Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “model exploration”

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 955 records · Page 53

New Efforts to Update NASA's Global Reference Atmospheric Models (GRAM)

NASA is at the forefront of planetary exploration. The inability to test planetary spacecraft in the flight environment prior to a mission requires engineers to rely on ground-based testing and models of the vehicle and expected environments. One of the most widely used engineering models of the atmosphere for many NASA projects is the Global Reference Atmospheric Model (GRAM) developed by the NASA Marshall Space Flight Center (MSFC). Over the past decade GRAM upgrades and maintenance have depended on inconsistent and waning project-specific support. Recently, the NASA Science Mission Directorate agreed to provide funding support in Fiscal Year 2018 and 2019 to upgrade the GRAMs. This poster summarizes the objectives, tasks and milestones of this effort.

Dwyer Cianciolo, Alicia↗

Model-driven software verification

In this paper we explore a different approach to software verification. With this approach, a software application can be included, without substantial change, into a verification test-harness and then verified directly, while presearving the ability to apply data abstraction techniques. Only the test-harness is written in the language of the model checker.

software verification↗

Quantum Hardware-Enabled Molecular Dynamics via Transfer Learning

The ability to perform ab initio molecular dynamics simulations using potential energy surfaces provided by quantum computers would open the door to virtually exact dynamics for a variety of chemical and biochemical systems, with impacts on catalysis and biophysics. Nonetheless, performing molecular dynamics on surfaces produced by quantum hardware has been hampered by the noisy energies typically produced by quantum computers and challenges associated with computing gradients and scaling to large systems interest. A recent set of advances in machine learning, known as transfer learning, provides a new path forward for molecular dynamics simulations on quantum hardware. Transfer learning offers a workaround, where one first trains models on larger, less accurate classical datasets and then refines them on smaller, more accurate quantum datasets. We explore this approach by training machine learning models to predict a molecule's potential energy based on its geometric structure using Behler-Parrinello neural networks. When successfully trained, the model enables energy gradient predictions necessary for dynamic simulations. To reduce the quantum resources needed, the model is initially trained with data derived from classical density functional theory and subsequently refined with a smaller dataset obtained from a variational quantum eigensolver optimization of the unitary coupled cluster ansatz. We show that this approach significantly reduces the size of the needed quantum training dataset while capturing the high accuracies needed within quantum chemistry simulations. The success of this two-step training method opens more opportunities to apply machine learning models on quantum data, a significant stride towards efficient quantum-classical hybrid computational models.

quantum computing↗

Adaptive Modeling of the International Space Station Electrical Power System

Software simulations provide NASA engineers the ability to experiment with spacecraft systems in a computer-imitated environment. Engineers currently develop software models that encapsulate spacecraft system behavior. These models can be inaccurate due to invalid assumptions, erroneous operation, or system evolution. Increasing accuracy requires manual calibration and domain-specific knowledge. This thesis presents a method for automatically learning system models without any assumptions regarding system behavior. Data stream mining techniques are applied to learn models for critical portions of the International Space Station (ISS) Electrical Power System (EPS). We also explore a knowledge fusion approach that uses traditional engineered EPS models to supplement the learned models. We observed that these engineered EPS models provide useful background knowledge to reduce predictive error spikes when confronted with making predictions in situations that are quite different from the training scenarios used when learning the model. Evaluations using ISS sensor data and existing EPS models demonstrate the success of the adaptive approach. Our experimental results show that adaptive modeling provides reductions in model error anywhere from 80% to 96% over these existing models. Final discussions include impending use of adaptive modeling technology for ISS mission operations and the need for adaptive modeling in future NASA lunar and Martian exploration.

Thomas, Justin Ray↗

Application of AI in the NAS - the Rationale for AI-Enhanced Airspace Management

This paper extends on the initial findings of "Application of Artificial Intelligence in the National Airspace System: A Primer" (Stroup & Niewoehner: Herndon, VA; ICNS-2019), and looks at why the current technologies, enterprise architecture, and future program plans may not be enough to address persistent operational challenges. This paper further explores why emergent operational concepts, business models, and demand profiles may necessitate AI-enhanced Communications, Navigation and Communications (CNS) infrastructure to disrupt current operational impediments. European airspace, as well as the NAS, has similar challenges. Key challenges explored in this study include: quantifiable improvements to NAS capacity, efficiency, and resiliency; traffic flow management of diverse users; UTM-ATM airspace integration; equitable access to airspace; and airborne-ground interoperability of AI applications. Finally, we examine why trustworthiness and resiliency will be key mileposts on the regulatory pathway to AI certification.

