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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Gramian-Based Characterization of Network Vulnerability to Nodal Impulse Inputs: Preprint

Impulsive inputs applied at influential nodes of a network system may result in undesirable behavior and degrade performance. This paper proposes the notion of vulnerability matrix (VM) to characterize the effect of impulse inputs on a network following either discrete-time or continuous-time dynamics. The VM describes the first-order effects of impulse inputs on edge flows and is based on the controllability Gramian. We provide explicit expressions for the elements of the vulnerability matrix for the class of directed line networks in terms of the edge weights. Simulations validate our results and highlight the utility of the proposed metric in capturing the transient effects of nodal impulse inputs on edge flows.

controllability Gramian↗
DOE OSTI2023

Detection of False Data Injection Attacks in Battery Stacks Using Input Noise-Aware Nonlinear State Estimation and Cumulative Sum Algorithms

Grid-scale battery energy storage systems (BESSs) are vulnerable to false data injection attacks (FDIAs), which could be used to disrupt state of charge (SoC) estimation. Inaccurate SoC estimation has negative impacts on system availability, reliability, safety, and the cost of operation. In this article a combination of a Cumulative Sum (CUSUM) algorithm and an improved input noise-aware extended Kalman filter (INAEKF) is proposed for the detection and identification of FDIAs in the voltage and current sensors of a battery stack. The series-connected stack is represented by equivalent circuit models, the SoC is modeled with a charge reservoir model and the states are estimated using the INAEKF. Further, the root mean squared error of the states’ estimation by the modified INAEKF was found to be superior to the traditional EKF. By employing the INAEKF, this article addresses the research gap that many state estimators make asymmetrical assumptions about the noise corrupting the system. Additionally, the INAEKF estimates the input allowing for the identification of FDIA, which many alternative methods are unable to achieve. The proposed algorithm was able to detect attacks in the voltage and current sensors in 99.16% of test cases, with no false positives. Utilizing the INAEKF compared to the standard EKF allowed for the identification of FDIA in the input of the system in 98.43% of test cases.

25 ENERGY STORAGE↗
DOE OSTI2024

Lyapunov-based nonlinear control of nonautonomous systems with individual input constraints

A control algorithm that can locally stabilize a specific class of multi-input multi-output nonautonomous nonlinear dynamical systems while satisfying individual input constraints is developed. The proposed Lyapunov-based state-feedback control law inherently accounts for the actuator amplitude saturation limits without the need for computationally expensive real-time optimization techniques. In addition to the control law, a formal definition for the local “controllable region” within which the controller can asymptotically drive the system states to the origin and satisfy the input saturation limits is also presented. The nonautonomous nature of the system dynamics implies that the “controllable region” continuously evolves with time. Therefore, a sufficient condition to maintain the system states within the “controllable region” is proposed in this work to make practical implementation feasible. The effectiveness of the controller is tested for a specific control problem arising in tokamaks, which are toroidal devices that use strong magnetic fields to confine a plasma (hot ionized gas). Here, the primary emphasis of tokamak research is to regulate the plasma properties around predetermined values to achieve stable plasma confinement. Nonlinear simulations show that the proposed controller can achieve the desired plasma control objectives in a DIII-D tokamak scenario.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Pilgrim Hot Springs: GEOPHIRES Inputs and Outputs for Direct-Use Geothermal District Heating and Cooling

This dataset includes files for a techno-economic analysis conducted using the GEOPHIRES simulator to examine the feasibility of expanding a larger district heating site in a remote location: Pilgrim Hot Springs, Alaska. Files included here are GEOPHIRES inputs and outputs for five different scenarios with varying demand, cycle, and system design characteristics to analyze. Also included is the link to the GEOPHIRES GitHub, as well as a link to the dataset that contains the energy modelling used to determine the heating demand for the district. For a list of the differences between scenarios, see the included "Input Overview.txt" file. Fields included in the input files are: subsurface technical parameters, surface technical parameters, financial parameters, capital and O&M parameters, as well as simulation parameters. The output files are case reports that summarize all equipment, reservoir characteristics, costs, and heating profiles.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Robustness of Deep Learning Classification to Adversarial Input on GPUs: Asynchronous Parallel Accumulation Is a Source of Vulnerability

