Search NASA⌕ Search

SEARCH · Search NASA

Results for “workflow development”

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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Oil-Pressure Based Apparatus for In-Situ High-Energy Synchrotron X-Ray Diffraction Studies During Biaxial Deformation

Background: Understanding biaxial loading response at the microstructural level is crucial in helping better design sheet manufacturing processes and calibrate/validate material deformation models. Objective: The objective of this work was to develop a low-cost testing apparatus to probe, with sufficient spatial resolution, the micro-mechanical response of a sheet material in-situ under biaxial loading conditions. Methods: The testing apparatus fabricated as a part of this study operates in a similar fashion to a standard bulge test and uses oil pressure to generate biaxial loading conditions. This biaxial testing apparatus was operated within a synchrotron beamline to characterize the mechanical response of a flash-processed steel sheet using in-situ high-energy X-ray diffraction (XRD) measurements. Further, the GSAS-II package was utilized to develop a workflow for the analysis of the large volume of diffraction data acquired. The workflow was then used to extract the peak position, width, and integrated intensity of the XRD peaks corresponding to the major body-centered cubic phase. Results: The equi-biaxial nature of the loading in the measured area was independently corroborated using experimental (XRD) and simulation (finite element analysis) methods. Furthermore, we discuss the evolution of elastic strain in the major body-centered cubic phase as a function of applied oil pressure and location on the steel sheet. Conclusions: A key advantage of the biaxial apparatus fabricated in this synchrotron study is demonstrated using the results obtained for the flash-processed steel sheet – i.e., mapping the lattice plane-dependent response to biaxial loading for a relatively large sample area in a spatially resolved manner.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Surrogate model evaluation and building energy benchmarking for commercial buildings

Building energy consumption benchmarking involves challenges associated with various energy patterns for different building types; heating, ventilating, and air-conditioning (HVAC) system types; and climates. Given significant variation in energy use patterns, accurate prediction of long-term energy use using surrogate models remains challenging. Multiple linear regression (MLR) is commonly used for building energy benchmarking because of its simple structure; however, it lacks accuracy compared to other black-box models. Although many studies have compared surrogate models and offer guidance on model selection based on metrics, they do not provide detailed analysis on improving the surrogate model accuracy. In this paper, we implement a surrogate model using polynomial ridge regression (i.e., MLR with interaction terms combined with ridge regularization) for small office and retail strip mall buildings across six HVAC system types and all climate zones, for electricity and natural gas in baseline and proposed scenarios. A simulation workflow is developed using OpenStudio TM /EnergyPlus TM to generate simulation data using measures over a wide range of efficiency inputs. Enhancements based on statistical insights are used for improving the model accuracy using filters, input transformations, and change points. Surrogate models achieved average coefficient of variation of the root mean squared error (CVRMSE) values of 2.17, 1.06, 2.05, and 3.26 for proposed electricity, proposed natural gas, baseline electricity, and baseline natural gas, respectively, with enhancements reducing CVRMSE by an average of 14.9% across all combinations. We provide model interpretation via Shapley additive explanations to determine which input variables most influence energy consumption and provide supportive arguments for enhancements.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Rapid Generation of Tandem Mass Spectrometry Reference Libraries Using Immediate Drop-on-Demand Liquid Handling Coupled to an Open Port Sampling Interface

In metabolomics, tandem MS (MS2) fragmentation libraries are important for the identification of unknown features, but generating these libraries takes many valuable hours of instrument and operator time. Here, an immediate droplet-on-demand/open port sampling interface was used to rapidly acquire tandem MS of standards arrayed in a 96-well plate format. A workflow was developed for automated, high-throughput control of MS2 library generation. Pure standard mass spectral libraries were collected on Orbitrap and Q-TOF mass spectrometers for 192 compounds using 6 different collision energies with a throughput of 4 and 7.8 s/spectrum, respectively. Libraries were acquired using different solvent additives, precursor adducts, and ion polarities.

