21 results about "Arrhenius equation" patented technology

The invention discloses a transformer health state determination method, system and device and a storage medium, and belongs to the technical field of transformers, and the method comprises the steps: carrying out the temperature detection of a key part of a transformer through a temperature sensor, and obtaining a temperature parameter set; performing stress measurement on the stress concentration area of the transformer to obtain a stress parameter set; inputting the temperature parameter set into an insulation thermal aging model to obtain a thermal aging damage index; performing damage evaluation on the stress parameter set through a Miner rule to obtain a mechanical fatigue damage index; and determining a health state value of the transformer based on the thermal aging damage index and the mechanical fatigue damage index. According to the method, the influence of the temperature on the insulation life is quantified based on the insulation thermal aging model constructed based on the Arrhenius equation, and the mechanical fatigue damage index is accumulated in combination with the Miner rule, so that the health state value of the transformer can be accurately estimated, the accuracy of transformer health state evaluation is improved, and the accident risk of a power system is reduced.

The invention relates to a deep energy level transient spectrum data processing method based on dictionary learning. The method comprises the following steps: acquiring transient capacitance signals of a sample at different temperatures by using deep energy level transient spectrum equipment; establishing an Arrhenius equation that the defect emissivity changes along with the temperature; the defect emissivity under each group of values is calculated according to all possible values of the coefficient of the exhaustion Arrhenius equation; constructing a dictionary matrix based on the defect emissivity amplitude introducing Arrhenius constraint, and reconstructing a transient capacitance signal by using the dictionary matrix; and constructing a convex optimization problem, solving a dictionary index variable which enables a reconstruction signal of the transient capacitor to be closest to an acquisition signal, and extracting a non-zero solution in the dictionary index variable to obtain a corresponding defect emissivity amplitude spectrum. According to the method, the detection precision of the deep energy level defect can be improved by introducing the Arrhenius constraint relation.

A trapezoidal approximation of the Arrhenius equation provides an accurate determination of an acceleration factor for aging, while using minimal storage. The current temperature is periodically sampled. Based on the current temperature, a previous temperature, and a reference temperature, an acceleration factor for the device is updated. Then the sampled temperature is stored in place of the previous temperature, for use in updating the acceleration factor in the next period. The remaining lifespan may be determined based on a reference lifespan and the acceleration factor. As a result, the benefits of more accurate lifespan prediction are obtained without the costs of storing a sequence of temperatures experienced by the device.

This invention provides a method for predicting the service life of concrete under alkali-aggregate reaction failure. Based on the kinetics of alkali-aggregate reaction expansion and the theory of temperature acceleration, the method first determines the activation energy Ea of the alkali-silica reaction expansion of reactive aggregates through accelerated tests at different temperatures. Then, concrete specimens are prepared using actual engineering mix proportions, and accelerated temperature tests are conducted in a simulated pore solution at 70℃ to obtain the time t1 required for the expansion rate to reach the failure threshold of 0.040%. Finally, based on the Arrhenius equation, a mathematical model is established comparing the failure time of the concrete specimens under accelerated temperature conditions with the failure time of the concrete in the engineering structure, and the service life t2 of the concrete under engineering conditions is calculated. This invention solves the problems of existing technologies lacking a unified physical basis and being difficult to quantitatively extrapolate, improving the engineering applicability and universality of service life assessment, and providing a scientific basis for the long-term safety evaluation of major infrastructure such as hydraulic engineering and transportation.

The invention relates to the technical field of water treatment intelligent control, and discloses a biochar adsorption capacity dynamics simulation and prediction system, which comprises a data acquisition module, an Arrhenius equation is utilized to introduce a temperature correction coefficient to carry out thermodynamic compensation on an adsorption dynamics constant; the flow state recognition module judges a forward adsorption or reverse desorption working condition based on a Langmuir model; the resistance decoupling module is used for calculating a mass transfer Biot number based on a double-membrane theory so as to distinguish diffusion resistance in a liquid membrane and particles, and distinguishing physical blockage and chemical saturation by utilizing a physical blockage index; the lag compensation module introduces a lag correction factor to correct the desorption equilibrium concentration, and calculates the recovery adsorption capacity during the reverse desorption period; and the life prediction module corrects the theoretical residual capacity by using the dynamic utilization rate coefficient and generates an operation and maintenance instruction. According to the invention, through resistance decoupling and lag compensation mechanisms, physical and chemical attenuation mechanisms are effectively distinguished, and accurate prediction of the adsorption penetration time under the dynamic working condition is realized.

