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NASA NTRS2011

HEASARC - The High Energy Astrophysics Science Archive Research Center

The High Energy Astrophysics Science Archive Research Center (HEASARC) is NASA's archive for high-energy astrophysics and cosmic microwave background (CMB) data, supporting the broad science goals of NASA's Physics of the Cosmos theme. It provides vital scientific infrastructure to the community by standardizing science data formats and analysis programs, providing open access to NASA resources, and implementing powerful archive interfaces. Over the next five years the HEASARC will ingest observations from up to 12 operating missions, while serving data from these and over 30 archival missions to the community. The HEASARC archive presently contains over 37 TB of data, and will contain over 60 TB by the end of 2014. The HEASARC continues to secure major cost savings for NASA missions, providing a reusable mission-independent framework for reducing, analyzing, and archiving data. This approach was recognized in the NRC Portals to the Universe report (2007) as one of the HEASARC's great strengths. This poster describes the past and current activities of the HEASARC and our anticipated developments in coming years. These include preparations to support upcoming high energy missions (NuSTAR, Astro-H, GEMS) and ground-based and sub-orbital CMB experiments, as well as continued support of missions currently operating (Chandra, Fermi, RXTE, Suzaku, Swift, XMM-Newton and INTEGRAL). In 2012 the HEASARC (which now includes LAMBDA) will support the final nine-year WMAP data release. The HEASARC is also upgrading its archive querying and retrieval software with the new Xamin system in early release - and building on opportunities afforded by the growth of the Virtual Observatory and recent developments in virtual environments and cloud computing.

Smale, Alan P.↗

NASA NTRS2013

Damage Detection Response Characteristics of Open Circuit Resonant (SansEC) Sensors

The capability to assess the current or future state of the health of an aircraft to improve safety, availability, and reliability while reducing maintenance costs has been a continuous goal for decades. Many companies, commercial entities, and academic institutions have become interested in Integrated Vehicle Health Management (IVHM) and a growing effort of research into "smart" vehicle sensing systems has emerged. Methods to detect damage to aircraft materials and structures have historically relied on visual inspection during pre-flight or post-flight operations by flight and ground crews. More quantitative non-destructive investigations with various instruments and sensors have traditionally been performed when the aircraft is out of operational service during major scheduled maintenance. Through the use of reliable sensors coupled with data monitoring, data mining, and data analysis techniques, the health state of a vehicle can be detected in-situ. NASA Langley Research Center (LaRC) is developing a composite aircraft skin damage detection method and system based on open circuit SansEC (Sans Electric Connection) sensor technology. Composite materials are increasingly used in modern aircraft for reducing weight, improving fuel efficiency, and enhancing the overall design, performance, and manufacturability of airborne vehicles. Materials such as fiberglass reinforced composites (FRC) and carbon-fiber-reinforced polymers (CFRP) are being used to great advantage in airframes, wings, engine nacelles, turbine blades, fairings, fuselage structures, empennage structures, control surfaces and aircraft skins. SansEC sensor technology is a new technical framework for designing, powering, and interrogating sensors to detect various types of damage in composite materials. The source cause of the in-service damage (lightning strike, impact damage, material fatigue, etc.) to the aircraft composite is not relevant. The sensor will detect damage independent of the cause. Damage in composite material is generally associated with a localized change in material permittivity and/or conductivity. These changes are sensed using SansEC. The unique electrical signatures (amplitude, frequency, bandwidth, and phase) are used for damage detection and diagnosis. An operational system and method would incorporate a SansEC sensor array on select areas of the aircraft exterior surfaces to form a "Smart skin" sensing surface. In this paper a new method and system for aircraft in-situ damage detection and diagnosis is presented. Experimental test results on seeded fault damage coupons and computational modeling simulation results are presented. NASA LaRC has demonstrated with individual sensors that SansEC sensors can be effectively used for in-situ composite damage detection of delamination, voids, fractures, and rips. Keywords: Damage Detection, Composites, Integrated Vehicle Health Monitoring (IVHM), Aviation Safety, SansEC Sensors

