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Uncertainty quantification of the wall thickness and stiffness in an idealized dissected aorta

Gheysen, Lise (author), Maes, Lauranne (author), Caenen, Annette (author), Segers, Patrick (author), Peirlinck, M. (author), Famaey, Nele (author)

Personalized treatment informed by computational models has the potential to markedly improve the outcome for patients with a type B aortic dissection. However, existing computational models of dissected walls significantly simplify the characteristic false lumen, tears and/or material behavior. Moreover, the patient-specific wall thickness...

journal article 2024

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A Bayesian defect-based physics-guided neural network model for probabilistic fatigue endurance limit evaluation

Tognan, A. (author), Patanè, Andrea (author), Laurenti, L. (author), Salvati, Enrico (author)

Accurate fatigue assessment of material plagued by defects is of utmost importance to guarantee safety and service continuity in engineering components. This study shows how state-of-the-art semi-empirical models can be endowed with additional defect descriptors to probabilistically predict the occurrence of fatigue failures by exploiting...

journal article 2024

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Unravelling uncertainty in trajectory prediction using a non-parametric approach

Li, G. (author), Li, Zirui (author), Knoop, V.L. (author), van Lint, J.W.C. (author)

Predicting the trajectories of road agents is fundamental for self-driving cars. Trajectory prediction contains many sources of uncertainty in data and modelling. A thorough understanding of this uncertainty is crucial in a safety-critical task like auto-piloting a vehicle. In practice, it is necessary to distinguish between the uncertainty...

journal article 2024

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How Far Ahead Should Autonomous Vehicles Start Resolving Predicted Conflicts? Exploring Uncertainty-Based Safety-Efficiency Trade-Off

Li, G. (author), Li, Zirui (author), Knoop, V.L. (author), van Lint, J.W.C. (author)

Resolving predicted conflicts is vital for safe and efficient autonomous vehicles (AV). In practice, vehicular motion prediction faces inherent uncertainty due to heterogeneous driving behaviours and environments. This spatial uncertainty increases non-linearly with prediction time horizons, leading AVs to perceive more road space occupied by...

journal article 2024

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Robust topology optimization considering part distortion and process variability in additive manufacturing

Komini, Ludian (author), Langelaar, Matthijs (author), Kriegesmann, Benedikt (author)

This paper presents a method to consider uncertainties in the distortion prediction of additive manufacturing processes within robust topology optimization. The random variable of the stochastic additive manufacturing process is the inherent thermomechanical strain, typically determined by process characterization experiments. The value of...

journal article 2023

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Sensitivity analysis for buckling characterisation using the vibration correlation technique

Baciu, Theodor D. (author), Degenhardt, Richard (author), Franzoni, Felipe (author), Gliszczynski, Adrian (author), Arbelo, Mariano A. (author), Castro, Saullo G.P. (author), Kalnins, Kaspars (author)

The Vibration Correlation Technique (VCT) is a non-destructive method to predict buckling loads for imperfection-sensitive structures. While successfully used to validate numerical models and predict experimental buckling loads, recommendations for defining the VCT experiment are scarce. Here, its sensitivity towards the number of load steps...

journal article 2023

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A View on Model Misspecification in Uncertainty Quantification

Kato, Y. (author), Tax, D.M.J. (author), Loog, M. (author)

Estimating uncertainty of machine learning models is essential to assess the quality of the predictions that these models provide. However, there are several factors that influence the quality of uncertainty estimates, one of which is the amount of model misspecification. Model misspecification always exists as models are mere simplifications...

conference paper 2023

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Generation of design allowables on the open hole tensile strength of composite laminates using sensitivity analysis and uncertainty quantification framework

Sasikumar, Aravind (author), Ninyerola, Joan (author), Ruiz, Ivan (author), Bessa, M.A. (author), Turon Travesa, Albert (author)

Aeronautical industries are concerned about the cost effective generation of design allowables for composite laminates. Design allowables take into account the variabilities arising from different sources (material, manufacturing, defects etc.,) which are determined using expensive and time consuming experimental campaigns....

conference paper 2022

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Uncertainty quantification of the lifetime of self-healing thermal barrier coatings based on surrogate modelling of thermal cyclic fracture and healing

Kumthekar, A. (author), Ponnusami, Sathiskumar A. (author), van der Zwaag, S. (author), Turteltaub, S.R. (author)

Computationally-efficient surrogate models based on a Polynomial Chaos Expansion (PCE) are developed to quantify the uncertainties in the fracture behavior and lifetime of a self-healing thermal barrier coating system (SH-TBC) and a benchmark conventional TBC system. The surrogate models are built using deterministic information from...

journal article 2022

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Poro-Mechanical Coupling for Flow Diagnostics

Sosa, L. Gutierrez (author), Geiger, S. (author), Doster, Florian (author)

Accounting for poro-mechanical effects in full-field reservoir simulation studies and uncertainty quantification workflows using complex reservoir models is challenging, mainly because of the high computational cost. We hence introduce an alternative approach that couples hydrodynamics through existing flow diagnostics simulations with poro...

