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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 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

The General Atomic and Molecular Electronic Structure System (GAMESS): Novel Methods on Novel Architectures

The primary focus of GAMESS over the last 5 years has been the development of new high-performance codes that are able to take effective and efficient advantage of the most advanced computer architectures, both CPU and accelerators. These efforts include employing density fitting and fragmentation methods to reduce the high scaling of well-correlated (e.g., coupled-cluster) methods as well as developing novel codes that can take optimal advantage of graphical processing units and other modern accelerators. Because accurate wave functions can be very complex, an important new functionality in GAMESS is the quasi-atomic orbital analysis, an unbiased approach to the understanding of covalent bonds embedded in the wave function. Finally, best practices for the maintenance and distribution of GAMESS are also discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Deep learning of electrochemical CO 2 conversion literature reveals research trends and directions

Large-scale and openly available material science databases are mainly composed of computer simulation results rather than experimental data. Some examples include the Materials Project, Open Quantum Materials Database, and Open Catalyst 2022. Unfortunately, building large-scale experimental databases remains challenging due to the difficulties in consolidating locally distributed datasets. In this work, focusing on the catalysis literature of CO 2 reduction reactions (CO 2 RRs), we present a machine learning (ML)-based protocol for selecting highly relevant papers and extracting important experimental data. First, we report a document embedding method (Doc2Vec) for collecting papers of greatest relevance to the specific target domain, which yielded 3154 CO 2 RR-related papers from six publishers. Next, we developed named entity recognition (NER) models to extract twelve entities related to material names (catalyst, electrolyte, etc.) and catalytic performance (Faradaic efficiency, current density, etc.). Further, among several tested models, the MatBERT-based approach achieved the highest accuracy, with an average F1-score of 90.4% and an F1-score of 95.2% in a boundary relaxation evaluation scheme. The accurate and accelerated NER-based data extraction from a large volume of catalysis literature enables temporal trend analyses of the CO 2 RR catalysts, products, and performances, revealing the potentially effective material space in CO 2 RRs. While this work demonstrates the effectiveness of our ML-based text mining methods for specifically CO 2 RR literature, the methods and approach are applicable to and may be used to accelerate the development of other catalytic chemical reactions.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Order conditions for nonlinearly partitioned Runge-Kutta methods

Recently, a new class of nonlinearly partitioned Runge–Kutta (NPRK) methods was proposed for nonlinearly partitioned systems of autonomous ordinary differential equations y' = F(y, y). The target class of problems are those in which different scales, stiffnesses, or physics are coupled in a nonlinear way, wherein the desired partition cannot be written in a classical additive or component-wise fashion. Here we use a rooted-tree analysis to derive full-order conditions for NPRKM methods, where M denotes the number of nonlinear partitions. Due to the nonlinear coupling and thereby the mixed product differentials, it turns out that the standard node-colored rooted tree analysis used in analyzing ODE integrators does not naturally apply. Instead we develop a new edge-colored rooted-tree framework to address the nonlinear coupling. The resulting order conditions are enumerated, are provided directly for up to fourth order with M = 2 and third order with M = 3, and are related to existing order conditions of additive and partitioned RK methods. We conclude with an example that shows how the nonlinear order conditions can be used to obtain an embedded estimate of the state-dependent nonlinear coupling strength in a dynamical system.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Reducing AI RAG Hallucination by Optimizing Routing Techniques

Large Language Models (LLMs), such as ChatGPT, tend to “hallucinate”, meaning they confidently generate false information. Retrieval Augmented Generation (RAG) attempts to diminish hallucination by providing context to the LLM from data stores (indexes) containing relevant information. The LLM uses this context to formulate its response. RAG systems can still suffer from hallucination because of bad embeddings or ineffective routing. For example, a router will often return context from an irrelevant index, resulting in a hallucinated answer. In this study, we aim to minimize the frequency of routing hallucinations by optimizing Index Summary Routing.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Nanotomography for Quantitative 3D Particle Reconstruction

