Search NASA⌕ Search

SEARCH · Search NASA

Results for “computational capability”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Sensing Electrical Networks Securely & Economically (SENSE)

The growing adoption of distributed energy resources (DERs) like battery energy storage systems and roof top solar/PV and the rapid penetration of electric vehicles (EVs), the electric grid is undergoing a major transformation with elevated stress on legacy grid assets. Despite a lot of expenditure to address these challenges, both in dollars and manpower, utilities have not been able to receive the value that was promised. The gains have been most visible at the transmission and substation level, especially where the main objective was improving operational and economic efficiency for the utility. Improving visibility and control at a few select points enhances the existing and established paradigm of centralized command and control. With changing load patterns, load types and the overall transition to an “active grid”, the centralized control and coordination paradigm gets challenged. To address the challenges, a new architecture and mechanism is needed, one that supports decentralized control and decision making, extracting value streams at the grid edge, particularly as the changes are fueled by transitions occurring in the distribution system. To address this, a communications and data processing platform, “GAMMA” was developed and demonstrated through the project. At the heart of the platform, are distributed, intelligent edge nodes with sensing and compute capabilities, that can record and analyze information locally. They are embedded in sensors and actuators specific to different distribution system applications. Phase 1 of the project focused on developing novel sensor technology that can be used for monitoring utility pole top distribution transformers. The sensors were designed with the objective of being low-cost, communicating with the GAMMA cloud using novel “delay-tolerant” networking using Bluetooth and a secure mobile application. They were non-intrusive in nature so that they can be installed quickly in the field, resulting in overall low cost of deployment and operations. Following the successful completion of Phase 1, the team manufactured 100 units for a field demonstration in Phase 2. The field demonstration was carried out on two real feeder systems with the local utility partner. In total, 100 sensors were installed and operated over a period of 6 months in the state of Georgia. The platform is operational end to end, with the cloud infrastructure deployed on a distributed, serverless environment that can serve multiple data streams, an analytics engine and a portal to securely view the data from multiple assets. The data collected through the GAMMA Mobile Phone app showcased the viability of the novel delay tolerant networking architecture, and the data processing algorithms developed through the course of the project, were successful in extracting important information about the overall network, improving the utility’s visibility and situational awareness in the distribution feeder.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

304L Can Crush Validation Studies

Accurate prediction of ductile behavior of structural alloys up to and including failure is essential in component or system failure assessment, which is necessary for nuclear weapons alteration and life extensions programs of Sandia National Laboratories. Modeling such behavior requires computational capabilities to robustly capture strong nonlinearities (geometric and material), rate- dependent and temperature-dependent properties, and ductile failure mechanisms. This study's objective is to validate numerical simulations of a high-deformation crush of a stainless steel can. The process consists of identifying a suitable can geometry and loading conditions, conducting the laboratory testing, developing a high-quality Sierra/SM simulation, and then drawing comparisons between model and measurement to assess the fitness of the simulation in regards to material model (plasticity), finite element model construction, and failure model. Following previous material model calibration, a J 2 plasticity model with a microstructural BCJ failure model is employed to model the test specimen made of 304L stainless steel. Simulated results are verified and validated through mesh and mass-scaling convergence studies, parameter sensitivity studies, and a comparison to experimental data. The converged mesh and degree of mass-scaling are the mesh discretization with 140,372 elements, and a mass scaling with a target time increment of 1.0e-6 seconds and time step scale factor of 0.5, respectively. Results from the coupled thermal-mechanical explicit dynamic analysis are comparable to the experimental data. Simulated global force vs displacement (F/D) response predicts key points such as yield, ultimate, and kinks of the experimental F/D response. Furthermore, the final deformed shape of the can and field data predicted from the analysis are similar to that of the deformed can, as measured by 3D optical CMM scans and DIC data from the experiment.

