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NASA NTRS2019

NICER Discovers Spectral Lines During Photospheric Radius Expansion Bursts from 4U 1820-30: Evidence for Burst-Driven Winds

We report the discovery with the Neutron Star Interior Composition Explorer (NICER) of narrow emission and absorption lines during photospheric radius expansion (PRE) X-ray bursts from the ultracompact binary 4U 1820 −30. NICER observed 4U 1820−30 in 2017 August during a low-flux, hard spectral state, accumulating about 60 ks of exposure. Five thermonuclear X-ray bursts were detected, of which four showed clear signs of PRE. We extracted spectra during the PRE phases and fit each to a model that includes a Comptonized component to describe the accretion-driven emission, and a blackbody for the burst thermal radiation. The temperature and spherical emitting radius of the fitted blackbody are used to assess the strength of PRE in each burst. The two strongest PRE bursts (burst pair 1) had blackbody temperatures of ≈0.6 keV and emitting radii of ≈100 km (at a distance of 8.4 kpc). The other two bursts (burst pair 2) had higher temperatures (≈0.67 keV) and smaller radii (≈75 km). All of the PRE bursts show evidence of narrow line emission near 1 keV. By coadding the PRE phase spectra of burst pairs 1 and, separately, 2, we find, in both coadded spectra, significant, narrow, spectral features near 1.0 (emission), 1.7, and 3.0 keV (both in absorption). Remarkably, all the fitted line centroids in the coadded spectrum of burst pair 1 appear systematically blueshifted by a factor of 1.046±0.006 compared to the centroids of pair 2, strongly indicative of a gravitational shift, a wind-induced blueshift, or more likely some combination of both effects. The observed shifts are consistent with this scenario in that the stronger PRE bursts in pair 1 reach larger photospheric radii, and thus have weaker gravitational redshifts, and they generate faster outflows, yielding higher blueshifts. We discuss possible elemental identifications for the observed features in the context of recent burst-driven wind models.

Strohmayer, T. E.↗

NASA NTRSDate not supplied

Stellar Energetic Particle-driven Production of Biologically Relevant Molecules in Atmospheres of Young Earth-like Exoplanets Around Active G-K Stars

The chemistry of N2, CO2, CH4-rich atmospheres of terrestrial-type exoplanets around active G-K stars is a complex problem: the star's ionizing radiation in the form of X-ray and Extreme UV radiation and the precipitating energetic particles accelerated in coronal mass ejection driven shocks can drive complex chemistry in the exoplanetary atmosphere, and ignite the production of complex molecules (Airapetian et al., 2016). We developed a set of atmospheric models to simulate the formation of organic molecules that can be considered as atmospheric pre-biosignatures from exoplanets around active stars. The model has recently been extended to account for the production of ions, their role in the enhanced chemistry in the lower atmosphere, as well as the interaction of these ions with the aerosols. We applied the photochemical-collisional model to simulate the chemistry of a young terrestrial-type exoplanets resembling the Hadean Earth to highlight the creation of atmospheric pre-biosignatures, relevant greenhouse gases, and the presence of aerosols driven by stellar activity sources. We also discuss the impact of that modified atmospheric chemistry on the exoplanetary climate.

Guillaume Philippe Gronoff↗

NASA NTRS2020

Global Declines in Human-Driven Mangrove Loss

Global mangrove loss has been attributed primarily to human activity. Anthropogenic loss hotspots across Southeast Asia and around the world have characterized the ecosystem as highly threatened, though natural processes such as erosion can also play a significant role in forest vulnerability. However, the extent of human and natural threats has not been fully quantified at the global scale. Here, using a Random Forest-based analysis of over one million Landsat images, we present the first 30-meter resolution global maps of the drivers of mangrove loss from 2000-2016, capturing both human-driven and natural stressors. We estimate that 62% of global losses between 2000-2016 resulted from land-use change, primarily through conversion to aquaculture and agriculture. Up to 80% of these human-driven losses occurred within six Southeast Asian nations, reflecting the regional emphasis on enhancing aquaculture for export to support economic development. Both anthropogenic and natural losses declined between 2000-2016, though slower declines in natural loss caused an increase in their relative contribution to total global loss area. We attribute the decline in anthropogenic losses to the regionally-dependent combination of increased emphasis on conservation efforts and a lack of remaining mangroves viable for conversion. While efforts to restore and protect mangroves appear to be effective over decadal time scales, the emergence of natural drivers of loss presents an immediate challenge for coastal adaptation. We anticipate that our results will inform decision making within conservation and restoration initiatives by providing a locally-relevant understanding of the causes of mangrove loss.

