![Ugur Mertyurek Ugur Mertyurek](/sites/default/files/styles/staff_profile_image_style/public/Mertyurek%2C%20Ugur.jpg?itok=JX5EY3h4)
Bio
Dr. Ugur Mertyurek is a recognized expert in nuclear reactor engineering, with extensive experience in reactor physics, design, and the computational modeling and simulation of nuclear reactors. Specializing in uncertainty quantification and reactor core physics, he has led and managed significant projects funded by the Department of Energy (DOE) and the Nuclear Regulatory Commission (NRC).
Dr. Mertyurek has played a pivotal role in optimizing light water reactor technology, developing innovative approaches for uncertainty prediction, and enhancing safety analysis through machine learning applications. His contributions are recognized both nationally and internationally, with collaborations across leading universities and research institutions.
Prior to his 14 years at Oak Ridge National Laboratory (ORNL), Dr. Mertyurek worked at General Electric/Global Nuclear Fuel (GE-GNF) in the reactor core monitoring group, where he developed a patented methodology to address the prediction of bias and uncertainties in core simulators for reactor startups. He was also responsible for the development and licensing of the next-generation lattice physics code LANCER for GNF.
He holds a Ph.D. and an M.S. degree in nuclear engineering, as well as a Master’s degree in Computer Science. His work has earned him two patents.
Some of Dr. Mertyurek’s key scientific contributions at ORNL include:
- Core and fuel assembly design of high burnup (HBU)-high enrichment (LEU+) fuel for reactor physics assessment of existing commercial light water reactor fleets (BWRs and PWRs).
- Development of the patent-pending Physics-Guided Analytical Model Validation technology, a methodology that ensures confidence in light water or advanced reactor digital twin applications, assuring that any adjustments or adaptations made by a digital twin remain within predicted boundaries.
- Development of the MAPPER mutual information-based bias and uncertainty mapping tool, which provides a new, mathematically proven methodology to use small-scale non-representative experiments to predict simulation errors for large systems.
- Development of the CRANE dimensionality reduction and surrogate generation tool to enable fast and accurate reactor calculations for simulation and optimization studies.
- Creation of a comprehensive validation and verification test suite to assess SCALE lattice physics calculation accuracy through high-fidelity numerical experiments and radiochemical assay measurements of spent fuel samples.
- Design and implementation of new analysis capabilities for the Sampler uncertainty quantification tool in SCALE, including similarity analysis to extend the validation basis for new fuel and reactor types beyond existing experimental coverage, simulation model optimization, and advanced parametric studies.