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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 OSTI2016

Formulation and numerical analysis of a fully-coupled dynamically deforming electromagnetic wire

Here, an electromagnetic beam model is developed for the simulation of actuated electronic textiles. The beam is solved using a nonlinear director-based kinematic description with additional temperature and electric potential fields along its length. The three fields are fully coupled by mutual dependences on the deformation, Lorenz force, back electromotive force, temperature dependent constitutive responses, and the Seebeck effect. Instead of solving Maxwell's equations in full detail, a quasistatic approximation is used to solve the electric potential in the presence of a moving material medium. The current-carrying beam approximation is used to further simplify the solution space for the potential. While this formulation alleviates the spatial and temporal discretization restrictions, the coupled problem is an index-1 semi-explicit Differential Algebraic Equation requiring special treatment. The time dependent problem is solved using different Runge-Kutta methods. Diagonally implicit Runge-Kutta methods and explicit Runge-Kutta methods using implicit solution of the electric potential problem are explored. The finite element model is implemented using the open source package FEniCS, which is able to automatically generate the linearizations of the multiphysics equations required for the implicit solutions. A model problem is constructed with which to test and analyze the physical formulation and numerical solution techniques. The time stepping methods are verified using the convergence orders of the higher-order Runge-Kutta methods. Runtime comparisons show that the explicit methods are generally more computationally efficient than the implicit schemes used for this problem. For the implicit schemes, a staggered solution is significantly faster than a monolithic solution at most time step sizes. However, at very large time steps, such as those that would be used for dynamic relaxation, the monolithic solution can be more efficient than the staggered solution.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A permafrost implementation in the simple carbon–climate model Hector v.2.3pf

Abstract. Permafrost currently stores more than a fourth of global soil carbon. A warming climate makes this carbon increasingly vulnerable to decomposition and release into the atmosphere in the form of greenhouse gases. The resulting climate feedback can be estimated using land surface models, but the high complexity and computational cost of these models make it challenging to use them for estimating uncertainty, exploring novel scenarios, and coupling with other models. We have added a representation of permafrost to the simple, open-source global carbon–climate model Hector, calibrated to be consistent with both historical data and 21st century Earth system model projections of permafrost thaw. We include permafrost as a separate land carbon pool that becomes available for decomposition into both methane (CH4) and carbon dioxide (CO2) once thawed; the thaw rate is controlled by region-specific air temperature increases from a preindustrial baseline. We found that by 2100 thawed permafrost carbon emissions increased Hector’s atmospheric CO2 concentration by 5 %–7 % and the atmospheric CH4 concentration by 7 %–12 %, depending on the future scenario, resulting in 0.2–0.25 ∘C of additional warming over the 21st century. The fraction of thawed permafrost carbon available for decomposition was the most significant parameter controlling the end-of-century temperature change in the model, explaining around 70 % of the temperature variance, and was distantly followed by the initial stock of permafrost carbon, which contributed to about 10 % of the temperature variance. The addition of permafrost in Hector provides a basis for the exploration of a suite of science questions, as Hector can be cheaply run over a wide range of parameter values to explore uncertainty and can be easily coupled with integrated assessment and other human system models to explore the economic consequences of warming from this feedback.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

A browser-based tool for visualization and analysis of diffusion MRI data

Human neuroscience research faces several challenges with regards to reproducibility. While scientists are generally aware that data sharing is important, it is not always clear how to share data in a manner that allows other labs to understand and reproduce published findings. Here we report a new open source tool, AFQ-Browser, that builds an interactive website as a companion to a diffusion MRI study. Because AFQ-Browser is portable—it runs in any web-browser—it can facilitate transparency and data sharing. Moreover, by leveraging new web-visualization technologies to create linked views between different dimensions of the dataset (anatomy, diffusion metrics, subject metadata), AFQ-Browser facilitates exploratory data analysis, fueling new discoveries based on previously published datasets. In an era where Big Data is playing an increasingly prominent role in scientific discovery, so will browser-based tools for exploring high-dimensional datasets, communicating scientific discoveries, aggregating data across labs, and publishing data alongside manuscripts.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Effects of MOF linker rotation and functionalization on methane uptake and diffusion