Particulates are ubiquitous across fuel cycle operations and carry critical information about particle formation, processing, and potential proliferation-related activities. Traditional analytical techniques, including micro-Raman spectroscopy and standard electron microscopy, are often limited in spatial resolution or dimensionality, particularly when used to examine metallic or submicron-scale features. Understanding particle morphology, phase distribution, and internal porosity is essential for constraining formation conditions, thermodynamic environments, and material transport behavior. In this report, we demonstrate the application of plasma focused ion beam nanotomography to reconstruct micron-scale particulates at nanoscale resolution. Using high-resolution backscattered electron imaging and Avizo software, we obtained 3D reconstructions that enabled quantitative analysis of particle morphology, phase composition, and internal voids. Representative examples include a Ta particle with a large central void and a composite particle with embedded tetrahedral crystalline structures. These reconstructions reveal structural and compositional details that are inaccessible through conventional 2D imaging. The results demonstrate that nanotomography provides both qualitative and quantitative insights into particle formation and behavior. Using nanotomography, porosity and phase distributions can be quantified to inform models of particle density, transport, and solidification conditions. Beyond technical insights, the workflow developed here establishes a transferable capability for analyzing heterogeneous particles and has potential applications in bulk materials studies via x-ray computed tomography or other volumetric imaging modalities. Ongoing efforts are focused on optimizing the workflow to process multiple particles simultaneously, increasing throughput and statistical robustness. Overall, this work illustrates the power of nanotomography as a tool for connecting particulate morphology to formation mechanisms, composition, and transport, thereby strengthening analytical capabilities for nuclear forensics, fuel cycle analysis, and related scientific investigations.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Transformer Neural Networks with Spatiotemporal Attention for Predictive Control and Optimization of Industrial Processes

In the context of real-time optimization and model predictive control of industrial systems, machine learning, and neural networks represent cutting-edge tools that hold promise for enhancing dynamic modeling. This work presents a novel transformer neural network architecture for real-time optimization and model predictive control. This network design includes a modified attention mechanism inspired by positional embedding attention from vision transformers and task-specific modifications to the input-output structure of the transformer’s decoder stack. Experiments were conducted using data from a 450 MW coal-fired power plant to evaluate this approach's effectiveness. The transformer neural network was compared with conventional recurrent models, including GRU and LSTM. The transformer exhibited a 6% increase in the R-squared (R2) value of predictions and an 83% reduction in mean squared error (MSE). Computation time was also reduced by 84% compared to conventional recurrent models.

Gallup, Ethan R.↗
DOE OSTI2019

A Comparison of GoldSim, RESRAD-Onsite, and NORMALYSA Modeling Tools at Radioactive Waste Sites - 19480

Radioactive waste disposal and associated decision making is a challenging problem around the world. A strong performance assessment model plays a crucial role in making good management and remediation decisions and in optimizing the performance of a site. The International Atomic Energy Agency (IAEA) MODARIA II Working Group 1 (WG1) is comparing the performance of various modeling platforms used at Naturally Occurring Radioactive Material (NORM) and legacy waste sites to support risk-informed decision making. Neptune and Company, Inc. (Neptune) has developed probabilistic performance assessment models using the GoldSim system modeling software at a variety of radioactive waste sites. This paper presents a comparison between GoldSim and two other tools used to evaluate radiation exposure and their associated risks: the NORM And Legacy Site Assessment (NORMALYSA) tool used to assess risk at the Zapadnoe mill tailings site, and the residual radioactivity (RESRAD-Onsite) computer program used to calculate Authorized Limits at the Los Alamos National Laboratory (LANL). The International Atomic Energy Agency (IAEA) supported development of NORMALYSA based off of Ecolego 6.0, in which the user connects several universal modules, representing various environmental transport and exposure pathways, and enters in site-specific data. Further work with NORMALYSA has continued under the MODARIA II WG 1. A radionuclide transport and dose calculation model of the Zapadnoe uranium mill tailings site at the Pridneprovsky Chemical Plant in the Ukraine has been configured by MODARIA II WG1 to test the capabilities of NORMALYSA. Under this test case, contaminant transport and effective dose calculations were performed for two exposure scenarios. The onsite worker is exposed via external ground irradiation and radon and radioactive dust inhalation. The offsite resident defines a residence established 300 years after institutional control has ended, 800 meters downstream from the site with exposure pathways including: ingestion of garden vegetables irrigated by contaminated ground water, ingestion of fish from the Konoplyanka River and agricultural activities in the contaminated garden. GoldSim modeling software is a dynamic probabilistic modeling platform that evaluates uncertainty in a robust and transparent manner. Although GoldSim provides contaminant and radionuclide transport modules to facilitate integrated modeling of radionuclide migration in the environment, these features are not embedded in an existing modeling framework, except as created by the user. Neptune developed a GoldSim model for the two exposure scenarios of the Zapadnoe test case. Neptune's Generic PA Model was used as a starting point for the Zapadnoe site. Input parameters for the GoldSim model include site-specific parameters from the MODARIA Working Group as well as parameters from the Generic PA Model and other PA models created by Neptune. For the other comparison, Version 7.0 of RESRAD-Onsite, developed by Argonne National Laboratory for the U.S. Department of Energy (DOE), was used to calculate Authorized Limits for LANL. Authorized Limits are concentrations of radionuclides in soil that support the evaluation process to clear land for release under different uses. The RESRAD-Onsite computer code was used to calculate Authorized Limits because this program was specifically designed to derive radionuclide soil guidelines corresponding to a specific dose criterion. The scope of the RESRAD dose or risk evaluation is constrained to the transport and exposure pathways that have been included in the computer code. To assist in illustrating the capability of GoldSim modeling software, a GoldSim model was created to replicate the calculation of LANL's Authorized Limits for soil. The main exposure pathways in this model are inhalation of dust and gas, plant ingestion, soil ingestion, and external ground irradiation. All of the processes and outputs of RESRAD-Onsite and NORMALYSA can be created in a GoldSim model, but the value of GoldSim lies in its flexibility for application to any of a wide variety of environmental modeling problems. GoldSim allows development of site-specific inventory, transport mechanisms and risk assessment scenarios. If all potentially complete release, transport, and exposure pathways are addressed in an existing dose and risk assessment computer code, and if the code's capabilities fulfill the project modeling objectives, then expending resources to create a GoldSim model may not be necessary. Site optimization and good decision making about site management are often best when supported with the flexibility of GoldSim as a modeling tool. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

