158 results about "Thermal diffusion coefficient" patented technology

The invention relates to the technical field of energy utilization, and discloses an urban heat and cold supply centralized energy comprehensive utilization method. The method comprises the following steps: S1, acquiring physical parameters and operation data of an urban heating and cooling system, wherein the physical parameters comprise a system initial temperature field, a thermal diffusion coefficient, heat source distribution, equipment performance parameters and heating and cooling demand loads; the invention further provides an urban heat and cold supply centralized energy comprehensive utilization system. The data acquisition module is used for acquiring physical parameters and operation data of the urban heating and cooling system; and the thermodynamic modeling module is used for constructing a thermodynamic model based on the law of conservation of energy. By constructing an optimization model based on energy conservation and entropy increase minimization, energy loss in a heating and cooling system is remarkably reduced, by optimizing energy distribution in real time and dynamically adjusting temperature field distribution, invalid energy consumption is reduced to the maximum extent, the overall energy utilization rate of the system is increased, and the energy utilization rate of the system is increased. And therefore, the heating and cooling system can operate efficiently under various working conditions.

The invention discloses a thermal tracing test device and method for determining the comprehensive hydrogeological condition of an aquifer, and the device comprises a water storage system, a medium groove, a heating system, and a distributed optical fiber data collection system. The device is simple in structure and intelligent and convenient to operate, instantaneous / continuous linear heat source excitation can be achieved through the heating system, and the water pressure and temperature change conditions of all positions are monitored in real time through the distributed optical fiber data acquisition system; the comprehensive hydrogeological parameters such as the flow velocity, the flow direction, the permeability coefficient, the thermal diffusion coefficient, the thermal capacity and the heat conductivity coefficient of underground water are studied, the heating system and the distributed optical fiber data acquisition system can be used for indoor tests and field tests, operation is easy and convenient, data precision is high, and practicability is high.

The invention discloses an energy shaft heat exchange efficiency optimization method based on stratum thermophysical property intelligent analysis, and the method comprises the steps: constructing a structured thermophysical property parameter database through integrating laboratory testing, on-site exploration and literature data, and covering key parameters such as a heat conductivity coefficient, specific heat capacity, a thermal diffusion coefficient and the like; a heat transfer model is verified and expanded by combining a reduced scale physical experiment and multi-parameter coupling numerical simulation; machine learning algorithms such as a random forest and a neural network are utilized to establish a nonlinear prediction model of the thermophysical parameters and the heat exchange efficiency of the energy shaft, the contribution degree of the parameters is quantified, and an adaptability threshold value is defined. Through similarity matching of drilling exploration data and a database, the optimal stratum depth is recommended, and finally a cloud intelligent platform is integrated, so that real-time data updating, online calculation and construction suggestions are realized. According to the method, the precision of stratum suitability evaluation in energy shaft ground source heat pump system development is remarkably improved, and the geothermal energy development efficiency is remarkably improved.

The invention discloses a preparation method of a graphene reinforced copper composite material, and belongs to the technical field of composite material preparation. According to the preparation method, high-activity carbon quantum dots are adopted and are dissolved in a solution, subjected to ultrasonic treatment and activated, iodine and copper powder are added at the same time, uniform stirring and freeze drying are conducted, a carbon source and nucleation sites are uniformly adsorbed to the surface of the copper powder of a complex microstructure, then the carbon source and the nucleation sites are catalyzed by iodine, and graphene grows in situ at low temperature; graphene / copper composite powder which is good in coating, uniform in structure and tight in combination is prepared, then the graphene reinforced copper composite material which is continuous in structure and strong in interface combination is prepared through sintering forming, the carrier and load transmission capacity of the composite material is improved by means of evenly-coated graphene with the large specific surface area, electricity, heat and force transmission is facilitated, and the composite material has the good application prospect. The conductivity of the composite material reaches up to 90-100.8% IACS, the tensile strength reaches 207-263 MPa, the thermal diffusion coefficient reaches 101.8-120.8 mm < 2 > / s, the comprehensive performance is superior to that of pure copper, and the method is simple in process and easy to produce.

