Shaowu Yin

Contribution from Urban Heating to China's 2020 Goal of Emission Reduction

To reduce inhalable particle and SO(x) pollution from coal-based urban central heating (UCH), China has been vigorously developing natural gas-based UCH for years. The CO(2) emissions of UCH, having an average annual growth rate of 10.3%, accounted for 4.4% of Chinas total CO(2) emissions in 2009. This paper analyzes the feasibility of replacing UCH with heat pump heating (HPH) in Chinas climatic suitable regions and evaluates the corresponding potential for energy saving and emission reduction. Current strategy of replacing coal-based UCH with natural gas-based UCH is expected to decrease CO(2) emissions by 63.5%. However, the CO(2) emissions of HPH are 55.4% less than those of natural gas-based UCH. Replacing coal-based UCH with HPH is capable of decreasing CO(2) emissions by 83.7% and consequently decreases the CO(2) emissions per unit of gross domestic product (GDP) by 4.2% by 2020 compared with 2005 level. This contributes about 10.5% to Chinas 2020 CO(2) emission reduction target. For controlling environmental pollution and protecting ecological environment better, China should adjust its strategy for CO(2) emission reduction by shifting its attention from replacing coal-based UCH with natural gas-based UCH to popularizing HPH in climatic suitable regions.

Kinetic study on the direct nitridation of silicon powders diluted with α-Si3N4 at normal pressure

Silicon nitride (Si3N4) powders were prepared by the direct nitridation of silicon powders diluted with α-Si3N4 at normal pressure. Silicon powders of 2.2 μm in average diameter were used as the raw materials. The nitriding temperature was from 1623 to 1823 K, and the reaction time ranged from 0 to 20 min. The phase compositions and morphologies of the products were analyzed by X-ray diffraction and scanning electron microscopy, respectively. The effects of nitriding temperature and reaction time on the conversion rate of silicon were determined. Based on the shrinking core model as well as the relationship between the conversion rate of silicon and the reaction time at different temperatures, a simple model was derived to describe the reaction between silicon and nitrogen. The model revealed an asymptotic exponential trend of the silicon conversion rate with time. Three kinetic parameters of silicon nitridation at atmospheric pressure were calculated, including the pre-exponential factor (2.27 cm·s−1) in the Arrhenius equation, activation energy (114 kJ·mol−1), and effective diffusion coefficient (6.2×10−8 cm2·s−1). A formula was also derived to calculate the reaction rate constant.

Intelligent simulation on refrigeration system using artificial neural network

Because of the dynamic, nonlinear and multi-parameter characteristic of the refrigeration system, it is difficult to keep the system operating under the optimal state. Based on the improved back-propagation (BP) of artificial neural network (ANN) with the momentum factor, the program to predict the performance of refrigeration system at part-load condition is established by Visual C++ 6.0. The training or testing data is from a refrigeration experiment system with HCFC22. The input layer includes 3 neurons, i.e. the indoor and outdoor air temperature and compressor frequency. The prediction result indicates that the artificial neural network method is a kind of effective way to analyze the performance of refrigeration system. This work can provide guidance on the saving-energy control method of refrigeration system at part-load condition.

Optimization of Indoor Thermal Comfort Parameters with the Adaptive Network-Based Fuzzy Inference System and Particle Swarm Optimization Algorithm

The goal of this study is to improve thermal comfort and indoor air quality with the adaptive network-based fuzzy inference system (ANFIS) model and improved particle swarm optimization (PSO) algorithm. A method to optimize air conditioning parameters and installation distance is proposed. The methodology is demonstrated through a prototype case, which corresponds to a typical laboratory in colleges and universities. A laboratory model is established, and simulated flow field information is obtained with the CFD software. Subsequently, the ANFIS model is employed instead of the CFD model to predict indoor flow parameters, and the CFD database is utilized to train ANN input-output “metamodels” for the subsequent optimization. With the improved PSO algorithm and the stratified sequence method, the objective functions are optimized. The functions comprise PMV, PPD, and mean age of air. The optimal installation distance is determined with the hemisphere model. Results show that most of the staff obtain a satisfactory degree of thermal comfort and that the proposed method can significantly reduce the cost of building an experimental device. The proposed methodology can be used to determine appropriate air supply parameters and air conditioner installation position for a pleasant and healthy indoor environment.

