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NASA NTRS2008

Earth Science Mining Web Services

To allow scientists further capabilities in the area of data mining and web services, the Goddard Earth Sciences Data and Information Services Center (GES DISC) and researchers at the University of Alabama in Huntsville (UAH) have developed a system to mine data at the source without the need of network transfers. The system has been constructed by linking together several pre-existing technologies: the Simple Scalable Script-based Science Processor for Measurements (S4PM), a processing engine at he GES DISC; the Algorithm Development and Mining (ADaM) system, a data mining toolkit from UAH that can be configured in a variety of ways to create customized mining processes; ActiveBPEL, a workflow execution engine based on BPEL (Business Process Execution Language); XBaya, a graphical workflow composer; and the EOS Clearinghouse (ECHO). XBaya is used to construct an analysis workflow at UAH using ADam components, which are also installed remotely at the GES DISC, wrapped as Web Services. The S4PM processing engine searches ECHO for data using space-time criteria, staging them to cache, allowing the ActiveBPEL engine to remotely orchestras the processing workflow within S4PM. As mining is completed, the output is placed in an FTP holding area for the end user. The goals are to give users control over the data they want to process, while mining data at the data source using the server's resources rather than transferring the full volume over the internet. These diverse technologies have been infused into a functioning, distributed system with only minor changes to the underlying technologies. The key to the infusion is the loosely coupled, Web-Services based architecture: All of the participating components are accessible (one way or another) through (Simple Object Access Protocol) SOAP-based Web Services.

Pham, Long↗

NASA NTRS2018

End-to-End Solution for Data Customization with NASA's Earthdata Search

The goal of NASA's Earthdata Search End-to-End Services workflow is to take the pain and headache out of searching for data and getting that data back in a format that is usable with only that data that is relevant for you. For too long scientists have had to jump through endless hoops, use tools that only offer specific data or specific services, and perform any number of other non-science tasks just to get started on their actual project. Earthdata Search leverages the Common Metadata Repository's (CMR) newly implemented Unified Metadata Models for Services and Variables as well as a new service broker to expose and seamlessly integrate a collection's service capabilities and variables into an intuitive user interface. Using the new End-to-End Services workflow, scientists will be able to quickly see what data is available to be customized, what customization options are available, and actually perform those customizations on the data all within Earthdata Search, regardless of who the data provider is. This talk will demonstrate the simple workflow that will be available to end users and also give an overview covering how the workflow is enabled by the metadata stored within the CMR. (https://search.earthdata.nasa.gov/)

Reese, Mark↗

NASA NTRS2019

Geometry Modeling for Unstructured Mesh Adaptation

The quantification and control of discretization error is critical to obtaining reliable simulation results. Adaptive mesh techniques have the potential to automate discretization error control, but have made limited impact on production analysis workflow. Recent progress has matured a number of independent implementations of flow solvers, error estimation methods, and anisotropic mesh adaptation mechanics. However, the poor integration of initial mesh generation and adaptive mesh mechanics to typical sources of geometry has hindered adoption of adaptive mesh techniques, where these geometries are often created in Mechanical Computer- Aided Design (MCAD) systems. The difficulty of this coupling is compounded by two factors: the inherent complexity of the model (e.g., large range of scales, bodies in proximity, details not required for analysis) and unintended geometry construction artifacts (e.g., translation, uneven parameterization, degeneracy, self-intersection, sliver faces, gaps, large tolerances be- tween topological elements, local high curvature to enforce continuity). Manual preparation of geometry is commonly employed to enable fixed-grid and adaptive-grid workflows by reducing the severity and negative impacts of these construction artifacts, but manual process interaction inhibits workflow automation. Techniques to permit the use of complex geometry models and reduce the impact of geometry construction artifacts on unstructured grid workflows are models from the AIAA Sonic Boom and High Lift Prediction are shown to demonstrate the utility of the current approach.

