Search NASA⌕ Search

SEARCH · Search NASA

Results for “orientation mapping”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

82 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Tracing cosmic gas in filaments and halos: Low-redshift insights from the kinematic Sunyaev-Zel’dovich effect

In this work, we leverage cosmic microwave background (CMB) data from the Atacama Cosmology Telescope (ACT) and LSS data from the imaging survey conducted by the Dark Energy Spectroscopic Instrument (DESI) to study the distribution of gas around galaxy groups at low redshift, z ≈ 0.3, via the kinematic Sunyaev-Zel'dovich (kSZ) effect. In particular, we perform velocity-weighted stacking on the photometric galaxies from the Bright Galaxy Survey (BGS) to isolate the monopole and quadrupole of the kSZ signal, orienting the stacked images along 2D filaments identified using the Hessian of the projected gravitational potential. We find a 7.2σ detection in the monopole of the signal (i.e., the gas density profile) and a 4σ detection in the quadrupole (m = 2), constituting the first measurement of the alignment between gas distribution and the cosmic web through the kSZ effect. As it is a linear probe of the local gas density, the kSZ has heightened sensitivity to the warm-hot intergalactic medium (WHIM), which is believed to house the majority of the "missing baryons."Mapping out the gas density at low redshifts, as enabled by our measurements, is crucial for weak lensing surveys, for which the impact of baryons on small scales is a major impediment. We compare the anisotropic signal against two hydrodynamical simulations, TNG300-1 and Illustris, which have very different baryonic feedback prescriptions. We find that the anisotropic signal measured in the data is comparable but slightly larger and more extended compared with the simulations. Further, this suggests that there is excess accretion and feedback taking place through the filaments, hinting at the possible presence of spin-filament alignment of the BGS objects.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Phase diagram and spectroscopic signatures of a supersolid in the quantum ising magnet K 2 Co(SeO 3 ) 2

Supersolid phases are quantum-entangled states of matter exhibiting the dual characteristics of superfluidity and solidity. Theory predicts that hard-core bosons on a triangular lattice can form such phases at half filling and near complete filling. Leveraging an exact mapping between bosons and spin-$\frac{1}{2}$ degrees of freedom, here we show that these phases are realized in the triangular-lattice antiferromagnet K 2 Co(SeO 3 ) 2 . At zero field, neutron diffraction reveals the development of quasi-two-dimensional $\sqrt3$ x $\sqrt3$ magnetic order with Z 3 translational symmetry breaking (solidity), though with reduced amplitude indicating strong quantum fluctuations. These fluctuations manifest as equidistant bands of continuum neutron scattering, where the lowest-energy mode is gapless at K ($\frac{1}{3}$ $\frac{1}{3}$), consistent with broken U(1) spin rotational symmetry (superfluidity). For c-axis-oriented magnetic fields near saturation, we find a second phase consistent with a high-field supersolid. These two supersolids are separated by a pronounced 1/3 magnetization plateau phase that supports coherent spin waves, from which we determine the underlying spin Hamiltonian.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Wind Turbine Sound Setbacks and Supply Curves: Ordinances and Extrapolated Trends, 110 Hub Height, 130 Rotor Diameter