Coastal wetlands↗

NASA NTRSDate not supplied

Application of Flux Conserved Modeling to an Unsteady Combustion Driven Pseudo-Shock

Quasi-steady correlation-based and flux conserved models of the pseudo-shock pressure profile are experimentally assessed in the Air Force Research Lab RC-18 Sub-Scale DirectConnect Isolator Combustion Facility. A bifurcated normal pseudo-shock is established in the 2.67 aspect ratio isolator test section by a downstream cavity-stabilized combustor. The Waltrup-Billig correlation, Smart flux conserved model, and Fiévet data driven flux conserved models are compared against measured pressure profiles. The literature models are found to perform poorly in a high-amplitude forced environment of RC-18, where shock speeds upwards of 20% the isolator Mach number occur. Two modifications to the quasi-steady Fiévet data driven flux conserved model are proposed including incorporating an estimate of pseudo-shock static pressure recovery and exercising the model in a shock stationary reference frame. It is shown that these modifications allow the quasi-steady flux conserved modeling approach to accurately predict pseudo-shock pressure profiles over the discrete phases of high-amplitude forced dynamics.

Shock Train↗

NASA NTRS2020

The Broad-lined Ic Supernova ZTF18aaqjovh (SN 2018bvw): An Optically Discovered Engine-driven Supernova Candidate with Luminous Radio Emission

We present ZTF18aaqjovh (SN 2018bvw), a high-velocity ("broad-lined") stripped-envelope (Type Ic) supernova (Ic-BL SN) discovered in the Zwicky Transient Facility one-day cadence survey. ZTF18aaqjovh shares a number of features in common with engine-driven explosions: the photospheric velocity and the shape of the optical light curve are very similar to those of the Type Ic-BL SN 1998bw, which was associated with a low-luminosity gamma-ray burst (LLGRB) and had relativistic ejecta. However, the radio luminosity of ZTF18aaqjovh is almost two orders of magnitude fainter than that of SN 1998bw at the same velocity phase, and the shock velocity is at most mildly relativistic (v = 0.06–0.4c). A search of high-energy catalogs reveals no compelling gamma-ray burst (GRB) counterpart to ZTF18aaqjovh, and the limit on the prompt GRB luminosity of L(γ(ꙩ)iso) ≈ 1.6 x 10^(48) erg/s excludes a classical GRB but not an LLGRB. Altogether, ZTF18aaqjovh represents another transition event between engine-driven SNe associated with GRBs and "ordinary" Ic-BL SNe.

Anna Y. Q. Ho↗

NASA NTRS2022

Strong Reconnection Electric Fields in Shock-Driven Turbulence

Turbulent magnetic reconnection in a quasi-parallel shock under parameters relevant to the Earth's bow shock is investigated by means of a two-dimensional particle-in-cell simulation. The addressed aspects include the reconnection electric field, the reconnection rate, and the electron and the ion outflow speeds. In the shock transition region, many current sheets are generated in shock-driven turbulence, and electron-only reconnection and reconnection where both ions and electrons are involved can occur in those current sheets. The electron outflow speed in electron-only reconnection shows a positive correlation with the theoretical speed, which is close to the local electron Alfvén speed, and a strong convection electric field is generated by the large electron outflow. As a result, the reconnection electric field becomes much larger than those in the standard magnetopause or magnetotail reconnection. In shock-driven reconnection that involves ion dynamics, both electron outflows and ion outflows can reach of the order of 10 times the Alfvén speed in the X-line rest frame, leading to a reconnection electric field the same order as that in electron-only reconnection. An electron-only reconnection event observed by the magnetospheric multiscale mission downstream of a quasi-parallel shock is qualitatively similar to those in the simulation and shows that the outflow speed reaches approximately half the local electron Alfvén speed, supporting the simulation prediction.