The invention relates to the technical field of industrial frequency conversion control, and discloses a fan frequency conversion energy-saving safety protection intelligent judgment system which comprises a multi-source data acquisition module, a multi-physics field coupling modeling module, a dynamic tensor decomposition module, a causal reinforcement learning control module, a dynamic closed-loop feedback module and a safety protection output module. The method comprises the following steps: modeling through multi-modal data fusion and multi-physical field coupling, extracting features in combination with tensor decomposition, generating a frequency conversion control instruction by utilizing causal reinforcement learning, and realizing closed-loop control of fan energy conservation and safety protection by dynamically adjusting a thermal diffusion coefficient and a residual hierarchical protection mechanism. According to the method, the monitoring capability is improved through multi-source data fusion and multi-field coupling modeling, energy-saving and safety collaborative optimization is realized in combination with tensor decomposition and causal reinforcement learning, a multi-stage protection system is constructed through closed-loop feedback, and the problems of multi-field dynamic unbalance and abnormal response lag of the frequency conversion fan are solved.

The invention provides a preparation method and application of a foamy copper reinforced aluminum-based composite material, and belongs to the technical field of aluminum-based composite material preparation. According to the foamy copper reinforced aluminum-based composite material, a variable-frequency and variable-power ultrasonic vibration assisted centrifugal casting technology is adopted, strong centrifugal force and the ultrasonic cavitation effect are utilized, so that the biological flowability of aluminum water is improved, the wettability between aluminum and copper is improved, and in combination with the effects of an auxiliary agent and Zn-Al or Sn-Al intermediate alloy, the foamy copper reinforced aluminum-based composite material is obtained. The problem that the binding force of foamy copper and an aluminum matrix is poor is solved, the adopted ultrasonic power is low (smaller than or equal to 1000 W), the preparation technology is simple, the obtained foamy copper reinforced aluminum matrix composite is free of obvious cracks and holes and high in density and thermal diffusion coefficient, and a light intelligent thermal management material can be provided for the field of electronic equipment.

The invention relates to a working parameter linkage regulation and control method and system for a medium-deep geothermal water source heat pump, in particular to the field of medium-deep geothermal water source heat pumps, firstly, disturbance signals are generated through reinforcement learning and injected into a geothermal loop, and therefore the dynamic mapping precision of an underground heat reservoir is improved; secondly, the thermal diffusion coefficient and the fracture rate of the rock stratum are inversed through the physical information neural network model, and real-time monitoring of underground thermal storage is achieved; secondly, predicting the difference between the heat supply load and the geothermal heat absorption capacity by using the meteorological data, the room temperature data and the heat pump operation parameters, generating a regulation and control decision, and setting a grading trigger condition; and finally, a phase change heat storage device and a compressor are precisely regulated and controlled through a double-actuator collaborative algorithm, the response speed and energy efficiency of the heat pump system are improved to the maximum extent, the room temperature fluctuation problem is relieved, frequent frequency modulation is avoided, starting of a standby boiler is reduced, and therefore the operation efficiency and stability of the heat supply system are optimized.

The invention discloses a liquid hydrogen leakage diffusion solving method and device based on the inlet velocity of an atmospheric boundary layer, and belongs to the field of numerical solution, and the method comprises the steps: based on the inlet velocity contour characteristics of the atmospheric boundary layer, combining the characteristics of the atmospheric boundary layer and a liquid hydrogen leakage diffusion model, solving the density through conjugate heat transfer, and solving a thermal diffusion coefficient at the same time; the method is closer to the actual physical process, so that the simulation result is more authentic. Residual errors are introduced, PBiCGStab is adopted in the virtual residual errors and the virtual search direction, and high convergence speed and high calculation efficiency are achieved. And through a PIMPLE algorithm pre-estimation-correction optimization strategy, the method has the advantages of high stability and precision. The method can accurately describe diffusion rules in complex environments, such as gas flow state, diffusion behavior characteristics, hydrogen concentration distribution and the like, of the hydrogen cloud cluster after liquid hydrogen leakage.

