Special Issue on Reliability and Safety Analysis, Uncertainty Quantification, and Prognostics of Fuel Cells
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering
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Incorporation of AI methods can aid these approaches. In particular, reliability analysis, diagnostics, and prognostics of fuel cells using AI-based uncertainty quantification, and data-driven or physics-based deep learning methods can be implemented. This special issue is focused on wholistic approaches for reliability, safety analyses, uncertainty quantification, diagnostics, and prognostics of a variety of fuel cells.
Topic Areas
THE SCOPE OF THIS ISSUE INCLUDES BUT IS NOT LIMITED TO:- Safety analysis of fuel cells of various types
- Failure mechanism and root cause analysis of fuel cell failures
- AI models for diagnostics and prognostics of fuel cells
- Physics-based or physics-informed fault detection and life cycle predictions (probabilistic and non-probabilistic)
- Confidence-based or reliability-based Remaining Useful Life (RUL) estimation of fuel cells
- Degradation prediction and classification using non-destructive measurements using AI methods
- Digital twin of various types of fuel cells
- Surrogate models for reliability and safety analysis as well as prognostics of fuel cells
- Novel nondestructive measurements or health indicators for the degradation analysis of fuel cells
- Image analysis to identify degradation patterns
Submission Instructions
Papers should be submitted electronically to the journal through the ASME Journal Tool. If you already have an account, log in as an author and select Submit Paper. If you do not have an account, you can create one here.Once at the Paper Submittal page, select the ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B: Mechanical Engineering, and then under the Special Issue field, select Special Issue on Reliability and Safety Analysis, Uncertainty Quantification, and Prognostics of Fuel Cells.
Papers received after the deadline or papers not selected for the Special Issue may be accepted for publication in a regular issue.
Guest Editors
Shweta Dabetwar, University of Arkansas at Little Rock, Arkansas, USA (sdabetwar@ualr.edu)
Matthias Faes, Technical University of Dortmund, Dortmund, Germany (matthias.faes@tu-dortmund.de)
Fisseha Alemayehu, West Texas A&M University, Texas, USA (falemayehu@wtamu.edu)
Suk Joo Bae, Hanyang University, Seoul, South Korea (sjbae@hanyang.ac.kr)