Modelling of the evolution of iron passivity: solving the moving boundaries problem

2012 ◽  
Vol 1475 ◽  
Author(s):  
Frantz A. Martin ◽  
Christian Bataillon
Keyword(s):  
Experimental Data ◽  
Corrosion Rate ◽  
Coupled Model ◽  
Oxide Thickness ◽  
Reaction Rates ◽  
Numerical Code ◽  
Fully Implicit ◽  
Geological Repository ◽  
Long Term Prediction

ABSTRACTIn the framework of long term prediction of corrosion in French geological repository systems, the modelling of the time evolution of the corrosion rate of iron over centuries is of high matter. The DPCM (Diffusion Poisson Coupled Model), implemented with the fully implicit CALIPSO numerical code can give access to the evolution of the oxide thickness grown on iron and the resulting corrosion rate. DPCM parameters for outer interfacial reactions have been set to fit to XANES experimental data provided by literature, and then the parameters for inner reaction rates have been evaluated from fitting to ellipsometry results taken from literature. The result is a good average fitting of the model to the experimental data.

scholarly journals LONG-TERM PREDICTION OF CASPIAN SEA LEVEL UNDER CMIP6 SCENARIOS USING ARTIFICIAL NEURAL NETWORKS

2020 ◽  
pp. 5
Author(s):  
S. Mahya Hoseini ◽  
Mohsen Soltanpour
Keyword(s):  
Sea Level ◽  
Caspian Sea ◽  
Coupled Model ◽  
Model Intercomparison ◽  
Climate Change Projections ◽  
Fluctuation Range ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Long Term Prediction

Artificial Neural Network (ANN) is employed to predict the long-term Caspian Sea level (CSL). 114-year observed CSL data (1900-2014) and the precipitation and temperature of historical and future scenarios of Coupled Model Intercomparison Phase 6 (CMIP6) are used to predict the future fluctuations of CSL (2015-2050). The values of the statistical indices in training, validating and testing periods (1900-2014) indicate the efficiency of the ANN in reconstruction of the CSL. Considering the outputs of different climate change projections (CMIP6) and excluding the human interventions, the study predicts the CSL fluctuation range of -28 m to -26m until 2050.Recorded Presentation from the vICCE (YouTube Link): https://youtu.be/Kfj-gr65TR8

scholarly journals A Wave Input-Reduction Method Incorporating Initiation of Sediment Motion

2020 ◽  
Vol 8 (8) ◽  
pp. 597
Author(s):  
Andreas Papadimitriou ◽  
Loukianos Panagopoulos ◽  
Michalis Chondros ◽  
Vasiliki Tsoukala
Keyword(s):  
Reduction Method ◽  
Coupled Model ◽  
Computational Effort ◽  
Acceleration Techniques ◽  
Subsequent Calculation ◽  
Bed Evolution ◽  
Long Term Prediction ◽  
Additional Reduction ◽  
Process Based Models

The long-term prediction of morphological bed evolution has been of interest to engineers and scientists for many decades. Usually, process-based models are employed to simulate bed-level changes in the scale of years to decades. To compensate for the major computational effort required by these models, various acceleration techniques have been developed, namely input-reduction, model-reduction and behaviour-oriented modelling. The present paper presents a new input-reduction method to obtain representative wave conditions based on the Shields criterion of incipient motion and subsequent calculation of the sediment pick-up rate. Elimination of waves unable to initiate sediment movement leads to additional reduction of model run-times. The proposed method was implemented in the sandy coastline adjusted to the port of Rethymno, Greece, and validated against two datasets consisting of 7 and 20 and 365 days, respectively, using the model MIKE21 Coupled Model FM. The method was compared with a well-established method of wave schematization and evaluation of the model’s skill deemed the simulations based on the pick-up rate schematization method as “excellent”. Additionally, a model run-time reduction of about 50% was observed, rendering this input-reduction method a valuable tool for the medium to long-term modelling of bed evolution.

