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About Falyn Eisiminger

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So far Falyn Eisiminger has created 182 blog entries.

NETL Establishes Full Cohort of Senior Fellows

With the recent announcement of Kelly Rose and James Bielenberg as the senior fellows for Computational Science & Engineering and Materials Science & Engineering, respectively, NETL establishes a full cohort of senior fellows, which also includes Nathan Weiland (Energy Conversion Engineering), Alexandra Hakala (Geologic & Environmental Systems), and John Wimer (Systems Analysis & Engineering).

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2024-12-19T14:30:48+00:00December 18th, 2024|

NETL Researchers Recognized in Stanford University’s Annual Top 2% of Scientists Worldwide List

A recent analysis published by Stanford University included 24 current and former NETL researchers in the top 2% of global scientists, demonstrating the level of talent and expertise the Lab is bringing to bear on the nation’s decarbonization goals.

“NETL brings together some of the best and brightest minds in the world to develop innovative solutions for clean energy and decarbonization,” NETL Director Marianne Walck said. “The Stanford list underscores this fact, showing the significant impact of our talented workforce.”

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2024-11-01T17:50:42+00:00October 24th, 2024|

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Aliquam erat volutpat. Sed non elit et neque ultricies eleifend. Vivamus quis ultrices nunc. Nulla pretium ante justo, et consequat dui lobortis quis. Aenean scelerisque mi vitae massa suscipit, ut blandit ante condimentum. Interdum et malesuada fames ac ante ipsum primis in faucibus. Sed eget odio augue. Pellentesque vel mauris ac turpis finibus eleifend nec a ligula. Praesent tristique accumsan maximus. Curabitur tempor leo vitae libero molestie finibus a vel sem. Praesent cursus sapien et lacus fringilla auctor.

Nunc porttitor ante sed vulputate placerat. Pellentesque pretium auctor ante, eget egestas nibh pellentesque ac. Suspendisse tempus, eros in varius posuere, quam lacus luctus eros, eu accumsan nisl nisl at ipsum. Duis dui dolor, mollis vitae accumsan ac, tincidunt vel mi. Fusce nec tristique est. Proin et neque laoreet, gravida libero in, imperdiet mauris. Ut condimentum nisl lacus, at ornare augue eleifend sit amet.

2024-10-18T15:51:54+00:00October 18th, 2024|

Advancing Diagnostic Efficiencies of Rotating Detonation Engine (RDE) with ML

Real-time diagnostic methods for gas turbines

  • Objective: Application of computer vision and machine learning techniques to monitor detonation wave behavior in real time to experimental RDEs.
  • Methods: Convoluted neural networks (CNNs) applied to high-speed conventional detonation imaging for image classification and object detection with You Only Look Once (YOLO) architecture, and time-series classification.
  • Results: No single diagnostic structure outperforms the others in all metrics. Each method offers solutions uniquely beneficial to a given study based on primary objectives and constraints, justifying the need for a portfolio of ML capabilities with networks tailored to specific needs throughout the research community.

Learn more here.

2024-09-18T17:41:21+00:00September 18th, 2024|
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