Search NASA⌕ Search

SEARCH · Search NASA

Results for “predictive model”

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 1,477 records · Page 82
All sourcesNASA NTRSDOE OSTI
NASA NTRS1991

A mathematical model of a sealed nickel-cadmium battery

A mathematical model for the charge and discharge of a sealed nickel-cadmium (Ni-Cd) battery is presented. The model is used to study the effect of transport properties of the electrolyte and kinetic parameters of the electrode reactions on the cell performance during the charge and discharge period. The model can also be used to demonstrate the changes of cell performance during cycling. Some comparisons between model predictions and experimental results indicate that the model predictions appear to fit the experimental data well. Sensitivity analyses illustrate that the sealed nickel-cadmium battery operates under activation control. It is also shown theoretically that oxygen generated on the positive electrode during charge is reduced electrochemically on the negative electrode.

Fan, Deyuan↗
NASA NTRS2004

A Multiple Constraint Queuing Model for Predicting Current and Future Terminal Area Capacities

A new queuing model is being developed to evaluate the capacity benefits of several new concepts for terminal airspace operations. The major innovation is the ability to support a wide variety of multiple constraints for modeling the scheduling logic of several concepts. Among the constraints modeled are in-trail separation, separation between aircraft landing on parallel runways, in-trail separation at terminal area entry points, and permissible terminal area flight times.

Meyn, Larry A.↗
NASA NTRS2007

A Comparison of Three Catastrophic On-Orbit Collisions

Orbital debris environment models, such as NASA's LEGEND model, show that accidental collisions between satellites will begin to be the dominant cause for future debris population growth within the foreseeable future. The collisional breakup models employed are obviously a critical component of the environment models. The Chinese Anti-Satellite (ASAT) test which destroyed the Fengyun-1C weather satellite provided a rare, but not unique, chance to compare the breakup models against an actual on-orbit collision. Measurements from the U.S. Space Surveillance Network (SSN), for debris larger than 10-cm, and from Haystack, for debris larger than 1-cm, show that the number of fragments created from Fengyun significantly exceeds model predictions using the NASA Standard Collisional Breakup Model. However, it may not be appropriate to alter the model to match this one, individual case. Two other on-orbit collisions have occurred in the past which have produced significant numbers of debris fragments. In September 1985, the U.S. conducted an ASAT test against the Solwind P-78 spacecraft at an altitude of approximately 525 km. A year later, in September 1986, the Delta 180 payload was struck by its Delta II rocket body in a planned collision at 220 km altitude. Although no Haystack data is available in 1985-6 and very few debris pieces were cataloged from Delta 180 due to its low altitude, measurements were collected in dedicated tests by phased array radars in the SSN in the days after each test. This paper will examine the available radar data from each test and compare and contrast the results with model predictions and with the results from the more recent Fengyun ASAT test.

Stansbery, Gene↗
DOE OSTI2022

Examination of stiff ion temperature gradient mode physics in simulations of DIII-D H-mode transport

A systematic evaluation of gyrokinetic and gyrofluid model predictions of ion temperature gradient (ITG) stability and transport using parameters from DIII-D high confinement mode (H-mode) plasmas has been performed. The nonlinear CGYRO code is used to make the gyrokinetic predictions, and the quasilinear TGLF model for the corresponding gyrofluid predictions. The assessments are made at three radii (normalized toroidal flux ρtor = 0.4, 0.55, and 0.7) in three different plasma scenarios with varying levels of neutral beam heating and torque. For each of the nine cases (3 radii × 3 scenarios) considered, ITG turbulence is found to be the dominant long-wavelength instability and transport mechanism. The inclusions of both transverse magnetic fluctuations and dynamic fast beam ions are stabilizing for all cases considered, with strongest effects seen at ρor = 0.4 where the fast ion population and normalized plasma pressure β = 2μ0nT/B2 are highest. The further inclusion of parallel magnetic fluctuations does not have a meaningful impact on the ITG turbulence in these scenarios, but does destabilize (in combination with fast ions) new high-frequency instabilities at ρtor = 0.4 in the high power scenarios. In each case the linear and nonlinear ITG critical gradients are predicted to be lower than the measured ITG scale lengths and their associated uncertainties. Inclusion of equilibrium flow shear in the transport predictions generally leads to an upshift in effective critical gradient rather than a qualitative change in the predicted stiffness, with stronger responses typically seen in the gyrokinetic predictions than in the gyrofluid results. However, in most cases these upshifted gradients still remain below the measured values and their uncertainties. Although the predicted critical gradients are below the measured gradients, both models predicted flux-matching gradients consistent with measured values in six of the nine cases considered, with no clear systematic over- or underprediction. Thus, while the experimental ion temperature profiles do not appear to be closely pinned to the ITG critical gradient, both gyrokinetic and gyrofluid models are able to accurately match the measured gradients reasonably well in most cases.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Data-driven global ocean modeling for seasonal to decadal prediction

