Value analysis of intermittent generation sources from the systemoperations perspective

The objective of this study is to determine the economic and operational impact on energy cost of incorporating large photovoltaic (PV) and wind energy conversion systems (WECS) into the electric utility generation mix. In most cases, PV and WECS power outputs are subtracted from the utility load with the expectation that conventional generation would meet the residual load. This approach is valid for small penetration levels and/or for PV and WECS facilities connected near load centers, However, several constraints such as thermal generation characteristics, fuel supply and delivery, spinning reserve requirements, and hydro availability are not adequately represented in this process. To determine the optimal value of large-scale PV and WECS applications, a new methodology that would take into account the aforementioned constraints as well as a more global penetration is developed. Results indicate that while high hydro availability increases PV penetration levels, high ramping rates can also significantly increase penetration levels     

Swift and Selective Reduction of Nitroaromatics to Aromatic Amines with Ni–Boride–Silica Catalysts System at Low Temperature

Various nitroaromatics are successfully reduced to amines with 100% conversion and selectivity in methanol at low temperature (≈5 °C), by using versatile system of 5% Ni–SiO₂ catalyst and NaBH₄ and in situ generation of Ni boride. The catalytic efficiency of Ni loading (5%, 10% and 15%) with silica or titania as support materials is investigated for reduction of nitrobenzene. The Ni–titania/NaBH₄ system recorded lower conversion and selectivity. The IR studies indicate that silica support does not have free –OH group on its surface. Thus the nickel boride is anchored to the silica to facilitate the catalytic process.     

Simulation and experimental analyses of electromagnetic transients behaviors of lightning surge on vertical conductors

Simulation of very fast surge phenomena in a three-dimensional (3-D) structure requires a method based on Maxwell's equations, such as the finite-difference time-domain (FDTD) method or the method of moments, because circuit-equation-based methods cannot handle the phenomena. This paper uses a method of thin-wire representation of the vertical conductor system for the FDTD method which is suitable for the 3-D surge simulation. The thin-wire representation is indispensable to simulate electromagnetic surges on wires or steel frames in which the radius is smaller than a discretized space step used in the FDTD simulation. In this paper, a general surge analysis program named the virtual surge test lab based on the Maxwell's equations formulated by the FDTD method, is used to simulate the surge phenomena of a vertical conductor, including the effects of horizontal wave incidence and vertical wave incidence. Experimental results on the reduced scale model have been presented in order to compare among the simulation results by the FDTD method and the results using numerical electromagnetic code based on the MoM.     

The functional reanimation of paralyzed limbs.

The functional reanimation of paralyzed limbs has been a longstanding goal of neural prosthetic research, but clinically successful applications have been elusive. Natural voluntary limb movement requires four major elements: actuators (i.e., motor units), sensors (i.e., somatosensory afferents), commands (i.e., cerebral cortical activity), and control (i.e., integration of the previous three elements at various levels of the neuraxis). Prosthetic equivalents of each of these elements are, as yet, primitive and often cumbersome to deploy, but new technologies promise substantial improvements for all. This article focuses on one such technology, bionic neuon (BION) modular microimplants, and its relationship to alternative and complementary technologies. The challenge remains to select and integrate them into systems that can be tailored efficiently to the widely disparate needs of patients with various patterns of weakness and paralysis.     

Modelling and controller design of an isolated diesel enginepermanent magnet synchronous generator

A method to analyze the steady-state performance of a stand-alone permanent magnet synchronous generator driven by a diesel engine is presented. The proposed method is based on equivalent d-q circuits and the phasor diagram of such a generator under steady-state conditions. A fixed capacitor-thyristor controlled reactor scheme is used to regulate the generator terminal voltage by controlling the thyristor ignition angle. Furthermore the overall system dynamics are modelled in terms of state variables and control inputs. Based on a reduced order linearized model, digital optimal state and output feedback controllers are designed by minimising a quadratic performance index using the dynamic programming technique. The objective of the controller is to maintain the load voltage and frequency constant under varying load conditions. The controller's effectiveness is assessed by examining the closed-loop system response to sudden load changes     

High-speed milling of titanium alloys using binderless CBN tools

The performance of conventional tools is poor when used to machine titanium alloys. In this paper, a new tool material, which is binderless cubic boron nitride (BCBN), is used for high-speed milling of a widely used titanium alloy Ti–6Al–4V. The performance and the wear mechanism of the BCBN tool have been investigated when slot milling the titanium alloy in terms of cutting forces, tool life and wear mechanism. This type of tool manifests longer tool life at high cutting speeds. Observations based on the SEM and EDX suggest that adhesion of workpiece and attrition are the main wear mechanisms of the BCBN tool when used in high-speed milling of Ti–6Al–4V.     

Thermal-stress-induced birefringence in bow-tie optical fibers

Birefringence induced by thermal stress in bow-tie optical fibers is studied in detail by the use of the finite-element method. Results of computer modeling show that a higher degree of birefringence can be obtained with the use of a larger cladding and larger stress-applying zones in the fiber.     

Ensemble learning of diffractive optical networks

A plethora of research advances have emerged in the fields of optics and photonics that benefit from harnessing the power of machine learning.Specifically,there has been a revival of interest in optical computing hardware due to its potential advantages for machine learning tasks in terms of parallelization,power efficiency and computation speed.Diffractive deep neural networks(D²NNs)form such an optical computing framework that benefits from deep learning-based design of successive diffractive layers to all-optically process information as the input light diffracts through these passive layers.D²NNs have demonstrated success in various tasks,including object classification,the spectral encoding of information,optical pulse shaping and imaging.Here,we substantially improve the inference performance of diffractive optical networks using feature engineering and ensemble learning.After independently training 1252 D²NNs that were diversely engineered with a variety of passive input filters,we applied a pruning algorithm to select an optimized ensemble of D²NNs that collectively improved the image classification accuracy.Through this pruning,we numerically demonstrated that ensembles of N=14 and N=30 D²NNs achieve blind testing accuracies of 61.14±0.23%and 62.13±0.05%,respectively,on the classification of GFAR-10 test images,providing an inference improvennent of>16%compared to the average performance of the individual D²NNs within each ensemble.These results constitute the highest inference accuracies achieved to date by any diffractive optical neural network design on the same dataset and might provide a significant leap to extend the application space of diffractive optical image classification and machine vision systems.