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At least 145 records · Page 8

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NASA NTRSDate not supplied

Increasing accessibility to deep learning-based analytics for space biology: pretrained models, transfer learning, and analytics platform development

Biological systems react in complex ways to the stressors of spaceflight, and the data capturing these relationships is concomitantly high-dimensional and complex. Deep learning and machine learning approaches are increasingly popular as an analytical approach for space biosciences, due to their ability to model complex relationships in complex data. However, such approaches often require large datasets and extensive computational resources. New approaches that minimize data sizes and computational power needed to leverage machine learning, and resources that make these approaches accessible, are needed to increase accessibility and adoption of machine learning in the space biosciences. Transfer learning, in which a pretrained model of broad utility is trained on a large dataset, and subsequently reused on downstream applications for which data is more limited, is one approach to minimizing data and computational intensity of deep learning applications. This transfer learning approach results in more performant models in high-dimensional, low-sample-size settings such as space biology, as compared to training models on limited data from scratch. This presentation will outline efforts to generate pretrained models for the space biology community, and highlight transfer learning applications modeling microbial antibiotic resistance during spaceflight. Finally, in order to increase accessibility of these models and tools, as well as others, for the broader space biology community, we present a modeling and analysis platform facilitating machine learning applications in space biology. This platform streamlines machine learning training and analysis in a notebook format, facilitates download and use of space biology data from the NASA GeneLab database, and can be utilized on NASA-hosted servers or downloaded and hosted locally. This effort, as part of the AI4LS (Artificial Intelligence for Life in Space) working group, will increase accessibility, feasibility, and performance of machine learning approaches for the space biology community.

Adrienne Hoarfrost↗

DOE OSTI2026

Half-ice, half-fire-driven ultranarrow phase crossover in one-dimensional decorated 𝑞-state Potts ferrimagnets: An AI-co-led exploration

OpenAI’s reasoning model o3-mini-high was used to carry out an exact analytic study of one-dimensional ferrimagnetic site- and bond-decorated 𝑞-state Potts models. We demonstrate that the finite-temperature ultranarrow phase crossover (UNPC), driven by a hidden “half-ice, half-fire” state recently discovered in the 𝑞=2 case (Ising model), persists for 𝑞>2. Moreover, we identify unique features for 𝑞>2, including the dome structure in the field-temperature phase diagram, and for large 𝑞 a secondary high-temperature UNPC to the fully disordered paramagnetic state. As the UNPC quickly approaches a genuine transition by enhancing 𝐽, the interaction between the backbone spins, two distinct behaviors emerge: In the site-decorated Potts model, 𝑇 0 is independent of 𝐽 and thus remains unchanged (Type-I UNPC), and in the bond-decorated Potts model with 𝑞>2, 𝑇 0 depends on 𝐽 and quickly shifts toward a finite temperature as 𝐽 increases (Type-II UNPC). These results establish a versatile framework for engineering controlled fast state-flipping switches in low-dimensional systems. Our nine-dan artificial intelligence (AI)-contribution framework assigns AI the meritorious status of AI-co-led discovery in this work.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

NASA NTRS1990

An experimental investigation of thrust vectoring two-dimensional convergent-divergent nozzles installed in a twin-engine fighter model at high angles of attack

An investigation was conducted in the Langley 16-Foot Transonic Tunnel to determine thrust vectoring capability of subscale 2-D convergent-divergent exhaust nozzles installed on a twin engine general research fighter model. Pitch thrust vectoring was accomplished by downward rotation of nozzle upper and lower flaps. The effects of nozzle sidewall cutback were studied for both unvectored and pitch vectored nozzles. A single cutback sidewall was employed for yaw thrust vectoring. This investigation was conducted at Mach numbers ranging from 0 to 1.20 and at angles of attack from -2 to 35 deg. High pressure air was used to simulate jet exhaust and provide values of nozzle pressure ratio up to 9.

