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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Anisotropic Goal-Based Mesh Adaptation Metric Clarification and Development

Adaptive unstructured mesh techniques have a limited, but growing impact on production analysis workflows to control discretization error for reliable simulation results. Multiple independent implementations of flow solvers, anisotropic metric construction methods, and anisotropic mesh adaptation mechanics have matured. Goal-based metrics target estimated error in output functions, such as lift and drag, through the guidance of an adjoint solution. A unification of goal-based anisotropic metrics is presented for steady viscous flows, which is an active area of research. These goal-based metrics drive robust and efficient anisotropic mesh adaptation for the calculation of output functions. The super-convergent functional output error behavior of stabilized finite-element methods is exploited without a formal proof, and evidence of super-convergence is shown in numerical experiments. Mesh adapted drag and lift outputs for two simple bodies in compressible viscous flow show convergence of error to less than a single drag count. Asymptotic behavior established for relatively coarse meshes shows the efficiency of this goal-based metric when compared to solution interpolation error control and expert-guided meshing. Anisotropic mesh adaptation techniques are applied to a transport aircraft in a high-lift configuration where variation between approaches decreases with mesh refinement, but asymptotic behavior is not observed with available resources.

goal-based↗
NASA NTRSDate not supplied

Verification of Viscous Goal-Based Anisotropic Mesh Adaptation

Adaptive unstructured mesh techniques have a limited, but growing impact on production analysis workflows where the control of discretization error is critical to obtaining reliable simulation results. Recent progress has matured a number of independent implementations of flow solvers, error estimation methods, and anisotropic mesh adaptation mechanics. Anisotropic metric construction methods are evaluated with analytically defined primal and adjoint fields. This allows the comparison of different metric formulations and different implementations of the same formulation without the complications of a flow and adjoint solution method. Unstructured mesh adaptation tools are verified by comparison on analytic primal and dual field before verification on benchmark aerodynamics cases. The documentation of these verification exercises helps to prepare these goal-based methods for routine use in production simulation workflows.

Mesh adaptation↗
DOE OSTI2023

A rule-free workflow for the automated generation of databases from scientific literature

Abstract In recent times, transformer networks have achieved state-of-the-art performance in a wide range of natural language processing tasks. Here we present a workflow based on the fine-tuning of BERT models for different downstream tasks, which results in the automated extraction of structured information from unstructured natural language in scientific literature. Contrary to existing methods for the automated extraction of structured compound-property relations from similar sources, our workflow does not rely on the definition of intricate grammar rules. Hence, it can be adapted to a new task without requiring extensive implementation efforts and knowledge. We test our data-extraction workflow by automatically generating a database for Curie temperatures and one for band gaps. These are then compared with manually curated datasets and with those obtained with a state-of-the-art rule-based method. Furthermore, in order to showcase the practical utility of the automatically extracted data in a material-design workflow, we employ them to construct machine-learning models to predict Curie temperatures and band gaps. In general, we find that, although more noisy, automatically extracted datasets can grow fast in volume and that such volume partially compensates for the inaccuracy in downstream tasks.

36 MATERIALS SCIENCE↗
NASA NTRS2019

Verification of Unstructured Grid Adaptation Components

Adaptive unstructured grid techniques have made limited impact on production analysis workflows where the control of discretization error is critical to obtaining reliable simulation results. Recent progress has matured a number of independent implementations of flow solvers, error estimation methods, and anisotropic grid adaptation mechanics. Known differences and previously unknown differences in grid adaptation components and their integrated processes are identified here for study. Unstructured grid adaptation tools are verified using analytic functions and the Code Comparison Principle. Three analytic functions with different smoothness properties are adapted to show the impact of smoothness on implementation differences. A scalar advection-diffusion problem with an analytic solution that models a boundary layer is adapted to test individual grid adaptation components. Laminar flow over a delta wing and turbulent flow over an ONERA M6 wing are verified with multiple, independent grid adaptation procedures to show consistent convergence to fine-grid forces and a moment. The scalar problems illustrate known differences in a grid adaptation component implementation and a previously unknown interaction between components. The wing adaptation cases in the current study document a clear improvement to existing grid adaptation procedures. The stage is set for the infusion of verified grid adaptation into production fluid flow simulations.