The invention provides a battery thermal field simulation calculation method and system and a storage medium, and the method comprises the steps: S1, inputting the product parameters and working condition parameters of a battery, constructing a three-dimensional simulation battery, and generating an electrochemical-thermal coupling model; s2, a heat generation source item in the electrochemical-thermal coupling model serves as a heat source, a thermal field model T (lambda0, p, t) is obtained through coupling, and lambda0 represents an initial set value of a thermal diffusion coefficient; s3, carrying out an incubator experiment on the solid battery to obtain a T-t characteristic curve; and S4, fitting a thermal diffusion coefficient of the thermal field model by using the T-t characteristic curve, optimizing to obtain T (lambda, p, t), correspondingly generating a T-t simulation curve in the model function according to the T-t characteristic curve, comparing the two groups of curves, and verifying the simulation precision. A thermal field model is established through a heat production source item, and thermal diffusion coefficient lambda is fitted through incubator experimental data, so that simulation precision is improved, dynamic prediction of the thermal field of the battery is realized, and the method can be used for predicting the temperature of any position of the battery under any time node in the battery operation process under different working conditions.

The invention relates to a calculation method and system for a non-load structural design value of the early temperature of a wall, and belongs to the technical field of construction control. The calculation method comprises the steps that geometric parameters, material parameters, environmental parameters and hydration heat parameters of a concrete continuous wall are obtained; according to the material parameters, a thermal diffusion coefficient is calculated, and a heat production rate function changing along with time is generated; based on the geometric parameters, the material parameters and the environmental parameters, calculating a Pitot number, and defining dimensionless coordinates and dimensionless time; solving the characteristic equation according to the Pitot number to obtain a characteristic value sequence and a corresponding vibration mode function sequence; calculating a weight coefficient of each mode based on the initial temperature distribution and the vibration mode function sequence; and solving a time domain ordinary differential equation and applying convolution integration, generating a time-varying modal amplitude function sequence, synthesizing a dimensionless temperature field, determining a convergence solution through a truncated modal number, and converting the dimensionless temperature field into a physical temperature field for output. The early temperature gradient distribution rule of the wall can be accurately captured.

The invention relates to the technical field of batteries, and discloses a battery early warning detection system and method based on a power supply side energy storage system, and the method comprises the steps: obtaining the state data of a battery, and obtaining the electrochemical impedance and thermal diffusion coefficient according to the state data; obtaining a charge state change rate according to the electrochemical impedance; obtaining chemical component data according to the gas release condition; comprehensively evaluating the health condition of the battery according to the thermal diffusion coefficient, the charge state change rate and the chemical component data; and performing early warning according to the comprehensive evaluation result. The health condition of the battery can be monitored in real time, and potential problems can be found in advance, so that sudden faults and safety accidents are avoided. Through accurate analysis of electrochemical impedance, thermal diffusion coefficient and chemical component data, the early warning detection system and method can timely discover tiny change of battery performance, and provide scientific basis for battery maintenance and replacement.

The invention provides a fluid-solid junction temperature simulation method and system based on a lattice Boltzmann method, and relates to the technical field of fluid mechanics. The method comprises the following steps: performing thermal fluid simulation by utilizing a lattice Boltzmann method to obtain grid point speed and grid point turbulence dynamic viscosity of each grid point; according to the turbulence dynamic viscosity of the multiple grid points, the turbulence dynamic viscosity at the position 1 / 2 grid away from the solid surface is obtained through calculation; calculating a dimensionless distance based on the turbulence dynamic viscosity, the thermal fluid physical parameters, the solid grid point speed and the first grid point speed; the solid surface temperature is obtained through correction according to the dimensionless distance and the turbulence thermal diffusion coefficient at the position 1 / 2 grid away from the solid surface; and solving a temperature lattice Boltzmann equation based on the solid surface temperature for simulation. According to the method, the simulation precision of the temperature of the fluid-solid interface near the complex solid surface when the lattice Boltzmann method is used for simulating the large-spatial-scale thermal fluid is improved to a great extent.

