Search NASA⌕ Search

SEARCH · Search NASA

Results for “Dynamic Timing Analysis”

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.

At least 1,531 records · Page 85
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Decadal Spiciness Variability in the Subtropical-Tropical Pacific in the CESM2 Large Ensemble

Tropical Pacific decadal variations impact weather and climate around the world and are also connected to variations in the global warming trend. The mechanisms driving these long-term modulations, particularly the role of subsurface ocean dynamics, are still debated. Here, we investigate the dynamics of spiciness (density-compensated temperature and salinity) anomalies in the tropical and subtropical Pacific, which are hypothesized as a possible driving mechanism of decadal climate variability. Based on the analysis of 100 realizations from the Community Earth System Model Version 2 Large Ensemble (CESM2-LE), we demonstrate a coupling between the subtropics and the equatorial Pacific by propagating spiciness anomalies at decadal time scales. The CESM2-LE simulates spiciness variability along a subduction path from the subtropics to the equator with frequency spectra that show the highest power at low frequencies and a power decay proportional to a −4 slope for frequencies greater than 0.01 cycles per months, corresponding to periods smaller than ∼8.5 years. Signals that originate in the Southern Hemisphere (SH) dominate and arrive with a larger magnitude at the equator compared to spiciness anomalies from the Northern Hemisphere (NH). Spiciness anomalies from the SH have shorter propagation times and are strengthened along their pathway as stochastic wind stress curl forcing generates anomalous baroclinic ocean pressure gradients. These pressure gradients generate spiciness anomalies via anomalous advection across climatological spiciness gradients in the SH. We conclude that the observed spiciness variance at decadal time scales is consistent with a forcing by stochastic wind variations that are low-pass filtered by ocean dynamics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

PROPERTIES OF A CORONAL SHOCK WAVE AS A DRIVER OF EARLY SEP ACCELERATION

Coronal mass ejectmons (CMEs) are thought to drive collisionless shocks in the solar corona, which in turn have been shown to be capable of accelerating solar energetic particles (SEPs) in minutes. It has been notoriously difficult to extract information about energetic particle spectra in the corona, owing to a lack of in situ measurements. It is possible, however, to combine remote observations with data-driven models in order to deduce coronal shock properties relevant to the local acceleration of SEPs and their heliospheric connectivity to near-Earth space. We present such novel analysis applied to the 2011 May 11 CME event on the western solar limb, focusing on the evolution of the eruption-driven, dome-like shock wave observed by the Atmospheric Imaging Assembly (AIA) EUV telescopes on board the Solar Dynamics Observatory spacecraft. We analyze the shock evolution and estimate its strength using emission measure modeling. We apply a new method combining a geometric model of the shock front with a potential field source surface model to estimate time-dependent field-to-shock angles and heliospheric connectivity during shock passage in the low corona. We find that the shock was weak, with an initial speed of ∼450 km s{sup –1}. It was initially mostly quasi-parallel, but a significant portion of it turned quasi-perpendicular later in the event. There was good magnetic connectivity to near-Earth space toward the end of the event as observed by the AIA instrument. The methods used in this analysis hold a significant potential for early characterization of coronal shock waves and forecasting of SEP spectra based on remote observations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Review: Recent advances of ToF-SIMS for environmental analysis and imaging

Background: Time-of-flight secondary ion mass spectrometry (ToF-SIMS) is a powerful surface analysis technique, initially developed and applied in inorganic materials and semiconductors. In past decades, ToF-SIMS has attracted more attention in its analysis capabilities of organic materials, with increased applications in biology, medical, and health development. It has also become a versatile and effective tool in environmental analysis due to its high mass resolution, mass accuracy, and depth profiling. Results: In this review, we first give an overview of the principle of ToF-SIMS and follow with recent ToF-SIMS applications in exemplary environmental study cases, including atmospheric aerosol, soil, water, plant, and organic solvent analysis. Moreover, sample preparation techniques are summarized in relation to corresponding environmental applications. Specifically, we call attention to ToF-SIMS investigations showcasing studies in surface chemical compositions, images, and depth profile analysis. These findings emphasize the important role of interfacial chemistry in environmental processes and provide valuable insights into dynamic processes, such as chemical transformation, particle formation, plant biology, and microbial inspired biotechnology development. The mass spectral imaging results acquired by ToF-SIMS offer a deeper understanding of intermediate stages and transient phases for environmental specimens. Significance: In situ and operando imaging offer new possibilities in studying phenomena in real time with high spatial resolution. Furthermore, it is anticipated that more research groups will use ToF-SIMS in environmental research given recent advances in measurement capabilities and surging needs in chemical mapping of complex analytes and systems.

