The Experts below are selected from a list of 80427 Experts worldwide ranked by ideXlab platform
Giovanni Quattrocchi - One of the best experts on this subject based on the ideXlab platform.
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data assimilative twin experiment in a high resolution bay of biscay configuration 4denoi based on Stochastic Modeling of the wind forcing
Ocean Modelling, 2016Co-Authors: V Vervatis, Charles-emmanuel Testut, J Chanut, Nadia Ayoub, Giovanni QuattrocchiAbstract:Abstract A twin-experiment is carried out introducing elements of an Ensemble Kalman Filter (EnKF), to assess and correct ocean uncertainties in a high-resolution Bay of Biscay configuration. Initially, an ensemble of 102 members is performed by applying Stochastic Modeling of the wind forcing. The target of this step is to simulate the envelope of possible realizations and to explore the robustness of the method at building ensemble covariances. Our second step includes the integration of the ensemble-based error estimates into a data assimilative system adopting a 4D Ensemble Optimal Interpolation (4DEnOI) approach. In the twin-experiment context, synthetic observations are simulated from a perturbed member not used in the subsequent analyses, satisfying the condition of an unbiased probability distribution function against the ensemble by performing a rank histogram. We evaluate the assimilation performance on short-term predictability focusing on the ensemble size, the observational network, and the enrichment of the ensemble by inexpensive time-lagged techniques. The results show that variations in performance are linked to intrinsic oceanic processes, such as the spring shoaling of the thermocline, in combination with external forcing modulated by river runoffs and time-variable wind patterns, constantly reshaping the error regimes. Ensemble covariances are able to capture high-frequency processes associated with coastal density fronts, slope currents and upwelling events near the Armorican and Galician shelf break. Further improvement is gained when enriching model covariances by including pattern phase errors, with the help of time-neighbor states augmenting the ensemble spread.
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data assimilative twin experiment in a high resolution bay of biscay configuration 4denoi based on Stochastic Modeling of the wind forcing
Ocean Modelling, 2016Co-Authors: V Vervatis, Charles-emmanuel Testut, J Chanut, Nadia Ayoub, Giovanni QuattrocchiAbstract:Abstract A twin-experiment is carried out introducing elements of an Ensemble Kalman Filter (EnKF), to assess and correct ocean uncertainties in a high-resolution Bay of Biscay configuration. Initially, an ensemble of 102 members is performed by applying Stochastic Modeling of the wind forcing. The target of this step is to simulate the envelope of possible realizations and to explore the robustness of the method at building ensemble covariances. Our second step includes the integration of the ensemble-based error estimates into a data assimilative system adopting a 4D Ensemble Optimal Interpolation (4DEnOI) approach. In the twin-experiment context, synthetic observations are simulated from a perturbed member not used in the subsequent analyses, satisfying the condition of an unbiased probability distribution function against the ensemble by performing a rank histogram. We evaluate the assimilation performance on short-term predictability focusing on the ensemble size, the observational network, and the enrichment of the ensemble by inexpensive time-lagged techniques. The results show that variations in performance are linked to intrinsic oceanic processes, such as the spring shoaling of the thermocline, in combination with external forcing modulated by river runoffs and time-variable wind patterns, constantly reshaping the error regimes. Ensemble covariances are able to capture high-frequency processes associated with coastal density fronts, slope currents and upwelling events near the Armorican and Galician shelf break. Further improvement is gained when enriching model covariances by including pattern phase errors, with the help of time-neighbor states augmenting the ensemble spread.
Aimy Bazylak - One of the best experts on this subject based on the ideXlab platform.
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Stochastic Modeling of polymer electrolyte membrane fuel cell gas diffusion layers part 1 physical characterization
International Journal of Hydrogen Energy, 2017Co-Authors: James Hinebaugh, Aimy BazylakAbstract:Abstract Stochastic Modeling of GDL structures requires a detailed characterization of the constituent elements of the material. In this work, a variety of imaging methods, including optical microscopy, microscale computed tomography, and scanning electron microscopy were used to characterize seven commercially available gas diffusion layers (GDLs). The result is a catalogue of the following geometrical characteristics: fiber diameter, fiber pitch and co-alignment, areal weight and volume, and microporous layer (MPL) crack size and frequency. This catalogue, when combined with previous GDL characterizations, is expected to provide enough information to create representative, predictive, Stochastic models of the GDL.
