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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.
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The Solar Uncertainty Integrator (SUNI) study was developed by the National Laboratory of the Rockies (NLR's) to provide a standardized "bulk uncertainty processing" method for solar irradiance data, which are essential for the successful deployment of solar energy systems. This poster provides an overview of key findings from NLR's SUNI application across 89 stations.
In general, there are two broad scientific objectives when using cloud resolving models (CRMs or cloud ensemble models-CEMs) to study tropical convection. The first one is to use them as a physics resolving models to understand the dynamic and microphysical processes associated with the tropical water and energy cycles and their role in the climate system. The second approach is to use the CRMs to improve the representation of moist processes and their interaction with radiation in large-scale models. In order to improve the credibility of the CRMs and achieve the above goals, CRMs using identical initial conditions and large-scale influences need to produce very similar results. Two CRMs produced different statistical equilibrium (SE) states even though both used the same initial thermodynamic and wind conditions. Sensitivity tests to identify the major physical processes that determine the SE states for the different CRM simulations were performed. Their results indicated that atmospheric horizontal wind is treated quite differently in these two CRMs. The model that had stronger surface winds and consequently larger latent and sensible heat fluxes from the ocean produced a warmer and more humid modeled thermodynamic SE state. In addition, the domain mean thermodynamic state is more unstable for those experiments that produced a warmer and more humid SE state. Their simulated wet (warm and humid) SE states are thermally more stable in the lower troposphere (from the surface to 4-5 km in altitude). The large-scale horizontal advective effects on temperature and water vapor mixing ratio are needed when using CRMs to perform long-term integrations to study convective feedback under specified large-scale environments. In addition, it is suggested that the dry and cold SE state simulated was caused by enhanced precipitation but not enough surface evaporation. We find some problems with the interpretation of these three phenomena.
The eROSITA Final Equatorial Depth Survey (eFEDS), completed in survey mode during the calibration and performance verification phase of the eROSITA instrument on Spectrum Roentgen Gamma, delivers data at and beyond the final depth of the 4-yr eROSITA all-sky survey (eRASS:8), f 0.5–2 keV = 1.1 × 10 –14 erg s –1 cm –2 , over 140 deg 2 . It provides the first view of normal galaxy X-ray emission from X-ray binaries (XRBs) and the hot interstellar medium at the full depth of eRASS:8. We used the Heraklion Extragalactic Catalogue (HECATE) of galaxies to correlate with eFEDS X-ray sources and identify X-ray detected normal galaxies. This flux-limited X-ray survey is relatively free from selection effects and enables the study of integrated normal galaxy X-ray emission and its relation to galaxy parameters such as the stellar mass, star formation rate (SFR), and metallicity. We cross-correlated 32 646 eFEDS X-ray sources to 1181 HECATE normal galaxies and obtained 94 matches. We classified galaxies as star-forming, early-type, composite, and active galactic nuclei (AGN) using Sloan Digital Sky Survey (SDSS) and Six-degree Field (6dF) optical spectroscopy. The eFEDS field harbours 37 normal galaxies: 36 late-type (star-forming) galaxies and one early-type galaxy. There are 1.9 times as many normal galaxies as predicted by scaling relations via simulations, with an overabundance of late-type galaxies and a dearth of early-type galaxies. When compared with empirical relations, eFEDS dwarf galaxies with a high specific SFR have elevated L X /SFR at a fixed specific SFR and metallicity, indicating an increase in XRB emission due to low metallicity. We expect that eRASS:8 will detect 12 500 normal galaxies, the majority of which will be star-forming, with the caveat that there are unclassified sources in eFEDS and galaxy catalogue incompleteness issues that could increase the actual number of detected galaxies over these current estimates. eFEDS observations detected a rare population of galaxies – the metal-poor dwarf starbursts – that do not follow known scaling relations. eRASS is expected to discover significant numbers of these high-redshift analogues, which are important for studying the heating of the intergalactic medium at high redshift. Further investigation of the hot gas emission from normal galaxies and stochastic effects in the dwarf galaxy population are required to constrain their X-ray output.
The main objective of this project was to better understand the geodynamics controlling the mountain building and topographic evolution in the central Andes using an integrated approach that combines GPS, seismological, and numerical studies.
This study dives into the applicability of using automated discovery of conserved quantities in dynamical systems relevant to accelerator physics. Specifically, we explore the performance of AI Poincaré in analyzing numerical trajectory data obtained using the McMillan system of non-linear integrable optics. A comprehensive evaluation of the algorithm's performance is conducted through diverse methodologies. These include the analysis of the estimated number of conserved quantities embedded in a dataset and the deviation of interpolated points on the inferred manifold with respect to points in actually in the dataset. the investigation identifies an optimal range of perturbation distances where the underlying manifold extraction algorithm inside AI Poincaré exhibits optimal performance. Additionally, an improved neural network architecture is proposed based on the observed results. Finally, we apply the algorithm to preliminary experimental data from the Integrable Optics Test Accelerator at Fermilab to successfully infer the number of conserved quantities even in the presence of fast decoherence of the measured signal.
