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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Emulation of Core Flight System Applications for Flight Software Development and Validation

The Mars Sample Return (MSR) campaign is an unprecedented attempt in the return of Martian samples back to Earth. The ascent from the surface will be performed by the Mars Ascent Vehicle (MAV), a critical element in the mission that National Aeronautics and Space Administration (NASA) Marshall Space Flight Center (MSFC) is developing. To this end, innovations in flight software development, verification, and validation are occurring. The MAV flight computer will run Core Flight System (cFS), an open-source software environment developed by NASA Goddard Space Flight Center (GSFC). NASA Marshall’s MAV Mission and Fault Management (M&FM) Team has implemented an emulation of two applications of this architecture: Limit Checker and Stored Command. Using an emulation of the functionalities of these applications allows for rapid prototyping of table-based algorithms. Further, M&FM is leveraging an in-house, low-fidelity but high-throughput State Analysis Model (SAM), an integrated MATLAB Stateflow Plant and Software model. This model is run in parallel with the cFS emulation for full flyout testing of the M&FM algorithms, verification of intent of these algorithms, and for future auto-generation of application-ingestible M&FM tables. The tables can then be delivered to the MAV Flight Software (FSW) team in a seamless process, reducing the cost of traditional FSW development and the risk of starting M&FM FSW development at later points in the NASA program life cycle.

Cody Wheeler↗
NASA NTRSDate not supplied

Flood Detection with Synthetic Aperture Radar: A Case Study of Houston, Texas Following Hurricane Harvey (2017) using C- and X-Band Observations

Hurricane Harvey produced record-breaking rainfall of up to 60 inches resulting in extensive flooding in Houston, Texas, in late August and early September of 2017. The slow forward motion of the storm following landfall left much of the area unobservable to optical remote sensing instruments for several days due to cloud cover. The active nature of Synthetic Aperture Radar (SAR) instruments allows for observations through clouds, which can supplement efforts to estimate hurricane-induced flood impacts. A growing fleet of SAR constellations has helped lower the latency of imagery following a hurricane, allowing for more timely detections of flooding to help support emergency response efforts. In this study, we leverage publicly available C-band SAR observations from the European Space Agency’s Sentinel-1B (S1B) satellite, collected on 30 August, and X-band SAR imagery collected on 1 September by the Airbus TanDEM-X (TDX) satellite made available through the NASA Commercial Smallsat Data Acquisition (CSDA) program. For each dataset, one co-polarized, StripMap, Radiometric Terrain Corrected (RTC) image was used to create a binary water/no water classification map by referencing permanent water in the Cropland Data Layer (CDL) dataset to determine thresholds. A validation dataset of randomly distributed “ground truth” points was generated using optical imagery from PlanetScope on 31 August, where the domain had relatively little cloud cover. We found that the S1B- and TDX-derived open water maps achieved overall accuracies of 94.17% and 91.57%, respectively. The variation in performance is attributed to both the penetrative abilities of C- and X-band SAR wavelengths in vegetated areas and the increased spatial resolution of the commercial SAR (~3 m) over Sentinel-1 (30 m). While publicly available Sentinel-1 observations are commonly relied upon in response and recovery efforts, these results suggest that including commercial X-band SAR imagery could be beneficial by providing both increased spatial resolution and more frequent revisits when deriving post-event flood mapping products.

Alexander M Melancon↗
DOE OSTI2020

Perovskite-Derivative Valleytronics

Halide perovskites are revolutionizing the renewable energy sector owing to their high photovoltaic efficiency, low manufacturing cost, and flexibility. Their remarkable mobility and long carrier lifetime are also valuable for information technology, but fundamental challenges like poor stability under an electric field prevent realistic applications of halide perovskites in electronics. Here, it is discovered that valleytronics is a promising route to leverage the advantages of halide perovskites and derivatives for information storage and processing. The synthesized all-inorganic lead-free perovskite derivative, Cs 3 Bi 2 I 9 , exhibits strong light-matter interaction and parity-dependent optically addressable valley degree of freedom. In this work, robust optical helicity in all odd-layer-number crystals with inversion symmetry breaking is observed, indicating excitonic coherence extending well beyond 11 layers. The excellent optical and valley properties of Cs 3 Bi 2 I 9 arise from the unique parallel bands, according to first principles calculations. This discovery points to new materials design principles for scalable valleytronic devices and demonstrates the promise of perovskite derivatives beyond energy applications.

