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Presentation for CCUS 2025 Conference held in Houston, Texas March 3-5, 2025. Offshore geologic carbon storage (GCS) efforts are accelerating worldwide. As additional projects come online and site characterizations take place, project information and data need to be aggregated into central locations to improve understanding of the success criteria for GCS.
Propellants mass gauging technologies designed to work in an accelerated environment, where the propellant remains settled at one end of the propellant tank, do not work well in a microgravity environment because the propellant is not necessarily settled. While some microgravity mass gauging technologies exist at various TRLs, most of them have major disadvantages. Improvements in microgravity propellant mass gauging will result in improvements to many areas of propellant management, which influences performance and mission assurance. Electrical Capacitance Tomography (ECT) is a sensing technology that has been used in the oil/gas industry for decades to measure multi-phase flow rate in pipes. ECT, when installed in a tank, is able to reconstruct the liquid distribution inside of the tank, which can then be integrated to obtain mass. ECT mass gauging recently achieved high accuracy in laboratory testing on the ground, and while ECT mass gauging will theoretically work during all phases of flight, it had not yet been tested in microgravity. The NASA KSC Launch Services Program, with support from the Flight Opportunities Program, successfully flew an ECT tank liquid mass gauging system experiment on a Zero-G parabolic flight aircraft. The experiment hardware was rented from a company; NASA LSP did no technology development work for this project. This STRIVES entry is for the entire raw data set, which consists of approximately 25GB of csv files of capacitance and motion data from the ground and flight tests of the experiment. The data set will be available on the NASA LSP Electronic Slosh Data Catalog (ESDC), and the public may request the data set from the authors (POC: Jed Storey, jedediah.m.nasa.gov).
Flight-test techniques are being used to generate a data base for identification of a full-envelope aerodynamic model of a V/STOL fighter aircraft, the YAV-8B Harrier. The flight envelope to be modeled includes hover, transition to conventional flight and back to hover, STOL operation, and normal cruise. Standard V/STOL procedures such as vertical takeoff and landings, and short takeoff and landings are used to gather data in the powered-lift flight regime. Long (3 to 5 min) maneuvers which include a variety of input types are used to obtain large-amplitude control and response excitations. The aircraft is under continuous radar tracking; a laser tracker is used for V/STOL operations near the ground. Tracking data are used with state-estimation techniques to check data consistency and to derive unmeasured variables, for example, angular accelerations. A propulsion model of the YAV-8B's engine and reaction control system is used to isolate aerodynamic forces and moments for model identification. Representative V/STOL flight data are presented. The processing of a typical short takeoff and slow landing maneuver is illustrated.
Flight-test techniques are being used to generate a data base for identification of a full-envelope aerodynamic model of a V/STOL fighter aircraft, the YAV-8B Harrier. The flight envelope to be modeled includes hover, transition to conventionally flight and back to hover, STOL operation, and normal cruise. Standard V/STOL operation, and normal cruise. Standard V/STOL procedures such as vertical takeoff and landings, and short takeoff and landings are used to gather data in the powered-lift flight regime. Long (3-5-min) maneuvers which include a variety of input types are used to obtain large-amplitude control and response excitations. The aircraft is under continuous radar tracking; a laser tracker is used for V/STOL operations near the ground. Tracking data are used with state-estimation techniques to check data consistency and to derive unmeasured variables, for example, angular accelerations. A propulsion model of the YAV-8B's engine and reaction control system is used to isolate aerodynamic forces and moments for model identification. Representative V/STOL flight data are presented. The processing of a typical short-takeoff and slow-landing maneuver is illustrated.
Spectral single and double power laws are common in high-energy phenomena, such as solar flares and solar energetic particles, including ground level enhancement events. It is not clear what determines the energy breaks and spectral indexes of these power laws. Here, we will describe a first-principles model of pitch-angle and energy distribution function evolution, which produces power laws and provides physical interpretation for such spectral features (Guidoni et al. 2022, ApJ). In this model, a prescribed fraction of particles sequentially “hops” between shrinking magnetic islands (accelerators) formed by flare reconnection. Each accelerator increases particles’ energies by a modest amount, but particles must visit only a few accelerators to increase their energies by orders of magnitude. Data from global magnetohydrodynamic simulations of an eruptive flare/coronal mass ejection provide ambient conditions for the evolving particle distributions. We will also describe the fully analytic method for forming and interpreting power laws, which requires only a few constrained physical parameters of the acceleration region and is independent of the acceleration model, as well as preliminary results extending the analytical model to the formation of double power laws.
