Synergy of elemental Fe and Ti promoting low temperature hydrogen sorption cycling of magnesium

We studied the catalytic effects of Titanium, Iron and FeTi intermetallic on the desorption kinetics of magnesium hydride. In order to separate the catalytic effects of each element from additional synergistic and alloying effects, Mg-Ti and Mg-Fe mixtures were studied as a baseline for Mg-Fe-Ti elemental and Mg-(FeTi) intermetallic composites. Sub-micron dimensions for MgH₂ particles and excellent nanoscale catalyst dispersion was achieved by high-energy ball-milling as confirmed by analytical electron microscopy techniques. The composites containing Fe shows desorption temperature of 170 K lower than as-received MgH₂ powder, which makes it suitable to be cycled at relatively low temperature of 523 K. Furthermore, the low cycling temperature prevents the formation of Mg₂FeH₆. In sorption cycling tests, Mg-10% Ti and Mg-10% (FeTi), after about 5 activation cycles, show fast desorption kinetics initially, but the kinetics also degrade faster than for all other composites, eventually slowing down by a factor of 7 and 4, respectively. The ternary Mg-Fe-Ti composite shows best performance. With the highest BET surface area of 40 m²/g, it also shows much less degradation during cycling. This is attributed to titanium hydride acting as a size control agent preventing agglomeration of particles; while Fe works as a very strong catalyst with uniform and nanoscale dispersion on the surface of MgH₂ particles.     

Qualitative performance assessment of semiconductor switching device,converter and generator candidates for 10 MW offshore wind turbine generators

This paper investigates the possibilities of viable power electronics converters, semiconductor switching devices and electric machines for 10 MW variable‐speed wind turbine generators. The maximum rated power of existing wind turbine configurations is in the range of 6 MW. The proposed alternatives are compared against several technical and economical factors, and their advantages over the present wind turbines are highlighted. A comprehensive performance comparison of modern power semiconductor devices, their electrical characteristics and the key differentiators among them are presented. The power electronics converters considered include all commercially available multilevel voltage source and current source converters as well as the opportunities offered by power electronics building block‐based design. The factors used for the comparison include the converter power range, capacitor voltage balancing, common mode voltage and current, electromagnetic interference emissions, fault ride‐through capability, reliability, footprint, harmonic performance, efficiency and losses, component count, risk of torsional vibration by the harmonics and inter‐harmonics, complexity, ease of back‐to‐back operation and filtering requirements. For the electric machines, this study concentrates on high‐temperature superconducting machines, multi‐phase induction machines and permanent magnet synchronous machines. These machines are compared against existing wind generator technologies in terms of their power range, torque density, efficiency, fault ride‐through capability, reliability, footprint, harmonic performance, ease of fault detection, excitation control, noise and vibration signature, oscillation damping, gearbox requirement, cost and the size of the associated converter. Copyright © 2010 John Wiley & Sons, Ltd.     

A stochastic security approach to energy and spinning reserve scheduling considering demand response program

In this paper a new algorithm for allocating energy and determining the optimum amount of network active power reserve capacity and the share of generating units and demand side contribution in providing reserve capacity requirements for day-ahead market is presented. In the proposed method, the optimum amount of reserve requirement is determined based on network security set by operator. In this regard, Expected Load Not Supplied (ELNS) is used to evaluate system security in each hour. The proposed method has been implemented over the IEEE 24-bus test system and the results are compared with a deterministic security approach, which considers certain and fixed amount of reserve capacity in each hour. This comparison is done from economic and technical points of view. The promising results show the effectiveness of the proposed model which is formulated as mixed integer linear programming (MILP) and solved by GAMS software.     

Optimization of Run-of-River Hydropower Plant Design under Climate Change Conditions

The assessment of climate change and its impacts on hydropower generation is a complex issue. This paper evaluates the application of representative concentration pathways (RCPs, 2.6, 4.5, and 8.5) with the change factor (CF) method and the statistical downscaling method (SDSM) to generate six climatic scenarios of monthly temperature and rainfall over the period 2020–2049 in the Karkheh basin, Iran. The identification of unit hydrographs and component flows from rainfall, evaporation and streamflow data (IHACRES) model was employed to simulate runoff for the purpose of designing a run-of-river hydropower plant in the Karkheh basin. The non-dominated sorting genetic algorithm (NSGA)-II was employed to maximize yearly energy generation and the plant factor, simultaneously. Results indicate the runoff scenarios associated with the SDSM lead to higher run-of-river hydropower generation in 2020–2049 compared to the CF results.     

