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DOE OSTI2024

ETHOS: An automated framework to generate multi-fidelity constitutive data tables and propagate uncertainties to hydrodynamic simulations

Accurate constitutive data, such as equations of state and plasma transport coefficients, are necessary for reliable hydrodynamic simulations of plasma systems such as fusion targets, planets, and stars. Here, we develop a framework for automatically generating transport-coefficient tables using a parameterized model that incorporates data from both high-fidelity sources (e.g., density functional theory calculations and reference experiments) and lower-fidelity sources (e.g., average-atom and analytic models). The framework incorporates uncertainties from these multi-fidelity sources, generating ensembles of optimally diverse tables that are suitable for uncertainty quantification of hydrodynamic simulations. We illustrate the utility of the framework with magnetohydrodynamic simulations of magnetically launched flyer plates, which are used to measure material properties in pulsed-power experiments. We explore how changes in the uncertainties assigned to the multi-fidelity data sources propagate to changes in simulation outputs and find that our simulations are most sensitive to uncertainties near the melting transition. The presented framework enables computationally efficient uncertainty quantification that readily incorporates new high-fidelity measurements or calculations and identifies plasma regimes where additional data will have high impact.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Reinforcement Learning for In-Spill Optimization of the Mu2e Resonant Extraction: Compensating Non-Stationarity

We present design considerations and challenges for the fast machine learning component of a third-order resonant beam extraction regulation system being commissioned to deliver steady beam rates to the mu2e experiment at Fermilab. Dedicated quadrupoles drive the tune toward the 29/3 resonance each spill, extracting beam at kV multiwire septa. The overall Spill Regulation System consists of (1) a “slow” process using ~100-spill averages to adjust the base quad ramp infrequently, (2) a feedforward harmonic content compensator, and (3) the “fast” ML agent reacting during each ongoing spill with on-the-fly additive corrections to the sum of (1) and (2). We have demonstrated improved beam-rate steadying for a fast ML agent compared to a PID controller using a quasi-physical spill simulation, and demonstrated distillation of that simulation into a predictive surrogate model. Current work includes a data-and-training pipeline to generate data-aware surrogates with real-world dynamics, even as the dynamics shift unpredictably. The surrogates are to act as RL environments against which to train our fast ML control agents before deploying them on FPGA in the live system. Further current efforts focus on modeling and controlling beam loss around the storage ring, understanding additional available hardware inputs to the model, and the interplay of these with beam-steadying performance.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

NASA NTRS2013

Inflight Performance of Cassini Reaction Wheel Bearing Drag in 1997-2013

As the first spacecraft to achieve orbit at Saturn in 2004, Cassini has collected science data throughout its four-year prime mission (2004-08), and has since been approved for a first and second extended missions through September 2017. Cassini is a three-axis stabilized spacecraft. It uses reaction wheels to achieve high level of spacecraft pointing stability that is needed during imaging operations of several science instruments. The Cassini flight software makes in-flight estimates of reaction wheel bearing drag torque and made them available to the mission operations team. These telemetry data are being trended for the purpose of monitoring the long-term health of the reaction wheel bearings. Anomalous drag torque signatures observed over the past 15 years are described in this paper. One of these anomalous drag conditions is bearing cage instability that appeared (and disappeared) spontaneously and unpredictably. Cage instability is an uncontrolled vibratory motion of the bearing cage that can produce high-impact forces internal to the bearing that will cause intermittent and erratic torque transients. Characteristics of the observed cage instabilities and other drag torque "spikes" are described in this paper. In day-to-day operations, the reaction wheels' rates must be neither too high nor too low. To protect against operating the wheels in any undesirable conditions (such as prolonged low spin rate operations), a ground software tool named Reaction Wheel Bias Optimization Tool (RBOT) was developed for the management of the wheels. Disciplined and long-term use of this ground software has led to significant reduction in the daily consumption rate of the wheels' low spin rate dwell time. Flight experience on the use of this ground software tool as well as other lessons learned on the management of Cassini reaction wheels is given in this paper.

Cassini/Huygens Mission↗

NASA NTRS2023

Best Practices for the Design, Development, and Operation of Robust and Reliable Space Vehicle Guidance, Navigation, and Control Systems

This document summarizes and updates the NESC Guidance, Navigation, and Control (GN&C) Technical Discipline Team’s (TDT) work to synthesize and document the current best practices for the design & development of robust and reliable GN&C systems for robotic and crewed (human-rated) spacecraft. These GN&C best practices for future science and exploration missions were derived from the lessons learned, both positive and negative, on earlier spaceflight projects, both robotic and crewed. An attempt has been made to capture preferred practices that reflect the key considerations, trades, and processes directly attributed to past mission success.