The invention relates to the technical field of blister thermoforming, and discloses a high-precision controllable thermoforming method of a blister machine, which comprises the following steps: extracting a feature recognition material type, and calculating to obtain a thermal diffusion coefficient; a temperature field is collected in real time, temperature distribution at the next moment is predicted, a fuzzy rule base is established to dynamically adjust PID parameters, a falling risk coefficient is calculated based on the actually measured temperature, and whether airflow compensation is started or not is judged; a mold cavity structure is constructed, and a vacuum degree curve is set in stages; material flow monitoring is conducted on the material, and the vacuum degree is corrected through fuzzy control; analyzing to obtain a coordinate deviation, and executing dynamic compensation; performing real-time correction according to the coordinate deviation, establishing an inter-station coupling coefficient matrix through multi-station cooperative control, and performing temperature compensation; and constructing a multi-dimensional quality evaluation model, performing analysis to obtain a comprehensive quality index, judging whether to trigger a key parameter optimization process or not based on the comprehensive quality index, and performing real-time feedback optimization.

The invention discloses a processing defect detection method and system based on a semiconductor integrated circuit, and belongs to the technical field of semiconductor manufacturing data processing and defect detection, and the method comprises the steps: obtaining a high-resolution surface topography scanning matrix, an interlayer stress distribution matrix and a transient electrical response matrix of a specified process layer of a target wafer, and constructing an original feature tensor after correction; generating a multi-dimensional coupling feature matrix, and constructing a spatial adjacency topology network and a cross-layer coupling energy propagation graph model; performing energy iteration deduction on the basis of introducing a current density direction vector and a thermal diffusion coefficient parameter to obtain a potential energy gathering path; a microdefect risk coupling coefficient distribution diagram is generated through time sequence folding mapping and nonlinear coupling calculation, hidden defect characterization parameters are extracted through multi-scale feature compression and abnormal clustering analysis, and defect space positioning and process compensation parameter adjustment suggestion output are achieved; according to the invention, the subcritical microscale latent defect can be identified, and the detection sensitivity and reliability are improved.

The invention discloses an active infrared thermal imaging thermal diffusion parameter inversion method and system based on a physical information neural network, and belongs to the technical field of infrared thermal imaging nondestructive testing and thermophysical parameter identification. In order to solve the problems that an active infrared thermal imaging method is difficult to steadily invert spatial distribution thermal diffusion parameters under the condition of only single-side surface temperature measurement and is sensitive to measurement noise, the method comprises the following steps: collecting surface space-time temperature field data of a tested piece under thermal excitation, and calculating a relative temperature field; normalizing the space-time coordinates, and constructing a multi-output physical information neural network; the equivalent surface heat conduction equation and the boundary and initial conditions thereof serve as a loss function of a physical constraint embedded neural network, and a Huber loss function is adopted to calculate a data fitting item; a neural network is optimized through joint training, a global volume thermal diffusion coefficient, a global equivalent thermal loss coefficient, a relative thermal diffusion coefficient field and a relative thermal loss coefficient field of spatial distribution are synchronously inverted, and a high signal-to-noise ratio temperature field is reconstructed. The method is used for material surface nondestructive testing.

The invention provides a liquid heat conductivity coefficient measurement method without absolute thickness information, and belongs to the field of liquid heat conductivity coefficient measurement. The problem that the universality and the accuracy are seriously restricted due to the fact that an unsteady state method must depend on the absolute thickness of a sample to be detected is solved. The method comprises the following steps: placing a to-be-detected liquid sample in a crucible, and adjusting an optical system to enable an infrared detector to be aligned with a to-be-detected area; changing the thickness of the liquid sample through an electric displacement table to obtain a plurality of thickness values; corresponding to each thickness, using a pulse laser to heat the sample, detecting a signal of the overheat residual temperature of the upper surface of the crucible along with time change, and obtaining a normalized temperature rise curve; repeatedly measuring under a plurality of different liquid thicknesses, and recording the half-peak time of the normalized heating curve corresponding to each thickness value; and calculating the thermal diffusion coefficient of the to-be-measured liquid according to the proportional relation between the half-peak time and the thickness square of the liquid, thereby determining the heat conductivity coefficient of the liquid. The method is used in practical industrial environments such as thermoelectricity and lithium ion batteries.

