SEARCH · Engineering Papers

Results for “unobservability”

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.

SourceResearch connectionFrom yearThrough year

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

At least 253 records · Page 14

All sources NASA NTRS DOE OSTI

NASA NTRSDate not supplied

Hybrid Approaches to Systems Health Management and Prognostics

To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Latest achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision making. In principle, data driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for an effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data driven methods to develop a unified hybrid approach. A hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Systems Health Management↗

NASA NTRS2022

Hybrid Model Based Approaches for Systems Health Management and Prognostics

This is a previously approved and published presentation. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Present achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision-making. In principle, data-driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data-driven methods to develop a unified hybrid approach. A hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety-critical systems is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Prognostics↗

NASA NTRSDate not supplied

Experimental Identification of Bistable Flow States on the Space Launch System at Liftoff Conditions

Time-averaged global force and moment measurements in wind tunnel tests can obscure complex flow dynamics, including bistability phenomena. This paper presents a detailed analysis into the potential cause of previously unobserved discrepancies among repeat runs during the 202114- by 22-Foot Subsonic Tunnel test of the Space Launch System. During the test, it was observed that liftoff configuration repeatability was not as strong as expected when compared to previous tests. These discrepancies occurred between wind angle azimuths of approximately 150◦ and 210◦, as well as between 330◦ and 30◦. After ruling out common confounding factors such as instrumentation hysteresis and temperature, it was hypothesized that the lack of repeatability was caused by the Coandă effect producing a bistable flow state at different locations between the cylindrical rocket centerbody and solid rocket boosters during the acquisition of data. Evidence to support this hypothesis is provided in this paper in the form of time-averaged force and moment coefficient data, time-dependent force and moment coefficient data, and still images from smoke flow and tuft flow visualization runs.

SLS↗

NASA NTRS2020

Exploring the Limits of the Data-Model-Theory Synergy: “Hot” MW Transitions for Rovibrational IR Studies

In order to further improve the accuracy of rovibrational IR line lists generated from the “Best Theory +Reliable High-resolution Experiment” (BTRHE) strategy from 0.01-0.05 cm-1, or 300-1500 MHz, to ~10 MHz, we explore the current limits of the Data-Model-Theory synergy by examining the accuracy and consistency of existing data, then propose that “hot” bands in microwave (MW) spectra is the solution we need for future enhancements. The Ames SO2 J=0-20 rovibrational energy levels computed on the semi-empirically refined Ames-2 potential energy surface (PES) are fit to the Effective Hamiltonian (EH) model regularly used in the experimental infrared (IR) analysis for SO2 isotopologues. In the fitted EH(Ames) model, the rotational constants A/B/C and all 5 quartic centrifugal distortion constants display clear, systematic, and consistent patterns along the vibrational state energy or quanta. Such consistent patterns may facilitate the vibrational assignments for MW hot bands and extract more information from high temperature MW spectra. Some EH(Expt) analyses were carried out with the lowest order Coriolis Coupling term, C1. Their constants should not be directly compared with other EH(Expt) and EH(Ames) results. After excluding them, our  = EH(Ames)- EH(Expt) analyses for 5 isotopologues (626, 636, 646, 628 and 828) indicates some loss of accuracy and consistency starting from vibrational states as low as 22 or 1000 cm-1. Some EH parameters, e.g. K, may have relative deviations as large as 50-100% and totally lose any recognizable patterns. This simply means that current EH(Expt) models do not have the system-wide consistency we need to further refine the EH(Ames) and Ames rovibrational IR line lists. A large part of such defects are probably inherited from the limited precision of experimental line positions, i.e. 1E-3 ~ 1E-4 cm-1, or 3-30 MHz. This is confirmed in a series of truncation tests using the Ames data. Although the EH(Ames) consistency may help identify unreliable rovibrational EH(Expt) parameters, and make reliable predictions for minor isotopologues and unobserved vibrational bands, we believe only the highly accurate “hot” MW transitions can provide real enhancements for EH(Expt) accuracy and consistency. “Hot” MW spectra should play a more significant role in the future synergy of Data, Model, and Theory in the field of rovibrational IR studies.