Accurate modeling of ocean dynamics is crucial for enhancing our understanding of complex ocean circulation processes, predicting climate variability, and tackling challenges posed by climate change. Although great efforts have been made to improve traditional numerical models, predicting global ocean variability over multiyear scales remains challenging. Here, we propose ORCA-DL (Oceanic Reliable foreCAst via Deep Learning), a data-driven three-dimensional ocean model for seasonal to decadal prediction of global ocean dynamics. ORCA-DL accurately simulates the three-dimensional structure of global ocean dynamics with high physical consistency and outperforms state-of-the-art numerical models in capturing extreme events, including El Niño–Southern Oscillation and upper ocean heat waves. Moreover, ORCA-DL stably emulates ocean dynamics at decadal timescales, demonstrating its potential even for skillful decadal predictions and climate projections. Our results demonstrate the high potential of data-driven models for providing efficient and accurate global ocean modeling and prediction.

Science & Technology - Other Topics↗
NASA NTRSDate not supplied

Spacecraft Fire Safety Predictions using Verified Saffire Model

A model developed using Fire Dynamics Simulator (FDS) that aimed to determine the effect of a fire in a spacecraft was validated by data collected during the Saffire campaign. The model used inlet and outlet temperatures and CO 2 concentrations of the Saffire payload where fire spread was taking place to determine the amount of heat and combustion products that made it into Northrop Grumman’s Cygnus vehicle. The model was then validated using six remote sensors in various places in the vehicle, as well as a far field device (FFD) in the open zenith section that was representative of average vehicle values. The current work focuses on using the model to predict fire safety scenarios. One simulation aimed to determine the fate of HCl, which sticks to surfaces. The model prediction showed that the HCl was removed from the atmosphere rapidly. This compared well against the FFD data in the Saffire VI campaign event where a bottle of 5% HCl was released into the vehicle. An additional simulation where the Environmental Control and Life Support System (ECLSS) was shut off once the FFD reached 5 ppm of HCl showed that HCl stayed in the atmosphere considerably longer. Continuing the simulation with the ECLSS activated and after temperatures returned to their initial conditions, resulted in a rapid removal of HCL similar to that observed in the original HCl release scenario model. Finally, a simulation that used the heat release rate from a lithium-ion battery test to determine the effect it would have on a spacecraft was performed. This simulation used a heat addition rate that is considerably higher than what was determined from the burning of solid fuels in the Saffire campaign and hence produced a non-trivial temperature increase in more locations within the vehicle.

Fire Safety↗
DOE OSTI2026

Interpreting AI for fusion: An application to plasma profile analysis for tearing mode stability

Artificial intelligence models have demonstrated strong predictive capabilities for various instabilities in fusion devices such as Tokamaks, including tearing modes (TM), edge localized modes, and disruptive events, but their opaque nature raises concerns about safety and trustworthiness when applied to fusion power plants. Here, we present a physics-based interpretation framework using a TM prediction model as a demonstration that is validated through a dedicated DIII-D TM avoidance experiment. By applying Shapley analysis, we identify how profiles such as rotation, temperature, and density contribute to the model's prediction of TM stability. Our analysis shows that in our experimental scenario, core electron temperature and rotation peaking play the primary role in TM stability, while density changes have smaller effects on stability. We show that off-axis ion temperature stabilizes TMs, suggesting that off-axis neutral beam heating can further stabilize this scenario. This work presents a generalizable ML-based event prediction methodology, from training to physics-driven interpretation, bridging the gap between physics understanding and opaque ML models.

Farre-Kaga, Hiro J. [Princeton Univ., NJ (United S↗
DOE OSTI2025

Front-end engineering design (FEED) studies: a quantitative analysis

NETL has devised a methodology for normalizing FEED study metrics of interest and allowing cautious quantitative comparison across FEED studies. This presentation introduces the novel quantitative comparison methodology and presents results from utilizing this methodology to examine data presented in recent FEED study reports. The quantitative methodology developed for the examination of FEED study performance and cost also allows comparison of real-world performance and costs against NETL TEA model predicted performance and cost. Learnings from examining NETL model predicted performance and cost versus real world reported values are highlighted. These learnings provide insight into NETL TEA model uncertainty and highlight opportunities for further model development.