36 MATERIALS SCIENCE↗
DOE OSTI2025

2D reactive transport model of shale chemical weathering and biogeochemical fluxes along a mountainous hillslope, East River Watershed, Colorado: Input files and simulation results

This data package contains input files and simulation results for a two-dimensional (2D) reactive transport model used to quantitatively analyze the coupled hydrological and biogeochemical processes governing shale weathering and associated biogeochemical fluxes under realistic environmental conditions in the high-elevation East River Watershed. These data support the conclusions presented in Stolze et al. (Water Resources Research, under review), "Model-based interpretation of solute exports and carbon partitioning during shale weathering in a mountainous hillslope". The model simulates atmospheric-subsurface gas exchange, subsurface water flow, and shale weathering processes under dynamic, year-scale conditions along a shale-underlain hillslope located in the East River watershed. The simulations were performed using the PFLOTRAN flow and reactive transport code and executed on the Perlmutter supercomputer to leverage its large-scale parallel computing capabilities. The data package contains two zipped folders, "model_input_files" and "simulation_results", and one readme.txt file. "model_input_files" contains the necessary input files to run the calibrated base-base model presented in Stolze et al. (Water Resources Research, under review). "simulation_results" contains a single hdf5 file ("Output_2D_hillslope_model.h5") which includes the results of simulation performed using the base-case model. This file can be opened with HDFView 3.1.4, Python, or MATLAB. "readme.txt" contains relevant information about the base-case model and provides guidelines on how to run the associated input files provided in the folder "model_input_files". Furthermore, readme.txt provides information regarding the model results provided in "Output_2D_hillslope_model.h5" such as matrix dimensionality and output units. Field datasets used to evaluate model performance were collected at three monitoring wells located along a hillslope transect (PLM1, PLM2, and PLM3). Dissolved ion concentration data were collected from November 2016 to October 2021 for Ca, Mg, DIC, Na, K, SO4 (Dong et al., 2025 - dic_npoc_data_2014_2024.zip - DOI:10.15485/1660459; Williams et al., 2025 - anion_data_2014_2024.zip - DOI:10.15485/1668054; Dong et al., 2025 - cation_data_2014_2024.zip - DOI:10.15485/1668055). Note that we used the files named er_PLM1_xx_yy, er_PLM2_xx_yy, and er_PLM3_xx_yy where xx stands for the name of the aqueous species and yy stands for the depth where the measurements were performed. Soil water content ([0 - 1] m) and water table depth were collected from November 2016 to October 2021 (Wan et al., 2024 - Dynamic_water_table__depthsFig2b.csv and Soil_water_content_Fig4e.csv - DOI:10.15485/2322567). Gaseous CO2 concentration were collected from October 2020 to December 2021(Wan et al., 2024 - Soil_CO2_concentrations_Fig4h.csv - DOI:10.15485/2322567) Gaseous CO2 flux from the subsurface to the atmosphere were collected in the vicinity of PLM2 from October 2019 to May 2022 (Wu et al., 2025). Soil microbial biomass concentration was measured from August 2016 to June 2017 (Sorensen et al., 2019 - 2017_East_River_Pumphouse_Microbial_Biomass__1_.csv - DOI:10.15485/1577267) All field data are published as CSV files compatible with Microsoft Excel, MATLAB, and Python, or as text files. The coordinates of the monitoring wells and the CO2(g) flux sensor in the coordinate system WGS84 are: -PLM1: [38.9197710 ; -106.9492750] -PLM2: [38.9201580 ; -106.9487170] -PLM3: [38.9207843 ; -106.9483668] -PLM4: 38.9210060 ; -106.9479528] -CO2(g) flux sensor: [38.9199180 ; -106.9489906] ------------------------------------------------------------------------------------------- This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231. This research used resources of the National Energy Research Scientific Computing Center (NERSC), a Department of Energy User Facility using NERSC award BER-ERCAP 23980, BER-ERCAP 28550, and BER-ERCAP 33789.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Embedded Fluidic Sensing and Control with Soft Open‐Cell Foams