magnetic reconnection↗

NASA NTRS2021

The Deep-Time Digital Earth program: data-driven discovery in geosciences

Current barriers hindering data-driven discoveries in deep-time Earth (DE) include: substantial volumes of DE data are not digitized; many DE databases do not adhere to FAIR (findable, accessible, interoperable and reusable) principles; we lack a systematic knowledge graph for DE; existing DE databases are geographically heterogeneous; a significant fraction of DE data is not in open-access formats; tailored tools are needed. These challenges motivate the Deep-Time Digital Earth (DDE) program initiated by the International Union of Geological Sciences and developed in cooperation with national geological surveys, professional associations, academic institutions and scientists around the world. DDE’s mission is to build on previous research to develop a systematic DE knowledge graph, a FAIR data infrastructure that links existing databases and makes dark data visible, and tailored tools for DE data, which are universally accessible. DDE aims to harmonize DE data, share global geoscience knowledge and facilitate data-driven discovery in the understanding of Earth’s evolution.

Chengshan Wang↗

NASA NTRSDate not supplied

AIAA ASCEND 2023 Panel Session Proposal: Community Perspectives on Establishing a Government-Funded National Laboratory in a Commercially-Driven LEO Ecosystem

AIAA ASCEND 2023 Panel Session Proposal: Community Perspectives on Establishing a Government-Funded National Laboratory in a Commercially-Driven LEO Ecosystem Submitted by the NASA Office of Technology, Policy, and Strategy March 8, 2023 Session Format: Panel Session Topic: Expanding and Evolving the Space Economy Session Title: Community Perspectives on Establishing a Government-Funded National Laboratory in a Commercially-Driven LEO Ecosystem Short Session Description: NASA is preparing for the retirement of the ISS and transition of government-sponsored low-Earth orbit (LEO) activities to Commercial LEO Destinations (CLDs), which includes a government-funded national lab. This transition necessitates new models to connect government-funded users of the LEO environment to CLDs. This panel will highlight updates on what may constitute a national laboratory, share perspectives from members of LEO user communities, and open the discussion for additional comments. Requested Session Duration: 90 minutes Extended Session Description: NASA is preparing for the retirement of the ISS and transition of low-Earth orbit (LEO) activities to Commercial LEO Destinations (CLDs), including planning for a national laboratory. This transition necessitates new models for connecting government-funded users of the LEO environment to CLDs. NASA has made progress on models and a stepwise strategy to enable this national laboratory. This session will discuss guiding national strategies and highlight perspectives on what constitutes a national laboratory from current user communities. A NASA panel participant will introduce the rationale for continuing a national laboratory utilizing CLDs and speak on progress-to-date. This includes furthering national priorities to continue a space-based microgravity national laboratory. It also may include a brief overview of a study conducted by NASA which elucidates a trade space of models for a national laboratory and highlights from an interagency working group on microgravity for additional context. A moderator and five panelists representing diverse LEO user communities will then share perspectives on topics relating to this new dynamic ecosystem, expectations for models and activities, and other thoughts on NASA’s progress. Panelists may consist of members from the following communities: NASA, other U.S. government agencies, startups, small businesses, academia, and non-profits. The panel then plans to open discussion to the audience for questions, input, and additional feedback. Session Goal(s)/Outcome(s): By hosting a panel session, NASA aims to accomplish two goals. First, NASA wishes to update the community on progress towards establishing a national laboratory in low-Earth orbit. This includes sharing key perspectives heard from stakeholder communities. The second goal is to highlight the collaborative nature of such an undertaking and to facilitate additional discussion with other current and future users of the LEO community. Participants (not exhaustive): Erica Rodgers; Robyn Gatens

Erica Marie Rodgers Heusinkveld↗

NASA NTRS2022

Hybrid data-driven and model-informed online tool wear detection in milling machines