An overview of simple basic equations used in HLW glass dissolution modeling: consequences for long term leaching and element profiles

2006 ◽  
Vol 932 ◽  
Author(s):  
Marc Aertsens
Keyword(s):  
Experimental Data ◽  
Analytical Solution ◽  
Water Solution ◽  
Numerical Code ◽  
Leach Rate ◽  
Glass Dissolution ◽  
Basic Assumptions ◽  
Basic Equations ◽  
The Relationship

ABSTRACTOver the last decades, several models describing glass dissolution have been published. Starting from the basic equations in their simplest form, the relationships between models are evaluated to address the following questions: 'What is the relationship between their basic assumptions?, What is the resulting long term leach rate? and, Which element profiles do they allow one to predict?' Although not part of it, this paper could complement the European project GLAMOR, where two models describing the dissolution of glass in a water solution, the r(t) and the GM models, are used by several groups to fit the same sets of experimental data. In this paper, other models are considered as well and all models are compared with each other.From comparison with the Boksay model, which uses the same equations, a simplification is suggested for the GM model concerning the water diffusion in the glass. The use of the numerical code developed to solve part of it, can mostly be avoided by using the analytical solution of the Boksay model.

Modelling of Moisture Movement in Wood during Outdoor Storage

2001 ◽  
Vol 6 (2) ◽  
pp. 3-14 ◽  
Author(s):  
R. Baronas ◽  
F. Ivanauskas ◽  
I. Juodeikienė ◽  
A. Kajalavičius
Keyword(s):  
Experimental Data ◽  
Finite Difference ◽  
Climatic Conditions ◽  
Two Dimensional ◽  
Moisture Movement ◽  
Finite Difference Technique ◽  
Long Term Storage ◽  
Space Formulation ◽  
Term Storage

A model of moisture movement in wood is presented in this paper in a two-dimensional-in-space formulation. The finite-difference technique has been used in order to obtain the solution of the problem. The model was applied to predict the moisture content in sawn boards from pine during long term storage under outdoor climatic conditions. The satisfactory agreement between the numerical solution and experimental data was obtained.

scholarly journals Novel robust time series analysis for long-term and short-term prediction

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hiroshi Okamura ◽  
Yutaka Osada ◽  
Shota Nishijima ◽  
Shinto Eguchi
Keyword(s):  
Robust Regression ◽  
Simulated Data ◽  
New Method ◽  
Short Term ◽  
Least Absolute Deviations ◽  
Term Prediction ◽  
Regression Approach ◽  
Short Term Prediction ◽  
Long Term Prediction

AbstractNonlinear phenomena are universal in ecology. However, their inference and prediction are generally difficult because of autocorrelation and outliers. A traditional least squares method for parameter estimation is capable of improving short-term prediction by estimating autocorrelation, whereas it has weakness to outliers and consequently worse long-term prediction. In contrast, a traditional robust regression approach, such as the least absolute deviations method, alleviates the influence of outliers and has potentially better long-term prediction, whereas it makes accurately estimating autocorrelation difficult and possibly leads to worse short-term prediction. We propose a new robust regression approach that estimates autocorrelation accurately and reduces the influence of outliers. We then compare the new method with the conventional least squares and least absolute deviations methods by using simulated data and real ecological data. Simulations and analysis of real data demonstrate that the new method generally has better long-term and short-term prediction ability for nonlinear estimation problems using spawner–recruitment data. The new method provides nearly unbiased autocorrelation even for highly contaminated simulated data with extreme outliers, whereas other methods fail to estimate autocorrelation accurately.

Transmitting Knowledge across Divides

2016 ◽  
Vol 46 (3) ◽  
pp. 313-359 ◽  
Author(s):  
Marta Jordi Taltavull
Keyword(s):  
Experimental Data ◽  
Quantum Mechanics ◽  
Quantum Theory ◽  
Scientific Understanding ◽  
Resonance Model ◽  
Optical Dispersion ◽  
History Of

One model, the resonance model, shaped scientific understanding of optical dispersion from the early 1870s to the 1920s, persisting across dramatic changes in physical conceptions of light and matter. I explore the ways in which the model was transmitted across these conceptual divides by analyzing the use of the model both in the development of theories of optical dispersion and in the interpretation of experimental data. Crucial to this analysis is the integration of the model into quantum theory because of the conceptual incompatibility between the model and quantum theory. What is more, a quantum understanding of optical dispersion set the grounds for the emergence of the first theories of quantum mechanics in 1925. A long-term history of the model’s transmission from the 1870s to the 1920s illuminates the ways in which the continuity of knowledge is possible across these discontinuities.

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