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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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At least 865 records · Page 48

The TRAPUM L -band survey for pulsars in Fermi -LAT gamma-ray sources

ABSTRACT More than 100 millisecond pulsars (MSPs) have been discovered in radio observations of gamma-ray sources detected by the Fermi Large Area Telescope (LAT), but hundreds of pulsar-like sources remain unidentified. Here, we present the first results from the targeted survey of Fermi-LAT sources being performed by the Transients and Pulsars with MeerKAT (TRAPUM) Large Survey Project. We observed 79 sources identified as possible gamma-ray pulsar candidates by a Random Forest classification of unassociated sources from the 4FGL catalogue. Each source was observed for 10 min on two separate epochs using MeerKAT’s L-band receiver (856–1712 MHz), with typical pulsed flux density sensitivities of $\sim 100\, \mu$Jy. Nine new MSPs were discovered, eight of which are in binary systems, including two eclipsing redbacks and one system, PSR J1526−2744, that appears to have a white dwarf companion in an unusually compact 5 h orbit. We obtained phase-connected timing solutions for two of these MSPs, enabling the detection of gamma-ray pulsations in the Fermi-LAT data. A follow-up search for continuous gravitational waves from PSR J1526−2744 in Advanced LIGO data using the resulting Fermi-LAT timing ephemeris yielded no detection, but sets an upper limit on the neutron star ellipticity of 2.45 × 10−8. We also detected X-ray emission from the redback PSR J1803−6707 in data from the first eROSITA all-sky survey, likely due to emission from an intrabinary shock.

79 ASTRONOMY AND ASTROPHYSICS↗

Anticorrelation between electron-phonon coupling strength and stability of ternary metal diborides

This paper endeavors to expand the exploration of prospective superconductivity within the 𝑃⁢6/𝑚⁢𝑚⁢𝑚−MgB 2 structural framework by substituting Mg atoms with a random selection of two distinct cations from a pool of 24 candidates. Employing a high-throughput screening methodology to scrutinize zone-center electron-phonon couplings, we found an inverse relationship between thermodynamic stability and electron-phonon coupling strength in the resulting ternary metal borides. Notably, within the MgNiB 4 system, we demonstrated that electron-phonon superconductivity is stronger in less dynamically stable configurations. Further, the qualitative reasons for the emerging instabilities when electron-phonon coupling becomes strong are discussed. This insight indicates that the quest for superior superconducting materials should concentrate on the intricate balance between electronic and phononic characteristics in structures that are on the brink of instability.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Probabilistic Voltage Sensitivity based Preemptive Voltage Monitoring in Unbalanced Distribution Networks

With increasing penetration of renewable energy and active consumers, control and management of power distribution networks has become challenging. Renewable energy sources can cause random voltage fluctuations as their output power depends on weather conditions. Conventional voltage control schemes such as tap changers and capacitor banks lack the foresight required to quickly alleviate voltage violations. Thus, there is an urgent need for effective approaches for predicting and mitigating voltage violations as a result of random fluctuations in power injections. This work proposes a novel voltage monitoring approach based on low-complexity, data-driven probabilistic voltage sensitivity analysis. The usefulness of this work is not only in predicting voltage violations in unbalanced distribution grids, but also in opening up the door for optimal voltage control. Using system data and forecasts, the proposed approach predicts the distribution of system node voltages which is then used to to identify nodes that may violate the nominal operational limits with high probability. The method is tested on the IEEE 37 node distribution system considering integrated distributed solar energy sources. The method is validated against the classic load flow based method and offers over 95% accuracy in predicting voltage violations.

Abujubbeh, Mohammad↗

2007 Chicago Regional Household Travel Inventory

To provide data for developing and refining travel demand forecast models, the 2007 Regional Household Travel Inventory studied the demographic and travel behavior characteristics of residents in the greater Chicago area. It included households in eight counties in Illinois and three in Indiana. The Chicago Metropolitan Agency for Planning conducted the study in coordination with the Illinois Department of Transportation, the Northwestern Indiana Regional Planning Commission, and the Indiana Department of Transportation. Travel and activity information for all household members (regardless of age) was collected during a randomly assigned 24- or 48-hour period. It relied on the willingness of households to provide detailed information about their members and vehicles, and to record all travel and associated activity during the collection period. This study also featured a subsample of households collecting data via in-vehicle and wearable global positioning system devices.