Precision machining tool wear is responsible for low product throughput and quality. Monitoring the tool wear online is vital to prevent degradation in machining quality. However, direct real-time tool wear measurement is not practical. This paper presents residual-based anomaly detection models, combining a hybrid model comprised of a physics-based model and a data-driven model (a decision tree or a neural network) to predict signals of interest (e.g., power or forces) under nominal conditions, followed by Page’s cumulative sum test for detecting tool wear on-line using the computer numerical control machine measurements. The most informative features are ranked using dynamic programming and its approximation variants from real-time measurements and machine settings, such as the width of cut, depth of cut, feed rate and spindle speed, that serve as inputs to the predictive models. The baseline nominal model is incrementally updated with experimental data via a gradient boosted adaptation model to generate the residuals that account for discrepancies between the actual machine data under normal conditions and the baseline nominal model predictions. The hybrid model is validated against 20 Mazak milling machine experimental tests and one Haas run-to-failure experiment. The proposed anomaly detector is applied to synthetic data from simulations of the physics-based model at different operating conditions, measurement noise levels, and tool wear levels, and the methods were able to achieve an overall 92% accuracy in data with 1% noise. The anomaly detection methods based on hybrid model reduced the false alarms of either the data-driven or physical-based models alone, and are found to be capable of good online detection of tool wear.

Online anomaly detection↗

NASA NTRSDate not supplied

Navigation Sensor Technology Assessment Capability for Data-Driven Systems Analysis

The capability to assess the mission performance of novel navigation technologies from a systems-level approach and to provide quantitative results is crucial. With growing interest and innovations from NASA’s commercial partners, data-driven results that quantify the technological impact will guide research developments and facilitate stakeholder decision-making while requiring less time and resources. This paper presents a method to evaluate various sensor combinations and their impact on the overall system performance using an existing six degrees-of-freedom, physics-based engineering simulation for a government reference lunar lander as a testbed. Selected navigation technologies over various technology readiness levels, including Inertial Measurement Units, Navigation Doppler Lidar, and radar altimeter-radar velocimeter, were studied in this paper. Results using this testbed to perform sensitivity studies and to provide quantitative assessments are reported. One key finding is that there are diminishing returns for reducing sensor errors. The most influential sensor parameter to landing success are vehicle configuration and mission dependent. Results from this method could be used by stakeholders to make data-driven systems-based decisions and by technology developers as guidance for the specific parameter improvements that will have the most significant impact on mission success. This capability could be further used to assess alternative scenarios should one type of technology become unavailable or to re-assess initial assumptions and identify appropriate requirements from a system perspective.

Esther Lee↗

NASA NTRS2024

Data-Driven Method for Nonlinear Modal Interaction Identification in Fighter Aircraft

The purpose of this paper is to report a new data-driven analysis methodology for identifying nonlinear modal interactions and its application to aeroelastic wind-tunnel data from a generic fighter configuration, which reveals global modal interactions in this system caused by multiple resonance capture events initiated by local nonlinearities. It is shown that during supersonic testing at the Transonic Dynamics Tunnel, vibro-impacts at the embedded external fuel tank shakers on the inboard underwing stations of the generic fighter introduce strong nonlinearity that gives rise to nonlinear modal interactions. Specifically, time–frequency analysis of the acceleration data demonstrates that 6∶5 and 7∶6 transient resonance captures occur across the aircraft exclusively during this vibro-impact forcing window. Further analysis suggests that these resonance captures are dependent on Mach number. These results show experimentally that local vibro-impacts can cause global nonlinear effects in an aeroelastic system and that these effects can be identified using the new methodology herein. The data-driven analysis scheme employed shows applicability to full-aircraft flight test data for identifying nonlinear modal interactions without knowledge of the precise nonlinear contributors.

nonlinear dynamics↗

NASA NTRS2023

A Data-driven, Physics-based Transport Model of Solar Energetic Particles Accelerated by Coronal Mass Ejection Shocks Propagating through the Solar Coronal and Heliospheric Magnetic Fields

