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DOE OSTI2026

Statistical inference of anomalous thermal transport with uncertainty quantification for interpretive 2D SOL models

The critical task of inferring anomalous cross-field transport coefficients is addressed in simulations of boundary plasmas with fluid models. A workflow for parameter inference in the UEDGE fluid code is developed using Bayesian optimization with parallelized sampling and integrated uncertainty quantification. In this workflow, transport coefficients are inferred by maximizing their posterior probability distribution, which is generally multidimensional and non-Gaussian. Uncertainty quantification is integrated throughout the optimization within the Bayesian framework that combines diagnostic uncertainties and model limitations. As a concrete example, we infer the anomalous electron thermal diffusivity $\chi_\perp$ from an interpretive 2D model describing electron heat transport in the conduction-limited region with radiative power loss. The workflow is first benchmarked against synthetic data and then tested on H-, L-, and I-mode discharges to match their midplane temperature and divertor heat flux profiles. We demonstrate that the workflow efficiently infers diffusivity and its associated uncertainty, generating 2D profiles that match 1D measurements. Future efforts will focus on incorporating more complicated fluid models and analyzing transport coefficients inferred from a large database of experimental results.

Bayesian optimization↗

DOE OSTI2019

Machine learning for disruption warnings on Alcator C-Mod, DIII-D, and EAST

In this paper, we report on disruption prediction using a shallow machinelearning method known as Random Forests, trained on large databases containingonly plasma parameters that are available in real-time on Alcator C-Mod, DIII-D, and EAST. The database for each tokamak contains parameters sampled ~10 6 times throughout ~10 4 discharges (disruptive and non-disruptive) over the last 4years of operation. It is found that a number of parameters (e.g.P rad /P input ,l i ,n/n G ,B n=1 /B 0 ) exhibit changes in aggregate as a disruption is approached on one ormore of these tokamaks. However, for each machine, the most useful parameters,as well as the details of their precursor behaviors, are markedly different. When the prediction problem is framed using a binary classification scheme to discriminate between time slices "close to disruption" and "far from disruption," it is found that the prediction algorithms differ substantially in performance among the three machines ona time slice-by-time slice basis, but have similar disruption detection rates (~80-90%) on a shot-by-shot basis after appropriate optimisation. This could have important implications for disruption prediction and avoidance on ITER, for which developmentof a training database of disruptions may be infeasible. The algorithm's output is interpretable using a method that identifies the most strongly contributing inputsignals, which may have implications for avoiding disruptive scenarios. Lastly, to further support its real-time capability, successful applications in inter-shot and real-time environments on EAST and DIII-D are also discussed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2019

Simulation of neutron emission in neutral beam injection heated plasmas with the real-time code RABBIT

In plasmas heated with deuterium beams a deficit of the expected fusion neutron rate is an indicator of the deterioration of the fast-ion confinement, caused, for instance, by magnetohydrodynamic instabilities. The capability of predicting this deficit during the discharge relies on the availability of real-time estimates of the neutron rate from NBI codes which must be fast and accurate at the same time. Therefore, the recently developed real-time RABBIT code for NBI simulations has been extended to output the distribution function and calculate the neutron emission. After the description of this newly installed diagnostics in RABBIT, benchmarks with NUBEAM, a massively used and validated Monte Carlo NBI solver, are discussed on ASDEX-Upgrade and JET cases. A first application for control-room intershot analysis on DIII-D is presented, and the results are compared on a large database with a slower NUBEAM analysis. Further application possibilities, e.g. for real-time control of Alfvén eigenmodes, are outlined.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2020

Physics-guided machine learning approaches to predict the ideal stability properties of fusion plasmas