The invention relates to the technical field of thermal conductivity measurement, in particular to a thermal conductivity measurement system and a material comprehensive thermophysical property measurement method, and the thermal conductivity measurement system comprises a vacuum heat insulation bin, a sample table, a refrigeration part, a heat source part and a measurement part; the sample table, the refrigerating component, the heat source component and the measuring component are arranged in the vacuum heat insulation bin; the sample table is used for fixing a to-be-tested sample; the measuring part is used for measuring parameters of a to-be-measured sample and sending the parameters to a controller of the vacuum heat insulation bin; the refrigeration component provides a cold source for the sample table; the heat source part provides a heat source for the sample table, so that a feasible method is provided for improving the thermophysical property measurement efficiency based on the steady-state thermal conductivity measurement system, and three parameters including the thermal conductivity, the specific heat capacity and the thermal diffusion coefficient of the sample are directly identified by collecting temperature data of the transient process of the sample. The data acquisition process from deep hypothermia to normal temperature only lasts for tens of minutes, and compared with several hours or even tens of hours of a steady-state method for measuring heat conductivity and specific heat capacity, the data acquisition process is greatly improved.

The invention relates to the technical field of new energy automobiles, and particularly provides a new energy automobile performance index evaluation method which comprises the following steps: calculating a probability density function of each parameter through a data distribution extraction technology according to a multi-dimensional alignment data set to obtain a parameter distribution feature set; according to the dimension reduction feature vector set, potential abnormal distribution features are generated by using a variational auto-encoder through a hidden variable extraction technology, and an abnormal distribution feature set is obtained; according to the abnormal distribution feature set, an abnormal sensitivity technology is adopted to detect distribution offset, and an abnormal offset score is obtained; according to an abnormal time period positioning result, calculating high-temperature duration to obtain defect duration data; querying associated thermal diffusion coefficients and historical data features in the knowledge graph according to defect types in the early warning information to obtain a supplementary acquisition parameter suggestion list; and according to the refined defect identification result, analyzing the thermal state of the battery through a thermal load distribution technology, and judging whether a final early warning report containing thermal characteristics is generated or not.

The invention discloses a transient thermal field inversion method and system based on deep learning, and the method comprises the steps: firstly obtaining thermal field evolution data and space grid coordinates of a measured object in a cooling process, and then designing a thermal field evolution network model composed of two sub-networks, the second sub-network outputs a deduced evolution thermal field, and deduces real material parameters of an object based on a finite difference deep learning model through joint optimization and fusion of internal and boundary thermal evolution equations as physical constraints so as to improve precision and robustness; on the basis, a time-containing inversion operator model is constructed, discrete cosine transform is utilized to map a thermal diffusion coefficient, a time step size and a final-state temperature field evolution sequence to a frequency domain, and accurate reasoning of an initial temperature field is realized through iterative optimization of an operator learning framework. The method has a good application prospect in material science and engineering detection, and can effectively assist in analysis and prediction of a complex heat conduction process.

The invention relates to the field of metal heat treatment and automatic control, and discloses a steel wire annealing quality on-line monitoring and closed-loop control method which comprises the following steps: applying pseudo-random binary sequence thermal excitation to an operating steel wire, collecting temperature response, identifying a channelized thermal response function, and determining the annealing quality of the operating steel wire according to the channelized thermal response function; the effective thermal diffusion coefficient representing the microstructure is obtained through inversion and serves as an agent parameter, the agent parameter is input into a Gaussian process regression model, a predicted mechanical property index and predicted uncertainty are obtained, during control, the deviation between the predicted index and a target value serves as a feedback signal, and the gain of a feedback controller is dynamically adjusted according to the predicted uncertainty; and meanwhile, the agent parameter change rate serves as a feedforward signal, and annealing process parameters are adjusted in combination with feedback and feedforward control quantities. According to the method, self-adaptive adjustment of the control system is realized by utilizing the prediction uncertainty, online and high-robustness closed-loop control can be carried out on the mechanical properties of the steel wire in combination with feed-forward compensation, and the consistency of product quality is improved.

