Search NASA⌕ Search

SEARCH · Search NASA

Results for “Abstract Interpretation”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Tokamak divertor plasma emulation with machine learning

Abstract Future tokamak devices that aim to create conditions relevant to power plant operations must consider strategies for mitigating damage to plasma facing components in the divertor. One of the goals of MAST-U tokamak operations is to inform these considerations by researching advanced divertor configurations that aid stable plasma detachment. Machine design, scenario planning and detachment control would all greatly benefit from tools that enable rapid calculation of scenario-relevant quantities given some input parameters. This paper presents a method for generating large, simulated scrape-off layer data sets, which was applied to generate a data set of steady-state Hermes-3 simulations of the MAST-U tokamak. A machine learning model was constructed using a Bayesian approach to hyperparameter optimisation to predict diagnosable output quantities given control-relevant input features. The resulting best-performing model, which is based on a feedforward neural network, achieves high accuracy when predicting electron temperature at the divertor target and carbon impurity radiation front position and runs in around 1 ms in inference mode. Techniques for interpreting the predictions made by the model were applied, and a high-resolution parameter scan of upstream conditions was performed to demonstrate the utility of rapidly generating accurate predictions using the emulator. This work represents a step forward in the design of machine learning-driven emulators of tokamak exhaust simulation codes in operational modes relevant to divertor detachment control and plasma scenario design.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications

Abstract Robust quantification of predictive uncertainty is a critical addition needed for machine learning applied to weather and climate problems to improve the understanding of what is driving prediction sensitivity. Ensembles of machine learning models provide predictive uncertainty estimates in a conceptually simple way but require multiple models for training and prediction, increasing computational cost and latency. Parametric deep learning can estimate uncertainty with one model by predicting the parameters of a probability distribution but does not account for epistemic uncertainty. Evidential deep learning, a technique that extends parametric deep learning to higher-order distributions, can account for both aleatoric and epistemic uncertainties with one model. This study compares the uncertainty derived from evidential neural networks to that obtained from ensembles. Through applications of the classification of winter precipitation type and regression of surface-layer fluxes, we show evidential deep learning models attaining predictive accuracy rivaling standard methods while robustly quantifying both sources of uncertainty. We evaluate the uncertainty in terms of how well the predictions are calibrated and how well the uncertainty correlates with prediction error. Analyses of uncertainty in the context of the inputs reveal sensitivities to underlying meteorological processes, facilitating interpretation of the models. The conceptual simplicity, interpretability, and computational efficiency of evidential neural networks make them highly extensible, offering a promising approach for reliable and practical uncertainty quantification in Earth system science modeling. To encourage broader adoption of evidential deep learning, we have developed a new Python package, Machine Integration and Learning for Earth Systems (MILES) group Generalized Uncertainty for Earth System Science (GUESS) (MILES-GUESS) ( https://github.com/ai2es/miles-guess ), that enables users to train and evaluate both evidential and ensemble deep learning. Significance Statement This study demonstrates a new technique, evidential deep learning, for robust and computationally efficient uncertainty quantification in modeling the Earth system. The method integrates probabilistic principles into deep neural networks, enabling the estimation of both aleatoric uncertainty from noisy data and epistemic uncertainty from model limitations using a single model. Our analyses reveal how decomposing these uncertainties provides valuable insights into reliability, accuracy, and model shortcomings. We show that the approach can rival standard methods in classification and regression tasks within atmospheric science while offering practical advantages such as computational efficiency. With further advances, evidential networks have the potential to enhance risk assessment and decision-making across meteorology by improving uncertainty quantification, a longstanding challenge. This work establishes a strong foundation and motivation for the broader adoption of evidential learning, where properly quantifying uncertainties is critical yet lacking.