This dataset provides a comprehensive set of wind turbine sound setbacks from every residential structure in the contiguous United States (CONUS). A sound setback is defined as the minimum required distance between a residential structure and a hypothetical turbine installation site to ensure that modeled sound levels received at the residence do not exceed local sound ordinances, which are commonly expressed in A-weighted decibels (dBA). Therefore, sound setbacks are a local spatial assessment combining multiple factors, including the sound pressure curve as a function of the observer location (distance and direction) relative to the turbine, local sound regulations, and the geographical distribution of residential structures. The dataset is organized into multiple scenario-based products, detailed as follows: 1. Existing and extrapolated sound setbacks. An existing scenario characterizes sound setbacks only in states or counties that have implemented sound regulations as of 2022. The extrapolated scenarios extend a constant sound threshold to counties that lack explicit sound regulations, with thresholds ranging from 35 to 60 dBA, in 5-dBA increments reflecting the variation observed in current sound ordinances. 2. Sound setbacks in directional and worst scenarios. The directional scenario accounts for the distance and orientation of residential structures relative to a hypothetical turbine location, utilizing the turbine's sound emissions in that specific direction. In contrast, the worst scenario takes loudest sound level at each distance step from the turbine, irrespective of directional considerations, which aligns with current industry practice. 3. Supply curves for Open and Reference Access scenarios. This dataset includes supply curves generated by the reV model, which integrates each of the above sound setbacks into both Open and Reference siting scenarios. In addition, two Open and Reference baselines scenarios were included which do not consider sound setbacks for comparative analysis. All sound setback data are stored in TIF files, with partial maps of the data provided in PNG format. The values in the sound setback raster range from 0 to 1, representing the fraction of developable land within a 90 meter by 90 meter pixel due to sound ordinances. A value of 0 indicates areas where wind energy development is prohibited, while a value of 1 signifies areas fully permissible. The wind turbine parameters used in the sound modeling are based on the land-based turbine from International Energy Agency (IEA), featuring a rated electrical power of 3.4 MW, a rotor diameter of 130 meters, and a hub height of 110 meters. The atmospheric conditions, including wind speed/direction, turbulence, air temperature, relative humidity, and air pressure, that drive the sound generation are obtained from the WIND Toolkit dataset.

Array↗
DOE OSTI2025

An open-source hybrid unstructured mesh - CAD fusion multiphysics analysis workflow in SALAMANDER

Plasma facing components in fusion devices will endure extreme neutron and heat fluxes. To facilitate their design using simulation tools, the open-source Fusion Module, Fusion ENergy Integrated multiphys-X (FENIX) framework is being developed to model these components with a high-fidelity multi-physics multi-dimensional approach. It can iteratively resolve couplings between all the physics at play, from neutron radiation, to thermomechanics, to near-wall plasma dynamics. This framework is based on the Multiphysics Object Oriented Simulation Environment (MOOSE), which is developed by a collaboration of US National Laboratories since 2008, for advanced nuclear, geomechanics simulations and other applications. FENIX couples numerous simulation tools, including OpenMC, the Tritium Migration Analysis Program v8, the NekRS CFD software, and most MOOSE modules. For the coupling of radiation transport and other physics, FENIX supports a hybrid workflow between Computer Assisted Design (CAD) and unstructured mesh geometries. The CAD can be generated from skinning the unstructured mesh, to enable a coarse geometry for efficient particle transport, but still resolving the local material compositions and temperature gradients. Neutron transport is performed using DAGMC on the CAD, and Cardinal, integrated in FENIX, maps tallied quantities, such as the heat deposition or the tritium generation rates, from a tally volumetric mesh to the other physics’ unstructured mesh. This coupling was exercised on a simplified tokamak geometry, coupling neutron transport with the heat conduction equation, and on a monoblock divertor problem, coupling additionally with tritium migration. Mesh convergence studies highlight the importance of the mapping conservativeness. Coupling with thermo-mechanics is further enabled by the generalization of the approach to moving meshes. The presentation will include these coupled analysis as well as an update on status of the FENIX framework.