Guidance, Navigation, and Control↗

DOE OSTI2020

Spatiotemporal Pattern Recognition in the PMU Signals in the WECC system

Phasor measurement unit (PMU) data has been used by multiple power system applications, including state estimation, post event analysis, oscillation detection, model validation, and many others. Still, due to its big data nature and availability to general research institutions, comprehensive understanding of the spatiotemporal patterns and underlying mechanisms are incomplete. This study applies a set of signal processing and machine learning approaches aiming at deciphering the characteristic behaviors of multiple phasor measurement units (PMUs) attributes (e.g., voltage, frequency, rate of change of frequency, phase angle), including their auto-correlation, cross-dependence, similarities and discrepancies across units and temporal scales, and distributions of anomalies and their linkages to potential external factors such as weather events. Data analytics are applied to PMUs from the U.S. Western Electricity Coordinating Council (WECC) system. The PMU measurements, recorded events, outages, and weather extremes are all from real world datasets. The findings from the study and mechanistic understanding of the PMU dynamics help provide guidance on system control or preventing blackouts. The derived metrics can be directly used for adjusting or filtering simulated PMU data used for advanced algorithm development.

Hou, Zhangshuan↗

DOE OSTI2026

Developing a complete AI-accelerated workflow for superconductor discovery

The quest to identify new superconducting materials with enhanced properties is hindered by the prohibitive cost of computing electron-phonon spectral functions, severely limiting the materials space that can be explored. Here, we introduce a Bootstrapped Ensemble of Equivariant Graph Neural Networks (BEE-NET), a machine-learning model trained to predict the Eliashberg spectral function and superconducting critical temperature with a mean-absolute-error of 0.87 K relative to DFT-based Allen-Dynes calculations. Intriguingly, BEE-NET achieves a true-negative-rate of 99.4%, enabling highly efficient screening for the rare property of superconductivity. Integrated into a multi-stage, AI-accelerated discovery pipeline that incorporates elemental-substitution strategies and machine-learned interatomic potentials, our workflow reduced over 1.3 million candidate structures to 741 dynamically and thermodynamically stable compounds with DFT-confirmed T c > 5 K. We report the successful synthesis and experimental confirmation of superconductivity in two of these previously unreported compounds. This study establishes a data-driven framework that integrates machine learning, quantum calculations, and experiments to systematically accelerate superconductor discovery.

Gibson, Jason B. [Quantum Formatics, Cambridge, MA↗

NASA NTRS2017

Development of Increasingly Autonomous Traffic Data Manager Using Pilot Relevancy and Ranking Data

NASA's Safe Autonomous Systems Operations (SASO) project goal is to define and safely enable all future airspace operations by justifiable and optimal autonomy for advanced air, ground, and connected capabilities. This work showcases how Increasingly Autonomous Systems (IAS) could create operational transformations beneficial to the enhancement of civil aviation safety and efficiency. One such IAS under development is the Traffic Data Manager (TDM). This concept is a prototype 'intelligent party-line' system that would declutter and parse out non-relevant air traffic, displaying only relevant air traffic to the aircrew in a digital data communications (Data Comm) environment. As an initial step, over 22,000 data points were gathered from 31 Airline Transport Pilots to train the machine learning algorithms designed to mimic human experts and expertise. The test collection used an analog of the Navigation Display. Pilots were asked to rate the relevancy of the displayed traffic using an interactive tablet application. Pilots were also asked to rank the order of importance of the information given, to better weight the variables within the algorithm. They were also asked if the information given was enough data, and more importantly the "right" data to best inform the algorithm. The paper will describe the findings and their impact to the further development of the algorithm for TDM and, in general, address the issue of how can we train supervised machine learning algorithms, critical to increasingly autonomous systems, with the knowledge and expertise of expert human pilots.

