Fuzzy Inversion Of Temporal And Spatial Distributive Thermal Boundary Conditions For Heat Conduction Process And Application

The inverse heat transfer problem(IHTP)is a typical inverse problem.IHTP involves the estimation of the unknown characteristic parameters,such as thermal boundary conditions,thermophysical parameters,geometry shape,internal heat source,by using the internal or surface temperature measurements in the heat transfer system.It is of great theoretical and practical significance to conduct in-depth research on IHTP,which is widely existed in various scientific research and engineering fields.IHTP is a typical ill-posed problem.Exploring effective inverse methods is the essential question of IHTP research.Fuzzy logic theory can utilize mathematical methods to imitate human thinking process to a certain extent,to make fuzzy recognition and fuzzy decision for complex things,especially good at fuzzy reasoning through the imprecise and incomplete information.These features can provides substantial help to solve the ill-posed problems including IHTP.The thermal boundary conditions and the observable information of the heat transfer system usually have significant temporal and spatial distribution characteristics.In this thesis,the fuzzy estimation problem of temporal and spatial distribution thermal boundary conditions of heat conduction process is studied.The main research work and research results of the thesis include:(1)For the estimation problem of transient and uniform boundary conditions for heat conduction process,a corresponding decentralized fuzzy inference mechanism is established.Based on this,a sequential decentralized fuzzy inference(SDFI)scheme is proposed.The proposed SDFI method is used to estimate the transient and uniform boundary heat flux in the two-dimensional heat conduction system.The results show that SDFI method can make inference and decision with inaccurate,uncertain and incomplete information,and has good anti-ill-posedness for solving transient inverse heat transfer problem.The research results in this part also lay a foundation for further study of the fuzzy estimation problem of temporal and spatial distribution thermal boundary conditions of heat transfer process.(2)For the estimation problem of transient and spatial boundary conditions for heat conduction process,a double decentralized fuzzy inference(DDFI)estimation scheme possessed of a decoupling characteristic in time and space is established in this paper.A set of decentralized fuzzy inference modules corresponding to the temperature measurement points are established.The fuzzy inference processes are,respectively,executed from the correspondingly observed temperature sequence.In the time domain,according to dynamic response sensitivity method,the weighing and synthesizing processes for the decentralized inference results are performed to get the temporal compensation vector for the unknown heat flux.Then,in the space domain,according to the normal distribution function and steady-state response sensitivity method,the weighing and synthesizing processes for the temporal compensation vectors are performed to get the spatial compensation vector for the unknown heat flux.Finally,the fuzzy estimation of spatial and temporal distribution parameters is realized.The above work provides a new method for the estimation problem of spatial and temporal distribution parameters of heat transfer system.(3)For the estimation problem in heat transfer process with complex heat transfer regions,the reasonable selection scheme of representative temperature measurement points in temperature observable space is studied.A selection mechanism of representative temperature measurement points based on fuzzy clustering technology is established.Based on dynamic response coefficients of heat transfer system,the characteristic vector of temperature observable point to thermal boundary conditions of heat transfer system is constructed,the fuzzy partition of temperature observable region is conducted by the fuzzy c-means(FCM)clustering of temperature characteristic vector,the spatial location of representative measurement points can be determined according to the shortest distance principle between the clustering center and the temperature observable points.Based on the obtained representative measurement points and the aforementioned DDFI inverse method,the fuzzy estimation in heat transfer process with complex heat transfer regions is realized,and the validity of the above scheme is verified.(4)By applying the DDFI inverse method,the estimation problems in two practical heat transfer systems are studied.The estimation problem of transient and distributed heat flux on the cross section of mold during continuous casting process is studied by using DDFI method.The influence of the functional forms of the heat flux distribution,the number of thermocouples,and measurement errors on the estimation process is discussed.The estimation problem of temporal and spatial distribution thermal load on the boiling surface of single bubble pool boiling is studied by using DDFI method.The effects of the number of temperature sensors and measurement errors on the estimation result of spatial and temporal distribution of thermal load on the boiling surface are discussed through numerical simulation.Furthermore,based on the measured temperature data of the single bubble pool boiling experiment,the heat flux distribution on the boiling surface was estimated.In addition,the reliability of the inversion results of the heat flux distribution on the boiling surface is verified by using related experimental data.(5)By applying the aforementioned selection scheme of representative temperature measurement points and the DDFI inverse method,the optimal arrangement of representative temperature measurement points in the estimation process of distributed thermal boundary of fin base was studied,and the thermal boundary conditions of the fin base were successfully estimated by the aforementioned DDFI inverse method.In addition,the validity of the selection scheme of representative temperature measurement points above was confirmed through the comparison of inversion results under different arrangement schemes of fin temperature measurement points.