Sensitivity Analysis, Uncertainty Quantification and Predictive Modeling of Nuclear Energy Systems
Deadline for manuscript submissions: closed (20 January 2021) | Viewed by 18802
Interests: all areas of nuclear engineering; sensitivity and uncertainty analysis of large-scale systems; predictive modeling by combining experimental and computational information to reduce uncertainties in predicted results
Special Issues, Collections and Topics in MDPI journals
This Special Issue of Energies presents ongoing progress and remaining challenges in sensitivity analysis, uncertainty quantification, model validation, and predictive modeling of nuclear energy systems.
As is well known, the results of measurements and computations are never perfectly accurate. On the one hand, results of measurements inevitably reflect the influence of experimental errors, imperfect instruments, or imperfectly known calibration standards. On the other hand, computations are afflicted by errors stemming from imperfectly known physical processes, problem geometry, known model parameters, boundary and initial conditions, and approximate numerical procedures. Therefore, knowing just the nominal values of experimentally measured and/or computed quantities is insufficient for applications. The quantitative uncertainties accompanying measurements and computations are also needed. Predictive modeling aims at extracting “best estimate” values for model parameters and predicted results, together with “best estimate” uncertainties for these parameters and results. Predictive modeling combines experimental and computational data to predict future outcomes based on all recognized errors and uncertainties and includes the following activities: (1) sensitivity analysis of model responses to model parameters; (2) quantification of model response uncertainties stemming from the imperfectly known underlying model parameters, physical processes, numerical solution; (3) data assimilation; (4) model validation; (5) model calibration (determination of optimal/best-estimate model parameters; and (6) best-estimate (optimal) predictions for model parameters and responses, with reduced predicted uncertainties. The numerical determination of quantities of interest for nuclear energy systems requires large-scale computations set in a high-dimensional phase–space and involves a very large number of imprecisely known parameters. Consequently, the “curse of dimensionality” limits the usefulness of naïve methods for sensitivity analysis, uncertainty quantification, and predictive modeling of nuclear energy systems. Developing innovative concepts and methods for accurate and validated predictive modeling, which overcome the curse of dimensionality while avoiding loss of physical information, remains a continuing challenge.
Prof. Dan Cacuci
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- deterministic and statistical methods for sensitivity analysis
- deterministic and statistical methods for uncertainty quantification
- data assimilation
- model calibration
- best-estimate predictions with reduced uncertainties
- nuclear reactor physics and shielding
- nuclear thermal–hydraulics
- nuclear safety