Xinchuan Huang↗

NASA NTRS2021

Local Time Dependence of Jupiter's Polar Auroral Emissions Observed by Juno UVS

Auroral brightness and color ratio imagery, captured using the Juno mission's Ultraviolet Spectrograph, display intense emissions poleward of Jupiter's northern main emission, and these are split into two distinctly different spectral or “color ratio” regimes. The most poleward region, designated the “swirl region” by Grodent et al. (2003), https://doi.org/10.1029/2003ja010017, exhibits a high color ratio, while low color ratio emissions are found within the collar around the swirl region but still poleward of the main emission. We confirm the apparent strong magnetospheric local time control within the polar collar (Grodent et al., 2003, https://doi.org/10.1029/2003ja010017), with the dusk side bright “active region” emissions extending from ∼11 to 22 hr of magnetospheric local time. These bright emissions dim by at least an order of magnitude between ∼0 and 11 hr magnetospheric local time, in the midnight to dawn side “dark region.” This magnetospheric local time structure holds true even when the entire northern oval is located on the night side of the planet (in ionospheric local time), a geometry unstudied prior to Juno, as it is unobservable from Earth. The swirl region brightens at ionospheric dawn (∼5–7 ionospheric local time) and diminishes or completely disappears at ionospheric local times of ∼20–22 hr. Finally, the southern auroral polar emissions appear to share all of the local time dependencies of its northern counterpart, but at a reduced intensity

Thomas Greathouse↗

NASA NTRSDate not supplied

Health Monitoring and Prognostics in Li-ion Batteries

Space applications need to overcome a very critical challenge of predicting remaining useful life of its critical systems/subsystems, with batteries being one of them. Batteries, power electronics conditioning system and motors and one of the most critical systems. Similarly in case of electric aircrafts, computing remaining flying time is safety-critical, since an aircraft that runs out of power (battery charge) while in the air will eventually lose control leading to catastrophe. To tackle and solve the prediction problem, it is essential to have awareness of the current state and health of the system, especially since it is necessary to perform condition-based predictions. To be able to accurately predict the future state of any system, it is required to possess knowledge of its current and future operations. Given models of the current and future system behavior, the general approach of model-based prognostics can be employed as a solution to the prior stated prediction problem. Two approaches are presented with battery prognostics application. The first approach presentation covers a physics based-modeling approach implemented for battery prognostics. Given models of the current and future system behavior, a general approach of model-based prognostics can be employed as a solution to the prediction problem and further for decision making. A second hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems i.e. batteries is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Batteries↗

NASA NTRS2022

Perspective on Satellite-Based Land Data Assimilation to Estimate Water Cycle Components in an Era of Advanced Data Availability and Model Sophistication

The beginning of the 21st century is marked by a rapid growth of land surface satellite data and model sophistication. This offers new opportunities to estimate multiple components of the water cycle via satellite-based land data assimilation (DA) across multiple scales. By resolving more processes in land surface models and by coupling the land, the atmosphere, and other Earth system compartments, the observed information can be propagated to constrain additional unobserved variables. Furthermore, access to more satellite observations enables the direct constraint of more and more components of the water cycle that are of interest to end users. However, the finer level of detail in models and data is also often accompanied by an increase in dimensions, with more state variables, parameters, or boundary conditions to estimate, and more observations to assimilate. This requires advanced DA methods and efficient solutions. One solution is to target specific observations for assimilation based on a sensitivity study or coupling strength analysis, because not all observations are equally effective in improving subsequent forecasts of hydrological variables, weather, agricultural production, or hazards through DA. This paper offers a perspective on current and future land DA development, and suggestions to optimally exploit advances in observing and modeling systems.

Data Assimilation↗

NASA NTRS2019

Isotopologue Consistency of Semi-Empirically Computed InfraRed Line Lists and Further Improvement for Rare Isotopologues: CO2 and SO2 Case Studies.

