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At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

The Impact of Anomalous Resistivity in Vacuum Contaminant Plasmas on the Electrothermal Instability

This manuscript presents an assessment of the electrothermal instability (ETI) in the presence of anomalous resistivity (AR) in vacuum contaminant plasmas (VCP) when applied to a magnetized liner inertial fusion (MagLIF)-like load.Pulsed-power driven dielectrically coated metallic liners, like in MagLIF, experience the current-driven electrothermal instability which occurs when a material’s resistivity changes with temperature and is subject to ohmic heating. Large scale pulsed-power facilities that use magnetically insulated transmission lines (MITL) have been shown to generate low-density plasma which enters the target chamber and coalesces around the load. The low-density high-temperature vacuum contaminant plasmas (VCP) can parasitically divert current from the load through causing a short in the anode-cathode gap inside the target chamber. Resistive magnetohydrodynamic (MHD) simulations of these VCP experience unphysical runaway ohmic heating due to under predicting the resistivity by using a purely collisional resistivity model.AR provides a physics-based way to address this runaway heating through increasing the resistivity in a proportional way with the drift speed. In this work, 1D simulations probe the effect that AR in VCP has on the magnetic diffusion rate, and 2D simulations show how this effect manifests in the nonlinear striation form of the ETI for a MagLIF-like load. Beryllium and aluminum dielectrically coated liners are used for the 1D and 2D simulations in this work. The1D simulations show that a VCP causes a delay in the current delivery to the load by upwards of 8 ns at 60 ns into atypical current pulse for an 1 × 10 7 A scale pulsed-power accelerator. The 2D simulations show the delay observed in the 1D simulations is reduced substantially (4 ns) by 2D turbulence that disrupts the VCP layer early in time. Here, the 2Dsimulated ETI growth varies across AR models, more so for the beryllium liner than the aluminum liner because the beryllium liner shows an enhanced rate of penetration for the magnetic diffusion wave in comparison to the aluminum liner. The 2D simulations show the bulk dielectric thickness varies across AR models with the Davidson AR model beingthe largest and the Buneman AR model being the smallest, and in connection with the thickness the Rayleigh-Taylor bubble-spike distances varies correspondingly.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Inert Gas Element as Active Infrared‐Absorption Source and Donor in Silicon for Forbidden‐Wavelength Sensing

Intrinsic silicon (Si) is forbidden for infrared (IR) sensing at the communication wavelength like 1.31 or 1.55 µm due to the well‐known bandgap limitation. In this work, an unexpected physical picture of using argon (Ar) is identified, which is usually inert to the surrounding chemical environment and used as a protective agent in semiconductor processing, to overcome the IR‐sensing‐forbidden problem in Si. Here, in this work, it is shown by an analysis of a dynamic secondary ion mass spectrometer that such a Si, when exposed to laser pulse in Ar gas, can contain a very high dose of Ar up to 10 20 cm −3 even after 1300 days. First‐principles calculations, molecular dynamics, and Hall effect measurements reveal that, due to both steric and dynamic repulsions by Ar orbitals to Si dangling bonds, the Ar‐filled‐vacancy produces a much wider defect band inside the gap, which is not only responsible for strong infrared absorption, but also causes a significant increase in n‐type conductivity, both in line with experiments. The study proves that originally inert elements in fact can act as active impurities in semiconductors for advanced applications, which updates the current knowledge of defect physics.

