pulverized coal particle combustion dynamics algorithm are fused, and hearth and flue temperature distribution is simulated and output; building a CFD-reinforcement learning fusion agent based on a CFD model, taking similar working condition agent parameters as initial values, combining historical and virtual data staged training, and reinforcing the weight of a key temperature measurement point through a focusing regulation and control strategy; an echo state network-predictive control collaborative algorithm is adopted, real-time data are input to obtain an initial adjustment amount, and after security constraint optimization, a compensation execution mechanism is controlled to act. The flue gas temperature is accurately and dynamically compensated, the adaptability to complex working conditions is improved, and equipment safety and operation efficiency are both considered.

The invention relates to a machining path planning system, in particular to the technical field of intelligent manufacturing and numerical control machining, and overcomes the defect that a thermal coupling effect is ignored in traditional geometric planning by constructing an evolution model fused with a physical law. A dynamic decoupling algorithm is utilized to extract a real cutting load from multi-source data and reconstruct a rigidity field changing along with material removal in real time, the problem that a physical boundary is difficult to perceive under a complex working condition is solved, and the system effectively eliminates size out-of-tolerance caused by heat accumulation through transient deformation advanced prediction. According to the method, the non-linear error is actively counteracted in cooperation with a reverse compensation strategy based on sensitivity analysis, the machining precision and surface quality of the thin-wall weak-rigidity part are remarkably improved, the rejection rate and trial cutting cost are reduced, and intelligent closed-loop control over the manufacturing process is achieved.

The invention discloses a photovoltaic photo-thermal coupling hydrogen production system control method for a hydrogen-burning heavy truck traffic network. The method comprises the following steps: S01, collecting operation data of a photovoltaic array, an electrolytic cell and a hydrogen storage tank; s02, based on a model prediction control algorithm, establishing a multi-objective optimization model containing an objective function and constraint conditions; s03, merging the sub-objective functions into a comprehensive objective function through a linear weighting method; s04, applying constraint conditions; S05, performing rolling optimization calculation by adopting a model prediction control algorithm; s06, outputting an optimal control variable; s07, adjusting corresponding parameters in the system; s08, monitoring the running state of the system; s09, whether the system meets constraint conditions is judged, according to the control method, through multi-target collaborative optimization and precise constraint control, the performance, efficiency, economical efficiency and safety of the photovoltaic photo-thermal coupling hydrogen production system can be remarkably improved, and clean, efficient and stable hydrogen energy supply is provided for a hydrogen combustion heavy truck traffic network.

The invention provides an intelligent calculation method and system for the loss of a semiconductor device, and the method comprises the steps: firstly collecting the operation electrical parameters of the semiconductor device and the temperature parameters of a plurality of temperature measurement points of a surrounding environment in real time, and forming a synchronous operation data set with a timestamp; a parameterized body loss model is established based on a semiconductor physical principle, and a relationship between conduction loss and switching loss along with changes of electrical parameters and junction temperature is described. A parameterized thermal coupling effect model is established according to the physical layout and the heat transfer path of the control cabinet, and a thermal resistance parameter to be identified is used to describe the heat transfer relationship between the device and other heat sources. And identifying and updating model parameters by adopting a parameter identification algorithm and utilizing the synchronous operation data set to obtain a self-adaptive model parameter set reflecting the current working condition and the coupling state. And in combination with the synchronous operation data set and the adaptive model parameter set, a real-time loss value of the semiconductor device is calculated through the body loss model and the thermal coupling effect model, and accurate loss evaluation under the actual working condition is realized.

The invention discloses a heat dissipation control method and device for a single-phase immersed liquid cooling charging pile and a medium, and relates to the technical field of battery thermal management, and the method comprises the steps: carrying out the high-precision scanning and dynamic feature extraction of a built-in fiber grating array of a cold plate according to a partition flow control signal, and obtaining a temperature field matrix and a dynamic thermal feature set; performing multi-dimensional fusion analysis and electrochemical coupling analysis on the temperature field matrix and the dynamic thermal feature set to generate a thermal coupling parameter set; performing real-time risk quantitative evaluation and risk topological mapping on the thermal coupling parameter set, and outputting a risk topological data set; and performing cooling intensity dynamic modulation according to the risk topology data set, and obtaining a partition flow redistribution instruction. According to the invention, fine monitoring of the cold plate-battery interface contact thermal resistance is realized through a high-precision pressure field reconstruction technology, the accuracy and reliability of pressure distribution detection are significantly improved, and the local overheating risk caused by an assembly gap is effectively eliminated.

