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All sources NASA NTRS DOE OSTI

NASA NTRS2016

Validation for Global Solar Wind Prediction Using Ulysses Comparison: Multiple Coronal and Heliospheric Models Installed at the Community Coordinated Modeling Center

The prediction of the background global solar wind is a necessary part of space weather forecasting. Several coronal and heliospheric models have been installed and/or recently upgraded at the Community Coordinated Modeling Center (CCMC), including the Wang-Sheely-Arge (WSA)-Enlil model, MHD-Around-a-Sphere (MAS)-Enlil model, Space Weather Modeling Framework (SWMF), and Heliospheric tomography using interplanetary scintillation data. Ulysses recorded the last fast latitudinal scan from southern to northern poles in 2007. By comparing the modeling results with Ulysses observations over seven Carrington rotations, we have extended our third-party validation from the previous near-Earth solar wind to middle to high latitudes, in the same late declining phase of solar cycle 23. Besides visual comparison, wehave quantitatively assessed the models capabilities in reproducing the time series, statistics, and latitudinal variations of solar wind parameters for a specific range of model parameter settings, inputs, and grid configurations available at CCMC. The WSA-Enlil model results vary with three different magnetogram inputs.The MAS-Enlil model captures the solar wind parameters well, despite its underestimation of the speed at middle to high latitudes. The new version of SWMF misses many solar wind variations probably because it uses lower grid resolution than other models. The interplanetary scintillation-tomography cannot capture the latitudinal variations of solar wind well yet. Because the model performance varies with parameter settings which are optimized for different epochs or flow states, the performance metric study provided here can serve as a template that researchers can use to validate the models for the time periods and conditions of interest to them.

Jian, L. K.↗

DOE OSTI2025

Meeting Global Health Needs via Infectious Disease Forecasting: Development of a Reliable Data-Driven Framework

Infectious diseases (IDs) have a significant detrimental impact on global health. Timely and accurate ID forecasting can result in more informed implementation of control measures and prevention policies. To meet the operational decision-making needs of real-world circumstances, we aimed to build a standardized, reliable, and trustworthy ID forecasting pipeline and visualization dashboard that is generalizable across a wide range of modeling techniques, IDs, and global locations. We forecasted 6 diverse, zoonotic diseases (brucellosis, campylobacteriosis, Middle East respiratory syndrome, Q fever, tick-borne encephalitis, and tularemia) across 4 continents and 8 countries. We included a wide range of statistical, machine learning, and deep learning models (n=9) and trained them on a multitude of features (average n=2326) within the One Health landscape, including demography, landscape, climate, and socioeconomic factors. The pipeline and dashboard were created in consideration of crucial operational metrics—prediction accuracy, computational efficiency, spatiotemporal generalizability, uncertainty quantification, and interpretability—which are essential to strategic data-driven decisions. While no single best model was suitable for all disease, region, and country combinations, our ensemble technique selects the best-performing model for each given scenario to achieve the closest prediction. For new or emerging diseases in a region, the ensemble model can predict how the disease may behave in the new region using a pretrained model from a similar region with a history of that disease. The data visualization dashboard provides a clean interface of important analytical metrics, such as ID temporal patterns, forecasts, prediction uncertainties, and model feature importance across all geographic locations and disease combinations. As the need for real-time, operational ID forecasting capabilities increases, this standardized and automated platform for data collection, analysis, and reporting is a major step forward in enabling evidence-based public health decisions and policies for the prevention and mitigation of future ID outbreaks.

60 APPLIED LIFE SCIENCES↗

NASA NTRS1984

Geometric modeling of large space antenna deployment

A dynamic visualization of large antenna deployment was created to be used as a precursor to further analysis of the structure as it is being deployed. The configuration chosen for this investigation was a box-truss structure consisting of a large reflector 60 meters wide by 120 meters long with a 120-meter mast. The entire space structure is folded and compressed into a container 17.8 meters long by 3.75 meters in diameter for transportation to orbit by the Space Transportation System (STS). A geometric model of this structure was created using an interactive solid modeling package. The building blocks of the structure are boxes and cylinders which are put together to form elements connected by joints and hinges. The elements, joints, and hinges make up the box-truss structure. The geometric model was then passed to the MOVIE.BYU software package. The "utility" module of this program was used to manipulate the elements of the structure to represent 13 different stages of deployment. The "animate" command was then used to create a smooth sequence between stages. Each step in the sequence was recorded on magnetic tape and a short movie was produced.

