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At least 271 records · Page 15

Auroral zone electric fields from DE 1 and 2 at magnetic conjunctions

Nearly simultaneous measurements of auroral zone electric fields are obtained by the Dynamics Explorer spacecraft at altitudes below 900 km and above 4,500 km during magnetic conjunctions. The measured electric fields are usually perpendicular to the magnetic field lines. The north-south meridional electric fields are projected to a common altitude by a mapping function which accounts for the convergence of the magnetic field lines. When plotted as a function of invariant latitude, graphs of the projected electric fields measured by both DE-1 and DE-2 show that the large-scale electric field is the same at both altitudes, as expected. Superimposed on the large-scale fields, however, are small-scale features with wavelengths less than 100 km which are larger in magnitude at the higher altitude. Fourier transforms of the electric fields show that the magnitudes depend on wavelength. Outside of the auroral zone the electric field spectrums are nearly identical. But within the auroral zone the high and low altitude electric fields have a ratio which increases with the reciprocal of the wavelength. The small-scale electric field variations are associated with field-aligned currents. These currents are measured with both a plasma instrument and magnetometer on DE-1.

Weimer, D. R.↗

Lift distributions for a 3-dimensional steady blade-vortex interaction

The interaction of a horizontally mounted V23010-1.58 semispan airfoil (simulating a helicopter rotor blade) with a tip vortex shed by a vertically mounted upstream vortex-generating wing (VGW) is investigated experimentally at 60 m/s (dynamic pressure 2.2 kPa, Reynolds number 850,000, and Mach number 0.17) in the NASA Ames 7 x 10-ft wind tunnel. The velocity field near the blade is determined using a three-dimensional zoom LDV; the spanwise lift distribution is measured by strain gages; and the results are compared with the predictions of the panel computer code VSAERO (Maskew, 1982) in graphs. Features noted include localized loss of lift due to the presence of residual VGW wake, loss of lift far inboard on the blade (indicating the large domain of VGW vorticity), little change in total lift with variations in vortex strength, and good agreement between VSAERO and experiment in overall lift distribution but not in all geometric variations).

Dunagan, Stephen E.↗

Application of Modified Meshgraphnets for Subsurface Prediction during CO2 Sequestration

In the face of the increasingly dire consequences of anthropogenic climate change, capturing and storing carbon dioxide is paramount. However, several impediments exist to the safe and effective subsurface storage of CO2, such as cost of transport, identification of suitable sites for subsurface storage, and assessment of long-term risk from storage in subsurface aquifers. Accurate subsurface modeling is necessary to ensure that CO2 storage is both safe and effective. Still, such modeling has traditionally required either substantial time and computational power (numerical simulation) or a substantial amount of pre-existing data for training (machine learning models). Additionally, these models lack flexibility in dealing with both changes in discretization of the input data and generalizability beyond the data on which they are trained. In order to address these issues, this research applies graph neural networks (GNNs) to predict subsurface saturation and pressure during CO₂ injection in a model of the Illinois Basin-Decatur Project (IBDP). GNNs provide a flexible, intuitive method for representing and manipulating complex unstructured data, which is often found in many practical domain problems such as fluid flow and subsurface characterization. These unstructured grids are easily represented in GNNs by representing spatially-localized features such as permeability, porosity, saturation, and pressure as nodes in a graph and relationships between these properties as edges connecting these nodes. This research applies a specific GNN model called MeshGraphNets (MGN) to model the change in CO2 saturation and pressure over a 50-month time period (36 months of injection, 14 months post-injection). The MGN model leverages a message passing process that allows the network to learn both the spatial and temporal dynamics of this system simultaneously. Additionally, training on a limited dataset (64 realizations, 20 time points each) resulted in a high degree of accuracy in saturation prediction both within the same timeframe as the training (20 months, 0.039 average RMSE) and when projecting out to the end of injection (36 months, 0.053 average RMSE). Temporal predictions such as those generated by MGNs and other similar models are prone to accumulated error over time; in order to address this, a multi-step rollout (MSR) training process was applied to calculate training loss. This method mimics the forward prediction during inference by “rolling out” multiple time points in a single training step using the previous prediction as input to the MGN model. By calculating the loss several time steps forward from the current prediction, the model is forced to find a more stable state over time. Application of MSR to the MGN model resulted in an average 15% reduction in inference error over time during forward prediction. This study showcases the immense potential of GNNs as a game-changing methodology for predicting pressure and saturation evolution in CCS projects, ultimately paving the way for more sustainable and effective carbon storage solutions. Presentation prepared for the 2024 AiChE Annual Meeting, October 27 to November 1 2024, San Diego, CA.

