快速预热型铷原子振荡器的研制

介绍了一种快速预热型铷原子振荡器的设计方法和试验数据 ,3 5min的锁定时间、0 4 5L的小巧体积和良好的频率重现性能 ,非常适合于战术应用。

磁铁矿粉粒度对球团矿预热焙烧性能的影响

研究了3种不同粒度的超细磁铁精矿粉,粒度小于0.044 mm的矿粉质量分数在89%以上。试验研究了其制备的球团矿的焙烧性能。试验结果表明,铁精矿粉粒度越细,球团矿在最佳焙烧制度下获得的抗压强度也越高,但适宜的预热温度也越低,而所需预热及焙烧时间也越长。在对生球孔隙率测试研究的基础上发现,精矿粒度越细,生球孔隙率越小,氧化变得越困难,需要较长的预热时间,而预热温度太高,表层易形成致密层,导致球团矿强度下降;在对适宜的焙烧制度下焙烧的球团矿的Fe O含量测定结果表明,对于焙烧球团矿,随矿粉粒度的变细,其Fe O含量则越高,表明氧化条件变差,需较长的焙烧时间。

预热焙烧参数对超细粒度磁铁矿粉球团性能的影响

国内某磁铁精矿粉粒度非常细,小于0.044mm的比例达到89%,对其制备的球团矿的预热焙烧性能进行试验研究,小型管式炉焙烧试验结果表明,预热焙烧温度太高或高温下预热焙烧时间过长,球团矿外层易形成硬壳,阻碍球团矿的进一步氧化,球团矿强度反而下降。矿相鉴定结果表明,预热焙烧球团矿的外层、中层、里层的氧化程度不同,整体来看,外层氧化速度快,氧化较好,而中层和里层氧化逐渐减弱,预热焙烧温度高或高温预热焙烧时间长是球团矿外层易形成硬壳的主要原因。因此,对于超细粒度磁铁矿粉球团矿,预热焙烧温度太高或高温下预热焙烧时间过长,反而不利于球团矿强度的提高。

GB/T 10184—2015与GB 10184—88锅炉效率计算差异分析

对GB/T 10184—2015与GB 10184—88锅炉效率计算方法进行了比较,指出2个标准在锅炉效率定义、基准温度、固体未完全燃烧热损失、散热损失、高温脱硝装置的影响、修正项目、空气预热器性能等方面的差异,通过锅炉效率算例对2个标准的计算结果进行了比对分析,并指出了GB/T 10184—2015存在的问题,对于火电机组锅炉效率测试具有指导意义。

Thermal-mechanical properties of short carbon fiber reinforced geopolymer matrix composites subjected to thermal load

Short carbon fiber preform reinforced geopolymer composites containing different contents of α-Al2O3 filler （Cr（a-Al2O3）/geopolymer composites） were fabricated, and the effects of heat treatment temperatures up to 1 200 ℃ on the thermal-mechanical properties were studied. The results show that the thermal shrinkage in the direction perpendicular to the lamination of the composites gradually increases with the increase of the heat treatment temperatures from room temperature （25 ℃ ） to 1000 ℃. However, the composites in the direction parallel to the lamination show an expansion behavior. Beyond 1 000℃, in the two directions the composites exhibit a larger degree of shrinkage due to the densification and crystallization. The mechanical properties of the composites show the minimum values in the temperature range from 600 to 800 ℃ as the hydration water of geopolymer matrix is lost. The addition of α-Al2O3 particle filler into the composites clearly increases the onset crystalline temperature of leucite （KAlSi2O6） from the amorphous geopolymer matrix. In addition, the addition of α-Al2O3 particles into the composites can not only help to keep volume stable at high temperatures but also effectively improve the mechanical properties of the composites subjected to thermal load to a certain extent. The main toughening mechanisms of the composites subjected to thermal load are attributed to fiber pulling-out.

Effect of double ageing on performance and establishment of prediction model for 6005 aluminum alloy

In the present investigation, the relation of pre-ageing temperature and pre-ageing time to mechanical properties was studied, and a model was established to predict the mechanical properties of AA6005 Al alloy. Compared with the experimental results, the deviation of the proposed model was limited to 8.1%, which showed reasonable accuracy of forecasting. It was found that the performance of AA6005 alloy was better at higher pre-ageing temperature with shorter pre-ageing time than that at T6 temper. The microstructure of the alloy was observed by transmission electron microscopy, and the results showed that high dislocation density and precipitate density existed at 160 ℃ and 200 ℃ pre-ageing, which were in good agreement with the model.

