Sökning: "uncertainty quantification"

Visar resultat 6 - 10 av 83 avhandlingar innehållade orden uncertainty quantification.

1. 6. Identification and synthesis of components for uncertainty propagation

   Författare :Mladen Gibanica; Chalmers tekniska högskola; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; NATURVETENSKAP; NATURAL SCIENCES; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; substructuring; uncertainty propagation; uncertainty quantification; surrogate modelling; Monte Carlo method; interface reduction; state-space models; experimental methods; system identification;

   Sammanfattning : For automotive structures, built-up of hundreds of components with property spread, knowing the effects of component variability and its propagation through the system assembly is important in order to mitigate noise and vibration problems. To increase the understanding of how the spread propagates into variability in built-up structures, both experimental and computational aspects are considered in this thesis. LÄS MER

3. 7. Uncertainty and Robustness in Aerospace Structures

   Författare :Anders Forslund; Chalmers tekniska högskola; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; simulation; geometry assurance; robust design; Geometrical variation; uncertainty quantification;

   Sammanfattning : Engineering is not an exact science. In fact, all engineering activity contain some degree of assumption, simplification, idealization, and abstraction. When engineered creations meet reality, every manufactured product behaves differently. This variation can be detrimental to product quality and functionality. LÄS MER

4. 8. Application of Uncertainty Quantification Techniques to Studies of Wall-Bounded Turbulent Flows

   Författare :Saleh Rezaeiravesh; Mattias Liefvendahl; Gunilla Kreiss; Pierre Sagaut; Uppsala universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Uncertainty Quantification; Large Eddy Simulation; Wall-Bounded Turbulent Flows; Wall Modeling; OpenFOAM; Beräkningsvetenskap med inriktning mot numerisk analys; Scientific Computing with specialization in Numerical Analysis;

   Sammanfattning : Wall-bounded turbulent flows occur in many engineering applications. The quantities of interest (QoIs) of these flows can be accurately obtained through experimental measurements and scale-resolving numerical approaches, such as large eddy simulation (LES). LÄS MER

5. 9. Nuclear data uncertainty quantification and data assimilation for a lead-cooled fast reactor : Using integral experiments for improved accuracy

   Författare :Erwin Alhassan; Henrik Sjöstrand; Rochman Dimitri; Michael Österlund; Stephan Pomp; Oscar Cabellos; Uppsala universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Total Monte Carlo; ELECTRA; nuclear data; uncertainty propagation; integral experiments; nuclear data adjustment; uncertainty reduction;

   Sammanfattning : For the successful deployment of advanced nuclear systems and optimization of current reactor designs, high quality nuclear data are required. Before nuclear data can be used in applications they must first be evaluated, tested and validated against a set of integral experiments, and then converted into formats usable for applications. LÄS MER

7. 10. Trustworthy explanations : Improved decision support through well-calibrated uncertainty quantification

   Författare :Helena Löfström; Ulf Seigerroth; Ulf Johansson; Patrick Mikalef; Jönköping University; []
   Nyckelord :SAMHÄLLSVETENSKAP; SOCIAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; Explainable Artificial Intelligence; Interpretable Machine Learning; Decision Support Systems; Uncertainty Estimation; Explanation Methods;

   Sammanfattning : The use of Artificial Intelligence (AI) has transformed fields like disease diagnosis and defence. Utilising sophisticated Machine Learning (ML) models, AI predicts future events based on historical data, introducing complexity that challenges understanding and decision-making. LÄS MER