1Hz data↗

Sensitivity analysis of numerical modeling input parameters on floating offshore wind turbine loads

Abstract. Floating wind turbines must withstand a unique and challenging set of loads from the wind and ocean environment. To de-risk development, accurate predictions of these loads are necessary. Uncertainty in modeling predictions leads to larger required safety factors, increasing project costs and the levelized cost of energy. Complex aero-hydro-elastic modeling tools use many input parameters to represent the wind, waves, current, aerodynamic loads, hydrodynamic loads, and structural properties. It is helpful to understand which of these parameters ultimately drives a design. In this work, an ultimate and fatigue-proxy load sensitivity analysis was performed with 35 different input parameters, using an elementary effects approach to identify the most influential parameters for a case study involving the National Renewable Energy Laboratory (NREL) 5 MW baseline wind turbine atop the OC4-DeepCwind semisubmersible during normal operation. The importance of each parameter was evaluated using 14 response quantities of interest across three operational wind speed conditions. The study concludes that turbulent wind velocity standard deviation is the parameter with the strongest sensitivity; this value is important not just for turbine loads, but also for the global system response. The system center of mass in the wind direction is found to have the highest impact on the system rotation and tower loads. The current velocity is found to be the most dominating parameter for the system global motion and consequently the mooring loads. All tested wind turbulence parameters in addition to the standard deviation are also found to be influential. Wave characteristics are influential for some fatigue-proxy loading but do not significantly impact the extreme ultimate loads in these operational load cases. The required number of random seeds for stochastic environmental conditions is considered to ensure that the sensitivities are due to the input parameters and not due to the seed. The required number of analysis points in the parameter space is identified so that the conclusions represent a global sensitivity. The results are specific to the platform, turbine, and choice of parameter ranges, but the demonstrated approach can be applied widely to guide focus in parameter uncertainty.

17 WIND ENERGY↗

Interfacial Exchange Phenomena Driven by Ferromagnetic Domains

Abstract Interfacial proximity effects in antiferromagnetic/ferromagnetic (AFM/FM) bilayers control the exchange‐bias (EB) phenomena exploited in most spintronic devices, although still is lack of full understanding. Discordant results, including different exchange‐bias field ( H E ), coercivity ( H C ), or blocking temperature ( T B ) found even in similar systems, are usually ascribed to uncontrolled parameters, namely dissimilar interfacial defects, structure, and thicknesses. Here, it is shown in the very same sample that the magnetic domain structure during the magnetization reversal of the FM layer controls those mentioned effects. Simultaneous transport and vectorial‐resolved magnetic measurements performed in a V 2 O 3 /Co system during warming after different field cooling (FC) procedures exhibit a strong dependence on the FC angle and the domain structure of the FM layer. Remarkably, magnetization reversal analysis reveals 35 K of variation in T B and up to a factor of two in H E . These observations can be explained within the random‐field model for the interfacial exchange coupling with a fixed AFM domain structure in contact with a variable (angle‐dependent) FM domain structure. The results highlight the importance of the domain structure and magnetization reversal of the FM layer (not previously considered) in the EB phenomena, with potential to tailor interfacial effects in future spintronic devices.

36 MATERIALS SCIENCE↗

Identification of hot water end-use process of electric water heaters from energy measurements

This paper presents an algorithm for the identification of parameters for a stochastic hot water end-use process that drives a homogeneous population of thermostatically controlled electric water heaters (EWH). Usually, only metered interval consumption data (kWh) is collected and the hot water end-use process is unobservable to utility and aggregators. However, the availability of EWHs for demand response (DR) is closely coupled with the hot water end-use process. In this context, the hot water end-use process is modeled as a two-state Markov chain (Use / No use), which causes the thermostatic ON-OFF switching process to behave as a Markov renewal process (MRP). A set of first passage-time problems is developed to obtain the moments of the transition probability densities of the MRP. These problems are addressed by establishing a system of coupled partial differential equations characterizing the temperature evolution of the EWH population. A key quantity in the methodology for estimating the parameters is the total time an EWH is ON within a period of interest. It is referred to as the total busy time. Total busy time in this approach is a random variable for which analytical expressions of the moments are developed as a function of the metered window length. The latter expressions become the basis of a hot water demand model identification algorithm which is validated using agent-based simulations of EWHs.

42 ENGINEERING↗

How Representative Are Uncrewed Aircraft System Measurements of the Convective Boundary Layer?

