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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.

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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Real-Time Detection of Charge Jumps in Superconducting Qubits with a Convolutional Neural Network

Ionizing radiation from cosmic rays and gammas can induce discontinuous jumps in the environmental charge of superconducting qubits (charge jumps), causing correlated errors that challenge fault-tolerant quantum computing while simultaneously providing a detection signature for quantum sensing applications. Current detection methods operate offline, introducing latency incompatible with in-the-loop qubit control. In this paper, an online detector of charge jumps for superconducting qubits, based on a dilated causal convolutional neural network (DCCNN) designed for in-the-loop deployment on the Quantum Instrumentation Control Kit (QICK) platform, is presented. The network is trained on synthetic Ramsey tomography scans generated from qubit templates measured at the Northwestern Experimental Underground Site (NEXUS) at Fermilab, and translated to FPGA firmware via hls4ml with ap_fixed$\langle 16,6 \rangle$ quantization, reaching a per-inference latency of $6.19 μ$s on the Zynq UltraScale+ RFSoC ZCU216. At this operating point the DCCNN matches the detection efficiency of the established offline $χ^2$ algorithm ($0.843 \pm 0.022$ vs. $0.866 \pm 0.020$ on $|Δq| \in [0.1, 0.5] e$ at matched false-positive rate), while requiring no per-qubit hyperparameter tuning. This shifts charge-jump detection from a post-hoc diagnostic to a control-loop primitive, enabling adaptive protocols that respond to radiation-induced events in situ, with applications to quantum-computing error mitigation and to the use of superconducting qubits as particle detectors.

Gaytan-Villarreal, Daniel [Carnegie Mellon U.]↗
DOE OSTI2026

Artificial Intelligence-Driven Management of Sustainable Energy Resources: Visibility, Operation, and Control

The rapid global transition toward sustainable energy resources (SERs) is reshaping how modern power systems are observed, optimized, and controlled. While SERs have significantly advanced decarbonization, their weather dependence, variability, and inverter-dominated characteristics challenge traditional, centralized, and deterministic grid operation. At the same time, the proliferation of high-resolution data from inverters, smart meters, and sensors offers unprecedented visibility into system dynamics. Yet, it also exceeds the analytical capability of conventional model-based approaches. Artificial intelligence (AI) provides a new foundation for addressing these challenges by bridging physical laws with data-driven learning, enabling accurate state awareness, adaptive operation, and coordinated control across distributed assets. This article examines how AI transforms the management of SER-rich power systems along three critical dimensions: 1) enhancing visibility by inferring behind-the-meter (BTM) activities, assessing SER flexibility, and reconstructing system states from sparse or noisy measurements; 2) improving operation through AI-enhanced SER service provision, volt/var control (VVC), and dynamic operating envelopes (DOE) for efficiency and security; and 3) advancing control by embedding learning-based intelligence into inverter coordination, voltage and frequency regulation, and long-term dispatch. Together, these developments reveal how AI can convert the variability of SERs from an operational challenge into a source of flexibility, resilience, and intelligence, paving the way toward sustainable, adaptive, and self-optimizing power systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1984

Environmentally-induced voltage limitations in large space power systems

Large power systems proposed for future space missions imply higher operating voltage requirements which, in turn, will interact with the space plasma environment. The effects of these interactions can only be inferred because of the limited data base of ground simulations, small test samples, and two space flight experiments. This report evaluates floating potentials for a 100 kW power system operating at 300, 500, 750, and 1000 volts in relation to this data base. Of primary concern is the possibility of discharging to space. The implications of such discharges were studied at the 500 volt operational setting. It was found that discharging can shut down the power system if the discharge current exceeds the array short circuit current. Otherwise, a power oscillation can result that ranges from 2 to 20 percent, depending upon the solar array area involved in the discharge. Means of reducing the effect are discussed.