Abstract The synthesis of soft matter intelligence with circuit‐driven logic has enabled a new class of robots that perform complex tasks or conform to specialized form factors in unique ways that cannot be realized through conventional designs. Translating this hybrid approach to fluidic systems, the present work addresses the need for sheet‐based circuit materials by leveraging the innate porosity of foam—a soft material—to develop pneumatic components that support digital logic, mixed‐signal control, and analog force sensing in wearables and soft robots. Analytical tools and experimental techniques developed in this work serve to elucidate compressible gas flow through porous sheets, and to inform the design of centimeter‐sized foam resistors with fluidic resistances on the order of 10 9 Pa s m −3 . When embedded inside soft robots and wearables, these resistors facilitate diverse functionalities spanning both sensing and control domains, including digital logic using textile logic gates, digital‐to‐analog signal conversion using ladder networks, and analog sensing of forces up to 40 N via compression‐induced changes in resistance. By combining features of both circuit‐based and materials‐based approaches, foam‐enabled fluidic circuits serve as a useful paradigm for future hybrid robotic architectures that fully embody the sensing and computing capabilities of soft fluidic materials.

Rajappan, Anoop↗
DOE OSTI2022

Probing the Kinetic Origin of Varying Oxidative Stability of Ethyl- vs. Propyl-spaced Amines for Direct Air Capture

Amine-based adsorbents are promising for direct air capture of CO 2 , yet oxidative degradation remains a key unmitigated risk hindering wide-scale deployment. Borrowing wisdom from the basic auto-oxidation scheme, insights are gained into the underlying degradation mechanisms of polyamines by quantum chemical, advanced sampling simulations, adsorbent synthesis, and accelerated degradation experiments. The reaction kinetics of polyamines are contrasted with that of typical aliphatic polymers and they elucidate for the first time the critical role of aminoalkyl hydroperoxide decomposition in the oxidative degradation of amino-oligomers. The experimentally observed variation in oxidative stability of polyamines with different backbone structures is explained by the relationship between the local chemical structure and the free energy barrier of aminoalkyl hydroperoxide decomposition, suggesting that its energetics can be used as a descriptor to screen and design new polyamines with improved stability. In conclusion, the developed computational capability sheds light on radical-induced degradation chemistry of other organic functional materials.

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

Calibration of elastoplastic constitutive model parameters from full-field data with automatic differentiation-based sensitivities

Here, we present a framework for calibration of parameters in elastoplastic constitutive models that is based on the use of automatic differentiation (AD). The model calibration problem is posed as a partial differential equation-constrained optimization problem where a finite element (FE) model of the coupled equilibrium equation and constitutive model evolution equations serves as the constraint. The objective function quantifies the mismatch between the displacement predicted by the FE model and full-field digital image correlation data, and the optimization problem is solved using gradient-based optimization algorithms. Forward and adjoint sensitivities are used to compute the gradient at considerably less cost than its calculation from finite difference approximations. Through the use of AD, we need only to write the constraints in terms of AD objects, where all of the derivatives required for the forward and inverse problems are obtained by appropriately seeding and evaluating these quantities. We present three numerical examples that verify the correctness of the gradient, demonstrate the AD approach's parallel computation capabilities via application to a large-scale FE model, and highlight the formulation's ease of extensibility to other classes of constitutive models.

42 ENGINEERING↗
DOE OSTI2023

Usage Pattern Analysis for the Summit Login Nodes

High performance computing (HPC) users interact with Summit through dedicated gateways, also known as login nodes. The performance and stability of these login nodes can have a significant impact on the user experience. In this study, the performance and stability of Summit’s five login nodes are evaluated by analyzing the log data from 2020 and 2021. The analysis focuses on the computing capability (CPU average load, users and tasks) and the storage performance, along with the associated job scheduler activity. The outcome of this study can serve as the foundation of a predictive modeling framework that enables the system admin of an HPC system to preemptively deploy countermeasures before the onset of a system failure.