Park, Michael A.↗
NASA NTRS2019

Verification of Unstructured Grid Adaptation Components

Adaptive unstructured grid techniques have made limited impact on production analysis workflows where the control of discretization error is critical to obtaining reliable simulation results. Recent progress has matured a number of independent implementations of flow solvers, error estimation methods, and anisotropic grid adaptation mechanics. Known differences and previously unknown differences in grid adaptation components and their integrated processes are identified here for study. Unstructured grid adaptation tools are verified using analytic functions and the Code Comparison Principle. Three analytic functions with different smoothness properties are adapted to show the impact of smoothness on implementation differences. A scalar advection-diffusion problem with an analytic solution that models a boundary layer is adapted to test individual grid adaptation components. The scalar problems illustrate known differences in a grid adaptation component implementation and a previously unknown interaction between components. Laminar flow over a delta wing is verified with multiple, independent grid adaptation procedures to show consistent convergence to fine-grid forces and pitching moment.

Park, Michael A.↗
NASA NTRSDate not supplied

Verification of Viscous Goal-Based Anisotropic Mesh Adaptation

Adaptive unstructured mesh techniques have a limited, but growing impact on production analysis workflows where the control of discretization error is critical to obtaining reliable simulation results. Recent progress has matured a number of independent implementations of flow solvers, anisotropic metric construction methods, and anisotropic mesh adaptation mechanics. A key ingredient for the broader acceptance of unstructured mesh adaptation is the verification of these implementations. Anisotropic metric construction methods are evaluated with analytically defined primal fields and the corresponding entropy variables as adjoint fields. This allows the comparison of different metric formulations and different implementations of the same formulation without the complications of a flow and adjoint solution method. The convergence of the output associated with the entropy variable adjoint is studied for mesh adaptation to these fields and a manufactured solution. Mesh adapted drag output is studied for two simple wings in compressible laminar flow to show fine-mesh convergence of multiple metric construction methods to less than a single drag count. The documentation of these verification exercises helps to prepare these goal-based methods for routine use in more complex simulations for production workflows.

mesh adaptation↗
DOE OSTI2023

Towards Lightweight Data Integration Using Multi-Workflow Provenance and Data Observability

Modern large-scale scientific discovery requires multidisciplinary collaboration across diverse computing facilities, including High Performance Computing (HPC) machines and the Edge-to-Cloud continuum. Integrated data analysis plays a crucial role in scientific discovery, especially in the current AI era, by enabling Responsible AI development, FAIR, Reproducibility, and User Steering. However, the heterogeneous nature of science poses challenges such as dealing with multiple supporting tools, cross-facility environments, and efficient HPC execution. Building on data observability, adapter system design, and provenance, we propose MIDA: an approach for lightweight runtime Multi-workflow Integrated Data Analysis. MIDA defines data observability strategies and adaptability methods for various parallel systems and machine learning tools. With observability, it intercepts the dataflows in the background without requiring instrumentation while integrating domain, provenance, and telemetry data at runtime into a unified database ready for user steering queries. We conduct experiments showing end-to-end multi-workflow analysis integrating data from Dask and MLFlow in a real distributed deep learning use case for materials science that runs on multiple environments with up to 276 GPUs in parallel. We show near-zero overhead running up to 100,000 tasks on 1,680 CPU cores on the Summit supercomputer.