FEED Studies↗
NASA NTRS1999

Five-Hole Flow Angle Probe Calibration for the NASA Glenn Icing Research Tunnel

A spring 1997 test section calibration program is scheduled for the NASA Glenn Research Center Icing Research Tunnel following the installation of new water injecting spray bars. A set of new five-hole flow angle pressure probes was fabricated to properly calibrate the test section for total pressure, static pressure, and flow angle. The probes have nine pressure ports: five total pressure ports on a hemispherical head and four static pressure ports located 14.7 diameters downstream of the head. The probes were calibrated in the NASA Glenn 3.5-in.-diameter free-jet calibration facility. After completing calibration data acquisition for two probes, two data prediction models were evaluated. Prediction errors from a linear discrete model proved to be no worse than those from a full third-order multiple regression model. The linear discrete model only required calibration data acquisition according to an abridged test matrix, thus saving considerable time and financial resources over the multiple regression model that required calibration data acquisition according to a more extensive test matrix. Uncertainties in calibration coefficients and predicted values of flow angle, total pressure, static pressure. Mach number. and velocity were examined. These uncertainties consider the instrumentation that will be available in the Icing Research Tunnel for future test section calibration testing.

Gonsalez, Jose C.↗
DOE OSTI2023

Explainability and extrapolation of machine learning models for predicting the glass transition temperature of polymers

Abstract Machine learning (ML) offers promising tools to develop surrogate models for polymers' structure–property relations. Surrogate models can be built upon existing polymer data and are useful for rapidly predicting the properties of unknown polymers. The accuracy of such ML models appears to depend on the feature space representation of polymers, the range of training data, and learning algorithms. Here, we establish connections between these factors for predicting the glass transition temperature (T g ) of polymers. Our analysis suggests linear models with fewer fitting parameters are as accurate as nonlinear models with many hidden and unexplainable parameters. Also, the performance of a monomer topology‐based ML model is found to be qualitatively identical to that of a physicochemical descriptor‐based ML model. We find that the ML models's performance in the extrapolative region is enhanced as the property range of the training data increases. Moreover, we establish newT g – polymer chemistry correlations via ML. Our work illustrates how ML can advance the fundamental understanding of polymer structure–property correlations and its efficacy for extrapolation problems.

Polymer Science↗
DOE OSTI2017

Strong Stellar-driven Outflows Shape the Evolution of Galaxies at Cosmic Dawn

We study galaxy mass assembly and cosmic star formation rate (SFR) at high redshift (z ≳ 4), by comparing data from multiwavelength surveys with predictions from the GAlaxy Evolution and Assembly (gaea) model. gaea implements a stellar feedback scheme partially based on cosmological hydrodynamical simulations, which features strong stellar-driven outflows and mass-dependent timescales for the re-accretion of ejected gas. In previous work, we have shown that this scheme is able to correctly reproduce the evolution of the galaxy stellar mass function (GSMF) up to z ∼ 3. We contrast model predictions with both rest-frame ultraviolet (UV) and optical luminosity functions (LFs), which are mostly sensitive to the SFR and stellar mass, respectively. We show that gaea is able to reproduce the shape and redshift evolution of both sets of LFs. We study the impact of dust on the predicted LFs, and we find that the required level of dust attenuation is in qualitative agreement with recent estimates based on the UV continuum slope. The consistency between data and model predictions holds for the redshift evolution of the physical quantities well beyond the redshift range considered for the calibration of the original model. In particular, we show that gaea is able to recover the evolution of the GSMF up to z ∼ 7 and the cosmic SFR density up to z ∼ 10.

79 ASTRONOMY AND ASTROPHYSICS↗
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↗
NASA NTRS2010

Meridional Distribution of CH3C2H and C4H2 in Saturn's Stratosphere from CIRS/Cassini Limb and Nadir Observations