Nazemetz, J.↗

NASA NTRS1989

Radiometry and a thermal modeling of asteroids

The steps required to determine a radiometric diameter of an asteroid from observations of its visual and thermal radiation are described together with thermal models required to predict the emission expected from a body of given size and bolometric albedo. It is pointed out, however, that, in some cases, these models were found to fail for a variety of reasons; in particular, they fail to characterize the shape, surface roughness, the rate and sense of rotation, and the maturity of the surface regolith, all of which affect the observed thermal flux. Several thermal models are examined, including the Standard Thermal Model for asteroids, the fast-rotating (isothermal-latitude) model, the thermophysical model, and the rough-surface thermophysical models.

Lebofsky, Larry A.↗

NASA NTRS1998

Object Creation and Human Factors Evaluation for Virtual Environments

The main objective of this project is to provide test objects for simulated environments utilized by the recently established Army/NASA Virtual Innovations Lab (ANVIL) at Marshall Space Flight Center, Huntsville, Al. The objective of the ANVIL lab is to provide virtual reality (VR) models and environments and to provide visualization and manipulation methods for the purpose of training and testing. Visualization equipment used in the ANVIL lab includes head-mounted and boom-mounted immersive virtual reality display devices. Objects in the environment are manipulated using data glove, hand controller, or mouse. These simulated objects are solid or surfaced three dimensional models. They may be viewed or manipulated from any location within the environment and may be viewed on-screen or via immersive VR. The objects are created using various CAD modeling packages and are converted into the virtual environment using dVise. This enables the object or environment to be viewed from any angle or distance for training or testing purposes.

Lindsey, Patricia F.↗

NASA NTRS2009

Aerodynamic and Aeroelastic Characteristics of a Tension Cone Inflatable Aerodynamic Decelerator

The supersonic aerodynamic and aeroelastic characteristics of a tension cone inflatable aerodynamic decelerator were investigated by wind tunnel testing. Two sets of tests were conducted: one using rigid models and another using textile models. Tests using rigid models were conducted over a Mach number range from 1.65 to 4.5 at angles of attack from -12 to 20 degrees. The axial, normal, and pitching moment coefficients were found to be insensitive to Mach number over the tested range. The axial force coefficient was nearly constant (C(sub A) = 1.45 +/- 0.05) with respect to angle of attack. Both the normal and pitching moment coefficients were nearly linear with respect to angle of attack. The pitching moment coefficient showed the model to be statically stable about the reference point. Schlieren images and video showed a detached bow shock with no evidence of large regions of separated flow and/or embedded shocks at all Mach numbers investigated. Qualitatively similar static aerodynamic coefficient and flow visualization results were obtained using textile models at a Mach number of 2.5. Using inflatable textile models the torus pressure required to maintain the model in the fully-inflated configuration was determined. This pressure was found to be sensitive to details in the structural configuration of the inflatable models. Additional tests included surface pressure measurements on rigid models and deployment and inflation tests with inflatable models.

Clark, Ian G.↗

DOE OSTI2025

Leveraging public AI tools to explore systems biology resources in mathematical modeling

Predictive mathematical modeling is an essential part of systems biology and is interconnected with information management. Systems biology information is often stored in specialized formats to facilitate data storage and analysis. These formats are not designed for easy human readability and thus require specialized software to visualize and interpret results. Therefore, comprehending modeling and underlying networks and pathways is contingent on mastering systems biology tools, which is particularly challenging for users with no or little background in data science or system biology. To address this challenge, we investigated the usage of public Artificial Intelligence (AI) tools in exploring systems biology resources in mathematical modeling. We tested public AI’s understanding of mathematics in models, related systems biology data, and the complexity of model structures. Our approach can enhance the accessibility of systems biology for non-system biologists and help them understand systems biology without a deep learning curve.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2010