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Stochastic Modeling of polymer electrolyte membrane fuel cell gas diffusion layers part 2 a comprehensive substrate model with pore size distribution and heterogeneity effects
International Journal of Hydrogen Energy, 2017Co-Authors: James Hinebaugh, Jeff T Gostick, Aimy BazylakAbstract:Abstract A Stochastic Modeling algorithm was developed that accounts for porosity distribution, fiber diameter, fiber co-alignment, fiber pitch, and binder and/or polytetrafluorethylene fractions. Materials representative of a commercially available GDL were digitally generated based on empirical measurements of these various properties. Materials made with varying fiber diameters and binder/fiber volume ratios were compared with a generated reference material through porosity heterogeneity calculations and mercury intrusion porosimetry simulations. Fiber diameters and binder/fiber ratios were found to be key Modeling parameters that exhibited non-negligible impacts on the pore space. These key parameters were found to positively correlate with heterogeneity and mean pore diameter and exhibit a complementary relationship in their impact on the pore space. Because both parameters directly impacted the number of fibers added to the domain, Modeling techniques and parameters pertaining to fiber count must be considered carefully.
V Vervatis - One of the best experts on this subject based on the ideXlab platform.
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data assimilative twin experiment in a high resolution bay of biscay configuration 4denoi based on Stochastic Modeling of the wind forcing
Ocean Modelling, 2016Co-Authors: V Vervatis, Charles-emmanuel Testut, J Chanut, Nadia Ayoub, Giovanni QuattrocchiAbstract:Abstract A twin-experiment is carried out introducing elements of an Ensemble Kalman Filter (EnKF), to assess and correct ocean uncertainties in a high-resolution Bay of Biscay configuration. Initially, an ensemble of 102 members is performed by applying Stochastic Modeling of the wind forcing. The target of this step is to simulate the envelope of possible realizations and to explore the robustness of the method at building ensemble covariances. Our second step includes the integration of the ensemble-based error estimates into a data assimilative system adopting a 4D Ensemble Optimal Interpolation (4DEnOI) approach. In the twin-experiment context, synthetic observations are simulated from a perturbed member not used in the subsequent analyses, satisfying the condition of an unbiased probability distribution function against the ensemble by performing a rank histogram. We evaluate the assimilation performance on short-term predictability focusing on the ensemble size, the observational network, and the enrichment of the ensemble by inexpensive time-lagged techniques. The results show that variations in performance are linked to intrinsic oceanic processes, such as the spring shoaling of the thermocline, in combination with external forcing modulated by river runoffs and time-variable wind patterns, constantly reshaping the error regimes. Ensemble covariances are able to capture high-frequency processes associated with coastal density fronts, slope currents and upwelling events near the Armorican and Galician shelf break. Further improvement is gained when enriching model covariances by including pattern phase errors, with the help of time-neighbor states augmenting the ensemble spread.
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data assimilative twin experiment in a high resolution bay of biscay configuration 4denoi based on Stochastic Modeling of the wind forcing
Ocean Modelling, 2016Co-Authors: V Vervatis, Charles-emmanuel Testut, J Chanut, Nadia Ayoub, Giovanni QuattrocchiAbstract:Abstract A twin-experiment is carried out introducing elements of an Ensemble Kalman Filter (EnKF), to assess and correct ocean uncertainties in a high-resolution Bay of Biscay configuration. Initially, an ensemble of 102 members is performed by applying Stochastic Modeling of the wind forcing. The target of this step is to simulate the envelope of possible realizations and to explore the robustness of the method at building ensemble covariances. Our second step includes the integration of the ensemble-based error estimates into a data assimilative system adopting a 4D Ensemble Optimal Interpolation (4DEnOI) approach. In the twin-experiment context, synthetic observations are simulated from a perturbed member not used in the subsequent analyses, satisfying the condition of an unbiased probability distribution function against the ensemble by performing a rank histogram. We evaluate the assimilation performance on short-term predictability focusing on the ensemble size, the observational network, and the enrichment of the ensemble by inexpensive time-lagged techniques. The results show that variations in performance are linked to intrinsic oceanic processes, such as the spring shoaling of the thermocline, in combination with external forcing modulated by river runoffs and time-variable wind patterns, constantly reshaping the error regimes. Ensemble covariances are able to capture high-frequency processes associated with coastal density fronts, slope currents and upwelling events near the Armorican and Galician shelf break. Further improvement is gained when enriching model covariances by including pattern phase errors, with the help of time-neighbor states augmenting the ensemble spread.
James Hinebaugh - One of the best experts on this subject based on the ideXlab platform.
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Stochastic Modeling of polymer electrolyte membrane fuel cell gas diffusion layers part 1 physical characterization
International Journal of Hydrogen Energy, 2017Co-Authors: James Hinebaugh, Aimy BazylakAbstract:Abstract Stochastic Modeling of GDL structures requires a detailed characterization of the constituent elements of the material. In this work, a variety of imaging methods, including optical microscopy, microscale computed tomography, and scanning electron microscopy were used to characterize seven commercially available gas diffusion layers (GDLs). The result is a catalogue of the following geometrical characteristics: fiber diameter, fiber pitch and co-alignment, areal weight and volume, and microporous layer (MPL) crack size and frequency. This catalogue, when combined with previous GDL characterizations, is expected to provide enough information to create representative, predictive, Stochastic models of the GDL.