The present disclosure relates to a thermal management system for a vehicle including a refrigerant system, a coolant system and a control unit. The coolant system includes a first control loop thermally coupled to an energy storage system, a second control loop thermally coupled to a drive train system, a third control loop thermally coupled to a radiator system, a first multiple valve unit and a second multiple valve unit, and a first heat exchanger configured to transfer heat to the refrigerant system. The first control loop, the second control loop and the third control loop are configured to transfer heat to the first heat exchanger. The first heat exchanger is arranged between the first multiple valve unit and the second multiple valve unit. The control unit is configured to switch the first multiple valve unit and the second multiple valve unit in a first mode to couple the first control loop with the second control loop to collectively transfer heat to the first heat exchanger independently of the third control loop.

The invention provides a high-strength aluminum alloy selective laser melting forming thermal stress prediction method based on deep learning. The method comprises the steps of 1, finite element model establishment and simulation, wherein a heat transfer model and a thermal coupling model are established; an SLM process is used as a simulation object, a Gaussian model is adopted to define a laser heat source, and thermal stress distribution under different process parameters and different sample sizes is simulated; wherein the process parameters comprise laser power, scanning speed and hatch spacing; step 2, data processing and deep learning model training; comprising the steps of data preprocessing, neural network model architecture determination, adversarial network part generation and model configuration and training. 3, evaluating and optimizing the model; and 4, thermal stress prediction and process optimization. By learning a complex mode in finite element simulation data through a deep learning model, efficient and accurate thermal stress prediction is achieved, and the problems that traditional finite element analysis is complex in calculation and large in resource consumption are solved.

The invention relates to the technical field of chemical automation control, and discloses an intelligent closed-loop control system for producing p-dichlorobenzene by a biomass method, which comprises a signal acquisition module, a residence time reconstruction module, a thermal coupling analysis module, a parameter self-adaptive scheduling module and an execution control module. The rheological characteristics of raw materials are monitored by collecting the current of a stirring motor, the liquid level is dynamically increased through a retention time reconstruction module to prolong the retention time of the materials, and a liquid level periodic migration strategy is introduced to prevent scaling of a gas-liquid interface; the thermal coupling analysis module and the parameter self-adaptive scheduling module are used for calculating thermal retardation post factors, temperature control parameters are corrected in real time, the upper limit of cooling output is limited, inner wall supercooling crystallization is prevented, and the problems that the conversion rate is reduced, the heat transfer efficiency is reduced and equipment is hung on the wall due to viscosity fluctuation of biomass raw materials are effectively solved. And the self-adaptive stable control of the reaction process is realized.

The invention relates to the technical field of metal plastic forming, and discloses a titanium alloy radial forging reduction rate collaborative optimization method based on material testing, which comprises the following steps of: acquiring dynamic characteristic data in a forging process in real time by acquiring multi-dimensional material performance parameters, and constructing a multi-target collaborative optimization model; and intelligent collaborative configuration of the reduction rate parameters is realized by adopting a self-adaptive weight distribution strategy. And a thermal coupling factor normalization equation and a phase field free energy functional normalization model are established, and the technical problems that in a traditional forging process, due to empirical configuration of reduction rate parameters, the structure is uneven, and residual stress exceeds the standard are solved. A lightweight model is deployed through edge computing nodes, millisecond-level dynamic correction of an optimization scheme is realized in combination with a 5G industrial private network, and the grain size qualification rate and the size precision of the titanium alloy forgings are improved. The method improves the yield of the grain size of the forge piece, ensures that the residual stress of the forge piece is reduced, stably controls the size precision, and improves the batch stability of high-end titanium alloy parts.