Schreck, John S.↗
DOE OSTI2024

A Scale‐Dependent Analysis of the Barotropic Vorticity Budget in a Global Ocean Simulation

Abstract The climatological mean barotropic vorticity budget is analyzed to investigate the relative importance of surface wind stress, topography, planetary vorticity advection, and nonlinear advection in dynamical balances in a global ocean simulation. In addition to a pronounced regional variability in vorticity balances, the relative magnitudes of vorticity budget terms strongly depend on the length‐scale of interest. To carry out a length‐scale dependent vorticity analysis in different ocean basins, vorticity budget terms are spatially coarse‐grained. At length‐scales greater than 1,000 km, the dynamics closely follow the Topographic‐Sverdrup balance in which bottom pressure torque, surface wind stress curl and planetary vorticity advection terms are in balance. In contrast, when including all length‐scales resolved by the model, bottom pressure torque and nonlinear advection terms dominate the vorticity budget (Topographic‐Nonlinear balance), which suggests a prominent role of oceanic eddies, which are of km in size, and the associated bottom pressure anomalies in local vorticity balances at length‐scales smaller than 1,000 km. Overall, there is a transition from the Topographic‐Nonlinear regime at scales smaller than 1,000 km to the Topographic‐Sverdrup regime at length‐scales greater than 1,000 km. These dynamical balances hold across all ocean basins; however, interpretations of the dominant vorticity balances depend on the level of spatial filtering or the effective model resolution. On the other hand, the contribution of bottom and lateral friction terms in the barotropic vorticity budget remains small and is significant only near sea‐land boundaries, where bottom stress and horizontal viscous friction generally peak.

Khatri, Hemant↗
DOE OSTI2025

The Milky Way and M31 orbital history: did the Local Group evolve in isolation?

ABSTRACT We use new measurements of the M31 proper motion to examine the Milky Way (MW) – M31 orbit and angular momentum. For Local Group (LG) mass consistent with measured values, and assuming the system evolves in isolation, we show a wide range of orbits is possible. We compare to a sample of LG-like systems in the Illustris simulation, and find that ∼13 per cent of these pairs have undergone a pericentric passage. Using the simulated sample, we examine how accurately an isolated, two-body model describes the MW–M31 orbit, and show that ∼10 per cent of the analogues in the simulation are well-modelled by such an orbit. Systems that evolve in isolation by this definition are found to have a lower rate of major mergers, and in particular have no major mergers since $z \approx 0.3$. For all systems, we find an increase in the orbital angular momentum which is fairly independent of the merger rate, and is possibly explained by the influence of tidal torques on the LG. Given the likely quiet recent major merger history of the MW, it is plausible that the isolated two-body model appropriately describes the orbit, though recent evidence for a major merger in M31 may complicate this interpretation.

Hartl, Odelia V. (ORCID:0000000202379726)↗
DOE OSTI2025

Continuous surface-to-distributed acoustic sensor snapshots explain reactivation of individual natural fractures during an unconventional reservoir stimulation

ABSTRACT Fiber-optic sensing technologies allow petroleum engineering teams to detect hydraulic fracture interaction with boreholes during unconventional reservoir stimulation. In combination with high-repeatability seismic sources, the same distributed acoustic sensors (DASs) enable vertical seismic profiling (VSP) of the fracture evolution away from the boreholes. We discovered clear signatures of seismic scattering on activated fractures during nine days of continuous seismic monitoring of the fracturing stages at the Austin Chalk/Eagle Ford Field Laboratory. The present study applies a novel approach for quantitative analysis of the scattering events in terms of the evolution of the geometry and elastic stiffness of individual fractures. Our characterization strategy sequentially refines the fracture models: from a stack of 1D soft layers to 3D rectangular inclusions. First, we estimate the number of fracture locations and reflectivity using a modified sparse-spike deconvolution of the stacked VSP traces. The fracture set consists of five fractures spaced by 15–30 m with a reflectivity of approximately 1%. Then, we develop a scattering integral method to refine these estimates along with an inversion of the fracture top and bottom for each monitoring vintage. We find that, initially, some of the fractures are located above the monitoring fiber with the height of approximately 100 m. Then we integrate the seismic interpretation with the low-frequency DAS and pressure and microseismic monitoring to reconstruct the activation process of the fractures. Most likely, some of the natural fractures slowly grew downward to the monitoring fiber as a result of fluid injections in the stimulated well. This led to bright strain anomalies but did not trigger seismicity. The top of the fractures remained almost constant and were limited by a lithologic boundary/stress barrier. To our knowledge, this is the first time VSP data enabled tracking of the fracture evolution with such high spatial and temporal resolution, which was previously only available for crosswell surveys and at a much smaller scale.

