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DOE OSTI · 3005568

Advanced Materials & Manufacturing Technology (AMMT): Development of Additive Manufacturing Agnostic Process Parameter Procedure, 316H Stainless Steel Readiness Level Data Sets, and Machine Maintenance Plan

Montoya, Robin Adriana [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000347225285)·Brand, Michael J. [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000314437097)·Le, Kevin [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:000900062971368X)·Hayne, Mathew Lindsay [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000256807095)·Beck, Peter Michael [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:000000027105751X)·Barta, Nicholas Evan [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000249673340)·Bloom, Rose Anne [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000170903844)·Goodrich, Joseph John [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)]·Carpenter, John S. [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:000000018821043X)·Mireles, Omar Roberto [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0009000770075234)·Mirabal, Alex James [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000245017602)·Martinez, Marisa Randi [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)]·Stull, Jamie Ann [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000204647456)·Debardeleben, Nathan A. [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000255939205)·Chakrabarti, Sharmistha [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000311053426)
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Abstract

The University of California, Davis is involved in a project to deploy and enhance an artificial intelligence (AI) system for predicting and preventing plasma disruptions on the DIII D tokamak, under the funding from Department of Energy DE-SC0023500 (title: AI/Deep Learning FRNN Software for Prediction & Real-Time Control of DIII-D Plasma Control System (PCS)). The overarching goal is to demonstrate that real-time, AI-guided intervention can proactively modify the plasma state to avoid or mitigate disruptions—a critical challenge for the future of fusion energy.

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Montoya, Robin Adriana [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000347225285), Brand, Michael J. [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000314437097), Le, Kevin [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:000900062971368X), Hayne, Mathew Lindsay [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000256807095), Beck, Peter Michael [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:000000027105751X), Barta, Nicholas Evan [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000249673340), Bloom, Rose Anne [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000170903844), Goodrich, Joseph John [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)], Carpenter, John S. [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:000000018821043X), Mireles, Omar Roberto [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0009000770075234), Mirabal, Alex James [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000245017602), Martinez, Marisa Randi [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)], Stull, Jamie Ann [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000204647456), Debardeleben, Nathan A. [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000255939205), Chakrabarti, Sharmistha [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000311053426). 2025-11-17. Advanced Materials & Manufacturing Technology (AMMT): Development of Additive Manufacturing Agnostic Process Parameter Procedure, 316H Stainless Steel Readiness Level Data Sets, and Machine Maintenance Plan. https://doi.org/10.2172/3005568

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The transition to a flexible and reliable energy infrastructure, using electro-thermal energy generation technologies such as geothermal, concentrated solar power, and nuclear, usually demands simultaneous advancement of thermal energy storage (TES) to support on-demand electricity generation and industrial applications while mitigating the inherent intermittency of renewable energy sources and power outages from direct energy generation. Among TES technologies, thermochemical energy storage (TCES) based on calcium looping emerges as a compelling high-power energy storage candidate due to its high reaction enthalpy, compatibility with elevated operating temperatures, and abundance of low-cost materials. However, the long-term durability of calcium-based sorbents for TCES is hindered by surface sintering and particle aggregation, leading to performance degradation over repeated thermal cycles. This study explores a moisture hydration-based strategy to regenerate a degraded calcium sorbent and mitigate performance degradation for long duration TCES. The addition of moisture transforms calcium oxide into calcium hydroxide and produces intercalation water layers, associated with a regenerated surface area and reduced calcium oxide crystallite size. Both these effects are beneficial in restoring the sorbents' reactivity for carbonization. Additionally, an optimized hydration-assisted reactivation protocol balances the recovered energy storage capacity with heating penalty required for moisture removal from hydrated samples, resulting in an enhanced energy storage capacity up to 176% compared to benchmark sorbents that undergo cycling without reactivation after 60 cycles. In conclusion, these results highlight the potential of hydration-assisted reactivation to enhance the long-term performance of TCES, providing an effective pathway to advancing electro-thermal storage technologies.

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