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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Impedance-Based Detection of NO 2 Using Ni-MOF-74: Influence of Competitive Gas Adsorption

Chemically robust, low-power sensors are needed for the direct electrical detection of toxic gases. Metal–organic frameworks (MOFs) offer exceptional chemical and structural tunability to meet this challenge, though further understanding is needed regarding how coadsorbed gases influence or interfere with the electrical response. To probe the influence of competitive gases on trace NO 2 detection in a simulated flue gas stream, a combined structure–property study integrating synchrotron powder diffraction and pair distribution function analyses was undertaken, to elucidate how structural changes associated with gas binding inside Ni-MOF-74 pores correlate with the electrical response from Ni-MOF-74-based sensors. Data were evaluated for 16 gas combinations of N 2 , NO 2 , SO 2 , CO 2 , and H 2 O at 50 °C. Fourier difference maps from a rigid-body Rietveld analysis showed that additional electron density localized around the Ni-MOF-74 lattice correlated with large decreases in Ni-MOF-74 film resistance of up to a factor of 6 × 10 3 , observed only when NO 2 was present. These changes in resistance were significantly amplified by the presence of competing gases, except for CO 2 . Without NO 2 , H 2 O rapidly (<120 s) produced small (1–3×) decreases in resistance, though this effect could be differentiated from the slower adsorption of NO 2 by the evaluation of the MOF’s capacitance. Furthermore, samples exposed to H 2 O displayed a significant shift in lattice parameters toward a larger lattice and more diffuse charge density in the MOF pore. Evaluating the Ni-MOF-74 impedance in real time, NO 2 adsorption was associated with two electrically distinct processes, the faster of which was inhibited by competitive adsorption of CO 2 . Together, this work points to the unique interaction of NO 2 and other specific gases (e.g., H 2 O, SO 2 ) with the MOF’s surface, leading to orders of magnitude decrease in MOF resistance and enhanced NO2 detection. Finally, understanding and leveraging these coadsorbed gases will further improve the gas detection properties of MOF materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Hydride and Seek: Comparing Crystallographic Hydride Placement Techniques with an Open-Shell Cobalt Complex

Locating hydrides is crucial in organometallic chemistry but difficult to do accurately using X-ray diffraction. Electron diffraction has been proposed as a way to overcome this problem but has not been systematically compared to neutron diffraction and to quantum crystallography (Hirshfeld atom refinement, HAR) to test this hypothesis. Here, we present a comparative analysis of methods for a terminal cobalt hydride complex by comparing a single-crystal neutron diffraction reference structure to results from single-crystal X-ray diffraction with and without Hirshfeld atom refinement (HAR, NoSpherA2), density functional theory (DFT), and electron diffraction (3D-ED/MicroED) refined under kinematical and dynamical formalisms. Conventional X-ray diffraction gives lower precision than neutron diffraction as expected. Despite expected improvements, HAR gives systematic deviation from the neutron benchmark. Interestingly, optimized DFT equilibrium geometries are closer to the neutron value than the value from HAR. On the other hand, electron diffraction with a high-quality data set coupled with dynamical refinement localizes the hydride in difference maps and gives excellent agreement with the neutron data. Dynamical refinement is crucial, as kinematical refinement does not allow assignment of a hydride peak. This cross-modal comparison defines the conditions under which 3D-ED/MicroED delivers high-precision metal–hydride distances for this open-shell cobalt hydride.