Aerosol↗
DOE OSTI2020

An integrated computational materials engineering framework to analyze the failure behaviors of carbon fiber reinforced polymer composites for lightweight vehicle applications

A bottom-up multi-scale modeling approach is used to develop an Integrated Computational Materials Engineering (ICME) framework for carbon fiber reinforced polymer (CFRP) composites, which has the potential to reduce development to deployment lead time for structural applications in lightweight vehicles. In this work, we develop and integrate computational models comprising of four size scales to fully describe and characterize three types of CFRP composites. In detail, the properties of the interphase region are determined by an analytical gradient model and molecular dynamics analysis at the nano-scale, which is then incorporated into micro-scale unidirectional (UD) representative volume element (RVE) models to characterize the failure strengths and envelopes of UD CFRP composites. Then, the results are leveraged to propose an elasto-plastic-damage constitutive law for UD composites to study the fiber tows of woven composites as well as the chips of sheet molding compound (SMC) composites. Subsequently, the failure mechanisms and failure strengths of woven and SMC composites are predicted by the meso-scale RVE models. Finally, building upon the models and results from lower scales, we show that a homogenized macro-scale model can capture the mechanical performance of a hat-section-shaped part under four-point bending. Along with the model integration, we will also demonstrate that the computational results are in good agreement with experiments conducted at different scales. The present work illustrates the potential and significance of integrated multi-scale computational modeling tools that can virtually evaluate the performance of CFRP composites and provide design guidance for CFRP composites used in structural applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Advancing spatiotemporal forecasts of CO 2 plume migration using deep learning networks with transfer learning and interpretation analysis

Accurate and timely forecasts of CO 2 plume distribution throughout the injection and post-injection phases are crucial for detecting plume migration, assessing leakage risks, and supporting operational decisions in geologic carbon storage (GCS). Current convolutional neural network-based approaches primarily focus on spatial information and overlook temporal dependencies in plume distributions, thus limiting their ability to capture dynamic movement effects and provide accurate predictions of plume migration. In this work, we propose two deep learning models, Auto-Encoder (AE)-LSTM and Encoder-Decoder (ED)-ConvLSTM, each uniquely designed to capture both spatial and temporal features. We apply the proposed methods to forecast the dynamic distribution of CO 2 plumes based on 108 reservoir simulations over a 30-year injection and a 30-year post-injection period. The results indicate that the ED-ConvLSTM model outperforms the AE-LSTM model in accurately predicting the spatiotemporal dynamics of CO 2 plume migration, achieving R 2 values above 0.99. To provide a deeper understanding of these model predictions, we employ a gradient-based explanation method on the trained models. This approach provides insights into the influence of input variables on plume migration forecasts and uncovers the underlying prediction mechanisms of the proposed models. Furthermore, we introduce a transfer learning technique, enabling fast and accurate plume migration forecasting in the post-injection phase by leveraging the trained model during the injection phase. This reduces the necessity for extensive data collection or re-training. In conclusion, the methods proposed in our work enhances the performance and interpretability of CO 2 plume migration forecasts, thereby facilitating informed decision-making throughout the entire lifecycle of GCS applications.

58 GEOSCIENCES↗
DOE OSTI2014

Glucose Metabolism Gene Expression Patterns and Tumor Uptake of {sup 18}F-Fluorodeoxyglucose After Radiation Treatment

Purpose: To investigate whether radiation treatment influences the expression of glucose metabolism genes and compromises the potential use of {sup 18}F-fluorodeoxyglucose positron emission tomography (FDG-PET) as a tool to monitor the early response of head and neck cancer xenografts to radiation therapy (RT). Methods and Materials: Low passage head and neck squamous cancer cells (UT14) were injected to the flanks of female nu/nu mice to generate xenografts. After tumors reached a size of 500 mm{sup 3} they were treated with either sham RT or 15 Gy in 1 fraction. At different time points, days 3, 9, and 16 for controls and days 4, 7, 12, 21, 30, and 40 after irradiation, 2 to 3 mice were assessed with dynamic FDG-PET acquisition over 2 hours. Immediately after the FDG-PET the tumors were harvested for global gene expression analysis and immunohistochemical evaluation of GLUT1 and HK2. Different analytic parameters were used to process the dynamic PET data. Results: Radiation had no effect on key genes involved in FDG uptake and metabolism but did alter other genes in the HIF1α and glucose transport–related pathways. In contrast to the lack of effect on gene expression, changes in the protein expression patterns of the key genes GLUT1/SLC2A1 and HK2 were observed after radiation treatment. The changes in GLUT1 protein expression showed some correlation with dynamic FDG-PET parameters, such as the kinetic index. Conclusion: {sup 18}F-fluorodeoxyglucose positron emission tomography changes after RT would seem to represent an altered metabolic state and not a direct effect on the key genes regulating FDG uptake and metabolism.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2015