The ability of machine learning (ML) classification models to resist small, targeted input perturbations—known as adversarial attacks—is a key measure of their safety and reliability. We show that floating-point non associativity (FPNA) coupled with asynchronous parallel programming on GPUs is sufficient to result in misclassification, without any perturbation to the input. Additionally, we show that this misclassification is particularly significant for inputs close to the decision boundary and that standard adversarial robustness results may be overestimated up to 4.6 when not considering machine-level details. We first study a linear classifier, before focusing on standard Graph Neural Network (GNN) architectures and datasets used in robustness assessments. We develop a novel black-box attack using Bayesian optimization to discover external workloads that can change the instruction scheduling which bias the output of reductions on GPUs and reliably lead to misclassification. Motivated by these results, we present a new learnable permutation (LP) gradient-based approach to learning floating-point operation orderings that lead to misclassifications. The LP approach provides a worst-case estimate in a computationally efficient manner, avoiding the need to run identical experiments tens of thousands of times over a potentially large set of possible GPU states or architectures. Finally, using instrumentation-based testing, we investigate parallel reduction ordering across different GPU architectures under external background workloads, when utilizing multi-GPU virtualization, and when applying power capping. Our results demonstrate that parallel reduction ordering varies significantly across architectures under the first two conditions, substantially increasing the search space required to fully test the effects of this parallel scheduler-based vulnerability. These results and the methods developed here can help to include machine-level considerations into adversarial robustness assessments, which can make a difference in safety and mission critical applications.

Shanmugavelu, Sanjif [Maxeler Technologies, a Groq↗
DOE OSTI2025

An Analysis of Input Parameters for Film-Based Flash X-Ray Radiography

Flash X-ray radiography (flash) is a commonly used diagnostic technique in dynamic experiments. An analysis of the effects of input parameters on resulting metrics of image quality can aid the experimentalist in configuring the X-ray input parameters to produce the highest quality radiograph for a given experiment. Here, a flash X-ray test bed with HS800 film and a LANEX Medium F intensifier screen was used with an L3 450 kVp pulser and Scandiflash X-ray tube for this study. Input parameters including charge voltage, source filtering, and film-pack assembly were investigated for their impact on contrast-to-noise ratio (CNR), contrast, and contrast transfer function (CTF). Using VIDAR’s NDT Pro industrial film digitizer, scanner parameters such as optical density range, pixel spacing, scan mode, and digital bit-depth were also examined for their impact on image quality metrics. The highest CNR values were found with two LANEX intensifiers and no filtering. Charge voltage had no direct impact on CNR values. LANEX screen count and filtering resulted both in direct effects on CNR and interaction effects with each other and CNR value. Uncertainty bounds for CNR comparisons and repeatability of CTF evaluations are also discussed. Finally, the film results are compared with a previous study using other detector types, specifically Carestream INDUSTREX Flex GP, Flex HR, Flex XL Blue, and HPX-DR 3543.

dynamic radiography↗
DOE OSTI2024

Soil Carbon Stocks Not Linked to Aboveground Litter Input and Chemistry of Old-Growth Forest and Adjacent Prairie

The long-standing assumption that aboveground plant litter inputs have a substantial influence on soil organic carbon storage (SOC) and dynamics has been challenged by a new paradigm for SOC formation and persistence. We tested the importance of plant litter chemistry on SOC storage, distribution, composition, and age by comparing two highly contrasting ecosystems: an old-growth coast redwood (Sequoia sempervirens) forest, with highly aromatic litter, and an adjacent coastal prairie, with more easily decomposed litter. We hypothesized that if plant litter chemistry was the primary driver, redwood would store more and older SOC that was less microbially processed than prairie. Total soil carbon stocks to 110 cm depth were higher in prairie (35 kg C m —2 ) than redwood (28 kg C m —2 ). Radiocarbon values indicated shorter SOC residence times in redwood than prairie throughout the profile. Higher amounts of pyrogenic carbon and a higher degree of microbial processing of SOC appear to be instrumental for soil carbon storage and persistence in prairie, while differences in fine-root carbon inputs likely contribute to younger SOC in redwood. We conclude that at these sites fire residues, root inputs, and soil properties influence soil carbon dynamics to a greater degree than the properties of aboveground litter.

