Numerical simulation of temperature field during gas preheating and start-up of drained aluminum reduction cell

1 Introduction The drained aluminum reduction cell is a new type cell and it is predicted that it can reduce the voltage of the cell 0.6 V and save the consumption of direct current 2400 kW?h/t, compared with the general cells[1,2]. Therefore, more and m…     

Constitutive model of rock under static-dynamic coupling loading and experimental investigation

1 Introduction Rock fragmentation, boring, explosive, rockburst etc in engineering domain of mineral are almost concerned with cracking of rock experiencing dynamic loading, and a plenty of results have been obtained[1]. But the related studies are almos…     

Synthesis and characterization of LiNi0.95-xCoxAl0.05O2 for lithium-ion batteries

Samples of LiNi0.95-xCoxAl0.05O2 （x = 0.10 and 0.15） and LiNiO2, synthesized by the solid-state reaction at 725℃ for 24 h from LiOH-H2O, Ni2O3, Co2O3, and AI（OH）3 under an oxygen stream, were characterized by TG-DTA, XRD, SEM, and electrochemical tests. Simultaneous doping of cobalt and aluminum at the Ni-site in LiNiO2 was tried to improve the cathode performance for lithium-ion batteries. The results showed that co-doping （especially, 5 at.% A1 and 10 at.% Co） definitely had a large beneficial effect in increasing the capacity （186.2 mA.h/g of the first discharge capacity for LiNio.s.42OoaoAlo.0502） and cycling behavior （180.1 mA-h/g after 10 cycles for LiNio.85CooaoAlo.osO2） compared with 180.7 mA.h/g of the first discharge capacity and 157.7 mA.h/g of the tenth discharge capacity for LiNiO2, respectively. Differen- tial capacity versus voltage curves showed that the co-doped LiNio.95_xCoxmlo.osO2 had less intensity of the phase transitions than the pristine LiNiO2.     

Security optimal power flow considering loading margin stability using hybrid FFA–PS assisted with brainstorming rules

This paper presents a new power system planning strategy which combines firefly algorithm (FFA) with pattern search algorithm (PS). The purpose is minimizing total fuel cost, total power loss and reducing total voltage deviation, with the objective of enhancing the loading margin stability and consequently the power system security. A new interactive and simple mechanism, inspired in brainstorming process, is proposed that allows FFA and PS algorithms to explore new regions of the search space. In this study the Static VAR compensator (SVC) is modeled and integrated in an efficient location which is chosen considering the voltage stability index. The proposed algorithm is interactive and tries to optimize a set of control variables at the same time, namely, active power generations, voltage of generators, tap transformers, and the reactive power of shunt compensators to optimize three objective functions such as: fuel cost, total power loss and total voltage deviation. These variables are optimized using a flexible interactive and competitive search mechanism. The proposed planning strategy has been examined and applied to two practical test systems IEEE 14-Bus and IEEE 30-Bus. Simulation results confirm the effectiveness of this hybrid strategy for solving the security optimal power flow.     

颗粒形状对多孔介质孔隙特征和渗流规律影响研究的探讨

讨论了颗粒形状对于多孔介质孔隙系统及其渗流规律的可能影响及其研究意义;综述相关领域的研究成果,探讨了颗粒形状的测量评定方法、颗粒形态和填筑的模拟方法、孔隙系统的可视化研究方法,以及渗流规律的试验对比研究方法;分析指出了颗粒形状对渗流规律影响的研究在促进水处理滤料开发和碎石料作为大坝填料利用方面的应用前景.     

Parallelisation of storage cell flood models using OpenMP

This paper describes the implementation and benchmarking of a parallel version of the LISFLOOD-FP hydraulic model based on the OpenMP Application Programming Interface. The motivation behind the study was that reducing model run time through parallelisation would increase the utility of such models by expanding the domains over which they can be practically implemented, allowing previously inaccessible scientific questions to be addressed. Parallel speedup was calculated for 13 models distributed over seven study sites and implemented on one, two, four and in selected cases eight processor cores. The models represent a range of previous applications from large area, coarse resolution models of the Amazon, to fine resolution models of urban areas, to orders of magnitude smaller models of rural floodplains. Parallel speedups were greater for larger model domains, especially for models with over 0.2–0.4 million cells where parallel efficiencies of up to 0.75 on four and eight cores were achieved. A key advantage of using OpenMP and an explicit rather than implicit model was the ease of implementation and minimal code changes required to run simulations in parallel.     

AVCOL: Availability-aware information aggregation in large distributed systems under uncollaborative behavior

Aggregation of system-wide information in large-scale distributed systems, such as p2p systems and Grids, can be unfairly influenced by nodes that are selfish, colluding with each other, or are offline most of the time. We present AVCOL, which uses probabilistic and gossip-style techniques to provide availability-aware aggregation. Concretely, AVCOL is the first aggregation system that: (1) implements any (arbitrary) global predicate that explicitly specifies any node’s probability of inclusion in the global aggregate, as a mathematical function of that node’s availability (i.e., percentage time online); (2) probabilistically tolerates large numbers of selfish nodes and large groups of colluders; and (3) scales well with hundreds to thousands of nodes. AVCOL uses several unique design decisions: per-aggregation tree construction where nodes are allowed a limited but flexible probabilistic choice of parents or children, probabilistic aggregation along trees, and auditing of nodes both during aggregation as well as in gossip-style (i.e., periodically). We have implemented AVCOL, and we experimentally evaluated it using real-life churn traces. Our evaluation and our mathematical analysis show that AVCOL satisfies arbitrary predicates, scales well, and withstands a variety of selfish and colluding attacks.     

Research on delamination monitoring for composite structures based on HHGA–WNN

Due to the deficiencies of the training algorithms for available wavelet neural network used for structural health monitoring, a new hybrid hierarchy genetic algorithm was introduced by combining hierarchy genetic algorithm and least-square method to improve the learning procedure of wavelet neural network. The hybrid algorithm was able to determine the structure and parameters of the wavelet neural network simultaneously. In this algorithm, adaptive crossover and mutation probability were used to accelerate the genetic speed and avoid the occurrence of prematurity. The modal frequencies of a glass/epoxy laminates beam with varying assumed delamination sizes and locations were computed using finite element method and fed into the wavelet neural network to predict the delamination location and its extent. The simulation demonstrates that the wavelet neural network based on hybrid hierarchy genetic algorithm is robust, promising and converges very fast.     

Multiple households energy consumption forecasting using consistent modeling with privacy preservation

Traditional data-driven energy consumption forecasting models, including machine learning and deep learning methods, showed outstanding performance in terms of forecasting accuracy and efficiency. The superior performances are based on enough training data samples. Moreover, the derived forecasting model is only applicable to the training dataset and usually is applied to specific household. In real-world smart city development, a centralized forecasting model is required to model and forecasting energy consumption patterns for multiple households, whereas the traditional data-driven forecasting approaches may become invalid. A consistent model is demanded in this scenario modeling multiple households’ energy consumption patterns. Additionally, privacy issues are also highly concerned in such scenarios. Accurate energy consumption forecasting with privacy preservations becomes a key point for the state-of-art research. In this study, we adopt an innovative privacy-preserving structure that combines deep learning and federated learning. Under the premise of guaranteeing forecasting accuracy and privacy preservation, this structure can achieve the forecasting of various household energy consumption with a consistent model that simultaneously forecast multiple household energy consumption data by transmission control protocol.