Eiffert, Brett↗
DOE OSTI2022

Fundamental Issues Identified for Thermodynamic Description of Molten Salt Systems

Thermodynamic modeling of the molten salt systems is critical in the design of advanced nuclear power plants, materials recycling, and many other important clean energy applications. As one of the most capable computational thermodynamic approaches, the CALPHAD (Calculation of Phase Diagrams) method has been widely used for multicomponent thermodynamic database development and further support more physics-based modeling. Although considerable CALPHAD modeling efforts for molten salt systems have been made, we found that no evaluation has been performed regarding the unary thermodynamic models for the molten salt systems despite the fact that more than one pure substance database is available for the CALPHAD modeling. Therefore, the thermodynamic descriptions of the pure salts should be critically evaluated to ensure a high-fidelity molten salt thermodynamic database. In this work, we comprehensively analyzed some selected molten salts using different available thermodynamic descriptions from two databases as an example. One is the SSUB database released by the SGTE (Scientific Group Thermodata Europe), and the other is the FactPS database from the FactSage. Finally, the significant discrepancies observed in the thermodynamic description between the existing CALPHAD pure substance databases and experiments call for an urgent effort to improve.

42 ENGINEERING↗
DOE OSTI2024

The contributions of microclimatic information in advancing ecosystem science

Drawing upon over 100 years of scholarly work on microclimate, we first present an overview of the history, key references, and critical issues surrounding the collection and utilization of microclimate records in ecosystem studies. We place particular emphasis on addressing specific and pressing issues related to the applications of microclimate at the community-ecosystem-landscape level, excluding those of controlled experiment such as growth chambers and greenhouses. Specifically, we: (1) highlight some key issues concerning the collection, quality assurance/quality control (QA/QC), and utilization of microclimatic data in ecosystem studies; (2) revisit microclimatic responses to the structural changes of ecosystems and landscapes; and (3) emphasize the significance of microclimate in understanding major ecosystem/landscape processes and functions. Vapor pressure deficit (VPD) is particularly emphasized for its calculation and use because of its burgeoning applications in the literature. Case studies for each of the three thematic topics are provided with selected references to demonstrate challenges and solutions. As the scientific community gears up to enhance microclimatic stations, we envision significant increases in the use of smart sensors, wireless access, networking, open databases, and computational capabilities. Understanding and addressing some of the issues raised in this synthesis paper may help advance microclimate research and foster collaboration with other relevant disciplines, such as ecosystem science.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Making sense of uncertain nuclear data

At its core, and in an imperfect world, the scientific method relies heavily on the concept of uncertain knowledge. Understanding the nature and magnitude of our lack of knowledge on nuclear data has important ramifications on a wide range of nuclear technologies that encompass defense, energy, medicine, astrophysics, among others. Over the span of a long and fruitful carrier, Dr. Massimo Salvatores has made significant contributions to the fields of nuclear reactor physics, nuclear data, and uncertainty quantification and propagation. Advances in machine learning techniques and computational capabilities are enabling a new level of confidence in our ability to predict key nuclear metrics as well as their associated uncertainties across many applications. Here this short article reviews some of those concepts with an eye towards developing a new paradigm to provide nuclear data libraries of benefit to various applications.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Graph neural networks for CO 2 solubility predictions in Deep Eutectic Solvents

Deep Eutectic Solvents (DESs) are a promising class of solvents for CO 2 capture. DESs are complex mixtures that can be designed to optimize CO solubility and overall capture process efficiency. However, the vast design landscape of DES mixtures makes experimental investigation prohibitive; as such, there is a need for computational models that can quickly and efficiently navigate the design space and inform data collection efforts. In this work, we propose Graph Neural Network (GNN) models for predicting CO 2 solubility for DESs; the GNN leverages a mixture graph representation that captures the molecular structure of the DES components as well as their intermolecular interactions. Here, we compare the GNN framework against alternative architectures (neural networks, graph convolution networks, and random forests) and data representations (molecular fingerprints, sigma profiles, and graphs). We show that the proposed approach offers superior predictive performance; specifically, we show that solubility can be predicted reliably directly from molecular structure (without the need of using sigma profiles as proposed in previous studies). This result is important, as obtaining sigma profiles requires expensive density functional theory computations. We also explored the ability of GNNs to predict solubility for new DES mixtures and operating conditions. We found that the model extrapolates across temperature reliably. However, we also found deficiencies in the ability of the model to predict solubility for DES mixtures, pressures, and molar ratio not included in the training sets; we show that this is due to an inherent lack of chemical diversity in datasets available in the literature. The proposed computational capabilities can thus help navigate the design space of DES and inform data collection efforts. Our models, data, and benchmarks are shared as Python code implemented in Jupyter notebooks.