Limb and nadir spectra acquired by Cassini/CIRS (Composite InfraRed Spectrometer) are analyzed in order to derive, for the first time, the meridional variations of diacetylene (C4H2) and methylacetylene (CH3C2H) mixing ratios in Saturn's stratosphere, from 5 hPa up to 0.05 hPa and 80 deg S to 45 deg N. We find that the C4H2 and CH3C2H meridional distributions mimic that of acetylene (C2H2), exhibiting small-scale variations that are not present in photochemical model predictions. The most striking feature of the meridional distribution of both molecules is an asymmetry between mid-southern and mid-northern latitudes. The mid-southern latitudes are found depleted in hydrocarbons relative to their northern counterparts. In contrast, photochemical models predict similar abundances at north and south mid-latitudes. We favor a dynamical explanation for this asymmetry, with upwelling in the south and downwelling in the north, the latter coinciding with the region undergoing ring shadowing. The depletion in hydrocarbons at mid-southern latitudes could also result from chemical reactions with oxygen-bearing molecules. Poleward of 60 deg S, at 0.1 and 0.05 hPa, we find that the CH3C2H and C4H2 abundances increase dramatically. This behavior is in sharp contradiction with photochemical model predictions, which exhibit a strong decrease towards the south pole. Several processes could explain our observations, such as subsidence, a large vertical eddy diffusion coefficient at high altitudes, auroral chemistry that enhances CH3C2H and C4H2 production, or shielding from photolysis by aerosols or molecules produced from auroral chemistry. However, problems remain with all these hypotheses, including the lack of similar behavior at lower altitudes. Our derived mean mixing ratios at 0.5 hPa of (2.4 +/- 0.3) 10(exp -10) for C4H2 and of (1.1 +/- 0.3) 10(exp -9) for CH3C2H are compatible with the analysis of global-average ISO observations performed by Moses et al. Finally, we provide values for the ratios [CH3C2H]/[C2H2] and [C4H2]/[C2H2] that can constrain the coupled chemistry of these hydrocarbons.

Guerlet, Sandrine↗
NASA NTRS1983

Potential impact of remote sensing data on sea-state analysis and prediction

The severe North Atlantic storm which damaged the ocean liner Queen Elizabeth 2 (QE2) was studied to assess the impact of remotely sensed marine surface wind data obtained by SEASAT-A, on sea state specifications and forecasts. Alternate representations of the surface wind field in the QE2 storm were produced from the SEASAT enhanced data base, and from operational analyses based upon conventional data. The wind fields were used to drive a high resolution spectral ocean surface wave prediction model. Results show that sea state analyses would have been vastly improved during the period of storm formation and explosive development had remote sensing wind data been available in real time. A modest improvement in operational 12 to 24 hour wave forecasts would have followed automatically from the improved initial state specification made possible by the remote sensing data in both numerical and sea state prediction models. Significantly improved 24 to 48 hour wave forecasts require in addition to remote sensing data, refinement in the numerical and physical aspects of weather prediction models.

Cardone, V. J.↗
DOE OSTI2018

Magnetic Inflation and Stellar Mass. II. On the Radii of Single, Rapidly Rotating, Fully Convective M-Dwarf Stars

Main-sequence, fully convective M dwarfs in eclipsing binaries are observed to be larger than stellar evolutionary models predict by as much as 10%–15%. A proposed explanation for this discrepancy involves effects from strong magnetic fields, induced by rapid rotation via the dynamo process. Although, a handful of single, slowly rotating M dwarfs with radius measurements from interferometry also appear to be larger than models predict, suggesting that rotation or binarity specifically may not be the sole cause of the discrepancy. We test whether single, rapidly rotating, fully convective stars are also larger than expected by measuring their Rsin i distribution. We combine photometric rotation periods from the literature with rotational broadening (vsin i) measurements reported in this work for a sample of 88 rapidly rotating M dwarf stars. Using a Bayesian framework, we find that stellar evolutionary models underestimate the radii by 10%--15%{sub −2.5}{sup +3}, but that at higher masses (0.18 < M < 0.4 M {sub Sun}), the discrepancy is only about 6% and comparable to results from interferometry and eclipsing binaries. At the lowest masses (0.08 < M < 0.18 M {sub Sun}), we find that the discrepancy between observations and theory is 13%–18%, and we argue that the discrepancy is unlikely to be due to effects from age. Furthermore, we find no statistically significant radius discrepancy between our sample and the handful of M dwarfs with interferometric radii. We conclude that neither rotation nor binarity are responsible for the inflated radii of fully convective M dwarfs, and that all fully convective M dwarfs are larger than models predict.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1972

A model for predicting aortic dynamic response to -G sub z impact acceleration.

A steady state dynamic response model for the radial motion of the aorta is developed from in vivo pressure-displacement and nerve stimulation experiments on canines. The model represented by a modified Van der Pol wave motion oscillator closely predicts steady state and perturbed response results. The applicability of the steady state canine aortic model to tailward acting impact forces is studied by means of the perturbed phase plane of the oscillator. The backflow through the aortic arch resulting from a specified acceleration-time profile is computed and an analysis for predicting the forced motion aortic response is presented.

Advani, S. H.↗