36 MATERIALS SCIENCE↗
DOE OSTI2024

UEDGE modeling of plasma detachment of CFETR with ITER ‐like divertor geometry by external impurity seeding

Abstract Efficient handling of high heat flux on the plasma‐facing components, particularly the divertor targets, poses a significant challenge for the Chinese Fusion Engineering Testing Reactor (CFETR) with fusion power of Gigawatt. This work investigates the divertor plasma detachment of CFETR with a standard ITER‐like divertor geometry by neon (Ne) or argon (Ar) impurity seeding using UEDGE code. The cross‐field drifts terms are switched off, and fluid neutral models and a “fixed‐fraction” impurity model are applied to enable efficient simulations for the study of CFETR detachment. In order to reduce the heat load on the divertor targets below the acceptable level (<10 MW/m 2 ), the impurity fraction ( f ), pumping speed ( S ), and upstream density are varied to identify the suitable operations window during Ne seeding. The effects of Ne and Ar impurities on the plasma detachment are compared. It is found that with the power across the core‐edge interface P SOL = 200 MW and separatrix density of 2.8 10 19 , Ne impurity fraction ≥1.7%, and Ar impurity fraction ≥0.24% can achieve the partial detachment. Achieving similar total radiation power (˜148 MW), the Ne fraction is 2.3% and the Ar fraction is 0.24%. Moreover, the simulation results indicate that Ar exhibits better power radiation efficiency and core compatibility compared with Ne.

Zhang, M. Z.↗
DOE OSTI2022

A window into atmospheric escape on early Mars provided by argon isotopes in a Martian meteorite

Novel analytical approaches to determining the trapped 38 Ar/ 36 Ar ratio of gases contained within Martian meteorites are presented and applied to the Martian regolith breccia Northwest Africa (NWA) 7034 and paired stone NWA 11220. The resulting data indicate that extensive mass-dependent fractionation of atmospheric Ar may have occurred within 150 million years of planetary formation, ostensibly as a result of diffusion-limited hydrodynamic escape. The inferred fractional loss of Ar and lighter atmospheric constituents exceeds 50%. In conclusion, these data suggest that volatiles derived from planetary outgassing and/or impactors may dominate the present abundance of atmospheric Ar.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2020

A carbon molecular sieve membrane-based reactive separation process for pre-combustion CO 2 capture

In this study, we discuss a hybrid system combining a membrane reactor (MR) and an adsorptive reactor (AR), with the MR's reject stream serving as the AR's feed. We apply this system for the water gas shift (WGS) reaction for H 2 generation and simultaneous CO 2 capture in the context of the Integrated Gas Combined Cycle (IGCC) process for power generation from coal and biomass. This MR-AR system attains a high conversion exceeding equilibrium, produces a pure H 2 product for power generation, and delivers a high-pressure CO 2 stream ready for sequestration. Specifically, in our study we use carbon molecular sieve membranes (CMSMs) and a commercial sour-shift WGS catalyst. Lab experiments were carried-out to determine the membrane characteristics, and the MR performance under IGCC-relevant conditions, i.e., for temperatures up to 250 °C and pressures up to 25 bar, employing a model coal gasifier syngas. The CMSM and the catalyst have displayed robust and stable performance during a long-term run (~750 h of syngas exposure). We evaluated the MR-AR system in multi-cycle runs and it has demonstrated superior performance to that of a conventional packed-bed reactor, producing a high-purity H 2 product directly useable in a turbine for power generation. We conclude from the study, that the CMSM-based MR-AR system is a good candidate technology for environmentally-benign power generation. We are currently constructing a pilot-scale system for field demonstration of the technology.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Down-regulation of A3AR signaling by IL-6-induced GRK2 activation contributes to Th17 cell differentiation

