Microstructure and Processing of 3D Printed Tungsten Microlattices and Infiltrated W–Cu Composites

Tungsten is of industrial relevance due its outstanding intrinsic properties (e.g., highest melting‐point of all elements) and therefore difficult to 3D‐print by conventional methods. Here, tungsten micro‐lattices are produced by room‐temperature extrusion‐based 3D‐printing of an ink comprising WO₃–0.5%NiO submicron powders, followed by H₂‐reduction and Ni‐activated sintering. The green bodies underwent isotropic linear shrinkage of ≈50% during the thermal treatment resulting in micro‐lattices, with overall 35–60% open‐porosity, consisting of 95–100% dense W–0.5%Ni struts having ≈80–300 μm diameter. Ball‐milling the powders and inks reduced the sintering temperature needed to achieve full densification from 1400 to 1200 °C and enabled the ink to be extruded through finer nozzles (200 μm). Partial sintering of the struts is achieved when NiO is omitted from the ink, with submicron interconnected‐porosity of ≈34%. Several tungsten micro‐lattices are infiltrated with molten copper at 1300 °C under vacuum, resulting in dense, anisotropic W–Cu composites with 40–65% tungsten volume fraction. Partially sintered struts (containing nickel) with submicron open porosity are also infiltrated with Cu, resulting in co‐continuous W–Cu composites with wide W struts/Cu channels at the lattice scale (hundreds of micrometers), and fine W–Cu interpenetrating network at the strut scale (hundreds of nanometers) allowing for the design of anisotropic mechanical and electrical properties.

     

Thermodynamic properties and phase equilibria for Pt-Rh alloys

The activity of rhodium in solid Pt-Rh alloys is measured in the temperature range from 900 to 1300 K using the solid-state cell

Visual information processing in primate cone pathways. I. A model

At the retinal level, the strategies utilized by biological visual systems allow them to outperform machine vision systems, serving to motivate the design of electronic or "smart" sensors based on similar principles. Design of such sensors in silicon first requires a model of retinal information processing which captures the essential features exhibited by biological retinas. In this paper, a simple retinal model is presented, which qualitatively accounts for the achromatic information processing in the primate cone system. The computer retina model exhibits many of the properties found in biological retinas such as data reduction through nonuniform sampling, adaptation to a large dynamic range of illumination levels, variation of visual acuity with illumination level, and enhancement of spatiotemporal contrast information. The main emphasis of the model presented here is to demonstrate how different adaptation mechanisms play a role in extending the operating range of the primate retina.     

Detection and quantification of valve stiction by the method of unknown input estimation

Valve stiction is one of the most common causes of oscillations in industrial process control loops. Such oscillations can degrade the overall performance of the loop and eventually the final product quality. The detection and quantification of valve stiction in industrial process control loops is thus important. From previous studies in the literature, a sticky valve has been shown to have a distinct signature of the stiction phenomena in its valve positioner data. However, the position of the modulating control valves is seldom available. We consider the problem of estimating the valve position as an unknown input estimation problem. In this work, we propose a novel application of the unknown input estimator in order to estimate the valve position given the process model and the data of the process variable and controller output. Using the estimated valve position, we can detect and also quantify the amount of stiction. We demonstrate the efficacy of the method through simulation examples where a sticky valve is deliberately introduced in the closed loop using a two-parameter stiction model available in the literature. Application of the proposed methodology to a laboratory scale flow control loop is presented. An industrial case study is also presented in which the algorithm accurately detects and quantifies stiction in the level control loop of a power plant.     

Consistency of noise covariance estimation in joint input–output closed-loop subspace identification with application in LQG benchmarking

The LQG trade-off curve has been used as a benchmark for control loop performance assessment. The subspace approach to estimating the LQG benchmark has been proposed in the literature which requires certain intermediate matrices in subspace identification as well as the covariance matrix of the noise. It is shown in this paper that many existing closed-loop identification methods do not give a consistent estimate of the noise covariance matrix. As a result, we propose an alternative subspace formulation for the joint input–output closed-loop identification for which the consistency of the required subspace matrices and noise covariance is guaranteed. Simulation studies and experimental results are provided to demonstrate the utility of the proposed method.     