Dynamic evolution of liquid-liquid phase separation during continuous cooling

Solidification from a multiphase fluid involves many unknown quantities due to the difficulty of predicting the impact of fluid flow on chemical partitioning. Real-time x-ray radiography has been used to observe liquideliquid phase separation in Al90In10 prior to solidification. Quantitative image analysis has been used to measure the motion and population characteristics of the dispersed indium-rich liquid phase during cooling. Here we determine that the droplet growth characteristics resemble well known steady-state coarsening laws with likely enhancement by concurrent growth due to supersaturation. Simplistic views of droplet motion are found to be insufficient until late in the reaction due to a hydrodynamic instability caused by the large density difference between the dispersed and matrix liquid phases.

Alloys↗
DOE OSTI2017

All-Atom Molecular Dynamics Study of Water–Dodecane Interface in the Presence of Octanol

In this work, classical atomistic molecular dynamics (MD) simulations were performed to investigate the structural properties of octanol at water/dodecane interface. Multiple combinations of simulation conditions and force fields were employed in order to :obtain high fidelity interfacial structure. The analysis of noncovalent interactions shows that the convergence of interfacial octanol distribution requires a simulation time scale larger than 100 ns. Finite size effect diminishes with increasing dimension of simulation box both laterally and vertically. In the largest simulated systems with vanishing finite size effect, the additive CHARMM force field shows similar interfacial aggregation behavior with the DRUDE polarizable model. Self-assembly of octanol molecules at water/oil interface is observed, expected to reduce;the free energy barrier between water phase and oil phase. This work provides an MD simulation benchmark for structural study of complex liquid/liquid interface with cosurfactant reducing interfacial tension between two immiscible phases.

Qiao, Baofu↗
DOE OSTI2026

Electron–Ion Covariance Reveals Geometry-Resolved Inner-Shell Spectra in CS 2 Photodissociation

The chemical shifts of inner-shell atomic orbitals are highly sensitive to chemical bonding and molecular structure. In evolving systems, however, the spectra of distinct molecular species and geometries overlap, obscuring the underlying chemical dynamics. Here we demonstrate the use of electron–ion covariance analysis to combine the structural sensitivity of Coulomb explosion imaging with inner-shell spectroscopy, yielding geometry-specific spectra of transient and product species. We apply this approach to the excited state dynamics of CS 2 probed by ionization above the S 2p edge. Electron–ion covariance with time- and momentum-selected S + and S 2+ ions isolates distinct S 2p photoelectron spectra for ground-state CS 2 , bent photoexcited CS 2 , the CS photoproduct, and bare atomic sulfur ─ species whose spectra overlap strongly in the channel-averaged measurement. Clear chemical shifts are observed in the covariance photoelectron spectrum for each of these species, all of which are consistent with high-level calculations. Here, by extracting the atomic S contribution to the photoelectron spectrum in a finely time-resolved manner we can disentangle this contribution to the overall time-resolved photoelectron spectrum as the photodissociation proceeds. These results demonstrate the promise of electron–ion covariance as a general approach to geometry-resolved inner-shell spectroscopy, opening a route to tracking structural evolution through chemical shifts in complex photoexcited molecules.

Ionization↗
DOE OSTI2026

Full-polarization millimeter wavelength variability of Sagittarius A * during the 2018 EHT campaign

Context. Sagittarius A* (Sgr A*), the supermassive black hole at the center of the Milky Way, provides a unique laboratory to study accretion dynamics and plasma processes near the event horizon. Aims. We investigated the variability and polarization properties of Sgr A* using ALMA observations during the 2018 Event Horizon Telescope campaign. Methods. We analyzed high-cadence full-polarization light curves from ALMA at millimeter wavelengths, performed time-series analysis, and investigated the temporal behavior during an X-ray flare observed by Chandra on 2018 April 24. The variability characteristics are compared with expectations from standard accretion flow models. Results. We find low variability in total intensity (σ/μ < 10%), but significantly higher variability in linear and circular polarization (∼30% and ∼50%, respectively). A time-series analysis reveals red-noise variability, with power spectral densities between −2 and −3 across all Stokes parameters. Polarized intensity shows stable intra-day timescales, while total intensity exhibits more variable timescales, suggesting distinct emission regions, with polarization likely arising from a coherent structure. On April 24, a statistically significant inter-band delay in polarized intensity coincides with a near-simultaneous X-ray and millimeter peak that deviates from the typical delayed flare scenario. This event also features enhanced millimeter variability and coherent polarization loop evolution. The observed simultaneity challenges standard models of transient synchrotron emission with cooling delays, favoring instead a scenario of continuous energy injection in an optically thin region. Conclusions. Our results offer new constraints on the physical mechanisms driving variability in Sgr A*, and provide key observational input for refining theoretical models of accretion and plasma behavior in the vicinity of supermassive black holes.