13C-NMR spectroscopy↗
DOE OSTI2022

Radium Inputs Into the Arctic Ocean From Rivers: A Basin‐Wide Estimate

Abstract Radium isotopes have been used to trace nutrient, carbon, and trace metal fluxes inputs from ocean margins. However, these approaches require a full accounting of radium sources to the coastal ocean including rivers. Here, we aim to quantify river radium inputs into the Arctic Ocean for the first time for 226 Ra and to refine the estimates for 228 Ra. Using new and existing data, we find that the estimated combined (dissolved plus desorbed) annual 226 Ra and 228 Ra fluxes to the Arctic Ocean are [7.0–9.4] × 10 14 dpm y −1 and [15–18] × 10 14 dpm y −1 , respectively. Of these totals, 44% and 60% of the river 226 Ra and 228 Ra, respectively are from suspended sediment desorption, which were estimated from laboratory incubation experiments. Using Ra isotope data from 20 major rivers around the world, we derived global annual 226 Ra and 228 Ra fluxes of [7.4–17] × 10 15 and [15–27] × 10 15 dpm y −1 , respectively. As climate change spurs rapid Arctic warming, hydrological cycles are intensifying and coastal ice cover and permafrost are diminishing. These river radium inputs to the Arctic Ocean will serve as a valuable baseline as we attempt to understand the changes that warming temperatures are having on fluxes of biogeochemically important elements to the Arctic coastal zone.

Bullock, Emma J.↗
DOE OSTI2023

Engineering activatable promoters for scalable and multi-input CRISPRa/i circuits

Dynamic, multi-input gene regulatory networks (GRNs) are ubiquitous in nature. Multilayer CRISPR-based genetic circuits hold great promise for building GRNs akin to those found in naturally occurring biological systems. We develop an approach for creating high-performing activatable promoters that can be assembled into deep, wide, and multi-input CRISPR-activation and -interference (CRISPRa/i) GRNs. By integrating sequence-based design and in vivo screening, we engineer activatable promoters that achieve up to 1,000-fold dynamic range in an Escherichia coli-based cell-free system. These components enable CRISPRa GRNs that are six layers deep and four branches wide. We show the generalizability of the promoter engineering workflow by improving the dynamic range of the light-dependent EL222 optogenetic system from 6-fold to 34-fold. Additionally, high dynamic range promoters enable CRISPRa systems mediated by small molecules and protein–protein interactions. We apply these tools to build input-responsive CRISPRa/i GRNs, including feedback loops, logic gates, multilayer cascades, and dynamic pulse modulators. Our work provides a generalizable approach for the design of high dynamic range activatable promoters and enables classes of gene regulatory functions in cell-free systems.

42 ENGINEERING↗
DOE OSTI2021

Effect of Soil Properties and Input Motion on Site Amplification Using Validated Nonlinear Soil Model

