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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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DOE OSTI2026

Self-driving thin film laboratory: autonomous epitaxial atomic-layer synthesis via real-time computer vision analysis of electron diffraction

Emerging materials science platforms with the ability to make autonomous decisions on the fly are fundamentally changing the outlook and protocols for materials optimization and discovery. Because AI-driven self-navigating schemes can effectively reduce the total number of iterations needed to arrive at the "answer" (i.e. the best stochiometric composition for a desired physical property, optimum materials processing parameters, etc.) by significant margins, they have the potential to revolutionize materials and chemical manufacturing processes at large in research laboratory settings as well as in industrial plants. Here, we demonstrate a successful implementation of real-time closed-loop autonomous navigation of a multi-dimensional materials synthesis parameter space for fabricating phase-pure epitaxial films of a metastable phase of a functional oxide in a combinatorial pulsed laser deposition chamber. Sequential epitaxial growth iterations in search of the optimized recipe to stabilize the desired crystal phase were performed using frame-by-frame quantitative computer vision analysis of reflection high-energy electron diffraction (RHEED) images of the unit-cell level film being deposited. The autonomous scheme regularly resulted in > 30-fold reduction in the number of required experiments compared to a comprehensive mapping of the parameter space. The real-time workflow developed here can be readily extended to a variety of thin film synthesis platforms opening the door for self-driving atomic-level materials design as well as autonomous optimization of semiconductor manufacturing.

36 MATERIALS SCIENCE↗

NASA NTRS2004

Transitioning to Intel-based Linux Servers in the Payload Operations Integration Center

The MSFC Payload Operations Integration Center (POIC) is the focal point for International Space Station (ISS) payload operations. The POIC contains the facilities, hardware, software and communication interface necessary to support payload operations. ISS ground system support for processing and display of real-time spacecraft and telemetry and command data has been operational for several years. The hardware components were reaching end of life and vendor costs were increasing while ISS budgets were becoming severely constrained. Therefore it has been necessary to migrate the Unix portions of our ground systems to commodity priced Intel-based Linux servers. hardware architecture including networks, data storage, and highly available resources. This paper will concentrate on the Linux migration implementation for the software portion of our ground system. The migration began with 3.5 million lines of code running on Unix platforms with separate servers for telemetry, command, Payload information management systems, web, system control, remote server interface and databases. The Intel-based system is scheduled to be available for initial operational use by August 2004 The overall migration to Intel-based Linux servers in the control center involves changes to the This paper will address the Linux migration study approach including the proof of concept, criticality of customer buy-in and importance of beginning with POSlX compliant code. It will focus on the development approach explaining the software lifecycle. Other aspects of development will be covered including phased implementation, interim milestones and metrics measurements and reporting mechanisms. This paper will also address the testing approach covering all levels of testing including development, development integration, IV&V, user beta testing and acceptance testing. Test results including performance numbers compared with Unix servers will be included. need for a smooth transition while maintaining real-time support. An important aspect of the paper will involve challenges and lessons learned. product compatibility, implications of phasing decisions and tracking of dependencies, particularly non- software dependencies. The paper will also discuss scheduling challenges providing real-time flight support during the migration and the requirement to incorporate in the migration changes being made simultaneously for flight support. This paper will also address the deployment approach including user involvement in testing and the , This includes COTS product compatibility, implications of phasing decisions and tracking of dependencies, particularly non- software dependencies. The paper will also discuss scheduling challenges providing real-time flight support during the migration and the requirement to incorporate in the migration changes being made simultaneously for flight support.

Guillebeau, P. L.↗

DOE OSTI2022

Apodization Specific Fitting for Improved Resolution, Charge Measurement, and Data Analysis Speed in Charge Detection Mass Spectrometry

Short-time Fourier transforms with short segment lengths are typically used to analyze single ion charge detection mass spectrometry (CDMS) data either to overcome effects of frequency shifts that may occur during the trapping period or to more precisely determine the time at which an ion changes mass or charge, or enters an unstable orbit. The short segment lengths can lead to scalloping loss unless a large number of zero-fills are used, making computational time a significant factor in real-time analysis of data. Apodization specific fitting leads to a 9-fold reduction in computation time compared to zero-filling to a similar extent of accuracy. This makes possible real-time data analysis using a standard desktop computer. Rectangular apodization leads to higher resolution than the more commonly used Gaussian or Hann apodization and makes it possible to separate ions with similar frequencies, a significant advantage for experiments in which the masses of many individual ions are measured simultaneously. Equally important is a >20% increase in S/N obtained with rectangular apodization compared to Gaussian or Hann, which directly translates to a corresponding improvement in accuracy of both charge measurements and ion energy measurements that rely on the amplitudes of the fundamental and harmonic frequencies. Finally, combined with computing the fast Fourier transform in a lower-level language, this fitting procedure eliminates computational barriers and should enable real-time processing of CDMS data on a laptop computer.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Distributed quantum approximate optimization algorithm on a quantum-centric supercomputing architecture