The invention provides a measuring method. The method comprises the following steps: doping boron ions into a part, exposed by a first opening, of a first semiconductor layer through the first opening of a first barrier layer; removing the first barrier layer and a part, which is not doped with boron ions, in the first semiconductor layer below the first barrier layer; measuring a first length of a remaining portion of the first semiconductor layer in a predetermined direction; boron ions are doped into the part, exposed by the second opening, of the second semiconductor layer through the second opening of the second barrier layer; removing the second barrier layer, and performing heat treatment on the second semiconductor layer; after the heat treatment is carried out, the part, not doped with the boron ions, in the second semiconductor layer is removed; measuring a second length of the remaining portion of the second semiconductor layer in the predetermined direction; and calculating the thermal diffusion coefficient of the boron ions along the predetermined direction according to the first length, the second length and the heating time of the heat treatment.

The invention relates to a preparation method of a flexible heat-conducting film with high surface emission, which comprises the following steps: (1) dispersing raw material powder in a solvent, and stirring and performing ultrasonic treatment to obtain precursor slurry; (2) taking a thermal shrinkage film as a substrate, coating the precursor slurry on the substrate, and drying to form a thin film on the substrate; (3) transferring the film with the substrate in the step (2) to a drying oven for high-temperature treatment, so that the film forms a wrinkle structure; (4) continuously coating precursor slurry on the thin film with the wrinkle structure in the step (3) to form a planar heat-conducting layer; and (5) removing the substrate from the film obtained in the step (4) through an organic solvent, thereby obtaining the flexible heat-conducting film. The method disclosed by the invention is simple and effective, and the prepared flexible heat-conducting film with the wrinkle structure has high emissivity and high thermal diffusion coefficient, and can meet the heat dissipation requirement of an electronic device.

The present invention relates to the technical field of circuit board processing, and in particular to a processing method for a radar circuit board product based on laser blind hole design. The method includes determining the laser blind hole processing environment based on whether there is an easily oxidizable material in the laser scanning path of the radar circuit board to be processed; determining whether to add a flying material shielding device and / or whether to use a gas-assisted processing method during laser blind hole processing based on the blind hole depth of the radar circuit board to be processed and the laser blind hole processing environment; when using the gas-assisted processing method, determining the gas-assisted processing method based on the magnetic permeability of the area where laser blind hole processing has not been performed and the thermal diffusion coefficient of the material of the blind hole to be processed; determining whether to adjust the processing process according to the roughness and blind hole depth of the blind holes of the radar circuit board after processing using the gas-assisted processing method. The present invention improves the processing accuracy of laser blind holes and the processing quality of blind holes by adding an auxiliary method to the laser blind hole processing process.

The application relates to a cooking utensil state detection method, an intelligent range hood and a computer device, wherein the method comprises the following steps: performing infrared thermal imaging on a target region above a heated cooking utensil to obtain a real-time temperature matrix corresponding to the target region; the real-time temperature matrix comprises a plurality of temperature values, and each temperature value corresponds to a corresponding position in the target region; gradient analysis is performed on each temperature value in the real-time temperature matrix to obtain a corresponding gradient amplitude matrix; the gradient amplitude matrix comprises a temperature gradient amplitude corresponding to each temperature value; based on each temperature value in the real-time temperature matrix and the gradient amplitude matrix, a heat diffusion coefficient of the cooking utensil is determined; and based on the heat diffusion coefficient, the current state of the cooking utensil is identified. Through the application, the problem that the actual state of the cooking utensil cannot be accurately detected is solved, and the state of the cooking utensil is accurately detected, so that the user can timely avoid the safety and health risks caused by the change of the state of the cooking utensil.

The invention discloses a preparation method of a graphene oxide film with the surface roughness of 0.5-5.0 microns. The preparation method comprises the following steps: (1) coating a base material with graphene oxide slurry; (2) drying the coated graphene oxide to obtain a graphene oxide film; wherein the drying temperature is 20-45 DEG C, the drying air speed is 1-10 m / s, and the drying time is 1.0-60 h. The invention also discloses a graphene oxide film, a graphene heat-conducting film with the surface roughness of 0.2-1.0 mu m and a preparation method of the graphene heat-conducting film. The thermal diffusion coefficient of the finally prepared graphene heat-conducting film is improved.