Galaxy: center↗
DOE OSTI2014

Quantum positron acoustic waves

Nonlinear quantum positron-acoustic (QPA) waves are investigated for the first time, within the theoretical framework of the quantum hydrodynamic model. In the small but finite amplitude limit, both deformed Korteweg-de Vries and generalized Korteweg-de Vries equations governing, respectively, the dynamics of QPA solitary waves and double-layers are derived. Moreover, a full finite amplitude analysis is undertaken, and a numerical integration of the obtained highly nonlinear equations is carried out. The results complement our previously published results on this problem.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

Direct photon production in high-energy nuclear collisions

Direct photons have always been considered a promising probe for the very early phases of high-energy nuclear collisions. Prompt photons reveal information about the initial state and its possible modifications in nuclei. In this context they should be one of the best probes for effects of gluon saturation. Thermal photons emitted from the produced matter in nuclear collisions carry information on the temperature of the very early phase. In particular a simultaneous measurement of yield and elliptic flow of thermal photons can put strong constraints on the early time dynamics of the system. I review the status of results on direct photon measurements at RHIC and LHC and their interpretation. Prompt photons at high p{sub T} are consistent with expectations from NLO pQCD in pp and show no strong nuclear modifications in A–A collisions. Recent analysis at RHIC has shown very intriguing results for lower p{sub T}, with high thermal photon yield and strong elliptic flow of direct photons, which are not fully understood theoretically. Also the ALICE experiment at the LHC has measured a high yield of thermal photons. Furthermore I discuss prospects for future measurements of forward direct photons at the LHC.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2016

Mechanical response of lithium fluoride under off-principal dynamic shock-ramp loading

Single crystal lithium fluoride (LiF), oriented [100], was shock loaded and subsequently shocklessly compressed in two experiments at the Z Machine. We employed velocimetry measurements in order to obtain an impactor velocity, shock transit times, and in-situ particle velocities for LiF samples up to ~1.8 mm thick. We also performed a dual thickness Lagrangian analysis on the in-situ velocimetry data to obtain the mechanical response along the loading path of these experiments. Finally, we observed an elastic response on one experiment during initial shockless compression from 100 GPa before yielding. The relatively large thickness differences utilized for the dual sample analyses (up to ~1.8 mm) combined with a relative timing accuracy of ~0.2 ns resulted in an uncertainty of less than 1% on density and stress at ~200 GPa peak loading on one experiment and <4% on peak loading at ~330 GPa for another. The stress-density analyses from these experiments compare favorably with recent equation of state models for LiF.

36 MATERIALS SCIENCE↗
DOE OSTI2026

A rapid embodied carbon assessment tool for priority materials

Embodied carbon limits within building materials are a driving factor in global trade, generating new research and analysis tools in industry. These product assessments, which require utilizing life-cycle assessment (LCA) across broad supply chains, can be expensive, time and data-intensive, and subject to significant variations. Existing methods and tools, such as specific environmental product declarations, typically do not capture these variations and dynamics in supply and manufacturing. Moreover, models and tools must enable stakeholders to assess customized supply chains and future scenarios. In this study, we present the Rapid Embodied Carbon Assessment and Target-setting for Emissions-intensive Materials (REDuCE) tool for building materials. We developed a tool that allows users to select production technologies, transportation mode and distances, concrete carbonation, fuel sources, and regional electricity mixes to supply customization for cement and concrete produced and consumed in California. We generated and integrated a material demand model using residential building stock projections. We provide the user with a wide range of mitigation alternatives through low-carbon production pathways, material use efficiency, transportation modes, and projected electricity grid mixes. In a case study application through four mitigation scenarios, we find emission savings up to 80% by maximizing user mitigation alternatives, primarily driven by reductions in material use intensities. This work represents a foundation for expanding LCA and embodied carbon tools to better enable stakeholders to rapidly and accurately assess customized supply chains while meeting trade requirements.