Quantum approximate optimization algorithm (QAOA) has shown promise in solving combinatorial optimization problems by providing quantum speedup on near-term gate-based quantum computing systems. However, QAOA faces challenges for high-dimensional problems due to the large number of qubits required and the complexity of deep circuits, limiting its scalability for real-world applications. In this study, we present a distributed QAOA (DQAOA), which leverages distributed computing strategies to decompose a large computational workload into smaller tasks that require fewer qubits and shallower circuits than are necessary to solve the original problem. These sub-problems are processed using a combination of high-performance and quantum computing resources. The global solution is iteratively updated by aggregating sub-solutions, allowing convergence toward the optimal solution. We demonstrate that DQAOA can handle considerably large-scale optimization problems (e.g., 1000-bit problem), achieving a high solution quality and short time-to-solution, outperforming existing strategies. Furthermore, we realize DQAOA on a quantum-centric supercomputing architecture, paving the way for practical applications of gate-based quantum computers in real-world optimization tasks. To extend DQAOA’s applicability to materials science, we further develop an active learning algorithm integrated with our DQAOA (AL-DQAOA), which involves machine learning, DQAOA, and active data production in an iterative loop. We successfully optimize photonic structures using AL-DQAOA, indicating that solving real-world optimization problems using gate-based quantum computing is feasible. We expect the proposed DQAOA to be applicable to a wide range of optimization problems and AL-DQAOA to find broader applications in material design.

Kim, Seongmin [ORNL] (ORCID:0000000159063004)↗

DOE OSTI2024

Grad–Shafranov equilibria via data-free physics informed neural networks

A large number of magnetohydrodynamic (MHD) equilibrium calculations are often required for uncertainty quantification, optimization, and real-time diagnostic information, making MHD equilibrium codes vital to the field of plasma physics. In this paper, we explore a method for solving the Grad–Shafranov equation by using physics-informed neural networks (PINNs). For PINNs, we optimize neural networks by directly minimizing the residual of the partial differential equation as a loss function. We show that PINNs can accurately and effectively solve the Grad–Shafranov equation with several different boundary conditions, making it more flexible than traditional solvers. This method is flexible as it does not require any mesh and basis choice, thereby streamlining the computational process. We also explore the parameter space by varying the size of the model, the learning rate, and boundary conditions to map various tradeoffs such as between reconstruction error and computational speed. Additionally, we introduce a parameterized PINN framework, expanding the input space to include variables such as pressure, aspect ratio, elongation, and triangularity in order to handle a broader range of plasma scenarios within a single network. Parameterized PINNs could be used in future work to solve inverse problems such as shape optimization.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

Forecasting high-dimensional spatio-temporal systems from sparse measurements

This paper introduces a new neural network architecture designed to forecast high-dimensional spatio-temporal data using only sparse measurements. The architecture uses a two-stage end-to-end framework that combines neural ordinary differential equations (NODEs) with vision transformers. Initially, our approach models the underlying dynamics of complex systems within a low-dimensional space; and then it reconstructs the corresponding high-dimensional spatial fields. Many traditional methods involve decoding high-dimensional spatial fields before modeling the dynamics, while some other methods use an encoder to transition from high-dimensional observations to a latent space for dynamic modeling. In contrast, our approach directly uses sparse measurements to model the dynamics, bypassing the need for an encoder. This direct approach simplifies the modeling process, reduces computational complexity, and enhances the efficiency and scalability of the method for large datasets. We demonstrate the effectiveness of our framework through applications to various spatio-temporal systems, including fluid flows and global weather patterns. Although sparse measurements have limitations, our experiments reveal that they are sufficient to forecast system dynamics accurately over long time horizons. Our results also indicate that the performance of our proposed method remains robust across different sensor placement strategies, with further improvements as the number of sensors increases. This robustness underscores the flexibility of our architecture, particularly in real-world scenarios where sensor data is often sparse and unevenly distributed.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Core-collapse supernova neutrino emission and detection informed by state-of-the-art three-dimensional numerical models