In an effort to develop computational tools for predicting radiation hazards from solar energetic particles (SEPs), we have created a data-driven physics-based particle transport model to calculate the injection, acceleration, and propagation of SEPs from coronal mass ejection (CME) shocks traversing through the solar corona and interplanetary magnetic fields. The model runs on an input of corona and heliospheric plasma and magnetic field configuration from a magnetohydrodynamic model driven by solar photospheric magnetic field measurements superposed with observed CME shocks determined from coronagraph images. SEP source particles are injected at the shock using the result of diffusive shock acceleration formulation from a characteristic obliquity-dependent injection from a heated solar wind thermal tail population. With several advanced computation techniques involving stochastic simulation and integration, the model obtains the particle intensity at any location in interplanetary space through the rigorous solution to the time-dependent 5D focus transport equation in the phase space that includes perpendicular diffusion. We apply the model to the 2011 November 3 CME event. The calculation results reproduce multispacecraft SEP observations at Earth and STEREO-B reasonably well without normalization of particle flux. The observations at STEREO-A can be reproduced by rescaling particle energy or modified energy dependence of particle diffusion coefficients. This circumsolar SEP event seen by spacecraft at Earth, STEREO-A, and STEREO-B at widely separated longitudes can be explained by diffusive shock acceleration by a single CME shock with a moderate speed.

Solar energetic particles↗

NASA NTRS2025

Shock-Stationary Application of Pseudoshock Models During High-Amplitude Combustion-Driven Unsteadiness

The isolator pseudo-shock provides necessary compression within a dual-mode scramjet engine and buffers the engine system against unstart. Quasi-1D flux-conserved models are the state-of-the-art reduced-order model for optimization and online control of dual-mode scramjet engines. The stability and efficacy of this modeling approach is evaluated against data from a combustor-driven direct-connect experiment. The experiment exhibited strong combustor-driven unsteadiness that produced upstream propagating weak shocks into the isolator, interacting with the pseudo-shock. While this configuration resulted in unsteadiness that is atypical of standard operation, the experiment provided an opportunity to evaluate the modeling techniques in highly transient states. Such transients could occur during maneuvering or result from unexpected combustor events. A flexible quasi-1D formulation, the Fievet flux-conserved model is fit using Bayesian inference in the laboratory and shock-stationary reference frames. Model performance is analyzed using the Bayesian posteriors and model evaluations over the measured shock train speed range. It is concluded that to produce consistent isolator pressure profile estimates in this unsteady environment, the model must be implemented in a shock-stationary reference frame. Implementing this conclusion in model-based engine controllers may reduce needed unstart safety margins and increase maximum performance.

Bayesian↗

NASA NTRS2023

Modeling the Formation and Eruption of Coronal Structures by Linking Data-Driven Magnetofrictional and MHD Simulations for AR 12673

Context .The data-driven and time-dependent modeling of coronal magnetic fields is crucial for understanding solar eruptions. These efforts are complicated by the challenges of finding a balance between physical realism and computing efficiency. One possible technique is to couple two modeling approaches. Aims . Our aim here is to showcase our progress in using time-dependent magnetofrictional model (TMFM) results as input to dynamical magnetohydrodynamic (MHD) simulations. However, due to the different evolution processes in these two models, using TMFM snapshots in an MHD simulation is nontrivial. We address these issues, both physically and numerically, discuss the incompatibility of the TMFM output to serve as the initial condition in MHD simulations, and show our methods of mitigating this. The evolution of the flux systems and the cause of the eruption are investigated. Methods . TMFM is a prevalent approach that has proven to be a very useful tool in the study of the formation of unstable structures in the solar corona. In particular, it is capable of incorporating observational data as initial and boundary conditions and requires shorter computational time compared to MHD simulations. To leverage the efficiency of data-driven TMFM and also to simulate eruptive events in the MHD framework, one can apply TMFM up to a certain time before the expected eruption(s) and then proceed with the simulation in the full or ideal MHD regime in order to more accurately capture the eruption process. Results . We show the results of a benchmark test case with a linked TMFM and MHD simulation to study the evolution of NOAA active region 12673. A rise of a twisted flux bundle through the MHD simulation domain is observed, but we find that the rate of the rise and the altitude reached depends on the time of the TMFM snapshot that was used to initialize the MHD simulation and the helicity injected into the system. The analysis suggested that torus instability and slip-running reconnection could play an important role in the eruption. Conclusions . The results show that the linkage of TMFM and zero-β MHD models can be successfully used to model the eruptive coronal magnetic fields.