One of the biggest challenges to achieve the goal of producing fusion energy in tokamak devices is the necessity of avoiding disruptions of the plasma current due to instabilities. The disruption event characterization and forecasting (DECAF) framework has been developed in this purpose, integrating physics models of many causal events that can lead to a disruption. Two different machine learning approaches are proposed to improve the ideal magnetohydrodynamic (MHD) no-wall limit component of the kinetic stability model included in DECAF. First, a random forest regressor (RFR), was adopted to reproduce the DCON computed change in plasma potential energy without wall effects for a large database of equilibria from the national spherical torus experiment (NSTX). This tree-based method provides an analysis of the importance of each input feature, giving an insight into the underlying physics phenomena. Secondly, a fully-connected neural network has been trained on sets of calculations with the DCON code, to get an improved closed form equation of the no-wall β limit as a function of the relevant plasma parameters indicated by the RFR. The neural network has been guided by physics theory of ideal MHD in its extension outside the domain of the NSTX experimental data. The estimated value has been incorporated into the DECAF kinetic stability model and tested against a set of experimentally stable and unstable discharges. Moreover, the neural network results were used to simulate a real-time stability assessment using only quantities available in real-time. Finally, the portability of the model was investigated, showing encouraging results by testing the NSTX-trained algorithm on the mega ampere spherical tokamak (MAST).

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2020

"BAAE" instabilities observed without fast ion drive

The instability that was previously identified as a fast-ion driven beta-induced Alfv´en-acoustic eigenmode (BAAE) in DIII-D was misidentified. In a dedicated experiment, low frequency modes (LFM) with characteristic “Christmas light” patterns of brief instability linked to the safety factor evolution occur in plasmas with electron temperature T e ≳ 2.1 keV but modest beta. To isolate the importance of different driving gradients on these modes, the electron cyclotron heating power and 80 keV, sub-Alfv´enic neutral beams are altered for 50-100 ms durations in reproducible discharges. Although beta-induced Alfv´en eigenmodes and reversed-shear Alfv´en eigenmodes stabilize when beam injection ceases (as expected for a fast-ion driven instability), the low frequency modes that were called BAAEs persist. Data mining reveals that characteristic LFM instabilities can occur in discharges with no beam heating but strong electron cyclotron heating. A large database of over 1000 discharges shows that LFMs are only unstable in plasmas with hot electrons but modest overall beta. The experimental LFMs have low frequencies (comparable to diamagnetic drift frequencies) in the plasma frame, occur near the minimum of the safety factor q min , and appear when q min is close to rational values. In conclusion, theoretical analysis suggests that the LFMs are a low frequency reactive instability of predominately Alfv´enic polarization.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2021

Prediction of electron density and pressure profile shapes on NSTX-U using neural networks

A new model for prediction of electron density and pressure profile shapes on NSTX and NSTX-U has been developed using neural networks. The model has been trained and tested on measured profiles from experimental discharges during the first operational campaign of NSTX-U. By projecting profiles onto empirically derived basis functions, the model is able to efficiently and accurately reproduce profile shapes. In order to project the performance of the model to upcoming NSTX-U operations, a large database of profiles from the operation of NSTX is used to test performance as a function of available data. The rapid execution time of the model is well suited to the planned applications, including optimization during scenario development activities, and real-time plasma control. Finally, a potential application of the model to real-time profile estimation is demonstrated.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2021

Verification of a quasi-linear model for gyrokinetic turbulent transport

The verification and calibration of a new quasi-linear transport model with a large database of gyrokinetic turbulence simulations is presented in this paper. In a previous paper (Staebler et al 2020 Plasma Phys. Control. Fusion 63 015013), a model for the saturated spectrum of electric potential fluctuations was developed based on the properties of the non-linear 3D spectrum. In this paper, a modification to the overall multiplicative factor in this model is found to be necessary to improve the fit to scans of the temperature and density gradients. The error in the fit of the quasi-linear fluxes of electron and ion energy fluxes is significantly better than for previous saturation models. The spectral shift model for the impact of equilibrium E × B velocity shear (Staebler et al 2013 Phys. Rev. Lett. 110 055003) and the zonal flow mixing model for electron-scale turbulence (Staebler et al 2016 Phys. Plasmas 23 062518) are both revised to be compatible with this new model. Furthermore, the models for the loss of bounce averaging and electron collisions in the TGLF reduced linear equations (Staebler et al 2005 Phys. Plasmas 12 102508) are also changed to improve the linear eigenmodes.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

