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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
DOE OSTI2025

Applying the FAIR Principles to computational workflows

Recent trends within computational and data sciences show an increasing recognition and adoption of computational workflows as tools for productivity and reproducibility that also democratize access to platforms and processing know-how. As digital objects to be shared, discovered, and reused, computational workflows benefit from the FAIR principles, which stand for Findable, Accessible, Interoperable, and Reusable. The Workflows Community Initiative’s FAIR Workflows Working Group (WCI-FW), a global and open community of researchers and developers working with computational workflows across disciplines and domains, has systematically addressed the application of both FAIR data and software principles to computational workflows. We present recommendations with commentary that reflects our discussions and justifies our choices and adaptations. These are offered to workflow users and authors, workflow management system developers, and providers of workflow services as guidelines for adoption and fodder for discussion. The FAIR recommendations for workflows that we propose in this paper will maximize their value as research assets and facilitate their adoption by the wider community.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2001

Cloud Structure and Cloud-Radiation Interaction: From Smallest Observable Scales (Less than 10 m) to GCM Grid Scales (More than 100 km)

In this presentation we review the fractal nature of internal cloud structure from cm- to km-scales as captured by in-situ probes during long horizontal penetrations by aircraft. We uncover the non-Poissonian spatial distribution of cloud droplets at submeter scales and confirm scale-invariant behavior for large scales. Based on these structural characteristics, we generate simple fractal cloud models that reproduce statistical scaling properties of real clouds. These stochastic models represent a link between nonlinear science, in general, and cloud-radiation interaction, in particular. Next we run three-dimensional radiative transfer computations on these synthetic fractal clouds and compare the structure of the resulting radiation fields with the known structure of the cloud model and with satellite images of real clouds. The different behaviors observed for small and large-scale variabilities will be discussed in detail. We find that while the large-scale fluctuations of the resulting radiation fields resemble those in the original scale-invariant cloud structure, the radiation at small scales is much smoother than its cloud liquid water counterpart. This violates scale-invariance and produces a scale-break at 0.2-0.5 km that is clearly observed in high-resolution satellite data such as from Landsat. Finally, we show how radiative transfer Green function theory in the photon diffusion limit explains (and predicts) the above phenomena of "radiative smoothing."

Marshak, Alexander↗
NASA NTRS1989

Non-contact temperature measurements in support of microgravity combustion experiments

Recent conceptual advances in the understanding of combustion science fundamentals in the context of microgravity processes and phenomenology have resulted in an increased demand for diagnostic systems of greater sophistication. Owing primarily to the severe operational constraints that accompany the space flight environment, measurement systems to date remain fairly primative in nature. Qualitative pictures provided by photographic recording media comprise the majority of the existing data, the remainder consisting of the output of conventional transducers, such as thermocouples, hot wires, and pressure transducers. The absence of the rather strong influence of buoyant convection renders microgravity combustion phenomena more fragile than their 1-G counterparts. The emphasis was placed on nonperturbing optical diagnostics. Other factors such as limited supplies of expendable reactants, and periods of microgravity time of sufficient duration, coupled with more fundamental questions regarding inherent length and time scales and reproducibility have favored multipoint or multidimensional techniques. While the development of optical diagnostics for application to combustion science is an extremely active area at present, the peculiarities of space flight hardware severely restrict the feasibility of implementing the majority of techniques which are being utilized in terrestrial applications. The additional requirements for system reliability and operational simplicity have tended to promote somewhat less commonly emphasized techniques such as refractive index mapping and molecular Rayleigh scattering, which are briefly discussed.

Greenberg, Paul S.↗
DOE OSTI2024

MSD CoP Webinar: Using Meta-Repositories To Facilitate Open Science in MSD Research

Context: This webinar was hosted by the MultiSector Dynamics Community of Practice (MSD CoP; https://multisectordynamics.org). Abstract: In this webinar, we will describe the use of a GitHub meta-repository, for documenting and disseminating the tools and data supporting MSD publications. The goal is to make it easier for others to understand the flow of data and code through your experiment and to be able to reproduce your results and figures with only the information you have provided for them. The webinar will cover the role of open science in the MSD community, the origins and purpose of meta-repositories, and step-by-step instructions and best practices for building a meta-repository starting from the GitHub template (https://github.com/IMMM-SFA/metarepo). We will also discuss how to leverage MSD-LIVE (https://msdlive.org/) in your meta-repository, provide links to numerous examples you can learn from, and discuss the role of meta-repositories in the IM3 project's open science mandates. Presenters : Chris R. Vernon, Casey D. Burleyson, Jennie Rice, and Mengqi Zhao Moderator: Pat M. Reed (MSD CoP Facilitation Team) This webinar was held on: February 22nd, 2024 from 2-3 PM ET