2D materials↗
DOE OSTI2024

Fabrication of a Point-Like Transmission Target for Reducing Computed Tomography Imaging Artifacts

In this study, we address the challenge of enhancing image quality and spatial resolution in computed tomography (CT) imaging by introducing simulation and fabrication of high aspect ratio, point-like transmission targets. Utilizing advanced electroplating techniques, traditionally employed in the fabrication of Through Substrate Via (TSV) interconnects for CMOS circuitry, we successfully embed copper targets within silicon substrates. This method allows us to create high-aspect-ratio features specifically designed for X-ray transmission targets, resulting in micro targets that exhibit a volume increase compared to conventional evaporated surface targets. Furthermore, we present simulation results of the X-ray spectrum generated by these targets, demonstrating their potential to significantly improve both image quality and spatial resolution in CT applications. Our findings suggest that leveraging advanced fabrication techniques can open new avenues for the development of enhanced imaging technologies in medical diagnostics and beyond.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Cyanobacterial circadian regulation enhances bioproduction under subjective nighttime through rewiring of carbon partitioning dynamics, redox balance orchestration, and cell cycle modulation

Abstract Background The industrial feasibility of photosynthetic bioproduction using cyanobacterial platforms remains challenging due to insufficient yields, particularly due to competition between product formation and cellular carbon demands across different temporal phases of growth. This study investigates how circadian clock regulation impacts carbon partitioning between storage, growth, and product synthesis in Synechococcus elongatus PCC 7942, and provides insights that suggest potential strategies for enhanced bioproduction. Results After entrainment to light-dark cycles, PCC 7942 cultures transitioned to constant light revealed distinct temporal patterns in sucrose production, exhibiting three-fold higher productivity during subjective night compared to subjective day despite moderate down-regulation of genes from the photosynthetic apparatus. This enhanced productivity coincided with reduced glycogen accumulation and halted cell division at subjective night time, suggesting temporal separation of competing processes. Transcriptome analysis revealed coordinated circadian clock-driven adjustment of the cell cycle and rewiring of energy and carbon metabolism, with over 300 genes showing differential expression across four time points. The subjective night was characterized by altered expression of cell division-related genes and reduced expression of genes involved in glycogen synthesis, while showing upregulation of glycogen degradation pathways, alternative electron flow components, the pentose phosphate pathway, and oxidative decarboxylation of pyruvate. These molecular changes created favorable conditions for product formation through enhanced availability of major sucrose precursors (glucose-1-phosphate and fructose-6-phosphate) and maintained redox balance through multiple mechanisms. Conclusions Our analysis of circadian regulatory rewiring of carbon metabolism and redox balancing suggests two potential approaches that could be developed for improving cyanobacterial bioproduction: leveraging natural circadian rhythms for optimizing cultivation conditions and timing of pathway induction, and engineering strains that mimic circadian-driven metabolic shifts through controlled carbon flux redistribution and redox rebalancing. While these strategies remain to be tested, they could theoretically improve the efficiency of photosynthetic bioproduction by enabling better temporal separation between cell growth, carbon storage accumulation, and product synthesis phases.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Dimensionally reduced machine learning model for predicting single component octanol–water partition coefficients