Risk Assessment Using the Three Dimensions of Probability (Likelihood), Severity, and Level of Control

Traditional hazard analysis techniques utilize a two-dimensional representation of the results determined by relative likelihood and severity of the residual risk. These matrices present a quick-look at the Likelihood (Y-axis) and Severity (X-axis) of the probable outcome of a hazardous event. A three-dimensional method, described herein, utilizes the traditional X and Y axes, while adding a new, third dimension, shown as the Z-axis, and referred to as the Level of Control. The elements of the Z-axis are modifications of the Hazard Elimination and Control steps (also known as the Hazard Reduction Precedence Sequence). These steps are: 1. Eliminate risk through design. 2. Substitute less risky materials for more hazardous materials. 3. Install safety devices. 4. Install caution and warning devices. 5. Develop administrative controls (to include special procedures and training.) 6. Provide protective clothing and equipment. When added to the twodimensional models, the level of control adds a visual representation of the risk associated with the hazardous condition, creating a tall-pole for the least-well-controlled failure while establishing the relative likelihood and severity of all causes and effects for an identified hazard. Computer modeling of the analytical results, using spreadsheets and threedimensional charting gives a visual confirmation of the relationship between causes and their controls

Watson, Clifford↗

NASA NTRS1997

Models of Speed Discrimination

The prime purpose of this project was to investigate various theoretical issues concerning the integration of information across visual space. To date, most of the research efforts in the study of the visual system seem to have been focused in two almost non-overlaping directions. One research focus has been the low level perception as studied by psychophysics. The other focus has been the study of high level vision exemplified by the study of object perception. Most of the effort in psychophysics has been devoted to the search for the fundamental "features" of perception. The general idea is that the most peripheral processes of the visual system decompose the input into features that are then used for classification and recognition. The experimental and theoretical focus has been on finding and describing these analyzers that decompose images into useful components. Various models are then compared to the physiological measurements performed on neurons in the sensory systems. In the study of higher level perception, the work has been focused on the representation of objects and on the connections between various physical effects and object perception. In this category we find the perception of 3D from a variety of physical measurements including motion, shading and other physical phenomena. With few exceptions, there seem to be very limited development of theories describing how the visual system might combine the output of the analyzers to form the representation of visual objects. Therefore, the processes underlying the integration of information over space represent critical aspects of vision system. The understanding of these processes will have implications on our expectations for the underlying physiological mechanisms, as well as for our models of the internal representation for visual percepts. In this project, we explored several mechanisms related to spatial summation, attention, and eye movements. The project comprised three components: 1. Modeling visual search for the detection of speed deviation. 2. Perception of moving objects. 3. Exploring the role of eye movements in various visual tasks.

Source record↗

DOE OSTI2023

Out of distribution evaluation for neural image compression

Official code for the NeurIPS 2023 paper "Neural Image Compression: Generalization, Robustness, and Spectral Biases". This code can be used to: - Computing spectral distortion errors of images compressed with traditional codes or neural image compression models (as proposed in the aformentioned paper) - Visualize power spectral densities and fourier heatmaps (as proposed in the aformentioned paper) - Train and test a variety of neural image compression models

Lieberman, KelseyA↗

NASA NTRS2004

Thermodynamic Modeling of a Solid Oxide Fuel Cell to Couple with an Existing Gas Turbine Engine Model