Stevens, N. J.↗
NASA NTRS1993

Observations of the marine environment from spaceborne side-looking real aperture radars

Real aperture, side looking X-band radars have been operated from the Soviet Cosmos-1500, -1602, -1766 and Ocean satellites since 1984. Wind velocities were inferred from sea surface radar scattering for speeds ranging from approximately 2 m/s to those of hurricane proportions. The wind speeds were within 10-20 percent of the measured in situ values, and the direction of the wind velocity agreed with in situ direction measurements within 20-50 deg. Various atmospheric mesoscale eddies and tropical cyclones were thus located, and their strengths were inferred from sea surface reflectivity measurements. Rain cells were observed over both land and sea with these spaceborne radars. Algorithms to retrieve rainfall rates from spaceborne radar measurements were also developed. Spaceborne radars have been used to monitor various marine hazards. For example, information derived from those radars was used to plan rescue operations of distressed ships trapped in sea ice. Icebergs have also been monitored, and oil spills were mapped. Tsunamis produced by underwater earthquakes were also observed from space by the radars on the Cosmos 1500 series of satellites. The Cosmos-1500 satellite series have provided all weather radar imagery of the earths surface to a user community in real time by means of a 137.4 MHz Automatic Picture Transmission channel. This feature enabled the radar information to be used in direct support of Soviet polar maritime activities.

Kalmykov, A. I.↗
DOE OSTI2024

Inferring fusion nuclear burnwidths with low gain photomultiplier impulse response functions

When an inertial confinement fusion implosion is compressed, it maintains thermonuclear density and temperatures for a very short time scale, about 100 ps. The Gamma Reaction History diagnostic measures the time evolution of the fusion burn, but its temporal resolution is limited by the use of a photomultiplier tube (PMT) to amplify the photon signal. Multichannel plate-based PMTs have a fast (~120 ps) full-width at half-max impulse response function (IRF), but the time scale is similar to the incoming physics signal. An analysis routine is used to remove the effect of the PMT IRF and infer the incident fusion burnwidth. With the National Ignition Facility achieving ignition and creating much brighter signals, the PMTs are run at gains three orders of magnitude lower than nominal operation. Calibration at these settings shows the PMT IRFs get ~15% wider. Taking the gain-dependent IRF can affect the inferred nuclear burnwidths by up to ~15%.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Neural Posterior Estimation for Scalable and Accurate Inverse Parameter Inference in Li-Ion Batteries

Diagnosing the internal state of Li-ion batteries is critical for battery research, operation of real-world systems, and prognostic evaluation of remaining lifetime. By using physics-based models to perform probabilistic parameter estimation via Bayesian calibration, diagnostics can account for the uncertainty due to model fitness, data noise, and the observability of any given parameter. However, Bayesian calibration in Li-ion batteries using electrochemical data is computationally intensive even when using a fast surrogate in place of physics-based models, requiring many thousands of model evaluations. A fully amortized alternative is neural posterior estimation (NPE). NPE shifts the computational burden from the parameter estimation step to data generation and model training, reducing the parameter estimation time from minutes to milliseconds, enabling real-time applications. The present work shows that NPE can infer parameters equally or more accurately than Bayesian calibration, even if it leads to higher voltage reconstruction errors. We also demonstrate that the higher computational costs for data generation are tractable even in high-dimensional cases (ranging from 6 to 27 estimated parameters). The NPE method also offers several interpretability advantages over Bayesian calibration, such as local parameter sensitivity to specific regions of the voltage curve. The NPE method is demonstrated using an experimental fast charge dataset, with parameter estimates validated against measurements of loss of lithium inventory and loss of active material. The implementation is made available in a companion repository (https://github.com/NatLabRockies/BatFIT).

25 ENERGY STORAGE↗
DOE OSTI2025

From sequence to protein structure and conformational dynamics with artificial intelligence/machine learning

The 2024 Nobel Prize in Chemistry was awarded in part for de novo protein structure prediction using AlphaFold2, an artificial intelligence/machine learning (AI/ML) model trained on vast amounts of sequence and three-dimensional structure data. AlphaFold2 and related models, including RoseTTAFold and ESMFold, employ specialized neural network architectures driven by attention mechanisms to infer relationships between sequence and structure. At a fundamental level, these AI/ML models operate on the long-standing hypothesis that the structure of a protein is determined by its amino acid sequence. More recently, AlphaFold2 has been adapted for the prediction of multiple protein conformations by subsampling multiple sequence alignments. Herein, we provide an overview of the deterministic relationship between sequence and structure, which was hypothesized over half a century ago with profound implications for the biological sciences ever since. We postulate that protein conformational dynamics are also determined, at least in part, by amino acid sequence and that this relationship may be leveraged for construction of AI/ML models dedicated to predicting protein conformational ensembles. Accordingly, we describe a conceptual model architecture, which may be trained on sequence data in combination with conformationally sensitive structural information, coming primarily from nuclear magnetic resonance (NMR) spectroscopy. Notwithstanding certain limitations in this context, NMR offers abundant structural heterogeneity conducive to conformational ensemble prediction. As NMR and other data continue to accumulate, sequence-informed prediction of protein structural dynamics with AI/ML has the potential to emerge as a transformative capability across the biological sciences.