Solar flares are among the most energetic and interesting phenomena in the Solar system, releasing up to 1032 ergs of energy on timescales of several tens of seconds to several tens of minutes. Much of this energy is in the form of suprathermal electrons and ions, which remain trapped at the Sun and produce a wide variety of radiations, as well as escape into interplanetary space, where they can be directly observed. The radiation from trapped particles consists in general of (1) continuum emission; (2) narrow gamma-ray nuclear deexcitation lines; and (3) high-energy neutrons observed in space or by ground-based neutron monitors. The particles that escape into space consist of both electrons and ions, which often have compositions quite different than that of the ambient solar atmosphere. Flares thus present many diagnostics of the particle acceleration mechanism(s), the identification of which is the ultimate goal of flare research. Moreover, flares in fact offer the only opportunity in astrophysics to study the simultaneous energization of both electrons and ions. Hopefully, an understanding of flares with their wealth of diagnostic data will lead to a better understanding of particle acceleration at other sites in the Universe. It is now generally accepted that flares are roughly divided into two classes: impulsive and gradual. Gradual events are large, occur high in the corona, have long-duration soft and hard X-rays and gamma rays, are electron poor, are associated with Type II radio emission and coronal mass ejections (CMEs), and produce energetic ions with coronal abundance ratios. Impulsive events are more compact, occur lower in the corona, produce short-duration radiation, and exhibit dramatic abundance enhancements in the energetic ions. Their He-3/He-4 ratio is - 1, which is a huge increase over the coronal value of about 5 x 10(exp -4), and they also posses smaller but still significant enhancements of Ne, Mg, Si, and Fe relative to He-4, C, N, and O. Specifically, above about 1 MeV nucleon(exp -1), the ratio of Fe to O is about 8 times larger than in the corona or in gradual flares, while the ratio of Ne, Mg, and Si to O is about 3 times higher; He-4, C, N, and 0 are not enchanced with respect to each other. In addition to these elemental enhancements, Ne and Mg have isotopic enhancements as well. The general scenario that has emerged from these (and other) observations is that energetic particles in gradual events are accelerated by a CME-driven shock, while those particles in impulsive events are accelerated by another mechanism(s).
The long-term thermal stability of structural alloys is essential for ensuring the safe and reliable operation of nuclear reactors and other power plants. While extensive research has explored the effects of thermal aging on conventional stainless steels, the behavior of additively manufactured (AM) alloys remains less understood. This study examines the thermal aging response of laser powder bed fusion (LPBF) 316H stainless steel (SS) at temperatures ranging from 550 °C to 750 °C over durations of up to 10,000 h (approximately 1.14 years). Advanced characterization techniques, including electron microscopy and synchrotron X-ray diffraction, were used to investigate dislocation recovery and phase evolution. Based on these findings, a time-temperature-precipitation (TTP) diagram was developed for LPBF 316H SS, revealing a 10- to100-fold acceleration in precipitation kinetics compared to wrought 316H SS. A physics-informed model was calibrated using the short-term experimental data, enabling predictions of average precipitate sizes, volume fractions of M 23 C 6 and Laves phases, and changes in molybdenum solute concentration for aging up to 1 × 10⁶ h (114 years). These microstructural insights were further utilized to estimate yield strength and extrapolate strength reduction factors over the extended aging period. Despite the accelerated aging kinetics, LPBF 316H SS demonstrated superior yield strength retention compared to its wrought counterpart. In conclusion, this study establishes a framework for evaluating long-term performance using short-term experimental data and supports the accelerated qualification of AM materials for high-temperature structural applications.
Through the use of theoretical predictions of fluid properties and experimental heat transfer and thrust measurements, the zones of laminar, transitional, and turbulent boundary layer flow were defined for the NASA Lewis 1030:1 area ratio rocket nozzle. Tests were performed on the nozzle at chamber pressures from 350 to 100 psia. For these conditions, the throat diameter Reynolds numbers varied from 300,000 to 1 million. The propellants used were gaseous hydrogen and gaseous oxygen. Thrust measurements and nozzle outer wall temperature measurements were taken during the 3-sec test runs. Comparison of experimental heat transfer and thrust data with the corresponding predictions from the Two-Dimensional Kinetics (TDK) nozzle analysis program indicated laminar flow in the nozzle at a throat diameter Reynolds number of 320,000 or chamber pressure of 360 psia. Comparison of experimental and predicted heat transfer data indicated transitional flow up to and including a chamber pressure of 1000 psia. Predicted values of the axisymmetric acceleration parameter within the convergent and divergent nozzle were consistent with the above results. Based upon an extrapolation of the heat transfer data and predicted distributions of the axisymmetric acceleration parameter, transitional flow was predicted up to a throat diameter Reynolds number of 220,000 or 2600-psia chamber pressure. Above 2600-psia chamber pressure, fully developed turbulent flow was predicted.