The Controls and Dynamics Technology Branch at NASA Glenn Research Center are interested in combining a solid oxide fuel cell (SOFC) to operate in conjunction with a gas turbine engine. A detailed engine model currently exists in the Matlab/Simulink environment. The idea is to incorporate a SOFC model within the turbine engine simulation and observe the hybrid system's performance. The fuel cell will be heated to its appropriate operating condition by the engine s combustor. Once the fuel cell is operating at its steady-state temperature, the gas burner will back down slowly until the engine is fully operating on the hot gases exhausted from the SOFC. The SOFC code is based on a steady-state model developed by The U.S. Department of Energy (DOE). In its current form, the DOE SOFC model exists in Microsoft Excel and uses Visual Basics to create an I-V (current-voltage) profile. For the project's application, the main issue with this model is that the gas path flow and fuel flow temperatures are used as input parameters instead of outputs. The objective is to create a SOFC model based on the DOE model that inputs the fuel cells flow rates and outputs temperature of the flow streams; therefore, creating a temperature profile as a function of fuel flow rate. This will be done by applying the First Law of Thermodynamics for a flow system to the fuel cell. Validation of this model will be done in two procedures. First, for a given flow rate the exit stream temperature will be calculated and compared to DOE SOFC temperature as a point comparison. Next, an I-V curve and temperature curve will be generated where the I-V curve will be compared with the DOE SOFC I-V curve. Matching I-V curves will suggest validation of the temperature curve because voltage is a function of temperature. Once the temperature profile is created and validated, the model will then be placed into the turbine engine simulation for system analysis.

Brinson, Thomas E.↗

NASA NTRS2010

Project M: Scale Model of Lunar Landing Site of Apollo 17

The basis of the project was creating a scale model representation of the Apollo 17 lunar landing site. Vital components included surface slope characteristics, crater sizes and locations, prominent rocks, and lighting conditions. The model was made for Project M support when evaluating approach and terminal descent as well as when planning surface operations with respect to the terrain. The project had five main mi lestones during the length of the project. The first was examining the best method to use to re-create the Apollo 17 landing site and then reviewing research fmdings with Dr. Tim Crain and EO staff which occurred on June 25, 2010 at a meeting. The second step was formulating a construction plan, budget, and schedule and then presenting the plan for authority to proceed which occurred on July 6,2010. The third part was building a prototype to test materials and building processes which were completed by July 13, 2010. Next was assembling the landing site model and presenting a mid-term construction status report on July 29, 2010. The fifth and final milestone was demonstrating the model and presenting an exit pitch which happened on August 4, 2010. The project was very technical: it needed a lot of research about moon topography, lighting conditions and angles of the sun on the moon, Apollo 17, and Autonomous Landing and Hazard Avoidance Technology (ALHAT), before starting the actual building process. This required using Spreadsheets, searching internet sources and conducting personal meetings with project representatives. This information assisted the interns in deciding the scale of the model with respect to cracks, craters and rocks and their relative sizes as the objects mentioned could interfere with any of the Lunar Landers: Apollo, Project M and future Landers. The project concluded with the completion of a three dimensional scale model of the Apollo 17 Lunar landing site. This model assists Project M members because they can now visualize approach phase, terminal descent phase, and surface phase operations on the physical model. The project had an additional requirement that was also satisfied: the granite table the model was placed on must be returnable to its original condition if needed in the future.

O'Brien, Hollie↗

NASA NTRS2016

Influence of Population Variation of Physiological Parameters in Computational Models of Space Physiology

The earliest manifestations of Visual Impairment and Intracranial Pressure (VIIP) syndrome become evident after months of spaceflight and include a variety of ophthalmic changes, including posterior globe flattening and distension of the optic nerve sheath. Prevailing evidence links the occurrence of VIIP to the cephalic fluid shift induced by microgravity and the subsequent pressure changes around the optic nerve and eye. Deducing the etiology of VIIP is challenging due to the wide range of physiological parameters that may be influenced by spaceflight and are required to address a realistic spectrum of physiological responses. Here, we report on the application of an efficient approach to interrogating physiological parameter space through computational modeling. Specifically, we assess the influence of uncertainty in input parameters for two models of VIIP syndrome: a lumped-parameter model (LPM) of the cardiovascular and central nervous systems, and a finite-element model (FEM) of the posterior eye, optic nerve head (ONH) and optic nerve sheath. Methods: To investigate the parameter space in each model, we employed Latin hypercube sampling partial rank correlation coefficient (LHSPRCC) strategies. LHS techniques outperform Monte Carlo approaches by enforcing efficient sampling across the entire range of all parameters. The PRCC method estimates the sensitivity of model outputs to these parameters while adjusting for the linear effects of all other inputs. The LPM analysis addressed uncertainties in 42 physiological parameters, such as initial compartmental volume and nominal compartment percentage of total cardiac output in the supine state, while the FEM evaluated the effects on biomechanical strain from uncertainties in 23 material and pressure parameters for the ocular anatomy. Results and Conclusion: The LPM analysis identified several key factors including high sensitivity to the initial fluid distribution. The FEM study found that intraocular pressure and intracranial pressure had dominant impact on the peak strains in the ONH and retro-laminar optic nerve, respectively; optic nerve and lamina cribrosa stiffness were also important. This investigation illustrates the ability of LHSPRCC to identify the most influential physiological parameters, which must therefore be well-characterized to produce the most accurate numerical results.

