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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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DOE OSTI2019

Development of Community, Capabilities and Understanding Through Unmanned Aircraft-Based Atmospheric Research: The LAPSE-RATE Campaign

Members of the international community developing unmanned aircraft for atmospheric research conducted a highly-successful coordinated flight week in Colorado during summer 2018 to collect measurements, develop capabilities, and enhance community in the field. Because unmanned aircraft systems (UAS) offer new perspectives on the atmosphere, their use in atmospheric science is expanding rapidly. In support of this growth, the International Society for Atmospheric Research using Remotely-piloted Aircraft (ISARRA) has been developed and have convened annual meetings and 'flight weeks'. The 2018 flight week, dubbed the Lower Atmospheric Profiling Studies at Elevation - a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE), involved a one-week deployment to Colorado's San Luis Valley. Between 14-20 July 2018 over 100 students, scientists, engineers, pilots and outreach coordinators conducted an intensive field operation using unmanned aircraft and ground-based assets to develop datasets, community, and capabilities. In addition to a coordinated 'community day' which offered a chance for groups to share their aircraft and science with the San Luis Valley community, LAPSE-RATE participants conducted nearly 1300 research flights totaling over 250 flight hours. The measurements collected have been used to advance capabilities (instrumentation, platforms, sampling techniques and modeling tools), conduct a detailed system intercomparison study, develop new collaborations, and foster community support for the use of UAS in atmospheric science.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

KBase partners with community developers from several Department of Energy Science Focus Areas to increase functionality of tools and analysis

Several SFAs were selected for training on the KBase Software Development Kit (SDK) to integrate tools and data into the KBase platform. Overall, the collaborative effort was viewed as highly successful. Every SFA project was able to achieve their proposed targets for training and functionality, while also gaining an appreciation for the role KBase plays/could play in their future science plans. Both the SFA and KBase teams felt that continued collaboration would be beneficial, especially if the programs were able to jointly prioritize scope and readiness of future projects. This poster will outline the scope of functionality added by SFA community developers, and highlight points of intersection across these efforts.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2013

Building a Community Infrastructure for Scalable On-Line Performance Analysis Tools around Open|SpeedShop

In this project we created a community tool infrastructure for program development tools targeting Petascale class machines and beyond. This includes tools for performance analysis, debugging, and correctness tools, as well as tuning and optimization frameworks. The developed infrastructure provides a comprehensive and extensible set of individual tool building components. We started with the basic elements necessary across all tools in such an infrastructure followed by a set of generic core modules that allow a comprehensive performance analysis at scale. Further, we developed a methodology and workflow that allows others to add or replace modules, to integrate parts into their own tools, or to customize existing solutions. In order to form the core modules, we built on the existing Open|SpeedShop infrastructure and decomposed it into individual modules that match the necessary tool components. At the same time, we addressed the challenges found in performance tools for petascale systems in each module. When assembled, this instantiation of community tool infrastructure provides an enhanced version of Open|SpeedShop, which, while completely different in its architecture, provides scalable performance analysis for petascale applications through a familiar interface. This project also built upon and enhances capabilities and reusability of project partner components as specified in the original project proposal. The overall project team’s work over the project funding cycle was focused on several areas of research, which are described in the following sections. The reminder of this report also highlights related work as well as preliminary work that supported the project. In addition to the project partners funded by the Office of Science under this grant, the project team included several collaborators who contribute to the overall design of the envisioned tool infrastructure. In particular, the project team worked closely with the other two DOE NNSA laboratories Los Alamos and Sandia leveraging co-funding for Krell by ASC’s Common Computing Environment (CCE) program as laid out in the original proposal. The ASC CCE co-funding, coordinated through LLNL, was for 50% of the total project funding, with the ASC CCE portion of the funding going entirely to Krell, while the ASCR funding itself was split between Krell and the funded partners. This report covers the entire project from both funding sources. Additionally, the team leveraged the expertise of software engineering researchers from Carnegie Mellon University, who specialize in software framework design, in order to achieve a broadly acceptable component framework. The Component Based Tool Framework (CBTF) software has been released to the community. Information related to the project and the released software can be found on the CBTF wiki page at: http://sourceforge.net/p/cbtf/wiki/Home.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Crowdsourcing Global Perspectives in Ecology Using Social Media