Holcomb, Paul↗

Cognitive engineering models in space systems

NASA space systems, including mission operations on the ground and in space, are complex, dynamic, predominantly automated systems in which the human operator is a supervisory controller. Models of cognitive functions in complex systems are needed to describe human performance and form the theoretical basis of operator workstation design, including displays, controls, and decision aids. Currently, there several candidate modeling methodologies. They include the Rasmussen abstraction/aggregation hierarchy and decision ladder, the goal-means network, the problem behavior graph, and the operator function model. The research conducted under the sponsorship of this grant focuses on the extension of the theoretical structure of the operator function model and its application to NASA Johnson mission operations and space station applications. The initial portion of this research consists of two parts. The first is a series of technical exchanges between NASA Johnson and Georgia Tech researchers. The purpose is to identify candidate applications for the current operator function model; prospects include mission operations and the Data Management System Testbed. The second portion will address extensions of the operator function model to tailor it to the specific needs of Johnson applications. At this point, we have accomplished two things. During a series of conversations with JSC researchers, we have defined the technical goal of the research supported by this grant to be the structural definition of the operator function model and its computer implementation, OFMspert. Both the OFM and OFMspert have matured to the point that they require infrastructure to facilitate use by researchers not involved in the evolution of the tools. The second accomplishment this year was the identification of the Payload Deployment and Retrieval System (PDRS) as a candidate system for the case study. In conjunction with government and contractor personnel in the Human-Computer Interaction Lab, the PDRS was identified as the most accessible system for the demonstration. Pursuant to this a PDRS simulation was obtained from the HCIL and an initial knowledge engineering effort was conducted to understand the operator's tasks in the PDRS application. The preliminary results of the knowledge engineering effort and an initial formulation of an operator function model (OFM) are contained in the appendices.

Mitchell, Christine M.↗

Probabilistic structural analysis of space propulsion system LOX post

The probabilistic structural analysis program NESSUS (Numerical Evaluation of Stochastic Structures Under Stress; Cruse et al., 1988) is applied to characterize the dynamic loading and response of the Space Shuttle main engine (SSME) LOX post. The design and operation of the SSME are reviewed; the LOX post structure is described; and particular attention is given to the generation of composite load spectra, the finite-element model of the LOX post, and the steps in the NESSUS structural analysis. The results are presented in extensive tables and graphs, and it is shown that NESSUS correctly predicts the structural effects of changes in the temperature loading. The probabilistic approach also facilitates (1) damage assessments for a given failure model (based on gas temperature, heat-shield gap, and material properties) and (2) correlation of the gas temperature with operational parameters such as engine thrust.

Newell, J. F.↗

The dynamics of particle disks. III - Dense and spinning particle disks

The kinetic theory of planetary rings developed by Araki and Tremaine (1986) and Araki (1988) is extended and refined, with a focus on the implications of finite particle size: (1) nonlocal collisions and (2) finite filling factors. Consideration is given to the derivation of the equations for the local steady state, the low-optical-depth limit, and the steady state at finite filling factors (including the effects of collision inelasticity, spin degrees of freedom, and self-gravity). Numerical results are presented in extensive graphs and characterized in detail. The importance of distinguishing effects (1) and (2) at low optical depths is stressed, and the existence of vertical density profiles with layered structures at high filling factors is demonstrated.