Energetic particle-induced geodesic acoustic modes on DIII-D

Various properties of the energetic particle-induced geodesic acoustic mode (EGAM) are explored in this large database analysis of DIII-D experimental data. EGAMs are n = 0 modes with m = 0 electrostatic potential fluctuations (where n/m = toroidal/poloidal mode number), m = 1 density fluctuations, and m = 2 magnetic fluctuations. The fundamental frequency (~20–40 kHz) of the mode is typically below that of the traditional geodesic acoustic mode frequency. EGAMs are most easily destabilized by beams in the counter plasma current (counter-I p ) direction as compared to co-Ip and off-axis beams. During counter beam injection, the mode frequency is found to have the strongest linear dependence (correlation coefficient r = –0.71) with the safety factor (q). Here, the stability of the mode in the space of q and poloidal beta (β p ) shows a clear boundary for the mode stability. The stability of the mode depends more strongly on damping rate than on fast-ion drive for a given injection geometry.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Metrics and extrapolation of resonant magnetic perturbation thresholds for ELM suppression

This large database study of resonant magnetic perturbation (RMP) edge localized mode (ELM) suppression thresholds in the AUG, DIII-D, EAST, and KSTAR tokamaks details the key strengths and weaknesses of RMP metrics. The RMP ELM suppression database used for this work contains plasma information at the time of transition from ELMing to ELM suppressed states where a clear experimental threshold is identified. The experimental threshold distributions are compared for five metrics: (1) the island overlap width, (2) pedestal top Chirikov overlap, (3) peeling edge displacement, (4) pedestal top resonant drive, and (5) edge dominant mode overlap. The distributions, the regularity of the dependence on RMP coil currents, and the sensitivities of a given metric to equilibrium reconstruction details are compared. The overlap metric proves to be a good compromise between including the appropriate plasma response physics and maintaining a numerical robustness. This quantity does not exhibit clear power-law scalings for projection, but machine learning can assist in predicting thresholds within the existing parameter ranges and providing uncertainty quantification of those predictions. Two new first-principles models, one utilizing a threshold from the non-linear Modified Rutherford equation evaluated at the pedestal top and one utilizing the SLAYER code to calculate the linear tearing threshold from torque balance, offer possible paths to extrapolation beyond the existing database parameter space.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

Coincidence anomaly detection for unsupervised locating of edge localized modes in the DIII-D tokamak dataset

Using supervised learning to train a machine learning model to predict an on-coming edge localized mode (ELM) requires a large number of labeled samples. Creating an appropriate data set from the very large database of discharges at a long-running tokamak, such as DIII-D, would be a very time-consuming process for a human. Considering this need and difficulty, we use coincidence anomaly detection, an unsupervised learning technique, to train an ELM-identifier to identify and label ELMs in the DIII-D discharge database. This ELM-identifier shows, simultaneously, a precision of 0.68 and a recall of 0.63 (AUC is 0.73) on identifying ELMs in example time series pulled from thousands of discharges spanning five years. In a test set of 50 discharges, the algorithm finds over 26 thousand ELM candidates, more than 5 times the existing catalog of ELMs labeled by humans.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2023

Computer-aided, resistance gene-guided genome mining for proteasome and HMG-CoA reductase inhibitors

