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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.

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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

Exploring Life Support Architectures for Evolution of Deep Space Human Exploration

Life support system architectures for long duration space missions are often explored analytically in the human spaceflight community to find optimum solutions for mass, performance, and reliability. But in reality, many other constraints can guide the design when the life support system is examined within the context of an overall vehicle, as well as specific programmatic goals and needs. Between the end of the Constellation program and the development of the "Evolvable Mars Campaign", NASA explored a broad range of mission possibilities. Most of these missions will never be implemented but the lessons learned during these concept development phases may color and guide future analytical studies and eventual life support system architectures. This paper discusses several iterations of design studies from the life support system perspective to examine which requirements and assumptions, programmatic needs, or interfaces drive design. When doing early concept studies, many assumptions have to be made about technology and operations. Data can be pulled from a variety of sources depending on the study needs, including parametric models, historical data, new technologies, and even predictive analysis. In the end, assumptions must be made in the face of uncertainty. Some of these may introduce more risk as to whether the solution for the conceptual design study will still work when designs mature and data becomes available.

Anderson, Molly S.↗
NASA NTRS1994

Kinetics model for initiation and promotion for describing tumor prevalence from HZE radiation

A kinetics model for cellular repair and misrepair for multiple radiation-induced lesions (mutation-inactivation) is coupled to a two-mutation model of initiation and promotion in tissue to provide a parametric description of tumor prevalence in the Harderian gland in a mouse. Dose-response curves are described for gamma-rays and relativistic ions. The effects of nuclear fragmentation are also considered for high-energy proton and alpha particle exposures The model described provides a parametric description of age-dependent cancer induction for a wide range of radiation fields. We also consider the two hypotheses that radiation acts either solely as an initiator or as both initiator and promoter and make model calculations for fractionation exposures from gamma-rays and relativistic Fe ions. For fractionated Fe exposures, an inverse dose-rate effect is provided by a promotion hypothesis using a mutation rate for promotion typical of single-gene mutations.

Cucinotta, Francis A.↗
NASA NTRS2012

The Effects of B/L-Dependent Heating on the Formation and Evolution of a Multi-Threaded Prominence

We have developed a comprehensive, multi-threaded, three-dimensional model of the plasma dynamics and energetics of a prominence and its overlying arcade (Luna et al. 2012). In this model, the basic magnetic structure is that of two interacting sheared arcades, while the cool condensations composing the prominence are formed by the well-studied thermal nonequilibrium mechanism. In a given filament-channel flux tube, the mass is evaporated from the chromosphere by heating localized near the footpoints, and condenses in the form of transient blobs or a persistent thread. Our previous studies of thermal nonequilibrium used steady or impulsive heating functions with no dependence on local physical. However, parametric active-region models with steady heating proportional to B/L, where B is the flux-tube magnetic field strength at each footpoint and L is the flux-tube length, yield the best agreement with observations (e.g., Schrijver et al. 2008). We have determined the effects of this active-region heating function on our model for the formation and evolution of prominence mass. We have also expanded the range of our computational domain to include more ofthe overlying arcade (the so-called "cavity") than in Luna et al. (2012), and have increased the number of selected flux tubes from 125 to 533. We will illustrate the time-dependent plasma behavior produced by the B/L heating function with synthetic images in several ALA passbands, and compare the resulting prominence properties with those predicted by our model with steady heating.

Karpen, Judith T.↗
DOE OSTI2025

Reduced basis approximations of parameterized dynamical partial differential equations via neural networks

Projection-based reduced order models are effective at approximating parameter-dependent differential equations that are parametrically separable. When parametric separability is not satisfied, which occurs in both linear and nonlinear problems, projection-based methods fail to adequately reduce the computational complexity. Devising alternative reduced order models is crucial for obtaining efficient and accurate approximations to expensive high-fidelity models. In this work, we develop a timestepping procedure for dynamical parameter-dependent problems, in which a neural-network is trained to propagate the coefficients of a reduced basis expansion. This results in an online stage with a computational cost independent of the size of the underlying problem. Here, we demonstrate our method on several parabolic partial differential equations, including a problem that is not parametrically separable.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2001