The invention relates to the technical field of tunnel engineering construction, in particular to a high and cold tunnel variable cross-section frozen soil thermal coupling safety hierarchical control construction method, which comprises the following steps: constructing a three-dimensional thermal coupling model considering frozen soil phase change characteristics based on an FLAC3d platform, and introducing a freezing frontal surface expansion speed prediction algorithm and ground temperature monitoring data to dynamically correct model parameters; a three-level safety threshold is established, a hierarchical control strategy is implemented, a pre-control period (heating and heat preservation), an excavation period (advanced small pilot tunnel and reverse top-picking expanding excavation) and a support period (asymmetric support) are divided, and a closed-loop system for monitoring, simulation, feedback and adjustment is established. The method comprises the following steps of: accurately regulating and controlling a temperature field, setting a phase change energy storage heat preservation layer, constructing a bionic drainage system, applying an intelligent construction platform with BIM (Building Information Modeling) and GIS (Geographic Information System) fused, integrating various sensing equipment for real-time monitoring, comparing monitoring data with a model prediction result, dynamically optimizing construction parameters and ensuring construction safety.

The invention relates to the field of computer heat dissipation control, and discloses a high-performance computer system temperature regulation and control method which comprises the following steps: dividing a computer system into a plurality of hot areas and initializing thermodynamic parameters; collecting temperature, power consumption and process data in real time, identifying a burst process and constructing a dynamic heat capacity model; the heat capacity parameter and the heat dissipation coefficient are updated online through a recursive least square method; the multi-target weight is dynamically adjusted based on the temperature deviation degree and the heat capacity change rate, a distributed optimization model containing heat diffusion coupling constraints is established, and an optimal heat dissipation strategy is solved by adopting an improved ADMM algorithm; model prediction deviation is corrected through a double-loop control mechanism, and data updating circulation is triggered in combination with burst process marks and temperature out-of-limit events. According to the method, through a dynamic modeling, weight self-adaption and closed-loop control fusion mechanism, the temperature control precision and the system safety in a complex thermal coupling scene are remarkably improved, and meanwhile heat dissipation efficiency, noise suppression and energy efficiency balance optimization are achieved.

The present application relates to the technical field of metal plastic forming, and discloses a titanium alloy radial forging reduction rate collaborative optimization method based on material testing, which acquires dynamic characteristic data of a forging process in real time through multi-dimensional material performance parameter collection, constructs a multi-objective collaborative optimization model, and realizes intelligent collaborative configuration of the reduction rate parameter by using an adaptive weight distribution strategy. A thermal coupling factor normalization equation and a phase field free energy functional normalization model are established to solve the technical problems of uneven structure and excessive residual stress caused by the empirical configuration of the reduction rate parameter in the traditional forging process. A lightweight model is deployed through an edge computing node, and millisecond-level dynamic correction of the optimization scheme is realized in combination with a 5G industrial private network, thereby improving the grain size qualification rate and size accuracy of titanium alloy forgings. The present application improves the grain size qualification rate of forgings, ensures stable control of the size accuracy while reducing the residual stress of forgings, and improves the batch stability of high-end titanium alloy components.

The invention discloses a mold partition temperature control system based on an air film adjustable thermal resistance boundary, and belongs to the technical field of mold temperature control and thermal management. The system comprises a mold body, at least two temperature control subareas and gas film adjustable thermal resistance boundary units arranged between the adjacent temperature control subareas. The air film adjustable thermal resistance boundary unit comprises a boundary air cavity, an air injection micropore, an exhaust adjusting structure and an air supply unit. Gas enters the partition boundary area through the gas injection micropores and forms a gas film layer, and the thickness, continuity, stability and coverage range of the gas film layer are changed by adjusting the gas supply pressure, the gas supply flow and the exhaust resistance, so that the thermal coupling strength between the adjacent temperature control partitions is adjusted. The system is suitable for injection molding, pressure casting, hot pressing and composite material forming molds.