Capone, Francis J.↗

NASA NTRS2003

Mixing and Transport in the Solar Nebula

Boss & Vanhala (2000, 2001) prepared reviews of triggered collapse and injection models, using Prudence Foster's finite differences code at very high spatial resolution (440 x 1440 cells) to demonstrate the convergence of the R-T fingers in triggered injection models. A two dimensional hydrodynamical calculation with unprecedentedly high spatial resolution (960 x 2880 zones, or almost 3 million grid points) demonstrated that it suitable shock front can both trigger the collapse of an otherwise stable presolar cloud, and inject shock front particles into the collapsing cloud through the formation of what become Rayleigh-Taylor fingers of compressed fluid layers falling into the gravitational potential well of the growing protostar. These calculations suggest that heterogeneity derived from these R-T fingers will persist down to the scale of their injection onto the surface of the solar nebula. Haghighipour developed a numerical code capable of calculating the orbital evolution of dust grains of varied sizes in a gaseous nebula, subject to Epstein and Stokes drag as well as the self-gravity of the disk. In collaboration with the PI and George W. Wetherill, Haghighipour has been involved in development of a new idea on the possibility of rapid formation of ice giant planets via the disk instability mechanism. Haghighipour studied the stability of a five-body system consisting of the Sun and four protoplanets by numerically integrating their equations of motions. Using Levison and Duncan s SWIFT integrator, Haghighipour showed that, depending on the orbital parameters of the bodies, such a system can be stable for 0.1-10 Myr. Time periods of 1 Myr or more are long enough to be consistent with the time scale proposed for the formation of giant planets by the disk instability mechanism and the photoevaporation of the gaseous envelopes of the outermost protoplanets by a nearby OB star, resulting in the formation of ice giant planets. The PI has used his three dimensional models of marginally gravitationally unstable disks to study the preservation of isotopic heterogeneity in evolving protoplanetary disks. Such heterogeneity might arise from the infall onto the disk s surface of solids processed in the X-wind region of the disk, or derived from stellar nucleosynthesis and injected by R-T fingers. The technique used consists of solving a color equation, identical to the gas continuity equation, which follows the time evolution in three space dimensions of an arbitrarily placed initial color field, i.e., a dye inserted the disk. The models show that significant concentrations of color could persist for time periods of about a thousand years or more, even in the most dynamically active region of such a disk. Such a time period might be long enough for solids to coagulate and grow to significant sizes while retaining the isotopic signature of their birth region in the nebula.

Boss, Alan P.↗

NASA NTRS2015

Verification and Validation of a Three-Dimensional Generalized Composite Material Model

A general purpose orthotropic elasto-plastic computational constitutive material model has been developed to improve predictions of the response of composites subjected to high velocity impact. The three-dimensional orthotropic elasto-plastic composite material model is being implemented initially for solid elements in LS-DYNA as MAT213. In order to accurately represent the response of a composite, experimental stress-strain curves are utilized as input, allowing for a more general material model that can be used on a variety of composite applications. The theoretical details are discussed in a companion paper. This paper documents the implementation, verification and qualitative validation of the material model using the T800-F3900 fiber/resin composite material

Plasticity↗

NASA NTRS2014

Verification and Validation of a Three-Dimensional Generalized Composite Material Model

Finite Element Method↗

NASA NTRS1994

Decision Making in Action: Applying Research to Practice

The importance of decision-making to safety in complex, dynamic environments like mission control centers, aviation, and offshore installations has been well established. NASA-ARC has a program of research dedicated to fostering safe and effective decision-making in the manned spaceflight environment: Because access to spaceflight is limited, environments with similar characteristics, including aviation and nuclear power plants, serve as analogs from which space-relevant data can be gathered and theories developed. Analyses of aviation accidents cite crew judgement and decision making as causes or contributing factors in over half of all accidents. Yet laboratory research on decision making has not proven especially helpful in improving the quality of decisions in these kinds of environments. One reason is that the traditional, analytic decision models are inappropriate to multi-dimensional, high-risk environments, and do not accurately describe what expert human decision makers do when they make decisions that have consequences. A new model of dynamic, naturalistic decision making is offered that may prove useful for improving decision making in complex, isolated, confined and high-risk environments. Based on analyses of crew performance in full-mission simulators and accident reports, features that define effective decision strategies in abnormal or emergency situations have been identified. These include accurate situation assessment (including time and risk assessment), appreciation of the complexity of the problem, sensitivity to constraints on the decision, timeliness of the response, and use of adequate information. More effective crews also manage their workload to provide themselves with time and resources to make good good decisions are appropriate to the demands of the situation. Effective crew decision making and overall performance are mediated by crew communication. Communication contributes to performance because it assures that all crew members have essential information, but it also regulates and coordinates crew actions and is the medium of collective thinking in response to a problem. This presentation will examine the relations between leadership, communication, decision making and overall crew performance. Implications of these findings for training will be discussed.