Aviation↗

Global empirical models of ionospheric electron temperature in the upper F-region and plasmasphere based on in situ measurements from the Atmosphere Explorer-C, ISIS-1 and ISIS-2 satellites

Langmuir probe measurements of electron temperature, T sub e, in the vicinity of 300, 400, 1400 and 3000 km from the Atmosphere Explorer-C and the ISIS satellites have been employed to construct empirical models of the global distribution of T sub e at each of these altitudes. Legendre polynomials are employed to describe the observations at solstice and equinox in terms of dip latitude and local time. Sources of T sub e variations, such as solar activity, magnetic activity and longitude are found to be of second order importance, although they are resolvable in some cases by comparisons of the data with the model. The behavior of T sub e at the altitudes of these models is discussed in terms of its implications for our understanding of the energy exchange between the F-region and the plasmasphere.

Brace, L. H.↗

Introduction to Medical Extensible Dynamic Probabilistic Risk Assessment Tool (MEDPRAT)

A key component in the development of NASA Human Research Programs (HRP) next generation risk model, the Medical Extensible Dynamic Probabilistic Risk Assessment Tool (MEDPRAT) intends to deliver a means to quantify how HRP products impact astronaut medical and health risks. MEDPRAT is extensible to the majority of exploration missions. Similar to other risk models, the tool utilizes the available space and terrestrial medical data. MEDPRAT is designed to be extended with additional human health research information, medical equipment, space and terrestrial standards and practices to assess space flight medical risk in a manner consistent with other risk measures used in spacecraft and mission design. This tool provides risk-based medical system design information necessary to evaluate new technologies, procedures, research insights and mission plans.

physiological response↗

X-Ray Gas Temperatures in the Arc Clusters MS0440+204 and MS0302+1658

The cluster of galaxies MS0440+02, originally discovered through its X-ray emission, was part of an optical observational program to search for arcs and arclets in a complete sample of X-ray luminous, medium-distant clusters of galaxies. Mauna Kea CCD images of MS0440+02 showed a remarkable optical morphology. The core of the cluster contains 6 bright galaxies and numerous fainter ones embedded in a low surface brightness halo. Besides, MS0440+02 is the most spectacular example that we have found of an arc system in a compact condensed cluster, with arcs symmetrically distributed to draw almost perfect circles around the cluster center. Giant arcs are magnified images of distant galaxies, gravitationally distorted by massive foreground clusters. It is of great importance to compare the results of the lensing studies with those derived from X-ray observations, as the two are independent methods of studying the mass distribution. Thus MS0440+02 was the ideal target to obtain temperature measurement with ASCA and good spatial resolution X-ray observations with ROSAT. The X-ray data have been used in conjunction with Hubble Space Telescope observations to put more stringent constrains on the mass estimates. Most of the different wavelength datasets have been reduced and analyzed. Mass determinations have been separately obtained from galaxy virial motions and X-ray profile fitting using the cluster gas temperature as measured by the ASCA satellite. Assuming that the hot gas is in hydrostatic equilibrium and in a spherical potential, we find from the X-ray data a mass distribution profile that is well described by a Beta model. From the multiple images formed by gravitational lensing (HST data) using the modelling of the gravitational lensed arcs, we have derived Beta model. To reconcile the mass estimates we have explored the possibility of having a supercluster surrounding the MOS0440 cluster, that is a model with two isothermal spheres, one embedded inside the other. These results have been published or are in press.

Gioia, Isabella M.↗

A Comparison of Geographic Information Systems, Complex Networks, and Other Models for Analyzing Transportation Network Topologies

This report reviews six classes of models that are used for studying transportation network topologies. The report is motivated by two main questions. First, what can the "new science" of complex networks (scale-free, small-world networks) contribute to our understanding of transport network structure, compared to more traditional methods? Second, how can geographic information systems (GIS) contribute to studying transport networks? The report defines terms that can be used to classify different kinds of models by their function, composition, mechanism, spatial and temporal dimensions, certainty, linearity, and resolution. Six broad classes of models for analyzing transport network topologies are then explored: GIS; static graph theory; complex networks; mathematical programming; simulation; and agent-based modeling. Each class of models is defined and classified according to the attributes introduced earlier. The paper identifies some typical types of research questions about network structure that have been addressed by each class of model in the literature.