Open Science↗
DOE OSTI2025

Ptychography at all wavelengths

Ptychography is a computational imaging technique that operates across multiple wavelength regimes, from electron (picometres) to X-ray (~0.1 nm), extreme ultraviolet (~10 nm) and visible light (micrometres). By reconstructing both amplitude and phase from diffraction patterns, ptychography enables high-resolution, quantitative imaging without conventional limitations imposed by lens-based optics. Ptychography has enabled advances across a range of scales: achieving deep-sub-angstrom resolution with electron microscopy, becoming an indispensable tool at X-ray synchrotron facilities worldwide and overcoming the trade-offs between resolution and field-of-view in optical imaging. This Primer provides a unified treatment of ptychography across these wavelength regimes. First, we discuss theoretical foundations, reconstruction algorithms, experimental considerations and wavelength-specific challenges. We then give examples of raw and processed data from various configurations and wavelengths. Next, we highlight key applications of ptychography in life sciences, materials science and industry. We also discuss data standards, open-source software implementations and best practices for ensuring reproducibility across different wavelength regimes. Finally, we consider limitations and future opportunities for ptychography. Together with accompanying datasets and code implementations, this Primer aims to serve newcomers and experienced practitioners in the field, facilitating broader adoption of ptychography across different disciplines.

47 OTHER INSTRUMENTATION↗
NASA NTRS2017

Engineering, Life Sciences, and Health/Medicine Synergy in Aerospace Human Systems Integration: The Rosetta Stone Project

In the realm of aerospace engineering and the physical sciences, we have developed laws of physics based on empirical and research evidence that reliably guide design, research, and development efforts. For instance, an engineer designs a system based on data and experience that can be consistently and repeatedly verified. This reproducibility depends on the consistency and dependability of the materials on which the engineer works and is subject to physics, geometry and convention. In life sciences and medicine, these apply as well, but individuality introduces a host of variables into the mix, resulting in characteristics and outcomes that can be quite broad within a population of individuals. This individuality ranges from differences at the genetic and cellular level to differences in an individuals personality and abilities due to sex and gender, environment, education, etc.

Medicine↗
NASA NTRS2016

Using Docker Containers to Extend Reproducibility Architecture for the NASA Earth Exchange (NEX)

NASA Earth Exchange (NEX) is a data, supercomputing and knowledge collaboratory that houses NASA satellite, climate and ancillary data where a focused community can come together to address large-scale challenges in Earth sciences. As NEX has been growing into a petabyte-size platform for analysis, experiments and data production, it has been increasingly important to enable users to easily retrace their steps, identify what datasets were produced by which process chains, and give them ability to readily reproduce their results. This can be a tedious and difficult task even for a small project, but is almost impossible on large processing pipelines. We have developed an initial reproducibility and knowledge capture solution for the NEX, however, if users want to move the code to another system, whether it is their home institution cluster, laptop or the cloud, they have to find, build and install all the required dependencies that would run their code. This can be a very tedious and tricky process and is a big impediment to moving code to data and reproducibility outside the original system. The NEX team has tried to assist users who wanted to move their code into OpenNEX on Amazon cloud by creating custom virtual machines with all the software and dependencies installed, but this, while solving some of the issues, creates a new bottleneck that requires the NEX team to be involved with any new request, updates to virtual machines and general maintenance support. In this presentation, we will describe a solution that integrates NEX and Docker to bridge the gap in code-to-data migration. The core of the solution is saemi-automatic conversion of science codes, tools and services that are already tracked and described in the NEX provenance system, to Docker - an open-source Linux container software. Docker is available on most computer platforms, easy to install and capable of seamlessly creating and/or executing any application packaged in the appropriate format. We believe this is an important step towards seamless process deployment in heterogeneous environments that will enhance community access to NASA data and tools in a scalable way, promote software reuse, and improve reproducibility of scientific results.