Park, Michael A.↗

NASA NTRSDate not supplied

Data Integrity Challenges in NASA Giovanni

The Geospatial Interactive Online Visualization ANd aNalysis Infrastructure (Giovanni) is an online tool developed by the NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC), one of 12 NASA Science Mission Directorate Data Centers (DAACs) to analyze and visualize NASA remote sensing and model data without downloading data and software. As of this writing, over 2000 Earth satellite and model variables are available in Giovanni, including several well-known NASA satellite missions (e.g., TRMM, GPM) and projects (e.g., MERRA-2, GPCP). There are twenty-two plots provided by Giovanni that can be used to analyze, compare, and explore Earth data across disciplines. Results can be shared with colleagues and downloaded for further analysis. Giovanni has helped publish over 3000 referral papers over the years. As open science policies roll in, data integrity has become a major challenge for Giovanni and other tools. For integrity, both data and workflows must be transparent. FAIR-compliant data, including input, intermediate, and result products, as well as their associated statistics, metadata, and information, are needed. The NASA Data Product Development Guide for Data Producers provides a key resource on how to develop FAIR-compliant data products. Data quality information is also needed from data producers and analysis services like Giovanni. The workflow part is quite challenging and requires workflow management improvements, such as recording workflows and making them available to users. In this presentation, we will discuss the data integrity challenges in Giovanni.

data analysis, visualization↗

DOE OSTI2024

Composable optimization and control toolkit for scientific applications

Applications of Artificial Intelligence (AI) and Machine Learning (ML) can improve the computational efficiency and scientific research output. In order to improve interoperability and reuse of AI/ML software, a composable approach is required. This talk presents a composable approach for scientific workflow development that allows seamless integration of various modules developed by independent researchers. These practices will reduce redundant software development by allowing re-use of workflow modules across projects, teams, departments and facilities. We will present three use cases that follow the composable approach namely, Scientific Optimization and Control Toolkit (SOCT), SciDAC QuantOm workflow, and JLab Nuclear Physics experimental workflows. This talk will dive deeper into SOCT and present the details of the composable code development for optimization and control algorithms using reinforcement learning.

Rajput, Kishansingh↗

DOE OSTI2024

Exploring Geothermal Potential of Great Basin Sub-Regions: Preprint

The INnovative Geothermal Exploration through Novel Investigations Of Undiscovered Systems (INGENIOUS) project aims to discover new, economically viable hidden geothermal systems in the Great Basin region by building on previous work in play fairway analysis and machine learning. A key objective of this project is to develop an exploration workflow to reduce geothermal exploration risks for hidden geothermal systems. A single preliminary play fairway workflow was developed from the assessment of the regional INGENIOUS geological, geophysical, and geochemical datasets. This workflow provided new preliminary predictive geothermal fairway maps for the INGENIOUS study area, which encompasses most of Nevada, western Utah, southern Idaho, southeastern Oregon, and easternmost California. However, a recent study (incorporating machine learning techniques) of a portion of Nevada identified four geologic domains and determined that the relative importance of individual datasets or features as indicators of geothermal potential may differ across these domains. The INGENIOUS study area includes a much larger and more geologically diverse region; therefore, additional geologic domains or sub-regions are expected. To assess the sub-regions in the INGENIOUS study area, principal component analysis and k-means clustering were applied. Preliminary results indicate that the INGENIOUS regional data cluster into groups that relate to different geologic domains in the Great Basin region. These include domains such as the Walker Lane, extensional western Great Basin region, broad lower strain region in the eastern Great Basin of western Utah and eastern Nevada, Quaternary volcanic fields, and the area adjacent to the Snake River Plain. These clusters are assessed to determine the key geologic drivers of the identified clusters. Understanding this variability can provide key insights for the exploration and characterization of hidden geothermal systems in the Great Basin region and could indicate the need to develop multiple geothermal conceptual models and play fairway workflows for the INGENIOUS study area.