Dudley, Kenneth L.↗

NASA NTRSDate not supplied

Lower Mekong Hydrological Decision Support system

The Lower Mekong Hydrological Decision Support system (LMHDSs) is a environmental data analysis tool developed at the NASA Goddard Space Flight Center with funding from the SERVIR Applied Sciences Team and technical support from SERVIR Science Coordination Office (SCO). The web application allows stakeholders and decision-makers to view and download the inputs and outputs to the Soil and Water Assessment Tool(SWAT) model temporally and spatially. The front end is developed using JavaScript libraries like OpenLayers and Stock charts and the backend uses Django, a Python-based web framework. The web app provides several features, including visualizing map products, time-series plots, land-use/land-cover and associated soil information, and a data cart for downloading data. In addition, LMHDSs incorporates the NASAaccess software package, which provides seamless access to various climate and weather data products from NASA’s Earth observations portfolio. The application is region agnostic (any valid SWAT model can be used), modular (different components of the applications can be customized), and open (anyone can download and run it on their end). The web app is currently in use by the Mekong River Commission (MRC), a treaty-based regional intergovernmental organization that is made up of Mekong countries, as part of its hydrological decision support.

Hydrology↗

DOE OSTI2025

A DECADE of dwarfs: first detection of weak lensing around spectroscopically confirmed low-mass galaxies

We present the first detection of weak gravitational lensing around spectroscopically confirmed dwarf galaxies, using the large overlap between DESI DR1 spectroscopic data and DECADE/DES weak lensing catalogs. A clean dwarf galaxy sample with well-defined redshift and stellar mass cuts enables excess surface mass density measurements in two stellar mass bins ($\log \rm{M}_*=[8.2, 9.2]~M_\odot$ and $\log \rm{M}_*=[9.2, 10.2]~M_\odot$), with signal-to-noise ratios of $5.6$ and $12.4$ respectively. This signal-to-noise drops to $4.5$ and $9.2$ respectively for measurements without applying individual inverse probability (IIP) weights, which mitigates fiber incompleteness from DESI's targeting. The measurements are robust against variations in stellar mass estimates, photometric shredding, and lensing calibration systematics. Using a simulation-based modeling framework with stellar mass function priors, we constrain the stellar mass-halo mass relation and find a satellite fraction of $\simeq 0.3$, which is higher than previous photometric studies but $1.5σ$ lower than $Λ$CDM predictions. We find that IIP weights have a significant impact on lensing measurements and can change the inferred $f_{\rm{sat}}$ by a factor of two, highlighting the need for accurate fiber incompleteness corrections for dwarf galaxy samples. Our results open a new observational window into the galaxy-halo connection at low masses, showing that future massively multiplexed spectroscopic observations and weak lensing data will enable stringent tests of galaxy formation models and $Λ$CDM predictions.

To, Chun-Hao [Chicago U., Astron. Astrophys. Ctr.;↗

DOE OSTI2026

SoK: What does it Mean to Benchmark Database Forensics?

Relational Database Management Systems are the backbone of modern enterprises and public-sector services, and are thus frequent targets of security incidents, insider threats, and thorough regulatory audits. Consequently, databases have become key sources of digital evidence, requiring investigators to reconstruct past activity from audit logs, transaction logs, and backups. Although benchmarking frameworks such as those developed by the Transaction Processing Performance Council (TPC) are widely used to evaluate database performance, they do not capture forensic requirements such as evidentiary completeness, tamper-evidence, chain of custody, or regulatory compliance under GDPR and CCPA. This survey examines the emerging domain of forensic database benchmarking. We gathered prior research on database forensics, secure logging, and tamper-evident data structures; we analyze modern forensic-ready features in commercial and open-source systems (SQL Server Ledger, Oracle Blockchain Tables, PostgreSQL pgAudit, Db2 Audit, Aurora Database Activity Streams, Oracle Real Application Security and IBM Guardium) and assess why existing benchmarks are insufficient. We propose forensic workloads, metrics, and methodologies that incorporate adversarial stressors, deleted-record recovery, and backup analysis. We also identify open research problems and call for a community-driven forensic benchmark suite. The result is an idea for evaluating not only database performance but also forensic soundness, bridging the gap between system engineering, compliance, and digital investigations.