The semi-empirical molecular rovibrational IR line lists, such as ExoMol, TheoReTs, and Ames, combine the experimental accuracy and theoretical power to reach better than 0.1 cm-1 accuracy for line positions and better than 80-90% agreement for line intensities. The quality of these existing semi-empirical IR lists allows further improvements of intensity and line positions for those unobserved minor isotopologues. This paper presents our new BTRHE (Best Theory + Reliable High-resolution Experiment) strategy implementation. For line intensity, the isotopologue consistency and the patterns of mass dependence in the Ames-296K SO2 and CO2 IR lists are quantitatively presented along the mass-inverse coordinates. The consistency and patterns are better than those in existing experimental data. The methodology proposed here can be used to identify inconsistencies, outliers, and mistakes in intensities, and help improve Effective Dipole Model (EDM) and molecular IR databases. We call for an experimental study on the 50006 and 60007 bands of CO2 628. For line position predictions, a simple approach combining the variational IR line lists with Effective Hamiltonian (EH) model may refine the effective rotational constants A0/B0/C0 and quartic centrifugal distortion constants of minor isotopologues. The prediction accuracy may be improved by two orders of magnitude, i.e. reaching 0-5 MHz prediction accuracy in the range of J<20-30, Ka<10-20, and 0.01-0.02 MHz accuracy for A0/B0/C0. Several important factors have been systematically investigated and discussed, e.g. convergence, uncertainties, higher order terms, fixing EH parameters, mass coordinates, etc. Amicrowave (MW) line set consisting of 644,636 strong transitions for all 30 isotopologues and corresponding refined EH(Ames) parameters are reported in the supplementary material. This approach may be easily extended to rovibrational bands, hot bands, and other molecular systems.

Xinchuan Huang↗

NASA NTRSDate not supplied

Health Monitoring and Prognostics in Li-ion Batteries

Battery Prognostics↗

NASA NTRSDate not supplied

Development of the Ames Global Hyperspectral Synthetic Dataset

This study develops the surface BRDF (bidirectional reflectance distribution function) product of the Ames Global Hyperspectral Synthetic Dataset (AGHSD), based on the corresponding MODIS products, to support the NASA Surface Biology and Geology mission development. A main challenge in deriving a hyperspectral dataset from the multi-band satellite products is how to identify a succinct yet robust algorithm that allow us to infer BRDF at unobserved wavelengths based on the few observed bands. Using the theories of radiative transfer in vegetation canopies, we arrive at a simple equation that accurately approximates hyperspectral surface BRDF as the weighted sum of components from the soil and the vegetation. Each of the components is modeled by the product of the spectrally-dependent optical properties of a surface element (the spectra of the soil surface reflectance, the leaf single albedo, or the canopy scattering coefficient) and a spectrally-independent bidirectional scattering function. The optical properties of the soil and the vegetation can be obtained from existing spectral libraries or model simulations. The bidirectional scattering functions are represented by the Ross-Thick-Li-Sparse BRDF model, where the linear coefficients are estimated with regression analysis from the multi-band MODIS data. We validate the algorithm with simulations by Monte Carlo Ray Tracing model experiments, and the results are highly consistent with the theoretic derivation. We apply the algorithm to generate the AGHSD BRDF product at 1km and 8-day resolutions for the year of 2019. The results are biogeochemically and physically coherent and consistent, and thus serve the goal to support the science and application development of the SBG community.

Hyperspectral↗

NASA NTRS2022

CryoEM Structures of the Nitrogenase Complex During Catalytic Turnover

The enzyme nitrogenase couples adenosine triphosphate (ATP) hydrolysis to the multi-electron reduction of atmospheric dinitrogen into ammonia. Despite extensive research, the mechanistic details of ATP-dependent energy transduction and dinitrogen reduction by nitrogenase are not well understood, requiring new strategies to monitor its structural dynamics during catalytic action. Here we report the cryogenic electron microscopic interrogation of the nitrogenase complex under enzymatic turnover conditions, which has enabled the structural characterization of the nitrogenase reaction intermediates at high resolution for the first time. Our structures show that asymmetry governs all aspects of nitrogenase mechanism including ATP hydrolysis, protein-protein interactions, and catalysis. Furthermore, they reveal several previously unobserved, mechanistically relevant conformational changes near the catalytic iron-molybdenum cofactor that are correlated with the nucleotide-hydrolysis state of the enzyme.