GEOTHERMAL ENERGY↗

DOE OSTI2025

Exploring Geothermal Potential of Great Basin Sub-Regions

exploration↗

DOE OSTI2025

Multi-agent AI collaboration for digital twin development and assessment

Developing a digital twin (DT) model involves different steps that encompass formulating requirements, model development, implementation, and assessment with respect to real applications. Human expertise is required to coordinate and implement different steps in the DT development and assessment process. However, certain parts of this process can be automated using artificial intelligence (AI) agents for efficient workflow development. In this work, we test and analyze a multiagent AI collaboration with humans in the loop to automate different elements of the DT development and assessment process. To implement the workflow for multiagent AI DT development and assessment, we use Autogen, a multiagent framework developed by Microsoft. Autogen offers a modular and flexible framework for configuring and designing task-specific multiagent workflows. In this framework, large language models (LLMs) form the core intelligence of the AI agents where the quality and performance of the automated element is governed by the inherent capabilities and knowledge base of the LLM. We use retrieval augmented generation to supplement the LLM with relevant domain-specific information for DT requirement formulation. We illustrate this multiagent workflow using a case study on a thermal energy storage system, focusing on how AI agents can collaborate with humans to expedite and optimize different elements of DT development and assessment process.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

High-Throughput Data Processing at FRIB Using ESnet

Real-time or nearly real-time (nearline) data processing methods are critical tools as detector technologies and data acquisition (DAQ) systems allow for higher data rates and volumes. The introduction of the energy sciences network (ESnet), a U.S. Department of Energy (DOE) supported high-speed network for scientific research, creates opportunities to leverage the computing power of DOE facilities like the National Energy Research Scientific Computing Center (NERSC). As a first step toward realizing a DOE Office of Science Integrated Research Infrastructure (IRI) pattern, an automated workflow was developed to remotely process data obtained from a nuclear physics experiment at the Facility for Rare Isotope Beams (FRIB) at NERSC with data transferred between FRIB and NERSC over ESnet. The workflow demonstrated the ability to process one week’s worth of experimental data in approximately 90 min and was used successfully for nearline analysis during a recently completed FRIB experiment. Here, a summary of the workflow development and results of recent demonstrations will be presented.

Data processing↗

DOE OSTI2025

Proceedings for the Workshop on Applied Nuclear Data Activities 2025

The 2025 Workshop for Applied Nuclear Data Activities (WANDA) covered four topic areas in nuclear data: Nuclear Data and Deterrence, Nuclear Data Prioritization for Fusion, High-Assay Low-Enriched Uranium and Novel Moderators for Advanced Reactors, and Data Preservation and Data Workflows. The intention of this workshop is to connect different communities that are invested in nuclear data and have their own unique sets of needs for the purposes of sharing information, fostering collaboration in areas of shared interest, and leveraging synergistic capabilities. The attendance of federal program managers at these workshops is essential in creating awareness of the needs of their respective communities and in providing information to better guide funding investments. In each of these topical sessions, a general description of the nuclear data needs and/or capabilities was presented, along with discussions of existing capabilities that could be leveraged, potential synergistic needs or resources, and challenges that must be overcome for the application space to progress. There are many synergistic nuclear data needs among these application spaces. The discussion largely focused on increasing the accuracy of the nuclear data and better quantifying the data uncertainties that have the greatest impact on applications. The full-day session on data processing and data workflows was by nature intended to be synergistic and applicable to all technical sessions at WANDA. A notable common theme that was highlighted across all sessions was the need for accelerated delivery of nuclear data products across complex and time-consuming workflows.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2024

Autonomous Synthesis of Thin Film Materials with Pulsed Laser Deposition Enabled by In Situ Spectroscopy and Automation

Autonomous systems that combine synthesis, characterization, and artificial intelligence can greatly accelerate the discovery and optimization of materials, however platforms for growth of macroscale thin films by physical vapor deposition techniques have lagged far behind others. Here this study demonstrates autonomous synthesis by pulsed laser deposition (PLD), a highly versatile synthesis technique, in the growth of ultrathin WSe 2 films. Further, by combing the automation of PLD synthesis and in situ diagnostic feedback with a high-throughput methodology, this study demonstrates a workflow and platform which uses Gaussian process regression and Bayesian optimization to autonomously identify growth regimes for WSe 2 films based on Raman spectral criteria by efficiently sampling 0.25% of the chosen 4D parameter space. With throughputs at least 10x faster than traditional PLD workflows, this platform and workflow enables the accelerated discovery and autonomous optimization of the vast number of materials that can be synthesized by PLD.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Applying queueing theory to evaluate wait-time-savings of triage algorithms