journal article 2022

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Bayesian-EUCLID: Discovering hyperelastic material laws with uncertainties

Joshi, A. (author), Thakolkaran, P. (author), Zheng, Y. (author), Escande, Maxime (author), Flaschel, Moritz (author), De Lorenzis, Laura (author), Kumar, Siddhant (author)

Within the scope of our recent approach for Efficient Unsupervised Constitutive Law Identification and Discovery (EUCLID), we propose an unsupervised Bayesian learning framework for discovery of parsimonious and interpretable constitutive laws with quantifiable uncertainties. As in deterministic EUCLID, we do not resort to stress data, but...

journal article 2022

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Verification, validation, and parameter study of a computational model for corrosion pit growth adopting the level-set method. Part II: Stress corrosion

Fayezioghani, A. (author), Dekker, R. (author), Sluys, Lambertus J. (author)

Structural components in corrosive environments such as pipelines, bridges, aircrafts, and turbines are imposed to stress corrosion. A stress corrosion model for pit growth should a) accurately consider the electrochemistry of the corrosion process, b) properly deal with the moving interface between solid and electrolyte, and c) effectively...

journal article 2022

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Verification, validation, and parameter study of a computational model for corrosion pit growth adopting the level-set method.: Part I: Corrosion

Fayezioghani, A. (author), Dekker, R. (author), Sluys, Lambertus J. (author)

Corrosion is a phenomenon observed in structural components in corrosive environments such as pipelines, bridges, aircrafts, turbines, etc. The computational model of corrosion should enjoy two features: a) accurately considering the electrochemistry of corrosion and b) properly dealing with the moving interface between solid and electrolyte....

journal article 2022

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Decision Making under Uncertainty for Construction Management of Offshore Wind Assets

Leontaris, G. (author)

Offshore wind is expected to be one of the important contributors to the energy transition towards a more renewable and sustainable energy future. This can be clearly seen from the amount of investments over the past years as well as from the substantial upcoming offshore wind projects in the years to come. Many technological implementation...

doctoral thesis 2021

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On the investigation of utility functions on optimal sensor locations

Igea, Felipe (author), Chatzis, Manolis N. (author), Cicirello, A. (author)

Structural Health Monitoring uses data collected from sensors placed on structures to determine their operating condition and whether maintenance is required. Often, optimal sensor placement strategies are used to find the optimal locations for the identification of their modal properties, structural parameters and/or abnormal behaviours under...

conference paper 2021

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Quantifying Accuracy and Uncertainty in Data-Driven Flight Trajectory Predictions with Gaussian Process Regression

Graas, R. (author), Sun, Junzi (author), Hoekstra, J.M. (author)

Several initiatives are developed to shift the current paradigm in Air Traffic Management from the tactical-based approach to more strategic-based coordination of flights. This transformation of the ATM system relies on the improvement of predictive models for the 4D flight trajectories. A variety of performance-based and data-driven approaches...

conference paper 2021

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Climate change induced trends and uncertainties in phytoplankton spring bloom dynamics

Mészáros, L. (author), van der Meulen, F.H. (author), Jongbloed, G. (author), El Serafy, G.Y.H. (author)

Spring phytoplankton blooms in the southern North Sea substantially contribute to annual primary production and largely influence food web dynamics. Studying long-term changes in spring bloom dynamics is therefore crucial for understanding future climate responses and predicting implications on the marine ecosystem. This paper aims to study...

journal article 2021

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Model-based probabilistic inversion using magnetic data: A case study on the kevitsa deposit

Güdük, N. (author), De La Varga, Miguel (author), Kaukolinna, Janne (author), Wellmann, Florian (author)

Structural geological models are widely used to represent relevant geological interfaces and property distributions in the subsurface. Considering the inherent uncertainty of these models, the non-uniqueness of geophysical inverse problems, and the growing availability of data, there is a need for methods that integrate different types of...

journal article 2021

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Role of sensors in error propagation with the dynamic constrained observability method

Peng, Tian (author), Nogal Macho, M. (author), Casas, Joan R. (author), Turmo, Jose (author)

The inverse problem of structural system identification is prone to ill‐conditioning issues; thus, uniqueness and stability cannot be guaranteed. This issue tends to amplify the error propagation of both the epistemic and aleatory uncertainties, where aleatory uncertainty is related to the accuracy and the quality of sensors. The analysis of...

journal article 2021

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Preliminary uncertainty and sensitivity analysis of the Molten Salt Fast Reactor steady-state using a Polynomial Chaos Expansion method

Santanoceto, M. (author), Tiberga, M. (author), Perko, Z. (author), Dulla, Sandra (author), Lathouwers, D. (author)

In this work, we present the results of a preliminary uncertainty quantification and sensitivity analysis study of the Molten Salt Fast Reactor (MSFR) behavior at steady-state performed by applying a non-intrusive Polynomial Chaos Expansion (PCE) approach. An in-house high-fidelity multi-physics simulation tool is used as reactor reference...

journal article 2021