Experimental investigation of parametric models in support of a NASA advanced transport technology program. Volume 3: Wing study, basic data and test description
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The conceptual model, the data classification scheme, and the analytic procedures are explained. The analytic results are summarized and specific software measures for collection and monitoring are recommended.
Control volume analysis models physics via the exchange of generalized fluxes between subdomains. Here, we introduce a scientific machine learning framework adopting a partition of unity architecture to identify physically-relevant control volumes, with generalized fluxes between subdomains encoded via Whitney forms. The approach provides a differentiable parameterization of geometry which may be trained in an end-to-end fashion to extract reduced models from full field data while exactly preserving physics. The architecture admits a data-driven finite element exterior calculus allowing discovery of mixed finite element spaces with closed form quadrature rules. An equivalence between Whitney forms and graph networks reveals that the geometric problem of control volume learning is equivalent to an unsupervised graph discovery problem. The framework is developed for manifolds in arbitrary dimension, with examples provided for H(div) problems in $\mathbb{R}$ establishing convergence and structure preservation properties. Finally, we consider a lithium-ion battery problem where we discover a reduced finite element space encoding transport pathways from high-fidelity microstructure resolved simulations. The approach reduces the 5.89M finite element simulation to 136 elements while reproducing pressure to under 0.1% error and preserving conservation.
In this work, we have introduced an optimized Debye-Grüneisen model that revolutionizes the determination of the Debye temperature and Grüneisen parameters. Unlike conventional methods, our model requires only the 0 K energy volume data for a material as input, eliminating the need to determine the bulk modulus and its pressure derivative, which often pose challenges due to numerical uncertainties. This unique feature sets our model apart from existing approaches and streamlines the process, enabling accurate predictions of thermal expansion behavior across various materials. To demonstrate its effectiveness, we showcase its excellent agreement with measured coefficients of thermal expansion (CTE) for the nickel-cobalt-chromium-aluminum-yttrium (Ni-Co-Cr-Al-Y) bond-coating system. Additionally, we apply our approach by conducting a high-throughput search for potential bond-coating materials among 90,000 compositions within the aluminum-cobalt-chromium-iron-nickel (Al-Co-Cr-Fe-Ni) system. From this extensive search, four compositions are synthesized, and the measured CTE values agree very well with theoretical predictions, hence validating our approach. In conclusion, the current optimized Debye-Grüneisen model combined with Density Functional Theory (DFT)-based thermodynamic database enables reliable and efficient high-throughput calculations of CTE of of a material without expensive phonon calculations.
Abstract Distributed acoustic sensing (DAS) has emerged as a promising seismic technology for monitoring microearthquakes (MEQs) with high spatial resolution. Efficient algorithms are needed for processing large DAS data volumes. This study introduces a deep learning (DL) model based on a Residual Convolutional Neural Network (ResNet) for detecting MEQs using DAS data, named as DASEventNet. The test data were collected from the Utah FORGE 16A (78)‐32 hydraulic stimulation experiments conducted in April 2022. The DASEventNet model achieves a remarkable accuracy of 100% when discriminating MEQs from noise in the raw test set of 260 examples. Surprisingly, the model identified weak MEQ signatures that have been manually categorized as noise. The decision‐making process with the model is decoded by the classic activation map, which illuminates learning features of the DASEventNet model. These features provide clear illustrations of weak MEQs and varied noise types. Finally, we apply the trained model to the entire period (∼7 days) of continuous DAS recordings and find that it discovers >5,700 new MEQs, previously unregistered in the public Silixa DAS catalog. The DASEventNet model significantly outperforms the traditional seismic method Short‐Term Average/Long‐Term Average (STA/LTA), which detected only 1,307 MEQs. The DASEventNet detection threshold is M w −1.80 compared to the minimum magnitude of M w −1.14 detected by STA/LTA. The spatiotemporal distribution of the newly identified MEQs defines an extensive stimulation zone and more accurately characterizes fracture geometry. Our results highlight the potential of DL for long‐term, real‐time microseismic monitoring that can improve enhanced geothermal systems and other activities that include subsurface hydraulic fracturing.
Graphical data obtained during experimental wind tunnel aerodynamic investigations of a 0.010 scale model (52-OTS) of the integrated space shuttle vehicle was presented. The purpose of this investigation was to obtain data with the solid rocket booster (SRB) in proximity to the orbiter/external tank (O/ET), over a large O/ET initial angle of attack and sideslip range, as well as data on the SRB alone (greatly separated from the O/ET). A captive trajectory system, which supported the SRB, was used with the tunnel primary sector (supporting the O/ET) to obtain grid type separation effects data. One symmetrical SRB model was used interchangeably to obtain right-hand and left-hand SRB data. The entire investigation was conducted at a free-stream Mach number of 4.5 at unit Reynolds number of 3.95 and 5.9 million per foot.
Tabular data obtained during experimental wind tunnel aerodynamic investigations of a 0.010 scale model (52-OTS) of the integrated space shuttle vehicle was presented. The purpose of this investigation was to obtain data with the solid rocket booster (SRB) in proximity to the orbiter/external tank (O/ET), over a large O/ET initial angle of attack and sideslip range, as well as data on the SRB alone (greatly separated from the O/ET). A captive trajectory system, which supported the SRB, was used with the tunnel primary sector (supporting the O/ET) to obtain grid type separation effects data. One symmetrical SRB model was used interchangeably to obtain right-hand and left-hand SRB data. The entire investigation was conducted at free-stream Mach number of 4.5 at unit Reynolds number of 3.95 and 5.9 million per foot.