Alexandrov, Natalia↗

Numerical experiments with model monophyletic and paraphyletic taxa

The problem of how accurately paraphyletic taxa versus monophyletic (i.e., holophyletic) groups (clades) capture underlying species patterns of diversity and extinction is explored with Monte Carlo simulations. Phylogenies are modeled as stochastic trees. Paraphyletic taxa are defined in an arbitrary manner by randomly choosing progenitors and clustering all descendants not belonging to other taxa. These taxa are then examined to determine which are clades, and the remaining paraphyletic groups are dissected to discover monophyletic subgroups. Comparisons of diversity patterns and extinction rates between modeled taxa and lineages indicate that paraphyletic groups can adequately capture lineage information under a variety of conditions of diversification and mass extinction. This suggests that these groups constitute more than mere "taxonomic noise" in this context. But, strictly monophyletic groups perform somewhat better, especially with regard to mass extinctions. However, when low levels of paleontologic sampling are simulated, the veracity of clades deteriorates, especially with respect to diversity, and modeled paraphyletic taxa often capture more information about underlying lineages. Thus, for studies of diversity and taxic evolution in the fossil record, traditional paleontologic genera and families need not be rejected in favor of cladistically-defined taxa.

Non-NASA Center↗

Scaling Equations for Ballistic Modeling of Solid Rocket Motor Case Breach

This paper explores the development of a series of scaling equations that can take a known nominal motor performance and scale it for small and growing case failures. This model was developed for the Malfunction-Turn Study as part of Return to Flight activities for the Space Shuttle program. To verify the model, data from the Challenger accident (STS- 51L) were used. The model is able to predict the motor performance beyond the last recorded Challenger data and show how the failed right hand booster would have performed if the vehicle had remained intact.

McMillin, Joshua E.↗

New Efforts to Update NASA's Global Reference Atmospheric Models (GRAM)

NASA is at the forefront of planetary exploration. The inability to test planetary space-craft in the flight environment prior to a mission requires engineers to rely on ground-based testing and models of the vehicle and expected environments. One of the most widely used engineering models of the atmosphere for many NASA projects is the Global Reference Atmospheric Model (GRAM) developed by the NASA Marshall Space Flight Center (MSFC). Over the past decade GRAM upgrades and maintenance have depended on inconsistent and waning project-specific support. Recently, the NASA Science Mission Directorate (SMD) has agreed to provide funding support in Fiscal Year 2018 and 2019 to upgrade the GRAMs.

Justh, H. L.↗

Quantifying Errors in 3D CME Parameters Derived from Synthetic Data Using White-Light Reconstruction Techniques

Current efforts in space weather forecasting of CMEs have been focused on predicting their arrival time and magnetic structure. To make these predictions, methods have been developed to derive the true CME speed, size, position, and mass, among others. Difficulties in determining the input parameters for CME forecasting models arise from the lack of direct measurements of the coronal magnetic fields and uncertainties in estimating the CME 3D geometric and kinematic parameters after eruption. White-light coronagraph images are usually employed by a variety of CME reconstruction techniques that assume more or less complex geometries. This is the first study from our International Space Science Institute (ISSI) team “Understanding Our Capabilities in Observing and Modeling Coronal Mass Ejections”, in which we explore how subjectivity affects the 3D CME parameters that are obtained from the Graduated Cylindrical Shell (GCS) reconstruction technique, which is widely used in CME research. To be able to quantify such uncertainties, the “true” values that are being fitted should be known, which are impossible to derive from observational data. We have designed two different synthetic scenarios where the “true” geometric parameters are known in order to quantify such uncertainties for the first time. We explore this by using two sets of synthetic data: 1) Using the ray-tracing option from the GCS model software itself, and 2) Using 3D magnetohydrodynamic (MHD) simulation data from the Magnetohydrodynamic Algorithm outside a Sphere code. Our experiment includes different viewing configurations using single and multiple viewpoints. CME reconstructions using a single viewpoint had the largest errors and error ranges overall for both synthetic GCS and simulated MHD white-light data. As the number of viewpoints increased from one to two, the errors decreased by approximately 4° in latitude, 22° in longitude, 14° in tilt, and 10° in half-angle. Our results quantitatively show the critical need for at least two viewpoints to be able to reduce the uncertainty in deriving CME parameters. We did not find a significant decrease in errors when going from two to three viewpoints for our specific hypothetical three spacecraft scenario using synthetic GCS white-light data. As we expected, considering all configurations and numbers of viewpoints, the mean absolute errors in the measured CME parameters are generally significantly higher in the case of the simulated MHD white-light data compared to those from the synthetic white-light images generated by the GCS model. We found the following CME parameter error bars as a starting point for quantifying the minimum error in CME parameters from white-light reconstructions: Δθ (latitude)=6° +2° -3° , Δϕ (longitude)=11° +18° -6° , Δγ (tilt)=25° +8° -7° , Δx (half-angle)=10° +12° -6° , Δh (height)=0.6 +1.2 -0.4 R ⨀ , and Δκ (ratio)=0.1 +0.03 -0.02 .

