Optimum Power Output Control of a Wind Turbine Rotor

© 2016 S. Wijewardana et al. An active and optimum controller is applied to regulate the power output from a wind turbine rotor. The controller is synthesized in two steps. The first step defines the equilibrium operation point and ensures that the desired equilibrium point is stable. The stability of the equilibrium point is guaranteed by a control law that is synthesized by applying the methodology of model predictive control (MPC). The method of controlling the turbine involves pitching the turbine blades. In the second step the blade pitch angle demand is defined. This involves minimizing the mean square error between the actual and desired power coefficient. The actual power coefficient of the wind turbine rotor is evaluated assuming that the blade is capable of stalling, using blade element momentum theory. This ensures that the power output of the rotor can be reduced to any desired value which is generally not possible unless a nonlinear stall model is introduced to evaluate the blade profile coefficients of lift and drag. The relatively simple and systematic nonlinear modelling and MPC controller synthesis approach adopted in this paper clearly highlights the main features on the controller that is capable of regulating the power output of the wind turbine rotor

How Can India Help Prevent Food Price Volatility?

This article is about India's role in reducing food price volatility in the world. India has come a long way from a ‘ship?to?mouth existence’ to a country that is ready to confer legal right to food to its citizens based on its own production. India has 18 per cent of the world's population and therefore food self?sufficiency of India would be a blessing for the struggle against price volatility. By improving productivity, by reducing energy use, by augmenting water resources and by conserving prime farm land, India can produce enough food for an estimated population of 1.5 billion by 2030. Further, by controlling speculative trade in food prices, by maintaining stable domestic prices and by sharing its agricultural and food policy expertise, India can help reduce food price volatility. However, to reduce global price volatility and to remove price distortions in the world market, it is important to resolve the issues of agricultural trade and to adopt a small farmer?friendly global trading system

Dynamic and wear study of an extremely bidisperse magnetorheological fluid

Acceso a la versión publicada en Smart Mater. Struct. 24(12) 127001 (http://iopscience.iop.org/0964-1726/24/12/127001)"This is an author-created, un-copyedited version of an article accepted for publication/published in Smart Materials and Structures. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at http://dx.doi.org/10.1088/0964-1726/24/12/127001."In this work the friction and wear properties of five magnetorheologicalfluids (MRFs)with varying compositions are investigated. Considering that many of the proposed applications for these fluids involve lubricated contact between mobile metal –metal or polymer– metal parts, the relationship between MR response and wear behavior appears to be of fundamental importance. One of the fluids(MR#1)contains only the iron microparticles and base oil; the second and third ones(MR#2 and MR#3) contain an anti-wear additive as well. The fourth one(MR#4)is a well known commercial MRF. Finally, MR#5 is stabilized by dispersing the iron particles in a magnetite ferrofluid. The MR response of the latter fluid is better(higher yield stress and post-yield viscosity)than that of the others. More importantly, it remains(and even improves)after the wear test: the pressure applied in the four-ball apparatus produces a compaction of the magnetite layer around the iron microparticles. Additionally, the friction coefficient is larger, which seems paradoxical in principle, but can be explained by considering the stability of MR#5 in comparison to the other four MRs, which appear to undergo partial phase separation during the test. In fact, electron and optical microscope observations confirm a milder wear effect of MR#5, with almost complete absence of scars from the steel test spheres and homogeneous and shallow grooves on them. Comparatively, MR#2, MR#3 and, particularly, MR#1 produce a much more significant wear.MINECO Ramón y Cajal Programme (RYC-2014-16901)MINECO FIS 2013-07666-C3-1-RCEI Biotic BS27.2015Junta de Andalucía, PE2012-FQM-069

Using Machine Learning Algorithms to Develop a Clinical Decision-Making Tool for COVID-19 Inpatients.

BACKGROUND: Within the UK, COVID-19 has contributed towards over 103,000 deaths. Although multiple risk factors for COVID-19 have been identified, using this data to improve clinical care has proven challenging. The main aim of this study is to develop a reliable, multivariable predictive model for COVID-19 in-patient outcomes, thus enabling risk-stratification and earlier clinical decision-making. METHODS: Anonymised data consisting of 44 independent predictor variables from 355 adults diagnosed with COVID-19, at a UK hospital, was manually extracted from electronic patient records for retrospective, case-control analysis. Primary outcomes included inpatient mortality, required ventilatory support, and duration of inpatient treatment. Pulmonary embolism sequala was the only secondary outcome. After balancing data, key variables were feature selected for each outcome using random forests. Predictive models were then learned and constructed using Bayesian networks. RESULTS: The proposed probabilistic models were able to predict, using feature selected risk factors, the probability of the mentioned outcomes. Overall, our findings demonstrate reliable, multivariable, quantitative predictive models for four outcomes, which utilise readily available clinical information for COVID-19 adult inpatients. Further research is required to externally validate our models and demonstrate their utility as risk stratification and clinical decision-making tools

A PIV-based method for estimating slamming loads during water entry of rigid bodies

Hydrodynamic impact of bodies onto the water surface is a problem of great importance in the design of off-shore and naval structures (wave energy converters, off-shore platforms, highspeed boats, etc). Classical measurement techniques, namely pressure sensors, present major drawbacks in the determination of impact loads because of their intrusive nature. In this paper, we propose a method to determine the impact loads on rigid bodies during water entry, based on high-speed particle image velocimetry. The method consists of two steps: firstly, an automated procedure is developed to determine the velocity field from high-speed images during water impact. Secondly, the unsteady pressure field is estimated from the velocity fields, using a Poisson-based solver. The method is validated on a rigid wedge slamming experiment and the results are compared with results from computational fluid dynamics simulations (performed with the software LS-Dyna) and from the literature