Highlights: • Cyclic AMP-PKA signaling is involved in Th17 cell differentiation. • IL-6 stimulation triggers the internalization of A{sub 3}AR and promotes cAMP production in T cells. • IL-6 induces A{sub 3}AR internalization in a GRK2 dependent manner. • GRK2 is a potential therapeutic target for treating Th17 driving immune diseases. IL-6-triggered Th17 cell expansion is responsible for the pathogenesis of many immune diseases including rheumatoid arthritis (RA). Traditionally, IL-6 induces Th17 cell differentiation through JAK-STAT3 signaling. In the present work, PKA inhibition reduces in vitro induction of Th17 cells, while IL-6 stimulation of T cells facilitates the internalization of A{sub 3}AR and increased cAMP production in a GRK2 dependent manner. Inhibition of GRK2 by paroxetine (PAR) or genetic depletion of GRK2 restored A{sub 3}AR distribution and prevented Th17 cell differentiation. Furthermore, in vivo PAR treatment effectively reduced the splenic Th17 cell proportion in a rat model of collagen-induced arthritis (CIA) which was accompanied by a significant improvement in clinical manifestations. These results indicate that IL-6-induced Th17 cell differentiation not only occurs through JAK-STAT3-RORγt but is also mediated through GRK2-A{sub 3}AR-cAMP-PKA-CREB/ICER-RORγt. This elucidates the significance of GRK2-controlled cAMP signaling in the differentiation of Th17 cells and its potential application in treating Th17-driven immune diseases such as RA.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

On the Mechanism of Catalytic Decarboxylation of Carboxylic Acids on Carbon-Supported Palladium Hydride

The high chemical stability of aliphatic carboxylic acid makes catalytic decarboxylation at low temperatures challenging. We show that arylaliphatic acids (Ar-CnH2n-COOH, n = 1) decarboxylate on carbon-supported Pd nanoparticles (Pd/C) at 90 °C with 100% selectivity. In situ XANES during decarboxylation of preadsorbed substrates indicates that the active phase is a-phase palladium hydride (a-PdHx). The reaction rate is enhanced by one order of magnitude when hydrogen is preadsorbed. Tracing deuterium labeling positions, it is concluded that carboxylic acid (Ar-CnH2n-COOH) undergoes an a-C-H bond dissociation on the Pd surface to the Ar-(CH2)n-1-CH*-COO* intermediate in the first step, followed by the C-COO scission, and finally, Ar-(CH2)n-1- CH* reacts with two sorbed H to produce Ar-(CH2)n-1-CH3. The high rates are related to the concentration of hydride present on the catalyst particles to complete the catalytic cycle in a Mars-van Krevelen-type mechanism and the rate of H/D exchange at the a-C- H position.

Pd/C catalyst, palladium hydride, arylaliphatic ca↗
DOE OSTI2020

Structure of an Atmospheric River over Australia and the Southern Ocean. Part I: Tropical and Midlatitude Water Vapor Fluxes

An atmospheric river (AR) impacting Australia-Tasmania and the Southern Ocean during the Austral summer on 28-29 January 2018 during the SOCRATES campaign is analyzed using both a modeling and observational approach. Gulfstream-V dropsonde measurements and GPM radar analyses were used in conjunction with Weather Research and Forecasting model simulations with water vapor tracers to investigate the relative contributions of tropical and mid-latitude moisture sources to the AR. Moisture associated with a monsoonal tropical depression became entrained into a mid-latitude frontal system that extended to 60°S over the Southern Ocean reaching the associated low pressure system 850 km off the east coast of Antarctica – effectively connecting the tropics and the polar region. Tropical moisture contributed to about 50% of the precipitable water within the AR. The tropical contribution to precipitation decreased with latitude, from > 70% over the Australian continent, to ~50% off the Australian coast, to less than 10% poleward of 60°S. Precipitation in the AR conformed to a seeder-feeder model, the seeder precipitation forming primarily from tropical sourced moisture above the altitude of the 0°C isotherm, and the feeder precipitation sourced from mid-latitude sourced moisture below the altitude of the 0°C isotherm. The integrated vapor transport (IVT) through the core of the AR reached above 500 kg m-1s-1 during 1200 UTC 28 January to 0600 UTC 29 January 29, 1.29 times the average amount of water carried by the world’s largest river, the Amazon. The high IVT strength might be attributed to the higher water vapor content associated with the warmer temperatures in Austral summer.

