表征真空玻璃熱性能的最重要參數(shù)-傳熱系數(shù)很難在線測量,因?yàn)樗鼤?huì)隨著時(shí)間的推移而增加,從而降低隔熱性能。確定真空的熱傳遞需要詳細(xì)了解它們不同元素的熱特性,這一領(lǐng)域存在一系列標(biāo)準(zhǔn)和指南�？傮w的熱性能既可以通過詳細(xì)的二維數(shù)值方法確定,也可以通過符合歐洲或國際標(biāo)準(zhǔn)的測量來確定。首先,我們研究基于真空玻璃傳熱機(jī)理的軟測量智能建模方法,以獲取真空玻璃性能數(shù)據(jù)。該方法保證了智能建模的可行性,為基于軟測量的智能建模預(yù)測真空玻璃隔熱性能參數(shù)提供了理論依據(jù)。研究并開發(fā)了一種有效的方法來模擬通過真空玻璃的傳熱。基于先進(jìn)的數(shù)值模擬技術(shù),利用計(jì)算流體動(dòng)力學(xué)軟件對(duì)傳熱過程進(jìn)行了分析,并將仿真結(jié)果用于指導(dǎo)和分析非穩(wěn)態(tài)測試方法。這種方法保證了加熱板的中心進(jìn)行一維傳熱,非受熱面中心的溫度測量具有實(shí)際意義,對(duì)于研究智能化保溫性能建模和預(yù)測是必要的。其次,我們應(yīng)用神經(jīng)網(wǎng)絡(luò)方法對(duì)真空玻璃的傳熱系數(shù)進(jìn)行了預(yù)測。基于MATLAB軟件,建立了神經(jīng)網(wǎng)絡(luò)智能模型,并對(duì)傳統(tǒng)的BP神經(jīng)網(wǎng)絡(luò)進(jìn)行了優(yōu)化。采用遺傳算法對(duì)自變量進(jìn)行降維。然后,利用思維進(jìn)化計(jì)算算法對(duì)初始權(quán)值和閾值進(jìn)行優(yōu)化。利用優(yōu)化后的BP神經(jīng)網(wǎng)絡(luò)智能模型對(duì)真空玻璃隔熱層傳熱系數(shù)進(jìn)... 