anions↗
DOE OSTI2022

Quantum-inspired method for solving the Vlasov-Poisson equations

Kinetic simulations of collisionless (or weakly collisional) plasmas using the Vlasov equation are often infeasible due to high-resolution requirements and the exponential scaling of computational cost with respect to dimension. Recently, it has been proposed that matrix product state (MPS) methods, a quantum-inspired but classical algorithm, can be used to solve partial differential equations with exponential speed-up, provided that the solution can be compressed and efficiently represented as a MPS within some tolerable error threshold. Here, in this work, we explore the practicality of MPS methods for solving the Vlasov-Poisson equations for systems with one coordinate in space and one coordinate in velocity, and find that important features of linear and nonlinear dynamics, such as damping or growth rates and saturation amplitudes, can be captured while compressing the solution significantly. Furthermore, by comparing the performance of different mappings of the distribution functions onto the MPS, we develop an intuition of the MPS representation and its behavior in the context of solving the Vlasov-Poisson equations, which will be useful for extending these methods to higher-dimensional problems.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Quantum/AI Topology-Aware Latency-Adaptive HPC Workflow Scheduling Optimization

The growing demand for more powerful high-performance computing (HPC) systems has led to a steady rise in energy consumption by supercomputing worldwide. This study is focused on comparing our Application-Topology Mapper (ATMapper) to the popular Simple Linux Utility for Resource Management (SLURM) for the purpose of exploring methods that can further optimize job-scheduling within HPC systems. ATMapper is an Artificial-Intelligence based approach to job-scheduling that is currently being enhanced with quantum annealing (QA) to generate optimal schedules faster. We are applying QA to speedup our ATMapper process to achieve higher computing efficiency, thereby reducing HPC energy consumption. Here, we examine how four job-scheduling approaches perform in processor node assignment when using an example network architecture of 4 interconnected nodes. Using a specialized script, we are assessing the schedule of a computation flow with 11 interdependent tasks. The data movements among nodes were tracked to count for the number of interactions (network hops) between nodes needed to complete the tasks. The total number of hops and the job completion time were then used to quantify the efficiency of the different mapping approaches. In addition to SLURM, we also compare our ATMapper to the QA-enabled LBNL TIGER and the D-Wave Distributed Computing processor assignment approaches. The preliminary results showed that our topology-aware, latency-adaptive ATMapper is significantly more efficient when compared to the other scheduling approaches due to its load-imbalance network allocation. The scheduler displayed a computing efficiency of 53% by performing significantly fewer network hops than its alternatives. By reducing the number of hops, ATMapper was able to perform all 11 tasks by using only 3 nodes out of given 4. This research indicates the potential to use QA/AI for HPC job-scheduling. Later, we will test a SLURM simulator program to draw further comparisons on the effectiveness of ATMapper's scheduling approach. The results of this comparison will serve as a baseline for later improving SLURM's performance using a QA-enhanced ATMapper approach.

Caraveo, Braulio [University of Huston - Clear Lak↗
DOE OSTI2020

Ultrafast structural response to charge redistribution within a photosinthetic reaction centre

This data set contains stream files, difference maps and SVD maps obtained from data processing of time-resolved serial femtosecond crystallography measurements performed at LCLS XFEL, used to construct the images and perform the analysis published in the paper "Ultrafast structural response to charge redistribution within a photosinthetic reaction centre". Two (2) time points plus a dark state from 2015 ('a' label) as well as five (5) time points plus a dark state from 2016 ('b' label) are provided.

CXI↗
DOE OSTI2025

Turbulence in Core-collapse Supernovae

It is understood in a general sense that turbulent fluid motion below the shock front in a core-collapse supernova stiffens the effective equation of state of the fluid and aids in the revival of the explosion. However, when one wishes to be precise and quantify the amount of turbulence in a supernova simulation, one immediately encounters the problem that turbulence is difficult to define and measure. Using the 3D magnetohydrodynamic code ELEPHANT, we study how different definitions of turbulence change one’s conclusions about the amount of turbulence in a supernova and the extent to which it helps the explosion. We find that, while all the definitions of turbulence we use lead to a qualitatively similar growth pattern over time of the turbulent kinetic energy in the gain region, the total amount of turbulent kinetic energy, and especially the ratios of turbulent to total kinetic energy, distinguish them. Some of the definitions appear to indicate turbulence is a necessary contributor to the explosion, and others indicate it is not. The different definitions also produce turbulence maps with different correlations with maps of the enstrophy, a quantity widely regarded as also indicating the presence of turbulence. We also compute the turbulent adiabatic index and observe that, in regions of low enstrophy, this quantity is sensitive to the definition used. As a consequence, the effective adiabatic index depends upon the method used to measure the turbulence, and thus it alters one’s conclusions regarding the impact of turbulence within the supernova.