Abstract MF-LOGP, a new method for determining a single component octanol–water partition coefficients ( $$LogP$$ LogP ) is presented which uses molecular formula as the only input. Octanol–water partition coefficients are useful in many applications, ranging from environmental fate and drug delivery. Currently, partition coefficients are either experimentally measured or predicted as a function of structural fragments, topological descriptors, or thermodynamic properties known or calculated from precise molecular structures. The MF-LOGP method presented here differs from classical methods as it does not require any structural information and uses molecular formula as the sole model input. MF-LOGP is therefore useful for situations in which the structure is unknown or where the use of a low dimensional, easily automatable, and computationally inexpensive calculations is required. MF-LOGP is a random forest algorithm that is trained and tested on 15,377 data points, using 10 features derived from the molecular formula to make $$LogP$$ LogP predictions. Using an independent validation set of 2713 data points, MF-LOGP was found to have an average $$RMSE$$ RMSE = 0.77 ± 0.007, $$MAE$$ MAE = 0.52 ± 0.003, and $${R}^{2}$$ R 2 = 0.83 ± 0.003. This performance fell within the spectrum of performances reported in the published literature for conventional higher dimensional models ( $$RMSE$$ RMSE = 0.42–1.54, $$MAE$$ MAE = 0.09–1.07, and $${R}^{2}$$ R 2 = 0.32–0.95). Compared with existing models, MF-LOGP requires a maximum of ten features and no structural information, thereby providing a practical and yet predictive tool. The development of MF-LOGP provides the groundwork for development of more physical prediction models leveraging big data analytical methods or complex multicomponent mixtures. Graphical Abstract

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2012

Monitoring of International Space Station Telemetry Using Shewhart Control Charts

Shewhart control charts have been established as an expedient method for analyzing dynamic, trending data in order to identify anomalous subsystem performance as soon as such performance would exceed a statistically established baseline. Additionally, this leading indicator tool integrates a selection methodology that reduces false positive indications, optimizes true leading indicator events, minimizes computer processor unit duty cycles, and addresses human factor concerns (i.e., the potential for flight-controller data overload). This innovation leverages statistical process control, and provides a relatively simple way to allow flight controllers to focus their attention on subtle system changes that could lead to dramatic off-nominal system performance. Finally, this capability improves response time to potential hardware damage and/or crew injury, thereby improving space flight safety. Shewhart control charts require normalized data. However, the telemetry from the ISS Early External Thermal Control System (EETCS) was not normally distributed. A method for normalizing the data was implemented, as was a means of selecting data windows, the number of standard deviations (Sigma Level), the number of consecutive points out of limits (Sequence), and direction (increasing or decreasing trend data). By varying these options, and treating them like dial settings, the number of nuisance alerts and leading indicators were optimized. The goal was to capture all leading indicators while minimizing the number of nuisances. Lean Six Sigma (L6S) design of experiment methodologies were employed. To optimize the results, Perl programming language was used to automate the massive amounts of telemetry data, control chart plots, and the data analysis.

Fitch, Jeffery T.↗
DOE OSTI2023

Long-pulse high-performance H-mode plasmas achieved on EAST

A record duration of a 310 s H-mode plasma (H 98y2 ~ 1.3, n e /n GW ~ 0.7, f BS > 50%) has been recently achieved on experimental advanced superconducting tokamak (EAST) with metal walls, exploiting the device's improved long-pulse capabilities. The experiment demonstrates good control of tungsten concentration, core/edge MHD stability, and particle and heat exhaust with an ITER-like tungsten divertor and zero injected torque, establishing a milestone on the path to steady-state long-pulse high-performance scenarios in support of ITER and CFETR. Important synergistic effects are leveraged toward this result, which relies purely on radio frequency (RF) powers for heating and current drive (H&CD). On-axis electron cyclotron heating enhances the H&CD efficiency from lower hybrid wave injection, increasing confinement quality and enabling fully non-inductive operation at high density (n e /n GW ~ 70%) and high poloidal beta (β P ~ 2.5). A small-amplitude grassy edge localized mode regime facilitates the RF power coupling to the H-mode edge and reduces divertor sputtering/erosion. The high energy confinement quality (H 98y2 ~ 1.3) is achieved with the experimental and simulated results pointing to the strong effect of Shafranov shift on turbulence. Transport analysis suggests that trapped electron modes dominate in the core region during the record discharge. The detailed physics processes (RF synergy, core-edge integration, confinement properties, etc.) of the steady-state operation will be illustrated in the content. In the future, EAST will aim at accessing more relevant dimensionless parameters to develop long-pulse high-performance plasma toward ITER and CFETR steady-state advanced operation.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Commissioning and operation of a real-time multi-point Thomson scattering evaluation system at Wendelstein 7-X