Glubokovskikh, Stanislav↗
DOE OSTI2025

The design space of E(3)-equivariant atom-centred interatomic potentials

Abstract Molecular dynamics simulation is an important tool in computational materials science and chemistry, and in the past decade it has been revolutionized by machine learning. This rapid progress in machine learning interatomic potentials has produced a number of new architectures in just the past few years. Particularly notable among these are the atomic cluster expansion, which unified many of the earlier ideas around atom-density-based descriptors, and Neural Equivariant Interatomic Potentials (NequIP), a message-passing neural network with equivariant features that exhibited state-of-the-art accuracy at the time. Here we construct a mathematical framework that unifies these models: atomic cluster expansion is extended and recast as one layer of a multi-layer architecture, while the linearized version of NequIP is understood as a particular sparsification of a much larger polynomial model. Our framework also provides a practical tool for systematically probing different choices in this unified design space. An ablation study of NequIP, via a set of experiments looking at in- and out-of-domain accuracy and smooth extrapolation very far from the training data, sheds some light on which design choices are critical to achieving high accuracy. A much-simplified version of NequIP, which we call BOTnet (for body-ordered tensor network), has an interpretable architecture and maintains its accuracy on benchmark datasets.

Computer Science↗
DOE OSTI2025

Subhaloes are anisotropically distributed and aligned with the smooth matter distribution of their host haloes

ABSTRACT We investigate the distributions of subhaloes about their hosts in two suites of zoom-in N-body simulations of halo growth – one suite focused on Milky Way-Mass haloes ($\sim 10^{12} \, \mathrm{M}_{\odot }$) and another focused on cluster-mass haloes ($\sim 10^{15} \, \mathrm{M}_{\odot }$) in the Symphony simulation suite. We find, in agreement with previous work on this subject, that subhaloes are distributed anisotropically about their host haloes. In particular, the positions of subhaloes lie preferentially near the major axes of their host haloes, possibly implying that satellite galaxies will exhibit a similar alignment. Furthermore, we show that in two-dimensional projection subhaloes are more likely to be observed near the halo centre (where the central galaxy presumably resides) when the host halo is projected nearly along its major axis. This projection effect is significant. Within projected radii of a few per cent of the virial radius of the host halo, the fraction of mass in subhaloes is $\sim 175~{{\ \rm per\ cent}}$ larger for Milky Way-mass haloes and as much as $\sim 195~{{\ \rm per\ cent}}$ larger for cluster haloes when projected along the major axis as compared to the average from a random projection. This result has consequences for many applications including the interpretation of gravitational lenses. Finally, we find that the orbital angular momentum vector of subhaloes is aligned with the angular momentum vector of their host halo, indicating that a significant component of a halo’s angular momentum may be carried in its subhaloes. This has consequences for galaxy formation models which use host halo angular momentum as a proxy for galaxy momentum.

Mezini, Lorena (ORCID:0000000300053973)↗
DOE OSTI2024

Dynamic changes in evapotranspiration, canopy photosynthesis and expansive growth in open field under rapid fluctuating radiation

Abstract In open fields, plants experience dynamic changes in environment, particularly radiation, temperature, wind, and humidity but their short-term responses have not been adequately characterized under natural conditions. In this study, we assessed causal effects of rapid radiation fluctuation on seven plant parameters in open fields of cotton and sweet corn canopies. The parameters are evapotranspiration ( ET ), canopy photosynthesis indicated by CO2 flux from air to canopy ( FCO2 ), sensible heat energy flux ( H ), canopy conductance for water vapor ( gcw ), canopy surface temperature ( Ts ), shoot and leaf elongation rate, and stem diameter change. The energy and CO2 fluxes were measured with Bowen ratio/energy balance/CO2 gradient (BREB+) technique, using averaging time of 5 min. Shoot + leaf elongation and stem diameter change were monitored with position transducers using averaging time of 1 min. All parameters were all found to respond to change in radiation and transpiration within minutes or sooner. While radiation effects on canopy gas exchanges are expected, illuminating are the indirect but immediate effects on shoot + leaf growth and stem diameter change through radiation effects on transpiration and plant water status. A novel finding is that gcw also responded within minutes or sooner to radiation fluctuations and that FCO2 was related almost linearly to gcw . Results are discussed in terms of soil-plant-atmosphere continuum, and interpreted in terms of dynamic interactions between transpiration and plant water status. The clear inverse relationship between ET and elongation rate or stem diameter changes provides additional evidence supporting the validity of 5-min averaging for the BREB + technique.