earth exchange↗
DOE OSTI2026

CAHS: Context-Aware Homology Search

Protein homology search is foundational to bioinformatics: it supports annotation transfer, structure/function inference, and evolutionary analysis over rapidly expanding sequence repositories (e.g., UniProtKB). Profile hidden Markov models (pHMMs), as implemented in HMMER, remain the most widely trusted approach because they provide statistically calibrated E-values; however, their gap behavior is fixed once a profile is trained, despite biological evidence that insertion/deletion tolerance varies across flexible loops and intrinsically disordered regions. We present CAHS (Context-Aware Homology Search), a lightweight query-time adapter for pHMM search that incorporates learned and biologically motivated signals without changing HMMER's downstream search pipeline or its calibrated E-value reporting. Given a query sequence, CAHS computes per-residue representations from a protein language model and a disorder predictor, maps these to profile coordinates, and modulates only match-state transition rows (gap-open and gap-extension probabilities) while preserving Plan7 constraints. We comprehensively evaluate CAHS across six structurally diverse protein families and multi-domain architectures against a 570k-sequence target corpus. CAHS expands detection capability, retrieving thousands of additional remote homologs at relaxed thresholds by maintaining alignment quality through flexible regions. For multi-domain proteins, context-aware modulation resolves 94% of fragmented alignments. Crucially, CAHS preserves hit-set invariance at stringent operating points (E<10-10), demonstrating increased statistical confidence without inflating false positives. Furthermore, sharper statistical distinction between homologs and background noise during early filter stages yields up to a 3.87× acceleration in end-to-end wall-clock time on high-performance computing clusters. Overall, CAHS illustrates a practical AI-for-science design pattern: augmenting a trusted probabilistic model with query-specific learned signals to improve interpretable, reproducible inference in data-rich biology.

Bhattaram, Swethasree [Georgia Institute of Techno↗
NASA NTRS2019

Accelerated Simulation of Air Pollution Using NVIDIA RAPIDS

Atmospheric chemistry models are a central tool to study and forecast the impact of air pollution on the environment, vegetation, and human health. However, the numerical simulation of chemical kinetics is computationally expensive due to the stiffness of the system of ordinary differential equations that describes atmospheric chemistry. Here we present an alternative approach to the computation of atmospheric chemistry based on machine learning. Our training data set is produced using the NASA Goddard Earth Observing System (GEOS) model with GEOS-Chem chemistry, run on the NASA Center for Climate Simulation (NCCS) Discover supercomputing cluster on 384 Intel Xeon Haswell cores. This model spends more than 50% of total run time on solving atmospheric chemistry. The data set contains as input features the air pollution concentrations before solving the differential equations, together with some key physical parameters such as temperature and sun intensity. As target variables we define the air pollution concentrations after solving the differential equations. Using Dask-cuDF and Dask-XGBoost on the NVIDIA RAPIDS platform on 8 Tesla V100 GPUs, we generate from this training set gradient boosted decision tree models that can reproduce the simulation of chemical kinetics. We do this on the NCCS Advanced Data Analytics Platform (ADAPT) science cloud environment. Our application takes full advantage of recent advances in Dask-XGBoost, such as multi-node and multi-GPU scaling for distributed training with large data sets. The increase in training data size enabled by this is critical to capture the full range of chemical environments encountered across the globe and all annual seasons.The boosted tree models offer good predictability and show many of the features of the full chemistry reference simulation. Further improvements can be achieved through mass balance considerations and by accounting for error correlations. We incorporate the boosted tree models into the GEOS reference model using XGBoost's C API. This enables a seamless integration of the GPU trained models into GEOS-Chem, which is written in Fortran and optimized for use in a massively parallel CPU environment. We show the benefits of this approach and discuss the potential speedup of this machine learning accelerated atmospheric chemistry model.

Keller, Christoph A.↗
DOE OSTI2021

Response to Andrew Fowlie’s Comments

This Letter to the Editor of Harvard Data Science Review is a rejoinder to a Letter to the Editor by Andrew Fowlie, titled “Comment on Reproducibility and Replication of Experimental Particle Physics Results”.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

High precision control of laser energy for laser–matter interaction studies

Precise, highly reproducible control of the laser energy is required for high confidence laser–matter interaction research such as in dynamic compression science and high energy density physics. The energy must be adjustable without affecting the pulse shape (time varying intensity) or beam smoothness. We have developed a convenient two-stage energy tuning method for a nominal 100 J, 351 nm (UV) laser. The energy is adjusted in 10 J (10%) increments by operating the laser at full energy and inserting a beam splitter in the laser output. As the splitter is located after the final frequency tripling optics, the UV pulse shape is unchanged. The energy is varied by substituting a splitter of different reflectivity. For finer 3 J (3%) increments, the infrared pulse is attenuated inside the laser before the final amplifier. This requires modest tuning to preserve the pulse shape. The demonstrated variation in shot-to-shot reproducibility is less than +/–2.5 J (5% of the full energy), irrespective of the laser output energy. These approaches can be adapted to most ~100 J class lasers. We describe these techniques and show two examples where they have elucidated the underlying physics in laser shock compression experiments. One used only the beam splitters to establish the pressure for melting in iron. The other combined both techniques to finely increment the peak stress (~2 GPa steps) in germanium to precisely determine the onset and completion of melting—including the melting kinetics. Furthermore, these unambiguous results would not be possible without the developments described here.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Opportunities in AI/ML for the Rubin LSST Dark Energy Science Collaboration