A real-time multi-point Thomson scattering (rtMPTS) evaluation system has recently been installed and commissioned at the Wendelstein 7-X (W7-X) stellarator during the 2024 -2025 operational phases. This system has demonstrated successful evaluation of Thomson scattered spectra on millisecond timescales. We discuss initial results from this system, consisting of a comparison of different evaluation techniques, assessment of system evaluation times, and proof-of-principle control experiments carried out with the W7-X poloidal correlation reflectometer (PCR) system. This diagnostic represents the first step towards the development of a density profile controller at W7-X, which can be leveraged to support steady-state operation.

Mohammed, Ameer [Princeton Plasma Physics Laborato↗
DOE OSTI2023

Plug-and-Play Methods for Integrating Physical and Learned Models in Computational Imaging: Theory, algorithms, and applications

Plug-and-play (PnP) priors constitute one of the most widely used frameworks for solving computational imaging problems through the integration of physical models and learned models. PnP leverages high-fidelity physical sensor models and powerful machine learning methods for prior modeling of data to provide state-of-the-art reconstruction algorithms. PnP algorithms alternate between minimizing a data fidelity term to promote data consistency and imposing a learned regularizer in the form of an image denoiser. Recent highly successful applications of PnP algorithms include biomicroscopy, computerized tomography (CT), magnetic resonance imaging (MRI), and joint ptychotomography. This article presents a unified and principled review of PnP by tracing its roots, describing its major variations, summarizing main results, and discussing applications in computational imaging. Additionally, we also point the way toward further developments by discussing recent results on equilibrium equations that formulate the problem associated with PnP algorithms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

PV-BESS DC-Series Integration for Regulated DC Systems

DC-series integration introduces a novel approach to seamlessly integrate a solar photovoltaic (PV) array and a battery energy storage (BES) in series. This system, referred to as the PV-integrated battery energy storage system—dc series (PVBESS-DCS), simplifies integration and enhances power density by leveraging the inherent voltage-source characteristics of batteries and adopting the concept of partial power processing. However, addressing voltage variations of a PV array and a BES under different operational conditions remains a challenge to apply the PVBESS-DCS to a regulated dc system, where the dc-bus voltage is fixed. This article proposes a PVBESS-DCS solution tailored for regulated dc systems. Within this framework, we present a controllable series voltage source compensating for the voltage differences between the PV and BES sources and the dc bus. Thus, the system can perform PV maximum power point tracking and curtailment control while charging and discharging BES and connected to a regulated dc bus. Additionally, we present a single-stage multiport partial power processing dc–dc converter designed for the proposed PVBESS-DCS. It utilizes a triple active bridge dc–dc converter topology. Finally, this article presents operating principles, control strategies, simulation results, and experimental findings of the proposed PVBESS-DCS for regulated dc systems.

25 ENERGY STORAGE↗
DOE OSTI2025

Fundamental mode excitation via Joule–Thomson light expansion in nonlinear optical lattices

Under linear conditions, power injected from a single waveguide into a multi-core fiber array results in multimode propagation, progressively diminishing the spatial coherence of light. In this work, we introduce a comprehensive approach to mitigate this coherence loss by means of a nonlinear thermodynamic Joule–Thomson expansion. By leveraging the tools of optical thermodynamics, we demonstrate that as light undergoes a sudden transition from a small to a larger nonlinear optical array, it can abruptly drop its optical temperature to near-zero values. During this cooling process, light irreversibly flows into the system's fundamental mode with very high efficiency, synchronizing all elements of the lattice with the input port. We show that this nonlinear effect is highly predictable even in systems of arbitrary geometry and shape and can be controlled precisely by the initial conditions at the input of the array. In particular, for a single injection point, the reduction in optical temperature can be directly determined by the total power, irrespective of the input location.