Influence of Rotation on BN Separation in Binary Particle System

Granular particles systems under vertical vibration exhibit Brazilian Nut separation (BN), Reversed BN (RBN) separation or transitional phases at different vibrating conditions. In the present work, we investigate the influence of rotation on the BN separation of a binary granular particle system by changing rotational speed. 13X molecular sieve particles with diameter 6.00 mm and 0.60 mm are used. Vibration frequency f is 30 Hz and dimensionless acceleration Γ is 1.52 or 1.75, in which the particle system mainly exhibits BN separation tendency. Rotational speed ω varies from 0 to 150rpm, while the upper surface of the particle system maintains flat. We took the pictures of the particles distribution and measured the particles mass layer by layer to obtain the 3-D distribution of the particles. The results show that rotation enhances the BN separation tendency at slow rotational speed. The BN separation becomes strongest when ω is approximately 50rpm, then the BN separation tendency reduces as ω continues...

Size separation of binary mixture under vibration

By considering as a thermodynamic system, a minimum energy principle is established, in which the granular system changes its size distribution to make itself to be at the lowest energy state as soon as possible on the precondition that it dissipates all of the energy supplied from the vibrating bottom. A model is presented based on this principle to clarify why and how binary mixture separates under vibration. The small particles tend to sink in order to lower the kinetic energy of system, while the heavy particles sink in order to lower the potential energy. The mixture separates finally based on the competition between the two effects. The results of our model qualitatively agree with the previous researches.

The prediction model of CVN for welded joint based on neural network and genetic algorithm

Based on the welding process parameters of high strength pipeline steel, the artificial neural network (ANN) model has been develped to predict Charpy_V notch (CVN) impact toughness of the welded joint. The model with back propagation (BP) algorithm is built in batch mode and optimized using momentum and adaptive learning rate. Futher more, it is also optimized using genetic algorithm (GA). Five process parameters, namely the welding layer, wall thickness, the welding processes, the preheat temperature, the mean energy input, are used as input variables and the CVN of the welded joints is considered as the output variable. The training and testing of the ANN model have been done using 119 datasets which were obtained from practical welding. The number of the testing samples with error less than 20% is about 74% in total testing data. It is found that the CVN of pipeline welded joints can be effectively predicted using the model and the GA outperforms the commonly BP algorithm used as an neural network training technique. Based on this model, the influence of the preheat temperature and the mean energy input on the CVN is also analysed. The results show that the preheat temperature and the mean energy input have little effects on the automatic welding, but large on the semi-automatic welding and the manual welding for root welding. So a reasonable choice of the preheat temperature and the mean energy input is necessary.

Study on Nitridation of Silicon Added With Amorphous Silicon Nitride

The direct nitridation process of silicon added with amorphous silicon nitride powder at atmospheric pressure was investigated and the product was analyzed by XRD and SEM. Based on the relationship between the conversion ratio of silicon and the reaction time at different temperatures, a physical and mathematical model was derived to describe the nitridation process of silicon particles. The results showed that the conversion ratio of silicon increased rapidly at the early stage of reaction. And the reaction would be accelerated by reducing the size of silicon particle and increasing the pressure of N2 . At the range of experimental temperature, the conversion ratio of silicon increases with improving temperature.© 2011 ASME

A Distributed Model for Air-to-Refrigerant Fin-and-Tube Evaporators With Special Emphasis on Two-Phase Zone

A general and simple model for simulating the steady behavior of air-to-refrigerant fin-and-tube evaporators, which accounts for detailed flow state inside the tubes, is introduced. To account for the heat transfer between air and the working fluid, the evaporator is divided into a number of control volumes. Space dependent partial differential equations group is obtained from the mass, energy and momentum balances for each one. The corresponding discretized governing equations are solved afterwards. Empirical correlations are also required to estimate the void fraction, the internal and external heat transfer coefficients, as well as the pressure drops. According to the phase of refrigerating fluid, the evaporator can be divided into two distinct zones on the refrigerant-side: the vapor zone and the two-phase zone, while special emphasis is performed on the treatment of the two-phase zone. The distribution of flow pattern has been evaluated with the aim of improving the calculation accuracy. The model prediction is validated against experimental data for an evaporator using R22 as the working fluid, which shows a reasonable level of agreement: the cooling capacity is predicted within the error band of 3%. The developed model will have wide applications in operational optimization, performance assessment and pipeline design.© 2010 ASME