The application relates to the technical field of intelligent water treatment control, and discloses a system for simulating and predicting the kinetics of the adsorption capacity of biochar, which comprises the following modules: a data acquisition module uses the Arrhenius equation to introduce a temperature correction coefficient to perform thermodynamic compensation on adsorption kinetic constants; a flow state recognition module determines a forward adsorption or reverse desorption working condition based on the Langmuir model; a resistance decoupling module calculates the mass transfer Biot number based on the double membrane theory to distinguish the liquid film and the internal diffusion resistance of the particles, and uses a physical blockage index to distinguish physical blockage and chemical saturation; a hysteresis compensation module introduces a hysteresis correction factor to correct the desorption equilibrium concentration and calculate the recovery adsorption capacity during the reverse desorption; and a service life prediction module uses a dynamic utilization rate coefficient to correct the theoretical residual capacity and generate operation and maintenance instructions. Through the resistance decoupling and hysteresis compensation mechanisms, the physical and chemical attenuation mechanisms are effectively distinguished, and the precise prediction of the adsorption breakthrough time under dynamic working conditions is realized.

The application discloses a kind of hydraulic oil life online monitoring method based on multi-sensor information fusion, first establish new oil baseline database;Real-time acquisition oil temperature, dielectric constant, viscosity, moisture, water activity and particle pollution data and carry out temperature compensation;Respectively calculate oil quality, particle pollution and temperature influence molecular score based on Arrhenius equation;According to the dynamic adjustment weight of operating phase or abnormal event, fusion calculation health status index HI;Through HI history trend prediction residual life;Construct oil deterioration reason analysis matrix, and according to the change mode matching diagnosis oil liquid deterioration reason of multiple parameters.This method solves the problem of single parameter monitoring easy misjudgment by multi-parameter fusion and dynamic weight adjustment, realizes the quantitative evaluation of oil health status, accurate diagnosis and life prediction, can effectively identify water, oxidation, wear and other failure modes, improve response speed, prolong oil life, reduce maintenance cost.

This invention discloses a method for process optimization and performance prediction of composite insulator silicone rubber, comprising the following steps: S1. Obtaining performance parameters of silicone rubber with the same composition as the silicone rubber to be predicted under different vulcanization times, temperatures, and pressures; S2. Based on the performance parameters obtained in step S1, determining the optimal vulcanization process conditions within the experimental range; S3. Obtaining a first silicone rubber performance prediction model using the least squares method based on the performance parameters, and then introducing the Arrhenius equation into the first silicone rubber performance prediction model to obtain a second silicone rubber performance prediction model; S4. Based on the first and second silicone rubber performance prediction models, inputting the vulcanization conditions to complete the silicone rubber performance prediction. This invention provides theoretical guidance for production processes and achieves accurate performance prediction.

The invention relates to a C6 repair-based HXD1 series locomotive high-voltage insulator residual life periodic performance research method, which comprises the following steps of: performing state grading, accelerated aging test and data fitting on a high-voltage insulator, establishing a thermal life model based on an Arrhenius equation, and accurately predicting the residual life of the insulator. The method has the advantages of being small in sample demand, easy and convenient to implement, high in prediction precision and the like, and is suitable for service life evaluation and maintenance decision making of the HXD1 series electric locomotive high-voltage insulator.

The application relates to a water-cooled damping resistor life prediction method for a converter valve, which comprises the following steps: S01: preparing samples and collecting aging data of the water-cooled damping resistor at different preset time points under each temperature; S02: obtaining a plurality of nonlinear fitting relationship formulas by using the collected aging data; S03: determining the life end point of the water-cooled damping resistor, and inversely calculating the life end value at multiple temperature points by using the nonlinear fitting formula; S04: obtaining a life prediction model by nonlinearly fitting the life end values at the multiple temperature points based on an Arrhenius equation; S05: optimizing parameters of the life prediction model to obtain optimal fitting coefficients; and S06: using the life prediction model with the optimal fitting coefficients as an optimal life prediction model. The application can accurately predict the life of the water-cooled damping resistor, and greatly improves the safe application of the water-cooled damping resistor in actual life.

The application discloses a kind of on-line evaluation methods of remaining service life of insulation bus duct, belong to the technical field of electric power equipment.The method comprises: obtaining the reference thermal life parameters of insulating material, including activation energy and frequency factor;Real-time temperature data in operation is obtained according to sampling period;Characteristic life value under current temperature is calculated based on the temperature data using Arrhenius equation, and the ratio of sampling period length and characteristic life value is taken as the thermal aging loss increment of the period;Loss increment is accumulated to historical cumulative thermal aging loss total value;Whether the cumulative value reaches the life termination threshold is judged, and the remaining service life evaluation result is calculated and output when not reached.The application combines thermal aging model with on-line temperature monitoring, to realize dynamic evaluation under variable temperature condition in the form of discrete time sampling and loss accumulation, solves the problem that offline evaluation cannot reflect actual operation condition cumulative aging, algorithm is simple, deployment cost is low, and is suitable for bus duct on-line monitoring.