Araki, Suguru↗

A Scale‐Adaptive Urban Hydrologic Framework: Incorporating Network‐Level Storm Drainage Pipes Representation

Abstract Below‐ground urban stormwater networks (BUSNs) significantly influence urban flood dynamics, yet their representation at the watershed or larger scales remains challenging. We introduce a scalable urban hydrologic framework that centers on a novel network‐level BUSN representation, balancing the needs for physical basis, parameter parsimony, and computational efficiency. Our framework conceptualizes an urban watershed into four interacting zones: hillslopes (natural), storm‐sewersheds (urban), a sub‐network channel (tributaries), and a main channel. We develop an innovative Graph Theory‐based algorithm to derive network‐level BUSN parameters from publicly available datasets, enabling efficient, scalable parameterization. We demonstrate this framework's applicability at nine representative watersheds in the Houston metropolitan region, USA, with urban imperviousness ranging from 0% to 64% and drainage areas ranging from 24 to 302 . Our model achieves satisfying computational efficiency, completing hourly time step simulations for 18 years in less than 5 sec per watershed on a standard PC. Validation against observed daily streamflow confirms that the model can capture small‐to‐large flood peaks and seasonal and annual water balance over these watersheds. Comparisons with the National Water Model show better performance in predicting flood peaks and overall water balance, underscoring the promises of our new framework for urban hydrologic modeling at large scales. Furthermore, analysis reveals nonlinear relationships between BUSNs' designed capacities and flood reduction effects. Our approach bridges the gap between detailed hydraulic and large‐scale hydrologic models, providing a valuable tool for urban flood prediction and management across broader spatial and temporal scales.

54 ENVIRONMENTAL SCIENCES↗

Prediction of carbon nanostructure mechanical properties and the role of defects using machine learning

Graphene-based nanostructures hold immense potential as strong and lightweight materials, however, their mechanical properties such as modulus and strength are difficult to fully exploit due to challenges in atomic-scale engineering. This study presents a database of over 2,000 pristine and defective nanoscale CNT bundles and other graphitic assemblies, inspired by microscopy, with associated stress–strain curves from reactive molecular dynamics (MD) simulations using the reactive INTERFACE force field (IFF-R). These 3D structures, containing up to 80,000 atoms, enable detailed analyses of structure-stiffness-failure relationships. By leveraging the database and physics- and chemistry-informed machine learning (ML), accurate predictions of elastic moduli and tensile strength are demonstrated at speeds 1,000 to 10,000 times faster than efficient MD simulations. Hierarchical Graph Neural Networks with Spatial Information (HS-GNNs) are introduced, which integrate chemistry knowledge. HS-GNNs as well as extreme gradient boosted trees (XGBoost) achieve forecasts of mechanical properties of arbitrary carbon nanostructures with only 3 to 6% mean relative error. The reliability equals experimental accuracy and is up to 20 times higher than other ML methods. Predictions maintain 8 to 18% accuracy for large CNT bundles, CNT junctions, and carbon fiber cross-sections outside the training distribution. The physics- and chemistry-informed HS-GNN works remarkably well for data outside the training range while XGBoost works well with limited training data inside the training range. The carbon nanostructure database is designed for integration with multimodal experimental and simulation data, scalable beyond 100 nm size, and extendable to chemically similar compounds and broader property ranges. The ML approaches have potential for applications in structural materials, nanoelectronics, and carbon-based catalysts.

Winetrout, Jordan J.↗

Templates for Risk Informed Assurance with Curvature Embeddings (TRACE)

We investigate recovery of geometric structure from networks embedded in manifolds with spatially varying curvature, extending the constant-curvature framework of Lubold et al. (2023). Our work supports cascade risk assessment in critical infrastructure through the Templates for Risk-informed Assurance with Curvature Embeddings (TRACE) framework. Simulations on a bi-modal Gaussian surface show that constant-curvature methods yield weighted averages shaped by clique patterns, while hierarchical clustering identifies distinct regimes. Localized estimation, however, reveals boundary contamination in transitional regions. To address heterogeneity, we develop distance metrics for graphs with edge and node features, proving their metric validity, and validate them via deterministic graph generation from canonical tilings. We further propose a diffusion-based anomaly detection approach that treats networks as glued manifolds, using curvature discontinuities to detect structural anomalies. Employing the carré-du-champ operator and scalar curvature, we achieve robust anomaly discrimination, demonstrated on the Singapore Water Treatment (SWaT) dataset with joint network-traffic and sensor features. Integration with TRACE reveals how curvature shapes cascade dynamics: positive curvature impedes, while negative curvature accelerates propagation. This geometric perspective provides interpretable risk metrics and visualization tools for critical infrastructure managers. While full validation remains ongoing, our contributions establish a rigorous foundation for geometric analysis of network resilience and cascade vulnerability.