Cahill, John [ORNL] (ORCID:0000000298664010)↗
DOE OSTI2024

Exploring electron-beam induced modifications of materials with machine-learning assisted high temporal resolution electron microscopy

Directed atomic fabrication using an aberration-corrected scanning transmission electron microscope (STEM) opens new pathways for atomic engineering of functional materials. In this approach, the electron beam is used to actively alter the atomic structure through electron beam induced irradiation processes. One of the impediments that has limited widespread use thus far has been the ability to understand the fundamental mechanisms of atomic transformation pathways at high spatiotemporal resolution. Here, we develop a workflow for obtaining and analyzing high-speed spiral scan STEM data, up to 100 fps, to track the atomic fabrication process during nanopore milling in monolayer MoS 2 . An automated feedback-controlled electron beam positioning system combined with deep convolution neural network (DCNN) was used to decipher fast but low signal-to-noise datasets and classify time-resolved atom positions and nature of their evolving atomic defect configurations. Through this automated decoding, the initial atomic disordering and reordering processes leading to nanopore formation was able to be studied across various timescales. Using these experimental workflows a greater degree of speed and information can be extracted from small datasets without compromising spatial resolution. This approach can be adapted to other 2D materials systems to gain further insights into the defect formation necessary to inform future automated fabrication techniques utilizing the STEM electron beam.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Machine learned potential for high-throughput phonon calculations of metal—organic frameworks

Metal–organic frameworks (MOFs) are highly porous and versatile materials studied extensively for applications such as carbon capture and water harvesting. However, computing phonon-mediated properties in MOFs, like thermal expansion and mechanical stability, remains challenging due to the large number of atoms per unit cell, making traditional Density Functional Theory (DFT) methods impractical for high-throughput screening. Recent advances in machine learning potentials have led to foundation atomistic models, such as MACE-MP-0, that accurately predict equilibrium structures but struggle with phonon properties of MOFs. In this work, we developed a workflow for computing phonons in MOFs within the quasi-harmonic approximation with a fine-tuned MACE model, MACE-MP-MOF0. The model was trained on a curated dataset of 127 representative and diverse MOFs. The fine-tuned MACE-MP-MOF0 improves the accuracy of phonon density of states and corrects the imaginary phonon modes of MACE-MP-0, enabling high-throughput phonon calculations with state-of-the-art precision. The model successfully predicts thermal expansion and bulk moduli in agreement with DFT and experimental data for several well-known MOFs. These results highlight the potential of MACE-MP-MOF0 in guiding MOF design for applications in energy storage and thermoelectrics.

Elena, Alin Marin↗
DOE OSTI2024

Mechanistic insights into a heterobifunctional degrader-induced PTPN2/N1 complex

PTPN2 (protein tyrosine phosphatase non-receptor type 2, or TC-PTP) and PTPN1 are attractive immuno-oncology targets, with the deletion of Ptpn1 and Ptpn2 improving response to immunotherapy in disease models. Targeted protein degradation has emerged as a promising approach to drug challenging targets including phosphatases. We developed potent PTPN2/N1 dual heterobifunctional degraders (Cmpd-1 and Cmpd-2) which facilitate efficient complex assembly with E3 ubiquitin ligase CRL4 CRBN , and mediate potent PTPN2/N1 degradation in cells and mice. To provide mechanistic insights into the cooperative complex formation introduced by degraders, we employed a combination of structural approaches. Our crystal structure reveals how PTPN2 is recognized by the tri-substituted thiophene moiety of the degrader. We further determined a high-resolution structure of DDB1-CRBN/Cmpd-1/PTPN2 using single-particle cryo-electron microscopy (cryo-EM). This structure reveals that the degrader induces proximity between CRBN and PTPN2, albeit the large conformational heterogeneity of this ternary complex. The molecular dynamic (MD)-simulations constructed based on the cryo-EM structure exhibited a large rigid body movement of PTPN2 and illustrated the dynamic interactions between PTPN2 and CRBN. Together, our study demonstrates the development of PTPN2/N1 heterobifunctional degraders with potential applications in cancer immunotherapy. Furthermore, the developed structural workflow could help to understand the dynamic nature of degrader-induced cooperative ternary complexes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Validation of a synthetic fast ion loss detector model for Wendelstein 7-X