Developing a Robust Multi-Attribute Decision-Making Framework to Evaluate Performance of Water System Design and Planning under Climate Change

In theory, emergence of robustness concept has pushed decision-makers toward designing alternatives, such as resistant against the potential fluctuations fueled by uncertain surrounding environment. This study promotes an objective-based multi-attributes decision-making framework that takes into account the uncertainties associated with the impacts of the climate change on water resources systems. To capture the uncertainties of climate change, Monte Carlo approach has been used to generate a series of ensembles. These generated ensembles represent the stochastic behavior of the hydro-climatic variables under climate change. This framework represents the inherent uncertainties associated with hydro-climatic simulations. Next, a coupled TOPSIS/Entropy multi-attribute decision-making framework has been formed to prioritize the feasible alternatives using system performance measures. The main objective of this framework is to minimize the risk of deceptive and subjective assessments during decision-making process. Karkheh River basin has been selected as a case study to demonstrate the implication of this framework. Using a set of system performance attributes, the performance of two hydropower systems has been estimated during the baseline period and under the future climate change conditions. According to the conducted frequency analysis, the alternative in which both hydropower projects would go under construction emerged as the robust solution (i.e., there was a 99.9% chance that it outperforms other solutions). The results indicate that the construction of these hydropower systems leads to the increase of Karkheh River basin robustness in the future.

     

Modeling the response of HCP polycrystals deforming by slip and twinning using a finite element representation of the orientation space

A continuum approach is presented for predicting the constitutive response of HCP polycrystals using a simple non-hardening constitutive model incorporating both slip and twinning. This has been achieved by considering a physically based methodology for restricting the amount of the twinning activity. A continuum approach is used in modeling the texture evolution that eliminates the need for increasing the number of discrete crystal orientations to account for new orientations created by twinning during deformation. The polycrystal is represented by an orientation distribution function using the Rodrigues parameterization. A total Lagrangian framework is used to model the evolution of microstructure. Numerical examples are used to show the application of the methodology for modeling deformation processes.     

Optical Pattern Switching in Semiconductor Microresonators as All‐Optical Switch

In this paper, we present a spatial perturbation method to control the optical patterns in semiconductor microresonators in the far‐field configuration. We propose a fast all‐optical switch which operates at a low light level. The switching beam controls the behavior of output beams with strong intensities. The method has been applied successfully to different optical patterns such as rolls, squares, and hexagons.     

Exploiting symmetries for scaling loopy belief propagation and relational training

Judging by the increasing impact of machine learning on large-scale data analysis in the last decade, one can anticipate a substantial growth in diversity of the machine learning applications for “big data” over the next decade. This exciting new opportunity, however, also raises many challenges. One of them is scaling inference within and training of graphical models. Typical ways to address this scaling issue are inference by approximate message passing, stochastic gradients, and MapReduce, among others. Often, we encounter inference and training problems with symmetries and redundancies in the graph structure. A prominent example are relational models that capture complexity. Exploiting these symmetries, however, has not been considered for scaling yet. In this paper, we show that inference and training can indeed benefit from exploiting symmetries. Specifically, we show that (loopy) belief propagation (BP) can be lifted. That is, a model is compressed by grouping nodes together that send and receive identical messages so that a modified BP running on the lifted graph yields the same marginals as BP on the original one, but often in a fraction of time. By establishing a link between lifting and radix sort, we show that lifting is MapReduce-able. Still, in many if not most situations training relational models will not benefit from this (scalable) lifting: symmetries within models easily break since variables become correlated by virtue of depending asymmetrically on evidence. An appealing idea for such situations is to train and recombine local models. This breaks long-range dependencies and allows to exploit lifting within and across the local training tasks. Moreover, it naturally paves the way for the first scalable lifted training approaches based on stochastic gradients, both in an online and a MapReduced fashion. On several datasets, the online training, for instance, converges to the same quality solution over an order of magnitude faster, simply because it starts optimizing long before having seen the entire mega-example even once.     

FPGA成为替代ASIC的最佳选择

多年来,Xilinx公司的可编程逻辑技术始终扮演着ASIC替代解决方案的角色.过去十多年来,每次当ASIC技术实现摩尔定律的预期,Xilinx FPGA和CPLD都迅速填补了由此而留下的间隙.最近,有些ASIC制造商推出了称为结构化ASIC(Structured ASIC)的改进ASIC结构,试图解决与基于标准单元的ASIC和门阵列相关的一些问题.但最终,人们都会问到这一决定性问题,"如果我们需要100万门至500万门的设计,到底哪种技术最佳地结合了硬件、软件和设计支持,从而可最好地满足我们的需要?"