Lenard, Ben↗

DOE OSTI2023

A highly proton conductive perfluorinated covalent triazine framework via low-temperature synthesis

Proton-conducting materials are essential to the emerging hydrogen economy. Covalent triazine frameworks (CTFs) are promising proton-conducting materials at high temperatures but need more effective sites to strengthen interaction for proton carriers. However, their construction and design in a concise condition are still challenges. Herein, we show a low temperature approach to synthesize CTFs via a direct cyclotrimerization of aromatic aldehyde using ammonium iodide as facile nitrogen source. Among the CTFs, the perfluorinated CTF (CTF-TF) was successfully synthesized with much lower temperature ( ≤ 160 °C) and open-air atmosphere. Due to the additional hydrogen-bonding interaction between fluorine atoms and proton carriers (H 3 PO 4 ), the CTF-TF achieves a proton conductivity of 1.82 × 10 -1 S cm -1 at 150 °C after H 3 PO 4 loading. Moreover, the CTF-TF can be readily integrated into mixed matrix membranes, displaying high proton conduction abilities and good mechanical strength. This work provides an alternative strategy for rational design of proton conducting media.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

A multiscale insight into the growth of h-BN: effect of the enclosure

Abstract There is a lack of knowledge on the fundamental growth mechanisms governing the characteristics of 2D materials synthesized by the chemical vapor deposition (CVD) technique and their correlation with experimentally controllable parameters, which hindered their wafer-scale synthesis. Here, we pursued an analytical and computational approach to access the system states that are not experimentally viable to address these critical needs. We developed a multiscale computational framework correlating the macroscale heat and mass flow with the mesoscale morphology of the as-grown 2D materials by solving the coupled system of heat/mass transfer and phase-field equations. We used hexagonal boron nitride (h-BN) as our model material and investigated the effect of substrate enclosure on its growth kinetics and final morphology. We revealed a lower concentration with a more uniform distribution on the substrate in an enclosed-growth than open-growth. It leads to a more uniform size distribution of the h-BN islands, consistent with existing experimental investigations.

Materials Science↗

NASA NTRS2008

Applications of TRMM-based Multi-Satellite Precipitation Estimation for Global Runoff Simulation: Prototyping a Global Flood Monitoring System

Advances in flood monitoring/forecasting have been constrained by the difficulty in estimating rainfall continuously over space (catchment-, national-, continental-, or even global-scale areas) and flood-relevant time scale. With the recent availability of satellite rainfall estimates at fine time and space resolution, this paper describes a prototype research framework for global flood monitoring by combining real-time satellite observations with a database of global terrestrial characteristics through a hydrologically relevant modeling scheme. Four major components included in the framework are (1) real-time precipitation input from NASA TRMM-based Multi-satellite Precipitation Analysis (TMPA); (2) a central geospatial database to preprocess the land surface characteristics: water divides, slopes, soils, land use, flow directions, flow accumulation, drainage network etc.; (3) a modified distributed hydrological model to convert rainfall to runoff and route the flow through the stream network in order to predict the timing and severity of the flood wave, and (4) an open-access web interface to quickly disseminate flood alerts for potential decision-making. Retrospective simulations for 1998-2006 demonstrate that the Global Flood Monitor (GFM) system performs consistently at both station and catchment levels. The GFM website (experimental version) has been running at near real-time in an effort to offer a cost-effective solution to the ultimate challenge of building natural disaster early warning systems for the data-sparse regions of the world. The interactive GFM website shows close-up maps of the flood risks overlaid on topography/population or integrated with the Google-Earth visualization tool. One additional capability, which extends forecast lead-time by assimilating QPF into the GFM, also will be implemented in the future.