Numerical Investigation of Fuel Dilution Effects on the Performance of the Conventional and the Highly Preheated and Diluted Air Combustion Furnaces

This numerical study investigates the effects of using a diluted fuel （50% natural gas and 50% N2） in an industrial furnace under several cases of conventional combustion （air with 21% O2 at 300 and 1273 K） and the highly preheated and diluted air （1273 K with 10% O2 and 90% N2） combustion （HPDAC） conditions using an in-house computer program. It was found that by applying a combined diluted fuel and oxidant instead of their uncombined and/or undiluted states, the best condition is obtained for the establishment of HPDAC＇s main unique features. These features are low mean and maximum gas temperature and high radiation/total heat transfer to gas and tubes; as well as more uniformity of theirs distributions which results in decrease in NOx pollutant formation and increase in furnace efficiency or energy saving. Moreover, a variety of chemical flame shape, the process fluid and tubes walls temperatures profiles, the required regenerator efficiency and finally the concentration and velocity patterns have been also qualitatively/quantitatively studied.

Function chain neural network prediction on heat transfer performance of oscillating heat pipe based on grey relational analysis

As for the factors affecting the heat transfer performance of complex and nonlinear oscillating heat pipe (OHP),grey relational analysis (GRA) was used to deal with the relationship between heat transfer rate of a looped copper-water OHP and charging ratio,inner diameter,inclination angel,heat input,number of turns,and the main influencing factors were defined.Then,forecasting model was obtained by using main influencing factors (such as charging ratio,interior diameter,and inclination angel) as the inputs of function chain neural network.The results show that the relative average error between the predicted and actual value is 4%,which illustrates that the function chain neural network can be applied to predict the performance of OHP accurately.

Microstructure evolution,mechanical properties and tailoring of coefficient of thermal expansion for Cu/Mo/Cu clad sheets fabricated by hot rolling

The law of microstructure evolution and mechanical properties of hot roll bonded Cu/Mo/Cu clad sheets were systematically investigated and the theoretical prediction model of the coefficient of thermal expansion(CTE)of Cu/Mo/Cu clad sheets was established successfully.The results show that the deformation of Cu and Mo layers was gradually coherent with an increase in rolling reduction and temperature and excellent interface bonding was achieved under the condition of a large rolling reduction.The development of the microstructure and texture through the thickness of Cu and Mo layers was inhomogeneous.This phenomenon can be attributed to the friction between the roller and sheet surface and the uncoordinated deformation between Cu and Mo.The tensile strength of the clad sheets increased with increasing rolling reduction and the elongation was gradually decreased.The CTE of Cu/Mo/Cu clad sheets was related to the volume fraction of Mo.The finite element method can simulate the deformation and stress distribution during the thermal expansion process.The simulation result indicates that the terminal face of the clad sheets was sunken inward.

Energy and long-term hygrothermal performance of building enclosures based on dynamic environmental simulation test

Dynamic environmental testing is an effective means to study the energy and long-term hygrothermal performance of building enclosures. Southeast University is designing and building a large-scale dynamic environment simulation testing facility. It can simuhaneously and dynamically simulate temperature, relative humidity, infrared solar radiation, UV radiation, and precipitation. A transformation is needed to predict the energy and long-term hygrothermal performance of building enclosures under real service conditions using data obtained from accelerated tests.

Modeling Thermal Protective Performance of Multilayer Fabrics for Firefighters

This paper is to report a prediction model for thermal protective performance of multilayer fabrics based on Matlab neural network toolbox. Then a back propagation (BP) neural network model is developed to predict thermal protective performance of multilayer fabrics for firefighters. The network consists of twelve input nodes, six hidden nodes, and one output node. The inputs are weight, thickness, density of warp and weft, limited oxygen index (LOI), and heat conductivity of each-layer fabric. Thermal protective performance (TPP) rating of multilayer fabrics is the output. In this paper, the data from the experiments are used as learning information for the neural network to develop a reliable prediction model. Finnally the model performance is verified, and the proposed model can be applied to predict the thermal protective performance of multilayer fabrics for firefighters.

Artificial neural network prediction of mechanical properties of hot rolled low carbon steel strip

Conventionally, direct tensile tests are employed to measure mechanical properties of industrially pro- duced products. In mass production, the cost of sampling and labor is high, which leads to an increase of total pro- duction cost and a decrease of production efficiency. The main purpose of this paper is to develop an intelligent pro- gram based on artificial neural network （ANN） to predict the mechanical properties of a commercial grade hot rolled low carbon steel strip, SPHC. A neural network model was developed by using 7 x 5 x 1 back-propagation （BP） neural network structure to determine the multiple relationships among chemical composition, product pro- cess and mechanical properties. Industrial on-line application of the model indicated that prediction results were in good agreement with measured values. It showed that 99.2 % of the products＇ tensile strength was accurately pre- dicted within an error margin of ~ 10 %, compared to measured values. Based on the model, the effects of chemical composition and hot rolling process on mechanical properties were derived and the relative importance of each in- put parameter was evaluated by sensitivity analysis. All the results demonstrate that the developed ANN models are capable of accurate predictions under real-time industrial conditions. The developed model can be used to sub- stitute mechanical property measurement and therefore reduce cost of production. It can also be used to control and optimize mechanical properties of the investigated steel.