Abstract In the past decade, artificial intelligence (AI) algorithms have made promising impacts in many areas of healthcare. One application is AI-enabled prioritization software known as computer-aided triage and notification (CADt). This type of software as a medical device is intended to prioritize reviews of radiological images with time-sensitive findings, thus shortening the waiting time for patients with these findings. While many CADt devices have been deployed into clinical workflows and have been shown to improve patient treatment and clinical outcomes, quantitative methods to evaluate the wait-time-savings from their deployment are not yet available. In this paper, we apply queueing theory methods to evaluate the wait-time-savings of a CADt by calculating the average waiting time per patient image without and with a CADt device being deployed. We study two workflow models with one or multiple radiologists (servers) for a range of AI diagnostic performances, radiologist’s reading rates, and patient image (customer) arrival rates. To evaluate the time-saving performance of a CADt, we use the difference in the mean waiting time between the diseased patient images in the with-CADt scenario and that in the without-CADt scenario as our performance metric. As part of this effort, we have developed and also share a software tool to simulate the radiology workflow around medical image interpretation, to verify theoretical results, and to provide confidence intervals for the performance metric we defined. We show quantitatively that a CADt triage device is more effective in a busy, short-staffed reading setting, which is consistent with our clinical intuition and simulation results. Although this work is motivated by the need for evaluating CADt devices, the evaluation methodology presented in this paper can be applied to assess the time-saving performance of other types of algorithms that prioritize a subset of customers based on binary outputs.

Thompson, Yee Lam Elim (ORCID:0000000196537707)↗

DOE OSTI2025

Multi-objective surrogate-assisted calibration of CPFEM models using macroscopic response and in situ EBSD measurements of grain reorientation trajectories

Crystal plasticity finite element method (CPFEM) models are widely used to simulate the deformation behaviour of polycrystalline materials, but their calibration is often limited by their high computational cost and the non-convexity of the optimisation landscape. Here, this study develops a multi-objective surrogate-assisted calibration workflow that couples a multi-objective genetic algorithm (MOGA) with an adaptively trained deep neural network (DNN) surrogate model to efficiently identify CPFEM parameters from experimental data. The workflow is demonstrated on three crystal plasticity (CP) formulations of increasing complexity — Voce hardening (VH), two-coefficient latent hardening (LH2), and six-coefficient latent hardening (LH6) — using in situ electron backscatter diffraction (EBSD) measurements of Alloy 617 under uniaxial tensile loading. The CPFEM models are calibrated against the experimentally observed stress–strain response and reorientation trajectories of eight grains, then validated against eight additional trajectories and overall texture evolution. Across the CP formulations, the macroscopic response was reproduced reliably, while differences emerged in the robustness and accuracy of the grain-scale predictions. Including grain reorientation trajectories in the multi-objective calibration improved texture evolution predictions and filtered out physically inconsistent parameter sets that can arise from calibrating against only the stress–strain data. The workflow also demonstrates good transferability of calibrated parameters from a low- to a high-fidelity microstructural model. These results provide practical guidance for integrating in situ microstructural data into CPFEM through efficient, repeatable, and physically meaningful multi-objective calibration.