The invention discloses a method and system for predicting the service life of a prestressed concrete cylinder pipe protective layer, and the method comprises the steps: building a diffusion permeation model of a corrosive medium in concrete based on a Fick diffusion law and an Arrhenius equation, calculating the performance degradation data of a material through the diffusion permeation model, and outputting a material degradation index; the method comprises the following steps: associating elastic modulus degradation of a protective layer with a crack growth rate through an improved CDM continuous damage mechanics theory, introducing a coupling factor of a dynamic stress field and a corrosion medium concentration, establishing a stress damage coupling model, inputting the structural stress monitoring data and a material degradation index into the stress damage coupling model for updating, and determining the structural stress monitoring data and the material degradation index. And establishing a life prediction model based on an LSTM (Long Short-Term Memory) network, inputting the protection layer environment parameters, the material degradation index and the stress distribution into the life prediction model for prediction, and outputting the residual life of the protection layer. And the protection layer prediction accuracy and efficiency are improved.

The application discloses a kind of testing evaluation methods of phase change heat storage material storage life, comprising the following steps: step 1, test and obtain the temperature control time before phase change heat storage material accelerated aging test;Step 2, according to Arrhenius equation, the acceleration factor of the phase change heat storage material is calculated;Step 3, according to the acceleration factor, the accelerated aging test time of the phase change heat storage material is calculated;Step 4, phase change heat storage material is placed in the accelerated aging test in forced air drying cabinet;Step 5, test the temperature control time after phase change heat storage material accelerated aging test;Step 6, compare the temperature control time change percentage of phase change heat storage material before and after accelerated aging test, if the absolute value of temperature control time change percentage is ≤5%, then determine the storage life of phase change heat storage material meets target life;Otherwise, it does not meet.The testing evaluation method of phase change heat storage material storage life provided by the application is simple, effective.

The invention discloses a method for predicting the service life of a metal oxide semiconductor type gas sensor, which comprises the following steps of: determining a normal stress (the working state of a rated local gas sensitive material of the sensor) temperature Top, and selecting at least three accelerated stress temperatures Tstressi higher than the Top; the method comprises the following steps: establishing an accelerated aging test system, placing a sensor sample in a constant-temperature environment box, and adjusting the voltage / power of a heating component through an independent programmable power supply, so that the temperature of a gas sensitive material is stabilized at each Tstressi; periodically monitoring and recording sensitivity, baseline resistance and a failure threshold value under a standard condition; for each Tstressi, calculating a median life L50i, and after converting the median life L50i into a Kelvin temperature, fitting based on an Arrhenius equation by taking 1 / Tstressi as an independent variable and ln (L50i) as a dependent variable to obtain failure activation energy Ea; and substituting Ea and Top into an Arrhenius equation, and extrapolating a predicted life Lpred under a normal use condition. The method can accurately and efficiently predict the life, and the effect is good.

The application discloses an individualized process parameter optimization method for aging treatment of aluminum alloy profiles, aiming at solving the problems of uneven performance caused by wall thickness difference in industrial heat treatment of complex cross-section profiles and low precision of traditional experience process. The method first determines the precipitation characteristic temperature of the material by differential scanning calorimetry, and sets the aging temperature boundary in combination with the solid solution supersaturation; a multi-objective optimization model is established based on the response surface method, and the user demand weight coefficient is introduced to balance the strength, toughness and corrosion resistance and other indexes; the actual furnace and profile cross-section temperature difference data are obtained through industrial grade temperature field coupling test; the precipitation activation energy parameter of the material is introduced, the equivalent compensation time is calculated by using the Arrhenius equation, and the hysteresis compensation strategy of "reducing temperature and prolonging holding time" is implemented. The application realizes the accurate conversion from laboratory theoretical parameters to industrial production, and significantly improves the uniformity of profile cross-section performance and the scientificity of process customization.

The invention provides an RFID-based fresh food transportation whole-process tracking method and system, and the method specifically comprises the steps: constructing a coupling state vector which comprises the three-dimensional position of a vehicle, the speed, the internal temperature and humidity of a cargo, and the remaining shelf life of the cargo based on an Arrhenius equation; constructing a parallel system model covering the possible state of the whole transportation process; generating an initial state for the current round of prediction for each model; for each model after interactive mixing, Kalman filtering state prediction is executed, and a process noise covariance matrix Q is dynamically configured according to the road type corresponding to the current position of the vehicle and real-time traffic flow information; receiving new measurement data, performing data validity test through a verification gate based on Mahalanobis distance, updating the state estimation and likelihood of each model by using valid data, and calculating the posterior probability of each model; and according to the updated model posterior probability, carrying out weighted fusion on the posterior state estimation of all parallel models, and outputting the optimal state estimation at the current moment.

