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International Journal for Uncertainty Quantification

Publicado 6 números por año

ISSN Imprimir: 2152-5080

ISSN En Línea: 2152-5099

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 1.7 To calculate the five year Impact Factor, citations are counted in 2017 to the previous five years and divided by the source items published in the previous five years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) 5-Year IF: 1.9 The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. Immediacy Index: 0.5 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.0007 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.5 SJR: 0.584 SNIP: 0.676 CiteScore™:: 3 H-Index: 25

Indexed in

A HOLISTIC APPROACH TO UNCERTAINTY QUANTIFICATION WITH APPLICATION TO SUPERSONIC NOZZLE THRUST

Volumen 2, Edición 4, 2012, pp. 363-381
DOI: 10.1615/Int.J.UncertaintyQuantification.2012003562
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SINOPSIS

In modeling and simulation (M&S), we seek to predict the state of a system using a computer-based simulation of a differential equation-based model. In general, the inputs to the model may contain uncertainty due to inherent randomness (aleatory uncertainty), a lack of knowledge (epistemic uncertainty), or a combination of the two. In many practical cases, there is so little knowledge of a model input that it should be characterized as an interval, the weakest statement of knowledge. When some model inputs are probabilistic and others are intervals, segregated uncertainty propagation should be used. The resulting uncertainty structure on the M&S output can take the form of a cumulative distribution function with a finite width; i.e., a p-box. Implications of sampling over interval versus probabilistic uncertainties in the outer loop are discussed and examples are given showing the effects of the choice of uncertainty propagation and characterization methods. In addition to the uncertainties in model inputs, uncertainties also arise due to modeling deficiencies and numerical approximations. Modeling uncertainties can be reduced by performing additional experiments and numerical uncertainties can be reduced by using additional computational resources; thus, both sources of uncertainty can be modeled as epistemic and can be characterized as intervals and included in the total predictive uncertainty by appropriately broadening the prediction p-box. A simple example is given for the M&S predictions of supersonic nozzle thrust that incorporates and quantifies all three sources of uncertainty.

CITADO POR
  1. Denham Casey L., Patil Mayuresh, Roy Christopher J., Estimating Uncertainty Bounds for Modified Configurations from an Aerodynamic Model of a Nominal Configuration, 2018 AIAA Atmospheric Flight Mechanics Conference, 2018. Crossref

  2. Choudhary Aniruddha, Roy Christopher J., Verification and Validation for Multiphase Flows, in Handbook of Multiphase Flow Science and Technology, 2018. Crossref

  3. Roy Christopher J., Verification, in Encyclopedia of Applied and Computational Mathematics, 2015. Crossref

  4. Roy Christopher J., Oberkampf W. L., Validation, in Encyclopedia of Applied and Computational Mathematics, 2015. Crossref

  5. Ewing Mark E., Liechty Brian C., Black David L., A General Methodology for Uncertainty Quantification in Engineering Analyses Using a Credible Probability Box, Journal of Verification, Validation and Uncertainty Quantification, 3, 2, 2018. Crossref

  6. Choudhary Aniruddha, Voyles Ian T., Roy Christopher J., Oberkampf William L., Patil Mayuresh, Probability Bounds Analysis Applied to the Sandia Verification and Validation Challenge Problem, Journal of Verification, Validation and Uncertainty Quantification, 1, 1, 2016. Crossref

  7. Roy Christopher J., Errors and Uncertainties: Their Sources and Treatment, in Computer Simulation Validation, 2019. Crossref

  8. Danquah Benedikt, Riedmaier Stefan, Rühm Johannes, Kalt Svenja, Lienkamp Markus, Statistical Model Verification and Validation Concept in Automotive Vehicle Design, Procedia CIRP, 91, 2020. Crossref

  9. Nicoletti Lorenzo, Bronner Matthias, Danquah Benedikt, Koch Alexander, Konig Adrian, Krapf Sebastian, Pathak Aditya, Schockenhoff Ferdinand, Sethuraman Ganesh, Wolff Sebastian, Lienkamp Markus, Review of Trends and Potentials in the Vehicle Concept Development Process, 2020 Fifteenth International Conference on Ecological Vehicles and Renewable Energies (EVER), 2020. Crossref

  10. Danquah Benedikt, Riedmaier Stefan, Meral Yasin, Lienkamp Markus, Statistical Validation Framework for Automotive Vehicle Simulations Using Uncertainty Learning, Applied Sciences, 11, 5, 2021. Crossref

  11. Riedmaier Stefan, Schneider Jakob, Danquah Benedikt, Schick Bernhard, Diermeyer Frank, Non-deterministic model validation methodology for simulation-based safety assessment of automated vehicles, Simulation Modelling Practice and Theory, 109, 2021. Crossref

  12. Riedmaier Stefan, Danquah Benedikt, Schick Bernhard, Diermeyer Frank, Unified Framework and Survey for Model Verification, Validation and Uncertainty Quantification, Archives of Computational Methods in Engineering, 28, 4, 2021. Crossref

  13. Gogu Christian, Uncertainty Modeling, in Mechanical Engineering under Uncertainties, 2021. Crossref

  14. Gel Aytekin, Li Tingwen, Gopalan Balaji, Shahnam Mehrdad, Syamlal Madhava, Validation and Uncertainty Quantification of a Multiphase Computational Fluid Dynamics Model, Industrial & Engineering Chemistry Research, 52, 33, 2013. Crossref

  15. Danquah Benedikt, Riedmaier Stefan, Lienkamp Markus, Potential of statistical model verification, validation and uncertainty quantification in automotive vehicle dynamics simulations: a review, Vehicle System Dynamics, 60, 4, 2022. Crossref

  16. Roache Patrick J., Verification and Validation in Fluids Engineering: Some Current Issues, Journal of Fluids Engineering, 138, 10, 2016. Crossref

  17. Gray Nicholas, Ferson Scott, De Angelis Marco, Gray Ander, Baumont de Oliveira Francis, Probability bounds analysis for Python, Software Impacts, 12, 2022. Crossref

  18. Denham Casey L., Patil Mayuresh, Roy Christopher J., Alexandrov Natalia, Framework for Estimating Performance and Associated Uncertainty for Modified Aircraft Configurations, Aerospace, 9, 9, 2022. Crossref

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