A review of channel estimation and security techniques for CRNS

Cognitive Radio Network (CRN) is an intelligent wireless communication system that adapts itself to variations in the incoming radio frequency stimuli by modifying the operating parameters. Using the spectrum sensing techniques, the idle channels are detected, and allocated to the Secondary Users (SUs). The existing cooperative spectrum sensing techniques such as centralized sensing technique, Distributed sensing technique, and External sensing technique exploit efficient prediction models for allocating the frequency spectrum to SUs. For an optimal assignment of the channel using channel parameters, the channel estimation techniques such as pilot-assisted channel estimation, blind and semi blind estimation technique, and decision directed channel estimation technique are analyzed. The flexible nature of the CRN introduces various security attacks such as Primary User Emulation Attack, Objective Function Attack, Jamming Attack, Spectrum Sensing Data Falsification (SSDF), Control Channel Saturation DoS Attack (CCSD), Selfish Channel Negotiation (SCN), Sinkhole Attacks, HELLO Flood Attacks and Lion Attack. From the surveyed results, it is observed that the existing spectrum sensing, and prediction-based techniques consume more energy, and minimal data transmission rate for detecting the idle channel. Further, the end-to-end delay, energy consumption, end-to-end delay, and bandwidth are not minimized by the existing techniques.     

Closed loop identification and control loop reconfiguration: an industrial case study

The results of a joint university–industry collaborative project for control loop reconfiguration using closed loop experimental data from a fuel gas pressure control system are described in this paper. The fuel gas pressure was being regulated using a butane stream. For economic reasons, it was necessary to switch control to the ethane stream. Previous attempts at effecting this changeover had proved unsuccessful. In this study, a powerful system identification technique namely Canonical Variate Analysis (CVA) was employed to obtain the empirical plant models. A PI controller was then designed using the direct synthesis method. Acceptable closed loop behavior was obtained with little online tuning.     

Computational haemodynamics: geometry and non-newtonian modelling using spectral/hp element methods

The increased availability of large scale computational facilities is enabling the use of computational modelling for the treatment of complex problems of industrial interest. Computational haemodynamics is an area where improved computational power and algorithmic development have permitted the application in biomechanics of techniques originally devised for direct numerical simulations in others areas of scientific interest. In this paper we present a highly accurate algorithm for the numerical solution of the incompressible Navier–Stokes equations which employs a spectral/hp element spatial discretisation and a high-order time splitting. Two important developments discussed in this paper are the coupling of these techniques with unstructured mesh generation, used in many everyday engineering applications, and the introduction of a non-Newtonian viscosity model to account for the shear thinning properties of blood. The proposed method will be applied to the analysis of shear stress distributions in flows of interest in haemodynamics. Received: 5 May 1999 / Accepted: 21 september 1999     

Diagnosis and treatment of aortic valve stenosis

The present review has attempted to summarize the classic symptoms and signs of aortic valve stenosis, especially in an adult. It is emphasized that all the classic signs rarely are present and their absence may mislead an unwary clinician. The diagnostic help provided by noninvasive tests, including echocardiography and phonocardiography, has been emphasized. A need for cardiac catheterization and angiography in most patients prior to corrective surgery is stressed. The natural history of the disease without operative intervention is dim and a significant risk of sudden death exists. The current surgical approach with immediate and long-term results is summarized. Finally, attention has been drawn to the special clinical circumstances when the aortic valve stenosis provides a strinkingly different clinical picture. We cannot find a better way to end this review than by quoting a warning note given by Thomas Lewis in 1920: "It is the faint cry of an anguished and fast failing muscle, which, when it comes, all should strain to hear, for it is not long repeated. A few months, a few years at most, and the end comes."     

Improving Water Supply Outlook in Korea with Ensemble Streamflow Prediction

Abstract

The purpose of this study is to propose an alternative forecasting approach for improving the current water supply outlook in Korea. Using a rainfall-runoff model, the existing technique for the water supply outlook in Korea produces monthly low, average, and high runoff forecasts. The proposed technique is called Ensemble Streamflow Prediction (ESP), and is currently implemented by the National Weather Service in the U.S.A. ESP appears particularly valid in Korea where the historical rainfall record is much more comprehensive than the historical streamflow record. This study applies ESP to runoff forecasting for a river basin in Korea to examine its applicability. An ensemble of one-month ahead runoff forecasts at the Gongju gauging station in the Keum River basin, Korea, was generated for each month. The resulting ESP forecasts were compared with the corresponding observed runoff data as well as the existing forecasts. Although this study is limited to one case study, the following conclusions can be made: (1) the ESP technique dominates the existing forecasting techniques in terms of both systematic and random errors; and (2) ESP is more accurate when high flows occur.