Orasanu, Judith↗

NASA NTRS1995

Decision Making in the Airplane

The Importance of decision-making to safety in complex, dynamic environments like mission control centers, aviation, and offshore installations has been well established. NASA-ARC has a program of research dedicated to fostering safe and effective decision-making in the manned spaceflight environment. Because access to spaceflight is limited, environments with similar characteristics, including aviation and nuclear power plants, serve as analogs from which space-relevant data can be gathered and theories developed. Analyses of aviation accidents cite crew judgement and decision making as causes or contributing factors in over half of all accidents. Yet laboratory research on decision making has not proven especially helpful In improving the quality of decisions in these kinds of environments. One reason is that the traditional, analytic decision models are inappropriate to multi-dimensional, high-risk environments, and do not accurately describe what expert human decision makers do when they make decisions that have consequences. A new model of dynamic, naturalistic decision making is offered that may prove useful for improving decision making in complex, isolated, confined and high-risk environments. Based on analyses of crew performance in full-mission simulators and accident reports, features that define effective decision strategies in abnormal or emergency situations have been identified. These include accurate situation assessment (including time and risk assessment), appreciation of the complexity of the problem, sensitivity to constraints on the decision, timeliness of the response, and use of adequate information. More effective crews also manage their workload to provide themselves with time and resources to make good decisions. In brief, good decisions are appropriate to the demands of the situation. Effective crew decision making and overall performance are mediated by crew communication. Communication contributes to performance because it assures that all crew members have essential information, but it also regulates and coordinates crew actions and is the medium of collective thinking In response to a problem, This presentation will examine the relations between leadership, communication, decision making and overall crew performance. Implications of these findings for training will be discussed.

Orasanu, Judith↗

NASA NTRS2011

Aerodynamic Simulation of Ice Accretion on Airfoils

This report describes recent improvements in aerodynamic scaling and simulation of ice accretion on airfoils. Ice accretions were classified into four types on the basis of aerodynamic effects: roughness, horn, streamwise, and spanwise ridge. The NASA Icing Research Tunnel (IRT) was used to generate ice accretions within these four types using both subscale and full-scale models. Large-scale, pressurized windtunnel testing was performed using a 72-in.- (1.83-m-) chord, NACA 23012 airfoil model with high-fidelity, three-dimensional castings of the IRT ice accretions. Performance data were recorded over Reynolds numbers from 4.5 x 10(exp 6) to 15.9 x 10(exp 6) and Mach numbers from 0.10 to 0.28. Lower fidelity ice-accretion simulation methods were developed and tested on an 18-in.- (0.46-m-) chord NACA 23012 airfoil model in a small-scale wind tunnel at a lower Reynolds number. The aerodynamic accuracy of the lower fidelity, subscale ice simulations was validated against the full-scale results for a factor of 4 reduction in model scale and a factor of 8 reduction in Reynolds number. This research has defined the level of geometric fidelity required for artificial ice shapes to yield aerodynamic performance results to within a known level of uncertainty and has culminated in a proposed methodology for subscale iced-airfoil aerodynamic simulation.

Broeren, Andy P.↗

NASA NTRS2002

Cloud-Scale Numerical Modeling of the Arctic Boundary Layer

The research objective of this NASA grant-funded project was to determine in detail how large-scale processes. in combination with cloud-scale radiative, microphysical, and dynamical processes, govern the formation and multi-layered structure of Arctic stratus clouds. This information will be useful for developing and improving 1D (one dimensional) boundary layer models for the Arctic. Also, to quantitatively determine the effects of leads on the large-scale budgets of sensible heat, water vapor, and condensate in a variety of Arctic winter conditions. This information will be used to identify the most important lead-flux processes that require parameterization in climate models. Our approach was to use a high-resolution numerical model, the 2D (two dimensional) University of Utah Cloud Resolving Model (UU CRM), and its 1D version, the University of Utah Turbulence Closure Model (UU TCM), a boundary layer model based on third-moment turbulence closure, as well as a large-eddy simulation (LES) model originally developed by C.H. Moeng.