Santos Souza, Renan↗
DOE OSTI2020

Block Design with Common Reference Samples Enables Robust Large-Scale Label-Free Quantitative Proteome Profiling

Label-free quantitative proteomics has become an increasingly popular tool for profiling global protein abundances. However, one major weakness is the potential performance drift of the LC-MS platform over time, which in turn limits its utility for analyzing large-scale sample sets. To address this, in this work we introduce an experimental and data analysis scheme based on a block-design with common controls within each block for enabling LC-MS-based large-scale label-free quantification. In this scheme, a large number of samples (e.g., >100 samples) are analyzed in smaller, and more manageable blocks, minimizing instrument drift and variability within a block. Furthermore, each designated block also contains common controls (or reference) samples for normalization within and across blocks. We demonstrated the effectiveness of this method by profiling the proteome response of human macrophage THP-1 cells to 11 engineered nanomaterials (ENMs) at two different doses. A total of 116 samples were analyzed in six blocks, yielding an average coverage of 4500 proteins per sample. The data revealed consistent quantification of proteins across all six blocks, as shown by highly stable quantification of house-keeping proteins in all samples and high levels of quantification correlation among samples from different blocks. The data also demonstrated that label-free quantification is robust and accurate enough to quantify even very subtle abundance changes as well as large fold-changes without potential ratio compression as often encountered with isobaric labeling. Our streamlined workflow is easy to implement and can be readily adapted to other large cohort studies for reproducible label-free proteome quantification.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

FAIR for AI: An interdisciplinary and international community building perspective

A foundational set of findable, accessible, interoperable, and reusable (FAIR) principles were proposed in 2016 as prerequisites for proper data management and stewardship, with the goal of enabling the reusability of scholarly data. The principles were also meant to apply to other digital assets, at a high level, and over time, the FAIR guiding principles have been re-interpreted or extended to include the software, tools, algorithms, and workflows that produce data. FAIR principles are now being adapted in the context of AI models and datasets. Here, we present the perspectives, vision, and experiences of researchers from different countries, disciplines, and backgrounds who are leading the definition and adoption of FAIR principles in their communities of practice, and discuss outcomes that may result from pursuing and incentivizing FAIR AI research. The material for this report builds on the FAIR for AI Workshop held at Argonne National Laboratory on June 7, 2022.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Machine Learning and Understanding for Intelligent Extreme Scale Scientific Computing and Discovery. DOE Workshop Report, January 7–9, 2015, Rockville, MD

Large-scale parallel simulations and data analysis drive scientific discovery across many disciplines. To drive larger and more detailed simulations, and deal with larger data volumes, exascale machines capable of 1018 operations per second are expected within the next five to ten years. However, the complexity of developing and adapting modern simulation codes for these architectures is increasing rapidly. Workloads on exascale machines will be billion-way parallel. Exploiting the full capability of the machine will require carefully assigning application tasks to cores, accelerators, deep memories, and other heterogeneous compute resources, while simultaneously optimizing for time to solution, data movement, power, and resilience. Optimizations that improve performance on one machine may slow down another. Worse, applications themselves are dauntingly complex. Production simulations comprise millions of lines of code and use sophisticated, adaptive algorithms whose performance is input-dependent. Complex workflows can couple multi-physics simulation with data preprocessing and post-processing modules.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2013

NASA Tech Briefs, November 2013

Topics include: Cryogenic Liquid Sample Acquisition System for Remote Space Applications; 5 Spatial Statistical Data Fusion (SSDF); GPS Estimates of Integrated Precipitable Water Aid Weather Forecasters; Integrating a Microwave Radiometer into Radar Hardware for Simultaneous Data Collection Between the Instruments; Rapid Detection of Herpes Viruses for Clinical Applications; High-Speed Data Recorder for Space, Geodesy, and Other High-Speed Recording Applications; Datacasting V3.0; An All-Solid-State, Room-Temperature, Heterodyne Receiver for Atmospheric Spectroscopy at 1.2 THz; Stacked Transformer for Driver Gain and Receive Signal Splitting; Wireless Integrated Microelectronic Vacuum Sensor System; Fabrication Method for LOBSTER-Eye Optics in <110> Silicon; Compact Focal Plane Assembly for Planetary Science; Fabrication Methods for Adaptive Deformable Mirrors; Visiting Vehicle Ground Trajectory Tool; Workflow-Based Software Development Environment; Mobile Thread Task Manager; A Kinematic Calibration Process for Flight Robotic Arms; Magnetostrictive Alternator; Bulk Metallic Glasses and Composites for Optical and Compliant Mechanisms; Detection of Only Viable Bacterial Spores Using a Live/Dead Indicator in Mixed Populations; and Intravenous Fluid Generation System.