Layered transition metal oxides are appealing cathodes for sodium-ion batteries due to their overall advantages in energy density and cost. But their stabilities are usually compromised by the complicated phase transition and the oxygen redox, particularly when operating at high voltages, leading to poor structural stability and substantial capacity loss. Here, in this study, an integrated strategy combing the high-entropy design with the superlattice-stabilization to extend the cycle life and enhance the rate capability of layered cathodes is reported. It is shown that the as-prepared high-entropy Na 2/3 Li 1/6 Fe 1/6 Co 1/6 Ni 1/6 Mn 1/3 O 2 cathode enables a superlattice structure with Li/transition metal ordering and delivers excellent electrochemical performance that is not affected by the presence of phase transition and oxygen redox. It achieves a high reversible capacity (171.2 mAh g –1 at 0.1 C), a high energy density (531 Wh kg –1 ), extended cycling stability (89.3% capacity retention at 1 C for 90 cycles and 63.7% capacity retention at 5 C after 300 cycles), and excellent fast-charging capability (78 mAh g –1 at 10 C). This strategy would inspire more rational designs that can be leveraged to improve the reliability of layered cathodes for secondary-ion batteries.
Over the last three decades, fiber optic sensors (FOS) have gained a lot of attention for their wide range of monitoring applications across many industries, including aerospace, defense, security, civil engineering, and energy. FOS technologies hold great promise to form the backbone for next‐generation intelligent sensing platforms that offer long‐distance, high‐accuracy, distributed measurement capabilities and multiparametric monitoring with resilience to harsh environmental conditions. The major limitations posed by FOS are 1) cross‐sensitivity, 2) enormous volume and large data generation, 3) low data processing speed, 4) degradation of signal‐to‐noise ratio over the fiber length, and 5) overall cost of sensor and interrogator systems. These challenges can be overcome by building advanced data analytics engines enabled by recent breakthroughs in machine learning (ML) and artificial intelligence (AI). This article presents a comprehensive review of recent studies that integrate ML and AI algorithms with FOS technologies. This review also highlights several FOS technology development directions that promise a significant impact on widespread use for several industrial applications, with an emphasis on energy systems monitoring. A perspective on future directions for further research development is also provided.
Abstract A novel series of heterometallic f ‐block‐frameworks including the first examples of transuranic heterometallic 238 U/ 239 Pu‐metal–organic frameworks (MOFs) and a novel monometallic 239 Pu‐analog are reported. In combination with theoretical calculations, we probed the kinetics and thermodynamics of heterometallic actinide(An)‐MOF formation and reported the first value of a U‐to‐Th transmetallation rate. We concluded that formation of uranyl species could be a driving force for solid‐state metathesis. Density of states near the Fermi edge, enthalpy of formation, band gap, proton affinity, and thermal/chemical stability were probed as a function of metal ratios. Furthermore, we achieved 97 % of the theoretical maximum capacity for An‐integration. These studies shed light on fundamental aspects of actinide chemistry and also foreshadow avenues for the development of emerging classes of An‐containing materials, including radioisotope thermoelectric generators or metalloradiopharmaceuticals.
Abstract A novel series of heterometallic f ‐block‐frameworks including the first examples of transuranic heterometallic 238 U/ 239 Pu‐metal–organic frameworks (MOFs) and a novel monometallic 239 Pu‐analog are reported. In combination with theoretical calculations, we probed the kinetics and thermodynamics of heterometallic actinide(An)‐MOF formation and reported the first value of a U‐to‐Th transmetallation rate. We concluded that formation of uranyl species could be a driving force for solid‐state metathesis. Density of states near the Fermi edge, enthalpy of formation, band gap, proton affinity, and thermal/chemical stability were probed as a function of metal ratios. Furthermore, we achieved 97 % of the theoretical maximum capacity for An‐integration. These studies shed light on fundamental aspects of actinide chemistry and also foreshadow avenues for the development of emerging classes of An‐containing materials, including radioisotope thermoelectric generators or metalloradiopharmaceuticals.
Deep Neural Networks (DNNs) are rapidly gaining popularity in a variety of important domains. Unfortunately, modern DNNs have been shown to be vulnerable to a variety of attacks and buggy behavior. This has motivated recent work in formally analyzing the properties of such DNNs. Furthermore, this paper introduces SyReNN, a tool for understanding and analyzing a DNN by computing its symbolic representation. The key insight is to decompose the DNN into linear functions. Our tool is designed for analyses using low-dimensional subsets of the input space, a unique design point in the space of DNN analysis tools. We describe the tool and the underlying theory, then evaluate its use and performance on three case studies: computing Integrated Gradients, visualizing a DNN’s decision boundaries, and repairing buggy DNNs.