Crystal plasticity finite element method↗

DOE OSTI2026

3D seismic imaging of a fracture damage zone controlling reservoir compartmentalization at the Raft River EGS using multi-azimuth walkaway VSP

Accurate imaging of steeply dipping fracture zones in crystalline enhanced geothermal systems (EGS) is critical for constraining permeability architecture and guiding stimulation design. However, such structures remain poorly resolved by conventional surface seismic methods. We present a fully three-dimensional (3D) elastic-waveform inversion-migration workflow applied to multi-azimuth walkaway vertical seismic profiling (VSP) data acquired in a deviated borehole at the Raft River EGS. The workflow integrates first-arrival traveltime tomography, multi-scale elastic waveform inversion (EWI), and elastic least-squares reverse-time migration (ELSRTM) to recover high-resolution compressional-and shear-wave velocity models and to image structural discontinuities in the crystalline basement. The results reveal a laterally continuous low-velocity anomaly, with shear-wave velocity reductions of 25-30%, consistent with fractureinduced mechanical weakening. Two steeply dipping discontinuities bound a 50-80 m wide fracture damage zone. Independent constraints from microseismic clustering and geochemical compartmentalization corroborate the geometry and structural significance of this feature. Synthetic modeling further confirms that structures of this scale are resolvable (~30 m). These findings indicate that the Narrows structure is a distributed fracture damage zone rather than a discrete fault plane. By resolving fracture-zone geometry at the tens-of-meters scale using a single borehole, this workflow provides a practical and transferable approach for improving structural characterization, reducing uncertainty in permeability architecture, and supporting reservoir modeling and stimulation design in fractured crystalline EGS reservoirs.

58 GEOSCIENCES↗

DOE OSTI2025

Modeling supercritical CO2 injection induced rupture of a minor fault embedded in a poroelastic layered reservoir-caprock system

CO2 injection for geologic carbon sequestration involves hydromechanical processes that lead to changes in fluid pressure and stresses that can activate existing faults. This paper presents a new method and workflow of modeling fault activation considering more complex three-dimensional geometry of natural faults using the TOUGH-FLAC multiphase fluid flow and geomechanical simulator. In this method and workflow, FLAC3D mechanical interfaces and TOUGH3 finite volume elements are discretized using computer aided design and gridding software along with a tailored mesh translation routine. The method and workflow are demonstrated with a model of a curved minor fault embedded in a poro-elastic layered reservoir-caprock system. The model is used for a comprehensive sensitivity analysis of fault responses to fault length, injection mass rate, injection schedule, well-fault distance, and well locations versus fault location. Four metrics (CO2 plume, shear state of fault, pressure and stress path at fault monitoring points) are selected to assess CO2 migration, pressure change, and the reactivation of faults. The results reveal that CO2 can bypass around the tip of the minor impermeable fault, building up pressure and poro-elastic stress on both sides that tends to impede fault rupture. Our study shows the benefit of carefully designing the injection to achieve the targeted final storage volume, starting at a relatively low rate for considerable time, and then ramping up the injection rate to the full rate of injection. The initial low injection has two distinct benefits: (1) it allows for the formation of an extensive CO2 plume with a much higher mobility through a low viscosity that will result in a lower pressure for a given injection rate, and (2) it allows for gradual build-up of horizontal poro-elastic stress within the reservoir that will tend to impede activation of steeply dipping faults. The injection scenario starting at a low injection rate, denoted here as conservative injection, can significantly reduce the risk of fault activation as high fluid mobility and reservoir strengthening poro-elastic stress has been established long before reaching the peak injection rates. Moreover, simultaneous injection in two injection wells on both sides of fault can provide further reservoir strengthening through poro-elastic stress buildup acting on a fault under normal faulting stress regime. The findings presented in the paper can provide practical and effective guidance on long-term, safe, and reliable geological CO2 storage.

Cao, Meng↗

DOE OSTI2025

Subject-specific modeling framework for particle deposition using computational fluid dynamics