Secondary metabolites (SMs) are biologically active small molecules, many of which are medically valuable. Fungal genomes contain vast numbers of SM biosynthetic gene clusters (BGCs) with unknown products, suggesting that huge numbers of valuable SMs remain to be discovered. It is challenging, however, to identify SM BGCs, among the millions present in fungi, that produce useful compounds. One solution is resistance gene-guided genome mining, which takes advantage of the fact that some BGCs contain a gene encoding a resistant version of the protein targeted by the compound produced by the BGC. The bioinformatic signature of such BGCs is that they contain an allele of an essential gene with no SM biosynthetic function, and there is a second allele elsewhere in the genome. We have developed a computer-assisted approach to resistance gene-guided genome mining that allows users to query large databases for BGCs that putatively make compounds that have targets of therapeutic interest. Working with the MycoCosm genome database, we have applied this approach to look for SM BGCs that target the proteasome β6 subunit, the target of the proteasome inhibitor fellutamide B, or HMG-CoA reductase, the target of cholesterol reducing therapeutics such as lovastatin. Our approach proved effective, finding known fellutamide and lovastatin BGCs as well as fellutamide- and lovastatin-related BGCs with variations in the SM genes that suggest they may produce structural variants of fellutamides and lovastatin. Gratifyingly, we also found BGCs that are not closely related to lovastatin BGCs but putatively produce novel HMG-CoA reductase inhibitors.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2018

Alpha Air Sample Counting Efficiency Versus Dust Loading: Evaluation of a Large Data Set

Dust loading on air sample filters is known to cause a loss of efficiency for direct counting of alpha activity on the filters, but the amount of dust loading and the correction factor needed to account for attenuated alpha particles is difficult to assess. In this paper, correction factors are developed by statistical analysis of a large database of air sample results for a uranium and plutonium processing facility at the Savannah River Site. As is typically the case, dust-loading data is not directly available, but sample volume is found to be a reasonable proxy measure; the amount of dust loading is inferred by a combination of the derived correction factors and a Monte Carlo model. The technique compares the distribution of activity ratios [beta/(beta + alpha)] by volume and applies a range of correction factors on the raw alpha count rate. The best-fit results with this method are compared with MCNP modeling of activity uniformly deposited in the dust and analytical laboratory results of digested filters. Finally, a linear fit is proposed to evenly-deposited alpha activity collected on filters with dust loading over a range of about 2 mg cm -2 to 1,000 mg cm -2 .

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2019

Machine learning the density functional theory potential energy surface for the inorganic halide perovskite CsPbBr 3

The temperature and pressure dependence of structural phase transitions determine the structure-functionality relationships in many technologically important materials. Harmonic Hamiltonians have proven successful in predicting the vibrational properties of many materials. However, they are inadequate for modeling structural phase transitions in crystals with potential energy surfaces that are either strongly anharmonic or nonconvex with respect to collective atomic displacements or homogeneous strains. In this paper we develop a framework to express highly anharmonic first-principles potential energy surfaces as polynomials of collective cluster deformations. We further adapt the approach to a nonlinear extension of the cluster expansion formalism through the use of an artificial neural net model. Finally, the machine learning models are trained on a large database of first-principles calculations and are shown to reproduce the potential energy surface with low error.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2020

ChemEnv : a fast and robust coordination environment identification tool

Coordination or local environments have been used to describe, analyze and understand crystal structures for more than a century. Here, a new tool called ChemEnv, which can identify coordination environments in a fast and robust manner, is presented. In contrast to previous tools, the assessment of the coordination environments is not biased by small distortions of the crystal structure. Its robust and fast implementation enables the analysis of large databases of structures. The code is available open source within the pymatgen package and the software can also be used through a web app available on http://crystaltoolkit.org through the Materials Project.

42 ENGINEERING↗

DOE OSTI2019

Using Map Matching for DeIdentification of Connected Vehicle Locations

In this paper we introduce a location deidentification procedure that uses road network structure to protect against certain types of inference-based attacks. Our target is large databases containing vehicle locations. Previous anonymization approaches are inappropriate because location generalization and perturbation of geopositions could negatively affect development of safety-critical applications that require precise position information. Furthermore, k-anonymity-based clustering approaches would lead to significant data suppression. Our algorithm attempts to balance privacy protection and data utility, while protecting against re-identification attacks. Our data is from the first connected vehicle model deployment in the United States.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Estimation of Body Fat Percentage for Clinical Pharmacokinetic Studies in Children

