Types of Uncertainty

Pelz, Peter F.; Pfetsch, Marc E.; Kersting, Sebastian; Köhler, Michael; Matei, Alexander; Melz, Tobias; Platz, Roland; Schaeffner, M; Ulbrich, Stefan

doi:10.1007/978-3-030-78354-9_2

Cited by 5 publications

(4 citation statements)

References 56 publications

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“…The pdf is used to derive the tailored FAT class that corresponds to a user-defined and location-specific reliability level. In general, uncertainty can be separated into two categories: aleatoric uncertainty (being irreducible and objective, often used to describe intrinsic randomness of a phenomenon) and epistemic uncertainty (being reducible and subjective and is used to describe the uncertainties related to a, e.g., lack of knowledge, model simplifications, or incompleteness) [23].…”

Section: Methodsmentioning

confidence: 99%

Tailoring the Fatigue Detail Category Class: A Deterministic Implementation of a Probabilistic-Based Approach to Consequence- and Uncertainty-Informed Fatigue Life Prediction of Ships

Deul,

van Battum,

Hoogeland

2023

JMSE

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The fatigue life of ship structures is typically based on deterministic methods in which underlying uncertainties are only implicitly taken into account and not explicitly reflected. Guidance for a probabilistic assessment is provided in class documents, but the methodology is too time consuming to apply in design practice. This paper proposes a novel approach based on DNV-CG-0129 to incorporate uncertainties and consequences explicitly. Using a probabilistic model, tailored deterministic FAT classes are derived to be applied in design practice. A tailored FAT class should be selected based on an acceptable probability of failure related to the severity of the consequences of a failure for the ship. Results show that tailored FAT classes are strongly dependent on the uncertainties provided as input when using the calculation method of DNV-CG-0129. This emphasizes the need for careful consideration and specification of the uncertainties. Furthermore, application of the First Order Reliability Method for a sensitivity study shows that the global model uncertainty is governing over other uncertainties considered in DNV-CG-0129. The proposed approach enables a low-effort and transparent probabilistic-based method, leading to optimized and improved designs due to reduction of overdimensioning in non-critical areas.

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Section: Methodsmentioning

confidence: 99%

Tailoring the Fatigue Detail Category Class: A Deterministic Implementation of a Probabilistic-Based Approach to Consequence- and Uncertainty-Informed Fatigue Life Prediction of Ships

Deul,

van Battum,

Hoogeland

2023

JMSE

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“…However, in the real world, uncertainty is commonly encountered in everyday life [25]. It is inherent to any piece of information and thus also present in any dataset, data model, or visualization and has been studied in many scientific disciplines and academic fields [13], [26], [27], [33], [31]. Given the universal relevance of uncertainty, it is not surprising to find various notations and categorizations in different fields.…”

Section: Sets and Uncertaintymentioning

confidence: 99%

“…Given the universal relevance of uncertainty, it is not surprising to find various notations and categorizations in different fields. Terms like aleatoric uncertainty or stochastic uncertainty are relevant in mechanical engineering [31]. But also more common terms such as incertitude, probability, or ignorance appear in the context of uncertainty.…”

Section: Sets and Uncertaintymentioning

confidence: 99%

Visualizing Uncertainty in Sets

Tominski¹,

Behrisch²,

Bleisch³

et al. 2023

Preprint

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“…In automated systems these confidence problems concern measurements on the observation side as well modeling in the virtual side, see e.g. [26][27][28]. The next sections regard these two categories of confidence.…”

Section: Complex Proceduresmentioning

confidence: 99%

Strategies for managing models regarding environmental confidence and complexity involved in intelligent control of energy systems - A review

Razek¹

2023

AEE

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This assessment aims to analyze, illustrate, and examine the complexity and confidence problem associated with monitoring dynamic energy systems and managing them through model coupling and reduction. The confidence problem, which is related to the proximity of models to reality, can be reduced in general by considering neglected secondary domains by coupling their models to that of the main domain. Moreover, the complexity must be properly accounted for in the system models without decreasing the monitoring efficiency, which can be done through appropriate numerical model reduction techniques. In the article, after having posed the problem to be solved, we discussed and analyzed the automated procedures involved in energy systems. The notions of complexity and confidence in these systems are then illustrated and analyzed. In this framework, a complete coupled physical model reducing the confidence problem is then discussed and demonstrated. Model reduction strategies needed to optimize matching in automated procedures are then reviewed and analyzed. Finally, the pairing behavior of digital twins involving complex procedures is discussed and assessed, using a literature review. At the end of the paper, the case of electric and intelligent vehicles is discussed as an example of energy systems.

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Types of Uncertainty

Cited by 5 publications

References 56 publications

Tailoring the Fatigue Detail Category Class: A Deterministic Implementation of a Probabilistic-Based Approach to Consequence- and Uncertainty-Informed Fatigue Life Prediction of Ships

Tailoring the Fatigue Detail Category Class: A Deterministic Implementation of a Probabilistic-Based Approach to Consequence- and Uncertainty-Informed Fatigue Life Prediction of Ships

Visualizing Uncertainty in Sets

Strategies for managing models regarding environmental confidence and complexity involved in intelligent control of energy systems - A review

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