A computer vision algorithm for interpreting lacustrine carbonate textures at Searles Valley, USA

Investigations of the paleohydrologies of pluvial lake systems have often employed lake carbonate deposits called “tufa” that grow subaqueously and can be preserved long after the drying of the lake. For this reason, tufa have been used as a proxy for minimum lake level. However, they exhibit a variety of textures that hold the potential to reveal richer paleoclimatological information. With the goal of determining if tufa texture can be used as a proxy for lake environment, this study investigates the textures of tufa at Mono Lake, California in comparison to the fossil tufa in Searles Valley, California. While observations in the last century suggest that the tufa in the Mono basin grew in waters similar to the modern, the tufa at Searles formed during the last glacial period, when the Great Basin contained a system of pluvial lakes on the scale of the modern Great Lakes. The tufa at both basins have been observed to have a range of classifiable textures, and new methods of inspecting visual data could be informative about what factors control these textures. To this end, a t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm is used to project images of the tufa at Searles and Mono into a coordinate space, allowing for simple, quantitative comparisons of the visual similarity of textures. In this work, the textures of tufa at Searles are compared to each other, as well as to the tufa at Mono. This study performs a robust assessment of the feasibility of Mono Lake as a modern analogue for Searles Valley. It finds that there is a justifiable basis for the comparison of certain fossil facies at Searles to the tufa at Mono, significant progress towards the goal of using texture as a metric for the environment in which tufa formed.

58 GEOSCIENCES↗
DOE OSTI2018

On the Ultrasolvability of p-Extensions of an Abelian Group by a Cyclic Kernel

The paper contains a solution of A. V. Yakovlev’s problem in the embedding theory for p-extensions of odd order with a cyclic normal subgroup and an Abelian quotient group: for such nonsplit extensions there exists a realization for the quotient group as a Galois group over number fields such that the corresponding embedding problem is ultrasolvable (i.e., this embedding problem is solvable and has only fields as solutions). A solution for embedding problems of p-extensions of odd order with kernel of order p and with a quotient group that is represented by a direct product of its proper subgroups is also given – this is a generalization for p > 2 of an analogous result for p = 2 due to A. Ledet.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Chlorophylls as primary electron acceptors in reaction centers