ABSTRACT Based on our recent three-dimensional core-collapse supernova (CCSN) simulations including both exploding and non-exploding models, we study the detailed neutrino signals in representative terrestrial neutrino observatories, namely Super-Kamiokande (Hyper-Kamiokande), DUNE, JUNO, and IceCube. We find that the physical origin of difference in the neutrino signals between 1D and 3D is mainly proto-neutron-star convection. We study the temporal and angular variations of the neutrino signals and discuss the detectability of the time variations driven by the spiral standing accretion shock instability (spiral SASI) when it emerges for non-exploding models. In addition, we determine that there can be a large angular asymmetry in the event rate (${\gtrsim} 50 {{\ \rm per\ cent}}$), but the time-integrated signal has a relatively modest asymmetry (${\lesssim} 20 {{\ \rm per\ cent}}$). Both features are associated with the lepton-number emission self-sustained asymmetry and the spiral SASI. Moreover, our analysis suggests that there is an interesting correlation between the total neutrino energy (TONE) and the cumulative number of neutrino events in each detector, a correlation that can facilitate data analyses of real observations. We demonstrate the retrieval of neutrino energy spectra for all flavours of neutrino by applying a novel spectrum reconstruction technique to the data from multiple detectors. We find that this new method is capable of estimating the TONE within the error of ∼20 per cent if the distance to the CCSN is ≲6 kpc.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2026

A practical guide to unbinned unfolding

Unfolding, in the context of high-energy particle physics, refers to the process of removing detector distortions in experimental data. The resulting unfolded measurements are straightforward to use for direct comparisons between experiments and a wide variety of theoretical predictions. For decades, popular unfolding strategies were designed to operate on data formatted as one or more binned histograms. In recent years, new strategies have emerged that use machine learning to unfold datasets in an unbinned manner, allowing for higher-dimensional analyses and more flexibility for current and future users of the unfolded data. This guide comprises recommendations and practical considerations from researchers across a number of major particle physics experiments who have recently put these techniques into practice on real data.

Canelli, Florencia [Univ. of Zurich (Switzerland)]↗

DOE OSTI2026

Port scanner and Testing Suite

This project addresses the challenge of identifying and managing open network ports across physical and virtual hosts. The current form of verifying ports in use required manually searching individual ports - a process that was both time- consuming and a potential bottleneck for deployment timelines. To resolve this, an automated port scanning tool was developed in Python. The tool supports simultaneous multiple port scans. To ensure functionality and long-term maintainability, a comprehensive testing suite was implemented using Python’s unittest framework. Edge cases, including valid port numbers, reversed ranges, and closed ports, were explicitly tested to ensure robust handling of real-world scenarios. The resulting tool reduces the time required to verify port security across a network, supporting both targeted and host checks and broader Classless Inter-Domain Routing (CIDR) -based network scans. This work demonstrates the value of automation and test-driven development in strengthening network security practices, and provides a foundation for future enhancements.

Rivera, Linda [Fermilab]↗

DOE OSTI2022

Bayesian Estimation of Oscillator Parameters: Toward Anomaly Detection and Cyber-Physical System Security

Cyber-physical system security presents unique challenges to conventional measurement science and technology. Anomaly detection in software-assisted physical systems, such as those employed in additive manufacturing or in DNA synthesis, is often hampered by the limited available parameter space of the underlying mechanism that is transducing the anomaly. As a result, the formulation of anomaly detection for such systems often leads to inverse or ill-posed problems, requiring statistical treatments. Here, we present Bayesian inference of unknown parameters associated with a generic actuator considered as a representative vital element of a cyber-physical system. Via a series of experimental input-output measurements, a transfer function for the actuator is obtained numerically, which serves as our model for the proposed method. Linear, nonlinear, and delayed dynamics may be assumed for the actuator response. By devising a code-based malicious signal, we study the efficacy of Bayesian inference for its potential to produce a detection, including uncertainty quantification, with a remarkably small number of input data points. Our approach should be adaptable to a variety of real-time cyber-physical anomaly detection scenarios.

47 OTHER INSTRUMENTATION↗

NASA NTRS1975

Sensitivity analysis of short-arc station coordinate determinations from range data

The accurate determination of the geocentric coordinates of a tracking station is essential for most geodetic and geophysical satellite applications. Since most of these satellites are close to the earth, the geopotential model is a dominant source of error which significantly influences station coordinate determinations. Other sources, such as GM error and drag, also influence the accuracy of the station coordinate determination. One technique for reducing the effect of these errors is to use short-arcs consisting of a few passes of the satellite over the tracking station. This paper analyzes the sensitivity of short-arc station coordinate estimates to various errors in the physical model, to the number of observations, and to the station-satellite geometry using simulated as well as real data.

Schutz, B. E.↗

NASA NTRS1976

Sensitivity analysis of short-arc station coordinate determinations from range data

Schutz, B. E.↗

NASA NTRS1979

Review of design and operational characteristics of the 0.3-meter transonic cryogenic tunnel

The fundamentals of cryogenic testing are validated both analytically and experimentally employing the 0.3-m transonic cryogenic tunnel. The tunnel with its unique Reynolds number capability has been used for a wide variety of aerodynamic tests. Techniques regarding real-gas effects have been developed and cryogenic tunnel conditions are set and maintained accurately. It is shown that cryogenic cooling, by injecting nitrogen directly into the tunnel circuit, imposes no problems with temperature distribution or dynamic response characteristics.