Artificial intelligence↗
DOE OSTI2024

The Simons Observatory: Combining cross-spectral foreground cleaning with multitracer B -mode delensing for improved constraints on inflation

The Simons Observatory (SO), due to start full science operations in early 2025, aims to set tight constraints on inflationary physics by inferring the tensor-to-scalar ratio r from measurements of cosmic microwave background (CMB) polarization B-modes. Its nominal design including three small-aperture telescopes (SATs) targets a precision σ⁡(r = 0) ≤ 0.003 without delensing. Achieving this goal and further reducing uncertainties requires a thorough understanding and mitigation of other large-scale B-mode sources such as Galactic foregrounds and weak gravitational lensing. We present an analysis pipeline aiming to estimate r by including delensing within a cross-spectral likelihood, and demonstrate it for the first time on SO-like simulations accounting for various levels of foreground complexity, inhomogeneous noise and partial sky coverage. As introduced in an earlier SO delensing paper, lensing B-modes are synthesized using internal CMB lensing reconstructions as well as Planck-like cosmic infrared background maps and LSST-like galaxy density maps. We then extend SO’s power-spectrum-based foreground-cleaning algorithm to include all auto- and cross-spectra between the lensing template and the SAT B-modes in the likelihood function. This allows us to constrain r and the parameters of our foreground model simultaneously. Within this framework, we demonstrate the equivalence of map-based and cross-spectral delensing and use it to motivate an optimized pixel-weighting scheme for power spectrum estimation. We start by validating our pipeline in the simplistic case of uniform foreground spectral energy distributions. In the absence of primordial B-modes, we find that the 1⁢σ statistical uncertainty on r, σ⁡(r), decreases by 37% as a result of delensing. Tensor modes at the level of r = 0.01 are successfully detected by our pipeline. Even when using more realistic foreground models including spatial variations in the dust and synchrotron spectral properties, we obtain unbiased estimates of r both with and without delensing by employing the moment-expansion method. In this case, uncertainties are increased due to the higher number of model parameters, and delensing-related improvements range between 27% and 31%. These results constitute the first realistic assessment of the delensing performance at SO’s nominal sensitivity level.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1987

Use of topographic and climatological models in a geographical data base to improve Landsat MSS classification for Olympic National Park

An unsupervised computer classification of vegetation/landcover of Olympic National Park and surrounding environs was initially carried out using four bands of Landsat MSS data. The primary objective of the project was to derive a level of landcover classifications useful for park management applications while maintaining an acceptably high level of classification accuracy. Initially, nine generalized vegetation/landcover classes were derived. Overall classification accuracy was 91.7 percent. In an attempt to refine the level of classification, a geographic information system (GIS) approach was employed. Topographic data and watershed boundaries (inferred precipitation/temperature) data were registered with the Landsat MSS data. The resultant boolean operations yielded 21 vegetation/landcover classes while maintaining the same level of classification accuracy. The final classification provided much better identification and location of the major forest types within the park at the same high level of accuracy, and these met the project objective. This classification could now become inputs into a GIS system to help provide answers to park management coupled with other ancillary data programs such as fire management.

Cibula, William G.↗
NASA NTRS2003

Initial closed operation of the CELSS Test Facility Engineering Development Unit

As part of the NASA Advanced Life Support Flight Program, a Controlled Ecological Life Support System (CELSS) Test Facility Engineering Development Unit has been constructed and is undergoing initial operational testing at NASA Ames Research Center. The Engineering Development Unit (EDU) is a tightly closed, stringently controlled, ground-based testbed which provides a broad range of environmental conditions under which a variety of CELSS higher plant crops can be grown. Although the EDU was developed primarily to provide near-term engineering data and a realistic determination of the subsystem and system requirements necessary for the fabrication of a comparable flight unit, the EDU has also provided a means to evaluate plant crop productivity and physiology under controlled conditions. This paper describes the initial closed operational testing of the EDU, with emphasis on the hardware performance capabilities. Measured performance data during a 28-day closed operation period are compared with the specified functional requirements, and an example of inferring crop growth parameters from the test data is presented. Plans for future science and technology testing are also discussed. Published by Elsevier Science Ltd on behalf of COSPAR.

