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Based on analyzing the mechanism of thermal conductivity of glazed hollow bead concrete, this paper divides the channels of thermal conductivity in concrete, constructs the model of thermal conductivity coefficient based on the Theory of Minimum Thermal Resistance, and confirms the model by using the data of other related literatures and the data of our own experiment.
The purpose of this paper is to construct the model of thermal conductivity coefficient based on the theory of minimum thermal resistance, and confirms the model by using the data of other related literatures and our own experiment, based on analyzing the mechanism of thermal conductivity of glazed hollow bead concrete.
The purpose of this paper is to construct the model of thermal conductivity coefficient based on the theory of minimum thermal resistance, and confirms the model by using the data of other related literatures and our own experiment, based on analyzing the mechanism of thermal conductivity of glazed hollow bead concrete.
Reduction of Model.
Supposing that the percentage of volume of aggregate, glazed hollow bead and mortar is Va , Vm and , we can make formula (5) and (6) further reduction, so (7) (8) Submitting formula (7) and (8) to (4), we can get the coefficient of thermal conductivity of this concrete is (9) Analyzing of Value K.

As compared with the data for submicrometer-grain-sized material with d ≈ 350 nm, nano-crystallization of ZrO2 ceramics less than d ≈ 90 nm can improve σf by a factor of 2.0-2.5.
The fracture strength σf monotonously increases with a reduction of grain size.
(bottom) 500nm (a) 500nm (a) Crack 200nm (c) Crack 200nm (c) toughness KIC or a reduction of the radius of critical flaw a.
This result is consistent well with the data that the transformation strengthening does not work for fine grained material; transformability of the t-ZrO2 phase is reduced by a decrease in the grain size [19-22].
According to the relation between σf and a in Eq. 3, on the other hand, a reduction of residual flaw size can also be effective in increasing σf [20].

The universal control computes an inverse kinematic based on application based data, executes an efficient path-planning and collision detection.
The control plans the movements of the robot and gripper, analyzes the data and runs algorithms e.g. for the best palletization of goods.
The surrounding data are collected with a SICK IVP Ranger M50 [12].
From these data an inverse kinematic is computed.
In addition, acquired 3D sensor data of the environment are delivered as basis for the path planning and collision detection.

The data in Fig. 1 imply that the saturation time of the VC formation at 1850 °C is less than the studied ones, i.e., thermal equilibrium for the VC concentration is established within less than 5 min.
Data are included both from the present work (1850 °C) and a previous study of isochronally annealed samples [3].
In Fig. 2, the average VC(-2/0) peak amplitude extracted from Fig. 1 is compared with data from isochronal treatment performed in Ref. [3].
Characterization of the second set of samples demonstrates a reduction of the VC concentration by lowering the cooling rate.
These data imply formation of VC in the bulk as the dominant process and not Schottky formation at the surface with subsequent in-diffusion.

Solution properties (i.e., density, viscosity, and diffusivity) required for data correlation were attained from the literature [18, 19].
The rate of heat transfer (Q) across the tank bottom is given by : Q=UAΔTmean (11) Fig. 8 shows a comparison between the present data and previously obtained data by Colton & Smith [12] and Marangozis & Johnson [13].
The present data lie below the benzoic acid technique data probably because: (i) The benzoic acid technique suffers from limitations such as benzoic acid attrition beside dissolution, furthermore, the development of surface roughness increases the area and induces micro eddies formation.
Johnson, A correlation of mass transfer data of solid‐liquid systems in agitated vessels, The Canadian Journal of Chemical Engineering. 40(6) (1962) 231-237
Post, Densities, viscosities, and diffusivities in aqueous sodium hydroxide-potassium ferri- and ferro-cyanide solutions, Journal of Chemical & Engineering Data. 30(2) (1985) 163-165

The reduction of volume is beneficial to improve PCP system performance.
The parameters of original design proposal by conventional method are: = 22, = 29, = 80, b = 52mm, m = 5mm, q = 3, = Y N Start Input the primary datum Calculate objective functions of vertexes Sequence vertexes according to objective functions Satisfy the condition of convergence?
According to the model and the solution procedure of reliability optimum design above, the result of the instance is therefore given by = [22.554 362 500, 53.258 470 380, 4.346 102 556] = 2 502 836.214 208 071 39 There are 8(i.e.) rounding points after rounding up the optimizing solution of real type data above and 6 rounding points of them are in the feasible zone.
The reduction of volume is beneficial to improve PCP system performance.