Three-Dimensional Aerodynamic Instabilities In Multi-Stage Axial Compressors

This thesis presents the conceptualization and development of a computational model for describing three-dimensional non-linear disturbances associated with instability and inlet distortion in multistage compressors. Specifically, the model is aimed at simulating the non-linear aspects of short wavelength stall inception, part span stall cells, and compressor response to three-dimensional inlet distortions. The computed results demonstrated the first-of-a-kind capability for simulating short wavelength stall inception in multistage compressors. The adequacy of the model is demonstrated by its application to reproduce the following phenomena: (1) response of a compressor to a square-wave total pressure inlet distortion; (2) behavior of long wavelength small amplitude disturbances in compressors; (3) short wavelength stall inception in a multistage compressor and the occurrence of rotating stall inception on the negatively sloped portion of the compressor characteristic; (4) progressive stalling behavior in the first stage in a mismatched multistage compressor; (5) change of stall inception type (from modal to spike and vice versa) due to IGV stagger angle variation, and "unique rotor tip incidence" at these points where the compressor stalls through short wavelength disturbances. The model has been applied to determine the parametric dependence of instability inception behavior in terms of amplitude and spatial distribution of initial disturbance, and intra-blade-row gaps. It is found that reducing the inter-blade row gaps suppresses the growth of short wavelength disturbances. It is also concluded from these parametric investigations that each local component group (rotor and its two adjacent stators) has its own instability point (i.e. conditions at which disturbances are sustained) for short wavelength disturbances, with the instability point for the compressor set by the most unstable component group. For completeness, the methodology has been extended to describe finite amplitude disturbances in high-speed compressors. Results are presented for the response of a transonic compressor subjected to inlet distortions.

Tan, Choon S.↗
NASA NTRS1997

Attributes of GRB Pulses: Analysis of BATSE TTE Data

Our extensive program of modeling GRB profiles is aimed at elucidating the physical processes responsible for the burst itself, as well as possible extrinsic phenomena (e.g. time dilation) as discussed in another paper in this Symposium (Norris et al., "GRB PROFILES AS COSMIC PROBES"). We have developed special methods to extract the wealth of short time-scale information contained in the BATSE time-tag event (TTE) data. Our algorithm yields a piecewise-constant representation of the light curve -- using only the raw photon arrival times, and based on Bayesian change-point methods. This representation in effect lets the data determine the bin size and location, and avoids unwanted effects due to arbitrary choices of the bin parameters. We have determined widths, separations, and amplitudes of pulses contained in the bursts, without invoking a specific pulse model. The effect of cosmic time dilation can be easily seen in a direct plot of amplitude vs. time scale for individual pulses, without the need to lump the data into a small number of brightness classes. We are also performing noise equalization on these data (to reduce a well-known bias of pulse width as a function of signal-to-noise ratio), as well as fits of parametric pulse-shape models -- including explicit energy dependence of the pulse parameters. Such refinements are expected to improve the quality and physical significance of these results.

Norris, J. P.↗
NASA NTRS2002

Modeling of the HiPco process for carbon nanotube production. I. Chemical kinetics

A chemical kinetic model is developed to help understand and optimize the production of single-walled carbon nanotubes via the high-pressure carbon monoxide (HiPco) process, which employs iron pentacarbonyl as the catalyst precursor and carbon monoxide as the carbon feedstock. The model separates the HiPco process into three steps, precursor decomposition, catalyst growth and evaporation, and carbon nanotube production resulting from the catalyst-enhanced disproportionation of carbon monoxide, known as the Boudouard reaction: 2 CO(g)-->C(s) + CO2(g). The resulting detailed model contains 971 species and 1948 chemical reactions. A second model with a reduced reaction set containing 14 species and 22 chemical reactions is developed on the basis of the detailed model and reproduces the chemistry of the major species. Results showing the parametric dependence of temperature, total pressure, and initial precursor partial pressures are presented, with comparison between the two models. The reduced model is more amenable to coupled reacting flow-field simulations, presented in the following article.