Hsiao, Theodore C.↗
DOE OSTI2024

PSInet: a new global water potential network

Abstract Given the pressing challenges posed by climate change, it is crucial to develop a deeper understanding of the impacts of escalating drought and heat stress on terrestrial ecosystems and the vital services they offer. Soil and plant water potential play a pivotal role in governing the dynamics of water within ecosystems and exert direct control over plant function and mortality risk during periods of ecological stress. However, existing observations of water potential suffer from significant limitations, including their sporadic and discontinuous nature, inconsistent representation of relevant spatio-temporal scales and numerous methodological challenges. These limitations hinder the comprehensive and synthetic research needed to enhance our conceptual understanding and predictive models of plant function and survival under limited moisture availability. In this article, we present PSInet (PSI—for the Greek letter Ψ used to denote water potential), a novel collaborative network of researchers and data, designed to bridge the current critical information gap in water potential data. The primary objectives of PSInet are as follows. (i) Establishing the first openly accessible global database for time series of plant and soil water potential measurements, while providing important linkages with other relevant observation networks. (ii) Fostering an inclusive and diverse collaborative environment for all scientists studying water potential in various stages of their careers. (iii) Standardizing methodologies, processing and interpretation of water potential data through the engagement of a global community of scientists, facilitated by the dissemination of standardized protocols, best practices and early career training opportunities. (iv) Facilitating the use of the PSInet database for synthesizing knowledge and addressing prominent gaps in our understanding of plants’ physiological responses to various environmental stressors. The PSInet initiative is integral to meeting the fundamental research challenge of discerning which plant species will thrive and which will be vulnerable in a world undergoing rapid warming and increasing aridification.

Forestry↗
DOE OSTI2024

Cosmic neutrino decoupling and its observable imprints: insights from entropic-dual transport

Abstract Very different processes characterize the decoupling of neutrinos to form the cosmic neutrino background (CνB) and the much later decoupling of photons from thermal equilibrium to form the cosmic microwave background (CMB). The CνB emerges from the fuzzy, energy-dependent neutrinosphere and encodes the physics operating in the early universe in the temperature rangeT∼ 10 MeV toT∼ 10 keV. This is the epoch where beyond Standard Model (BSM) physics, especially in the neutrino sector, may be influential in setting the light element abundances, the necessarily distorted fossil neutrino energy spectra, and other light particle energy density contributions. Here we use techniques honed in extensive CMB studies to analyze the CνB as calculated in detailed neutrino energy transport and nuclear reaction simulations of the protracted weak decoupling and primordial nucleosynthesis epochs. Our moment method, relative entropy, and differential visibility approach can leverage future high precision CMB and light element primordial abundance measurements to provide new insights into the CνB and any BSM physics it encodes. We demonstrate that the evolution of the energy spectrum of the CνB throughout the weak decoupling epoch is accurately captured in the Standard Model by only three parameters per species, a non-trivial conclusion given the deviation from thermal equilibrium and the impact of the decrease of electron-positron pairs. Furthermore, we can interpret each of the three parameters as physical characteristics of a non-equilibrium system. Though the treatment presented here makes some simplifying assumptions including ignoring neutrino flavor oscillations, the success of our compact description within the Standard Model motivates its use also in BSM scenarios. We further demonstrate how observations of primordial light element abundances can be used to place constraints on the CνB energy spectrum, deriving response functions that can be applied for general deviations from a thermal spectrum. Combined with the description of those deviations that we develop here, our methods provide a convenient and powerful framework to constrain the impact of BSM physics on the CνB.