Photosynthetic Reaction Centers (RCs) can be considered blueprints for highly efficient energy transfer. Embedded with an array of cofactors, including (bacterio)chlorophyll ((B)Chl) and (B)pheophytin ((B)Pheo) molecules, RCs function with a high quantum yield that spans a wide spectral range. Understanding the principles that underlie their function can influence the design of the next generation of artificial photosynthetic devices. We are particularly interested in the factors that influence the early stages of light-driven charge separation in RCs. With the recent publication of several highly anticipated RC structures and advanced computational methods available, it is possible to probe both the geometric and electronic structures of an array of RCs. In this chapter, we review the electronic and geometric structures of the (B)Chl and (B)Pheo primary electron acceptors from fiveRCs, comprising both Type I and Type II RCs and representing both heterodimeric and homodimeric systems. We showcase the dimeric A0•- state of Type I RCs, whereby the unpaired electron is delocalized, to various extents, over two (B)Chl molecules (B)Chl2 and (B)Chl3. This delocalization is controlled by several factors, including the structure of the (B)Chls, interactions with the surrounding protein matrix, and the orientation and distances of the cofactors themselves. In contrast, the primary acceptors of Type II RCs are entirely monomeric, with electron density residing solely on the (B)Pheo. We compare the natural design of the primary acceptors of the Type I and Type II RCs from both an evolutionary and application based perspective.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Finch: Toxicity Dose Response Curve Prediction of Chemical Compounds and Mixtures

A paradigm shift in chemical risk assessment is emphasizing mixture testing over single compound analysis, eliminating animal testing, and adopting advanced modeling approaches to understand mixture activity profiles. However, existing computational models largely focus on single chemicals, with few effective solutions for modeling complex mixtures that account for synergistic or antagonistic effects and multiple Modes of Action (MoA). Conventional methods like concentration addition (CA) and independent action (IA) are insufficient for this task as they are designed for simplistic interactions and struggle to account for the dynamic and multifaceted nature of chemical mixtures, such as overlapping MoA and non-linear interactions. Finch offers a novel approach utilizing deep learning (DL) embeddings and multi-task quantitative structure-activity relationship (QSAR) models to improve chemical exposure prediction. By leveraging molecular descriptors, physiochemical properties, and large language model (LLM) embeddings from SMILES inputs, Finch preserves critical information in a latent space thereby enhancing predictive accuracy. The multi-task learning aspect of Finch is highly advantageous, as it simultaneously optimizes multiple loss functions, leveraging all available data across tasks to develop generalized representations that effectively capture complex ingredient interactions within mixtures.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Magnetic impurity as a local probe of the $U(1)$ quantum spin liquid with spinon Fermi surface

We solve the problem of a magnetic impurity coupled to a U(1) quantum spin liquid with a spinon Fermi surface, and we compute the impurity spectral function. Using the slave rotor mean-field approach combined with gauge field fluctuations, we find that a peak located at the top of the lower Hubbard band and one at the bottom of the upper Hubbard band can emerge in the impurity spectral function. The peaks at the Hubbard band edges arise from the gauge field fluctuation-induced spinon-chargon binding inside the spinon Kondo screening cloud of the magnetic impurity. For a magnetic impurity embedded in a Mott insulator, our findings suggest that the emergence of a pair of peaks at the Hubbard band edges in the impurity density of states spectra provides strong evidence that the host Mott insulator is a U(1) quantum spin liquid with a spinon Fermi surface.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A Parallel Cut-Cell Algorithm for the Free-Boundary Grad--Shafranov Problem

A parallel cut-cell algorithm is described to solve the free-boundary problem of the Grad--Shafranov equation. The algorithm reformulates the free-boundary problem in an irregular bounded domain and its important aspects include a searching algorithm for the magnetic axis and separatrix, a surface integral along the irregular boundary to determine the boundary values, an approach to optimize the coil current based on a targeting plasma shape, Picard iterations with Aitken's acceleration for the resulting nonlinear problem, and a Cartesian grid embedded boundary method to handle the complex geometry. Here the algorithm is implemented in parallel using a standard domain-decomposition approach and a good parallel scaling is observed. Numerical results verify the accuracy and efficiency of the free-boundary Grad--Shafranov solver.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Higgs Parity grand unification