Evaluation Studies↗
NASA NTRS2023

New Seawater Dielectric Constant Parametrization and Application to SMOS Retrieved Salinity

The accuracy of the Sea Surface Salinity (SSS) retrieved from L-Band radiometer measurements is strongly dependent on the reliability of the dielectric constant model. Two new parametrizations were recently developed based on one hand on the Soil Moisture and Ocean Salinity (SMOS) satellite multi-angular brightness temperature measurements by Boutin et al. (2021) (BV), and on the other hand, on new George Washington University laboratory measurements by Zhou et al. (2021) (GW2020). These two approaches are fully independent. For most SSS and Sea Surface Temperature (SST) conditions commonly observed over the open ocean, the relative variations of brightness temperatures Tb simulated through the BV and GW2020 parametrizations agree particularly well, and better than with earlier parametrizations previously used in the SMOS, Soil Moisture Active Passive (SMAP) and Aquarius SSS retrievals. Nevertheless, uncertainty remains, especially below 10 °C where a ∼ 0.1 K relative difference between the two models is observed. This motivates the development of a revised parameterization, BVZ, based on a methodology similar to that used to derive BV but using GW2020 instead of SMOS measurements. Compared to the GW2020 parameterization, BVZ is derived with a reduced number of degrees of freedom, it relies on the TEOS10 PSS78 conductivity-salinity relationship, and on the previously derived static permittivity of fresh water. One month per season of SMOS data have been reprocessed in 2018 using BV, GW2020, and BVZ. We find the best overall agreement between SMOS SSS and Argo SSS with BVZ parametrization, with noticeable improvement in the 5 °C–15 °C SST range.

sea measurements↗
DOE OSTI2025

Physics-informed latent neural operator for real-time predictions of time-dependent parametric PDEs

Deep operator network (DeepONet) has shown significant promise as surrogate models for systems governed by partial differential equations (PDEs), enabling accurate mappings between infinite-dimensional function spaces. However, when applied to systems with high-dimensional input-output mappings arising from large numbers of spatial and temporal collocation points, these models often require heavily overparameterized networks, leading to long training times. Latent DeepONet addresses some of these challenges by introducing a two-step approach: first learning a reduced latent space using a separate model, followed by operator learning within this latent space. While efficient, this method is inherently data-driven and lacks mechanisms for incorporating physical laws, limiting its robustness and generalizability in data-scarce settings. Here, in this work, we propose PI-Latent-NO, a physics-informed latent neural operator framework that integrates governing physics directly into the learning process. Our architecture features two coupled DeepONets trained end-to-end: a Latent-DeepONet that learns a low-dimensional representation of the solution, and a Reconstruction-DeepONet that maps this latent representation back to the physical space. By embedding PDE constraints into the training via automatic differentiation, our method eliminates the need for labeled training data and ensures physics-consistent predictions. The proposed framework is both memory and compute-efficient, exhibiting near-constant scaling with problem size and demonstrating significant speedups over traditional physics-informed operator models. We validate our approach on a range of parametric PDEs, showcasing its accuracy, scalability, and suitability for real-time prediction in complex physical systems.

Latent representations↗
NASA NTRS2020

Convection Parametrization and Multi-Nesting Dependence of a Heavy Rainfall Event over Namibia with Weather Research and Forecasting (WRF) Model

Namibia is considered as one of the countries that are most vulnerable to climate change due to its generally dry climate and the percentage of its population that rely on subsistence agriculture for their livelihoods. Early warning systems are an important aspect of adapting to climate change. Weather forecasting relies on the use of numerical weather prediction models and these need to be configured properly. In this study we investigate the effects of using multi-nests and a convection scheme on the simulation of a heavy rainfall event over the Northwestern region of Kunene, Namibia. The event which was associated with a cut-off low system, was short-lived and resulted in over 45 mm of rainfall in one hour. For the multi-nest, a 9 km grid length parent domain is nested within the Global Forecast System (GFS) simulations, which in turn forces a 3 km grid spacing child domain. A different set of simulations are produced using a single nest of 3 km grid spacing, nested directly inside the GFS data. The simulations are produced with the convection scheme switched on and off. The impact of a single versus multi nest is found to be small in general, with slight differences in the location of high rainfall intensity. Switching off the convection schemes results in high rainfall intensity and increased detail in the simulations, including when a grid spacing of 9 km mis used. Using a grid spacing of 3 km with the convection scheme on, results in a loss of detail in the simulations as well as lower rainfall amounts. The study shows a need for different configurations to be tested before an optimum configuration can be selected for operational forecasting. We recommend further tests with different synoptic forcing to be conducted to identify a suitable configuration for Namibia.