The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will produce unprecedented volumes of heterogeneous astronomical data (images, catalogs, and alerts) that challenge traditional analysis pipelines. The LSST Dark Energy Science Collaboration (DESC) aims to derive robust constraints on dark energy and dark matter from these data, requiring methods that are statistically powerful, scalable, and operationally reliable. Artificial intelligence and machine learning (AI/ML) are already embedded across DESC science workflows, from photometric redshifts and transient classification to weak lensing inference and cosmological simulations. Yet their utility for precision cosmology hinges on trustworthy uncertainty quantification, robustness to covariate shift and model misspecification, and reproducible integration within scientific pipelines. This white paper surveys the current landscape of AI/ML across DESC's primary cosmological probes and cross-cutting analyses, revealing that the same core methodologies and fundamental challenges recur across disparate science cases. Since progress on these cross-cutting challenges would benefit multiple probes simultaneously, we identify key methodological research priorities, including Bayesian inference at scale, physics-informed methods, validation frameworks, and active learning for discovery. With an eye on emerging techniques, we also explore the potential of the latest foundation model methodologies and LLM-driven agentic AI systems to reshape DESC workflows, provided their deployment is coupled with rigorous evaluation and governance. Finally, we discuss critical software, computing, data infrastructure, and human capital requirements for the successful deployment of these new methodologies, and consider associated risks and opportunities for broader coordination with external actors.

Aubourg, Eric [APC, Paris] (ORCID:000000025592023X↗
DOE OSTI2023

Say where you sample: Increasing site selection transparency in urban ecology

Urban ecological studies have the potential to extend our understanding of socio-ecological systems beyond that of an individual city or region. Cross-comparative empirical work and synthesis are imperative to develop a general urban ecological theory. This can be achieved only if studies are replicable and generalizable. Transparency in methods reporting facilitates generalizability and replicability by documenting the decisions scientists make during the various steps of research design; this is particularly true for sampling design and selection because of their impact on both internal and external validity and the potential to unintentionally introduce bias. Three interdependent aspects of sample design are study sample selection (e.g., specific organisms, soils, or water), sample specification (measurement of specific variable of interest), and site selection (locations sampled). Of these, documentation of site selection—the where component of sample design—is underrepresented in the urban ecology literature. Using a stratified random sample of 158 papers from 12 major urban ecology journals, we investigated how researchers selected study sites in urban ecosystems and evaluated whether their site selection methods were transparent. We extracted data from these papers using a 50-question, theory-based questionnaire and a multiple-reviewer approach. Our sample represented almost 45 years of urban ecology research across 40 different countries. We found that more than 80% of the papers read were not transparent in their site selection methodology. We do not believe site selection methods are replicable for 70% of the papers read. Key weaknesses include incomplete descriptions of populations and sampling frames, urban gradients, sample selection methods, and property access. Low transparency in reporting the where methodology limits urban ecologists’ ability to assess the internal and external validity of studies’ findings and to replicate published studies; it also limits the generalizability of existing studies. The challenges of low transparency are particularly relevant in urban ecology, a field where standard protocols for site selection and delineation are still being developed. These limitations interfere with the fields’ ability to build theory and inform policy. We conclude by offering a set of recommendations to increase transparency, replicability, and generalizability.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Challenges for monitoring and data analytics in a leadership public data repository

The availability and disposition of data has assumed increasing importance in large-scale computational science. Data repositories are evolving to meet new classes of requirements: compliance with government access guidelines, support for reproducibility of experimental results, and long-term availability of data products. The Constellation public data repository at the Oak Ridge Leadership Computing Facility faces these issues while being situated in one of the most productive data centers in the world. While monitoring and operational data analysis are ingrained in the operation of the OLCF’s large-scale high performance computing platforms, data repositories do not have this history of support. Problems faced by Constellation range from data size (over 7 petabytes in current holdings) to analytic complexity (detailed curation is both absolutely necessary for many data sets and absolutely impossible for humans to accomplish in any practical manner) to deployment environment (OLCF storage resources are oriented toward the needs of the compute platforms). In this paper we describe some of the challenges for collecting monitoring and analytic data from a leadership public data repository. We also discuss various strategies we are pursuing in order to address these challenges, from manual data collection to plans for introducing machine learning-based curatorial techniques.