computational astronomy↗
DOE OSTI2025

Evidence-based Graph Adversary Mapping (EGRAM) [Poster]

Cybersecurity companies such as CrowdStrike, Dragos, Microsoft and Unit 42 categorize Advanced Persistent Threats (APTs) using their own naming schemes. As a result, these APTs are mapped to different malware sources and campaigns, all from differing sources, leading to inconsistent mapping. Inconsistent mapping causes confusion and adds further obscurity around these groups, making it difficult to track and mitigate APT cyberattacks. The Evidence-based Graph Adversary Mapping (EGRAM) tool remediates the mapping challenge by collecting, updating and converting adversary data and their sources into a valid, codified STIX v2.1 bundle which is then stored in a Neo4j graph database. It utilizes graph traversal methods and centrality analysis to generate actionable information as a Structured Threat Intelligence Graph (STIG), based on user queries. EGRAM exists as Python code and a Jupyter Notebook that acts as a searchable, evidence-based, source of intelligence for APT groups’ artifacts and cyber campaigns.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Method and apparatus for two-layer copy-on-write

A system, apparatus and method are provided in which a range of virtual memory addresses and a copy of that range are mapped to the same first system address range in a data processing system until an address in the virtual memory address range, or its copy, is written to. The common system address range includes a number of divisions. Responsive to a write request to an address in a division of the common address range, a second system address range is generated. The second system address range is mapped to the same physical addresses as the first system address range, except that the division containing the address to be written to and its corresponding division in the second system address range are mapped to different physical addresses. First layer mapping data may be stored in a range table buffer and updated when the second system address range is generated.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Application of the Chloride Susceptibility Index to Study the Effects of Ni, Cr, Mn and Mo on the Repassivation of Stainless Steels

The effects of Ni, Cr, Mn and Mo on the very earliest stages of repassivation of stainless steels are quantified using the Chloride Susceptibility Index (CSI), which is an ab initio-based index for the evaluation of repassivation tendency. The quinary system of Fe-Ni-Cr-Mn-Mo is studied with density functional theory analysis and an electrochemisorption model developed previously by the authors, which are required to determine the CSI. The adsorption energies of O and Cl to different surface configurations are calculated, and then surface coverage maps of different species on the surface are obtained from the adsorption energies based on the Langmuir isotherm. Finally, CSI is calculated for different compositions of stainless steels. It is found that the effect of alloying elements on promoting repassivation of Fe alloys is in the order of Mn > ≈Ni > Cr > Mo when solute composition is less than 28 wt.%. A strong synergy is found between Cr and Mo such that a combination of these two elements at a certain ratio can give an optimal (low) CSI. Here, the usage of CSI for evaluating repassivation tendency of CRAs is validated by experimental measured repassivation potential, which shows a strong monotonic negative relation with CSI.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A Simple Standard for Sharing Ontological Mappings (SSSOM)