Abstract Uncrewed aircraft systems (UAS) demonstrate significant potential for filling data gaps in the atmospheric boundary layer. However, the extent to which UAS observations—typically vertical profiles taken over 15 min—are representative of the boundary layer as a whole remains poorly characterized. Using large eddy simulations (LES) of the daytime convective boundary layer (CBL), we quantify random errors in UAS measurements that occur due to insufficient statistical convergence of the time average to the true ensemble mean. Random errors in first‐order moments increase as the CBL becomes increasingly unstable, and are largest near the surface for most quantities. Errors are on the order of 2–6 m for wind speed, 15–60 for wind direction, 0.2–3 K for potential temperature, and 0.1–1 g for specific humidity, with errors in turbulent fluxes on the order of 50%–100%. Sampling strategies that mitigate random errors are discussed in light of our results.

Greene, Brian R. [Now at Verisk Extreme Event Solu↗

MArVD2: a machine learning enhanced tool to discriminate between archaeal and bacterial viruses in viral datasets

Abstract Our knowledge of viral sequence space has exploded with advancing sequencing technologies and large-scale sampling and analytical efforts. Though archaea are important and abundant prokaryotes in many systems, our knowledge of archaeal viruses outside of extreme environments is limited. This largely stems from the lack of a robust, high-throughput, and systematic way to distinguish between bacterial and archaeal viruses in datasets of curated viruses. Here we upgrade our prior text-based tool (MArVD) via training and testing a random forest machine learning algorithm against a newly curated dataset of archaeal viruses. After optimization, MArVD2 presented a significant improvement over its predecessor in terms of scalability, usability, and flexibility, and will allow user-defined custom training datasets as archaeal virus discovery progresses. Benchmarking showed that a model trained with viral sequences from the hypersaline, marine, and hot spring environments correctly classified 85% of the archaeal viruses with a false detection rate below 2% using a random forest prediction threshold of 80% in a separate benchmarking dataset from the same habitats.

Vik, Dean (ORCID:000000027546899X)↗

Machine Learning for Searching the Dark Energy Survey for Trans-Neptunian Objects

In this paper we investigate how implementing machine learning could improve the efficiency of the search for Trans-Neptunian Objects (TNOs) within Dark Energy Survey (DES) data when used alongside orbit fitting. The discovery of multiple TNOs that appear to show a similarity in their orbital parameters has led to the suggestion that one or more undetected planets, an as yet undiscovered “Planet 9”, may be present in the outer solar system. DES is well placed to detect such a planet and has already been used to discover many other TNOs. Here, we perform tests on eight different supervised machine learning algorithms, using a data set consisting of simulated TNOs buried within real DES noise data. We found that the best performing classifier was the Random Forest which, when optimized, performed well at detecting the rare objects. We achieve an area under the receiver operating characteristic (ROC) curve, (AUC) = 0.996 ± 0.001. After optimizing the decision threshold of the Random Forest, we achieve a recall of 0.96 while maintaining a precision of 0.80. Finally, by using the optimized classifier to pre-select objects, we are able to run the orbit-fitting stage of our detection pipeline five times faster.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

New Capability at ORNL: High Precision Uranium Titration

Destructive analytical measurements establish a nuclear facility’s nuclear material inventory and inventory differences for Nuclear Material Accountancy and Control. A nuclear laboratory’s ability to perform accurate high-precision analytical measurements is key for tracking large inventories within a facility’s material balance areas and during production to track material movement through dynamic processes. For uranium, these measurements are made by using several established high-precision measurement protocols. These include isotope dilution mass spectrometry, gravimetry, and potentiometric titrations. Whatever measurement technique a nuclear lab chooses to use, reference materials (RMs) with certified attributes and accompanying uncertainties are used to calibrate measurement systems, and are the cornerstone for accurate results. In addition to calibration, RMs provide for metrological traceability, are used for method development and validation, and thus provide critical evaluations of the appropriateness and performance of analytical processes used. Evaluations may include validation that a method is fit-for-purpose, quantification of systematic and random biases, and the evaluation of long-term and short-term performance metrics. High-precision measurement techniques require that measured attributes be certified to a high degree of precision in the RMs used—ultimately, to a higher degree than that of the measurement technique itself. The US authority on the production of special nuclear material Certified Reference Materials is the NBL Program Office (NBLPO), formally known as New Brunswick Laboratory (NBL). The NBLPO is responsible for the sales and distribution of existing NBL certified reference materials (CRMs) and for the production of the next generation of nuclear RMs. To accomplish its mission, NBLPO is establishing key base capabilities within the DOE laboratory complex that formerly existed at the NBL laboratory. The Nuclear Analytical Chemistry (NAC) section within the Chemical Sciences Division (CSD) at Oak Ridge National Laboratory (ORNL) is currently working with NBLPO to set up laboratory and measurement capabilities to provide measurements and capabilities for production and/or recertification of existing and future CRMs for uranium assay. The NBL-developed high-precision titration (HPT) method is a critically-evaluated, extremely precise and accurate primary method utilized for the determination of uranium content in a variety of uranium materials. The HPT method, combined with detailed balance weighing protocols, provides for an analytical methodology that is unsurpassed in precision and one in which all sources of error have been evaluated, a requirement of CRM certification. HPT capability within the United States was lost with the closure of the labs at NBL. The NAC has been collaborating with NBLPO to stand up and demonstrate the capability to perform uranium assay via HPT at ORNL. HPT can produce results with an expanded uncertainty of approximately 0.01% for pure uranium compounds, with typical precisions of <0.006%. The major tasks required to stand up the method at ORNL were the refurbishment of a dedicated lab and equipment setup, procedure development, analyst training, establishing method-specific quality assurance, and qualification of an analyst to perform the method. This report summarizes these tasks, outlines the documents drafted, and gives the outcome of the qualification titrations.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

