Biodiesel Exhaust Treatment with HFAC Plasma supported by Red Mud: Study on DeNOx and power consumption

The concentration of Nitrogen Oxides (NOx) in engines which use biodiesel as fuel is higher compared to conventional diesel engine exhaust. In this paper, an attempt has been made to treat this exhaust using a combination of High frequency AC (HFAC) plasma and an industrial waste, Red Mud which shows proclivity towards Nitrogen dioxide (NO2) adsorption. The high frequency AC source in combination with the proposed compact double dielectric plasma reactors is relatively more efficient in converting Nitric Oxide (NO) to NO2. It has been shown that the plasma treated gas enhances the activity of red mud as an adsorbent/catalyst and about 60-72% NOx removal efficiency was observed at a specific energy of 250 J/L. The advantage in this method is the cost effectiveness and abundant availability of the waste red mud in the industry. Further, power estimation studies were carried out using Manley's equation for the two reactors employed in the experiment and a close agreement between experimental and predicted powers was observed. (C) 2015 The Authors. Published by Elsevier Ltd.

Optimal Guidance for Accurate Lunar Soft Landing with Minimum Fuel Consumption using Model Predictive Static Programming

In this paper the soft lunar landing with minimum fuel expenditure is formulated as a nonlinear optimal guidance problem. The realization of pinpoint soft landing with terminal velocity and position constraints is achieved using Model Predictive Static Programming (MPSP). The high accuracy of the terminal conditions is ensured as the formulation of the MPSP inherently poses final conditions as a set of hard constraints. The computational efficiency and fast convergence make the MPSP preferable for fixed final time onboard optimal guidance algorithm. It has also been observed that the minimum fuel requirement strongly depends on the choice of the final time (a critical point that is not given due importance in many literature). Hence, to optimally select the final time, a neural network is used to learn the mapping between various initial conditions in the domain of interest and the corresponding optimal flight time. To generate the training data set, the optimal final time is computed offline using a gradient based optimization technique. The effectiveness of the proposed method is demonstrated with rigorous simulation results.

Combustion and heat transfer characteristics of nanofluid fuel droplets: A short review

With the pressing need to meet an ever-increasing energy demand, the combustion systems utilizing fossil fuels have been the major contributors to carbon footprint. As the combustion of conventional energy resources continue to produce significant Green House gas (GHG) emissions, there is a strong emphasis to either upgrade or find an energy-efficient eco-friendly alternative to the traditional hydrocarbon fuels. With recent developments in nanotechnology, the ability to manufacture materials with custom tailored properties at nanoscale has led to the discovery of a new class of high energy density fuels containing reactive metallic nanoparticles (NPs). Due to the high reactive interfacial area and enhanced thermal and mass transport properties of nanomaterials, the high heat of formation of these metallic fuels can now be released rapidly, thereby saving on specific fuel consumption and hence reducing GHG emissions. In order to examine the efficacy of nanofuels in energetic formulations, it is imperative to first study their combustion characteristics at the droplet scale that form the fundamental building block for any combustion system utilizing liquid fuel spray. During combustion of such multiphase, multicomponent droplets, the phenomenon of diffusional entrapment of high volatility species leads to its explosive boiling (at the superheat limit) thereby leading to an intense internal pressure build-up. This pressure upsurge causes droplet fragmentation either in form of a microexplosion or droplet puffing followed by atomization (with formation of daughter droplets) featuring disruptive burning. Both these atomization modes represent primary mechanisms for extracting the high oxidation energies of metal NP additives by exposing them to the droplet flame (with daughter droplets acting as carriers of NPs). Atomization also serves as a natural mechanism for uniform distribution and mixing of the base fuel and enhancing burning rates (due to increase in specific surface area through formation of smaller daughter droplets). However, the efficiency of atomization depends on the thermo-physical properties of the base fuel, NP concentration and type. For instance, at dense loading NP agglomeration may lead to shell formation which would sustain the pressure upsurge and hence suppress atomization thereby reducing droplet gasification rate. Contrarily, the NPs may act as nucleation sites and aid boiling and the radiation absorption by NPs (from the flame) may lead to enhanced burning rates. Thus, nanoadditives may have opposing effects on the burning rate depending on the relative dominance of processes occurring at the droplet scale. The fundamental idea in this study is to: First, review different thermo-physical processes that occur globally at the droplet and sub-droplet scale such as surface regression, shell formation due to NP agglomeration, internal boiling, atomization/NP transport to flame zone and flame acoustic interaction that occur at the droplet scale and second, understand how their interaction changes as a function of droplet size, NP type, NP concentration and the type of base fuel. This understanding is crucial for obtaining phenomenological insights on the combustion behavior of novel nanofluid fuels that show great promise for becoming the next-generation fuels. (C) 2016 Elsevier Ltd. All rights reserved.

Low power consumption and high sensitivity carbon monoxide gas sensor using indium oxide nanowire.

In this paper, micro gas sensor was fabricated using indium oxide nanowire for effective gas detection and monitoring system. Indium oxide nanowire was grown using thermal CVD, and their structural properties were examined by the SEM, XRD and TEM. The electric properties for microdropped indium oxide nanowire device were measured, and gas response characteristics were examined for CO gas. Sensors showed high sensitivity and stability for CO gas. And with below 20 mw power consumption, 5 ppm CO could be detected.