Hong, Yang↗

NASA NTRS2017

Collaborative Data Publication Utilizing the Open Data Repository's (ODR) Data Publisher

Introduction: For small communities in diverse fields such as astrobiology, publishing and sharing data can be a difficult challenge. While large, homogenous fields often have repositories and existing data standards, small groups of independent researchers have few options for publishing standards and data that can be utilized within their community. In conjunction with teams at NASA Ames and the University of Arizona, the Open Data Repository's (ODR) Data Publisher has been conducting ongoing pilots to assess the needs of diverse research groups and to develop software to allow them to publish and share their data collaboratively. Objectives: The ODR's Data Publisher aims to provide an easy-to-use and implement software tool that will allow researchers to create and publish database templates and related data. The end product will facilitate both human-readable interfaces (web-based with embedded images, files, and charts) and machine-readable interfaces utilizing semantic standards. Characteristics: The Data Publisher software runs on the standard LAMP (Linux, Apache, MySQL, PHP) stack to provide the widest server base available. The software is based on Symfony (www.symfony.com) which provides a robust framework for creating extensible, object-oriented software in PHP. The software interface consists of a template designer where individual or master database templates can be created. A master database template can be shared by many researchers to provide a common metadata standard that will set a compatibility standard for all derivative databases. Individual researchers can then extend their instance of the template with custom fields, file storage, or visualizations that may be unique to their studies. This allows groups to create compatible databases for data discovery and sharing purposes while still providing the flexibility needed to meet the needs of scientists in rapidly evolving areas of research. Research: As part of this effort, a number of ongoing pilot and test projects are currently in progress. The Astrobiology Habitable Environments Database Working Group is developing a shared database standard using the ODR's Data Publisher and has a number of example databases where astrobiology data are shared. Soon these databases will be integrated via the template-based standard. Work with this group helps determine what data researchers in these diverse fields need to share and archive. Additionally, this pilot helps determine what standards are viable for sharing these types of data from internally developed standards to existing open standards such as the Dublin Core (http://dublincore.org) and Darwin Core (http://rs.twdg.org) metadata standards. Further studies are ongoing with the University of Arizona Department of Geosciences where a number of mineralogy databases are being constructed within the ODR Data Publisher system. Conclusions: Through the ongoing pilots and discussions with individual researchers and small research teams, a definition of the tools desired by these groups is coming into focus. As the software development moves forward, the goal is to meet the publication and collaboration needs of these scientists in an unobtrusive and functional way.

easy to use and implement software tool↗

DOE OSTI2021

An Open-Source Virtual Testbed for a Real Net-Zero Energy Community

Net zero energy communities (NZECs) are critical to ensure sustainability and resilience of modernized power systems. System modeling helps overcome technical challenges in designing and operating NZECs. In this paper, we present the modeling work based on a real NZEC. Two sets of models are developed: higher-fidelity physics-based models considering the interaction between subsystems of the studied NZEC and capturing fast-dynamics; and lower-fidelity data-driven models requiring less resource to establish and/or run. All models are validated against measurements from this real NZEC. In addition, we create a simulation framework which streamlines the processes for simulation and thus allows using developed models to form a virtual testbed. To demonstrate the usage of the virtual testbed, a case study is conducted where a building-to-grid integration control is evaluated via simulation. The evaluation results suggest that the tested control significantly smooths the power draw of the studied community and doesn’t sacrifice the thermal comfort to a great extent.

Huang, Sen↗

NASA NTRS1995

Forced three-dimensional magnetic reconnection due to linkage of magnetic flux tubes

During periods of southward interplanetary magnetic field (IMF) orientation the magnetic field geometry at the dayside magnetopause is susceptible to magnetic reconnection. It has been suggested that reconnection may occur in a localized manner at several patches on the magnetopause. A major problem with this picture is the interaction of magnetic flux ropes which are generated by different reconnection processes. An individual flux rope is bent elbowlike where it intersects the magnetopause and the magnetic field changes from magnetospheric to interplanetary magnetic field orientation. Multiple patches of reconnection can lead to the formation of interlinked magnetic flux tubes. Although the corresponding flux is connected to the IMF the northward and southward connected branches are hooked into each other and cannot develop independently. We have studied this problem in the framework of three-dimensional magnetohydrodynamic simulations. The results indicate that a singular current sheet forms at the interface of two interlinked flux tubes if no resistivity is present in the simulation. This current sheet is strongly tilted compared to the original current sheet. In the presence of resistivity the interaction of the two flux tubes forces a fast reconnection process which generates helically twisted closed magnetospheric flux. This linkage induced reconnection generates a boundary layer with layers of open and closed magnetospheric flux and may account for the brightening of auroral arcs poleward of the boundary between open and closed magnetic flux.

