2015
DOI: 10.1137/140960499
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Goal-Oriented A Posteriori Error Estimation For The Travel Time Functional In Porous Media Flows

Abstract: Abstract. In this article we consider the a posteriori error estimation and adaptive mesh refinement for the numerical approximation of the travel time functional arising in porous media flows. The key application of this work is in the safety assessment of radioactive waste facilities; in this setting, the travel time functional measures the time taken for a non-sorbing radioactive solute, transported by groundwater, to travel from a potential site deep underground to the biosphere. To ensure the computabilit… Show more

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Cited by 11 publications
(28 citation statements)
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References 46 publications
(77 reference statements)
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“…Three numerical experiments are conducted in Section 4: two simple, academic-style examples aim to build confidence in the proposed a posteriori error estimate, while the last one adaptively simulates the leakage of radioactive waste within a domain inspired by the (albeit greatly simplified) Sellafield site, located in Cumbria, in the UK. This final, physically motivated example, matches the experiment conducted in [25] but uses the new linearisation result instead. Lastly, some concluding remarks are discussed in Section 5.…”
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confidence: 70%
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“…Three numerical experiments are conducted in Section 4: two simple, academic-style examples aim to build confidence in the proposed a posteriori error estimate, while the last one adaptively simulates the leakage of radioactive waste within a domain inspired by the (albeit greatly simplified) Sellafield site, located in Cumbria, in the UK. This final, physically motivated example, matches the experiment conducted in [25] but uses the new linearisation result instead. Lastly, some concluding remarks are discussed in Section 5.…”
mentioning
confidence: 70%
“…To each of the sub-domains we assign a fixed, constant, permeability and porosity (cf. Example III), given by the dataset used in [25].…”
Section: Example IImentioning
confidence: 99%
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