Evaluation of Thermal Insulation Properties of Fibrous Mineral Fine Powders

2010 ◽  
Vol 178 ◽  
pp. 339-343
Author(s):  
Fei Wang ◽  
Jin Sheng Liang ◽  
Chong Yan Ren ◽  
Qing Guo Tang
Keyword(s):  
Experimental Data ◽  
Thermal Resistance ◽  
Thermal Insulation ◽  
Thermal Conduction ◽  
Fibrous Materials ◽  
Fine Powders ◽  
Resistance Model ◽  
Fibrous Mineral ◽  
The Relationship ◽  
Sepiolite Nanofibers

The equivalent thermal resistance model of sepiolite mineral nanofibers has been presented in this paper to predict the thermal insulation properties of fibrous mineral fine powders. The model was based on the correlation between thermal conduction and gas & solid conduction in the fibrous system. According to the analysis about the process of heat transfer in sepiolite nanofibers, the total thermal conduction can be described as the synergism of the solid thermal conduction and the gaseous thermal conduction. From the equivalent thermal resistance model of fibrous materials in the accumulative condition, it can be seen that the thermal conduction of fibrous mineral fine powders can be evaluated by the relationship between bulk density and thermal conduction of sepiolite nanofibers. Comparing the theoretical values with experimental data obtained from thermal conduction instrument, it was found that the theoretical values corresponded well with experimental data.

scholarly journals Thermal-wet model of knitted double jersey based on backpropagation algorithm of neural network

2020 ◽  
Vol 15 ◽  
pp. 155892501990083
Author(s):  
Xintong Li ◽  
Honglian Cong ◽  
Zhe Gao ◽  
Zhijia Dong
Keyword(s):  
Neural Network ◽  
Water Vapor ◽  
Thermal Resistance ◽  
Resistance Test ◽  
Training Algorithm ◽  
Backpropagation Algorithm ◽  
Optimal Network ◽  
Resistance Model ◽  
Hidden Layer ◽  
The Relationship

In this article, thermal resistance test and water vapor resistance test were experimented to obtain data of heat and humidity performance. Canonical correlation analysis was used on determining influence of basic fabric parameters on heat and humidity performance. Thermal resistance model and water vapor resistance model were established with a three-layered feedforward-type neural network. For the generalization of the network and the difficulty of determining the optimal network structure, trainbr was chosen as training algorithm to find the relationship between input factors and output data. After training and verification, the number of hidden layer neurons in the thermal resistance model was 12, and the error reached 10−3. In the water vapor resistance model, the number of hidden layer neurons was 10, and the error reached 10−3.

Thermal Insulation Coatings Containing Sepiolite Mineral Fibers and their Performance

2010 ◽  
Vol 178 ◽  
pp. 318-323 ◽  
Author(s):  
Cong Chen ◽  
Fei Wang ◽  
Jin Sheng Liang ◽  
Qing Guo Tang
Keyword(s):  
Thermal Conductivity ◽  
Heat Flow ◽  
Thermal Resistance ◽  
Thermal Insulation ◽  
Thermal Conduction ◽  
Glass Beads ◽  
Optimum Ratio ◽  
Mineral Fibers ◽  
Hollow Glass ◽  
Insulation Effect

In this text, the effective thermal conductivity of different shape filler particles was investigated. The thermal insulation coatings were prepared using hollow glass beads and sepiolite as thermal insulation fillers and the thermal insulation effect was evaluated. The results show that the optimum ratio of sepiolite and hollow glass beads is 6:1, and the temperature difference of upper box and lower box is up to 18 °C. The main reason for this phenomenon is that the thermal conduction chain is difficult to form in the direction of heat flow, thus leading to the increase of thermal resistance and decrease of thermal conductivity.

Identifying Physico-Chemical Laws from the Robotically Collected Data

2019 ◽  
Author(s):  
Liwei Cao ◽  
Danilo Russo ◽  
Vassilios S. Vassiliadis ◽  
Alexei Lapkin
Keyword(s):  
Experimental Data ◽  
Numerical Models ◽  
Predictor Variable ◽  
Physical Models ◽  
Training Data ◽  
Mixed Integer ◽  
Physico Chemical ◽  
Data Points ◽  
Future Work ◽  
The Relationship

<p>A mixed-integer nonlinear programming (MINLP) formulation for symbolic regression was proposed to identify physical models from noisy experimental data. The formulation was tested using numerical models and was found to be more efficient than the previous literature example with respect to the number of predictor variables and training data points. The globally optimal search was extended to identify physical models and to cope with noise in the experimental data predictor variable. The methodology was coupled with the collection of experimental data in an automated fashion, and was proven to be successful in identifying the correct physical models describing the relationship between the shear stress and shear rate for both Newtonian and non-Newtonian fluids, and simple kinetic laws of reactions. Future work will focus on addressing the limitations of the formulation presented in this work, by extending it to be able to address larger complex physical models.</p><p><br></p>