Through the use of theoretical predictions of fluid properties and experimental heat transfer and thrust measurements, the zones of laminar, transitional, and turbulent boundary layer flow were defined for the NASA Lewis 1039:1 area ratio rocket nozzle. Tests were performed on the nozzle at chamber pressures from 350 to 100 psia. For these conditions, the throat diameter Reynolds numbers varied from 300,000 to 1 million. The propellants used were gaseous hydrogen and gaseous oxygen. Thrust measurements and nozzle outer wall temperature measurements were taken during the 3-sec test runs. Comparison of experimental heat transfer and thrust data with the corresponding predictions from the Two-Dimensional Kinetics (TDK) nozzle analysis program indicated laminar flow in the nozzle at a throat diameter Reynolds number of 320,000 or chamber pressure of 360 psia. Comparison of experimental and predicted heat transfer data indicated transitional flow up to and including a chamber pressure of 1000 psia. Predicted values of the axisymmetric acceleration parameter within the convergent and divergent nozzle were consistent with the above results. Based upon an extrapolation of the heat transfer data and predicted distributions of the axisymmetric acceleration parameter, transitional flow was predicted up to a throat diameter Reynolds number of 220,000 or 2600-psia chamber pressure. Above 2600-psia chamber pressure, fully developed turbulent flow was predicted.
The GlueX experiment has produced the world?s largest statistics for peripheral photoproduction of mesons with a goal to search for exotic mesons. This document presents results on the search for possible intermediate resonances which decay to ??. Little is known for the states seen in this decay channel such as the ?(1420) and ?(1650). This channel also allows to search for the never seen exotics JPC = 0??, 2+?, and the elusive 2??. The ?, in this reaction, is identified via the ?+???0 decay mode and the ? and ?0 are identified via the decay to 2?. Data from the GlueX Phase I was analyzed. This data was obtained at the Thomas Jefferson National Accelerator Facility utilizing the GlueX detector and a tagged linearly polarized photon beam incident on a liquid hydrogen target. A model using the expected major wave contributors, 1+? and 1??, shows two structure in the region where the ?(1420) and ?(1650) are expected. A third structure is seen at threshold with the quantum numbers of an h1 resonance, however, more studies are needed to assert the nature of this structure. Instabilities on the m = ±1 were shown to come from mathematical ambiguities. This only occurs between the m = 1 and m = ?1 projections when both reflectivities are present in close amount. Expanding the intensity equation for a single wave allows to extract coefficients that can help describe the data. A tangent study is also presented in this document which consists on a direct fit to the lineshape of an isolated N(1535). This baryonic resonance is well established, but its Breit-Wigner parameters have only been obtained from partial wave analysis. In contrast, this study obtained the Breit-Wigner parameters using a direct fit and finds the value of the width to be 70.5± 9.2 MeV; this is half of the accepted value.
TorchBraid is a high-performance implementation of layer-parallel training for deep neural networks (DNNs) supporting MPI-based parallelism and GPU acceleration. Layer-parallel training has been developed to overcome the serialization inherent in forward and backward propagation of DNNs that limits utilization of computational resources in the strong scaling limit. To achieve this, TorchBraid integrates the PyTorch neural network framework with the state-of-the-art XBraid time-parallel library. Furthermore, this article presents the use and performance of TorchBraid, in addition to solutions for overcoming the algorithmic challenges inherent in combining automatic differentiation with layer-parallel. Results are presented with and without GPU acceleration for the Tiny ImageNet and MNIST image classification data sets, as well as recurrent neural networks. Overall, TorchBraid enables fast training of DNNs, both in a strong and weak scaling context. In addition to the TorchBraid software, several new advances in applying layer-parallel algorithms are detailed. Integration of layer-parallel with data-parallel algorithms is presented for the first time, showing the computational advantages of the combination. Standard deep learning techniques, like batch-normalization, are developed for layer-parallel training. Finally, a new approach combining layer-parallel with spatial coarsening in order to accelerate training for 3D image classification shows roughly a 10× speedup over serial execution.
