The Experts below are selected from a list of 28677 Experts worldwide ranked by ideXlab platform
Jurgen Schmiegel - One of the best experts on this subject based on the ideXlab platform.
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Stochastic Intermittency Fields in a von Kármán Experiment
'MDPI AG', 2021Co-Authors: Jurgen Schmiegel, Flavio PonsAbstract:We discuss the application of stochastic Intermittency fields to describe and analyse the statistical properties of time series of the generalised turbulence intensity in an anisotropic and inhomogeneous turbulent flow and provide a parsimonious description of the one-, two-, and three-point statistics. In particular, we show that the three-point correlations can be predicted from observed two-point statistics. Our analysis is motivated by observed stylised features of the energy dissipation in homogeneous and isotropic situations where these statistical properties are well represented within the framework of stochastic Intermittency fields. We find a close resemblance and conclude that stochastic Intermittency fields may be relevant in more general situations
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assessing relative volatility Intermittency energy dissipation
Electronic Journal of Statistics, 2014Co-Authors: Ole E Barndorffnielsen, Mikko S Pakkanen, Jurgen SchmiegelAbstract:We introduce the notion of relative volatility/Intermittency and demonstrate how relative volatility statistics can be used to estimate consistently the temporal variation of volatility/Intermittency when the data of interest are generated by a non-semimartingale, or a Brownian semistationary process in particular. This estimation method is motivated by the assessment of relative energy dissipation in empirical data of turbulence, but it is also applicable in other areas. We develop a probabilistic asymptotic theory for realised relative power variations of Brownian semistationary processes, and introduce inference methods based on the theory. We also discuss how to extend the asymptotic theory to other classes of processes exhibiting stochastic volatility/Intermittency. As an empirical application, we study relative energy dissipation in data of atmospheric turbulence.
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assessing relative volatility Intermittency energy dissipation
CREATES Research Papers, 2013Co-Authors: Ole E Barndorffnielsen, Mikko S Pakkanen, Jurgen SchmiegelAbstract:We introduce the notion of relative volatility/Intermittency and demonstrate how relative volatility statistics can be used to estimate consistently the temporal variation of volatility/Intermittency even when the data of interest are generated by a non-semimartingale, or a Brownian semistationary process in particular. While this estimation method is motivated by the assessment of relative energy dissipation in empirical data of turbulence, we apply it also to energy price data. Moreover, we develop a probabilistic asymptotic theory for relative power variations of Brownian semistationary processes and Ito semimartingales and discuss how it can be used for inference on relative volatility/Intermittency.
Ole E Barndorffnielsen - One of the best experts on this subject based on the ideXlab platform.
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assessing relative volatility Intermittency energy dissipation
Electronic Journal of Statistics, 2014Co-Authors: Ole E Barndorffnielsen, Mikko S Pakkanen, Jurgen SchmiegelAbstract:We introduce the notion of relative volatility/Intermittency and demonstrate how relative volatility statistics can be used to estimate consistently the temporal variation of volatility/Intermittency when the data of interest are generated by a non-semimartingale, or a Brownian semistationary process in particular. This estimation method is motivated by the assessment of relative energy dissipation in empirical data of turbulence, but it is also applicable in other areas. We develop a probabilistic asymptotic theory for realised relative power variations of Brownian semistationary processes, and introduce inference methods based on the theory. We also discuss how to extend the asymptotic theory to other classes of processes exhibiting stochastic volatility/Intermittency. As an empirical application, we study relative energy dissipation in data of atmospheric turbulence.
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assessing relative volatility Intermittency energy dissipation
CREATES Research Papers, 2013Co-Authors: Ole E Barndorffnielsen, Mikko S Pakkanen, Jurgen SchmiegelAbstract:We introduce the notion of relative volatility/Intermittency and demonstrate how relative volatility statistics can be used to estimate consistently the temporal variation of volatility/Intermittency even when the data of interest are generated by a non-semimartingale, or a Brownian semistationary process in particular. While this estimation method is motivated by the assessment of relative energy dissipation in empirical data of turbulence, we apply it also to energy price data. Moreover, we develop a probabilistic asymptotic theory for relative power variations of Brownian semistationary processes and Ito semimartingales and discuss how it can be used for inference on relative volatility/Intermittency.
Mikko S Pakkanen - One of the best experts on this subject based on the ideXlab platform.