Rauber, Robert M.↗
DOE OSTI2023

Future Atmospheric Rivers and Impacts on Precipitation: Overview of the ARTMIP Tier 2 High–Resolution Global Warming Experiment

Atmospheric rivers (ARs) are long, narrow synoptic scale weather features important for Earth’s hydrological cycle typically transporting water vapor poleward, delivering precipitation important for local climates. Understanding ARs in a warming climate is problematic because the AR response to climate change is tied to how the feature is defined. The Atmospheric River Tracking Method Intercomparison Project (ARTMIP) provides insights into this problem by comparing 16 atmospheric river detection tools (ARDTs) to a common data set consisting of high resolution climate change simulations from a global atmospheric general circulation model. ARDTs mostly show increases in frequency and intensity, but the scale of the response is largely dependent on algorithmic criteria. Across ARDTs, bulk characteristics suggest intensity and spatial footprint are inversely correlated, and most focus regions experience increases in precipitation volume coming from extreme ARs. The spread of the AR precipitation response under climate change is large and dependent on ARDT selection.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Co-Occurring Atmospheric Features and Their Contributions to Precipitation Extremes

Object-based identification algorithms for atmospheric features are commonly utilized to attribute global precipitation. This study employs a systematic approach to examine feature co-occurrences and their relationships to mean and extreme precipitation. Four features are identified using existing data sets for atmospheric rivers (ARs), mesoscale convective systems (MCSs), low-pressure systems (LPSs), and fronts (FTs). Often, a single atmospheric phenomenon satisfies the criteria set by multiple feature identification algorithms, yielding an association between precipitation and multiple features. Over the extra-tropics, the number of features attributed to a single event typically increases with precipitation intensity. Over two-thirds of the precipitation is from co-occurring features, with a considerable fraction related to AR-FT co-occurrences. Over the tropics, about one-quarter of precipitation is associated with co-occurring features, with LPS-MCS co-occurrences contributing substantially in monsoon regions. MCSs are the leading single-feature contributors over tropical land and oceans. In the extra-tropics, FTs, ARs, and their co-occurrences account for over half of the total precipitation over oceans. AR-FT-MCS and FT-MCS co-occurrences contribute to extremes (precipitation exceeding the 95th percentile) over both oceans (over 30%) and land (over 20%). Any combination of features involving MCSs shows a larger contribution to high percentiles of precipitation intensity. A case analysis indicates that AR-FT-MCS co-occurrences exhibit convective instability and deep vertical motion, suggesting that the feature trackers and reanalysis are capturing physics relevant to both convective and frontal systems. The results here emphasize the need for simultaneous identifications of multiple features when attributing precipitation to atmospheric phenomena.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

The role of interdecadal climate oscillations in driving Arctic atmospheric river trends

Abstract Atmospheric rivers (ARs), intrusions of warm and moist air, can effectively drive weather extremes over the Arctic and trigger subsequent impact on sea ice and climate. What controls the observed multi-decadal Arctic AR trends remains unclear. Here, using multiple sources of observations and model experiments, we find that, contrary to the uniform positive trend in climate simulations, the observed Arctic AR frequency increases by twice as much over the Atlantic sector compared to the Pacific sector in 1981-2021. This discrepancy can be reconciled by the observed positive-to-negative phase shift of Interdecadal Pacific Oscillation (IPO) and the negative-to-positive phase shift of Atlantic Multidecadal Oscillation (AMO), which increase and reduce Arctic ARs over the Atlantic and Pacific sectors, respectively. Removing the influence of the IPO and AMO can reduce the projection uncertainties in near-future Arctic AR trends by about 24%, which is important for constraining projection of Arctic warming and the timing of an ice-free Arctic.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

