Bayesian calibration of thermodynamic parameters for geochemical speciation modeling of cementitious materials

Sarkar, Sohini; Kosson, David S.; Mahadevan, Sankaran; Meeussen, J.C.L.; Sloot, H. van der; Arnold, J.; Brown, Kevin G.

doi:10.1016/j.cemconres.2012.02.004

Cited by 22 publications

(17 citation statements)

References 35 publications

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“…Recent applications of the model include coupling of ORCHESTRA with LeachXS database to improve understanding and prediction of the long-term performance of cementitious barriers used in nuclear applications. This thermo-hydro-mechanical (THM) coupling has also been used to assess long-term release behavior of sulfidic mining wastes [134], carbonation-induced corrosion in highlevel waste tanks [135], and uncertainty quantification of thermodynamic parameters [136]. More information on code development and example applications is available at http://www.macaulay.ac.uk/ORCHESTRA/.…”

Section: Orchestramentioning

confidence: 99%

Reactive transport codes for subsurface environmental simulation

et al. 2014

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A general description of the mathematical and numerical formulations used in modern numerical reactive transport codes relevant for subsurface environmental simulations is presented. The formulations are followed by short descriptions of commonly used and available subsurface simulators that consider continuum representations of flow, transport, and reactions in porous media. These formulations are applicable to most of the subsurface environmental benchmark problems included in this special issue. The list of codes described briefly here includes PHREEQC, HPx, PHT3D, OpenGeoSys (OGS), HYTEC, ORCHESTRA, TOUGHREACT, eSTOMP, HYDROGEOCHEM, CrunchFlow, MIN3P, and PFLOTRAN. The descriptions include a C. I. Steefel ( ) · B. Arora · S. Molins · N. Spycher

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Section: Orchestramentioning

confidence: 99%

Reactive transport codes for subsurface environmental simulation

et al. 2014

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“…However, this additional complexity seemed unnecessary in our case, since we had a large and comprehensive training set from which to construct the ROM. If we had had a limited training set, as in [24], a Bayesian construction of the ROM would have been warranted.…”

Section: Discussionmentioning

confidence: 99%

Bayesian inference of substrate properties from film behavior

Aggarwal¹,

Demkowicz²,

Marzouk³

2014

Modelling Simul. Mater. Sci. Eng.

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Abstract.We demonstrate that, by observing the behavior of a film deposited on a substrate, certain features of the substrate may be inferred with quantified uncertainty using Bayesian methods. We carry out this demonstration on an illustrative film/substrate model, where the substrate is a Gaussian random field and the film is a two-component mixture that obeys the Cahn-Hilliard equation. We construct a stochastic reduced order model to describe the film/substrate interaction and use it to infer substrate properties from film behavior. This quantitative inference strategy may be adapted to other film/substrate systems.

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“…hyperelastic constitutive models for soft tissue [28], phenomenological models for tumour growth [29], models for damage progression in composites due to fatigue [30] and fatigue models for metals [31]. Sarkar et al [32] used the Bayesian method to identify thermodynamical parameters of cementitious materials. BI is also used in the fields of heat transfer and 55 fluid mechanics for inverse problems [5,33].…”

Section: Introductionmentioning

confidence: 99%