biomechanics↗

NASA NTRS2019

The Effect of Turbulence Modeling on the Mixing Characteristics of Several Fuel Injectors at Hypervelocity Flow Conditions

CFD analysis is presented on the effects of turbulence modeling choices on the mixing characteristics and performance of three fuel injectors at hypervelocity flow conditions. The analyses were carried out with the VULCAN-CFD solver using Reynolds-Averaged Simulations (RAS). The hypervelocity flow conditions match the high Mach number flow of the experiments conducted as a part of the Enhanced Injection and Mixing Project (EIMP) at the NASA Langley Research Center. The three injectors are the baseline configurations used in the experiments and represent three categories of injectors typically considered individually or in combination for fueling high-speed propulsive devices. The current work discusses the impact of the turbulence model and the turbulent Schmidt number on the mixing flow field behavior and the mixing performance as described by the one-dimensional values of the Mach number, total pressure recovery, and the mixing efficiency. Because planar laser induced fluorescence (PLIF) images are available from the EIMP experiments, the sensitivity of the synthetic LIF signal to turbulence modeling choices is also examined to determine whether PLIF can be extended beyond its intended qualitative visualization purpose and used to guide CFD turbulence model and parameter selections. It is found that the mixing performance, as quantified using mixing efficiency, exhibits a strong sensitivity to both turbulence model choice and turbulent Schmidt number value. However, the synthetic LIF signal only demonstrates a modest level of sensitivity, which suggests that PLIF is of limited use for guiding CFD turbulence model and parameter selections.

Drozda, Tomasz G.↗

NASA NTRS2010

Risk Presentation Using the Three Dimensions of Likelihood, Severity, and Level of Control

Traditional hazard analysis techniques utilize a two-dimensional representation of the results determined by relative likelihood and severity of the residual risk. These matrices present a quick-look at the Likelihood (Y-axis) and Severity (X-axis) of the probable outcome of a hazardous event. A three-dimensional method, described herein, utilizes the traditional X and Y axes, while adding a new, third dimension, shown as the Z-axis, and referred to as the Level of Control. The elements of the Z-axis are modifications of the Hazard Elimination and Control steps (also known as the Hazard Reduction Precedence Sequence). These steps are: 1. Eliminate risk through design. 2. Substitute less risky materials for more hazardous materials. 3. Install safety devices. 4. Install caution and warning devices. 5. Develop administrative controls (to include special procedures and training.) 6. Provide protective clothing and equipment. When added to the two-dimensional models, the level of control adds a visual representation of the risk associated with the hazardous condition, creating a tall-pole for the leastwell-controlled failure while establishing the relative likelihood and severity of all causes and effects for an identified hazard. Computer modeling of the analytical results, using spreadsheets and three-dimensional charting gives a visual confirmation of the relationship between causes and their controls.