70 - PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Multiomics and deep learning dissect regulatory syntax in human development

Transcription factors establish cell identity during development by binding regulatory DNA in a sequence-specific manner, often promoting local chromatin accessibility and regulating gene expression1. Mapping accessible chromatin offers critical insights into transcriptional control, but available datasets for human development are restricted to bulk tissue, single organs or single modalities2. Here we present the Human Development Multiomic Atlas, a single-cell atlas of chromatin accessibility and gene expression from 817,740 fetal cells across 12 organs, spanning 203 cell types and more than 1 million candidate cis-regulatory elements, many of which exhibit organ-specific in vivo enhancer activity. Deep learning models trained to predict accessibility from local DNA sequence unravel a comprehensive lexicon of motifs that influence accessibility, including composite motifs exhibiting distinct syntactic constraints that are predicted to mediate transcription factor cooperativity. We identify ‘hard’ syntactic rules requiring precise motif spacing and orientation, ‘soft’ rules allowing flexible motif arrangements, and ubiquitous motifs inhibiting accessibility. Model-based interpretation of genetic variants reveals that disruption of motifs with positive and negative effects is associated with concordant effects on gene expression. Our work delineates how motif syntax governs cell-type-specific chromatin accessibility and provides a foundational resource for decoding cis-regulatory logic and interpreting genetic variation during human development.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Combining Theory and Experiment to Map the Atomic-Level Structure–Energy Pathways of Adsorbate-Mediated Phase Changes in a Cooperatively Flexible Metal–Organic Framework

An important subclass of metal–organic frameworks (MOFs) exhibits cooperative flexibility, wherein individual crystallites undergo global structural phase changes in response to external stimuli. Where cooperative flexibility results in reversible changes between crystalline states of distinct accessible porosity, these frameworks can exhibit rare yet desirable behaviors that cannot be explained by local dynamics alone. Yet, the chemical and structural origins of cooperative flexibility and how frameworks undergo these reversible phase changes at the atomic level remain poorly understood. Deliberate design for specific applications is therefore exceedingly difficult, and there is great impetus to develop a fundamental understanding of this phenomenon. Here, an effective and widely accessible computational approach is developed, which is designed to provide microscopic resolution via direct comparison to experimental data along the desorption-guided pathway. The strategy is applied to explain the desorption-induced phase change in an experimentally well-characterized framework, CdIF-13 (sod-Cd(benzimidazolate)2), where experiment alone was unable to resolve the atomistically detailed phase change landscape. Our findings reveal that the cooperative phase change pathways are adsorbate dependent with thermodynamics of intermediate structural states dictated by a nuanced interplay of ligand orientation, skeletal symmetry, and modes of surface adsorption. The results reveal that this isotropically flexible framework is “chaperoned” through a complex energy landscape by specific adsorbates, revealed by the reported computational approach with atomic-level insight and validated by experimentally determined structures. Thus, this work facilitates both understanding and future design of flexible materials for applications in gas storage, transport, delivery, and separation technologies.

03 NATURAL GAS↗
DOE OSTI2025

Fracture Network Quantification during CO2 Injection

This is the presentation prepared for the ARMA 2025 (59th US Rock Mechanics/Geomechanics Symposium) Conference held in Santa Fe, New Mexico, June 8-11, 2025. Accurate mapping and quantification of these networks are essential to ensure the integrity of CO2 storage reservoirs, understand and reduce potential leakage, and maintain long-term environmental safety. This study presents a novel machine learning-driven approach, integrated with geomechanical analysis, to quantify fracture networks and assess their spatial distribution during CO2 injection. This paper combines microseismic monitoring data with principles of hydraulic diffusivity and geomechanical analysis to characterize reservoir scale fracture network. The novelty of our approach lies in its capacity to assimilate time-dependent pressure data and microseismicity into a cohesive framework, which not only identifies microseismic triggering fronts but also tracks fracture distribution during active injection. Besides, leveraging image log data and analysis our approach also provides another angle of the insights to solidate the fracture networks understanding and geomechanical impacts. Key results from our study include the detection of over 100 distinct fracture clusters across the injection site, with fracture orientations strongly correlated with the prevailing in-situ stress field.