Hannah L. Rutledge↗

NASA NTRS2021

The Science Case for Spacecraft Exploration of the Uranian Satellites: Candidate Ocean Worlds in an Ice Giant System

The 27 satellites of Uranus are enigmatic, with dark surfaces coated by material that could be rich in organics. Voyager 2 imaged the southern hemispheres of Uranus’s five largest “classical” moons—Miranda, Ariel, Umbriel, Titania, and Oberon, as well as the largest ring moon, Puck—but their northern hemispheres were largely unobservable at the time of the flyby and were not imaged. Additionally, no spatially resolved data sets exist for the other 21 known moons, and their surface properties are essentially unknown. Because Voyager 2 was not equipped with a near-infrared mapping spectrometer, our knowledge of the Uranian moons’ surface compositions, and the processes that modify them, is limited to disk-integrated data sets collected by ground- and space-based telescopes. Nevertheless, images collected by the Imaging Science System on Voyager 2 and reflectance spectra collected by telescope facilities indicate that the five classical moons are candidate ocean worlds that might currently have, or had, liquid subsurface layers beneath their icy surfaces. To determine whether these moons are ocean worlds, and to investigate Uranus’s ring moons and irregular satellites, close-up observations and measurements made by instruments on board a Uranus orbiter are needed.

Richard J. Cartwright↗

NASA NTRS2021

Decoding the effects of synonymous variants

Synonymous single nucleotide variants (sSNVs) are common in the human genome but are often overlooked. However, sSNVs can have significant biological impact and may lead to disease. Existing computational methods for evaluating the effect of sSNVs suffer from the lack of gold-standard training/evaluation data and exhibit over-reliance on sequence conservation signals. We developed synVep (synonymous Variant effect predictor), a machine learning-based method that overcomes both of these limitations. Our training data was a combination of variants reported by gnomAD (observed) and those unreported, but possible in the human genome (generated). We used positive-unlabeled learning to purify the generated variant set of any likely unobservable variants. We then trained two sequential extreme gradient boosting models to identify subsets of the remaining variants putatively enriched and depleted in effect. Our method attained 90% precision/recall on a previously unseen set of variants. Furthermore, although synVep does not explicitly use conservation, its scores correlated with evolutionary distances between orthologs in cross-species variation analysis. synVep was also able to differentiate pathogenic vs. benign variants, as well as splice-site disrupting variants (SDV) vs. non-SDVs. Thus, synVep provides an important improvement in annotation of sSNVs, allowing users to focus on variants that most likely harbor effects.

Zishuo Zeng↗

NASA NTRS2023

The Variable Detection of Atmospheric Escape around the Young, Hot Neptune AU Mic b

Photoevaporation is a potential explanation for several features within exoplanet demographics. Atmospheric escape observed in young Neptune-sized exoplanets can provide insight into and characterize which mechanisms drive this evolution and at what times they dominate. AU Mic b is one such exoplanet, slightly larger than Neptune (4.19 R ⊕ ). It closely orbits a 23 Myr pre-main-sequence M dwarf with an orbital period of 8.46 days. We obtained two visits of AU Mic b at Lyα with Hubble Space Telescope (HST)/Space Telescope Imaging Spectrograph. One flare within the first HST visit is characterized and removed from our search for a planetary transit. We present a nondetection in our first visit, followed by the detection of escaping neutral hydrogen ahead of the planet in our second visit. The outflow absorbed ∼30% of the star’s Lyα blue wing 2.5 hr before the planet’s white-light transit. We estimate that the highest-velocity escaping material has a column density of 10 13.96 cm −2 and is moving 61.26 km s −1 away from the host star. AU Mic b’s large high-energy irradiation could photoionize its escaping neutral hydrogen in 44 minutes, rendering it temporarily unobservable. Our time-variable Lyα transit ahead of AU Mic b could also be explained by an intermediate stellar wind strength from AU Mic that shapes the escaping material into a leading tail. Future Lyα observations of this system will confirm and characterize the unique variable nature of its Lyα transit, which, combined with modeling, will tune the importance of stellar wind and photoionization.