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

Towards next-generation optical potentials for nuclear reactions and structure calculations

Optical-model potentials (OMPs) are critical ingredients for basic and applied nuclear physics. Present-day computational capabilities allow us to generate data-driven nucleon-nucleus OMPs that are non-local and exactly dispersive (as theoretically required to be), include statistically-sound uncertainty quantification, and are trained on both scattering and bound-state data from a wide area of the nuclear chart. Combined together, these features allow for significant improvement in fidelity and extrapolative power of the model. Here, we present preliminary work toward the development and training of such an OMP. The capability of the model to describe data at this first stage is encouraging.

Perrotta, Salvatore Simone [Lawrence Livermore Nat↗
DOE OSTI2021

Particle resonances in stellarators

Resonances of high energy particles in magnetic confinement devices due to electromagnetic instabilities can strongly modify the particle distribution, leading to a reduction in fusion power and even discharge termination and particle loss to the device walls through an avalanche. The existence of a mode particle resonance depends on the properties of the equilibrium and particle parameters, and their number, location, and density can vary with device design. Recently, the advent of more powerful computing capabilities and advanced theoretical understanding has led to the design of non-axisymmetric devices or stellarators, which could prove to be more advantageous than tokamaks. Stellarators have the advantage of being immune to major disruptions because of the very low plasma current. One of the problems shared by both types of devices is the existence of resonances in particle orbits, which can lead to large amplitude high frequency instabilities and subsequent induced particle loss. We examine the number of resonances, their location, and dependence on particle energy for some stellarator designs.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Quantum subspace expansion in the presence of hardware noise

Finding ground state energies on current quantum processing units (QPUs) using algorithms such as the variational quantum eigensolver (VQE) continues to pose challenges. Hardware noise severely affects both the expressivity and trainability of parameterized quantum circuits, limiting them to shallow depths in practice. Here, we demonstrate that both issues can be addressed by synergistically integrating VQE with a quantum subspace expansion, allowing for an optimal balance between quantum and classical computing capabilities and costs. We perform a systematic benchmark analysis of the iterative quantum-assisted eigensolver in the presence of hardware noise. We determine ground state energies of 1D and 2D mixed-field Ising spin models on noisy simulators and the IBM QPUs ibmq_quito (5 qubits) and ibmq_guadalupe (16 qubits). To maximize accuracy, we propose a suitable criterion to select the subspace basis vectors according to the trace of the noisy overlap matrix. Finally, we show how to systematically approach the exact solution by performing controlled quantum error mitigation based on probabilistic error reduction on the noisy backend fake_guadalupe.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Harnessing ferro-valleytricity in pentalayer rhombohedral graphene for memory and compute

Two-dimensional materials with multiple degrees of freedom, including spin, valleys, and orbitals, open up an exciting avenue for engineering multifunctional devices. Beyond spintronics, these degrees of freedom can lead to novel quantum effects such as valley-dependent Hall effects and orbital magnetism, which could revolutionize next-generation electronics. However, achieving independent control over valley polarization and orbital magnetism has been a challenge due to the need for large electric fields. A recent breakthrough involving pentalayer rhombohedral graphene has demonstrated the ability to individually manipulate anomalous Hall signals and orbital magnetic hysteresis, forming what is known as a valley-magnetic quartet. Here, we leverage the electrically tunable ferro-valleytricity of pentalayer rhombohedral graphene to develop nonvolatile memory and in-memory computation applications. We propose an architecture for a dense, scalable, and selector-less nonvolatile memory array that harnesses the electrically tunable ferro-valleytricity. In our designed array architecture, nondestructive read and write operations are conducted by sensing the valley state through two different pairs of terminals, allowing for independent optimization of read/write peripheral circuits. The power consumption of our PRG-based array is remarkably low, with only ∼6 nW required per write operation and ∼2.3 nW per read operation per cell. This consumption is orders of magnitude lower than that of the majority of state-of-the-art cryogenic memories. Additionally, we engineer in-memory computation by implementing majority logic operations within our proposed nonvolatile memory array without modifying the peripheral circuitry. In conclusion, our framework presents a promising pathway toward achieving ultra-dense cryogenic memory and in-memory computation capabilities.