CO2 storage and sequestration↗
DOE OSTI2025

Fracture Network Quantification during CO2 Injection

This is the conference paper accompanying an oral presentation at the ARMA 2025 (59th US Rock Mechanics/Geomechanics Symposium) Conference held in Santa Fe, New Mexico, June 8-11, 2025. Accurate mapping and quantification of these networks are essential to ensure the integrity of CO2 storage reservoirs, understand and reduce potential leakage, and maintain long-term environmental safety. This study presents a novel machine learning-driven approach, integrated with geomechanical analysis, to quantify fracture networks and assess their spatial distribution during CO2 injection. This paper combines microseismic monitoring data with principles of hydraulic diffusivity and geomechanical analysis to characterize reservoir scale fracture network. The novelty of our approach lies in its capacity to assimilate time-dependent pressure data and microseismicity into a cohesive framework, which not only identifies microseismic triggering fronts but also tracks fracture distribution during active injection. Besides, leveraging image log data and analysis our approach also provides another angle of the insights to solidate the fracture networks understanding and geomechanical impacts. Key results from our study include the detection of over 100 distinct fracture clusters across the injection site, with fracture orientations strongly correlated with the prevailing in-situ stress field.

CO2 storage and sequestration↗
DOE OSTI2026

Machine learning inversion of interatomic force constants from single-crystal inelastic neutron scattering

Atomic vibrations govern many macroscopic properties of materials, but experiments to comprehensively probe them remain challenging. Inelastic neutron scattering (INS) is a powerful technique to map phonon dispersions in crystals, especially when leveraging modern time-of-flight (ToF) spectrometers with large detectors. However, efficiently and robustly extracting interatomic force constants (FCs) parameterizing phonon dynamics from experimental spectra remains a bottleneck due to the complexity and high dimensionality of ToF INS datasets. Here, we present a machine learning approach for the direct inversion of FCs from single-crystal INS measurements. The framework leverages synthetic training data generated using universal machine-learned force fields and an efficient physics-based forward model. We benchmark two neural architectures–one emphasizing structured latent representation learning and the other direct, supervised spectral regression–across simulated datasets for two materials under idealized and noisy conditions. The latent-representation model is subsequently applied to experimental single-crystal INS data on germanium. The model is shown to reproduce FCs derived from both first-principles simulations and from iterative optimization, and furthermore achieves reliable inference even from sparse, single-orientation measurements representing short data acquisitions. Analysis of the learned latent space reveals semantically continuous and physically interpretable encodings that support strong cross-domain generalization. By bridging theoretical and experimental domains, we establish a path toward rapid inversion of experimental spectra and data-driven interpretation of temperature-dependent lattice dynamics.

42 ENGINEERING↗
DOE OSTI2024

Controlling topology through targeted composite symmetry manipulation in magnetic systems

The possibility of selecting magnetic space groups by orienting the magnetization direction or tuning magnetic orders offers a vast playground for engineering symmetry-protected topological phases in magnetic materials. In this work, we study how selective tuning of symmetry and magnetism can influence and control the resulting topology in a two-dimensional magnetic system, and we illustrate such a procedure in the ferromagnetic monolayer MnPSe 3 . Density functional theory calculations reveal a symmetry-protected accidental semimetallic (SM) phase for out-of-plane magnetization, which becomes an insulator when the magnetization is tilted in-plane, reaching band-gap values close to 100 meV. We identify an order-2 composite antiunitary symmetry and threefold rotational symmetry that induce the band crossing, and we classify the possible topological phases using symmetry analysis, which we support with tight-binding and k · p models. Breaking of inversion symmetry opens a gap in the SM phase, giving rise to a Chern insulator. We demonstrate this explicitly in the isostructural Janus compound Mn 2 ⁢P 2 ⁢S 3 ⁢Se 3 , which naturally exhibits Rashba spin-orbit coupling that breaks inversion symmetry. Our results map out the phase space of topological properties of ferromagnetic transition-metal phosphorus trichalcogenides, and they demonstrate the potential of the magnetization-dependent metal-to-insulator transition as a spin switch in integrated two-dimensional electronics.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