Kruegen, Steven K.↗

DOE OSTI2024

Predicting Drug Effects from High-dimensional Asymmetric Drug Data Sets using Graph Neural Networks: A Comprehensive Analysis of Multi-target Drug Effect Prediction

Graph neural networks (GNNs) have emerged as one of the most effective Machine learning (ML) techniques for drug effect prediction from drug molecular graphs. Despite having immense potential, GNN models lack performance when using data sets that contain high dimensional asymmetrically co-occurrent drug effects as targets with complex correlations between them. Training individual learning models for each drug effect and incorporating every prediction result for a wide spectrum of drug effects is beyond practicality. Such an implication provides a testbed to address this challenge as multi-target prediction problems, aiming to predict all drug effects at a time. We develop standard and hybrid graph neural networks (GNNs)to perform two separate tasks that are multi-regression for continuous values and multi-label classification for categorical values contained in our data sets. Since this step makes the target data even more sparse and introduces asymmetric label co-occurrence, the learning of multi-label classification models becomes difficult and heavily impacts the GNN's performance. To address these challenges, we propose a new data oversampling technique to improve multi-label classification performances on all the given imbalanced molecular graph data sets. Using the technique, we improve the data imbalance ratio of the drug effects better than before while protecting the data set's integrity. Finally, we evaluate multi-label classification performance using the best-performant hybrid GNN model on all the oversampled data sets obtained from the proposed oversampling technique. These results outperform those of other ML models including GNN models when they are trained on the original data sets or oversampled data sets using MLSMOTE (a well-known oversampling technique) in all evaluation metrics precision, recall, and F1 score by a significant margin.

Bose, Avishek [ORNL]↗

DOE OSTI2026

Intrusive Uncertainty Quantification and Optimal Experiment Design in the Open-Source Pyomo Ecosystem

This contribution describes ParmEst and Pyomo.DoE, two pillars of the open-source Python-based Pyomo ecosystem for computational optimization with (partial differential) algebraic equation mathematical models. Specifically, ParmEst facilitates intrusive frequentist parameter estimation (PE) and uncertainty quantification (UQ) through built-in features, such as covariance matrix estimation, bootstrapping, and likelihood ratio tests. Complementary, Pyomo.DoE enables optimal experiment design by maximizing various metrics of the Fisher information matrix, such as A-optimality (trace), D-optimality (determinant), E-optimality (minimum eigenvalue), and ME-optimality (condition number). ParmEst and Pyomo.DoE can solve high-dimensional optimization problems by leveraging the model structure and exact derivative information. Finally, we will discuss future opportunities to integrate PE and UQ capabilities with optimization under uncertainty, including robust optimization with non-convex models via PyROS.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1989

Analysis of the transient compressible vapor flow in heat pipe

The transient compressible one-dimensional vapor flow dynamics in a heat pipe is modeled. The numerical results are obtained by using the implicit non-iterative Beam-Warming finite difference method. The model is tested for simulated heat pipe vapor flow and actual flow in cylindrical heat pipes. A good comparison of the present transient results for the simulated heat pipe vapor flow with the previous results of a two-dimensional numerical model is achieved and the steady state results are in agreement with the existing experimental data. The transient behavior of the vapor flow under subsonic, sonic, and supersonic speeds and high mass flow rates are successfully predicted. The one-dimensional model also describes the vapor flow dynamics in cylindrical heat pipes at high temperatures.

Jang, Jong Hoon↗

NASA NTRS1989

Analysis of the transient compressible vapor flow in heat pipes

The transient compressible one-dimensional vapor flow dynamics in a heat pipe is modeled. The numerical results are obtained by using the implicit non-iterative Beam-Warming finite difference method. The model is tested for simulated heat pipe vapor flow and actual vapor flow in cylindrical heat pipes. A good comparison of the present transient results for the simulated heat pipe vapor flow with the previous results of a two-dimensional numerical model is achieved and the steady state results are in agreement with the existing experimental data. The transient behavior of the vapor flow under subsonic, sonic, and supersonic speeds and high mass flow rates are successfully predicted. The one-dimensional model also describes the vapor flow dynamics in cylindrical heat pipes at high temperatures.

Jang, J. H.↗

NASA NTRS1989

Pressure-confined Lyman-alpha clouds

Results are presented of numerical models of pressure-confined spherical gas clouds which produce absorption resembling the low to intermediate atomic column density lines found in high-redshift QSO spectra. One-dimensional hydrodynamical models including electron conduction are described, and the rate equations are solved to find ionization and excitation states. Results are presented for both static and adiabatically expanding confining media covering a range of initial pressures. It is found that Ly-alpha lines are very similar over a wide range of conditions and that the most promising diagnostic of pressure is to compare the column density in H I to that in He I and He II. No single-pressure model can explain the wide range of observed H I column densities.