Astronomy & Astrophysics↗
DOE OSTI2025

Constraining aerosol–cloud adjustments by uniting surface observations with a perturbed parameter ensemble

Abstract. Aerosol–cloud interactions (ACIs) are the largest source of uncertainty in inferring the magnitude of future warming consistent with the observational record. The effective radiative forcing due to ACI (ERFaci) is dominated by liquid clouds and is composed of two terms: the change in cloud albedo due to redistributing liquid over a larger number of cloud droplets (Nd) and the change in cloud macrophysical properties due to changes in cloud microphysics. These terms are, respectively, referred to as the radiative forcing due to ACI (RFaci) and aerosol–cloud adjustments. While the magnitude of RFaci is uncertain, its sign is confidently negative and results in a cooling in the historical record. In contrast, the adjustment of cloud liquid water path (LWP) to enhanced Nd and associated radiative forcing is uncertain in sign. Increased LWP in response to increased Nd is consistent with precipitation suppression, while decreased LWP in response to increased Nd is consistent with enhanced evaporation from cloud top. Observational constraints of these processes are poor in part because of causal ambiguity in the relationship between Nd and LWP. To better understand this relationship, precipitation (P), Nd, and LWP surface observations from the Eastern North Atlantic (ENA) atmospheric observatory are combined with the output from a perturbed parameter ensemble (PPE) hosted in the Community Atmosphere Model version 6 (CAM6). This allows for causal interpretation of observed covariability. Observations of precipitation and cloud from ENA constrain the range of possible LWP aerosol–cloud adjustments relative to the prior from the PPE by 15 %, resulting in a global value that is confidently positive (a historical cooling) ranging from 2.1 to 6.9 g m−2. It is found that observed covariability between Nd and LWP is dominated by coalescence scavenging and that this observed covariability is not strongly related to aerosol–cloud adjustments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Riverine dissolved organic matter transformations increase with watershed area, water residence time, and Damköhler numbers in nested watersheds

Abstract Quantifying the relative influence of factors and processes controlling riverine ecosystem function is essential to predicting future conditions under global change. Dissolved organic matter (DOM) is a fundamental component of riverine ecosystems that fuels microbial food webs, influences nutrient and light availability, and represents a significant carbon flux globally. The heterogeneous nature of DOM molecular composition and its propensity for interaction (i.e., functional diversity) can characterize riverine ecosystem function across spatiotemporal scales. To investigate fundamental drivers of DOM diversity, we collected seasonal water samples from 42 nested locations within five watersheds spanning multiple watershed sizes (~ 5 to 30,000 km 2 ) across the United States. Patterns in DOM molecular richness, aromaticity, relative abundance of N-containing formulas, and putative biochemical transformations derived from high-resolution mass spectrometry were assessed across gradients of explanatory variables associated with watershed characteristics (e.g., watershed area, water residence time, land cover). We found that putative biochemical transformations were more strongly related to explanatory variables across watersheds than common bulk DOM parameters and that watershed area, surface water residence time and derived Damköhler numbers representing DOM reactivity timescales were strong predictors of DOM diversity. The data also indicate that catchment-specific land cover factors can significantly influence DOM diversity in diverging directions. Overall, the results highlight the importance of considering water residence time and land cover when interpreting longitudinal patterns in DOM chemistry and the continued challenge of identifying generalizable drivers that are transferable across watershed and regional scales for application in Earth system models. This work also introduces a Findable Accessible Interoperable Reusable (FAIR) dataset (> 300 samples) to the community for future syntheses.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

32 examples of LLM applications in materials science and chemistry: towards automation, assistants, agents, and accelerated scientific discovery

Abstract Large language models (LLMs) are reshaping many aspects of materials science and chemistry research, enabling advances in molecular property prediction, materials design, scientific automation, knowledge extraction, and more. Recent developments demonstrate that the latest class of models are able to integrate structured and unstructured data, assist in hypothesis generation, and streamline research workflows. To explore the frontier of LLM capabilities across the research lifecycle, we review applications of LLMs through 32 total projects developed during the second annual LLM hackathon for applications in materials science and chemistry, a global hybrid event. These projects spanned seven key research areas: (1) molecular and material property prediction, (2) molecular and material design, (3) automation and novel interfaces, (4) scientific communication and education, (5) research data management and automation, (6) hypothesis generation and evaluation, and (7) knowledge extraction and reasoning from the scientific literature. Collectively, these applications illustrate how LLMs serve as versatile predictive models, platforms for rapid prototyping of domain-specific tools, and much more. In particular, improvements in both open source and proprietary LLM performance through the addition of reasoning, additional training data, and new techniques have expanded effectiveness, particularly in low-data environments and interdisciplinary research. As LLMs continue to improve, their integration into scientific workflows presents both new opportunities and new challenges, requiring ongoing exploration, continued refinement, and further research to address reliability, interpretability, and reproducibility.