The flexible degrees of freedom in metal–organic frameworks (MOFs) can have significant effects on guest molecule behavior. However, in the majority of studies applying molecular simulations to MOFs, the framework is assumed to be rigid in order to minimize computational cost. Here, in this work, we assess the significance of this assumption on a representative example of methane uptake and diffusion in UiO-66. We introduce an open-source code to modify MOFs through functionalization and linker rotation and we perform Grand Canonical Monte Carlo and molecular dynamics simulations of methane in each of the functionalized and linker-rotated derivatives of UiO-66. We find that linker rotation moderately influences methane uptake and significantly influences methane diffusion. Our assessment provides ranges of property values that serve as measures of uncertainty of these two properties associated with linker rotation. We further determine that void volume fraction and minimum pore size are the features that govern methane uptake and diffusion, respectively. These findings illustrate the impact of linker rotation on MOFs and provide design principles to guide future investigations.

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

Changuinola peat soil characteristics and gas emission raw data October 2019

This dataset comprises radiocarbon and geochemical measurements from peat and porewater samples collected across various depths at a site in Bocas del Toro, Panama. The study focuses on carbon cycling dynamics in tropical peatlands by examining carbon isotopic signatures (¹⁴C and ¹³C) and elemental compositions of bulk peat, dissolved organic carbon (DOC), carbon dioxide (CO₂), and methane (CH₄). Key parameters include radiocarbon ages and isotopic ratios (δ¹³C) of bulk peat, concentrations of carbon (%C) and nitrogen (%N), and radiocarbon content of porewater gases and dissolved organic carbon (DOC). The data provide insights into the vertical and spatial distribution of carbon sources and possible preservation and decomposition processes within tropical peat profiles, offering critical information for understanding carbon storage and greenhouse gas emissions in these ecosystems.This dataset is comprised of one main data folder containing (1) file-level metadata; (2) data dictionary; (3) field metadata; (4) carbon isotopic signatures (¹⁴C and ¹³C); (5) concentrations of carbon (%C) and nitrogen (%N); (6) radiocarbon content of porewater carbon dioxide (CO₂), and methane (CH₄) ; (7) porewater DOC; (8) bulk peat sampling protocol; (9) porewater sampling protocol; (10) porewater gas collection methods; and (11) gas extraction methods. All files are in .csv format and can be opened with any software that supports this file types.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Optimizing Repowering and Lifecycle Decisions with PV ICE and SAM

Should you repower or extend the life of your PV system? Are high-efficiency modules, durable modules, or recyclable modules the best option for your site and goals? Evaluating the trade-offs in design and lifecycle strategies can be complex. The PV in Circular Economy (PV ICE) tool is an open-source model designed to help developers, modelers, and decision-makers assess material flows, energy return on investment (EROI), and financial viability of PV systems. Now integrated with the System Advisor Model (SAM), PV ICE enables site-specific comparisons of lifecycle strategies - such as repowering benefits, module selection for reliability and recyclability, among others. This interactive tutorial will provide hands-on experience with PV ICE using Google Collab, exploring scenario-based analyses on these topics.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Facile Integration of Robots into Experimental Orchestration at Scientific User Facilities

Integration of robots into scientific user facilities, such as the National Synchrotron Light Source II, improves their efficiency and capacity. Many such facilities use the opensource Bluesky project for experimental control and orchestration. However, there remains an open challenge in deploying robotic solutions at these facilities that are reconfigurable, extensible, and compatible with pre-existing software infrastructure. Herein, we introduce a framework that uses the Robotic Operating System 2 (ROS2) and Bluesky to provide extensible robotic applications, while working under the operational constraints of a large-scale user facility. We demonstrated this framework by integrating a robotic arm to pick and place a sample holder at a beamline, recording a 90% repeatability rate. This provides the groundwork for further new robotics applications at large-scale scientific user facilities that depend on Bluesky.