【文章來源】：海南大學(xué)海南省 211工程院校

【文章頁數(shù)】：183 頁

【學(xué)位級(jí)別】：博士

【文章目錄】：
摘要
Abstract
Nomenclature
Chapter 1 Introduction general
    1.1 Introduction
    1.2 Research Background
    1.3 Research heat transfer coefficients of VG and development trends in China and abroad
        1.3.1 Study performance prediction of vacuum glass insulation at Home and abroad
        1.3.2 Main content of research of the heat transfer coefficients of vacuum glass insulation
    1.4 Conclusion
Chapter 2 Mechanism of Vacuum Glass Heat Transfer by Soft Sensor Intelligent Modelling
    2.1 Introduction
    2.2 Data preprocessing
        2.2.1 Implementation of a model of Soft Sensor intelligent modelling
        2.2.2 Online correction of the smart sensor model intelligent modeling
        2.2.3 Modeling methods
    2.3 Selection of historical data
        2.3.1 Pretreatment and transformation of data
        2.3.2 Cleaning and reduction of data
    2.4 Reduction of dimensionality
    2.5 Model selection,training and validation of Prediction coefficient heat transfer VG
        2.5.1 Model training soft sensor intelligent for heat transfer coefficient prediction
        2.5.2 Validation of the model
    2.6 Flexible sensor applications
        2.6.1 Data for flexible sensor modeling
        2.6.2 Modeling approaches
    2.7 White box templates
    2.8 Mathematical modeling equation white boxes on heat transfer coefficient vacuum glass
        2.8.1 Vacuum glass heat transfer principle
        2.8.2 Main factors affecting insulation performance parameters
        2.8.3 Principles of Soft Sensor Intelligent Modeling Technology
        2.8.4 Auxiliary variable selection
    2.9 Gray Box Controller Model
        2.9.1 Data-Driven Modeling Soft Sensor Intelligent Modeling
    2.10 Black box model
    2.11 Feasibility Analysis of the SS Intelligent Modeling of Vacuum Glass
    2.12 Summary of this chapter
Chapter 3 Computational fluid-dynamics-based simulation of heat transfer through vacuum glass
    3.1 Introduction
    3.2 Application of vacuum glass
    3.3 Heat transfer coefficient of vacuum glass
    3.4 Research significance,main content,and innovation points
    3.5 Method of heat transfer in vacuum glass
    3.6 CFD-based vacuum glass heat transfer simulation
    3.7 Discussion
        3.7.1 Steady heat transfer
        3.7.2 Unsteady heat transfer
    3.8.Grid-independent modelling of heat transfer
        3.8.1 Mathematical model
        3.8.2 Numerical methods
    3.9.Vacuum glass thermal property parameters non-steady-state test principle
        3.9.1 Unsteady measuring device
        3.9.2 Analysis of factors affecting the accuracy of non-steady-state measuring devices
        3.9.3 Selection and design of hardware components for temperature measurement system
        3.9.4 System software program
        3.9.5 Physical system
    3.10.Conclusion
Chapter 4 Intelligent modelling to predict heat transfer coefficient of vacuum glass insulation based on thinking evolutionary neural network
    4.1 Introduction
    4.2 Thermal formulation analytical modelling approach
    4.3 Artificial NN(ANN)structure and methodology
    4.4 BPNN model
        4.4.1 BP algorithm
        4.4.2 BPNN algorithm
        4.4.3 Learning process of BPNN algorithms
    4.5 Prediction model of Vacuum Glass insulation performance based on BPNN
        4.5.1 Variable dimension reduction
        4.5.2 Optimisation of initial weights and thresholds
        4.5.3 Simulation results
        4.5.4 Measurements and error analysis
    4.6 Conclusions
Chapter 5 Performance Monitoring of Vacuum Glazing Based on LSSVM
    5.1.Introduction and motivation
    5.2 PCA-RBFNN and Establishing an RBF Neural Network Model
        5.2.1 RBF Neural Network Design
        5.2.2 Analysis of simulation results
        5.2.3 Comparison of PCA and Non-PCA Neural Network Models
    5.3 Vector machine supports least squares
        5.3.1 Prediction modeling Based on LSSVM
        5.3.2 Selection of modeling variables
        5.3.3 Model establishment
        5.3.4 Selecting the parameters of the LSSVM model
        5.3.5 Comparison of modeling methods
    5.4 Conclusion
Chapter 6 Predicting the lifetime of vacuum glass based on fuzzy
    6.1 Introduction
    6.2 Fuzzy Sets,Numbers and Operations
    6.3 Determination of fuzzy regression parameter
        6.3.1 Fuzzy linear regression
        6.3.2 Direct estimation of the parameters
        6.3.3 Fuzzy failure probabilities
        6.3.4 Fuzzy degradation analysis
        6.3.5 Parameter Estimation,Two-Stage Least-Squares
        6.3.6 Maximum Likelihood
        6.3.7 Bayesian Approach
        6.3.8 Estimation of and Prediction from Failure Time Distributions
    6.4 Predicting the lifetime service of vacuum glass based on fuzzy
        6.4.1 Vacuum glass degradation analysis
        6.4.2 Detecting Degraded Data
        6.4.3 Application Fuzzy degradation analysis
    6.5 Fuzzy set theory to predict the probability of lifetime Vacuum Glass
        6.5.1 Failure Time Distribution
Chapter 7 Conclusions general
    7.1 Conclusions
    7.2 Future prospects
Reference
Acknowledgements
Research achievements during the Ph D
致謝


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