Abstract Despite progress in the development of standards for describing and exchanging scientific information, the lack of easy-to-use standards for mapping between different representations of the same or similar objects in different databases poses a major impediment to data integration and interoperability. Mappings often lack the metadata needed to be correctly interpreted and applied. For example, are two terms equivalent or merely related? Are they narrow or broad matches? Or are they associated in some other way? Such relationships between the mapped terms are often not documented, which leads to incorrect assumptions and makes them hard to use in scenarios that require a high degree of precision (such as diagnostics or risk prediction). Furthermore, the lack of descriptions of how mappings were done makes it hard to combine and reconcile mappings, particularly curated and automated ones. We have developed the Simple Standard for Sharing Ontological Mappings (SSSOM) which addresses these problems by: (i) Introducing a machine-readable and extensible vocabulary to describe metadata that makes imprecision, inaccuracy and incompleteness in mappings explicit. (ii) Defining an easy-to-use simple table-based format that can be integrated into existing data science pipelines without the need to parse or query ontologies, and that integrates seamlessly with Linked Data principles. (iii) Implementing open and community-driven collaborative workflows that are designed to evolve the standard continuously to address changing requirements and mapping practices. (iv) Providing reference tools and software libraries for working with the standard. In this paper, we present the SSSOM standard, describe several use cases in detail and survey some of the existing work on standardizing the exchange of mappings, with the goal of making mappings Findable, Accessible, Interoperable and Reusable (FAIR). The SSSOM specification can be found at http://w3id.org/sssom/spec. Database URL: http://w3id.org/sssom/spec

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2022

Crustal and uppermost mantle structure of Cape Verde from ambient noise tomography

SUMMARY We present a seismic ambient noise tomography of the Cape Verde archipelago, located in the Atlantic Ocean, approximately 600 km west of Senegal. We used 38 seismic broad-band stations that continuously recorded for 10 months, in order to construct the first 3-D model of Sv-wave velocities for the crust and uppermost mantle beneath the Cape Verde region. We started by computing phase cross-correlations for vertical component recordings using all possible inter-island station pairs. Next, a time–frequency phase-weighted stack was applied to obtain robust Rayleigh-wave group-velocity dispersion curves in the period band between 10 and 24 s. Group-velocity maps at different periods are obtained by inverting the dispersion curves. We then inverted the group-velocity maps to obtain the 3-D shear wave velocity structure of the crust and uppermost mantle beneath Cape Verde. The final 3-D model extends from 8 km down to 23 km and has a lateral resolution of about 50 km. The crust in the southwestern sector, encompassing Fogo, presents lower S-wave velocities that may be caused by the presence of melt pockets and/or hydrothermal fluids circulation. The uppermost mantle beneath the northwestern sector is characterized by higher S-wave velocities in agreement with previous results obtained from Ps and Sp receiver functions. Those high-velocity anomalies can reflect non-altered crust or remnants of magma chambers or solidified basaltic intrusions, which fed the volcanism in these islands. Our maps revealed the presence of crustal underplating across the entire archipelago, yet stronger beneath the groups Santo Antão—São Vicente—São Nicolau and Fogo—Santiago—Maio.

Carvalho, J. (ORCID:0000000310778994)↗
DOE OSTI2023

DEEPEN 3D PFA Index Models for Exploration Datasets at Newberry Volcano

DEEPEN stands for DE-risking Exploration of geothermal Plays in magmatic ENvironments. As part of the development of the DEEPEN 3D play fairway analysis (PFA) methodology for magmatic plays (conventional hydrothermal, superhot EGS, and supercritical), index models needed to be developed to map values in geoscientific exploration datasets to favorability index values. This GDR submission includes those index models. Index models were created by binning values in exploration datasets into chunks based on their favorability, and then applying a number between 0 and 5 to each chunk, where 0 represents very unfavorable data values and 5 represents very favorable data values. To account for differences in how exploration methods are used to detect each play component, separate index models are produced for each exploration method for each component of each play type. Index models were created using histograms of the distributions of each exploration dataset in combination with literature and input from experts about what combinations of geophysical, geological, and geochemical signatures are considered favorable at Newberry. This is in attempt to create similar sized bins based on the current understanding of how different anomalies map to favorable areas for the different types of geothermal plays (i.e., conventional hydrothermal, superhot EGS, and supercritical). For example, an area of partial melt would likely appear as an area of low density, high conductivity, low vp, and high vp/vs. This means that these target anomalies would be given high (4 or 5) index values for the purpose of imaging the heat source. To account for differences in how exploration methods are used to detect each play component, separate index models are produced for each exploration method for each component of each play type. Index models were produced for the following datasets: - Geologic model - Alteration model - vp/vs - vp - vs - Temperature model - Seismicity (density*magnitude) - Density - Resistivity - Fault distance - Earthquake cutoff depth model