2D materials↗
DOE OSTI2024

Development of Whole System Digital Twins for Advanced Reactors: Leveraging Graph Neural Networks and SAM Simulations

Here, in this work, we introduce a novel method to develop whole system digital twins (DTs) for advanced nuclear reactors. This method treats a complex reactor system as a heterogeneous graph: with the system components as different types of graph nodes and their physical interconnections as edges. Based on the heterogeneous graph, a graph neural network combining graph convolution and temporal node attention is developed as the DT, facilitating a comprehensive understanding of the system's dynamic behavior. By utilizing the System Analysis Module (SAM) code for simulating various operational transients, we develop a graph-based database that trains the DT. This DT is characterized by two primary functions: It can infer the entire system's status using sparse node information, and it can predict the progress of transients based on current and historical system information. Our approach is validated through case studies on the Experimental Breeder Reactor II (EBR-II) system and a generic Fluoride-salt-cooled High-temperature Reactor (gFHR), demonstrating the DT's accuracy in forecasting operational transients. The DT's rapid computation capabilities enhance its potential for supporting advanced reactor operations, offering benefits in intelligent simulation, autonomous control, and anomaly detection, paving the way for improved safety analysis and intelligent component health management for advanced reactor systems and reducing their operations and maintenance cost.

EBR-II↗
DOE OSTI2022

The NASA Carbon Monitoring System Phase 2 synthesis: scope, findings, gaps and recommended next steps

Abstract Underlying policy efforts to address global climate change is the scientific need to develop the methods to accurately measure and model carbon stocks and fluxes across the wide range of spatial and temporal scales in the Earth system. Initiated in 2010, the NASA Carbon Monitoring System is one of the most ambitious relevant science initiatives to date, exploiting the satellite remote sensing resources, computational capabilities, scientific knowledge, airborne science capabilities, and end-to-end system expertise that are major strengths of the NASA Earth Science program. Here we provide a synthesis of ‘Phase 2’ activities (2011–2019), encompassing 79 projects, 482 publications, and 136 data products. Our synthesis addresses four key questions: What has been attempted? What major results have been obtained? What major gaps and uncertainties remain? and What are the recommended next steps? Through this review, we take stock of what has been accomplished and identify future priorities toward meeting the nation’s needs for carbon monitoring reporting and verification.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Geometric GNNs for charged particle tracking at GlueX

Nuclear physics experiments are aimed at uncovering the fundamental building blocks of matter. The experiments involve high-energy collisions that produce complex events with many particle trajectories. Tracking charged particles resulting from collisions in the presence of a strong magnetic field is critical to enable the reconstruction of particle trajectories and precise determination of interactions. It is traditionally achieved through combinatorial approaches that scale worse than linearly as the number of hits grows. Since particle hit data naturally form a point cloud and can be structured as graphs, graph neural networks (GNNs) emerge as an intuitive and effective choice for this task. In this study, we evaluate the GNN model for track finding on the data from the GlueX experiment at Jefferson Lab. We use simulation data to train the model and test on both simulation and real GlueX measurements. We demonstrate that GNN-based track finding outperforms the currently used traditional method at GlueX in terms of segment-based efficiency at a fixed purity while providing faster inferences. We show that the GNN model can achieve significant speedup by processing multiple events in batches, which exploits the parallel computation capability of graphical processing units (GPUs). Finally, we compare the GNN implementation on GPU and field-programmable gate array and describe the trade-off.

batched GNN pipeline↗