The invention relates to the technical field of solar heat utilization, and discloses an electric energy conversion and power regulation and control method based on a solar heat exchange coupling system, which comprises the following steps: calculating the phase change latent heat release rate and pressure and temperature sensitivity of a working medium in real time through a nonlinear prediction model, and establishing a compensation factor to dynamically correct the fusion weight of temperature and pressure signals; the shape memory alloy runner is driven to adjust the branch angle in a self-adaptive mode within the range of 45-75 degrees; constructing an energy potential matching matrix of the temperature gradient of the heat storage module and the output voltage of the power generation system, and combining a pulse type energy release strategy to maintain power stability; and controlling adjacent flow channel branches to generate a phase difference based on pressure fluctuation spectrum characteristics to realize fluctuation offset. According to the method, through closed-loop control of thermodynamic parameter compensation and flow channel dynamic adjustment and space-time collaborative optimization of energy potential matching and pulse release, continuous and stable power generation under the intermittent irradiation condition is achieved, the overall energy efficiency of the system is improved, and operation is more stable.

The invention belongs to the field of winding temperature control, and relates to a PCA-FCNN-based converter transformer valve side winding oil flow temperature rise calculation method, which analyzes a thermodynamic conduction process in a valve side winding, determines a characteristic variable having the highest correlation with a temperature field, performs high-correlation characteristic variable parameterization setting by using Latin hypercube sampling, and calculates the oil flow temperature rise of the valve side winding. A valve side winding flow-heat coupling calculation model is established, and finite element calculation software is used for flow-heat coupling calculation; constructing a data set of a calculation model by taking the parameterized characteristic variable as an input characteristic quantity and taking the valve side winding oil flow temperature rise calculated by a finite element as an output characteristic quantity, dividing the data set into a training set and a test set, and generating a valve side winding oil flow temperature rise rapid calculation model with relatively high precision by setting model training parameters; a validation set other than the model data set is set to validate the accuracy of the computational model. According to the method, the required calculation time is shortened by about 300 times compared with the calculation time of a finite element method, and the calculation speed of the oil flow temperature rise of the valve side winding is increased.

The invention provides a tip Raman-based metal oxide nanoparticle in-situ detection method, which comprises: S1, dispersing metal oxide nanoparticles in a nanoparticle dispersion liquid, focusing laser to the tip of a nanometer tip through an objective lens, capturing a single nanoparticle by using an optical tweezers technology, and attaching the single nanoparticle to the nanometer tip; s2, focusing Raman laser on a nano needle tip-particle interface, generating a local high-temperature field through a photo-thermal near-field coupling effect, triggering a thermal chemical reaction of nano particles, and synchronously collecting a Raman spectrum; and S3, carrying out pretreatment and characteristic peak analysis on the collected Raman signal of the Raman spectrum, and determining the reaction critical temperature and the dynamic process in combination with temperature calibration data. According to the invention, capture and accurate heating of the nanoparticles are realized through photo-thermal coupling of the optical tweezers and the needle tip, and thermal chemical reaction monitoring of the nanoparticles is realized.

The invention belongs to the field of machine tool linear feed shaft thermal error prediction, and provides a cross-working-condition migration machine tool thermal error step-by-step prediction method and a cross-working-condition migration machine tool thermal error step-by-step prediction system. According to the technical scheme, the method comprises the following steps: acquiring thermal error data of the linear feed shaft of the machine tool under two working conditions of natural cooling and forced cooling and time-temperature distribution data of each temperature measurement point of the linear feed shaft; decoupling the thermal error data under each working condition into a linear component and a nonlinear deviation component; according to a heat conduction and convective heat transfer coupling mechanism, the inhibition effect of low-temperature water on the temperature rise of the lead screw is quantified, and a temperature rise inhibition coefficient is calculated; based on the inhibition coefficient, mapping the original temperature rise data and the nonlinear deviation component under the natural cooling working condition to an equivalent temperature rise characteristic space under the forced cooling working condition; and predicting a cross-working-condition linear component and a non-linear deviation component according to the equivalent temperature rise characteristic space, and performing superposition to obtain a thermal error prediction value of the linear feed shaft. And high-precision prediction of the thermal error of the linear feed shaft under different cooling working conditions is realized.