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

When less is more: How increasing the complexity of machine learning strategies for geothermal energy assessments may not lead toward better estimates

Previous moderate- and high-temperature geothermal resource assessments of the western United States utilized data-driven methods and expert decisions to estimate resource favorability. Although expert decisions can add confidence to the modeling process by ensuring reasonable models are employed, expert decisions also introduce human and, thereby, model bias. This bias can present a source of error that reduces the predictive performance of the models and confidence in the resulting resource estimates. Our study aims to develop robust data-driven methods with the goals of reducing bias and improving predictive ability. We present and compare nine favorability maps for geothermal resources in the western United States using data from the U.S. Geological Survey's 2008 geothermal resource assessment. Two favorability maps are created using the expert decision-dependent methods from the 2008 assessment (i.e., weight-of-evidence and logistic regression). With the same data, we then create six different favorability maps using logistic regression (without underlying expert decisions), XGBoost, and support-vector machines paired with two training strategies. The training strategies are customized to address the inherent challenges of applying machine learning to the geothermal training data, which have no negative examples and severe class imbalance. We also create another favorability map using an artificial neural network. We demonstrate that modern machine learning approaches can improve upon systems built with expert decisions. We also find that XGBoost, a non-linear algorithm, produces greater agreement with the 2008 results than linear logistic regression without expert decisions, because the expert decisions in the 2008 assessment rendered the otherwise linear approaches non-linear despite the fact that the 2008 assessment used only linear methods. The F1 scores for all approaches appear low (F1 score < 0.10), do not improve with increasing model complexity, and, therefore, indicate the fundamental limitations of the input features (i.e., training data). Until improved feature data are incorporated into the assessment process, simple non-linear algorithms (e.g., XGBoost) perform equally well or better than more complex methods (e.g., artificial neural networks) and remain easier to interpret.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Union: A Unified HW-SW Co-Design Ecosystem in MLIR for Evaluating Tensor Operationson Spatial Accelerators

To meet the extreme compute demands for deep learning across commercial and scientific applications, dataflow accelerators are becoming increasingly popular. While these“domain-specific” accelerators are not fully programmable like CPUs and GPUs, they retain varying levels of flexibility with respect to data orchestration, i.e., dataflow and tiling optimizations to enhance efficiency. There are several challenges when designing new algorithms and mapping approaches to execute the algorithms for a target problem on new hardware. Previous works have addressed these challenges individually. To address this challenge as a whole, in this work, we present an HW-SW co-design ecosystem for spatial accelerators called Union within the popular MLIR compiler infrastructure. Our framework allows exploring different algorithms and their mappings on several accelerator cost models. Union also includes a plug-and-play library of accelerator cost models and mappers which can easily be extended. The algorithms and accelerator cost models are connected via a novel mapping abstraction that captures the map space of spatial accelerators which can be systematically pruned based on constraints from the hardware, workload, and mapper. We demonstrate the value of Union for the community with several case studies which examine offloading different tensor operations (CONV/GEMM/Tensor Contraction) on diverse accelerator architectures using different mapping schemes.

Jeong, Geonhwa↗
DOE OSTI2021

Tracking Soiling Losses: Assessment, Uncertainty, and Challenges in Mapping

Several models have been presented in the recent years to estimate the magnitude of soiling from environmental parameters. However, these models are often based on data from a single site, or at most a few sites, and only limited data are, as of yet, available on their uncertainty. The present work aims to present a first comparative analysis of soiling estimation models, using measured soiling data from various locations in the USA. The study also investigates the impact that the source of the input data can have on the estimation. The results show that the model selection is only one of the factors that can affect the evaluation. Indeed, the use of satellite-derived or ground-mounted particulate matter data can lead to the generation of different soiling maps, with factors greater than 2x between the modeled losses. The current challenges and the unanswered questions that can bias soiling estimation are discussed. Additionally, potential research directions to improve the quality of soiling modeling are identified.