Here, three-dimensional (3-D) nonlinear site response analyses are conducted using finite element models of actual soil profiles from ten nuclear power plant (NPP) sites in the United States to investigate the effects of soil properties and input motions on site amplification. The modeling approach developed in this study combines several novel elements, such as 3-D analysis (including vertical motions), nonlinear inelastic behavior of soil (strain-dependent shear modulus reduction and hysteretic damping), formulation of nonreflecting boundary conditions at the base, and generation of realistic outcrop ground motions for specific sites. All these elements of the modeling approach are first validated using actual data from five earthquakes at three downhole array stations recorded in the Kiban-Kyoshin network (KiK-net), Japan. The same approach is then used to develop site models of ten NPP sites in the United States and corresponding ground motions that are spectrally matched to the site hazard spectra. Eight sets of three-component input motions are used in the study and are categorized on the basis of presence or absence of a near-field pulse in the seed ground motions used for spectral matching. It is found that all sites retain a definite site amplification function regardless of the input motion, provided that the seed motion is spectrally matched to the site hazard spectra. The magnitude of site amplification and frequencies at which they occur depend upon soil properties, particularly the shear wave velocity profile and the constitutive relationship (strain-dependent shear modulus reduction and hysteretic damping) of soil. Amplification of spectral acceleration in the vertical direction (up-down motion) is found to be just as much as, if not more than, the amplification in the horizontal direction. Peak shear strain is found to be about 20% larger for near-field motions compared to far-field motions whereas maximum horizontal site amplification for far-field motions is found to be consistently larger than that of near-field motions, even though the differences between the two remain within the scatter resulting from individual ground motions.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Microbiome processing of organic nitrogen input supports growth and cyanotoxin production of Microcystis aeruginosa cultures

Abstract Nutrient-induced blooms of the globally abundant freshwater toxic cyanobacterium Microcystis cause worldwide public and ecosystem health concerns. The response of Microcystis growth and toxin production to new and recycled nitrogen (N) inputs and the impact of heterotrophic bacteria in the Microcystis phycosphere on these processes are not well understood. Here, using microbiome transplant experiments, cyanotoxin analysis, and nanometer-scale stable isotope probing to measure N incorporation and exchange at single cell resolution, we monitored the growth, cyanotoxin production, and microbiome community structure of several Microcystis strains grown on amino acids or proteins as the sole N source. We demonstrate that the type of organic N available shaped the microbial community associated with Microcystis, and external organic N input led to decreased bacterial colonization of Microcystis colonies. Our data also suggest that certain Microcystis strains could directly uptake amino acids, but with lower rates than heterotrophic bacteria. Toxin analysis showed that biomass-specific microcystin production was not impacted by N source (i.e. nitrate, amino acids, or protein) but rather by total N availability. Single-cell isotope incorporation revealed that some bacterial communities competed with Microcystis for organic N, but other communities promoted increased N uptake by Microcystis, likely through ammonification or organic N modification. Our laboratory culture data suggest that organic N input could support Microcystis blooms and toxin production in nature, and Microcystis-associated microbial communities likely play critical roles in this process by influencing cyanobacterial succession through either decreasing (via competition) or increasing (via biotransformation) N availability, especially under inorganic N scarcity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Data and scripts associated with “Sequential Precipitation Input Tagging (SPIT) to Estimate Water Transit Times and Hydrologic Tracer Dynamics within Water-Tagging Enabled Hydrologic Models” (v3)

This data package is associated with the publication “Sequential Precipitation Input Tagging (SPIT) to Estimate Water Transit Times and Hydrologic Tracer Dynamics within Water-Tagging Enabled Hydrologic Models” submitted to Journal of Advances in Modeling Earth Systems (Butler et al. 2025). This study developed the Sequential Precipitation Input Tagging (SPIT) framework to tag input precipitation and estimate water transit times and hydrologic tracers. SPIT tags all precipitation events at regular intervals over an extended period (monthly tags over seven years) in a hydrologic model from 2016-2022. SPIT is applied at six National Ecological Observatory Network (NEON) sites across the continental United States to calculate transit time distributions (TTD) and derive from these mean transit times (MTT), fractions of young water (Fyw), and hydrologic tracer concentrations in stream water (δ18O) within a water-tagging enabled version of the Weather Research and Forecast (WT-WRF-Hydro) model with national water model (NWM) configurations. We go on to validate WT-WRF-Hydro estimates against Butler et al. (2023), who analyzed the same NEON sites using stable water isotope data to estimate water transit times. This new tracking method provides a detailed picture of water movement and helps improve predictions about water availability in the future. This data package was originally published in January 2025. It was updated May 2025 (v2; new and modified files) and October 2025 (v3; new and modified files). File and folder names were not revised to indicate changes. See the change history section in the readme for more details. This data package contains the data and scripts used to develop the SPIT framework WT-WRF-Hydro (Water Tagging Weather Research and Forecasting Hydrologic) model and is associated with the following GitHub repository: https://github.com/zbutler33/SPIT-Framework. This data package contains five parent folders: (1) “Manipulated_outputs”, (2) “Metadata”, (3) “Observed”, (4) “Outputs”, and (5) “Scripts”. Each of these parent folders contains additional subfolders and files. Please see the FLMD (“v*_Butler_2024_WT_WRF_Hydro_flmd.csv”) for a list of all the files contained in this data package and descriptions for each. See the data dictionary (“v*_Butler_2024_WT_WRF_Hydro_dd.csv”) for definitions and units of all of the tabular (files ending in “.csv” and ".tsv") column headers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