97 MATHEMATICS AND COMPUTING↗

The use of underwater dynamometry to evaluate two space suits

Four Astronauts were instrumented and donned one of three extravehicular activity (EVA) suits: the currently in use shuttle suit (STS), the Mark III (MK3), and the AX5. The STS was used as the comparison suit because of approved status. Each subject performed ten different exercises in each suit in three different manners (static, dynamic and fatigue) in two different environments, WETF and KC-135 (KC-135 not completed as of this report). Data were recorded from a flight qualified underwater dynamometer (Cybex power head) with a TEAC multichannel recorder/tape and downloaded into the VAX computer system for analysis. Also direct hard copy strip chart recordings were made for backup comparisons. Data were analyzed using the ANOVA procedure and results were graphed and reported without interpretation to the NASA/JSC ABL manager.

Squires, W. G.↗

Results of dynamic testing of GP-B spherical gyroscopes

Laboratory tests of the spherical electrostatically levitated cryogenically cooled coated gyroscope being developed for the Gravity Probe B (GP-B) spacecraft (Bardas et al., 1986) are reported. Spin speed and the dc components of the trapped magnetic field are measured with three orthogonal pickup loops attached to SQUID detectors as the levitated gyro is brought up to speed by an He gas jet. Data on the spin-vector time history, mass unbalance, higher rotor-shape harmonics, and spin-vector position are presented in extensive graphs and characterized in detail, and a mathematical model of the electrostatic suspension torques is derived. Prototype gyro 86-4 is found to have mass unbalance within the range required for the GP-B mission (to detect the geodetic and motional effects predicted by general relativity theory).

Keiser, G. M.↗

Ion heating in the cusp

Data from satellite observations and theoretical simulations of ion heating in the magnetospheric cusp region are compiled in tables, graphs, and diagrams and discussed. Consideration is given to the mixing of ionospheric and magnetosheath plasmas, the instability of downward-flowing ring distributions of H(+) and He(2+) to lower-hybrid waves, and oxygen and hydrogen heating at finite k(parallel). A range of unstable propagation angles of + or - 20 deg about the perpendicular is estimated for M(H)/M(e) = 50, including superthermal and background electron dynamics.

Hudson, M. K.↗

A superelement methodology for coupled loads analysis of Space Station Freedom

A technique for calculating the effects of on-orbit loads on the structural dynamics of the International Space Station is developed on the basis of MSC/NASTRAN superelement solution sequences. The complexity of the Space Station program with its multiple configurations, international partners, and contractors is stressed, and the impact of this complexity on the dynamic-load analysis process is indicated. The MSC/NASTRAN-based approach is outlined, and its application to two versions of the Permanent Manned Capability configuration is described in detail, including component mode synthesis, GFSC/FLAME coupled-loads analysis (with components from the system FEM model or from the Work Package), and ISI/MATRIX-X generation of closed-loop forcing functions. The results of these sample analyses are presented in extensive tables and graphs and briefly characterized.

Del Basso, Steve↗

The interdisciplinary role of space geodesy; Proceedings of the International Workshop, Erice, Italy, July 23-29, 1988

The current status of space geodesy and research plans for the 1990s are discussed in reviews and reports. Sections are devoted to the short-term and long-term dynamics of the solid earth, interactions between space geodesy and other disciplines and programs (geodynamics, earth structure, ocean physics, atmosphere and climate, planetary science, and fundamental physics), instrumentation, data analysis, reference coordinate systems, and education. Also provided are overviews of the French, ESA, and NASA geodetic research programs and descriptions of the Soviet Glonass navigation satellites, the WEGENER/MEDLAS observation program, and the Lageos II laser-ranging satellites. Diagrams, graphs, maps, and tables of numerical data are provided.