36 MATERIALS SCIENCE↗
DOE OSTI2024

PyPop: a mature open-source software pipeline for population genomics

Python for Population Genomics (PyPop) is a software package that processes genotype and allele data and performs large-scale population genetic analyses on highly polymorphic multi-locus genotype data. In particular, PyPop tests data conformity to Hardy-Weinberg equilibrium expectations, performs Ewens-Watterson tests for selection, estimates haplotype frequencies, measures linkage disequilibrium, and tests significance. Standardized means of performing these tests is key for contemporary studies of evolutionary biology and population genetics, and these tests are central to genetic studies of disease association as well. Here, we present PyPop 1.0.0, a new major release of the package, which implements new features using the more robust infrastructure of GitHub, and is distributed via the industry-standard Python Package Index. New features include implementation of the asymmetric linkage disequilibrium measures and, of particular interest to the immunogenetics research communities, support for modern nomenclature, including colon-delimited allele names, and improvements to meta-analysis features for aggregating outputs for multiple populations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Source characterization of a detector for heavy and superheavy nuclei

A new focal plane detector system for the Berkeley Gas-filled Separator (BGS) was designed, constructed, and tested offline with various α-decay and conversion-electron sources. The SuperHeavy RECoils (SHREC) detector comprises sets of double-sided silicon strip detectors arranged in an open-faced cuboid geometry. Alongside the detector upgrade new digital data acquisition electronics have been commissioned offline. This setup aims to detect separated recoiling heavy and superheavy nuclei as well as their correlated radioactive decay paths with improved energy resolution and overall sensitivity.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Probing the light scattering properties of insulin secretory granules in single live cells

Light scattering was recently demonstrated to serve as an intrinsic indicator for pancreatic islet cell mass and secretion. The insulin secretory granule (ISG), in particular, was proposed to be a reasonable candidate as the main intracellular source of scattered light due to the densely-packed insulin semi-crystal in the granule lumen. This scenario, if confirmed, would in principle open new perspectives for label-free single-granule imaging, tracking, and analysis. Contrary to such expectations, here we demonstrate that ISGs are not a primary source of scattering in primary human β-cells, as well as in immortalized β-like cells, quantitatively not superior to other intracellular organelles/structures, such as lysosomes and internal membranes. This result is achieved through multi-channel imaging of scattered light along with fluorescence arising from selectively-labelled ISGs. Co-localization and spatiotemporal cross-correlation analysis is performed on these signals, and compared among different cell lines. Obtained results suggest a careful re-thinking of the possibility to exploit intrinsic optical properties originating from ISGs for single-granule imaging purposes.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Optimization of Desalination Systems with Detailed Water Chemistry through Integration of Reaktoro in WaterTAP

Chemistry predictions are critical for an accurate estimation of performance and costs in desalination process models, which allows for the estimation of the value of new technologies and the viability of treating new water sources. Herein, we present how an implicit function formulation can be used to integrate the chemical modeling package, Reaktoro, into the techno-economic assessment and modeling platform, WaterTAP. This approach resolves the critical issues of integrating large-scale thermodynamic models and databases into equation-oriented process models while allowing more flexibility relative to previously presented surrogate-based methods. We describe how this integration into Pyomo and WaterTAP models is implemented and used through the open-source package Reaktoro-PSE . We first validate this integration approach by performing optimization on a previously presented desalination treatment train with softening and acid addition as the pretreatment steps. Then, to demonstrate the value of this approach, we extend the cost-optimization problem to include the simultaneous addition of lime and soda ash for softening, and HCl and H 2 SO 4 in the acidification steps. Finally, we were able to confirm the previously established results that were obtained by using surrogate models and demonstrate that the implicit function approach enables exploration of different feedwater compositions and a larger number of chemicals and their combinations.

