Quantifying Uncertainty in Subsurface Systems

Scheidt, Céline; Li, Lewis; Caers, Jef

doi:10.1002/9781119325888

Cited by 113 publications

(142 citation statements)

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“…In addition, it has to be emphasized that the impact of ASC complexity on social development can only be anticipated based on the theoretical assumption that higher organizational complexity, induced by a company's favorable development, could positively influence social sustainability. In such a case, it would be expected that organizational complexity is growing not only along various dimensions, such as production volume, but also with the increasing number of employees [40,41]. However, the related literature is more or less ambivalent about whether organizational complexity has positive or negative effects on firm performance [42].…”

Section: Discussion Of Results Implications and Limitationsmentioning

confidence: 99%

Complexity Assessment of Assembly Supply Chains from the Sustainability Viewpoint

2019

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Assembly supply chain systems are becoming increasingly complex and, as a result, there is more and more need to design and manage them in a way that benefits the producers and also satisfies the interests of community stakeholders. The structural (static) complexity of assembly supply chain networks is one of the most important factors influencing overall system complexity. Structures of such networks can be modeled as a graph, with machines as nodes and material flow between the nodes as links. The purpose of this paper is to analyze existing assembly supply chain complexity assessment methods and propose such complexity metric(s) that will be able to accurately reflect not only specific criteria for static complexity measures, but also selected sustainability aspects. The obtained results of this research showed that selected complexity indicators reflect sustainability facets in different ways, but one of them met the mentioned requirements acceptably.

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Section: Discussion Of Results Implications and Limitationsmentioning

confidence: 99%

Complexity Assessment of Assembly Supply Chains from the Sustainability Viewpoint

2019

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“…If an accurate prediction is difficult to obtain, one solution is to update the prior model in order to preferentially sample models in the area of the data. Such prior falsification approaches have been successfully applied in different contexts (Hermans et al, 2015a;Park et al, 2013;Scheidt et al, 2015aScheidt et al, , 2018.…”

Section: Discussionmentioning

confidence: 99%

Uncertainty Quantification of Medium‐Term Heat Storage From Short‐Term Geophysical Experiments Using Bayesian Evidential Learning

Hermans

Nguyen

Klepikova

et al. 2018

Water Resources Research

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In theory, aquifer thermal energy storage (ATES) systems can recover in winter the heat stored in the aquifer during summer to increase the energy efficiency of the system. In practice, the energy efficiency is often lower than expected from simulations due to spatial heterogeneity of hydraulic properties or non‐favorable hydrogeological conditions. A proper design of ATES systems should therefore consider the uncertainty of the prediction related to those parameters. We use a novel framework called Bayesian Evidential Learning (BEL) to estimate the heat storage capacity of an alluvial aquifer using a heat tracing experiment. BEL is based on two main stages: pre‐ and postfield data acquisition. Before data acquisition, Monte Carlo simulations and global sensitivity analysis are used to assess the information content of the data to reduce the uncertainty of the prediction. After data acquisition, prior falsification and machine learning based on the same Monte Carlo are used to directly assess uncertainty on key prediction variables from observations. The result is a full quantification of the posterior distribution of the prediction conditioned to observed data, without any explicit full model inversion. We demonstrate the methodology in field conditions and validate the framework using independent measurements.

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“…For instance, the quantification of epistemic uncertainties serves only as a guide to additional research or to a qualitative argument (i.e., a "safety case") about the possible long-term behavior of the repository. To ensure that the safety case approach meets this objective, some have recommended that the uncertainty quantification of sub-surface systems start by defining clearly and unambiguously all hypotheses (e.g., uncertainties on all parameters), a priori [20]. In the context of geological disposal systems, this corresponds to making explicit the safety strategy and its associated assumptions early in the development of a safety case.…”

Section: Discussionmentioning

confidence: 99%

Probabilistic Performance Assessment vs. the Safety Case Approach

Diaz-Maurin

Ewing

2019

MRS Advances

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The “safety case” approach has been developed to address the issue of evaluating the performance of a geologic repository in the face of the large uncertainty that results for evaluations that extend over hundreds of thousands of years. This paper reviews the concept of the safety case as it has been defined by the international community. We contrast the safety case approach with that presently used in the U.S. repository program. Especially, we focus on the role of uncertainty quantification. There are inconsistencies between the initial proposal to dealing with uncertainties in a safety case and current U.S. practice. The paper seeks to better define the safety case concept so that it can be usefully applied to the regulatory framework of the U.S. repository program.

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Quantifying Uncertainty in Subsurface Systems

Cited by 113 publications

References 0 publications

Complexity Assessment of Assembly Supply Chains from the Sustainability Viewpoint

Complexity Assessment of Assembly Supply Chains from the Sustainability Viewpoint

Uncertainty Quantification of Medium‐Term Heat Storage From Short‐Term Geophysical Experiments Using Bayesian Evidential Learning

Probabilistic Performance Assessment vs. the Safety Case Approach

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