The dataset comprises Soil Temperature and Deformation monitoring data gathered from a watershed situated along the Nome-Teller road at Mile 47 in Alaska. Its primary objective is to enhance comprehension of deformation mechanisms in permafrost environments by using 51 probes designed following Wielandt et al. (2022). The dataset comprises a description of Probe_ID, Location, start time, end time and probe length in the "ProbesLocations_StartEndDates.csv" file, the 51 data files encapsulated within "Data_files.zip" and the computed deformation for 2023 and 2022 in "Deformation_T47.zip".Each file in Data_files.zip is named as followed "ProbeId_StartMonth_EndMonth.csv". Within each file, the initial column denotes the timestamp in UTC, followed by the sensor’s battery voltage and temperature and acceleration values (X, Y, Z) in subsequent columns. The data collection frequency is set at 30-minute intervals. Each file in Deformation_T47.zip is named as followed "ProbeId_Temp_def_year.csv". Within each file, the initial column denotes the timestamp in UTC, followed by the temperature at each sensor and cumulative deformation from last to first sensor in subsequent columns. The data collection is daily averaged.This dataset forms an integral component of the Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic). The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).
This report documents the development of the data acquisition system (DAS) and data reduction methodologies for the Irradiated Material Property Accelerated Characterization Test (IMPACT) experiment at the Advanced Test Reactor (ATR). The IMPACT experiment is designed to enable in-pile measurement of thermal conductivity in metallic nuclear fuels, specifically U-10Zr, using an instrumented thermal conductivity probe. The DAS supports both passive temperature monitoring and active thermal interrogation of the probe through controlled AC and DC excitation. Significant modifications to laboratory-scale systems were required to accommodate the higher resistance paths associated with the in-pile application. Custom electronics and relay-controlled measurement sequencing were developed to enable the measurement and sufficient power delivery to the sensing region. A reduced-order, axisymmetric thermal model based on the thermal quadrupoles method is presented to support data interpretation. This model enables efficient evaluation of transient heat transfer behavior and facilitates solution of the inverse problem required to extract thermal properties from measured signals. Multiple boundary condition formulations are discussed to address varying experimental time scales and geometries. Additionally, machine learning techniques are introduced to support data reduction and improve confidence in inverse solutions. Convolutional neural networks are applied to identify the presence of gas gaps and other evolving geometric features that significantly impact thermal response during irradiation. These efforts contribute to the broader integration of digital twin frameworks and real-time modeling capabilities within the Advanced Fuels Campaign.
An accelerated test matrix is outlined that includes acceptance, baseline and post-cycling tests, chemical and physical analyses, and the data analysis procedures to be used in determining the feasibility of an accelerated test for sealed, nickel cadmium cells.
As an early proponent of grid computing, Ames Research Center awarded Small Business Innovation Research (SBIR) funding to 3DGeo Development Inc., of Santa Clara, California, (now FusionGeo Inc., of The Woodlands, Texas) to demonstrate a virtual computer environment that linked geographically dispersed computer systems over the Internet to help solve large computational problems. By adding to an existing product, FusionGeo enabled access to resources for calculation- or data-intensive applications whenever and wherever they were needed. Commercially available as Accelerated Imaging and Modeling, the product is used by oil companies and seismic service companies, which require large processing and data storage capacities.
The Booster Neutrino Beamline experiment re-quested a new secondary electron emission multiwire profile monitor installation. The device had to be durable in high radiation conditions and mounted within a large 10-foot airtight steel fixture for installation near the beam target. Previous iterations of multiwire suffered radiation damage to both the connectors and wires. To ensure accurate horizontal and vertical beam profile measurements, an updated design was pro-posed, designed, and constructed. The new BNB multiwire utilizes 3 mil diameter gold-plated tungsten sense wires soldered to vertical and horizontal Alumi-na-96 ceramic planes, 50 wires per plane. Radiation hard Kapton insulated 30-gauge wires carry the output signals. Profiles are readout through charge integrator scanner electronics. This paper will detail the design and functionality of the BNB target multiwire and present relevant beam profile data.
LiNi 0.5 Mn 1.5 O 4 (LNMO) is a high-capacity spinel-structured material with an average lithiation/de-lithiation potential at ca. 4.6–4.7 V vs Li + /Li, far exceeding the stability limits of electrolytes. An efficient way to enable LNMO in lithium-ion batteries is to reformulate an electrolyte composition that stabilizes both graphitic (Gr) negative electrode with solid-electrolyte-interphase and LNMO with cathode-electrolyte-interphase. In this study, we select and test a diverse collection of 28 single and dual additives for the Gr||LNMO battery system. Subsequently, we train machine learning models on this dataset and employ the trained models to suggest 6 binary compositions out of 125, based on predicted final area-specific-impedance, impedance rise, and final specific-capacity. Such machine learning-generated new additives outperform the initial dataset. This finding not only underscores the efficacy of machine learning in identifying materials in a highly complicated application space but also showcases an accelerated material discovery workflow that directly integrates data-driven methods with battery testing experiments.