The invention discloses a dynamic modeling method for a carbon capture system based on intelligent estimation of reaction kinetic parameters, and belongs to the technical field of carbon capture. On the basis of a double-deep neural network structure, an Arrhenius equation and a primary reaction kinetic equation are respectively embedded into a grid result calculation and loss function, so that a physical information neural network model for realizing intelligent estimation of a reaction rate constant is formed; and in combination with an estimated value output by the physical information neural network and a carbon capture system mechanism model based on a constitutive equation, a carbon capture system'data driving + mechanism 'model considering nonlinear dynamic characteristics of reaction kinetic parameters is constructed. According to the method, the reaction dynamic characteristics of the carbon capture system in the actual operation process are fully considered, and the adaptability and precision of the carbon capture system model in the complex industrial environment can be improved.

The invention discloses an experimental verification method of a water electrolyser multi-field intelligent simulation technology, and relates to the technical field of water electrolyser simulation experiments. Comprising the steps that S1, a multi-physical field simulation model comprising a thermal field, a fluid field and at least one other physical field selected from an electric field, a chemical field or a structural field is constructed, dynamic coupling of the model is achieved through inter-field correlation parameters, and coupling of the thermal field and the fluid field comprises the steps that bubble generation kinetic parameters of the fluid field are dynamically corrected based on thermal field temperature distribution, and the correction adopts an Arrhenius equation to correlate the temperature and the bubble generation activation energy. The method has the advantages that temperature-bubble-heat transfer linkage is captured through a heat flow field dynamic coupling closed loop, multi-field optimization is driven, heat flow priority iteration and simulation experiments are used for synchronously improving efficiency in real time, convergence threshold values are dynamically adjusted, precision is guaranteed through full-dimensional verification, and accurate and reliable simulation support is provided for structural optimization and parameter debugging of the water electrolyser.

The invention relates to the technical field of metal material treatment and quality monitoring, in particular to a method for predicting the thickness of a decarburized layer of spring steel. Comprising the following steps: S1, calculating a diffusion energy barrier of C atoms through microscopic simulation VASP software; s2, solving a diffusion coefficient expression of the C atoms by combining the calculated diffusion energy barrier of the C atoms with an Arrhenius equation to obtain a diffusion coefficient of the C atoms; s3, a mathematical model of the spring steel decarburization process is established, the numerical value of the mathematical model is solved and calculated to obtain the carbon content distribution in the spring steel, the area with C smaller than or equal to 85% C0 is taken as the decarburization layer area, and the decarburization layer thickness of the spring steel under different heating systems can be predicted. According to the method, the decarburized layer thickness of the final product can be accurately and quantitatively predicted in the heat treatment process design stage.

The application discloses a method for tracking electrode zero-point drift based on Kalman filtering combined with Arrhenius equation, and belongs to the field of water quality monitoring sensor signal processing. The application establishes a temperature-related electrode aging drift rate model through the Arrhenius equation, embeds the drift law into the state transition process of Kalman filtering, realizes prior prediction of the zero point and posterior correction after observation, can filter out measurement noise and predict long-term aging drift trend at the same time, significantly reduces the frequency of manual calibration, improves the long-term stability and precision of online measurement of an ion selective electrode, and is suitable for unattended long-term operation of water quality monitoring equipment such as ammonia nitrogen sensors.

This invention relates to the technical field of hexafluorobutadiene preparation process, and provides a method for calculating the reaction thermodynamics and kinetics of trifluorochloroethylene dimerization. The method constructs the initial structure of the trifluorochloroethylene molecule, predicts all corresponding reaction pathways, and selects the optimal functional and basis set. For the target reaction, rigidity scanning, transition state optimization, and vibrational frequency analysis are performed sequentially, and the reaction pathway is predicted based on the confirmed transition state. For the predicted reaction pathway, the initial molecular structures of all possible reactants, target products, byproducts, possible intermediates, and transition states are constructed. Gibbs free energy or enthalpy values ​​at specified temperatures and pressures are extracted from the frequency calculation output file, and the thermodynamic function changes of the predicted reaction pathway are calculated. The free energy changes of different reaction pathways are compared, and a reaction energy barrier diagram is plotted. The reaction rate constant is obtained using transition state theory and tunneling coefficient, and the pre-exponential factor and activation energy are obtained by fitting the Arrhenius equation.