Le Vie, Lisa R.↗

DOE OSTI2026

A non-intrusive framework using acoustic signals and deep learning for boiling diagnostics in visual-limited environments

Accurate monitoring of boiling heat transfer is critical for safeguarding high-power systems operating in environments where conventional optical diagnostics are hindered by radiation fields or restricted visual accessibility. This study presents a non-intrusive framework that integrates hydroacoustic sensing with deep learning to infer near-wall boiling characteristics and enable predictive thermal assessment without visual access. In a prototypical subcooled flow-boiling facility representative of the Isotope Production Facility (IPF) at Los Alamos, hydrophones capture boiling-induced acoustic emissions that are transformed into background-removed Short-Time Fourier Transform (STFT) spectrograms. A convolutional neural network (CNN) then regresses heat flux, wall superheat, and key bubble parameters directly from these spectrograms. The CNN achieved predictive accuracy under nominal conditions and demonstrated robustness and generalization under acoustic noise for Signal-to-Noise Ratios (SNRs) down to approximately 0 dB. When integrated into an ANSYS CFX wall-boiling model, the acoustically inferred parameters reproduced boiling curve and critical heat flux (CHF) values consistent with image-based benchmarks. Furthermore, the model retained reliable performance under moderate variations in bulk temperature, flow rate, and hydrophone placement, confirming its generalizability across practical boundary conditions. These results demonstrate the feasibility of hydroacoustic-based deep learning as a viable path toward real-time, radiation-tolerant boiling diagnostics and predictive thermal safety assessment in inaccessible systems such as the IPF.

42 ENGINEERING↗

NASA NTRS1996

DGPS with NASA's ACTS

The U.S. Army Topographic Engineering Center, concerned about frequency license problems inherent with operating data-ready radios to support differential GPS (DGPS), learned of an experiment program the National Aeronautics and Space Administration (NASA) was sponsoring with its prototype digital satellite - the Advanced Communications Technology Satellite (ACTS). ACTS was designed to evaluate many new, untested technologies for eventual transfer to U.S. telecommunications and related industries. The new technologies promised greater data transmission rates and significantly reduced transmission latency. recognizing these benefits, the Topographic Engineering Center designed a DGPS experiment using an ACTS datalink with baselines of 200 kilometers and longer to learn what positional accuracy was possible with leading-edge satellite telecommunications technology.

Dendy, Roger↗

NASA NTRS2021

Test Bed for Investigating Anomalous Pump Behavior in a Single Phase Mechanically Pumped Flow Loop and Lessons Learned

NASA’s Europa Clipper mission is tasked with investigating the habitability of this icy moon of Jupiter and determining if it is capable of supporting life. The suite of powerful instruments necessary for this mission, combined with the harsh environment, require an elaborate single phase, mechanically pumped flow loop (MPFL) to redistribute waste heat around the spacecraft, thus minimizing energy requirements and avoiding the need for constant heating of the propulsion tanks. At the heart of this MPFL is a pump which will circulate fluid for the entirety of the 12-year mission; however, during initial qualification testing, anomalous behavior raised serious concerns regarding the pump and system design. Due to the critical nature of the pump, a major investigation was launched to evaluate the pump, system design, and the testing methodology employed to investigate this anomaly. This investigation resulted in a highly detailed analysis of the test loop, revised procedures, and the isolation/removal of variables impacting the quality of the observed pump behavior. The resulting test loop permitted comprehensive monitoring of the pump in the MPFL, including: flow rate, inlet and outlet static pressures, temperature, power consumption, and pump speed. Additionally, the investigation of the anomaly revealed several key lessons learned and helped institute a new set of best practices for future MPFL tests.

Miers, Collier↗

DOE OSTI2021

An adaptive approach to machine learning for compact particle accelerators

Abstract Machine learning (ML) tools are able to learn relationships between the inputs and outputs of large complex systems directly from data. However, for time-varying systems, the predictive capabilities of ML tools degrade if the systems are no longer accurately represented by the data with which the ML models were trained. For complex systems, re-training is only possible if the changes are slow relative to the rate at which large numbers of new input-output training data can be non-invasively recorded. In this work, we present an approach to deep learning for time-varying systems that does not require re-training, but uses instead an adaptive feedback in the architecture of deep convolutional neural networks (CNN). The feedback is based only on available system output measurements and is applied in the encoded low-dimensional dense layers of the encoder-decoder CNNs. First, we develop an inverse model of a complex accelerator system to map output beam measurements to input beam distributions, while both the accelerator components and the unknown input beam distribution vary rapidly with time. We then demonstrate our method on experimental measurements of the input and output beam distributions of the HiRES ultra-fast electron diffraction (UED) beam line at Lawrence Berkeley National Laboratory, and showcase its ability for automatic tracking of the time varying photocathode quantum efficiency map. Our method can be successfully used to aid both physics and ML-based surrogate online models to provide non-invasive beam diagnostics.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Data driven investigation to understand the influence of total solids on biological biogas upgrading

