Problems of Interpretation and Experimental Data Assimilation

2008 ◽  
Vol 40 (7) ◽  
pp. 26-36
Author(s):  
Vyacheslav F. Gubarev
Keyword(s):  
Experimental Data ◽  
Data Assimilation

scholarly journals A Reduced Order Kalman Filter for Computational Fluid-Dynamics Applications

2018 ◽  
Author(s):  
Carolina Introini ◽  
Stefano Lorenzi ◽  
Antonio Cammi ◽  
Davide Baroli
Keyword(s):  
Fluid Dynamics ◽  
Experimental Data ◽  
Computational Fluid Dynamics ◽  
Kalman Filter ◽  
Numerical Model ◽  
Data Assimilation ◽  
Computational Cost ◽  
High Fidelity ◽  
Dimensional Manifold ◽  
Reduced Order

In the last decade, the importance of numerical simulations for the analysis of complex engineering systems, such as thermo-fluid dynamics in nuclear reactors, has grown exponentially. In spite of the large experimental databases available for validation of mathematical models, in order to identify the most suitable one for the system under investigation, the inverse integration of such data into the CFD model is nowadays an ongoing challenge. In addition, such integration could tackle the problem of propagation of epistemic uncertainties, both in the numerical model and in the experimental data. In this framework, the data-assimilation method allows for the dynamic incorporation of observations within the computational model. Perhaps the most famous among these methods, due to its simple implementation and yet robust nature, is the Kalman filter. Although this approach has found success in fields such as weather forecast and geoscience, its application in Computational Fluid-Dynamics (CFD) is still in its first stages. In this setting, a new algorithm based on the integration between the segregated approach, which is the most common method adopted by CFD applications for the solution of the incompressible Navier-Stokes equations, and a Kalman filter modified for fluid-dynamics problems, while preserving mass conservation of the solution, has already been developed and tested in a previous work. Whereas such method is able to robustly integrate experimental data within the numerical model, its computational cost increases with model complexity. In particular, in high-fidelity realistic scenarios the error covariance matrix for the model, which represents the uncertainties associated with it, becomes dense, thus affecting the efficiency and computational cost of the method. For this reason, due to the promised reduction of computational requirements recently investigated, which combines model reduction and data-assimilation, in this work a combination of reduced order model and mass-conservative Kalman filter within a segregated approach for CFD analysis is proposed. The novelty lies in the peculiar formulation of the Kalman filter and how to construct a low-dimensional manifold to approximate, with sufficient accuracy, the high fidelity model. With respect to literature, in which the full-order Kalman filter is applied to a reduced model, the reduction is performed directly on the integrated model in order to obtain a reduced-order Kalman filter already optimised for fluid-dynamics applications. In order to verify the capabilities of this approach, this reduced-order algorithm has been tested against the lid-driven cavity test case.

Improvement to Numerical Predictions of Aerodynamic Flows Using Experimental Data Assimilation

1999 ◽  
Vol 36 (3) ◽  
pp. 611-614 ◽  
Author(s):  
G. Barakos ◽  
D. Drikakis ◽  
W. Lefebre
Keyword(s):  
Experimental Data ◽  
Data Assimilation ◽  
Numerical Predictions ◽  
Aerodynamic Flows

Improvement to Numerical Predictions of Aerodynamic Flows Using Experimental Data Assimilation

1999 ◽  
Vol 36 (4) ◽  
pp. 736-736
Author(s):  
G. Barakos ◽  
D. Drikakis ◽  
W. Lefebre
Keyword(s):  
Experimental Data ◽  
Data Assimilation ◽  
Numerical Predictions ◽  
Aerodynamic Flows

Microdiffraction Patterns of Small Metallic Particles II; Theoretical Analysis

1982 ◽  
Vol 40 ◽  
pp. 668-669
Author(s):  
A. Gómez ◽  
P. Schabes-Retchkiman ◽  
M. José-Yacamán ◽  
T. Ocaña
Keyword(s):  
Experimental Data ◽  
Electron Diffraction ◽  
Theoretical Analysis ◽  
Size Range ◽  
Dynamical Theory ◽  
Metallic Particles ◽  
Splitting Effect

The splitting effect that is observed in microdiffraction pat-terns of small metallic particles in the size range 50-500 Å can be understood using the dynamical theory of electron diffraction for the case of a crystal containing a finite wedge. For the experimental data we refer to part I of this work in these proceedings.

Evidence for ordered Fe3Ni

1987 ◽  
Vol 45 ◽  
pp. 216-217
Author(s):  
K.B. Reuter ◽  
D.B. Williams ◽  
J.I. Goldstein
Keyword(s):  
Experimental Data ◽  
Martensitic Transformation ◽  
Electron Diffraction ◽  
Room Temperature ◽  
Direct Evidence ◽  
Transformation Product ◽  
Iron Meteorites ◽  
X Ray ◽  
Ni Content ◽  
Temperature Transformation

In the Fe-Ni system, although ordered FeNi and ordered Ni3Fe are experimentally well established, direct evidence for ordered Fe3Ni is unconvincing. Little experimental data for Fe3Ni exists because diffusion is sluggish at temperatures below 400°C and because alloys containing less than 29 wt% Ni undergo a martensitic transformation at room temperature. Fe-Ni phases in iron meteorites were examined in this study because iron meteorites have cooled at slow rates of about 10°C/106 years, allowing phase transformations below 400°C to occur. One low temperature transformation product, called clear taenite 2 (CT2), was of particular interest because it contains less than 30 wtZ Ni and is not martensitic. Because CT2 is only a few microns in size, the structure and Ni content were determined through electron diffraction and x-ray microanalysis. A Philips EM400T operated at 120 kV, equipped with a Tracor Northern 2000 multichannel analyzer, was used.

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