The invention discloses a whole-process method for improving the thermal diffusion coefficient of a tungsten-rhenium alloy plate through rhenium segregation. The whole-process method comprises the following steps: preparing tungsten powder and rhenium powder with different particle sizes in proportion to obtain uniformly mixed powder of the tungsten powder and the rhenium powder; carrying out cold isostatic pressing on the mixed powder and sintering to prepare a sintered blank; carrying out heating rolling deformation treatment on the sintered blank; and performing high-temperature annealing treatment. According to the method, local segregation of rhenium is formed in a tungsten-rhenium alloy sintered blank by strictly controlling the sizes of the added tungsten powder and rhenium powder, after thermal deformation, rhenium segregation blocks can form anisotropic rhenium component segregation, the tungsten-rhenium alloy shows a higher thermal diffusion coefficient in the RD direction, and after high-temperature annealing, the tungsten-rhenium alloy has a higher thermal diffusion coefficient in the RD direction. The thermal diffusion coefficient in each direction can be further improved, and the tungsten-rhenium alloy plate with the high thermal diffusion coefficient is obtained.

The invention discloses a characterization sample for the in-plane thermal diffusion coefficient of a carbon-carbon composite material with sieve pores and a preparation method. The characterization sample comprises a manufacturing method of a thickened carbon-carbon composite material structural member with sieve pores, a designing and manufacturing method of a long-strip-shaped sample and an assembling method of a splicing type thermal diffusion coefficient test sample. The method provided by the invention can guide to prepare the sample for representing the thermal diffusion coefficient in a certain specific direction in the surface of the ultra-thin carbon-carbon screen.

The invention relates to an overmolding method for manufacturing an overmolded part (2) forming a decoration to be added to a part (1) or the part itself, said overmolded part (2) being made of an at least partially amorphous metal alloy (8), the method comprising the following steps: providing a blank (3) for use as an injection tool, a blank (3) having at least one through-hole (4) forming a channel (5) for injecting the metal alloy (8), the blank (3) being made of a first material having a thermal diffusion coefficient of less than or equal to 7000 W K-1 m-2 s 1 / 2, or being at least partially plated with a layer made of the first material; positioning the blank (3) in an injection mould (6); injecting the metal alloy (8) through the through-hole (4) opening into the cavity in order to obtain the overmolded part (2); the overmolded part (2) is separated from the blank (3) in order to form the trim or the part itself to be added.

The application discloses a kind of loose coal body thermal property coefficient measurement method and system of exothermic oxidation stage, this measurement method experiment constructs temperature field, determines the loose coal body exothermic heat source term of coal body under high temperature oxidation (70 DEG C above) condition, when the boundary condition of measurement and calculation model function is consistent, the temperature rise curve of measuring point;Then construct thermal property parameter calculation model function and golden section search calculation model, measure the thermal diffusivity of loose coal body.The measurement method and system of the application are high in degree of automation, and the measurement result is reliable and stable, easy to operate;It comprehensively uses finite difference equation and golden section search algorithm, and introduces heat source term, eliminates the influence of self-heating of coal body in oxidation environment on thermal diffusivity test, improves the measurement accuracy and efficiency;Convenient for engineering application, the algorithm is packaged by program, and the calculation result can be obtained by inputting corresponding parameters.

The invention relates to the technical field of perovskite photovoltaic, in particular to a perovskite photovoltaic device and an improved preparation method. The method comprises a step of preparing one or more ultrathin metal layers on a substrate, and further comprises a step of preparing a perovskite photoactive layer by adopting a low-temperature annealing method, the thickness of the ultrathin metal layer is 1-10 nm, and the temperature of low-temperature annealing is less than or equal to 100 DEG C. According to the method, the ultrathin metal layer is prepared on the substrate, so that the obtained electrode has higher thermal diffusion coefficient and thermal conductivity, and the subsequent step of preparing the perovskite photoactive layer can be carried out under a low-temperature condition, so that the thermal stress of an interface is lower, and the conversion efficiency of the perovskite photovoltaic device is higher; the method is simple in process, high in repeatability and low in cost, and can be used for preparing a large-area perovskite photovoltaic device.