The vanishing of the Higgs quartic coupling of the Standard Model at high energies may be explained by spontaneous breaking of Higgs Parity. Taking Higgs Parity to originate from the Left-Right symmetry of the $SO(10)$ gauge group, leads to a new scheme for precision gauge coupling unification that is consistent with proton decay. We compute the relevant running of couplings and threshold corrections to allow a precise correlation among Standard Model parameters. The scheme has a built-in solution for obtaining a realistic value for $m_b/m_\tau$, which further improves the precision from gauge coupling unification, allowing the QCD coupling constant to be predicted to the level of 1 % or, alternatively, the top quark mass to 0.2 %. Future measurements of these parameters may significantly constrain the detailed structure of the theory. We also study an $SO(10)$ embedding of quark and lepton masses, showing how large neutrino mixing is compatible with small quark mixing, and predict a normal neutrino mass hierarchy. The strong CP problem may be explained by combining Higgs Parity with space-time parity.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

Fractal Dimension Calculation for Big Data Using Box Locality Index

The box-counting approach for fractal dimension calculation is scaled up for big data using a data structure named box locality index (BLI). The BLI is constructed as key-value pair style with the key (an integer array) indexing the location of a “box” (i.e., a gird cell on the multi-dimensional space embedding a given dataset) and the value counting the number of data points inside the box (called “box occupancy”). Compared with the traditionally used E-dim tree, the BLI avoids complex hierarchical structure and encodes only necessary information required by box-counting approach for fractal dimension calculation. Moreover, the key-value pair nature of BLI, together with the fact that the box occupancy is aggregatable, grants box-counting approach the needed scalability for fractal dimension calculation of big data using distributed computing techniques (e.g., MapReduce and Spark). Taking the advantage of the BLI, MapReduce and Spark methods for fractal dimension calculation of big data are developed, which conduct box-counting for each grid level as a cascade of MapReduce/Spark jobs in a bottom-up fashion. In an empirical validation, the MapReduce and Spark methods demonstrated good effectiveness and efficiency in fractal calculation of a big synthetic dataset. In summary, this work provides an efficient solution for the estimation of intrinsic dimension of big data that is essential for many machine learning methods and data analytics.

Fractal dimension, Intrinsic dimension, Box-counti↗
DOE OSTI2018

Plasmon dispersion in a multilayer solid torus in terms of three-term vector recurrence relations and matrix continued fractions

Toroidal confinement, which has played a crucial role in magnetized plasmas and Tokamak physics, is emerging as an effective means to obtain useful electronic and optical response in solids. In particular, excitation of surface plasmons in metal nanorings by photons or electrons finds important applications due to the engendered field distribution and electromagnetic energy confinement. However, in contrast to the case of a plasma, often the solid nanorings are multilayered and/or embedded in a medium. The non-simply connected geometry of the torus results in surface modes that are not linearly independent. A three-term difference equation was recently shown to arise when seeking the nonretarded plasmon dispersion relations for a stratified solid torus (Garapati et al 2017 Phys. Rev. B 95 165422). The reported generalized plasmon dispersion relations are here investigated in terms of the involved matrix continued fractions and their convergence properties including the determinant forms of the dispersion relations obtained for computing the plasmon eigenmodes. We also present the intricacies of the derivation and properties of the Green's function employed to solve the three term amplitude equation that determines the response of the toroidal structure to arbitrary external excitations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2017

Multi-Scale Modeling of Swelling in Accident-Tolerant U 3 Si 2 Fuel

U 3 Si 2 is a promising candidate for use as an accident-tolerant fuel for light water reactors. A multi-scale computational approach was used to calculate swelling in pellet-form U 3 Si 2 fuel. Swelling was assumed to be equal to the volume fraction of fission gas bubbles in the fuel, and the evolution of bubble volume fraction was determined from phase-field simulations. To parameterize the phase-field model, density-functional theory and molecular dynamics simulations were performed. To enable molecular dynamics simulations, a new interatomic potential for the U-Si system was developed based on the modified embedded atom method. A new phase-field model based on a grand-potential functional was also developed. The model is applicable to both intergranular and intragranular bubbles. To calculate the volume fraction of bubbles, the microstructure was decomposed into regions consisting of only intragranular or intergranular bubbles based on a truncated octahedral grain structure, and growth of the bubbles in the two regions was simulated separately. The total swelling was then calculated based on a a weighted average of bubble volume fraction in the two regions. Total swelling was of the same order of magnitude, but larger than that predicted by the existing empirical swelling model used in BISON and a rate-theory based model. Limitations of the present approach and suggestions for improvement are presented.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