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Fusion Science and Technology
August 2025
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From operator to entrepreneur: David Garcia applies outage management lessons
David Garcia
If ComEd’s Zion plant in northern Illinois hadn’t closed in 1998, David Garcia might still be there, where he got his start in nuclear power as an operator at age 24.
But in his ninth year working there, Zion closed, and Garcia moved on to a series of new roles—including at Wisconsin’s Point Beach plant, the corporate offices of Minnesota’s Xcel Energy, and on the supplier side at PaR Nuclear—into an on-the-job education that he augmented with degrees in business and divinity that he sought later in life.
Garcia started his own company—Waymaker Resource Group—in 2014. Recently, Waymaker has been supporting Holtec’s restart project at the Palisades plant with staffing and analysis. Palisades sits almost exactly due east of the fully decommissioned Zion site on the other side of Lake Michigan and is poised to operate again after what amounts to an extended outage of more than three years. Holtec also plans to build more reactors at the same site.
For Garcia, the takeaway is clear: “This industry is not going away. Nuclear power and the adjacent industries that support nuclear power—and clean energy, period—are going to be needed for decades upon decades.”
In July, Garcia talked with Nuclear News staff writer Susan Gallier about his career and what he has learned about running successful outages and other projects.
M. Santos, A. J. Cantos
Fusion Science and Technology | Volume 58 | Number 2 | October 2010 | Pages 706-713
Selected Paper from the Sixth Fusion Data Validation Workshop 2010 (Part 1) | doi.org/10.13182/FST10-A10895
Articles are hosted by Taylor and Francis Online.
In the analysis and classification of signals from massive databases, it is highly desirable to use automatic mechanisms. The synergy of artificial intelligence and advanced signal processing techniques is becoming very efficient in developing this kind of task. In this work we employ a signal processing strategy based on the wavelet transform and then genetic algorithms for classification purposes. An in-depth analysis of the waveforms has been carried out, and an analytical preprocessing has been applied to prepare the signals for their classification. Each individual of the simulated population represents a classifying rule, composed of an antecedent and a consequent. The codification of the knowledge is one of the main contributions of this paper. This genetic classification system has been applied to six different classes of plasma signals of the TJ-II stellarator database at CIEMAT in Spain with satisfactory results.