Therapeutic targeting of membrane-associated proteins in central nervous system tumors

The activity of the most complex system, the central nervous system (CNS) is profoundly regulated by a huge number of membrane-associated proteins (MAP). A minor change stimulates immense chemical changes and the elicited response is organized by MAP, which acts as a receptor of that chemical or channel enabling the flow of ions. Slight changes in the activity or expression of these MAPs lead to severe consequences such as cognitive disorders, memory loss, or cancer. CNS tumors are heterogeneous in nature and hard-to-treat due to random mutations in MAPs; like as overexpression of EGFRvIII/TGFβR/VEGFR, change in adhesion molecules α5β3 integrin/SEMA3A, imbalance in ion channel proteins, etc. Extensive research is under process for developing new therapeutic approaches using these proteins such as targeted cytotoxic radiotherapy, drug-delivery, and prodrug activation, blocking of receptors like GluA1, developing viral vector against cell surface receptor. The combinatorial approach of these strategies along with the conventional one might be more potential. Henceforth, our review focuses on in-depth analysis regarding MAPs aiming for a better understanding for developing an efficient therapeutic approach for targeting CNS tumors.

60 APPLIED LIFE SCIENCES↗

Ab initio property predictions of quinary solid solutions using small binary cells

The Set of Small Ordered Structures (SSOS) approach is an ab initio technique for modelling random solid solutions in which many small structures are averaged so that their correlation functions match those of a desired composition. SSOS has been shown to be effective in reducing the cost of density functional theory calculations relative to other well-known techniques such as cluster expansions and special quasirandom structures for modelling solid solutions. Here in this work, we demonstrate that SSOS’s can be constructed using cells with only a subset of elements while still accurately modelling multi-component systems. Specifically, we show that small binary cells can effectively model two quinary high entropy alloys – NbTaTiHfZr and MoNbTaVW – accurately capturing properties such as formation energy, lattice parameters, elastic constants, and root-mean-square atomic displacements. Overall, this insight is useful for those looking to construct databases of such small structures for predicting the properties of multi-component solid solutions, as it greatly decreases the number of structures that needs to be considered.

36 MATERIALS SCIENCE↗

From pixels to patterns: Coupling Optical Coherence Tomography and machine learning for monitoring coastal wetland root systems

Coastal wetlands are crucial in shoreline stabilization, carbon sequestration, and storm protection. Yet, due to limitations in traditional destructive sampling techniques, the belowground biomass (live root mass) and necromass (dead and decaying roots) remain difficult to assess in coastal wetlands, limiting our understanding on coastal resilience, nutrient cycling, and soil structure. This study employs Optical Coherence Tomography (OCT) as a high-resolution imaging technique to analyze root biomass and necromass in the Terrebonne Basin, Louisiana. A Random Forest (RF) model was developed to classify root health states based on OCT-derived features, achieving an accuracy of 70% in distinguishing live from dead root segments. The results demonstrate that OCT, combined with ML, offers a promising novel approach to root analysis, providing fine-scale insights into root morphology and decay patterns that are not easily captured by conventional methods. This research lays the foundation for future integration of OCT with complementary imaging modalities such as X-ray Computed Tomography (XCT) and advanced ML algorithms to enhance classification accuracy and scalability. Future work aims to expand the dataset diversity across different wetland types and apply the methodology for large-scale, repeatable assessments of root biomass turnover and accumulation, with important implications for wetland monitoring, conservation, and restoration under changing environmental conditions.