Mueller, Ivan I.↗

MUADEE: Mars Upper Atmosphere Dynamics, Energetics, and Evolution discovery mission. Executive summary volume

This document is the final report of the MAUDEE (Mars Upper Atmosphere Dynamics, Energetics, and Evolution) consortium. It describes a low cost Discovery mission to investigate the upper atmosphere of Mars and to understand the manner in which Mars has evolved over geologic time. In keeping with the innovative philosophy permeating the Discovery Program and in order to minimize the burden of reading an extensive prose exposition, a new presentation format has been adopted. The format involves a series of view graphs with facing text. The view graphs form the basis of a complete oral presentation of the MAUDEE mission and the facing text provides more detailed, but still brief, explanatory descriptions. Readers can scan the view graphs and/or read the facing text at their discretion. The oral presentation of this study was given to code SL personnel at NASA Headquarters on February 23, 1994. MAUDEE is an essential component of the Mars Exploration Program. It provides the information required to understand the evolution of the planet via the escape of volatiles. It provides the key measurements needed to understand the upper atmosphere of the last of the three terrestrial planets to be so studied. It connects and supplements investigations based on other Mars missions: Mars Surveyor, Planet-B and Mars-96. The MAUDEE mission plan involves a combination of remote and in-situ sensors, housed in three instrument packages. The sensors make measurements of the atmospheric regions between 60-200 km. These instruments are based on extensive heritage from Earth explorers and Pioneer Venus. The mission scenario has several phases and employs aerobraking maneuvers to lower initial apoapsis, thereby reducing fuel requirements. The spacecraft has body-mounted solar cells, enabling deep diving into the Martian atmosphere. The orbital inclination allows for pole-to-pole latitudinal sweeps in an initial elliptical phase, followed by a circular phase affording detailed diurnal measurements. The nominal mission duration at Mars is one Mars year.

Killeen, Timothy L.↗

Graph-Based Attention Mechanisms for Solving the AC Optimal Power Flow Problem in Electrical Power Networks

With the increasing complexity and data availability in modern power systems, learning-based approaches to AC Optimal Power Flow (AC OPF) have garnered significant attention. In particular, the structure of smart grids lends itself naturally to graph-based representations, where Graph Neural Networks (GNNs) can capture spatial and relational dependencies. This paper investigates attention-based GNN architectures tailored to heterogeneous graph representations of electric grids. We evaluate two major paradigms: relational attention, which distinguishes between edge types during message passing, and meta-path attention, which captures high-level semantics through multi-hop, typed paths. Using a large corpus of public AC OPF scenarios, we benchmark representative models of each type of attention. Our results demonstrate the benefits of heterogeneous attention-based models in accurately capturing grid dynamics; heterogeneous attention models achieve superior performance in both standard and perturbed settings. The findings highlight the importance of semantic-aware architectures for improving prediction robustness and interpretability in power system applications.

Trigui, Ali [Qubit Engineering Inc.]↗

Optimal trajectories for an aerospace plane. Part 2: Data, tables, and graphs

Data, tables, and graphs relative to the optimal trajectories for an aerospace plane are presented. A single-stage-to-orbit (SSTO) configuration is considered, and the transition from low supersonic speeds to orbital speeds is studied for a single aerodynamic model (GHAME) and three engine models. Four optimization problems are solved using the sequential gradient-restoration algorithm for optimal control problems: (1) minimization of the weight of fuel consumed; (2) minimization of the peak dynamic pressure; (3) minimization of the peak heating rate; and (4) minimization of the peak tangential acceleration. The above optimization studies are carried out for different combinations of constraints, specifically: initial path inclination that is either free or given; dynamic pressure that is either free or bounded; and tangential acceleration that is either free or bounded.

Miele, Angelo↗

The Mathematics of Dispatchability, Revisited

Dispatchability is an important property for the efficient execution of temporal plans where the temporal constraints are represented as a Simple Temporal Network (STN). It has been shown that every STN may be reformulated as a dispatchable STN, and dispatchability ensures that the temporal constraints need only be satisfied locally during execution. Recently, it has also been shown that Simple Temporal Networks with Uncertainty, augmented with wait edges, are Dynamically Controllable provided every projection is dispatchable. Thus, dispatchability has considerable theoretical as well as practical significance. One thing that hampers further work in this area is the underdeveloped theory. Moreover, the existing foundation is inadequate in certain respects. In this paper, we develop a new mathematical theory of dispatchability and its relationship to execution. We also provide several characterizations of dispatchability, including characterizations in terms of the structural properties of the STN graph. This facilitates the potential application of the theory to other areas.

mathematical models↗