The caculation of k.Parameter k can be calculated by Eq.6.Indicated by transcendental equations,however,they can’t be solved with common method.Therefore,it should be solved by Matlab,the process is as follows: The reduction of equation.
Let T=0.5,1,1.5,2,2.5,3,3.5,4,4.5,5,respectively,draw k corresponding to different periods,as follows: Tab.1 Value of k corresponding to different period T（s） 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 K 16 4 1.7806 1.0327 0.7171 0.5535 0.4536 0.3859 0.3365 0.2988 The caculation of DA.According to Eq.4 and Tab.1,the calculation of DA requires great effort,so it’s better to use Excel to process the data.The steps as shown below: (1) Input the original data.Input the data of table 1 in Excel,T occupy column A and k occupy column B
The reduction of equation.
Take T=0.5 as example，obtain the first 20 value of kn，as is shown below： Tab.2 Value of kn corresponding to T=0.5 k1 k2 k3 k4 k5 k6 k7 k8 k9 k10 0.8107 2.4316 4.051 5.668 7.2821 8.893 10.5006 12.1048 13.7059 15.3042 k11 k12 k13 k14 k15 k16 k17 k18 k19 k20 16.8997 18.4929 20.084 21.6732 23.2607 24.8467 26.4314 28.0149 29.5973 31.1789 The caculation of mA.As the method of the caculation of DA,mA should be caculated by Excel.Take T=0.5 as example,the steps as shown below: (1)Input the riginal data.Input the data of table 2 in A1~A20 of Excel
Summary In this paper，a caculation method of wave-maker mechanical parameters based on Matlab and Excel is introduced.This method makes full use of the powerful data processing function of Matlab and Excel,and improve not noly the working efficiency but also the calculation accuracy.

Different factors that affect the reduction of the laterite ores are tested and analyzed by Differential Scanning Calorimeter(DSC), X-ray Diffraction(XRD), Brunauer Emmett Teller (BET) and Scanning Electron Microscope(SEM).
Their weight reduction rates are calculated respectively.
Related thermodynamic data are shown in Tab. 2
(2) (3) (4) (5) Tab. 2 Thermodynamic Data of Reactants Formula a (cal/mol/K) b c d Temperature /K (kcal/mol) (cal/mol/K) H2O(g) 6.790 2.982 0.307 0.086 298-1100 -57.798 45.132 7.514 3.371 -5.964 -0.438 1100-2800 - - FeOOH 19.167 6.812 -3.020 0.000 298-1500 -133.843 14.436 Fe2O3 34.313 -8.681 -7.513 17.159 298-700 -196.702 20.889 152.440 -230.290 -106.927 134.071 700-950 - - -52471.880 69336.660 90566.060 -25616.900 950-1050 - - 19.172 13.378 40.006 -2.964 1050-1812 - - MgSiO3 22.000 7.900 -0.042 0.000 298-903 -370.100 16.220 28.760 0.000 0.000 0.000 903-1258

Data of CO2 emission and absorption in full life cycle could be obtained through market and literature research.
The initial interface of system includes “Input Data” and “Analysis Results” (Fig.1), according to the real-time information from dynamic display of CO2 emission, CO2 absorption and net CO2 emission.
The owner can input the corresponding data in the system according to the actual operation stage.
The input data of property come from two ways, one is construction drawing and corresponding acceptance records, the other is operational data acquired from daily management.
CO2 factor comes from reliable research, providing scientific data for the operation of the evaluating model.

In the case of Kallend and Davies we could actually from their own data detect a texture difference between copper and brass already at 20% reduction.
Typically this texture component first appears at about 60% reduction, reaches its maximum at about 80% reduction, then decreases with further reduction and disappears at very high reductions (higher than 95%).
Nevertheless the final texture after high reduction is a reasonably normal brass-type texture in both cases.
The experimental observations point at a composite deformation pattern at intermediate reductions.
As mentioned earlier there is a transition from MA to PSA with increasing reduction, increasing flatness of the grains.