AI/ML↗

Quantum Embedding Theory for Strongly Correlated States in Materials

Quantum embedding theories are promising approaches to investigate strongly correlated electronic states of active regions of large-scale molecular or condensed systems. Notable examples are spin defects in semiconductors and insulators. We present a detailed derivation of a quantum embedding theory recently introduced, which is based on the definition of effective Hamiltonians. The effect of the environment on a chosen active space is accounted for through screened Coulomb interactions evaluated using density functional theory. Importantly, the random phase approximation is not required, and the evaluation of virtual electronic orbitals is circumvented with algorithms previously developed in the context of calculations based on many-body perturbation theory. In addition, we generalize the quantum embedding theory to active spaces composed of orbitals that are not eigenstates of Kohn–Sham Hamiltonians. Finally, we report results for spin defects in semiconductors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Is Terzan 5 the remnant of a building block of the Galactic bulge? Evidence from APOGEE

ABSTRACT It has been proposed that the globular cluster-like system Terzan 5 is the surviving remnant of a primordial building block of the Milky Way bulge, mainly due to the age/metallicity spread and the distribution of its stars in the α–Fe plane. We employ Sloan Digital Sky Survey data from the Apache Point Observatory Galactic Evolution Experiment to test this hypothesis. Adopting a random sampling technique, we contrast the abundances of 10 elements in Terzan 5 stars with those of their bulge field counterparts with comparable atmospheric parameters, finding that they differ at statistically significant levels. Abundances between the two groups differ by more than 1σ in Ca, Mn, C, O, and Al, and more than 2σ in Si and Mg. Terzan 5 stars have lower [α/Fe] and higher [Mn/Fe] than their bulge counterparts. Given those differences, we conclude that Terzan 5 is not the remnant of a major building block of the bulge. We also estimate the stellar mass of the Terzan 5 progenitor based on predictions by the Evolution and Assembly of GaLaxies and their Environments suite of cosmological numerical simulations, concluding that it may have been as low as ∼3 × 108 M⊙ so that it was likely unable to significantly influence the mean chemistry of the bulge/inner disc, which is significantly more massive (∼1010 M⊙). We briefly discuss existing scenarios for the nature of Terzan 5 and propose an observational test that may help elucidate its origin.

79 ASTRONOMY AND ASTROPHYSICS↗

Interactions Between Climate Mean and Variability Drive Future Agroecosystem Vulnerability

ABSTRACT Agriculture is crucial for global food supply and dominates the Earth's land surface. It is unknown, however, how slow but relentless changes in climate mean state, versus random extreme conditions arising from changing variability , will affect agroecosystems' carbon fluxes, energy fluxes, and crop production. We used an advanced weather generator to partition changes in mean climate state versus variability for both temperature and precipitation, producing forcing data to drive factorial‐design simulations of US Midwest agricultural regions in the Energy Exascale Earth System Model. We found that an increase in temperature mean lowers stored carbon, plant productivity, and crop yield, and tends to convert agroecosystems from a carbon sink to a source, as expected; it also can cause local to regional cooling in the earth system model through its effects on the Bowen Ratio. The combined effect of mean and variability changes on carbon fluxes and pools was nonlinear, that is, greater than each individual case. For instance, gross primary production reduces by 9%, 1%, and 13% due to change in mean temperature, change in temperature variability, and change in both temperature mean and variability, respectively. Overall, the scenario with change in both temperature and precipitation means leads to the largest reduction in carbon fluxes (−16% gross primary production), carbon pools (−35% vegetation carbon), and crop yields (−33% and −22% median reduction in yield for corn and soybean, respectively). By unambiguously parsing the effects of changing climate mean versus variability and quantifying their nonadditive impacts, this study lays a foundation for more robust understanding and prediction of agroecosystems' vulnerability to 21st‐century climate change.

54 ENVIRONMENTAL SCIENCES↗

STATISTICAL ANALYSIS OF IN-SERVICE ULTRASONIC INSPECTION DATA OF WASTE TANKS AT THE SAVANNAH RIVER Site-25021

CONCLUSIONS AND RECOMMENDATIONS •“There is no evidence of pit depth growth over the last 15-years demonstrating the effectiveness of the corrosion control program. •“Incipient pits” are not growing and are likely pre-service pits. • No need to continue measuring these tiny pits. However, due to the random nature of pit initiation and the potentially accelerated growth of a pit, periodic monitoring for pit growth is prudent. 1. No reportable pits were identified. All identified pits are far less than the reportable depth of 25% of the nominal wall thickness. 2. No cracking was identified. 3. There is no evidence that active, systemic corrosion has occurred in Tank 29 during its 38-year service history as of 2009.

Harris, Stephen P. [Savannah River National Labora↗