AmeriFlux FLUXNET-1F US-OPE Optimizing Production Inputs for Economic and Environmental Enhancement (OPE3)

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-OPE Optimizing Production Inputs for Economic and Environmental Enhancement (OPE3). This is the FLUXNET version of the carbon flux data for the site US-OPE Optimizing Production Inputs for Economic and Environmental Enhancement (OPE3) produced by applying the standard ONEFlux (1F) software. Site Description - The Optimizing Production Inputs for Economic and Environmental Enhancement (OPE3) site is located at the Beltsville Agricultural Research Center in Prince George's County, MD, and consists of a 22-ha production field, with an adjacent riparian wetland and first-order stream. Scientists from several U.S. Federal agencies, universities (foreign and domestic), and private industry have conducted multidisciplinary research at this location since 1998.

Alfieri, Joe [USDA-ARS Hydrology and Remote Sensin↗
DOE OSTI2024

Community Solar Program Design Considerations & Modeling Inputs

Designing and modeling a community solar (CS) program is a complex process with numerous variable inputs that are interconnected. Modeling a CS program can be useful to inform the programs design itself while also providing stakeholders of all types with information. Accurate data inputs and assumptions are key to ensuring that a model is informative and as representative of real market conditions as possible. This report is an exploration of community solar program modeling considerations, especially as it relates to data inputs such as capital costs, administrative fees, and subscription size, using a CS program in North Carolina as a case study.

14 SOLAR ENERGY↗
DOE OSTI2025

Polaris-PARCS Sensitivity Study on LWR Fuel Cycles: Polaris Input Options

This study is the first of a multi-phase effort to assess the sensitivity of light-water reactor (LWR) core-level prediction biases to changes in lattice-level calculation parameters. Prediction bias is the measured-to-predicted difference in a core-level quantity of interest (QOI) which can be estimated by comparing the simulation results with the plant-measured data for key nuclear parameters. The LWR two-step neutronics codes employed herein are the SCALE–Polaris lattice physics code (v6.3.1) and the Purdue Advanced Reactor Core Simulator (PARCS) nodal diffusion simulator (v3.4.2), both funded and used for confirmatory analysis to support licensing by the US Nuclear Regulatory Commission (NRC). Polaris–PARCS is used to model Watts Bar Unit 1 cycles 1–3 and Peach Bottom Unit 2 cycles 1–3. This study focuses on the impact of changes to Polaris input options such as scattering treatment or quadrature settings and how these input options induce changes in core-level quantities of interest (QOIs)bias. The report documents multiple bias assessments for different modeling choices and compares the bias magnitude to the QOI measurement uncertainties. Future companion reports will investigate the sensitivity of core-level LWR prediction bias to Polaris input options and Polaris-computed QOIs such as few-group assembly-homogenized cross sections to gain an understanding of the key drivers of prediction bias at lattice and core levels for application of a two-step LWR neutronics procedure in a licensing scenario.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

FY25 MOOSE Usability Improvements: 3D Meshing Capabilities, Initiation of Geometry Support for Monte Carlo Tools, and Enhancement of MOOSE/Workbench User Input Interactions