Pyrialakos, Georgios G. (ORCID:0000000286129694)↗
DOE OSTI2022

Long-time memory effects in a localizable central spin problem

We study the properties of the Nakajima–Zwanzig memory kernel for a qubit immersed in a many-body localized (i.e. disordered and interacting) bath. We argue that the memory kernel decays as a power law in both the localized and ergodic regimes, and show how this can be leveraged to extract t → ∞ populations for the qubit from finite time (Jt ≤ 10 2 ) data in the thermalizing phase. This allows us to quantify how the long-time values of the populations approach the expected thermalized state as the bath approaches the thermodynamic limit. This approach should provide a good complement to state-of-the-art numerical methods, for which the long-time dynamics with large baths are impossible to simulate in this phase. Additionally, our numerics on finite baths reveal the possibility for unbounded exponential growth in the memory kernel, a phenomenon rooted in the appearance of exceptional points in the projected Liouvillian governing the reduced dynamics. In small systems amenable to exact numerics, we find that these pathologies may have some correlation with delocalization.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Dispersion Leverage Coronagraph: a nulling coronagraph for use on primary objective grating telescopes

Here, we present the Dispersion Leverage Coronagraph (DLC), a variation of the Achromatic Interfero Coronagraph (AIC) that is designed for optical systems featuring large, dispersive primary objective gratings. DLC was originally designed for the Diffractive Interfero Coronagraph Exoplanet Resolver (DICER), a notional 20 m class infrared space telescope utilizing the enhanced one-dimensional angular resolution of large diffraction gratings to discover and characterize near-Earth exoplanets. Here we develop the theoretical foundation for DLC, and apply it to DICER as an example use case. We derive important properties of the DLC system, including focal plane transmission maps, stellar leakage, residual optical path difference tolerance, and pointing error/jitter considerations. Ultimately, we found that DLC effectively nulls an on-axis target across the entire spectrum in the focal plane, allowing for 2D/λ diffraction-limited imaging. It requires asymmetrical fine-guidance tolerances on pointing error/jitter. We work through a benchmark DICER design, explaining the need for a second disperser to reduce background from Zodiacal light, and showing that it could plausibly find and characterize ∼4 nearby, habitable exoplanets around Sun-like stars in a 7-year mission; about 30% of the habitable exoplanets within 8 pc were found in our simulation. The DLC may be useful for any application requiring extremely high-resolution, close companion spectroscopy.

AIC↗
DOE OSTI2021

Regressing Nuclear Reactor Power Level Using Low-Cost Sensor Network Data

Multisensor networks deployed at nuclear facilities can be leveraged to collect data used as inputs to machine learning models predicting nuclear safeguard relevant information. This work demonstrates an application of this idea by regressing nuclear reactor power levels, a key indicator for nuclear safeguard verification, at the McClellan Nuclear Research Center using data collected by five Merlyn multisensor platforms with LASSO and LSTM models. This work also demonstrates the use of Leave One Node Out to measure the importance of each multisensor for this regression problem providing insight into model explainability and allowing inferential hypotheses about the nuclear facility to be made. This work can be used as a starting point for future development of methods for regression on reactor power levels at nuclear facilities using multisensor network data.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Top-Down Fabrication of Atomic Patterns in Twisted Bilayer Graphene