More frequent atmospheric rivers slow the seasonal recovery of Arctic sea ice

In recent decades, Arctic sea ice coverage experienced a drastic decline in winter, when sea ice is expected to recover following the melting season. It is unclear to what extent atmospheric processes such as atmospheric rivers (ARs), intense corridors of moisture transport, contribute to this reduced recovery of sea ice. Here, in this study, using observations and climate model simulations we find a robust frequency increase in ARs in early winter over the Barents-Kara Seas and the central Arctic (ABK) between 1979 and 2021. The moisture carried by more frequent ARs has intensified surface downward longwave radiation and rainfall, caused stronger melting of thin, fragile ice cover, and slowed the seasonal recovery of sea ice, accounting for 34% of the sea ice cover decline in ABK. A series of model ensemble experiments suggests that, in addition to a uniform AR increase in response to anthropogenic warming, tropical Pacific variability also contributes to the observed Arctic AR changes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Multiscale drivers of extreme southern California flooding: ENSO, MJO, North Pacific jet, and atmospheric rivers

Extreme rainfall and flooding, driven by a powerful atmospheric river (AR) and a persistent Madden-Julian Oscillation (MJO), hit Southern California in February 2024 during the 2023–2024 El Niño, affecting over 10 million people. ARs are key contributors to extreme rainfall and flooding along the U.S. West Coast. Although the AR-MJO link has been documented, its spatio-temporal variability remains a major forecasting and risk-management challenge. Combining precipitation, stream gauge and demographic data, we quantify the physical drivers and population exposure to this extreme event. Leveraging a Lagrangian MJO precipitation tracking algorithm, we unravel the multiscale interactions responsible for the AR’s development. El Niño favored a large, long-lived MJO that interacted with the North Pacific Jet (NPJ) over more than three weeks. The MJO convective outflow modulated the NPJ by inducing negative potential vorticity advection along the tropopause. The ensuing NPJ extension and acceleration induced explosive cyclogenesis, whose AR-driven moisture transport resulted in extreme rainfall.

Atmospheric dynamics↗
DOE OSTI2025

X-ray absorption spectroscopy measurements of radiatively ionized argon gas

X-ray absorption spectroscopy is a diagnostic tool that can characterize the temperature and ionization state of a plasma. This technique requires experiments to characterize the platform, careful data calibration, and comparison with atomic models to understand the plasma parameters. We performed ionizing radiation wave experiments at the OMEGA Laser Facility that used an ∼ 80 eV X-ray source to heat an Argon (Ar) gas cell at fill pressures of 3 atm. To diagnose the Ar plasma, we used a capsule backlighter offset 10 mm from the gas cell, to produce X-rays that were absorbed by the ionized Ar gas. The absorption analysis was calibrated using the significant line structure in the backlighter spectrum, which served as independent energy fiducials enabling definitive measurements of a 50-eV shift in the energy of the Ar K-edge due to ionization. We compare the measured absorption spectra to two independent atomic models, PrismSPECT and SCRAM, and show that the observed K-edge shift in the heated gas is consistent with ionization up to Ar 4+ and temperatures of 10 eV.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Radio-frequency magnetron sputter deposition of ultrathick boron carbide films

The deposition of thick B 4 C films with low residual stress by conventional direct-current magnetron sputtering is accompanied by the formation of dust particulates contaminating the target, chamber, and substrates and leading to the formation of nodular defects in films. Here, we demonstrate that the formation of particulates is greatly reduced during radio-frequency magnetron sputtering (RFMS). We systematically study properties of B 4 C films deposited by RFMS with a substrate temperature of 330 °C, a target-to-substrate distance of 10 cm, Ar working gas pressure in the range of 4.5–12.0 mTorr (0.6–1.6 Pa), and substrate tilt angles of 0°–80°. All films are x-ray amorphous. A columnar structure develops with increasing either Ar pressure or substrate tilt. For columnar films, the column tilt angle decreases with increasing Ar pressure, which we attribute to a corresponding increase in the width of the distribution of impact angles of deposition flux. In contrast to the Keller–Simmons rule, the deposition rate increases with increasing Ar pressure, which suggests a better coupling of the RF energy to the plasma processes that lead to target sputtering at higher pressures. There is a critical substrate tilt angle above which the total residual stress is close to zero. This critical substrate tilt angle is ~0° for an Ar pressure of 12 mTorr (1.6 Pa). The lower residual stress state, necessary for depositing ultrathick films, is characterized by a larger concentration of nanoscale inhomogeneities and decreased mechanical properties. Based on these results, RFMS deposition of 60-μm-thick B 4 C films is demonstrated.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Intercomparison of Deep Learning Model Architectures for Atmospheric River Prediction