14 SOLAR ENERGY↗
DOE OSTI2025

FM4NPP: A Scaling Foundation Model for Nuclear and Particle Physics

Large language models have revolutionized artificial intelligence by enabling large, generalizable models trained through self-supervision. This paradigm has inspired the development of scientific foundation models (FMs). However, applying this capability to experimental particle physics is challenging due to the sparse, spatially distributed nature of detector data, which differs dramatically from natural language. This work addresses if an FM for particle physics can scale and generalize across diverse tasks. We introduce a new dataset with more than 11 million particle collision events and a suite of downstream tasks and labeled data for evaluation. We propose a novel self-supervised training method for detector data and demonstrate its neural scalability with models that feature up to 188 million parameters. With frozen weights and task-specific adapters, this FM consistently outperforms baseline models across all downstream tasks. The performance also exhibits robust data-efficient adaptation. Further analysis reveals that the representations extracted by the FM are task-agnostic but can be specialized via a single linear mapping for different downstream tasks.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Microstructural Evaluation of Phase Instability in Large Bandgap Metal Halide Perovskites

The optoelectronic performance of organic–inorganic halide perovskite (OIHP)-based devices has been improved in recent years. Particularly, solar cells fabricated using mixed-cations and mixed-halides have outperformed their single-cation and single-halide counterparts. Yet, a systematic evaluation of the microstructural behavior of mixed perovskites is missing despite their known composition-dependent photoinstability. Here, we explore microstructural inhomogeneity in (FAPbI 3 ) x (MAPbBr 3 ) 1– x using advanced scanning probe microscopy techniques. In this work, contact potential difference (CPD) maps measured by Kelvin probe force microscopy show an increased fraction of grains exhibiting a low CPD with flat topography as MAPbBr 3 concentration is increased. The higher portion of low CPD contributes to asymmetric CPD distribution curves. Chemical analysis reveals these grains being rich in MA, Pb, and I. The composition-dependent phase segregation upon illumination, reflected on the emergence of a low-energy peak emission in the original photoluminescence spectra, arises from the formation of such grains with flat topology. Bias-dependent piezo-response force microscopy measurements, in these grains, further confirm vigorous ion migration and cause a hysteretic piezo-response. Our results, therefore, provide insights into the microstructural evaluation of phase segregation and ion migration in OIHPs pointing toward process optimization as a mean to further enhance their optoelectronic performance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Probe the Localized Electrochemical Environment Effects and Electrode Reaction Dynamics for Metal Batteries using In Situ 3D Microscopy

Uncontrollable dendrite growth is closely related to non-uniform reaction environments. However, there is a lack of understanding and analysis methods to probe the localized electrochemical environment (LEE). Here the effects of the LEE are investigated, including localized ion concentrations, current density, and electric potential, on metal plating/stripping dynamics and dendrite minimization. A novel in situ 3D microscopy technique is developed to image the morphology dynamics and deposition rate of Zn plating/stripping processes on 3D Zn–Mn anodes. Using the in situ 3D microscope, the electrode morphology changes during the reactions are directly imaged and Zn deposition rate maps at different time points are obtained. It is found that reaction kinetics are highly correlated to LEE and electrode morphology. To further quantify the LEE effects, the digital twin technique is employed that allows the accurate calculation of the electrochemical environments, such as localized ion concentrations, current density, and electric potential, which cannot be directly measured from experiments. We found that the curvature of the 3D electrode surface determines the LEE and significantly influences reaction kinetics. This provides a new strategy to minimize the dendrite formation by designing and optimizing the 3D geometry of the electrode to control the LEE.

36 MATERIALS SCIENCE↗