flooding↗
NASA NTRS2005

Uncertainty Modeling for Robustness Analysis of Control Upset Prevention and Recovery Systems

Formal robustness analysis of aircraft control upset prevention and recovery systems could play an important role in their validation and ultimate certification. Such systems (developed for failure detection, identification, and reconfiguration, as well as upset recovery) need to be evaluated over broad regions of the flight envelope and under extreme flight conditions, and should include various sources of uncertainty. However, formulation of linear fractional transformation (LFT) models for representing system uncertainty can be very difficult for complex parameter-dependent systems. This paper describes a preliminary LFT modeling software tool which uses a matrix-based computational approach that can be directly applied to parametric uncertainty problems involving multivariate matrix polynomial dependencies. Several examples are presented (including an F-16 at an extreme flight condition, a missile model, and a generic example with numerous crossproduct terms), and comparisons are given with other LFT modeling tools that are currently available. The LFT modeling method and preliminary software tool presented in this paper are shown to compare favorably with these methods.

Belcastro, Christine M.↗
NASA NTRS2001

Parametric Study of a YAV-8B Harrier in Ground Effect Using Time-Dependent Navier-Stokes Computations

Flow simulations using the time-dependent Navier-Stokes equations remain a challenge for several reasons. Principal among them are the difficulty to accurately model complex flows, and the time needed to perform the computations. A parametric study of such complex problems is not considered practical due to the large cost associated with computing many time-dependent solutions. The computation time for each solution must be reduced in order to make a parametric study possible. With successful reduction of computation time, the issue of accuracy, and appropriateness of turbulence models will become more tractable.

Shishir, Pandya↗
NASA NTRS1991

A parametric study of dissociation and ionization models at 12 km/sec

Thermochemical nonequilibrium-solution dependence on available models for the chemical reaction rates is examined. Solutions from the Kang and Dunn (1973) reaction-rate set, the Park rate set of 1987, and the Park rate set of 1991 are compared. The blunt-nosed, axisymmetric geometry considered is a 60-deg sphere cone with nose radius of 1.07 m and cicular aft skirt. The nonequilibrium test case is 12 km/sec entry into the earth's atmosphere at 80 km altitude. The model variations are implemented into the Langley aerothermodynamics upwind relaxation algorithm code. While variations in the reaction rates have no effect on the surface pressure distribution and little effect on the convective heating, the effect on degree of ionization and radiative heating can be a factor of three.

Mitcheltree, R. A.↗
DOE OSTI2024

Data-driven models of nonautonomous systems

Nonautonomous dynamical systems are characterized by time-dependent inputs, which complicates the discovery of predictive models describing the spatiotemporal evolution of the state variables of quantities of interest from their temporal snapshots. When dynamic mode decomposition (DMD) is used to infer a linear model, this difficulty manifests itself in the need to approximate the time-dependent Koopman operators. Our approach is to approximate the original nonautonomous system with a modified system derived via a local parameterization of the time-dependent inputs. The modified system comprises a sequence of local parametric systems, which are subsequently approximated by a parametric surrogate model using the DRIPS (dimension reduction and interpolation in parameter space) framework. The offline step of DRIPS relies on DMD to build a linear surrogate model, endowed with reduced-order bases for the observables mapped from training data. The online step interpolates on suitable manifolds to construct a sequence of iterative parametric surrogate models; the target/test parameter points on these manifolds are specified by a local parameterization of the test time-dependent inputs. Here, we use numerical experimentation to demonstrate the robustness of our method and compare its performance with that of deep neural networks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Survival analysis of localized prostate cancer with deep learning