With a rapid surge in the application of machine learning (ML) for a diverse range of tasks in climate science, the present study addresses a challenge for climate scientists when selecting the optimal ML or deep learning (DL) architecture for a given application. In particular, a DL intercomparison study was performed with a focus on forecasting the position of atmospheric rivers (ARs) on short-range time scales (up to 5-day lead times). AR predictions from multiple DL architectures, including various types of convolutional autoencoders and a vision transformer (ViT), were compared against ECMWF ERA5 reanalysis and hindcasts from a global climate model. DL models with similar trainable parameters were trained on ERA5 reanalysis data and AR positions derived from a thresholding algorithm to ensure a fair comparison among the DL models. Each model’s performance and accuracy in forecasting AR location and key input fields within a 5-day window were assessed using metrics of root-mean-square error, anomaly correlation, and mean intersection over union. The ViT architecture outperformed other autoencoder models in most of the metrics. Incorporating additional meteorological fields only yielded slight improvements in forecasting certain fields at longer lead times. The results also suggest that a smaller number of input time steps or smaller number of autoregressive steps can achieve better prediction skills, while also improving the overall computational efficiency. This research offers valuable insights into the strengths and weaknesses of different DL techniques for AR forecasting, hopefully guiding the development of improved models for forecasting this phenomenon.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Alpha Spectrometry Results for Groundwater Samples Collected in Northern Iraq and a Summary of the Environmental Setting of the Adaya Burial Site

The Radiation Protection Center (RPC) of the Iraqi Ministry of Environment continues to evaluate the potential health impacts associated with the Adaya Burial Site, which is located 33 kilometers (20.5 miles) southwest of Mosul. This report documents the radiological analyses of 16 groundwater samples collected from wells located in the vicinity of the Adaya Burial Site and at other sites in northern Iraq. The Adaya Burial Site is a high-risk dump site because a large volume of radioactive material and contaminated soil is located on an unsecure hillside above the village of Tall ar Ragrag. The uranium activities for the 16 water samples in northern Iraq are considered to be naturally occurring and do not indicate artificial (man-made) contamination. With one exception, the alpha spectrometry results for the 16 wells that were sampled in 2019 indicate that the water quality concerning the three uranium isotopes (Uranium-233/234, Uranium-235/236, and Uranium-238) was acceptable for potable purposes (drinking and cooking). However, Well 7 in Mosul had a Uranium-233/234 activity concentration that slightly exceeded the World Health Organization guidance level. Eight of the 16 wells are located in the villages of Tall ar Ragrag and Adaya and had naturally occurring uranium concentrations. Wells in the villages of Tall ar Ragrag and Adaya are located near the Adaya Burial Site and should be sampled on an annual schedule. The list of groundwater analytes should include metals, total uranium, isotopic uranium, gross alpha/beta, gamma spectroscopy, organic compounds, and standard water quality parameters. Our current understanding of the hydrogeologic setting in the vicinity of the Adaya Burial Site is solely based on villager's domestic wells, topographic maps, and satellite imagery. To better understand the hydrogeologic setting, a Groundwater Monitoring Program needs to be developed and should include the installation of twelve groundwater monitoring wells in the vicinity of Tall ar Ragrag and the Adaya Burial Site. Characterization of the limestone aquifer and overlying alluvium is needed. RPC should continue to support health assessments for the villagers in Tall ar Ragrag and Adaya. Collecting samples for surface water (storm water), airborne dust, vegetation, and washway sediment should be conducted on a routine basis. Human access to the Adaya Burial Site needs to be strictly limited. Livestock access on or near the burial site needs to be eliminated. The surface-water exposure pathway is likely a greater threat than the groundwater exposure pathway. Installation of a surface-water diversion or collection system is recommended in order to reduce the potential for humans and livestock to come in contact with contaminated water and sediment. To reduce exposure to villagers, groundwater treatment should be considered if elevated uranium or other contaminants are detected in drinking water. Installing water-treatment systems would likely be quicker to accomplish than remediation and excavation of the Adaya Burial Site. The known potential for human exposure to uranium and metals (such as arsenic, chromium, selenium, and strontium) at the Adaya Burial Site is serious. Additional characterization , mitigation, and remediation efforts should be given a high priority.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Artificial Intelligence/Machine Learning Technologies for Advanced Reactors (Workshop Summary Report)