Computer Science↗
DOE OSTI2025

High‐Resolution National‐Scale Water Modeling Is Enhanced by Multiscale Differentiable Physics‐Informed Machine Learning

Abstract The National Water Model (NWM) is a key tool for flood forecasting, planning, and water management. Key challenges facing the NWM include calibration and parameter regionalization when confronted with big data. We present two novel versions of high‐resolution (∼37 km 2 ) differentiable models (a type of hybrid model): one with implicit, unit‐hydrograph‐style routing and another with explicit Muskingum‐Cunge routing in the river network. The former predicts streamflow at basin outlets whereas the latter presents a discretized product that seamlessly covers rivers in the conterminous United States (CONUS). Both versions use neural networks to provide a multiscale parameterization and process‐based equations to provide a structural backbone, which were trained simultaneously (“end‐to‐end”) on 2,807 basins across the CONUS and evaluated on 4,997 basins. Both versions show great potential to elevate future NWM performance for extensively calibrated as well as ungauged sites: the median daily Nash‐Sutcliffe efficiency of all 4,997 basins is improved to around 0.68 from 0.48 of NWM3.0. As they resolve spatial heterogeneity, both versions greatly improved simulations in the western CONUS and also in the Prairie Pothole Region, a long‐standing modeling challenge. The Muskingum‐Cunge version further improved performance for basins >10,000 km 2 . Overall, our results show how neural‐network‐based parameterizations can improve NWM performance for providing operational flood predictions while maintaining interpretability and multivariate outputs. The modeling system supports the Basic Model Interface (BMI), which allows seamless integration with the next‐generation NWM. We also provide a CONUS‐scale hydrologic data set for further evaluation and use.

Song, Yalan [Civil and Environmental Engineering T↗
DOE OSTI2024

Applying queueing theory to evaluate wait-time-savings of triage algorithms

Abstract In the past decade, artificial intelligence (AI) algorithms have made promising impacts in many areas of healthcare. One application is AI-enabled prioritization software known as computer-aided triage and notification (CADt). This type of software as a medical device is intended to prioritize reviews of radiological images with time-sensitive findings, thus shortening the waiting time for patients with these findings. While many CADt devices have been deployed into clinical workflows and have been shown to improve patient treatment and clinical outcomes, quantitative methods to evaluate the wait-time-savings from their deployment are not yet available. In this paper, we apply queueing theory methods to evaluate the wait-time-savings of a CADt by calculating the average waiting time per patient image without and with a CADt device being deployed. We study two workflow models with one or multiple radiologists (servers) for a range of AI diagnostic performances, radiologist’s reading rates, and patient image (customer) arrival rates. To evaluate the time-saving performance of a CADt, we use the difference in the mean waiting time between the diseased patient images in the with-CADt scenario and that in the without-CADt scenario as our performance metric. As part of this effort, we have developed and also share a software tool to simulate the radiology workflow around medical image interpretation, to verify theoretical results, and to provide confidence intervals for the performance metric we defined. We show quantitatively that a CADt triage device is more effective in a busy, short-staffed reading setting, which is consistent with our clinical intuition and simulation results. Although this work is motivated by the need for evaluating CADt devices, the evaluation methodology presented in this paper can be applied to assess the time-saving performance of other types of algorithms that prioritize a subset of customers based on binary outputs.

Thompson, Yee Lam Elim (ORCID:0000000196537707)↗
DOE OSTI2026

Data from: "Towards CONUS-Wide ML-Augmented Conceptually-Interpretable Modeling of Catchment-Scale Precipitation-Storage-Runoff Dynamics"