First-mile last-mile (FMLM) mobility services that connect riders to public transit can lead to improved transit accessibility and network efficiency if such services are convenient and reliable. However, many current FMLM services are inefficient and costly because they are inflexible (e.g., fixed supply of shuttles) and do not leverage collected data for optimized decision making. At the same time, new forms of shared mobility can provide added flexibility and real-time analytics to FMLM systems when carefully integrated. This study evaluates performance and cost implications of public/private coordination between transit shuttles and transportation network companies (TNC) in the FMLM context. A real-time operations model was developed to simulate daily operations for an existing FMLM system using real-world demand data. Three supply strategies were tested with varying levels of flexibility: (1) Status Quo (two 23-passenger on-demand shuttles), (2) Hybrid (one 23-passenger on-demand shuttle + TNC), and (3) TNC Only (exclusively use TNC services). Results indicated that the added flexibility of the Hybrid service design (using shuttles and TNCs) improved service performance (a 7.7% improvement), reduced daily operating costs (– 6.0%), and improved service reliability (95th percentile travel times decreased by up to 40% during peak periods). In addition, the Hybrid service design was more robust to variations in demand. Here, the Hybrid service was significantly cheaper to operate (– 31.6%) at reduced demand levels (50% of normal), and improved service performance (a 10.2% improvement) when demand levels were increased (150% of normal). These findings emphasize the importance of flexibility in FMLM service designs, especially when demand is sparse and variable.
Abstract Catalysis research has witnessed remarkable progress with the advent of in situ and operando techniques. These methods enable the study of catalysts under actual operating conditions, providing unprecedented insights into catalytic mechanisms and dynamic catalyst behavior. This review discusses key in situ techniques and their applications in catalysis research. Advances in in situ electron microscopy allow direct visualization of catalysts at the atomic scale under reaction conditions. In situ spectroscopy techniques like X-ray absorption spectroscopy and nuclear magnetic resonance spectroscopy can track chemical states and reveal transient intermediates. Synchrotron-based techniques offer enhanced capabilities for in situ studies. The integration of in situ methods with machine learning and computational modeling provides a powerful approach to accelerate catalyst optimization. However, challenges remain regarding radiation damage, instrumentation limitations, and data interpretation. Overall, continued development of multi-modal in situ techniques is pivotal for addressing emerging challenges and opportunities in catalysis research and technology.
Sand production encountered in the 2013 offshore field gas production tests at the Nankai Trough, Japan, could be attributed to well failure during reservoir compaction. In this study, well integrity under various reservoir compaction patterns for the Nankai Trough case is examined using a well-formation finite element model. Here, the modelling details include the inclusion of a cement sheath as well as the modelling of construction processes (such as cement shrinkage). Well elongation in the overburden layer becomes significant when the reservoir subsidence is localized near the wellbore under large depressurization. Results show that the maximum plastic deviatoric strain level in the cement could reach 0.7% when the maximum reservoir subsidence reaches 0.85 m and cement shrinkage is limited. When cement shrinkage rises to 0.75%, the maximum plastic deviatoric strain increases to 2.4% as the cement accumulates additional plastic strain during shrinkage due to its deformation being constrained by the casing. In order to prevent the cement from failure, it might be effective to hold the pressure drawdown at a low level (e.g., several MPa) until the hydrate dissociation front advances to a certain radius from the well (e.g., a couple of tens of metres).
In this study, we integrated the high-speed synchrotron X-ray phase-contrast imaging (PCI) with a modified Kolsky compression bar loading platform to visualize the dynamic failure processes of cross-ply glass fiber reinforced composites (GFRCs). Four S-2 glass/SC-15 composite specimens were prepared, having similar thicknesses but different stacking sequences, namely as [0 12 /90 12 ], [90 12 /0 12 ], [0 8 /90 8 /0 8 ], and [90 8 /0 8 /90 8 ]. Three-dimensional synchrotron X-ray computed tomography and scanning electron microscopy (SEM) were employed to examine the microstructures and quantify the fiber volume fractions. Each specimen was notched and subjected to a dynamic three-point flexural loading. The onset of cracking close to the notch tip, crack propagation in 0° or 90° plies and their interface, crack opening, and ultimately failure of the specimen were captured by high-speed synchrotron X-ray PCI. Additional dynamic experiments were performed to determine the average time when the stress wave propagated through the specimen and correlate the X-ray images with the specimen's force-deflection response. Finally, the surface morphology of each specimen after the dynamic loading was imaged by SEM. Comparison between real-time X-ray images and post-fracture SEM images demonstrated the capability of the X-ray method to record damage evolution inside composites. Furthermore, the high spatial and temporal resolutions of the X-ray setup and edge enhancement by PCI enabled the identification of microscale damage features within 1 μs. Two damaging processes were identified, crack growth was quantified, and fracture toughness of the composites was evaluated. The method is deemed useful to reveal microscale damage mechanisms and track cracking behaviors inside cross-ply GFRCs under dynamic loading in real time.