Coronal mass ejections↗

Quantifying Errors in 3D CME Parameters Derived From Synthetic Data Using White-Light Reconstruction Techniques

Current efforts in space weather forecasting of CMEs have been focused on predicting their arrival time and magnetic structure. To make these predictions, methods have been developed to derive the true CME speed, size, position, and mass, among others. Difficulties in determining the input parameters for CME forecasting models arise from the lack of direct measurements of the coronal magnetic fields and uncertainties in estimating the CME 3D geometric and kinematic parameters after eruption. White-light coronagraph images are usually employed by a variety of CME reconstruction techniques that assume more or less complex geometries. This is the first study from our International Space Science Institute (ISSI) team “Understanding Our Capabilities in Observing and Modeling Coronal Mass Ejections”, in which we explore how subjectivity affects the 3D CME parameters that are obtained from the Graduated Cylindrical Shell (GCS) reconstruction technique, which is widely used in CME research. To be able to quantify such uncertainties, the “true” values that are being fitted should be known, which are impossible to derive from observational data. We have designed two different synthetic scenarios where the “true” geometric parameters are known in order to quantify such uncertainties for the first time. We explore this by using two sets of synthetic data: 1) Using the ray-tracing option from the GCS model software itself, and 2) Using 3D magnetohydrodynamic (MHD) simulation data from the Magnetohydrodynamic Algorithm outside a Sphere code. Our experiment includes different viewing configurations using single and multiple viewpoints. CME reconstructions using a single viewpoint had the largest errors and error ranges overall for both synthetic GCS and simulated MHD white-light data. As the number of viewpoints increased from one to two, the errors decreased by approximately 4° in latitude, 22° in longitude, 14° in tilt, and 10° in half-angle. Our results quantitatively show the critical need for at least two viewpoints to be able to reduce the uncertainty in deriving CME parameters. We did not find a significant decrease in errors when going from two to three viewpoints for our specific hypothetical three spacecraft scenario using synthetic GCS white-light data. As we expected, considering all configurations and numbers of viewpoints, the mean absolute errors in the measured CME parameters are generally significantly higher in the case of the simulated MHD white-light data compared to those from the synthetic white-light images generated by the GCS model. We found the following CME parameter error bars as a starting point for quantifying the minimum error in CME parameters from white-light reconstructions: Δθ (latitude)=6° +2° -3° , Δϕ (longitude)=11° +18° -6° , Δγ (tilt)=25° +8° -7° , Δx (half-angle)=10° +12° -6° , Δh (height)=0.6 +1.2 -0.4 R ⨀ , and Δκ (ratio)=0.1 +0.03 -0.02 .

Coronal mass ejections↗

Neurovascular Responses to Simulated Deep Space Radiation in a Human Organ-on-a-Chip Model