In situ biogas upgrading achieves CO 2 conversion to CH 4 via hydrogenotrophic methanogenesis; however, gas-liquid mass transfer constraints limit the upgrading performance. Recognizing that optimization studies often underrepresent the effects of total solids (TS) and organic loading rate (OLR), this study undertook a holistic, statistics driven assessment of operating conditions for in situ H 2 assisted biogas upgrading, centering the analysis on TS and OLR. A dataset of 31 studies was compiled and comprised 99 observations. A rigorous analytical framework was employed, combining data standardization, fixed- and random-effects (REML) weighted regressions with cluster-robust errors, stratified analyses, and machine learning. Mixed-effects meta regression indicated that TS was the main factor explaining differences of methane fraction (CH 4 %) when considering the between studies heterogeneity. Focusing on a near-stoichiometric subset (H 2 /CO 2 ≈ 4:1), TS remained significant. Stratified results showed a stronger negative relationship between TS and CH 4 % in UASB reactors than in CSTRs, with a negative effect under mesophilic conditions and no significant effect under thermophilic conditions. A Random Forest model corroborated the statistical findings, consistently ranking H 2 /CO 2 ratio, OLR, TS, and hydrogen injection rate (HIR) as the most influential predictors. These findings delineate trends across increasing TS levels, particularly between 1% and 10%, and provide preliminary insights for TS above 15% in in situ biogas upgrading. They further provide insights for the influence of TS by reactor type and temperature, thereby advancing the evidence base for implementing biological CO 2 conversion to CH 4 in practice.

In situ biogas upgrading↗

DOE OSTI2025

Root exudate lipids: Uncovering chemodiversity and carbon stability potential

Root-derived carbon has been shown to contribute more to soil carbon stocks than aboveground litter. Yet the molecular chemodiversity of root exudates remains poorly understood due to limited characterization and annotation. In this study, we characterized the molecular chemodiversity and production of metabolites and lipids in root exudates from field grown mature tall wheatgrass (Thinopyrum ponticum). We discovered a diversity of lipids, including substantial levels of triacylglycerols (∼19 μg/g fresh root per min), fatty acyls, sphingolipids, sterol lipids, and glycerophospholipids, some of which have not been previously documented in root exudates. By integrating tandem mass spectral library searching and deep learning-based chemical class assignment, our metabo-lipidomics approach significantly expanded the known molecular diversity of root exudates. Rates of lipid derived carbon production were approximately double that of polar metabolites (lipids: 81.52 ± 13.81 vs polar metabolites: 38.41 ± 5.93 μg C g −1 fresh root mass min −1 ) with an order of magnitude higher carbon to nitrogen ratios (lipids: 459 ± 90 vs polar metabolites: 14.40 ± 0.58). Exudate lipids displayed highly negative nominal oxidation state of carbon (−1.182 to −1.909), indicating that these compounds may be less favorable for microbial decomposition. Together our results suggest the potential of root exudate lipids to contribute to stable carbon pools in soil, supporting long-term carbon storage. This work advances understanding of plant-derived lipid inputs to soil and underscores the need for future studies on the functional roles of lipids in shaping root-microbe-soil interactions, microbial activity, soil structure, and nutrient availability – contributing to soil health.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Driving Next-Generation Workflows from the Data Plane

We observe the emergence of a new generation of scientific workflows that process data produced at a sustained rate by scientific instruments and large scale numerical simulations. This data is consumed by multiple analysis, visualization, or Machine Learning components not only to enable inference and justify the scientific program, but also to monitor and steer the evolution of these experiments. In such workflows, moving intermediate data efficiently is key to performance, more than efficiently scheduling computational tasks. However, most traditional workflow management systems focus on optimizing task scheduling and then deal with data management, assuming a “move little, compute for long” model, which makes them unfit to the efficient management of this new generation of workflows. Therefore, we advocate for a new way to manage scientific workflows. We propose to consider an efficiently and independently managed data plane that can store and stream data. Workflows compute components, in the application plane can then interact with the data plane, abstracted from complexities of data management. Then, the role of a workflow management system would become that of a control plane that allows users to connect services together to execute the workflow and manages connections between the application and data planes. In this position paper, we characterize several next-generation workflow motifs and describe how their interaction with the data plane is a challenge to traditional workflow management systems. Then, we express a set of requirements that a workflow management system should meet to efficiently manage next-generation workflows at different scales. Based on these requirements, we expose our vision of driving next-generation workflows from the data plane and list remaining open challenges.