Efectos de la política monetaria en un modelo de precios rígidos en distintas monedas

Resumen: En un modelo dinámico, de dos países y con precios rígidos, este trabajo analiza la transmisión de la política monetaria cuando las empresas fijan sus precios en distintas monedas. Siguiendo el modelo de Betts y Devereux (2000) suponemos que las empresas pueden fijar un único precio para el mercado local y extranjero en moneda del país al cual exportan. Algunas empresas segmentan el mercado por país y otras fijan un único precio en su propia moneda o en la del país vecino. Los precios rígidos en moneda del país vecino aumentan la variabilidad del tipo de cambio y reducen los efectos positivos que la política monetaria tiene sobre el consumo y la tasa de interés real, respecto a una situación donde las empresas sólo segmentan el mercado o fijan un único precio en su propia moneda. En ausencia de segmentación de mercado, a mayor número de empresas que fijen su precio en moneda del país vecino, mayor es el efecto positivo que un shock monetario en el país extranjero tiene sobre su bienestar y el del otro, pero es menor en ambos cuando se produce en el país local.

Fiscal Policy and Economic Stability: Does PIGS stand for Procyclicality In Government Spending?

The Financial Crisis has hit particularly hard countries like Ireland or Spain. Procyclical fiscal policy has contributed to a boom-bust cycle that undermined fiscal positions and deepened current account deficits during the boom. We set up an RBC model of a small open economy, following Mendoza (1991), and introduce the effect of fiscal policy decisions that change over the cycle. We calibrate the model on data for Ireland, and simulate the effect of different spending policies in response to supply shocks. Procyclical fiscal policy distorts intertemporal allocation decisions. Temporary spending boosts in booms spur investment, and hence the need for external finance, and so generates very volatile cycles in investment and the current account. This economic instability is also harmful for the steady state level of output. Our model is able to replicate the relation between the degree of cyclicality of fiscal policy, and the volatility of consumption, investment and the current account observed in OECD countries.

Smiling under stochastic volatility

This paper studies the behavior of the implied volatility function (smile) when the true distribution of the underlying asset is consistent with the stochastic volatility model proposed by Heston (1993). The main result of the paper is to extend previous results applicable to the smile as a whole to alternative degrees of moneyness. The conditions under which the implied volatility function changes whenever there is a change in the parameters associated with Hestons stochastic volatility model for a given degree of moneyness are given.

Autorregresive conditional volatility, skewness and kurtosis

This paper proposes a GARCH-type model allowing for time-varying volatility, skewness and kurtosis. The model is estimated assuming a Gram-Charlier series expansion of the normal density function for the error term, which is easier to estimate than the non-central t distribution proposed by Harvey and Siddique (1999). Moreover, this approach accounts for time-varying skewness and kurtosis while the approach by Harvey and Siddique (1999) only accounts for nonnormal skewness. We apply this method to daily returns of a variety of stock indices and exchange rates. Our results indicate a significant presence of conditional skewness and kurtosis. It is also found that specifications allowing for time-varying skewness and kurtosis outperform specifications with constant third and fourth moments.

Population age structure and private consumption in Spain

In this paper we study the effect of population age distribution upon private consumption expenditure in Spain from 1964 to 1997 using aggregate data. We obtain four main results. First, changes in the population pyramid have substantial effects upon the behaviour of private consumption. Second, the pattern of the coefficients of the demographic variables is not consistent with the simplest version of the life cycle hypothesis. Third, we estimate the impact of the demographic transition upon consumption and find positive values associated with episodes in which the shares of groups of individuals with expenditure levels higher (lower) than the mean increased (decreased). Fourth, the results are robust to alternative specifications for the population age distribution.

Data Revisions in the Estimation of DSGE Models

Revisions of US macroeconomic data are not white-noise. They are persistent, correlated with real-time data, and with high variability (around 80% of volatility observed in US real-time data). Their business cycle effects are examined in an estimated DSGE model extended with both real-time and final data. After implementing a Bayesian estimation approach, the role of both habit formation and price indexation fall significantly in the extended model. The results show how revision shocks of both output and inflation are expansionary because they occur when real-time published data are too low and the Fed reacts by cutting interest rates. Consumption revisions, by contrast, are countercyclical as consumption habits mirror the observed reduction in real-time consumption. In turn, revisions of the three variables explain 9.3% of changes of output in its long-run variance decomposition.

Analysis of volatility transmissions in integrated and interconnected markets: The case of the Iberian and French markets

This paper models the mean and volatility spillovers of prices within the integrated Iberian and the interconnected Spanish and French electricity markets. Using the constant (CCC) and dynamic conditional correlation (DCC) bivariate models with three different specifications of the univariate variance processes, we study the extent to which increasing interconnection and harmonization in regulation have favoured price convergence. The data consist of daily prices calculated as the arithmetic mean of the hourly prices over a span from July 1st 2007 until February 29th 2012. The DCC model in which the variances of the univariate processes are specified with a VARMA(1,1) fits the data best for the integrated MIBEL whereas a CCC model with a GARCH(1,1) specification for the univariate variance processes is selected to model the price series in Spain and France. Results show that there are significant mean and volatility spillovers in the MIBEL, indicating strong interdependence between the two markets, while there is a weaker evidence of integration between the Spanish and French markets. We provide new evidence that the EU target of achieving a single electricity market largely depends on increasing trade between countries and homogeneous rules of market functioning.

Consumption and portfolio rules whit stochastic hyperbolic discounting

We extend the classic Merton (1969, 1971) problem that investigates the joint consumption-savings and portfolio-selection problem under capital risk by assuming sophisticated but time-inconsistent agents. We introduce stochastic hyperbolic preferences as in Harris and Laibson (2013) and find closed-form solutions for Merton's optimal consumption and portfolio selection problem in continuous time. We find that the portfolio rule remains identical to the time-consistent solution with power utility and no borrowing constraints. However,the marginal propensity to consume out of wealth is unambiguously greater than the time-consistent, exponential case and,importantly, it is also more responsive to changes in risk. These results suggest that hyperbolic discounting with sophisticated agents offers promise for contributing to explaining important aspects of asset market data.