Atomic-scale engineering typically involves bottom-up approaches, leveraging parameters such as temperature, partial pressures, and chemical affinity to promote spontaneous arrangement of atoms. These parameters are applied globally, resulting in atomic-scale features scattered probabilistically throughout the material. In a top-down approach, different regions of the material are exposed to different parameters, resulting in structural changes varying on the scale of the resolution. In this work, the application of global and local parameters is combined in an aberration-corrected scanning transmission electron microscope (STEM) to demonstrate atomic-scale precision patterning of atoms in twisted bilayer graphene. The focused electron beam is used to define attachment points for foreign atoms through the controlled ejection of carbon atoms from the graphene lattice. The sample environment is staged with nearby source materials such that the sample temperature can induce migration of the source atoms across the sample surface. Under these conditions, the electron-beam (top-down) enables carbon atoms in the graphene to be replaced spontaneously by diffusing adatoms (bottom-up). Using image-based feedback control, arbitrary patterns of atoms and atom clusters are attached to the twisted bilayer graphene with limited human interaction. Finally, the role of substrate temperature on adatom and vacancy diffusion is explored by first-principles simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Bayesian learning of orthogonal embeddings for multi-fidelity Gaussian Processes

Uncertainty propagation in complex engineering systems often poses significant computational challenges related to modeling and quantifying probability distributions of model outputs, as those emerge as the result of various sources of uncertainty that are inherent in the system under investigation. Gaussian Processes regression (GPs) is a robust meta-modeling technique that allows for fast model prediction and exploration of response surfaces. Multi-fidelity variations of GPs further leverage information from cheap and low fidelity model simulations in order to improve their predictive performance on the high fidelity model. In order to cope with the high volume of data required to train GPs in high dimensional design spaces, a common practice is to introduce latent design variables that are typically projections of the original input space to a lower dimensional subspace, and therefore substitute the problem of learning the initial high dimensional mapping, with that of training a GP on a low dimensional space. Here in this paper, we present a Bayesian approach to identify optimal transformations that map the input points to low dimensional latent variables. The \projection" mapping consists of an orthonormal matrix that is considered a priori unknown and needs to be inferred jointly with the GP parameters, conditioned on the available training data. The proposed Bayesian inference scheme relies on a two-step iterative algorithm that samples from the marginal posteriors of the GP parameters and the projection matrix respectively, both using Markov Chain Monte Carlo (MCMC) sampling. In order to take into account the orthogonality constraints imposed on the orthonormal projection matrix, a Geodesic Monte Carlo sampling algorithm is employed, that is suitable for exploiting probability measures on manifolds. We extend the proposed framework to multi-fidelity models using GPs including the scenarios of training multiple outputs together. We validate our framework on three synthetic problems with a known lower-dimensional subspace. The benefits of our proposed framework, are illustrated on the computationally challenging aerodynamic optimization of a last-stage blade for an industrial gas turbine, where we study the effect of an 85-dimensional shape parameterization of a three-dimensional airfoil on two output quantities of interest, specifically on the aerodynamic efficiency and the degree of reaction

42 ENGINEERING↗
DOE OSTI2026

Tin–Lead Liquid Metal Alloy Source for Focused Ion Beams

Focused Ion Beam (FIB) systems are increasingly utilized in nanotechnology for nanostructuring, surface modification, doping, and rapid prototyping. Recently, their potential for quantum applications has been explored, leveraging FIB’s direct-write capabilities for in situ single ion implantation, which is crucial for fabricating single photon emitters. Color centers in diamond can function as qubits and are of particular interest due to their capacity to store and transmit quantum information. While Group-IV color centers exhibit high brightness, they require low temperatures to retain coherence. However, lead-vacancy in diamond (PbV) operates at the higher end (4 K) of this temperature spectrum due to larger ground-state splitting, making them particularly interesting. In this context, our study presents results for lead (Pb)-containing alloys with eutectic points below 600 °C and results on using tantalum (Ta) and titanium (Ti) as emitter materials for a Pb liquid metal alloy ion source. We show that a standard FIB system is able to resolve the different Pb isotopes and achieve nanoscale spot sizes, as required for quantum information science applications.

color center↗