Electrical impedance spectrometry involves measurement of the complex resistance of a load at multiple frequencies. With this information in the form of impedance magnitude and phase, or resistance and reactance, basic structure or function of the load can be estimated. The "load" targeted for measurement and estimation in this study consisted of the water-bearing tissues of the human calf. It was proposed and verified that by measuring the electrical impedance of the human calf and fitting this data to a model of fluid compartments, the lumped-model volume of intracellular and extracellular spaces could be estimated, By performing this estimation over time, the volume dynamics during application of stimuli which affect the direction of gravity can be viewed. The resulting data can form a basis for further modeling and verification of cardiovascular and compartmental modeling of fluid reactions to microgravity as well as countermeasures to the headward shift of fluid during head-down tilt or spaceflight.
Modern transportation research relies on seamlessly integrating traffic signal data with robust network representation and simulation tools. This study presents utdf2gmns, an open-source Python tool that automates conversion of the Universal Traffic Data Format, including network representation, signalized intersections, and turning volumes into the General Modeling Network Specification (GMNS) Standard. The resulting GMNS-compliant network can be converted for microsimulation in SUMO. By automatically extracting intersection control parameters and aligning them with GMNS conventions, utdf2gmns minimizes manual preprocessing and data loss. utdf2gmns also integrates with the Sigma-X engine to extract and visualize key traffic control metrics, such as phasing diagrams, turning volumes, volume-tocapacity ratios, and control delays. This streamlined workflow enables efficient scenario testing, accurate model building, and consistent data management. Validated through case studies, utdf2gmns reliably models complex urban corridors, promoting reproducibility and standardization. Documentation is available on GitHub and PyPI, supporting easy integration and community engagement.
Selective ion transport in polymer membranes depends critically on how penetrant motion couples to polymer dynamics and hydration. Yet, the mechanistic interplay between polymer rigidity, water content, and penetrant size remains poorly understood, especially in the regime where the penetrant diameter, polymer Kuhn length, and correlation length are comparable. Here, we employ coarse-grained molecular dynamics simulations to systematically investigate penetrant diffusion in hydrated polymer networks across a broad range of water volume fractions, chain rigidities, and penetrant sizes. The results reveal a transition from a decoupled regime, where small penetrants diffuse nearly independently of polymer relaxation, to a coupled regime in which large penetrants require cooperative polymer motion for transport. Increasing polymer rigidity amplifies the sensitivity of diffusivity to hydration, particularly at low water content, leading to pronounced deviations from Stokes−Einstein scaling. Comparison with scaling theories and free-volume models shows that classical nanoparticle-based frameworks fail to capture this intermediate regime. To address this gap, we extend the Yasuda model to incorporate polymer rigidity through a single parameter that quantifies the dynamic contribution of chain stiffness to free-volume fluctuations. The resulting model collapses diffusivity data across all sizes, water contents, and rigidities, providing a unified description of penetrant transport in hydrated polymer matrices. Furthermore, these findings establish polymer rigidity as a key, tunable determinant of diffusion and offer a framework for interpreting size-dependent transport in ion-selective membranes.
Space shuttle abort stage separation wind tunnel tests of orbiter and booster vehicles - booster proximity data, Mach number 5
Space Shuttle abort stage separation wind tunnel tests of orbiter and booster vehicles - orbiter proximity data, Mach number 5
Space shuttle abort stage separation wind tunnel tests of orbiter and booster vehicles - booster proximity data, Mach number 3
Space shuttle abort stage separation wind tunnel tests of orbiter and booster vehicles - orbiter proximity data, Mach number 3
Space shuttle abort stage separation wind tunnel tests of orbiter and booster vehicles - booster proximity data, Mach number 2
Space shuttle abort stage separation wind tunnel tests of orbiter and booster vehicles - orbiter proximity data, Mach number 2
Archived wind tunnel data are available for flyback booster or other alternative recoverable configurations as well as reusable orbiters studied during initial development (Phase B) of the Space Shuttle. Considerable wind tunnel data was acquired by the competing contractors and the NASA Centers for an extensive variety of configurations with an array of wing and body planforms. All contractor and NASA wind tunnel data acquired in the Phase B development have been compiled into a data base and are available for application to current winged flyback or recoverable booster aerodynamic studies. The Space Shuttle Phase B Wind Tunnel Database is structured by vehicle component and configuration type. Basic components include booster, orbiter and launch vehicle. Booster configuration types include straight and delta wings, canard, cylindrical, retroglide and twin body. Orbital configuration types include straight and delta wings, lifting body, drop tanks and double delta wings. This is Volume 3 (Part 2) of the report -- Launch Configuration -- which includes booster and orbiter components in various stacked and tandem combinations.
Differences in scaling relationships for crater formation and the generation of impact melt should lead to a variety of observable features and phenomena. These relationships infer that the volume of the transient cavity (and final crater) relative to the volume of impact melt (and the depth to which melting occurs) decreases as the effects of gravity and impact velocity increase. Since planetary gravity and impact velocity are variables in the calculation of cavity and impact-melt volumes, the implications of the model calculation will vary between planetary bodies. Details of the model calculations of impact-melt generation as a function of impact and target physical conditions were provided elsewhere, as were attempts to validate the model through ground-truth data on melt volumes, shock attenuation, and morphology from terrestrial impact craters.