Ray, E. J.↗

NASA NTRS1986

A decade of SETI observations

A full time dedicated search for extraterrestrial radio signals of intelligent origin has been in progress at the Ohio State University Radio Observatory since 1973. The radio telescope has a collecting area of 2200 square meters, which is equivalent to a circular dish 175 feet in diameter. The search concentrates on a 500 kHz bandwidth centered on the 1420 MHz hydrogen line, Doppler corrected to the galactic standard of rest. A large portion of the sky visible from Ohio was searched, with particulat emphasis on the galactic center region and the M31 Andromeda galaxy. The survey is largely computer automated, and all data reduction is done in real time. Two distinct populations of signals were detected. The first is a relatively small number of signals which persist for over a minute and which are clearly extraterrestrial in origin. The second is the large number of signals which persist less than 10 seconds whose locations are anticorrelated with the galactic plane but show clumps along the galactic axis. None of these signals were observed to recur, despite repeated observations. The cause of these signals were not determined.

Dixon, R. S.↗

NASA NTRS1988

Antenna pointing compensation based on precision optical measurement techniques

The pointing control loops of the Deep Space Network 70 meter antennas extend only to the Intermediate Reference Structure (IRS). Thus, distortion of the structure forward of the IRS due to unpredictable environmental loads can result in uncompensated boresight shifts which degrade blind pointing accuracy. A system is described which can provide real time bias commands to the pointing control system to compensate for environmental effects on blind pointing performance. The bias commands are computed in real time based on optical ranging measurements of the structure from the IRS to a number of selected points on the primary and secondary reflectors.

Schumacher, L. L.↗

NASA NTRS1992

Time management situation assessment (TMSA)

TMSA is a concept prototype developed to support NASA Test Directors (NTDs) in schedule execution monitoring during the later stages of a Shuttle countdown. The program detects qualitative and quantitative constraint violations in near real-time. The next version will support incremental rescheduling and reason over a substantially larger number of scheduled events.

Richardson, Michael B.↗

NASA NTRS1991

Observations of space debris

Optical observations of near Earth and deep-space debris conducted at M.I.T.'s artificial satellite observatory will be discussed. A brief review of observing technique, regions of high debris density, and amount of debris in orbit will be given. The unique, duplex facilities of the observatory allow the discrimination of debris from meteors, the construction of an orbital element set, and real-time identification of catalogued artificial satellites. Near-Earth debris is present in large numbers in all the popular near-Earth orbits; at least 5-6 times the 5000-6000 objects in the NORAD catalog. In deep-space, the new presence of Ariane-related debris adds significantly to the existing population which is treble that catalogued by NORAD.

Taff, L. G.↗

NASA NTRS1993

Streak camera based SLR receiver for two color atmospheric measurements

To realize accurate two-color differential measurements, an image digitizing system with variable spatial resolution was designed, built, and integrated to a photon-counting picosecond streak camera, yielding a temporal scan resolution better than 300 femtosecond/pixel. The streak camera is configured to operate with 3 spatial channels; two of these support green (532 nm) and uv (355 nm) while the third accommodates reference pulses (764 nm) for real-time calibration. Critical parameters affecting differential timing accuracy such as pulse width and shape, number of received photons, streak camera/imaging system nonlinearities, dynamic range, and noise characteristics were investigated to optimize the system for accurate differential delay measurements. The streak camera output image consists of three image fields, each field is 1024 pixels along the time axis and 16 pixels across the spatial axis. Each of the image fields may be independently positioned across the spatial axis. Two of the image fields are used for the two wavelengths used in the experiment; the third window measures the temporal separation of a pair of diode laser pulses which verify the streak camera sweep speed for each data frame. The sum of the 16 pixel intensities across each of the 1024 temporal positions for the three data windows is used to extract the three waveforms. The waveform data is processed using an iterative three-point running average filter (10 to 30 iterations are used) to remove high-frequency structure. The pulse pair separations are determined using the half-max and centroid type analysis. Rigorous experimental verification has demonstrated that this simplified process provides the best measurement accuracy. To calibrate the receiver system sweep, two laser pulses with precisely known temporal separation are scanned along the full length of the sweep axis. The experimental measurements are then modeled using polynomial regression to obtain a best fit to the data. Data aggregation using normal point approach has provided accurate data fitting techniques and is found to be much more convenient than using the full rate single shot data. The systematic errors from this model have been found to be less than 3 ps for normal points.

Varghese, Thomas K.↗