Exoplanets↗

NASA NTRSDate not supplied

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

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

Alexander M Melancon↗

NASA NTRSDate not supplied

Noise Prediction of Multi-stream Internally Mixed Jets with External and Internal Plugs using Large-Eddy Simulation

This paper presents results from ongoing research on jet noise prediction using wall-modeled large eddy simulations (WMLES) conducted within the Launch Ascent and Vehicle Aerodynamics (LAVA) computational framework. The study primarily focuses on the aeroacoustic implications of multi-stream nozzle configurations with internal mixing and both internal and external plugs, at a Reynolds number of 1×10 6 based on the nozzle exit diameter. While internal mixing nozzles have long been considered for jet noise reduction, the complexities of their impact on overall noise levels remain insufficiently understood. This research applies established best practices for WMLES to these complex nozzle configurations, aiming to assess their efficacy and identify limitations in accurately predicting noise behaviors. Through detailed comparisons with experimental data obtained from NASA’s Glenn Research Center, initial findings underscore the challenges inherent in current simulation practices when confronted with intricate geometric and operational conditions. Responding to these challenges, the study explores innovative computational approaches to rectify discrepancies noted between experimental outcomes and CFD predictions. Once example is the introduction of a resonating sound source in the simulation environment to mimic potential unobserved acoustic phenomena.A second example is the strategic modifications to the geometry of external plugs to account for real-world deformations caused by heating and gravity. These novel strategies aim to enhance the accuracy and reliability of noise predictions from supersonic jets, advancing our understanding and capability to effectively reduce jet noise in commercial supersonic aircraft

CST↗

NASA NTRSDate not supplied

A Low-Cost Innovative Observatory for the Reionization Explorer Mission (REX)

The early universe, from 400 to 800 million years after the Big Bang, or “Cosmic Dawn,” is characterized by large scale distributions of neutral and ionized intergalactic gas undergoing reionization on a cosmic scale at redshifts effectively unobservable through Earth’s atmosphere. Characterizing this active period of cosmological history requires wide-field, narrow passband surveys of galactic Lyman-alpha light red-shifted to the near infrared. REX combines low noise Near Infrared detectors and wide field optics to rapidly map large areas of space during the crucial Cosmic Dawn epoch of 400-800 million years after the Big Bang (Red Shift z=7 to z=10+).

T Casey↗

NASA NTRS2024

The Mega-MUSCLES Treasury Survey: X-Ray to Infrared Spectral Energy Distributions of a Representative Sample of M Dwarfs

We present 5–1 × 107 Å spectral energy distributions (SEDs) for 12 M dwarf stars covering spectral types M0–M8. Our SEDs are provided for community use as a sequel to the Measurements of the Ultraviolet Spectral Characteristics of Low-mass Exoplanetary Systems (MUSCLES) survey. The 12 stars include eight known exoplanet hosts and four stars chosen to fill out key parameter space in spectral type and rotation period. The SEDs are constructed from Hubble Space Telescope ultraviolet spectroscopy and XMM Newton, Chandra, and/or Swift X-ray observations, and completed with various model data, including Lyα reconstructions, PHOENIX optical models, APEC coronal models, and differential emission measure models in the currently-unobservable extreme ultraviolet. We provide a complete overview of the Mega-MUSCLES program, including a description of the observations, models, and SED construction. The SEDs are available as MAST high-level science products and we describe the various data products here. We also present ensemble measurements from our sample that are of particular relevance to exoplanet science, including the high-energy fluxes in the habitable zone and the farultraviolet to near-ultraviolet ratio. Combined with MUSCLES, Mega-MUSCLES provides SEDs covering a wide range of M dwarf spectral types and ages such that suitable proxies for any M dwarf planet host of interest may be found in our sample. However, we find that ultraviolet and X-ray fluxes can vary even between stars with similar parameters, such that observations of each exoplanet host star will remain the gold standard for interpreting exoplanet atmosphere observations.

David J. Wilson↗