Otto, A.↗

DOE OSTI2026

Neural-network quantum states for the nuclear many-body problem

A long-standing goal of nuclear theory is to explain how the structure and dynamics of atomic nuclei and neutron-star matter emerge from the underlying interactions among protons and neutrons. Achieving this goal requires solving the nuclear quantum many-body problem with high accuracy across a wide range of length scales and density regimes. In this review, we discuss how artificial neural network representations of the nuclear many-body wave function have significantly extended the capabilities of continuum quantum Monte Carlo methods. In particular, neural network quantum states enable calculations of larger systems than were previously accessible and provide a flexible framework for capturing phenomena that challenge conventional approaches, including the emergence of nuclear clusters and superfluid phases in dense matter. We highlight recent applications to finite nuclei, infinite nuclear and neutron matter, and dynamical processes relevant to lepton-nucleus and nucleus-nucleus scattering. We also discuss conceptual and methodological connections with condensed matter physics, emphasizing developments in neural network quantum states that bridge strongly correlated systems across disciplines. Together, these developments demonstrate how neural-network methods open new avenues toward unified and accurate descriptions of nuclear structure, matter, and reactions.

Lovato, Alessandro [Argonne; TIFPA-INFN, Trento; V↗

DOE OSTI2025

Introducing GPU Acceleration into the Python-Based Simulations of Chemistry Framework

We introduce the first version of GPU4P Y SCF, a module that provides GPU acceleration of methods in P Y SCF. As a core functionality, this provides a GPU implementation of two-electron repulsion integrals (ERIs) for contracted basis sets comprising up to g functions using the Rys quadrature. As an illustration of how this can accelerate a quantum chemistry workflow, we describe how to use the ERIs efficiently in the integral-direct Hartree–Fock build and nuclear gradient construction. Benchmark calculations show a significant speedup of 2 orders of magnitude with respect to the multithreaded CPU Hartree–Fock code of P Y SCF and the performance comparable to other open-source GPU-accelerated quantum chemical packages, including GAMESS and QUICK, on a single NVIDIA A100 GPU.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Principal Landau determinants

We reformulate the Landau analysis of Feynman integrals with the aim of advancing the state of the art in modern particle-physics computations. We contribute new algorithms for computing Landau singularities, using tools from polyhedral geometry and symbolic/numerical elimination. Inspired by the work of Gelfand, Kapranov, and Zelevinsky (GKZ) on generalized Euler integrals, we define the principal Landau determinant of a Feynman diagram. We illustrate with a number of examples that this algebraic formalism allows to compute many components of the Landau singular locus. We adapt the GKZ framework by carefully specializing Euler integrals to Feynman integrals. For instance, ultraviolet and infrared singularities are detected as irreducible components of an incidence variety, which project dominantly to the kinematic space. We compute principal Landau determinants for the infinite families of one-loop and banana diagrams with different mass configurations, and for a range of cutting-edge Standard Model processes. Furthermore, our algorithms build on the Julia package this http URL and are implemented in the new open-source package this http URL available at this https URL.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

Pseudospectral convex optimization for on-ramp merging control of connected vehicles