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

Decoding substance use disorder severity from clinical notes using a large language model

Substance use disorder (SUD) poses a major concern due to its detrimental effects on health and society. SUD identification and treatment depend on a variety of factors such as severity, co-determinants (e.g., withdrawal symptoms), and social determinants of health. Existing diagnostic coding systems used by insurance providers, like the International Classification of Diseases (ICD-10), lack granularity for certain diagnoses, but American clinicians will add this granularity (as that found within the Diagnostic and Statistical Manual of Mental Disorders classification or DSM-5) as supplemental unstructured text in clinical notes. Traditional natural language processing (NLP) methods face limitations in accurately parsing such diverse clinical language. Large language models (LLMs) offer promise in overcoming these challenges by adapting to diverse language patterns. This study investigates the application of LLMs for extracting severity-related information for various SUD diagnoses from clinical notes. We propose a workflow employing zero-shot learning of LLMs with carefully crafted prompts and post-processing techniques. Through experimentation with Flan-T5, an open-source LLM, we demonstrate its superior recall compared to the rule-based approach. Focusing on 11 categories of SUD diagnoses, we show the effectiveness of LLMs in extracting severity information, contributing to improved risk assessment and treatment planning for SUD patients.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

PCAfold 2.0—Novel tools and algorithms for low-dimensional manifold assessment and optimization

We describe an update to our open-source Python package, PCAfold, designed to help researchers generate, analyze and improve low-dimensional data manifolds. In the current version, PCAfold 2.0, we introduce novel tools and algorithms for assessing and optimizing low-dimensional manifolds. This includes a method that generates a “map” of local feature sizes that can help pinpoint researchers to problematic regions on a manifold. We introduce a novel cost function that characterizes the quality of a manifold topology with a single number. We develop two algorithms for feature selection based on principal component analysis (PCA) that use the cost function as an objective function to minimize. We introduce a quantity of interest (QoI)-aware dimensionality reduction strategy where data projections are computed using an artificial neural network and are directly optimized towards representing various projection-independent and projection-dependent QoIs. We also introduce an implementation of partition of unity networks (POUnets) for efficient reconstruction of QoIs from low-dimensional manifolds based on combining neural network classification with localized polynomial regression. Our software can be broadly applicable in all domains of science and engineering that aim to reduce data dimensionality, as well as in the fundamental research on representation learning.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Predicting Trends in VOC Through Rapid, Multimodal Characterization of State-of-the-Art p-i-n Perovskite Devices

Perovskite photovoltaic technologies are approaching commercial deployment, yet single junction and tandem architectures both still have significant room to improve power conversion efficiency and stability. The ability to perform rapid screening of material quality after altering processing conditions is critical to accelerating the optimization and commercialization of perovskite-based technologies. Currently, researchers utilize a wide range of stand-alone metrology tools to isolate sources of power loss throughout a device stack, which can be slow and labor intensive. Here, we demonstrate the use of a multimodal metrology approach to rapidly determine the maximum achievable and predicted open circuit voltages of >100 perovskite devices during fabrication. Acquisition of these different data is facilitated by combining them into a single integrated measurement platform. We show that these data and automated analysis can be used to rapidly understand and ultimately predict quantitative trends in open circuit voltages of state-of-the-art device architectures. The data and automated analysis workflow presented provides a reliable approach to quickly identify absorber and charge transport layer combinations that can lead to improved open circuit voltages.