building materials↗
DOE OSTI2017

Quantitative 3D evolution of colloidal nanoparticle oxidation in solution

Watching nanomaterials transform in time Real-time analysis of chemical transformations of nanoparticles is usually done with electron microscopy of a few particles. One limitation is interference by the electron beam. Sun et al. monitored the oxidation of iron nanoparticles in solution by using small- and wide-angle x-ray scattering and molecular dynamics simulations (see the Perspective by Cadavid and Cabot). These methods revealed the formation of voids within the nanoparticles, diffusion of material into and out of the nanoparticles, and ultimately the coalescence of the voids. Science , this issue p. 303 ; see also p. 245

36 MATERIALS SCIENCE↗
DOE OSTI2019

Investigating the Dynamic Compaction Behavior of Brittle Powders

The compression of porous materials to full density is a complex physical and mechanical process. Modeling efforts to capture the compaction behavior of distended solids (e.g., porous materials or powders) have produced a variety of compaction models, each functionalized to address specific compaction mechanisms. Of the available models, those suited for high-strain-rate deformation associated with shock loading have generally been developed for ductile materials. Compaction models for brittle materials have only been developed for quasistatic loading conditions. No predictive models currently exist that successfully capture the physical processes and mechanics for shock compaction of brittle distended materials, such as sand and oxide powders. The primary hindrance in producing a dynamic compaction model for brittle distended materials is the lack of availability of high-fidelity, high-precision shock compaction data for validation. The proposed work is aimed at alleviating this issue by developing a high-precision testing method in combination with high-fidelity analysis methods to accurately and precisely determine the shocked state of an ideal test material, ceria powder, under uniaxial strain dynamic compaction. The measured shock compaction data from these brittle powder experiments is then used in initial computations to design a cylindrically converging shock compaction experiment within situ time-resolved shocked density measurements, employing proton radiography. The preliminary experimental and computational results obtained from these uniaxial strain dynamic compaction and cylindrically converging shock compaction experiments are presented. An experimental and computational path forwards to predictive model development is then proposed.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Laser Powder Bed Fusion Additive Manufacturing In-Process Monitoring and Optimization Using Thermionic Emission Detection

In this project, the emission of electrons during laser powder bed fusion additive manufacturing was identified and investigated to resolve the underlying dynamics driven by laser-material interactions. Laser powder bed fusion involves the selective laser melting of metal powder particles layer by layer which fuse together forming a larger 3D structure. Laser-based additive manufacturing approaches such as laser powder bed fusion hold the potential to revolutionize manufacturing of complex metal components in the aerospace, medical, and automotive industries. However, the difficulty in producing defect-free components by metal 3D printing is a major hurdle for widespread commercial adoption. This project resolved important dynamics in laser powder bed fusion including the identification of plasma formation mechanisms, transitions between laser-induced melting modes and stochastic precursors to defect formation. These findings were realized through implementation of electronic sensing diagnostics into an existing testbed system and harnessing capabilities developed for the time dependent analysis of laser powder bed fusion datasets. Importantly, the results advance our understanding of laser-material interactions and reveal that detection of thermionic emission can also resolve information critical to optimization of the laser powder bed fusion fabrication process. The findings are critical for advancing our fundamental understanding of the laser powder bed fusion process and can help assure the requisite fidelity of fabricated components.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Bridging Equipment Reliability Data and Robust Decisions in a Plant Operation Context

In order to reduce operation and maintenance (O&M) costs, nuclear power plants (NPPs) are moving from corrective and periodic maintenance to predictive maintenance strategies. Such transition requires changes on the data that needs to be retrieved and on the type of decision processes to be employed. Advanced monitoring and data analysis technologies are essential to support predictive strategies. They can in fact provide precise information about health of a component, track its degradation trends, and provide information of its expected failure time. With such information, maintenance operations for a component can be performed right before its expected failure time. This dynamic context of O&M operations requires new methods to analyze data, propagate component health information from the component to the system level, and optimize plant resources. In this respect, the risk informed asset management (RIAM) project has been tasked to develop and test this new class of methods into a risk analytics toolset. This toolset consists of data analytics tools coupled with reliability methods designed to manage plant assets and performances in a predictive maintenance context. This report shows the latest improvements on such development and the initial testing of our methods on the three main research areas that the RIAM project is focusing on. These areas are the following: equipment reliability data analytics, system reliability modeling, and plant resources optimization methods. We show how the methods developed in these areas can support predictive maintenance strategies by: 1) analyzing equipment reliability data (either in numeric and textual form), 2) assessing component and system health through an innovative margin-based reliability approach, and 3) identifying the most critical components and set optimal maintenance schedule based on plant economic and operational constraints.

97 MATHEMATICS AND COMPUTING↗