In recent years, data-driven, deep-learning-based models have shown great promise in medical risk prediction. By utilizing the large-scale Electronic Health Record data found in the U.S. Department of Veterans Affairs, the largest integrated healthcare system in the United States, we have developed an automated, personalized risk prediction model to support the clinical decision-making process for localized prostate cancer patients. This method combines the representative power of deep learning and the analytical interpretability of parametric regression models and can implement both time-dependent and static input data. To collect a comprehensive evaluation of model performances, we calculate time-dependent C-statistics C td over 2-, 5-, and 10-year time horizons using either a composite outcome or prostate cancer mortality as the target event. The composite outcome combines the Prostate-Specific Antigen (PSA) test, metastasis, and prostate cancer mortality. Our longitudinal model Recurrent Deep Survival Machine (RDSM) achieved C td 0.85 (0.83), 0.80 (0.83), and 0.76 (0.81), while the cross-sectional model Deep Survival Machine (DSM) attained C td 0.85 (0.82), 0.80 (0.82), and 0.76 (0.79) for the 2-, 5-, and 10-year composite (mortality) outcomes, respectively. In addition to estimating the survival probability, our method can quantify the uncertainty associated with the prediction. The uncertainty scores show a consistent correlation with the prediction accuracy. We find PSA and prostate cancer stage information are the most important indicators in risk prediction. Our work demonstrates the utility of the data-driven machine learning model in prostate cancer risk prediction, which can play a critical role in the clinical decision system.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1976

Some factors influencing radiation of sound from flow interaction with edges of finite surfaces

Edges of surfaces which are exposed to unsteady flow cause both strictly acoustic effects and hydrodynamic effects, in the form of generation of new hydrodynamic sources in the immediate vicinity of the edge. An analytical model is presented which develops the explicit sound-generation role of the velocity and Mach number of the eddy convection past the edge, and the importance of relative scale lengths of the turbulence, as well as the relative intensity of pressure fluctuations. The Mach number (velocity) effects show that the important paramater is the convection Mach number of the eddies. The effects of turbulence scale lengths, isotropy, and spatial density (separation) are shown to be important in determining the level and spectrum of edge sound radiated for the edge dipole mechanism. Experimental data is presented which provides support for the dipole edge noise model in terms of Mach number (velocity) scaling, parametric dependence on flow field parameter, directivity, and edge diffraction effects.

Hayden, R. E.↗
NASA NTRS1973

Numerical experiments in climate stability.

Two semiempirical climate models are formulated as time-dependent problems in order to study the stability of their asymptotic steady state equilibrium solutions to perturbations in internal (in this case, initial) conditions. For fixed external conditions the time-dependent versions of these models exhibit fully transitive behavior to positive perturbations in initial conditions, slight intransivity to negative perturbations up to -18 K, and an ice-covered earth regime is obtained for extremely large negative perturbations in initial temperatures (below -18 K). The parametrization found to be most critical in these models is the albedo-temperature coupling, especially in tropical regions. The temperature albedo parametrization from an intransitive climate model used in a time-dependent form of one of the preceding models is found to be highly sensitive to negative perturbations in both internal and external conditions. Numerical experimentation with these semiempirical models shows the important role of the tropics in maintaining the equilibrium climate and suggests that the radiation balance in equatorial latitudes might have a controlling influence on the equatorward extent of the polar ice cap.

Schneider, S. H.↗
NASA NTRS2005

The Minimum-Mass Surface Density of the Solar Nebula using the Disk Evolution Equation

The Hayashi minimum-mass power law representation of the pre-solar nebula (Hayashi 1981, Prog. Theo. Phys.70,35) is revisited using analytic solutions of the disk evolution equation. A new cumulative-planetary-mass-model (an integrated form of the surface density) is shown to predict a smoother surface density compared with methods based on direct estimates of surface density from planetary data. First, a best-fit transcendental function is applied directly to the cumulative planetary mass data with the surface density obtained by direct differentiation. Next a solution to the time-dependent disk evolution equation is parametrically adapted to the planetary data. The latter model indicates a decay rate of r -1/2 in the inner disk followed by a rapid decay which results in a sharper outer boundary than predicted by the minimum mass model. The model is shown to be a good approximation to the finite-size early Solar Nebula and by extension to extra solar protoplanetary disks.

Davis, Sanford S.↗