14 SOLAR ENERGY↗
DOE OSTI2026

Non-equilibrium reducing flame aerosol process to create supported high-entropy alloy nanoparticles

High-entropy alloy (HEA) nanomaterials provide opportunities and property combinations for energy and electronic applications, but their practical synthesis faces challenges of elemental immiscibility, metal reducibility, and particle aggregation during their synthesis. Herein, we report a broadly applicable non-equilibrium, scalable, and in-situ reducing flame aerosol process for synthesis of supported HEA nanoparticles. This versatile process can directly load a high concentration of 2 ~ 4 nm HEA nanoparticles on various 1- to 3-dimensional supports. Notably, simultaneous formation of HEA nanoparticles and a mesoporous silica support was successfully realized in a single step. Exploration of this process demonstrates the role of kinetics and entropy on decreasing alloy particle size and altering the reducibility of elements. We propose an entropy-induced reduction mechanism to incorporate oxidizable elements into HEAs, which extends the compositional space of HEA nanoparticles. As a representative catalytic application, we present a RuPdOsIrPt/graphene electrocatalyst with high activity and stability for hydrogen oxidation reaction. Our findings open horizons for high-performance HEA design and applications in diverse fields such as catalysis, electrochemistry, and sensing.

organic↗
DOE OSTI2024

Proton discrimination in CLYC for fast neutron spectroscopy

The Cs 2 LiYCl 6 :Ce (CLYC) elpasolite scintillator is known for its response to fast and thermal neutrons along with good γ-ray energy resolution. While the 35 Cl(n,p) reaction has been identified as a potential means for CLYC-based fast neutron spectroscopy in the absence of time-of-flight (TOF), previous efforts to functionalize CLYC as a fast neutron spectrometer have been thwarted by the inability to isolate proton interactions from 6 Li(n,α) and 35 Cl(n,α) signals. This work introduces a new approach to particle discrimination in CLYC for fission spectrum neutrons using a multi-gate charge integration algorithm that provides excellent separation between protons and heavier charged particles. Neutron TOF data were collected using a 252 Cf source, an array of EJ-309 organic liquid scintillators, and a 6 Li-enriched CLYC scintillator outfitted with fast electronics. Modal waveforms were constructed corresponding to the different reaction channels, revealing significant differences in the pulse characteristics of protons and heavier charged particles at ultrafast, fast, and intermediate time scales. These findings informed the design of a pulse shape discrimination algorithm, which was validated using the TOF data. This study also proposes an iterative subtraction method to mitigate contributions from confounding reaction channels in proton and heavier charged particle pulse height spectra, opening the door for CLYC-based fast neutron and γ-ray spectroscopy while preserving sensitivity to thermal neutron capture signals.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

Automatic and Transparent Resource Contention Mitigation for Improving Large-Scale Parallel File System Performance

Proportional to the scale increases in HPC systems, many scientific applications are becoming increasingly data intensive, and parallel I/O has become one of the dominant factors impacting the large-scale HPC application performance. On a typical large-scale HPC system, we have observed that the lack of a global workload coordination coupled with the shared nature of storage systems cause load imbalance and resource contention over the end-to-end I/O paths resulting in severe performance degradation. I/O load imbalance on HPC systems is generally a self-inflicted wound and mostly occurs between the I/O paths and resources consumed by each individual job. In this paper, we introduce TAPP-IO, a dynamic, shared load balancing framework for mitigating resource contention. TAPP-IO extends our previous work and solves two major limitations: First, it transparently intercepts file creation calls during runtime to balance the workload over all available storage targets. The usage of TAPP-IO requires no application source code modifications and is independent from any I/O middleware. The framework can be applied to almost any HPC platform and is suitable for systems that lack a centralized file system resource manager. Second, the framework proposes a new placement strategy to support not only file-per-process I/O, but also single shared file I/O. This opens the door to a new class of scientific applications that can leverage the placement library for improved performance. We demonstrate the effectiveness of our integration on the Titan system at the Oak Ridge National Laboratory. Our experiments with a synthetic benchmark and real-world HPC workload show that, even in a noisy production environment, TAPP-IO can improve large-scale application performance significantly.

Neuwirth, Sarah↗