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Nuclear Energy Conference & Expo (NECX)
September 8–11, 2025
Atlanta, GA|Atlanta Marriott Marquis
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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.
Cihang Lu, Zeyun Wu, Xu Wu
Nuclear Technology | Volume 207 | Number 5 | May 2021 | Pages 692-710
Technical Paper | doi.org/10.1080/00295450.2020.1805259
Articles are hosted by Taylor and Francis Online.
Thermal stratification (TS) is a thermal-fluid phenomenon that can introduce large uncertainties to nuclear reactor safety. The stratified layers caused by TS can lead to temperature oscillations in the reactor core. They can also result in damages to both the reactor vessel and in-vessel components due to the growth of thermal fatigue cracks. More importantly, TS can impede the establishment of natural circulation, which is widely used for passive cooling and ensures the inherent safety of numerous reactor designs. A fast-running one-dimensional (1-D) model was recently developed in our research group to predict the TS phenomenon in pool-type sodium-cooled fast reactors. The efficient 1-D model provided reasonable temperature predictions for the test conditions investigated, but nonnegligible discrepancies between the 1-D predictions and the experimental temperature measurements were observed. These discrepancies are attributed to the model uncertainties (also known as model bias or errors) in the 1-D model and the parameter uncertainties in the input parameters.
In this study, we first recognized through a forward uncertainty analysis that the observed discrepancies between the computational predictions and the experimental temperature measurements could not be explained solely by input uncertainty propagation. We then performed an inverse uncertainty quantification (UQ) study to reduce the model uncertainties of the 1-D model using a modular Bayesian approach based on experimental data. Inverse UQ serves as a data assimilation process to simultaneously minimize the mismatches between the predictions and experimental measurements, while quantifying the associated parameter uncertainties. The solutions of the modular Bayesian approach were in the form of posterior probability density functions, which were explored by rigorous Markov Chain Monte Carlo sampling. Results showed that the quantified parameters obtained from the inverse UQ effectively improved the predictive capability of the 1-D TS model.