It can be a daunting task for human drivers to merge into highways because of the intricate vehicle negotiations and potential risk within limited time and space. Connected vehicle (CV) technologies could be a solution to this problem and offer many benefits to the road safety, traffic mobility, and energy efficiency. However, real-time optimal control of CVs is still an open challenge, due to the nonlinear vehicle dynamics, non-convex fuel consumption model, and highly dynamic uncertain inter-vehicle interactions. To tackle these issues, a novel real-time optimal control approach that balances the computational efficiency and solution optimality is proposed for the purpose of onboard application. To this end, the pseudospectral collocation method is integrated with a sequential convex programming approach to develop two new optimization algorithms, which are implemented within a model predictive control (MPC) framework to allow for real-time generation of optimal merging speed profiles. One algorithm leverages the line search technique to improve convergence, and the other benefits from the trust region method for better computational efficiency. The optimality and convergence process of both proposed algorithms are investigated by comparing their solutions with a popular non-linear solver. Furthermore, simulation results show that the proposed methods outperform the benchmark in terms of computational cost, fuel consumption, and traffic efficiency. In particular, the proposed fuel-economy merging rule can save 57.1% fuel consumption on average on four different traffic volumes. Meanwhile, the proposed optimal control algorithms can reduce 2.2% travel time on average comparing to the “first-in-first-out” merging rule.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2022

From predicting to learning dissipation from pair correlations of active liquids

Active systems, which are driven out of equilibrium by local non-conservative forces, can adopt unique behaviors and configurations. Towards designing such materials, an important challenge is to precisely connect the static structure of active systems to the dissipation of energy induced by the local driving. Here, we use tools from liquid-state theories and machine learning to take on these challenges. We first demonstrate analytically for an isotropic active matter system that dissipation and pair correlations are closely related when driving forces behave like an active temperature. We then extend a nonequilibrium mean-field framework for predicting these pair correlations which, unlike most existing approaches, is applicable even for strongly interacting particles and far from equilibrium, to predict dissipation in these systems. Based on this theory, we reveal analytically a robust relation between dissipation and structure which holds even as the system approaches a nonequilibrium phase transition. Finally, we construct a neural network which maps static configurations of particles to their dissipation rate without any prior knowledge of the underlying dynamics. Furthermore, our results open novel perspectives on the interplay between dissipation and organization out-of-equilibrium.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Insights into distorted lamellar phases with small-angle scattering and machine learning

Lamellar phases are essential in various soft matter systems, with topological defects significantly influencing their mechanical properties. In this report, we present a machine-learning approach for quantitatively analyzing the structure and dynamics of distorted lamellar phases using scattering techniques. By leveraging the mathematical framework of Kolmogorov–Arnold networks, we demonstrate that the conformations of these distorted phases – expressed as superpositions of complex waves – can be reconstructed from small-angle scattering intensities. Through the contour analysis of wave field phase singularities, we obtain the statistics of the spatial distribution of topological defects. Furthermore, we establish that the temporal evolution of these defects can be derived from the time-dependent traveling wave field, informed by the dispersion relation of spectral components. This method opens new avenues for investigating the dynamics of distorted lamellar phases using various dynamic scattering techniques such as neutron spin echo and X-ray photon correlation spectroscopy. These findings enhance our microscopic understanding of how defects influence the physical properties of lamellar materials, with implications for both equilibrium and non-equilibrium states in general lamellar systems.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Network Reconfiguration for Enhanced Operational Resilience Using Reinforcement Learning

This paper proposes a reinforcement learning-based approach for distribution network reconfiguration(DNR) to enhance the resilience of the electric power supply. Resilience enhancements usually require solving large-scale stochastic optimization problems that are computationally expensive and sometimes infeasible. The exceptional performance of reinforcement learning techniques has encouraged their adoption in various power system control studies, specifically resilience-based real-time applications. In this paper, a single agent framework is developed using an Actor-Critic algorithm (ACA) to determine statuses of tie-switches in a distribution feeder impacted by an extreme weather event. The proposed approach provides a fast-acting control algorithm that reconfigures the feeder topology to reduce or even avoid load shedding. The problem is formulated as a discrete Markov decision process in such a way that a system state captures the system topology and its operational characteristics. An action is made to open or close a specific set of tie-switches after which a reward is calculated to evaluate the practicality and advantage of that action. The iterative Markov process is used to train the proposed ACA under diverse failure scenarios and is demonstrated on the 33-node distribution feeder system. Results show the capability of the proposed ACA to determine proper switching action of tie-switches with accuracy exceeding 93%.

actor critic↗