Obesity is a prevalent childhood condition and the degree of adiposity appears likely to be an important covariate in the pharmacokinetics (PKs) of many drugs. We undertook these studies to facilitate the evaluation and, where appropriate, quantification of the covariate effect of body fat percentage (BF%) on PK parameters in children. We examined two large databases to determine the values and variabilities of BF% in children with healthy body weights and in those with obesity, comparing the accuracy and precision of BF% estimation by both clinical methods and demographically derived techniques. Additionally, we conducted simulation studies to evaluate the utility of the several methods for application in clinical trials. BF% was correlated with body mass index (BMI), but was highly variable among both children with healthy body weights and those with obesity. Bio‐impedance and several demographically derived techniques produced mean estimates of BF% that differed from dual x‐ray absorptiometry by < 1% (accuracy) and a SD of 5% or less (precision). Simulation studies confirmed that when the differences in precision among the several methods were small compared with unexplained between‐subject variability of a PK parameter, the techniques were of similar value in assessing the contribution of BF%, if any, as a covariate for that PK parameter. The combination of sex and obesity stage explained 68% of the variance of BF% with BMI. The estimation of BF% from sex and obesity stage can routinely be applied to PK clinical trials to evaluate the contribution of BF% as a potential covariate.

Green, Thomas P.↗

DOE OSTI2025

Intelligent Manufacturing Support: Specialized LLMs for Composite Material Processing and Equipment Operation

Engineering educational curriculum and standards cover many material and manufacturing options. However, engineers and designers are often unfamiliar with certain composite materials or manufacturing techniques. Large language models (LLMs) could potentially bridge the gap. Their capacity to store and retrieve data from large databases provides them with a breadth of knowledge across disciplines. However, their generalized knowledge base can lack targeted, industry-specific knowledge. To this end, we present two LLM-based applications based on the GPT-4 architecture: (1) The Composites Guide: a system that provides expert knowledge on composites material and connects users with research and industry professionals who can provide additional support and (2) The Equipment Assistant: a system that provides guidance for manufacturing tool operation and material characterization. By combining the knowledge of general AI models with industry-specific knowledge, both applications are intended to provide more meaningful information for engineers. In this paper, we discuss the development of the applications and evaluate it through a benchmark and two informal user studies. The benchmark analysis uses the Rouge and Bertscore metrics to evaluate our models’ performance against GPT-4o. The results show that GPT-4o and the proposed models perform similarly or better on the ROUGE and BERTScore metrics. The two user studies supplement this quantitative evaluation by asking experts to provide qualitative and open-ended feedback about our model’s performance on a set of domain-specific questions. The results of both studies highlight a potential for more detailed and specific responses with the Composites Guide and the Equipment Assistant.

Kapoor, Gunnika [Oak Ridge National Laboratory (OR↗

DOE OSTI2020

MECHANICAL SHOCK AND VIBRATION ANALYSIS OF SPENT NUCLEAR FUEL CARRIED BY THE ATLAS RAILCAR

Researchers at Pacific Northwest National Laboratory have completed a structural-dynamic analysis of spent nuclear fuel subjected to the mechanical shock and vibration environment that is anticipated during normal conditions of transport in casks carried by the Atlas railcar. The Atlas railcar is a new railcar design that is being developed specifically for the purpose of carrying spent nuclear fuel casks. The analysis used best-estimate railcar dynamics models of the Atlas railcar and considered 17 different spent nuclear fuel transportation cask systems, representing the current fleet of cask options. This work used NUCARS, a specialized railcar dynamics explicit finite element code to calculate railcar dynamic response to prescribed speeds and track configurations. The railcar dynamics models provided cask transient motion for a wide range of speeds and track conditions, generating a relatively large database of potential cask motion. All of the cask motion transients were then applied as loading conditions to LS-DYNA structural-dynamic models of a single fuel rod. The analyses predict that the Equipos Nucleares S.A./U.S. Department of Energy (ENSA/DOE) multimodal transportation test of 2017 provided a relatively stronger vibration environment than is expected from the Atlas railcar. This paper describes the analysis methods, the analysis results, and compares the results of the Atlas transportation analysis to the test results and analyses of the ENSA/DOE multimodal transportation test of 2017.

Klymyshyn, Nicholas A.↗