Research Of Thermal Conductivity Inversion Method Based On The Second Kind Of Boundary Conditions

Temperature,as one of the seven basic physical quantities,is a macroscopic manifestation of the movement of molecules inside an object,and heat conduction is a physical phenomenon that is occurring all the time in nature.Thermal conductivity is a very important physical parameter in thermal conduction,and it characterizes the efficiency of heat transfer between objects.The ability to obtain accurate thermal conductivity has important practical significance for the application of engineering insulation,frozen ground and other engineering applications.The thermal imager can obtain the temperature information of the material change in a non-contact manner,and use the inversion algorithm on this basis.The thermal conductivity coefficient of the object can be inversed by adding the boundary conditions and initial conditions of the measured target.The problem is called an inverse problem.Because it is closely related to heat transfer,this type of problem is collectively referred to as the inverse problem of heat conduction.In this paper,based on the one-dimensional semi-infinite heat conduction model,based on the second type of boundary conditions,a set of positive inversion platform for thermal conductivity inversion is built.This set of forward generation platforms can generate the data needed for thermal conductivity inversion to test and optimize the mathematical model of the positive problem and provide input for inversion of the thermal conductivity using the measured data,thus providing a basis for verifying thermal conduction inversion.algorithm.In the process of building the experimental platform,this article firstly designed the mechanical structure of the device according to the technical specifications of the device and the thermal conductivity model,so that it can carry the sample to be tested.Then choose the appropriate type of electrical equipment and data acquisition equipment such as platinum resistance thermometer,heat source heating power,heat flow sensor and so on.Stainless steel 304 was selected for testing.The communication protocol of the communication between the lower computer and the upper computer is clarified,and a MFC framework is used to write a host computer with an interface that can display the measured temperature in real time,save the data,and repeat the test.Temperature data.Next,finite difference is used to calculate the heat conduction equation based on the second type of boundary,and the calculated temperature field is compared with the actual measured temperature field of the forward generating platform.According to the actual conditions,the mathematical model is continuously optimized and adjusted so that the maximum likelihood is consistent with the actual data.Finally,the difference between the calculated data and the measured data of the positive question is less than or equal to 8%.Next,using simulation methods to study the noise immunity of quantum behavioral particle swarm optimization(QPSO)when there is an error in the temperature data.It turns out that when there is an error of ±2% in the simulation data,the error in the inverse thermal conductivity value is within 3%.Then the temperature data generated by the forward generation platform is brought into the algorithm for inversion of thermal conductivity,and the error is 10.5%.