The modern energy-economy is increasingly causing the production of highly saline brines, including from produced water. Supercritical water desalination can concentrate and extract minerals from these brines, but the effects of mixed salt interactions, organic degradation with additives, and the technology's economics are not well understood at supercritical condition. The present study evaluated and experimentally studied an integrated supercritical water desalination and oxidation (SCWDO) process for treating real-produced water samples from oil/natural gas field. The complex interactions between the various anions and cations in produced water were extensively evaluated. Most of the divalent and trivalent ions were extracted below 250 °C while the majority of the monovalent salt were removed between 380 to 410 °C. The treated real produced water was of drinking water quality, with <500 mg/l of total dissolved solid (TDS) and with 100% organics removal. Furthermore, the heat liberated during the organic oxidation could be utilized internally and for electricity generation for enhanced the energy efficiency and lower cost of produced water treatment. With system optimization, the proposed SCWDO process can essentially be made a net zero energy process. A novel process flow diagram for the commercial scale self-powered hybrid SCWDO technology was proposed as a cost-effective produced water treatment to mitigate the environmental crises. Techno-economic analysis showed that produced water treatment cost with SCWDO can be reduced to 2–3 $/m 3 and can be up to 60 % cheaper to traditional deep well reinjection. Additionally, the proposed SCWDO process could achieve net negative CO 2 emission.
Constituent redistribution is a defining feature of irradiated U-Pu-Zr metallic fuels, yet its mechanisms and effects on fuel performance are not sufficiently resolved to guide model development. Although decades of irradiation testing have established broad trends, a true mechanistic understanding of constituent redistribution has not been achieved. Here, in this study, we use electron probe microanalysis (EPMA), scanning electron microscopy (SEM), and transmission electron microscopy-based (TEM) selective area electron diffraction (SAED) on a EBR-II irradiated U-19 wt.% Pu-6 wt.% Zr fuel pin cross-section to correlate the composition, porosity, and crystallographic phases formed after irradiation. Constituent redistribution is thought to consist of three distinct zones, in which uranium and zirconium migrate while plutonium remains relatively unchanged. Our EPMA results resolve eight distinct compositional regions, and more importantly, show that plutonium redistributes alongside zirconium, contrary to historical assumptions. The distribution of fission products was highly asymmetric with a few large lanthanide precipitates observed at isolated sites on the pin periphery instead of a uniform distribution of smaller precipitates around the periphery. Using thermodynamic data from TAF-ID and the measured EPMA compositions, matrix phase fractions were predicted across the fuel radius. Phase predictions based on composition did not match TEM/SAED results, which revealed a much higher fraction of α−U phase than would be expected if phases were retained from reactor temperatures. These findings highlight the need for expanded SAED phase identification to capture post-irradiation and storage effects, as well as rigorous uncertainty quantification in fuel performance and phase diagram modeling to better constrain predictions from compositional data.
Improved neutron inelastic scattering cross section data are needed to inform integral benchmark studies and advance applications in a wide variety of areas including nuclear energy, stockpile stewardship, nonproliferation, and space exploration. Neutron inelastic scattering also serves as a non-selective probe of low-lying nuclear structure. To help meet these needs, the Gamma Energy Neutron Energy Spectrometer for Inelastic Scattering (GENESIS) was constructed at the 88-Inch Cyclotron at Lawrence Berkeley National Laboratory. This array couples high-resolution γ-ray detectors and fast neutron detectors to achieve single and coincident n/γ detection over a broad energy range. The current configuration of the array includes 26 organic liquid scintillators and four high-purity germanium detectors (two single-crystal and two four-crystal CLOVER detectors with two-fold segmentation). The array was constructed with minimal supporting material and designed to cover a wide range of secondary particle angles and energies with limited inter-element scattering. Data acquisition is accomplished using Mesytec MDPP-16 multi-channel high-resolution digital pulse processing modules. The array characteristics, including γ-ray and neutron energy resolution, timing resolution, and detection efficiency were measured and used to validate a Geant4 model of the array. Furthermore, the primary sources of neutron background and the uncertainties in the determination of incident and secondary neutron energy were assessed. GENESIS provides a new capability to address nuclear data needs and facilitates the advancement of a wide range of nuclear applications.