Source record↗
DOE OSTI2019

DeepDriveMD: Deep-Learning Driven Adaptive Molecular Simulations for Protein Folding

Simulations of biological macromolecules are important in understanding the physical basis of complex processes such as protein folding. However, even with increasing computational capacity and specialized architectures, the ability to simulate protein folding at atomistic scales still remains challenging. This stems from the dual aspects of high dimensionality of protein conformational landscapes, and the inability of atomistic molecular dynamics (MD) simulations to sufficiently sample these landscapes to observe folding events. Machine learning/deep learning (ML/DL) techniques, when combined with atomistic MD simulations offer the opportunity to potentially overcome these limitations by: (1) effectively reducing the dimensionality of MD simulations to automatically build latent representations that correspond to biophysically relevant reaction coordinates (RCs), and (2) driving MD simulations to automatically sample potentially novel conformational states based on these RCs. We examine how coupling DL approaches with MD simulations can lead to effective approaches to fold small proteins on supercomputers. In particular, we study the computational costs and effectiveness of scaling DL-coupled MD workflows implemented using RADICAL-Cybertools in folding two prototypical systems, namely Fs-peptide and the fast-folding variant of the villin head piece protein. We demonstrate that a DL-coupled MD workflow is able to effectively learn latent representations and drive adaptive simulations. Compared to traditional MD-based approaches, our approach achieves an effective performance gain in sampling the folded states by at least 2.3x. Together, our study provides quantitative basis to understand how coupling DL approaches to MD simulations, can lead to effective performance gains and reduced times to solution on supercomputing resources.

Lee, Hyungro↗
DOE OSTI2016

MO-E-BRC-00: Online Adaptive Radiotherapy - Considerations for Practical Clinical Implementation

Online adaptive radiation therapy has the potential to ensure delivery of optimal treatment to the patient by accounting for anatomical and potentially functional changes that occur from one fraction to the next and over the course of treatment. While on-line adaptive RT (ART) has been a topic of many publications, discussions, and research, it has until very recently remained largely a concept and not a practical implementation. However, recent advances in on-table imaging, use of deformable image registration for contour generation and dose tracking, faster and more efficient plan optimization, as well as fast quality assurance method has enabled the implementation of ART in the clinic in the past couple of years. The introduction of these tools into routine clinical use requires many considerations and progressive knowledge to understand how processes that have historically taken hours/days to complete can now be done in less than 30 minutes. This session will discuss considerations to perform real time contouring, planning and patient specific QA, as well as a practical workflow and the required resources. Learning Objectives: To understand the difficulties, challenges and available technologies for online adaptive RT. To understand how to implement online adaptive therapy in a clinical environment and to understand the workflow and resources required. To understand the limitations and sources of uncertainty in the online adaptive process I have research funding from ViewRay Inc. and Philips Medical Systems.; R. Kashani, I have research funding from ViewRay Inc. and Philips Medical Systems.; X. Li, Research supported by Elekta Inc.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

MO-E-BRC-02: MRI-Guided Online Adaptive Radiotherapy: The UCLA Approach to Quality Management