Lettuce/growth & development/metabolism↗
NASA NTRS2014

Chemical Evidence for Smectites and Zeolites on Mars: Criteria and Limitations

Aqueous alteration on Mars can produce a range of tell-tale secondary minerals [1]. Surface missions typically obtain detailed and highly localized element compositional information, but not always mineralogical information, whereas orbital missions deduce mineralogy from relatively high spatial resolution IR spectral mapping (decameters scale, for CRISM), but obtain element data only over much larger areas of martian terrain (~200 km). Surface missions have also discovered several occurrences of major geochemical alteration of igneous precursors, for many of which elemental compositional is the only diagnostic information available. Many types of clays and zeolites have quasi-unique element profiles which may be used to implicate their presence. In some cases, one or more candidate minerals are sufficiently close in their component elements and their stoichiometry that ambiguity must remain, unless other constraints can be brought to bear. Geochemical characteristics of alteration products most likely on Mars can be compared to results from MER and MSL rover missions (e.g. Independence [4] and Esperance samples). These considerations are needed for MER Opportunity rover now that Mini-TES is no longer operational. It also has importance for exploration by the MSL Curiosity rover because inferences and deductions available from ChemCam (CCAM) remote LIBS and/or in situ x-ray fluorescence (APXS) can be used as indicators for triage to select materials to sample for limited-resource instruments, SAM and Chemin.

Clark, B. C.↗
NASA NTRS1986

An Expert-System Engine With Operative Probabilities

Program enables proof-of-concepts tests of expert systems under development. AESOP is rule-based inference engine for expert system, which makes decisions about particular situation given user-supplied hypotheses, rules, and answers to questions drawn from rules. If knowledge base containing hypotheses and rules governing environment is available to AESOP, almost any situation within that environment resolved by answering questions asked by AESOP. Questions answered with YES, NO, MAYBE, DON'T KNOW, DON'T CARE, or with probability factor ranging from 0 to 10. AESOP written in Franz LISP for interactive execution.

Orlando, N. E.↗
NASA NTRS1983

The inference of tropical cyclone dynamics using GOES VISSR/VAS data

The sequence of events observed during tropical cyclone Emily, was suggested as a possible mechanism for cyclogenesis. Geostationary Operational Environmental Satellite (GOES) East VISSR/VAS sensors were used. The VISSR visible imagery obtained every 15 minutes was used to define the low tropospheric cyclonic vortex and upper tropospheric horizontal convergence. The VAS water vapor (channels 9 and 10) and carbon dioxide (channels 3 and 4) channels were used to infer upper and middle tropospheric subsidence by monitoring the Adiabatic compressional drying and warming, respectively, occurring within this layer. Evidence of an existing lower tropospheric cyclonic vortex was seen. The satellite derived wind vectors (length of vector is proportional to wind velocity, where the strongest winds were approximately 35 knots) are superimposed on the GOES visible image of tropical storm Emily. Vectors and low level clouds depict the center of the cyclonic vortex immediately south of the large convective cell in the center of the image. Upper tropospheric cloud tracers and rawinsonde reports along the Eastern United States suggest that the southwesterly environmental upper atmospheric flow is converging with the outflow from the convective cell north of the vortex.

Rodgers, E. B.↗
NASA NTRS1990

Silicon Detectors For Helium Liquid And Vapor

Simple electrical-resistance devices made of silicon indicate whether helium liquid or helium vapor present. Devices designed primarily for use in outer space, were tested and found to operate in normal Earth gravity. Silicon cubes supported by stainless-steel wires and strips. Voltage across each cube at fixed current indicates whether immersed in helium liquid or vapor. Liquid cools more than vapor does, resulting in greater electrical resistance. Such helium-liquid/vapor detectors incorporated into ducts or containers of laboratory equipment, and used to infer locations of liquid/vapor interfaces in order to measure quantities of liquid and vapor or to control refill operations.