Usability improvements have been made to MOOSE and Workbench in FY25 to enhance usability and user workflows. Assorted enhancement have been made to MOOSE’s intrinsic meshing capabilities in order to enable more flexible and complex meshing of nuclear reactor systems, in particular for 3D applications. Mesh generators have been added to perform operations such as batch mesh generation, surface mesh generation, and creation of 3D transition layers. These mesh generation capabilities make it much easier to generate high quality non-extruded 3D meshes. Additionally, work to integrate Monte Carlo reactor physics simulations into MOOSE-based multi-physics workflows has reached another milestone with the implementation of the Constructive Solid Geometry (CSG) base framework. This framework lays the foundation for mesh generators to offer the user a generic CSG output option (as opposed to a finite element mesh). To support users, workshop on the MOOSE Reactor Module was delivered which featured hands-on examples using the NEAMS Workbench on INL’s High Performance Computing system. Recent updates to the NEAMS Workbench, WASP, and the MOOSE language server have introduced several improvements aimed at making MOOSE-based simulation setup and input management faster, more accurate, and easier to use. Key capabilities that have been added include multi-tab-stop autocompletion, visual input diagnostics, developer-directed data visualizations, upgraded ParaView integration, and Workspace-level file tracking. Together, these changes make it easier for users to build, validate, and manage complex MOOSE-based simulation models — especially those involving reusable components, included files, and datasets. The improvements are designed to save time, reduce input errors, and help users get to a successful simulation run faster, with more confidence in the results.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Evaluation of an Image-Derived Input Function for Kinetic Modeling of Nicotinic Acetylcholine Receptor-Binding PET Ligands in Mice

Positron emission tomography (PET) radioligands that bind with high-affinity to α4β2-type nicotinic receptors (α4β2Rs) allow for in vivo investigations of the mechanisms underlying nicotine addiction and smoking cessation. Here, we investigate the use of an image-derived arterial input function and the cerebellum for kinetic analysis of radioligand binding in mice. Two radioligands were explored: 2-[ 18 F]FA85380 (2-FA), displaying similar pKa and binding affinity to the smoking cessation drug varenicline (Chantix), and [ 18 F]Nifene, displaying similar pKa and binding affinity to nicotine. Time–activity curves of the left ventricle of the heart displayed similar distribution across wild type mice, mice lacking the β2-subunit for ligand binding, and acute nicotine-treated mice, whereas reference tissue binding displayed high variation between groups. Binding potential estimated from a two-tissue compartment model fit of the data with the image-derived input function were higher than estimates from reference tissue-based estimations. Rate constants of radioligand dissociation were very slow for 2-FA and very fast for Nifene. We conclude that using an image-derived input function for kinetic modeling of nicotinic PET ligands provides suitable results compared to reference tissue-based methods and that the chemical properties of 2-FA and Nifene are suitable to study receptor response to nicotine addiction and smoking cessation therapies.

2-FA85380↗
DOE OSTI2026

Sensitivity Analysis of Numerical Modeling Input Parameters on Wind Turbine Loads in Deterministic Transient Load Cases

Aero-hydro-elastic-servo numerical models used to design and analyze wind turbines are based on thousands of variable input parameters that dictate the inflow, aerodynamic, structural, and control characteristics of the system as well as sea state, hydrodynamic, and mooring characteristics for fixed-bottom and floating offshore wind turbines. Each of these parameters has some level of uncertainty, which can significantly impact the predicted loads. Understanding the uncertainty in the inputs is critical to understanding the uncertainty in the outputs. This work demonstrates a screening technique to identify which parameters ultimate loads are most sensitive to so that more focus can be given to quantifying the possible range of those parameters. This technique has been demonstrated previously for different turbine and load case types and is extended here for a floating offshore wind turbine in design load cases with transient events both in the inflow and operations. Each load case features a deterministic gust, including variations in wind speed, direction, and shear. Load cases are considered with an operating turbine as well as with prescribed fault, startup, and shutdown procedures. The study found that key input parameters with a large impact on loads include the length of the gust, the magnitude of direction change and speed in the gust, the initial wind speed, and the shape of the gust profile.

17 WIND ENERGY↗