This data package was generated to support the manuscript “Towards CONUS-Wide Machine Learning-Augmented Conceptually Interpretable Modeling of Catchment-Scale Precipitation-Storage-Runoff Dynamics.” It provides input files, model outputs, plotting data, scripts, notebooks, and documentation used to develop, evaluate, and reproduce Mass-Conserving Perceptron (MCP)-based hydrologic modeling experiments across 513 selected Catchment Attributes and Meteorology for Large-sample Studies in the United States (CAMELS-US) basins. The files are organized by modeling component and analysis purpose, including rainfall–runoff experiments, snow module experiments, coupled hydrologic-snow experiments, Long Short-Term Memory (LSTM) benchmark results, model skill metrics, initialization and epoch records, cell-state normalization files, Akaike Information Criterion (AIC)-based model comparison files, and data used to generate manuscript figures. Tabular files can be opened using standard spreadsheet software or Python/R data-analysis tools. Python scripts, Jupyter notebooks, and selected MATLAB scripts are included for model execution, postprocessing, plotting, and statistical analysis. Quality assurance and quality control were conducted through the source-data selection and modeling workflow. Meteorological forcing, streamflow, and static catchment attributes were derived from the CAMELS-US dataset, and snow water equivalent data were derived from the University of Arizona (UA) Snow Water Equivalent dataset. Selected basins and time periods were screened during the associated research workflow to avoid missing observations or poor-quality cases. Static geospatial features were processed primarily using Quantum Geographic Information System (QGIS) and Geospatial Data Abstraction Library (GDAL) workflows. Additional details are provided in the associated manuscript and documentation.

ESS-DIVE CSV File Formatting Guidelines Reporting ↗
DOE OSTI2024

Wall heating by subcritical energetic electrons generated by the runaway electron avalanche source *

Abstract Subcritical energetic electrons (SEEs) produced by the runaway electron (RE) avalanche source at energies below the runaway threshold are found to be the primary contributor to surface heating of plasma-facing components (PFCs) during final loss events. This finding is supported by theoretical analysis, computational modeling with the Kinetic Orbit Runaway electrons Code (KORC), and qualitative agreement with DIII-D experimental observations. The avalanche source generates significantly more secondary electrons below the runaway threshold, which thermalize rapidly when well-confined. However, during a final loss event, the RE beam impacts the first wall, and SEEs are deconfined before they can thermalize. Additionally, because the energy deposition length decreases faster than energy, the deposited energy density, and thus the maximum PFC surface temperature change, is larger for SEEs than REs. KORC simulations employ an analytic first wall to model particle deconfinement onto a non-axisymmetric wall composed of individual tiles. PFC surface heating is calculated using a 1D model extended to include an energy-dependent deposition length scale. Simulations of DIII-D qualitatively agree with infrared (IR) imaging only when SEEs from the avalanche source are included. These results demonstrate that SEEs are the dominant contributor to PFC surface heating and indicate that the avalanche source plays a critical role in the PFC damage caused during final loss events. The prominence of SEEs also has important implications for interpreting IR imaging, one of the primary diagnostics for RE-wall interaction diagnosis, despite REs dominating the energy and current density. This result improves predictions of wall damage due to post-disruption REs to estimate material lifetime and design RE mitigation systems for ITER and future reactors.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Decoupling of peeling and ballooning thresholds for pedestal stability and reduction in ELM frequency via enhanced turbulence with edge electron cyclotron heating in DIII-D

Abstract The edge localized mode (ELM) frequency ( f ELM ) decreased by 63% when electron cyclotron heating (ECH) deposition location is shifted from ρ = 0.4 to ρ = 0.8 in DIII-D discharges where the power ratio between neutral beam injection (NBI) and ECH ( P NBI / P ECH ) is kept at ∼1. The performance of the pedestal in the ECH heated case is compared with a pure NBI reference discharge while keeping the total input power constant. All these discharges are performed at balanced input torque conditions. Furthermore, in the pure NBI discharge a strong decoupling of the peeling–ballooning (PB) thresholds is observed. The PB decoupling is preserved when the ECH is deposited at ρ = 0.8 and P NBI / P ECH ∼ 1, while the thresholds manifest a closed stability boundary when the ECH is deposited at ρ = 0.4. The inter-ELM pedestal recovery time is considerably larger for the ECH at ρ = 0.8 case. Increased pedestal turbulence is observed in beam emission spectroscopy (BES), Doppler backscattering and magnetic diagnostics for the ECH at the ρ = 0.8 case. Strong growth of a TEM-like mode is observed in BES and the mode growth is correlated with the decrease in f ELM . In view of these observations, the increased pedestal turbulence seems to be the plausible reason behind the delayed pedestal recovery following an ELM event in the ECH at ρ = 0.8 case, and the preservation of PB decoupling through temperature pedestal profile widening. TRANSP interpretative simulations show that the ECH at the ρ = 0.8 case is more susceptible to ITG/TEM turbulence.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