Di Pirro, M. J.↗
DOE OSTI2026

The Double-edged Sword of Data-driven Super-Resolution: Adversarial Super-resolution Models

Data-driven super-resolution (SR) methods are often integrated into imaging pipelines as preprocessing steps to improve downstream tasks such as classification and detection. However, these SR models introduce a previously unexplored attack surface into imaging pipelines. In this paper, we present AdvSR, a framework demonstrating that adversarial behavior can be embedded directly into SR model weights during training, requiring no access to inputs at inference time. Unlike prior attacks that perturb inputs or rely on backdoor triggers, AdvSR operates entirely at the model level. By jointly optimizing for reconstruction quality and targeted adversarial outcomes, AdvSR produces models that appear benign under standard image quality metrics while inducing downstream misclassification. We evaluate AdvSR on three SR architectures (SRCNN, EDSR, SwinIR) paired with a YOLOv11 classifier and demonstrate that AdvSR models can achieve high attack success rates with minimal quality degradation. These findings highlight a new model-level threat for imaging pipelines, with implications for how practitioners source and validate models in safety-critical applications.

Sullivan, Haley [ORNL] (ORCID:0000000274069217)↗
NASA NTRS1982

Analysis of combustion spectra containing organ pipe tone by cepstral techniques

Signal reinforcements and cancellations due to standing waves may distort constant bandwidth combustion spectra. Cepstral techniques previously applied to the ground reflection echo problem are used to obtain smooth broadband data and information on combustion noise propagation. Internal fluctuating pressure measurements made using a J47 combustor attached to a 6.44 m long duct are analyzed. Measurements made with Jet A and hydrogen fuels are compared. The acoustic power levels inferred from the measurements are presented for a range of low heat release rate operating conditions near atmospheric pressure. For these cases, the variation with operating condition of the overall acoustic broadband power level for both hydrogen and Jet A fuels is consistent with previous results showing it was proportional to the square of the heat release rate. However, the overall acoustic broadband power level generally is greater for hydrogen than for Jet A.

Miles, J. H.↗
NASA NTRS1983

FINDS: A fault inferring nonlinear detection system. User's guide

The computer program FINDS is written in FORTRAN-77, and is intended for operation on a VAX 11-780 or 11-750 super minicomputer, using the VMS operating system. The program detects, isolates, and compensates for failures in navigation aid instruments and onboard flight control and navigation sensors of a Terminal Configured Vehicle aircraft in a Microwave Landing System environment. In addition, FINDS provides sensor fault tolerant estimates for the aircraft states which are then used by an automatic guidance and control system to land the aircraft along a prescribed path. FINDS monitors for failures by evaluating all sensor outputs simultaneously using the nonlinear analytic relationships between the various sensor outputs arising from the aircraft point mass equations of motion. Hence, FINDS is an integrated sensor failure detection and isolation system.

Lancraft, R. E.↗
NASA NTRS2000

Monitoring Global Sea Level: Eustatic Variations, Local Variations, and Solid Earth Effects

Project BIFROST (Baseline Inferences for Fennoscandian Rebound Observations. Sea-level and Tectonics) combines networks of continuously operating CPS receivers in Sweden and Finland to measure ongoing crustal deformation due to glacial isostatic adjustment, (CIA). We present an analysis of data collected in the years 1993-1998. We compare the CPS determinations of three-dimensional crustal motion to predictions calculated using the high resolution Fennoscandian deglaciation model recently proposed by Lambeck et al. We find that the the maximum observed uplift rate (approx. 10 mm/ yr) and the maximum predicted uplift rate agree to better than 1 mm/ yr. The patterns of uplift also agree quite well, although differences are discernible. The chi(exp 2) difference between predicted and GPS-observed radial rates is reduced by a factor of 5-6 compared to that for the "null" (no uplift) model, depending on whether a mean difference is first removed. The north components of velocity agree at about the same relative level. whereas the agreement for the east components is worse, a problem possibly related to the lack of bias fixing. We have also compared the values for the observed radial deformation rates to those based on sea-level rates from Baltic tide gauges. The weighted RMS difference between CPS and tide-gauge rates (after removing a mean) is 0.6 mm/ yr, giving an indication of the combined accuracy of the CPS and tide-gauge measurement systems. Spectral analysis of the time series of position estimates yields spectral indices in the range -1 to -2. An EOF analysis indicates, however, that much of this power is correlated among the sites. The correlation appears to be regional and falls off only slightly with distance. Some of this correlated noise is associated with snow accumulation on the antennas or, for those antennas with radomes, on the radomes. This problem has caused us to modify the radomes used several times, leading to one of our more significant sources of uncertainty.

Davis, James L.↗