Abstract We present the first validated synthetic diagnostic for fast ion loss detectors (FILDs) in the Wendelstein 7-X (W7-X) stellarator. This model has been developed on, and validated against experimental data from, a FILD provided by the National Institute for Fusion Science (NIFS-FILD), with potential future applicability to the existing Faraday Cup FILD (FC-FILD) on W7-X as well as the scintillating FILD (S-FILD) currently under development. A workflow combining Monte Carlo codes BEAMS3D and ASCOT5 is used to track fast ions produced by neutral beam injection from the moment of ionization until they are thermalized or lost from the last closed flux surface, and from there to a virtual plane which serves as a projection of the entrance aperture to the FILD. Simulations in ASCOT5 are analyzed via a geometric method to determine the probability of transmission through the FILD aperture and onto the detector as a function of normalized momentum, pitch angle, gyrophase, and position at the virtual plane. This probability is then applied to the simulated ions arriving from the plasma, producing a simulated signal from a computationally tractable number of simulated fast ions. Simulated signals are presented for two W7-X experiments with neutral beam injection and quantitatively compared with experimental measurements from the NIFS-FILD diagnostic. An estimate of the frequency of charge-exchange with neutral particles in the edge is performed, and it is found that this process may have a significant impact on the measured signals.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

M3SF-24LL010302052-Comprehensive Analysis of Radionuclide Interaction with Hydrothermally Altered Repository Materials

This progress report (Level 3 Milestone Number M3SF-24LL010302052) summarizes research conducted at Lawrence Livermore National Laboratory (LLNL) within the Crystalline Work Package Number SF-24LL01030205. The research is focused on actinide and radionuclide sequestration in hydrothermally altered repository materials. In FY24, we completed a rigorous analysis of Se sorption to iron oxide phases using our L-SCIE sorption database. This effort explicitly accounts for surface titration behavior of oxide surfaces that was the subject of a recently published manuscript (Han et al., 2023). With this in mind, our Se sorption analysis now yields a more robust workflow for developing self-consistent surface complexation modeling approaches that can be adapted to specific SCM conceptual and numerical approaches (i.e. non-electrostatic, diffuse layer, triple layer models). In late FY24, we plan to publish the results of our comprehensive surface complexation modeling of Se(IV) and Se(VI) sorption to iron oxide mineral phases and provide a path forward to developing robust radionuclide sorption models for use in performance assessment. In FY24, we also submitted a manuscript summarizing our approach to integrating radionuclide sorption and coprecipitation phenomena and evaluation of radionuclide partitioning values across a range of radionuclides relevant to performance assessment. We demonstrated our approach in detail using Se sorption and coprecipitation with iron oxide minerals as a test case. This manuscript was recently accepted for publication in Applied Geochemistry (Balboni et al., Accepted). We also continued experiments to identify radionuclide interaction with hydrothermally altered crystalline repository and backfill materials. Recent research performed at Los Alamos National Laboratory (LANL) and Sandia National Laboratory (SNL) has provided key insights regarding the hydrothermal alteration behavior of bentonite backfill in the presence of repository materials (steel, concrete, etc.). We are now examining how mineral alteration affects retardation behavior of a suite of radionuclides of interest to repository performance assessment. These experiments also allow us to test the predictive ability of our component additivity approach to surface complexation and ion exchange. Our guiding hypothesis is that a robust surface complexation/ion exchange model and associated database, developed using our L-SCIE approach, can effectively predict changes in radionuclide sorption behavior resulting from the hydrothermal alteration of mineralogy in a repository near field. A short update of results to date is presented below.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2025

Remote Sensing and Fluxes Upscaling for Real-world Impact (Workshop Report)

The "Remote Sensing and Fluxes Upscaling for Real-world Impact" workshop, held on July 9-10, 2024, at Lawrence Berkeley National Lab, was a collaborative effort led by the AmeriFlux Management Project, NEON, and the Carbon Dew Community of Practice. The event brought together over 200 registrants and approximately 100 attendees each day, including leading experts, researchers, and practitioners. The primary focus was on bridging the gap between cutting-edge research and practical applications in environmental monitoring by integrating remote sensing and flux data. Key themes included the importance of site-level measurements for validating remote sensing products, providing nature-based climate solutions, and addressing challenges such as instrument costs and the need for standardized methods. At the regional scale, discussions centered on addressing spatial heterogeneity and using high-resolution remote sensing and machine learning methods to enhance data interpretation. Global scale challenges included data consistency, gap filling, and accurate emission source identification, with opportunities for international collaboration and standardized practices to improve global carbon budget assessments. The workshop emphasized the critical need for integrating data across local, regional, and global scales through explicit scale-matching and developed a workflow for scaling flux data using "straight shot" and "explicit nesting" approaches. The event highlighted the importance of connecting scientific research with real-world applications in carbon, energy, and water management, ensuring that advancements translate into tangible societal benefits. These insights will guide future research, technology transfer, and collaboration, maximizing the potential of environmental fluxes to address real-world challenges.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Image processing workflow yielding high contrast synchrotron nanoscale computed tomography data from Ni-YSZ electrodes