Widener, Patrick [ORNL] (ORCID:0000000258820816)↗
DOE OSTI2021

Deep graph representations embed network information for robust disease marker identification

We report that the accurate disease diagnosis and prognosis based on omics data rely on the effective identification of robust prognostic and diagnostic markers that reflect the states of the biological processes underlying the disease pathogenesis and progression. In this article, we present GCNCC, a Graph Convolutional Network-based approach for Clustering and Classification, that can identify highly effective and robust network-based disease markers. Based on a geometric deep learning framework, GCNCC learns deep network representations by integrating gene expression data with protein interaction data to identify highly reproducible markers with consistently accurate prediction performance across independent datasets possibly from different platforms. GCNCC identifies these markers by clustering the nodes in the protein interaction network based on latent similarity measures learned by the deep architecture of a graph convolutional network, followed by a supervised feature selection procedure that extracts clusters that are highly predictive of the disease state. By benchmarking GCNCC based on independent datasets from different diseases (psychiatric disorder and cancer) and different platforms (microarray and RNA-seq), we show that GCNCC outperforms other state-of-the-art methods in terms of accuracy and reproducibility.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Criticality Accident Alarm System Shielding Benchmark: Integral Experiment Request 498, Critical Engineering Decision 2 Report

A workable design to perform a CAAS benchmark experiment is detailed herein. Key dimensions, materials, source intensity levels, and detectors are listed in this report. Sensitivity to 21 perturbations was determined to be acceptable. The next step will be for NCSP management to determine whether procurement should occur and if the experiment should proceed. The perturbation study suggests that the room return shield cavity radius and runout should be maintained to within a millimeter, the room return shield should be positioned carefully (perhaps with a laser range finder), and that the detectors should be mounted in a lightweight fixture such as aluminum, so their positioning is assured. Using a 3D scanner or photogrammetry to record part shapes may be beneficial. Further work is also needed to verify source reproducibility.

36 MATERIALS SCIENCE↗
NASA NTRS2012

Multi-Color QWIP FPAs for Hyperspectral Thermal Emission Instruments

Infrared focal plane arrays (FPAs) covering broad mid- and long-IR spectral ranges are the central parts of the spectroscopic and imaging instruments in several Earth and planetary science missions. To be implemented in the space instrument these FPAs need to be large-format, uniform, reproducible, low-cost, low 1/f noise, and radiation hard. Quantum Well Infrared Photodetectors (QWIPs), which possess all needed characteristics, have a great potential for implementation in the space instruments. However a standard QWIP has only a relatively narrow spectral coverage. A multi-color QWIP, which is compromised of two or more detector stacks, can to be used to cover the broad spectral range of interest. We will discuss our recent work on development of multi-color QWIP for Hyperspectral Thermal Emission Spectrometer instruments. We developed QWIP compromising of two stacks centered at 9 and 10.5 ?m, and featuring 9 grating regions optimized to maximize the responsivity in the individual subbands across the 7.5-12 ?m spectral range. The demonstrated 1024x1024 QWIP FPA exhibited excellent performance with operability exceeding 99% and noise equivalent differential temperature of less than 15 mK across the entire 7.5-12 ?m spectral range.

Infrared focal plane arrays (FPAs)↗
NASA NTRS2013

Multi-color QWIP FPAs for hyperspectral thermal emission instruments

Infrared focal plane arrays (FPAs) covering broad mid- and long-IR spectral ranges are the central parts of the spectroscopic and imaging instruments in several Earth and planetary science missions. To be implemented in the space instrument these FPAs need to be large-format, uniform, reproducible, low-cost, low 1/f noise, and radiation hard. Quantum Well Infrared Photodetectors (QWIPs), which possess all needed characteristics, have a great potential for implementation in the space instruments. However a standard QWIP has only a relatively narrow spectral coverage. A multi-color QWIP, which is compromised of two or more detector stacks, can to be used to cover the broad spectral range of interest. We will discuss our recent work on development of multi-color QWIP for Hyperspectral Thermal Emission Spectrometer instruments. We developed QWIP compromising of two stacks centered at 9 and 10.5 μm, and featuring 9 grating regions optimized to maximize the responsivity in the individual subbands across the 7.5-12 μm spectral range. The demonstrated 1024x1024 QWIP FPA exhibited excellent performance with operability exceeding 99% and noise equivalent differential temperature of less than 15 mK across the entire 7.5-12 μm spectral range.

Gunapala, Sarath D.↗