Simulation on Extrusion Comminution Model of Roller Press

2010 ◽  
Vol 156-157 ◽  
pp. 1702-1707
Author(s):  
Xiang Wen Cheng ◽  
Jinchao Liu ◽  
Qi Zhi Ding ◽  
Li Ming Song ◽  
Zhan Lin Wang
Keyword(s):  
Experimental Data ◽  
Particle Size ◽  
Field Experiments ◽  
Size Ratio ◽  
Optimum Operating ◽  
Extrusion Force ◽  
Particle Size Ratio ◽  
Small Constant ◽  
Working Parameters ◽  
The Relationship

How to predict the relationship among particle size and among product size, to establish the relationship between the granularity and working parameters in the process of grinding and to determine the optimum operating parameters. With proposing BS squeeze crush model by L. Bass and the idea of roll surface division as the material uneven extrusion force are adopted. Based on field experiments the experimental data is analyzed, the select function and the breakage functions are fitted with MATLAB software, and obtaining their model. The comminution model is determined by the roller division. We obtain the model parameter through the experimental data. Through model analysis shows: the relationship between particle breakage and energy absorption, namely the smaller size of the same power, the lower broken; the breakage diminishes with the decrease of particle size ratio and it will be tending to a small constant when the smaller particle size ratio. The breakage functions rapidly decrease within ratio of between 0.2-0.7. This shows: the energy consumption will rapidly increase when the particle size of less than 0.2 in broken; the selection diminish with the decrease of particle size. Pressure (8-9MPa) should be the most appropriate value.

scholarly journals Effect of Weave Structure on Thermo-Physiological Properties of Cotton Fabrics

2015 ◽  
Vol 15 (1) ◽  
pp. 30-34 ◽  
Author(s):  
Sheraz Ahmad ◽  
Faheem Ahmad ◽  
Ali Afzal ◽  
Abher Rasheed ◽  
Muhammad Mohsin ◽  
...  
Keyword(s):  
Thermal Resistance ◽  
Cotton Fabrics ◽  
Air Permeability ◽  
Climatic Conditions ◽  
Plain Weave ◽  
Weave Structure ◽  
Physiological Properties ◽  
The Relationship

Abstract This paper aims to investigate the relationship between fabric weave structure and its comfort properties. The two basic weave structures and four derivatives for each selected weave structure were studied. Comfort properties, porosity, air permeability and thermal resistance of all the fabric samples were determined. In our research the 1/1 plain weave structure showed the highest thermal resistance making it suitable for cold climatic conditions. The 2/2 matt weave depicted the lowest thermal resistance which makes it appropriate for hot climatic conditions.

The Tensile Property of the SPF/Cotton Blended Yarn

2011 ◽  
Vol 321 ◽  
pp. 192-195
Author(s):  
Qing Bin Yang ◽  
Xiao Yang
Keyword(s):  
Experimental Data ◽  
Simple Model ◽  
Tensile Property ◽  
Cotton Fibers ◽  
Blended Yarn ◽  
The Difference ◽  
The Relationship

In order to analysis the relationship between the strength and elongation and the blended ratio of SPF/Cotton blended yarn, the strength and elongation of SPF /cotton blended yarn with different blended ratio were measured and compared with the simple model. The results indicated that For the SPF/cotton blended yarn, the difference between the experimental data and the model value is remarkable because of the high cohesion of the cotton fibers.

Experimental and theoretical investigation of heat transfer characteristics of cylindrical heat pipe using Al2O3–SiO2/W-EG hybrid nanofluids by RSM modeling approach

2021 ◽  
Vol 68 (1) ◽  
Author(s):  
R. Vidhya ◽  
T. Balakrishnan ◽  
B. Suresh Kumar
Keyword(s):  
Heat Transfer ◽  
Experimental Data ◽  
Heat Transfer Coefficient ◽  
Thermal Resistance ◽  
Transfer Coefficient ◽  
Heat Pipe ◽  
Diffraction Spectrum ◽  
Step Method ◽  
Ceramic Nanoparticles ◽  
Transfer Mechanisms

AbstractNanofluids are emerging two-phase thermal fluids that play a vital part in heat exchangers owing to its heat transfer features. Ceramic nanoparticles aluminium oxide (Al2O3) and silicon dioxide (SiO2) were produced by the sol-gel technique. Characterizations have been done through powder X-ray diffraction spectrum and scanning electron microscopy analysis. Subsequently, few volume concentrations (0.0125–0.1%) of hybrid Al2O3–SiO2 nanofluids were formulated via dispersing both ceramic nanoparticles considered at 50:50 ratio into base fluid combination of 60% distilled water (W) with 40% ethylene glycol (EG) using an ultrasonic-assisted two-step method. Thermal resistance besides heat transfer coefficient have been examined with cylindrical mesh heat pipe reveals that the rise of power input decreases the thermal resistance and inversely increases heat transfer coefficient about 5.54% and 43.16% respectively. Response surface methodology (RSM) has been employed for the investigation of heat pipe experimental data. The significant factors on the various convective heat transfer mechanisms have been identified using the analysis of variance (ANOVA) tool. Finally, the empirical models were developed to forecast the heat transfer mechanisms by regression analysis and validated with experimental data which exposed the models have the best agreement with experimental results.

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