Published paper on Bayesian calibration of fiber optic sensors for nuclear systems

View profile for Lauren Kohler

Ph.D. Candidate at NCSU for Machine Learning in Nuclear Reactors | Edison Engineer at GE Vernova

I’m excited to share that my paper, “Bayesian Calibration and Sensitivity Analysis of Rayleigh Scattering Fiber Optic Distributed Temperature Sensing in Water Flow Loop,” is now published in Nuclear Science and Engineering as part of the BEPU 2024 special issue. This work presents a Bayesian framework to calibrate and analyze distributed fiber optic sensors (DFOSs). Using Bayesian inference, we reduced prediction errors by nearly half. We also applied global sensitivity analysis (Sobol’, Morris, and correlation coefficients) to identify the most important measurement locations for future validation. The study shows how uncertainty-aware calibration can enhance sensor performance in complex thermal-fluid environments and support advanced instrumentation strategies for next-generation nuclear systems. Read more here: https://lnkd.in/djGFd4qx

Eirini Klemes

Nuclear Engineering Undergraduate Student at North Carolina State University

1mo

I am so proud of you!

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Eirini Klemes

Nuclear Engineering Undergraduate Student at North Carolina State University

1mo

Wow! This is amazing Lauren!

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Amanuel Zelalem

Follower of Jesus | Former Test Engineer at Analog Devices

2w

Wow! Congrats Lauren!

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