A major health risk for human deep space exploration is central nervous system (CNS) damage by galactic cosmic ray radiation. Simulated galactic cosmic rays or their components, especially the high-linear energy transfer (LET) particles such as 56Fe ions, have been shown to cause CNS damage, neuroinflammation and cognitive dysfunction in rodent models, but their effects on human CNS remain to be investigated. CNS damage from any insult, including ionizing radiation, is partially mediated by the blood-brain barrier (BBB), which regulates interactions between CNS and the rest of the body. The main cellular regulators of BBB permeability are astrocytes, which also modulate neuroinflammation. However, there have been few studies on BBB and astrocyte functions in regulating CNS responses, especially in human tissue analogs. Therefore, we utilized a high-throughput 3D organ-on-a-chip system, seeded with human induced pluripotent stem cell-derived astrocytes and brain endothelial cells, or brain endothelial cells alone, to study human neurovascular responses to simulated deep space radiation. We investigated the permeability and morphology of vascular structures formed by endothelial cells, as well as oxidative stress and secreted cytokines and chemokine levels over 1-7 days after irradiation with 0.25 – 0.5 Gy 5-ion simplified simulated galactic cosmic rays or 0.3 – 0.8 Gy high-LET 600 MeV/n 56Fe particles, and compared the outcomes to low-LET X-ray irradiation. We observed that simulated deep space radiation caused delayed astrocyte activation in a pattern resembling CNS responses to brain injury, caused oxidative stress and the production of inflammatory cytokines, and compromised BBB integrity by damaging tight junctions, thus increasing vascular permeability. Furthermore, our results indicate that astrocytes have a dual role in regulating radiation responses: they exacerbate blood-brain barrier permeability early after irradiation, followed by switching to a more protective scar-like phenotype by reducing oxidative stress and pro-inflammatory cytokine and chemokine secretion. In a follow-up study using the same platform, we investigated the dose-rate effects of ionizing radiation, by exposing our model to chronic, low dose-rate, gamma radiation. Our model was significantly improved by adding additional cell types composing the BBB, modelling immune cell infiltration into the brain, and studying the effect of an antioxidant, to measure more complex outcomes and model more closely the effect of deep space radiation on the human BBB. In summary, our results present a human neurovascular model for space radiation studies and potential future automated payload adaptation, and suggest astrocyte regulatory mechanisms as targets for countermeasures to mitigate human neurovascular impairments during deep space exploration.

Ionizing radiation↗

Physics of the Inner Heliosphere 1-10 R(sub s): Plasma Diagnostics and Models

While the mechanisms responsible for heating the solar corona and accelerating the fast and slow solar wind streams are still unknown, model computations offer the only means for exploring and predicting the properties of such mechanisms in light of the empirical constraints currently available. During the time covered by this grant, modeling and data analysis efforts were aimed at: 1) the study of the propagation and damping of ion-cyclotron waves in the fast solar wind 2) the exploration of the role of instabilities in the development of temperature anisotropies in the inner corona 3) the coupling of neutral hydrogen and protons in the fast solar wind 4) the morphology of the source region of the solar wind. Summarized are some of the highlights of these studies. Two PhD theses by Xing Li and Lorraine Allen were partially supported by this grant.

Habbal, Shadia R.↗

Planets of the solar system

Venera and Mariner spacecraft and ground based radio astronomy and spectroscopic observations of the atmosphere and surface of venus are examined. The composition and structural parameters of the atmosphere are discussed as the basis for development of models and theories of the vertical structure of the atmosphere, the greenhouse effect, atmospheric circulation and cloud cover. Recommendations for further meteorological studies are given. Ground based and Pioneer satellite observation data on Jupiter are explored as well as calculations and models of the cloud structure, atmospheric circulation and thermal emission field of Jupiter.

Kondratyev, K. Y.↗

Uncertainty in Model Predictions of Vibrio Vulnificus Response to Climate Variability and Change: A Chesapeake Bay Case Study

The effect that climate change and variability will have on waterborne bacteria is a topic of increasing concern for coastal ecosystems, including the Chesapeake Bay. Surface water temperature trends in the Bay indicate a warming pattern of roughly 0.3-0.4 C per decade over the past 30 years. It is unclear what impact future warming will have on pathogens currently found in the Bay, including Vibrio spp. Using historical environmental data, combined with three different statistical models of Vibrio vulnificus probability, we explore the relationship between environmental change and predicted Vibrio vulnificus presence in the upper Chesapeake Bay. We find that the predicted response of V. vulnificus probability to high temperatures in the Bay differs systematically between models of differing structure. As existing publicly available datasets are inadequate to determine which model structure is most appropriate, the impact of climatic change on the probability of V. vulnificus presence in the Chesapeake Bay remains uncertain. This result points to the challenge of characterizing climate sensitivity of ecological systems in which data are sparse and only statistical models of ecological sensitivity exist.

model predictions↗