The operating lifetime of Ni-YSZ fuel electrodes used in solid oxide electrolysis cells and fuel cells (SOECs and SOFCs) is limited by Ni redistribution, one of the primary degradation mechanisms that must be overcome to extend the longevity and maximize the performance of SOECs and SOFCs. To achieve this, 3D microstructural data is needed to relate both initial performance and performance loss over time to microstructural properties and their evolution throughout operation under various conditions. However, 3D microstructure data remains relatively scarce within the literature due to multiple challenges in acquiring and analyzing such data reliably. This work presents a workflow for acquiring and processing synchrotron X-ray nanoscale computed tomography (nano-CT) data from Ni-YSZ electrodes. Parameters for each step in the nano-CT workflow are described up to the final result (a 3D reconstruction), with particular emphasis on image alignment using freely available software. Following the results of a parametric sweep of the image alignment step, high contrast, low signal-to-noise 3D nano-CT data is obtained with relatively short compute times. While the exact methods best suited to samples with different microstructural qualities, or similar Ni-YSZ nano-CT data obtained from other sources may deviate from the solution found herein, this work also generalizes the decision points and evaluation of each step to provide a starting point to adapt this workflow to other datasets.

08 HYDROGEN↗
NASA NTRSDate not supplied

Modeling the Worst-Case-Scenario of Soil Drydown for Agricultural Drought Monitoring in Kansas Using NASA Earth Observations and In-situ Parameters

Climatologists in Kansas have observed rapid shifts from wet to dry conditions and rapid intensification of drought conditions for the state in recent years. As a leading state in agricultural production, drought events can lead to decreased crop yields and impact the state’s economy. The exponential decay of soil moisture content is a major consequence of drought and can exacerbate these impacts on agricultural production. This study examined the rate of soil moisture drydown in Kansas to understand and monitor the worst-case-scenario of short-term soil water shortage. Soil Moisture Active Passive (SMAP) L-band Radiometer rootzone soil moisture imagery from March 2015 to June 2019 was used to estimate the minimum and maximum soil moisture capacity throughout the state. In situ soil moisture parameters, such as the exponential decay constant, were approximated from the Kansas Mesonet system. These variables were inputs into an exponential decay model that forecasted gridded rootzone soil moisture in a scenario that assumed no precipitation for a user-identified period of time. The forecasts were output on the order of weeks to capture the rapid shifts in soil moisture content. Another model was used to identify areas in Kansas currently below a given percentage of the relative soil moisture saturation, measured as a function of minimum, maximum, and current soil moisture. This model forecasted the number of days until each cell in the soil moisture grid reached the input percentage of relative soil moisture saturation. The gridded model outputs and workflow were developed in partnership with the Kansas Water Office and Kansas Office of the State Climatologist at Kansas State University, as a part of their drought monitoring efforts.

NASA DEVELOP↗
NASA NTRS2023

Leveraging Automated Fiber Placement Computer Aided Process Planning Framework for Defect Validation and Dynamic Layup Strategies

Process planning represents an essential stage of the Automated Fiber Placement (AFP) workflow. It develops useful and efficient machine processes based upon the working material, composite design, and manufacturing resources. The current state of process planning requires a high degree of interaction from the process planner and could greatly benefit from increased automation. Therefore, a list of key steps and functions are created to identify the more difficult and time-consuming phases of process planning. Additionally, a set of metrics must exist by which to evaluate the effectiveness of the manufactured laminate from the machine code created during the Process Planning stage. Layup strategies, in addition to dog ears, stagger shifts, steering constraints, and starting points, represented the group of functions labeled as process optimization and ranked the highest in terms of priority for automation. The laminates resulting from the selected parameters are evaluated through the occurrences of principal defect metrics such as fiber gaps, overlaps, angle deviation and steering violations. This document presents an automated software solution to the layup strategy and starting point selection phase of process planning. A series of ply scenarios are generated with variations of these ply parameters and evaluated according to a set of metrics entered by the Process Planner. These metrics are generated through use of the Analytical Hierarchy Process (AHP), where relative importance between each of the fiber features are defined. The ply scenarios are selected which reduce the overall fiber feature scores based on the defects the Process Planner wishes to minimize.