Watson, Clifford↗

NASA NTRS2003

Using Computational Cognitive Modeling to Diagnose Possible Sources of Aviation Error

We present a computational model of a closed-loop, pilot-aircraft-visual scene-taxiway system created to shed light on possible sources of taxi error. Creating the cognitive aspects of the model using ACT-R required us to conduct studies with subject matter experts to identify experiential adaptations pilots bring to taxiing. Five decision strategies were found, ranging from cognitively-intensive but precise, to fast, frugal but robust. We provide evidence for the model by comparing its behavior to a NASA Ames Research Center simulation of Chicago O'Hare surface operations. Decision horizons were highly variable; the model selected the most accurate strategy given time available. We found a signature in the simulation data of the use of globally robust heuristics to cope with short decision horizons as revealed by errors occurring most frequently at atypical taxiway geometries or clearance routes. These data provided empirical support for the model.

Pilots↗

NASA NTRS1992

Liquid propulsion turbomachinery model testing

For the past few years an extensive experimental program to understand the fluid dynamics of the Space Shuttle Main Engine hot gas manifold has been in progress. This program includes models of the Phase II and II+ manifolds for each of the air and water flow facilities, as well as two different turbine flow paths and two simulated power levels for each manifold. All models are full-scale (geometric). The water models are constructed partially of acrylic to allow flow visualization. The intent of this paper is to discuss the concept, including the test objectives, facilities, and models, and to summarize the data for an example configuration, including static pressure data, flow visualization, and the solution of a specific flow problem.

Mcdaniels, David M.↗

NASA NTRS2016

Probabilistic Modeling Of Ocular Biomechanics In VIIP: Risk Stratification

Visual Impairment and Intracranial Pressure (VIIP) syndrome is a major health concern for long-duration space missions. Currently, it is thought that a cephalad fluid shift in microgravity causes elevated intracranial pressure (ICP) that is transmitted along the optic nerve sheath (ONS). We hypothesize that this in turn leads to alteration and remodeling of connective tissue in the posterior eye which impacts vision. Finite element (FE) analysis is a powerful tool for examining the effects of mechanical loads in complex geometries. Our goal is to build a FE analysis framework to understand the response of the lamina cribrosa and optic nerve head to elevations in ICP in VIIP. To simulate the effects of different pressures on tissues in the posterior eye, we developed a geometric model of the posterior eye and optic nerve sheath and used a Latin hypercubepartial rank correlation coef-ficient (LHSPRCC) approach to assess the influence of uncertainty in our input parameters (i.e. pressures and material properties) on the peak strains within the retina, lamina cribrosa and optic nerve. The LHSPRCC approach was repeated for three relevant ICP ranges, corresponding to upright and supine posture on earth, and microgravity [1]. At each ICP condition we used intraocular pressure (IOP) and mean arterial pressure (MAP) measurements of in-flight astronauts provided by Lifetime Surveillance of Astronaut Health Program, NASA Johnson Space Center. The lamina cribrosa, optic nerve, retinal vessel and retina were modeled as linear-elastic materials, while other tissues were modeled as a Mooney-Rivlin solid (representing ground substance, stiffness parameter c1) with embedded collagen fibers (stiffness parameters c3, c4 and c5). Geometry creationmesh generation was done in Gmsh [2], while FEBio was used for all FE simulations [3]. The LHSPRCC approach resulted in correlation coefficients in the range of 1. To assess the relative influence of the uncertainty in an input parameter on the peak strains, we ranked and then normalized these coefficients, considering that normalized values 0.5 implied a substantial influence on the range of the peak strains in the optic nerve head (ONH). IOP and ICP were found to have a major influence on the peak strains in the ONH, as did optic nerve and LC stiffness. Interestingly, the stiffness of the sclera far from the scleral canal did not have a large influence on peak strains in ONH tissues; however, the collagen fiber stiffness in the peripapillary sclera and annular ring both influenced the peak strains within the ONH. We have created a physiologically relevant model that incorporated collagen fibers to study the effects of elevated ICP. Elevated ICP resulted in strains in the optic nerve that are not predicted to occur on earth: the upright or supine conditions. We found that IOP, ICP, lamina cribrosa stiffness and optic nerve stiffness had the highest association with these extreme strains in the ONH. These extreme strains may activate mechanosensitive cells that induce tissue remodeling and are a risk factor for the development of VIIP.

biomechanics↗