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assessing relative volatility Intermittency energy dissipation
Electronic Journal of Statistics, 2014Co-Authors: Ole E Barndorffnielsen, Mikko S Pakkanen, Jurgen SchmiegelAbstract:We introduce the notion of relative volatility/Intermittency and demonstrate how relative volatility statistics can be used to estimate consistently the temporal variation of volatility/Intermittency when the data of interest are generated by a non-semimartingale, or a Brownian semistationary process in particular. This estimation method is motivated by the assessment of relative energy dissipation in empirical data of turbulence, but it is also applicable in other areas. We develop a probabilistic asymptotic theory for realised relative power variations of Brownian semistationary processes, and introduce inference methods based on the theory. We also discuss how to extend the asymptotic theory to other classes of processes exhibiting stochastic volatility/Intermittency. As an empirical application, we study relative energy dissipation in data of atmospheric turbulence.
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assessing relative volatility Intermittency energy dissipation
CREATES Research Papers, 2013Co-Authors: Ole E Barndorffnielsen, Mikko S Pakkanen, Jurgen SchmiegelAbstract:We introduce the notion of relative volatility/Intermittency and demonstrate how relative volatility statistics can be used to estimate consistently the temporal variation of volatility/Intermittency even when the data of interest are generated by a non-semimartingale, or a Brownian semistationary process in particular. While this estimation method is motivated by the assessment of relative energy dissipation in empirical data of turbulence, we apply it also to energy price data. Moreover, we develop a probabilistic asymptotic theory for relative power variations of Brownian semistationary processes and Ito semimartingales and discuss how it can be used for inference on relative volatility/Intermittency.
El Hassane Aglzim - One of the best experts on this subject based on the ideXlab platform.
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Game model to optimally combine electric vehicles with green and non-green sources into an end-to-end smart grid architecture
Journal of Network and Computer Applications, 2017Co-Authors: Mohamed Attia, Hichem Sedjelmaci, Sidi Mohammed Senouci, El Hassane AglzimAbstract:The integration of information and communication technologies into the Smart Grid (SG) will make it smarter to provide a more efficient power delivery, economically viable and safe. In fact, electrical systems should be controlled in more flexible way to manage critical situations such as the Intermittency of renewable energy and the development of new consumers like electric vehicles (EVs). In this paper, we first propose an end-to-end SG architecture with all its associated actors such as main production, transmission, distribution, SG and cyber-security managers. In contrast to the literature, this architecture takes into account both the power and communication aspects of the SG as well as the relationship between all its components. Then, we focus on EVs as prosumers (producers and consumers thanks to their energy storage capacity) and how integrate them efficiently into the power grid. Hence, we propose in a first phase a Bayesian game-theory model that aims to integrate them into the SG and maintain the equilibrium between the offer and the demand, hence avoiding electricity intermittence. In a second phase, we concentrate on EVs as consumers and propose a new Bayesian game model to optimally integrate green electricity sources into this electricity network in order to charge EVs’ batteries. This aims to promote the electricity network capacity and have an eco-friendly effect on the environment by minimizing the usage of polluting energy sources. The obtained simulation results prove that our models help to efficiently integrate the EVs into the SG and use green electricity sources with more flexibility and less loss.
Grzegorz Kowal - One of the best experts on this subject based on the ideXlab platform.
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velocity field of compressible magnetohydrodynamic turbulence wavelet decomposition and mode scalings
The Astrophysical Journal, 2010Co-Authors: Grzegorz Kowal, A LazarianAbstract:We study compressible magnetohydrodynamic turbulence, which holds the key to many astrophysical processes, including star formation and cosmic-ray propagation. To account for the variations of the magnetic field in the strongly turbulent fluid, we use wavelet decomposition of the turbulent velocity field into Alfven, slow, and fast modes, which presents an extension of the Cho & Lazarian decomposition approach based on Fourier transforms. The wavelets allow us to follow the variations of the local direction of the magnetic field and therefore improve the quality of the decomposition compared to the Fourier transforms, which are done in the mean field reference frame. For each resulting component, we calculate the spectra and two-point statistics such as longitudinal and transverse structure functions as well as higher order Intermittency statistics. In addition, we perform a Helmholtz- Hodge decomposition of the velocity field into incompressible and compressible parts and analyze these components. We find that the turbulence Intermittency is different for different components, and we show that the Intermittency statistics depend on whether the phenomenon was studied in the global reference frame related to the mean magnetic field or in the frame defined by the local magnetic field. The dependencies of the measures we obtained are different for different components of the velocity; for instance, we show that while the Alfven mode Intermittency changes marginally with the Mach number, the Intermittency of the fast mode is substantially affected by the change.