F. Daei↗

NASA NTRS2026

Remote Sensing-Driven Hydrodynamic Modeling in Data-Scarce Regions: Integrating ICESat-2, Sentinel-2, SWOT and Re-analysis Models for Coastal Monitoring

Hydrodynamic models in coastal and estuarine systems are typically constrained by sparse bathymetry, boundary, and validation data, especially in regions where field campaigns are costly or impractical. Here we develop and test a fully satellite-driven framework for hydrodynamic modeling in South Africa’s Langebaan Lagoon without using any local in situ measurements. Bathymetry is derived by training multispectral Sentinel-2 reflectance against ICESat-2 ATL24 photon-derived depths using an XGBoost model optimized with Bayesian search. The final satellite-derived bathymetry reproduces independent ATL24 points with RMSE = 0.45 m and R 2 = 0.97. This bathymetry was used in a depth-averaged Delft3D Flexible Mesh model driven at the open boundary by TPXO tidal harmonics and by ERA5 winds. We validate modeled water surface elevation against 16 SWOT low-rate (250 m, unsmoothed) passes in 2023. SWOT–model comparisons yield an overall RMSE of 0.11 m and R 2 = 0.61, with typical point differences <0.10 m (∼7% of the 1.5 m tidal range), and showed consistent spatial gradients in water level from the offshore boundary, through Saldanha Bay, and into the lagoon. At the offshore boundary, TPXO and SWOT sea surface heights agree closely (R 2 = 0.86). A simple phase adjustment of ∼26,min between TPXO and SWOT lowers the RMSE from 0.18,m to 0.11,m, showing that phase offset accounts for some of the discrepancy, with additional errors likely linked to non-tidal signals. Our results demonstrate that combining passive optical, photon-counting LiDAR, radar interferometry, and global tidal/atmospheric models enables robust, transferrable hydrodynamic modeling in data-scarce coastal systems, offering a cost-effective pathway for monitoring.

ICESat-2↗

DOE OSTI2020

A Physics-Driven Deep-Learning Network for Solving Nonlinear Inverse Problems

Geosteering inversion, which can be viewed as a nonlinear inverse problem, is an important technique used by directional drilling. Traditional methods that rely on iterative procedures and regularization are sensitive to the selection of initial values and can be slow due to convergence issues. In industrial applications, a lookup table is used to produce fast predictions. However, performance is not guaranteed by this approach due to the limitation of the hardware. In this paper, we propose a novel physics-driven deep-learning framework for providing a fast, accurate surrogate to solve the inverse problem. Particularly, leveraged by the forward physical model and 1D convolutional neural network (1D-CNN), the proposed method provides more reliable solutions to the inverse problem with improved performance. In addition, a new physics-driven loss function is introduced to accommodate both the model misfit and the data misfit. In conclusion, our experiments demonstrate the effectiveness of our method.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2020

System and method for generation of event driven, tuple-space based programs

In a system for automatic generation of event-driven, tuple-space based programs from a sequential specification, a hierarchical mapping solution can target different runtimes relying on event-driven tasks (EDTs). The solution uses loop types to encode short, transitive relations among EDTs that can be evaluated efficiently at runtime. Specifically, permutable loops translate immediately into conservative point-to-point synchronizations of distance one. A runtime-agnostic which can be used to target the transformed code to different runtimes.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Data-driven model construction for industrial asset decision boundary classification

In some embodiments, a system model construction platform may receive, from a system node data store, system node data associated with an industrial asset. The system model construction platform may automatically construct a data-driven, dynamic system model for the industrial asset based on the received system node data. A synthetic attack platform may then inject at least one synthetic attack into the data-driven, dynamic system model to create, for each of a plurality of monitoring nodes, a series of synthetic attack monitoring node values over time that represent simulated attacked operation of the industrial asset. The synthetic attack platform may store, in a synthetic attack space data source, the series of synthetic attack monitoring node values over time that represent simulated attacked operation of the industrial asset. This information may then be used, for example, along with normal operational data to construct a threat detection model for the industrial asset.

97 MATHEMATICS AND COMPUTING↗