Baron, E.↗

NASA NTRS2008

On the Sensitivity of Atmospheric Ensembles to Cloud Microphysics in Long-Term Cloud-Resolving Model Simulations

Month-long large-scale forcing data from two field campaigns are used to drive a cloud-resolving model (CRM) and produce ensemble simulations of clouds and precipitation. Observational data are then used to evaluate the model results. To improve the model results, a new parameterization of the Bergeron process is proposed that incorporates the number concentration of ice nuclei (IN). Numerical simulations reveal that atmospheric ensembles are sensitive to IN concentration and ice crystal multiplication. Two- (2D) and three-dimensional (3D) simulations are carried out to address the sensitivity of atmospheric ensembles to model dimensionality. It is found that the ensembles with high IN concentration are more sensitive to dimensionality than those with low IN concentration. Both the analytic solutions of linear dry models and the CRM output show that there are more convective cores with stronger updrafts in 3D simulations than in 2D, which explains the differing sensitivity of the ensembles to dimensionality at different IN concentrations.

Zeng, Xiping↗

NASA NTRS2018

Greedy Sampling and Incremental Surrogate Model-Based Tailoring of Aeroservoelastic Model Database for Flexible Aircraft

This paper presents a data analysis and modeling framework to tailor and develop linear parameter-varying (LPV) aeroservoelastic (ASE) model database for flexible aircrafts in broad 2D flight parameter space. The Kriging surrogate model is constructed using ASE models at a fraction of grid points within the original model database, and then the ASE model at any flight condition can be obtained simply through surrogate model interpolation. The greedy sampling algorithm is developed to select the next sample point that carries the worst relative error between the surrogate model prediction and the benchmark model in the frequency domain among all input-output channels. The process is iterated to incrementally improve surrogate model accuracy till a pre-determined tolerance or iteration budget is met. The methodology is applied to the ASE model database of a flexible aircraft currently being tested at NASA/AFRC for flutter suppression and gust load alleviation. Our studies indicate that the proposed method can reduce the number of models in the original database by 67%. Even so the ASE models obtained through Kriging interpolation match the model in the original database constructed directly from the physics-based tool with the worst relative error far below 1%. The interpolated ASE model exhibits continuously-varying gains along a set of prescribed flight conditions. More importantly, the selected grid points are distributed non-uniformly in the parameter space, a) capturing the distinctly different dynamic behavior and its dependence on flight parameters, and b) reiterating the need and utility for adaptive space sampling techniques for ASE model database compaction. The present framework is directly extendible to high-dimensional flight parameter space, and can be used to guide the ASE model development, model order reduction, robust control synthesis and novel vehicle design of flexible aircraft.

numerical analysi↗

NASA NTRS2013

Scalable Adaptive Graphics Environment (SAGE) Software for the Visualization of Large Data Sets on a Video Wall

The use of collaborative scientific visualization systems for the analysis, visualization, and sharing of "big data" available from new high resolution remote sensing satellite sensors or four‐dimensional numerical model simulations is propelling the wider adoption of ultra‐resolution tiled display walls interconnected by high speed networks. These systems require a globally connected and well‐integrated operating environment that provides persistent visualization and collaboration services. This abstract and subsequent presentation describes a new collaborative visualization system installed for NASA's Shortterm Prediction Research and Transition (SPoRT) program at Marshall Space Flight Center and its use for Earth science applications. The system consists of a 3 x 4 array of 1920 x 1080 pixel thin bezel video monitors mounted on a wall in a scientific collaboration lab. The monitors are physically and virtually integrated into a 14' x 7' for video display. The display of scientific data on the video wall is controlled by a single Alienware Aurora PC with a 2nd Generation Intel Core 4.1 GHz processor, 32 GB memory, and an AMD Fire Pro W600 video card with 6 mini display port connections. Six mini display‐to‐dual DVI cables are used to connect the 12 individual video monitors. The open source Scalable Adaptive Graphics Environment (SAGE) windowing and media control framework, running on top of the Ubuntu 12 Linux operating system, allows several users to simultaneously control the display and storage of high resolution still and moving graphics in a variety of formats, on tiled display walls of any size. The Ubuntu operating system supports the open source Scalable Adaptive Graphics Environment (SAGE) software which provides a common environment, or framework, enabling its users to access, display and share a variety of data‐intensive information. This information can be digital‐cinema animations, high‐resolution images, high‐definition video‐teleconferences, presentation slides, documents, spreadsheets or laptop screens. SAGE is cross‐platform, community‐driven, open‐source visualization and collaboration middleware that utilizes shared national and international cyberinfrastructure for the advancement of scientific research and education.

Jedlovec, Gary↗