Online adaptive radiation therapy has the potential to ensure delivery of optimal treatment to the patient by accounting for anatomical and potentially functional changes that occur from one fraction to the next and over the course of treatment. While on-line adaptive RT (ART) has been a topic of many publications, discussions, and research, it has until very recently remained largely a concept and not a practical implementation. However, recent advances in on-table imaging, use of deformable image registration for contour generation and dose tracking, faster and more efficient plan optimization, as well as fast quality assurance method has enabled the implementation of ART in the clinic in the past couple of years. The introduction of these tools into routine clinical use requires many considerations and progressive knowledge to understand how processes that have historically taken hours/days to complete can now be done in less than 30 minutes. This session will discuss considerations to perform real time contouring, planning and patient specific QA, as well as a practical workflow and the required resources. Learning Objectives: To understand the difficulties, challenges and available technologies for online adaptive RT. To understand how to implement online adaptive therapy in a clinical environment and to understand the workflow and resources required. To understand the limitations and sources of uncertainty in the online adaptive process I have research funding from ViewRay Inc. and Philips Medical Systems.; R. Kashani, I have research funding from ViewRay Inc. and Philips Medical Systems.; X. Li, Research supported by Elekta Inc.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

MO-E-BRC-03: Fast Online Replanning Techniques

Online adaptive radiation therapy has the potential to ensure delivery of optimal treatment to the patient by accounting for anatomical and potentially functional changes that occur from one fraction to the next and over the course of treatment. While on-line adaptive RT (ART) has been a topic of many publications, discussions, and research, it has until very recently remained largely a concept and not a practical implementation. However, recent advances in on-table imaging, use of deformable image registration for contour generation and dose tracking, faster and more efficient plan optimization, as well as fast quality assurance method has enabled the implementation of ART in the clinic in the past couple of years. The introduction of these tools into routine clinical use requires many considerations and progressive knowledge to understand how processes that have historically taken hours/days to complete can now be done in less than 30 minutes. This session will discuss considerations to perform real time contouring, planning and patient specific QA, as well as a practical workflow and the required resources. Learning Objectives: To understand the difficulties, challenges and available technologies for online adaptive RT. To understand how to implement online adaptive therapy in a clinical environment and to understand the workflow and resources required. To understand the limitations and sources of uncertainty in the online adaptive process I have research funding from ViewRay Inc. and Philips Medical Systems.; R. Kashani, I have research funding from ViewRay Inc. and Philips Medical Systems.; X. Li, Research supported by Elekta Inc.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

A High-Quality Workflow for Multi-Resolution Scientific Data Reduction and Visualization

Multi-resolution methods such as Adaptive Mesh Refinement (AMR) can enhance storage efficiency for HPC applications generating vast volumes of data. However, their applicability is limited and cannot be universally deployed across all applications. Furthermore, integrating lossy compression with multi-resolution techniques to further boost storage efficiency encounters significant barriers. To this end, we introduce an innovative workflow that facilitates high-quality multi-resolution data compression for both uniform and AMR simulations. Initially, to extend the usability of multi-resolution techniques, our workflow employs a compression-oriented Region of Interest (ROI) extraction method, transforming uniform data into a multi-resolution format. Subsequently, to bridge the gap between multi-resolution techniques and lossy compressors, we optimize three distinct compressors, ensuring their optimal performance on multi-resolution data. These optimizations can improve the compression ratio of SOTA approaches by up to 3.3× under the same data quality loss. Lastly, we incorporate an advanced uncertainty visualization method into our workflow to understand the potential impacts of lossy compression. Experimental evaluation demonstrates that our workflow achieves significant compression quality improvements.

Wang, Daoce↗
DOE OSTI2026

Ecosystems for Scientific Computing in the Age of AI

Scientific computing is at an inflection point. Artificial intelligence (AI) is reshaping how scientific software is developed, how teams collaborate, how projects are governed, and how the next generation is trained. Drawing on insights from a 2025 workshop report, this article argues that the future of discovery will depend on agile, robust ecosystems built through socio-technical co-design—the intentional integration of technical and human systems. This perspective is essential for ensuring that future scientific computing remains trustworthy, sustainable, and scalable. It combines advances in AI, high-performance computing, and software with new models for cross-disciplinary collaboration, education, and workforce development. Key recommendations include building modular, trustworthy AI-enabled software ecosystems; enabling teams to integrate AI into scientific workflows while preserving human creativity, integrity, and rigor; and developing adaptive training pathways that keep pace with rapid technological change. By sharing these perspectives, we hope to stimulate broader community dialogue and encourage coordinated action.

AI↗