HiCAM↗
NASA NTRS2023

Shoshone River Water Resources II​: Quantifying Sediment Input in the Shoshone River in Wyoming using the Soil and Water Assessment Tool for Enhanced Water Quality Monitoring

The Willwood Dam, an irrigation diversion dam located on the Shoshone River (Wyoming, USA), has faced ongoing issues with sediment accumulation and needs frequent sediment flushing to remain operable. However, high suspended sediment levels during flushing events have negatively impacted downstream aquatic ecology and recreational opportunities. To address these problems, DEVELOP partnered with the Wyoming Department of Environmental Quality, Shoshone River Partners, and United States Geologic Survey. During term one, the team developed a workflow to map turbidity using PlanetScope imagery, analyze time series precipitation data, and create landcover maps. For this term, we focused on three methods to gain a better understanding of sediment sources: 1) improving remote sensing of turbidity, 2) modeling sediment transport within the watershed using the Soil and Water Assessment Tool (SWAT), and 3) conducting a snow cover time series analysis using Suomi NPP VIIRS imagery. Through remote sensing, we found Dry Creek/Homesteader Creek and Penney Gulch/Rough Gulch had the highest concentration sediment plumes. The SWAT+ model created a high-resolution grid model of the watershed, identifying high sedimentation in the western and southern subbasins but displayed low-correlated calibration and validation results due to limited observed data. We analyzed the snow cover extent alongside other hydrologic variables and found that snow melt events correlated with increases in suspended sediment concentration and turbidity values. Coupling remote sensing with hydrological modeling will give watershed managers a new perspective on high-priority regions for implementation of remediation to reduce the sediment build-up at the Willwood Dam.

Suspended Sediment↗
DOE OSTI2024

Data Generation Workflow for Meso/Microscale Coupled Offshore Wind Farm Simulations

A robust, simple-to-use workflow is developed in this study which allows mesoscale information from the NOW-23 database to be easily incorporated into microscale wind farm simulations. This process will enable many different wind farm configurations to be simulated under realistic inflow conditions spanning a variety of atmospheric phenomena.

AMR-Wind↗
DOE OSTI2024

Empowering Scientific Innovation Through An Integrated Research Infrastructure: The Role of the Advanced Computing Ecosystem

As the landscape of computational science evolves, the Department of Energy (DOE) is reimagining the roles of its large-scale computing facilities to meet emerging research challenges. The Integrated Research Infrastructure (IRI) program aims to transform how experiments are designed, conducted, and shared, with significant impacts on all stakeholders. In response, the Oak Ridge Leadership Computing Facility (OLCF) has established the Advanced Computing Ecosystem (ACE), a strategic framework to prepare its hardware, software, and experimental capabilities for the IRI era. ACE focuses on integrating novel compute environments, orchestrating advanced workflows, and developing foundational technologies, ensuring a seamless transition to IRI while accelerating scientific discovery. This paper outlines ACE's role in advancing OLCF's mission and its impact on the future of computational science.

Widener, Patrick↗
NASA NTRSDate not supplied

Earth Science Data Processing With Nextflow

Earth science data processing tasks present many challenges. These tasks often process large input datasets and require scores of CPU-hours to generate results. All but the simplest tasks will be decomposed into a series of computational or data manipulation steps, also known as a scientific workflow. In order to reduce the burden of orchestrating and running the dependent processing steps, a workflow execution engine is required. This poster describes the lessons learned by the CLARREO Pathfinder (CPF) team while developing multiple scientific workflows and utilizing the open-source Nextflow engine to execute them in a cloud computing environment. The Nextflow engine is designed with the following stated goals: first, the engine does not dictate how individual steps in the task are implemented (i.e. it is language and interface agnostic); second, the engine supports easy configuration and modularity at the workflow level so that others can easily execute our workflows to reproduce results; lastly, the engine eases development by transparently scaling execution from local to remote environments. Nextflow was developed for the bioinformatics domain but is a good fit for other scientific workflows where the overall task is well-described by a dataflow diagram. The CPF team has developed Nextflow pipelines (i.e. scientific workflows) to simulate CLARREO radiance, generate large look-up tables for inter-calibration algorithms, and generate L4 intercalibration data products. These pipelines consume from single-digits to hundreds of thousands of CPU-hours. In the development and evolution of these pipelines we have discovered many design patterns, pitfalls, and solutions to common problems. Our goal is to demonstrate important aspects of how to design, implement, run, and ultimately share Nextflow pipelines in the domain of Earth science.

Aron D Bartle↗