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Stochastic Modeling of polymer electrolyte membrane fuel cell gas diffusion layers part 2 a comprehensive substrate model with pore size distribution and heterogeneity effects
International Journal of Hydrogen Energy, 2017Co-Authors: James Hinebaugh, Jeff T Gostick, Aimy BazylakAbstract:Abstract A Stochastic Modeling algorithm was developed that accounts for porosity distribution, fiber diameter, fiber co-alignment, fiber pitch, and binder and/or polytetrafluorethylene fractions. Materials representative of a commercially available GDL were digitally generated based on empirical measurements of these various properties. Materials made with varying fiber diameters and binder/fiber volume ratios were compared with a generated reference material through porosity heterogeneity calculations and mercury intrusion porosimetry simulations. Fiber diameters and binder/fiber ratios were found to be key Modeling parameters that exhibited non-negligible impacts on the pore space. These key parameters were found to positively correlate with heterogeneity and mean pore diameter and exhibit a complementary relationship in their impact on the pore space. Because both parameters directly impacted the number of fibers added to the domain, Modeling techniques and parameters pertaining to fiber count must be considered carefully.
Charles-emmanuel Testut - One of the best experts on this subject based on the ideXlab platform.
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data assimilative twin experiment in a high resolution bay of biscay configuration 4denoi based on Stochastic Modeling of the wind forcing
Ocean Modelling, 2016Co-Authors: V Vervatis, Charles-emmanuel Testut, J Chanut, Nadia Ayoub, Giovanni QuattrocchiAbstract:Abstract A twin-experiment is carried out introducing elements of an Ensemble Kalman Filter (EnKF), to assess and correct ocean uncertainties in a high-resolution Bay of Biscay configuration. Initially, an ensemble of 102 members is performed by applying Stochastic Modeling of the wind forcing. The target of this step is to simulate the envelope of possible realizations and to explore the robustness of the method at building ensemble covariances. Our second step includes the integration of the ensemble-based error estimates into a data assimilative system adopting a 4D Ensemble Optimal Interpolation (4DEnOI) approach. In the twin-experiment context, synthetic observations are simulated from a perturbed member not used in the subsequent analyses, satisfying the condition of an unbiased probability distribution function against the ensemble by performing a rank histogram. We evaluate the assimilation performance on short-term predictability focusing on the ensemble size, the observational network, and the enrichment of the ensemble by inexpensive time-lagged techniques. The results show that variations in performance are linked to intrinsic oceanic processes, such as the spring shoaling of the thermocline, in combination with external forcing modulated by river runoffs and time-variable wind patterns, constantly reshaping the error regimes. Ensemble covariances are able to capture high-frequency processes associated with coastal density fronts, slope currents and upwelling events near the Armorican and Galician shelf break. Further improvement is gained when enriching model covariances by including pattern phase errors, with the help of time-neighbor states augmenting the ensemble spread.
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data assimilative twin experiment in a high resolution bay of biscay configuration 4denoi based on Stochastic Modeling of the wind forcing
Ocean Modelling, 2016Co-Authors: V Vervatis, Charles-emmanuel Testut, J Chanut, Nadia Ayoub, Giovanni QuattrocchiAbstract:Abstract A twin-experiment is carried out introducing elements of an Ensemble Kalman Filter (EnKF), to assess and correct ocean uncertainties in a high-resolution Bay of Biscay configuration. Initially, an ensemble of 102 members is performed by applying Stochastic Modeling of the wind forcing. The target of this step is to simulate the envelope of possible realizations and to explore the robustness of the method at building ensemble covariances. Our second step includes the integration of the ensemble-based error estimates into a data assimilative system adopting a 4D Ensemble Optimal Interpolation (4DEnOI) approach. In the twin-experiment context, synthetic observations are simulated from a perturbed member not used in the subsequent analyses, satisfying the condition of an unbiased probability distribution function against the ensemble by performing a rank histogram. We evaluate the assimilation performance on short-term predictability focusing on the ensemble size, the observational network, and the enrichment of the ensemble by inexpensive time-lagged techniques. The results show that variations in performance are linked to intrinsic oceanic processes, such as the spring shoaling of the thermocline, in combination with external forcing modulated by river runoffs and time-variable wind patterns, constantly reshaping the error regimes. Ensemble covariances are able to capture high-frequency processes associated with coastal density fronts, slope currents and upwelling events near the Armorican and Galician shelf break. Further improvement is gained when enriching model covariances by including pattern phase errors, with the help of time-neighbor states augmenting the ensemble spread.