Curing kinetics and mechanical properties of bio-based composite using rosin-sourced anhydrides as curing agent for hot-melt prepreg

In this study, a novel bio-based thermosetting system has been developed from epoxy resin （EP）, with rosin-sourced anhydrides （maleopimaric acid, RAM） as curing agent and imidazole type latent catalyst （two amino imidazole salt complex, IMA）, to be used as matrix for hot-melt prepreg curing at mid-temperature. For comparison, the epoxy resin system with petroleum sourced hardener methylhexahydrophthalicanhydride （MHHPA） was also examined. The curing behaviour and mechanism were investigated by non-isothermal differential scanning calorimeter （DSC） analysis and Fourier transform infrared （FTIR） spectra. The results showed that the curing course of bio-based epoxy resin system containing RAM included two stages, which were the reaction between the free carboxyl group of RAM and oxirane ring under the acceleration of IMA, and the main reaction attributed to the reaction between anhydride and oxirane. According to Kissinger method, the reaction activation energy （E,） of two stages were 68.9 and 86.5kJmo1-1, respectively. The Eo of EP/MHHPA and EP/IMA resin system were 81.04 and 77.9kJmol-I. The processing property of EP/RAM/IMA system, i.e. the relationship between viscosity-temperature-time, was characterized by cone-plate viscometer aim to decide the processing parameter ofprepreg preparation. The effect of RAM content on mechanical performance and dynamic mechanical property was investigated. Noteworthily, compared with the laminates with EP/MHHPA as matrix, the laminates with RAM as hardeners achieved a 44%, 73% and 70℃ increase in bending strength, bending modulus and the glass transition temperature, respectively, due to the bulky hydrogenated phenanthrene ring structure incorporated into the cross-linking networks. When the fiber volume fraction reached 47%, the mechanical property of the laminates prepared with hot melt prepreg was superior or comparable to that of composites with pure petroleum sourced matrix. RAM as cross-linking agent of epoxy resin holds a great potential to satisfy the requirement of composites such as structure and secondary structure parts preparation.

Performance analysis of a solar-aided power generation(SAPG) plant using specific consumption theory

In contrast to a traditional coal-fired power generation plant where steam extracted from a turbine is used to preheat the feedwater in all preheating stages, a solar-aided power generation(SAPG) plant uses solar heat to replace a part or all of the extracted steam in one or more preheating stages. The performance of an SAPG plant with different replacements is investigated in this study by using specific consumption theory(SCT). Fuel-specific and cost-specific consumption models for SAPG plants are built based on the SCT. A typical 330 MW coal-fired power plant is used as the study case. The performance of the SAPG plant in terms of specific consumption, with steam obtained from the first through the eighth(except for the fourth) stages of extraction replaced by solar heat, is compared with that of the reference coal-fired power plant. The fuel-specific consumption of the SAPG plant is determined to be lower than that of the reference coal-fired power plant. The fuel-specific consumption accrual distribution in SAPG plants is used to assess the effect of each individual replacement. Effective strategies to reduce the specific costs of the SAPG and coal-fired power plants are proposed based on the results of this study.

Prediction of thermal conductivity of polymer-based composites by using support vector regression

Support vector regression (SVR) combined with particle swarm optimization (PSO) for its parameter optimization, was proposed to establish a model to predict the thermal conductivity of polymer-based composites under different mass fractions of fillers (mass fraction of polyethylene (PE) and mass fraction of polystyrene (PS)). The prediction performance of SVR was compared with those of other two theoretical models of spherical packing and flake packing. The result demonstrated that the estimated errors by leave-one-out cross validation (LOOCV) test of SVR models, such as mean absolute error (MAE) and mean absolute percentage error (MAPE), all are smaller than those achieved by the two theoretical models via applying identical samples. It is revealed that the generalization ability of SVR model is superior to those of the two theoretical models. This study suggests that SVR can be used as a powerful approach to foresee the thermal property of polymer-based composites under different mass fractions of polyethylene and polystyrene fillers.

Wrinkle-free graphene with spatially uniform electrical properties grown on hot-pressed copper

The chemical vapor deposition （CVD） of graphene on Cu substrates enables the fabrication of large-area monolayer graphene on desired substrates. However, during the transfer of the synthesized graphene, topographic defects are unavoidably formed along the Cu grain boundaries, degrading the electrical properties of graphene and increasing the device-to-device variability. Here, we introduce a method of hot-pressing as a surface pre-treatment to improve the thermal stability of Cu thin film for the suppression of grain boundary grooving. The flattened Cu thin film maintains its smooth surface even after the subsequent high temperature CVD process necessary for graphene growth, and the formation of graphene without wrinkles is realized. Graphene field effect transistors （FETs） fabricated using the graphene synthesized on hot-pressed Cu thin film exhibit superior field effect mobility and significantly reduced device-to-device variation.