A workshop on artificial intelligence and machine learning (AI/ML) for advanced reactors (AR) was held October 5-6, 2021. The workshop was to be attended in-person at ANL but COVID restrictions forced the workshop to go virtual. The objectives of the workshop were to identify the most promising AI/ML opportunities for improving advanced reactor design, optimizing plant performance, and enhancing economic competitiveness and to develop an understanding of the scientific, engineering and licensing challenges facing their application. The workshop planning committee included GAIN, EPRI and NEI and members of three national laboratories (ANL, INL, and ORNL). The workshop was attended by more than 200 individuals representing academic and scientific institutions and the nuclear power industry. The definition put forth for an AI/ML system was one that perceives its environment and takes actions that maximize its chance of achieving its goals. In this report AI/ML refers to next generation algorithms that include deep learning, statistical analysis and data analytics and associated scientific computing and their potential application to the design, licensing, operation and maintenance of ARs. These methods typically incorporate models built from process data and may also include data generated by simulations that represent the behavior of a system. The workshop was organized in response to the growing interest in application of AI/ML for improving the economic competitiveness of nuclear energy. Increasingly more resources are being allocated to investigating the benefits of AI/ML methods. The DOE created the Artificial Intelligence & Technology Office to promote their development. And within the Office of Nuclear Energy, resources have been allocated to explore and understand the potential benefits of AI/ML. Additionally, the national laboratories are strategically positioned with DOE computing facilities such as Summit, Perlmutter, Aurora and Frontier that support large-scale simulations, hybrid HPC models with AI surrogates, and the exploration of new types of generative models emerging from multi-model data streams and sources. The workshop was organized with members of the AR community to understand the effort and to identify the level of interest and progress in this emerging technology. The workshop discussions focused on identifying opportunities for AI/ML across diverse areas of the nuclear industry and identifying current scientific and engineering challenges for advanced reactors that might be addressed through transformational uses of AI/ML. Discussion panels focused on four high-interest technical domains for advanced reactors: design, maintenance and operations, energy storage, and materials. The results of those discussions are summarized in this report. This includes opportunities that were identified for exploiting AI techniques and methods to improve the efficacy and efficiency of reactor analysis and to improve the operation and optimization of advanced reactors. Advanced reactor developers expressed an interest in learning more about AI/ML methods and their application. This included understanding whether ML methods can provide an advantage over existing nonlinear data regression methods for collapsing high-fidelity simulation results into faster running models. A consensus emerged that AR advances planned for the next decade will benefit from the use of AI/ML tools. The need exists to understand and model complex systems across length scales and modalities. AI/ML is a tool for discovery that can yield a set of engineering principles for use by nuclear engineers, licensing bodies, and operators to solve problems in plant design, safety analyses, autonomous operation, and predictive maintenance. While AI/ML represents a new set of tools, an awareness by the nuclear community of the full potential is still in the early stages so there is a need to increase awareness. It appears that the wide-spread adoption of AI/ML tools for ARs would be facilitated by future educational workshops that describe foundational methods and capabilities and describe successful applications.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