Suter, Fred↗

DOE OSTI2024

Learning Functions Varying along a Central Subspace

Many functions of interest are in a high-dimensional space but exhibit low-dimensional structures. This paper studies regression of an s-Hölder function in $R^D$ which varies along a central subspace of dimension $d$ while $d \ll D$. A direct approximation of $f$ in $R^D$ with an accuracy $\varepsilon$ requires the number of samples in the order of $\varepsilon^{-(2s+D)/s}$. In this paper, we analyze the generalized contour regression (GCR) algorithm for the estimation of the central subspace and use piecewise polynomials for function approximation. GCR is among the best estimators for the central subspace, but its sample complexity is an open question. In this paper, we partially answer this questions by proving that if a variance quantity is exactly known, GCR leads to a mean squared estimation error of $O(n^{-1})$ for the central subspace. The estimation error of this variance quantity is also given in this paper. The mean squared regression error of $f$ is proved to be in the order of $(n/\log n)^{-\frac{2s}{2s+d}}$, where the exponent depends on the dimension of the central subspace instead of the ambient space . This result demonstrates that GCR is effective in learning the low-dimensional central subspace. We also propose a modified GCR with improved efficiency. Here, the convergence rate is validated through several numerical experiments.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

EMSL-Computing/PeakDecoder

PeakDecoder is a machine learning (ML)-based metabolite identification algorithm for multidimensional mass spectrometry measurements incorporating liquid chromatography (LC) and ion mobility spectrometry (IM) separations, and collecting extensive fragmentation spectra with data-independent acquisition (DIA) methods: LC-IM-DIA-MS. The algorithm learns to distinguish true co-elution and co-mobility from raw data and calculates metabolite identification error rates.

Bilbao Pena, Aivett↗

DOE OSTI2020

NEON AOP foliar trait maps, maps of model uncertainty estimates, and conifer map, East River, CO 2018

This data package contains mapped trait estimates and their uncertainties, and conifer map, for the National Ecological Observatory Network's Airborne Observation Platform survey data acquired over the Upper East River, Colorado in 2018. For full details, please see associated reference. in brief, trait models were developed independently for needle and non-needle leaf species using partial least squares regression (PLSR) using ground data from additional datasets: doi:10.15485/1618130, doi:10.15485/1618132, and doi:10.15485/1631278, merged with extracted spectral data from doi:10.15485/1618131. We separated vegetated pixels into needle and non-needle classes in order to generate a classification map based on the spectral differences between these leaf types (conifer.tif). We trained a deep learning model with custom architecture, detailed in Chadwick et al. In Press. The model performed with 0.998 true positive rate and 0.982 true negative rate, with ‘positives’ being non-needle identification. We then utilized PLSR to generate models of foliar traits for each leaf type. So that we could also map uncertainty in these predictions, we generated ten different models for needle and non-needle leaf species using different testing holdout sets of discrete sites. Each of these models was developed with a 100-fold cross validation procedure that utilized a 70% training set and 30% validation set with each fold, and then assessed based on the 10% of testing sites that were not included in that model’s development. The mean predicted value across the 10 models is used for the trait estimate in each pixel across the study area. The models are applied according to the leaf type designation in the conifer.tif map. The errors are the standard deviation across the 10 different models developed, with high error suggesting instability in model prediction and areas where values may not be reliable for ecological inference. These maps are only applied to areas with a NDVI > 0.5 to exclude non-vegetated areas. Shade masks could be applied to these data (doi:10.15485/1618131), but have not been for this data package. These data are also available on Google Earth Engine: https://code.earthengine.google.com/?asset=users/kdc/ER_NEON

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2005

Program Facilitates CMMI Appraisals

A computer program has been written to facilitate appraisals according to the methodology of Capability Maturity Model Integration (CMMI). [CMMI is a government/industry standard, maintained by the Software Engineering Institute at Carnegie Mellon University, for objectively assessing the engineering capability and maturity of an organization (especially, an organization that produces software)]. The program assists in preparation for a CMMI appraisal by providing drop-down lists suggesting required artifacts or evidence. It identifies process areas for which similar evidence is required and includes a copy feature that reduces or eliminates repetitive data entry. It generates reports to show the entire framework for reference, the appraisal artifacts to determine readiness for an appraisal, and lists of interviewees and questions to ask them during the appraisal. During an appraisal, the program provides screens for entering observations and ratings, and reviewing evidence provided thus far. Findings concerning strengths and weaknesses can be exported for use in a report or a graphical presentation. The program generates a chart showing capability level ratings of the organization. A context-sensitive Windows help system enables a novice to use the program and learn about the CMMI appraisal process.

Sweetser, Wesley↗