Quantifying particle deposition and dose in the respiratory tract requires a physiologically realistic representation and reproducible computational workflows. However, existing modeling frameworks, such as the International Commission on Radiological Protection (ICRP) compartmental models and the Multiple Path Particle Dosimetry (MPPD) tool, lack detailed deposition profiles and subject-specific capabilities. The combination of advances in computer vision algorithms applied to the respiratory tract and Computational Fluid and Particle Dynamics (CFPD) allows high-fidelity simulations of particle behavior in anatomically accurate geometries derived from individual CT scans. The segmentation, preprocessing, and file preparation task for a CFPD simulation was often time-consuming, and no prior studies to-date have yet presented a fully automated framework. This work presents a fully automated workflow to obtain individualized particle deposition profiles in the human respiratory tract. The pipeline starts with segmenting upper and lower airway geometries using morphological and deep learning-based methods, generating three-dimensional (3D) models from CT imaging data. Next, a series of algorithms are presented to quality check and prepare the 3D geometry for a CFD or CFPD simulation. The preprocessing step includes correcting geometric artifacts, enforcing a physically consistent mesh, and automatically identifying and capping multiple outlets, which is required for CFD/CFPD simulations. These processed models are then input into open-source (OpenFOAM) or commercial (StarCCM+) CFD solvers, where flow and transient particle transport equations — including turbulence and particle–wall interactions are solved under realistic breathing conditions. Finally, the resulting particle deposition profiles can be integrated with Monte Carlo radiation transport codes and state-of-the-art computational phantoms to assess organ-specific absorbed doses in scenarios of radioactive aerosol inhalation. The presented work streamlines respiratory tract segmentation, preprocessing for CFD/CFPD simulations, and integration with dose assessment workflows, reducing manual intervention and improving access to high-fidelity, subject-specific modeling. The high precision in predicted particle deposition and dose distributions can improve personalized treatment strategies in respiratory medicine and refine dose estimates for radiation protection.

AI↗

DOE OSTI2025

Predicting metal-binding proteins and structures through integration of evolutionary-scale and physics-based modeling

Metals are essential elements in all living organisms, binding to approximately 50% of proteins. They serve to stabilize proteins, catalyze reactions, regulate activities, and fulfill various physiological and pathological functions. While there have been many advancements in determining the structures of protein-metal complexes, numerous metal-binding proteins still need to be identified through computational methods and validated through experiments. Here, to address this need, we have developed the ESMBind workflow, which combines evolutionary scale modeling (ESM) for metal-binding prediction and physics-based protein-metal modeling. Our approach utilizes the ESM-2 and ESM-IF models to predict metal-binding probability at the residue level. In addition, we have designed a metal-placement method and energy minimization technique to generate detailed 3D structures of protein-metal complexes. Our workflow outperforms other models in terms of residue and 3D-level predictions. To demonstrate its effectiveness, we applied the workflow to 142 uncharacterized fungal pathogen proteins and predicted metal-binding proteins involved in fungal infection and virulence.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Integrative Multi-PTM Proteomics Reveals Dynamic Global, Redox, Phosphorylation, and Acetylation Regulation in Cytokine-treated Pancreatic Beta Cells

Studying regulation of protein function at a systems level necessitates an understanding of the interplay among diverse post-translational modifications (PTMs). A variety of proteomics sample processing workflows are currently used to study specific PTMs but rarely characterize multiple types of PTMs from the same sample inputs. Method incompatibilities and laborious sample preparation steps complicate large-scale physiological investigations and can lead to variations in results. The single-pot, solid-phase-enhanced sample preparation (SP3) method for sample cleanup is compatible with different lysis buffers and amenable to automation, making it attractive for high-throughput multi-PTM profiling. Herein, we describe an integrative SP3 workflow for multiplexed quantification of protein abundance, cysteine thiol oxidation, phosphorylation, and acetylation. The broad applicability of this approach is demonstrated using cell and tissue samples, and its utility for studying interacting regulatory networks is highlighted in a time-course experiment of cytokine-treated ß-cells. We observed a swift response in global regulation of protein abundances consistent with rapid activation of JAK-STAT and NF-?B signaling pathways. Regulators of these pathways as well as proteins involved in their target processes displayed multi-PTM dynamics indicative of a complex cellular response stages: acute, adaptation, and chronic (prolonged stress). PARP14, a negative regulator of JAK-STAT, had multiple co-localized PTMs that may be involved in intraprotein regulatory crosstalk. Our workflow provides a high-throughput platform that can profile multi-PTMomes from the same sample set, which is valuable in unraveling the functional roles of PTMs and their co-regulation.

proteomics, PTM, automation, SP3, cysteine thiol o↗