Transparent, open, and reproducible research is still far from routine, and the full potential of open science has not yet been realized. Crowdsourcing–defined as the usage of a flexible open call to a heterogeneous group of individuals to recruit volunteers for a task –is an emerging scientific model that encourages larger and more outwardly transparent collaborations. While crowdsourcing, particularly through citizen- or community-based science, has been increasing over the last decade in ecological research, it remains infrequently used as a means of generating scientific knowledge in comparison to more traditional approaches. We explored a new implementation of crowdsourcing by using an open call on social media to assess its utility to address fundamental ecological questions. We specifically focused on pervasive challenges in predicting, mitigating, and understanding the consequences of disturbances. In this paper, we briefly review open science concepts and their benefits, and then focus on the new methods we used to generate a scientific publication. We share our approach, lessons learned, and potential pathways forward for expanding open science. Our model is based on the beliefs that social media can be a powerful tool for idea generation and that open collaborative writing processes can enhance scientific outcomes. We structured the project in five phases: (1) draft idea generation, (2) leadership team recruitment and project development, (3) open collaborator recruitment via social media, (4) iterative paper development, and (5) final editing, authorship assignment, and submission by the leadership team. We observed benefits including: facilitating connections between unusual networks of scientists, providing opportunities for early career and underrepresented groups of scientists, and rapid knowledge exchange that generated multidisciplinary ideas. We also identified areas for improvement, highlighting biases in the individuals that self-selected participation and acknowledging remaining barriers to contributing new or incompletely formed ideas into a public document. While shifting scientific paradigms to completely open science is a long-term process, our hope in publishing this work is to encourage others to build upon and improve our efforts in new and creative ways.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Nuclear Science User Facilities High Performance Computing: Provide a Science Gateway for HPC Users

Idaho National Laboratory (INL), supported by the Department of Energy Office of Nuclear Energy (DOE-NE) through the Nuclear Science User Facilities (NSUF), provides direct access to the Barracuda Virtual Reactor and 18 Multiphysics Object-Oriented Simulation Environment (MOOSE) applications via a web-based science gateway developed using Open Ondemand on the INL high performance computing (HPC) systems. This gateway features the computational tools of the Nuclear Computational Resource Center (NCRC) and the computing resources of the INL high performance computing systems. These computational tools are a key foundation of collaboration and innovation in nuclear energy systems research. High performance computing resources and INL staff directly support the mission and objectives of DOE-NE. The Barracuda Virtual Reactor was the first science gateway deployed in Jan 2021 to support NSUF users. In July 2021, the science gateway was expanded to support access to NCRC codes for use across all supported INL HPC systems. The HPC science gateway currently supports 20 total applications. The gateway also includes access to training resources specific to NCRC tools.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2026

Benchmark for two-dimensional large scale coherent structures in partially magnetized E × B plasmas—community collaboration & lessons learned

Low-temperature plasmas (LTPs) are essential to both fundamental scientific research and critical industrial applications. As in many areas of science, numerical simulations have become a vital tool for uncovering new physical phenomena and guiding technological development. Code benchmarking remains crucial for verifying implementations and evaluating performance. This work continues the Landmark benchmark initiative, a series specifically designed to support the verification of LTP codes. In this study, seventeen simulation codes from a collaborative community of nineteen international institutions modeled a partially magnetized E × B Penning discharge. The emergence of large scale coherent structures, or rotating plasma spokes, endows this configuration with an enormous range of time scales, making it particularly challenging to simulate. The codes showed excellent agreement on the rotation frequency of the spoke as well as key plasma properties, including time-averaged ion density, plasma potential, and electron temperature profiles. Achieving this level of agreement came with challenges, and we share lessons learned on how to conduct future benchmarking campaigns. Comparing code implementations, computational hardware, and simulation runtimes also revealed interesting trends, which are summarized with the aim of guiding future plasma simulation software development.

benchmarking↗
DOE OSTI2019

Changing the Rules of the Game: Used Fuel Studies Outside of a Remote Handling Facility

Recent advances in micro-fabrication tools like the FEI Helios NanoLab 660 dual Focused Ion Beam (FIB) and Scanning Electron Microscope (SEM) now make it possible to easily and reliably prepare micron (or smaller) specimens for analyses or further experimentation. Pacific Northwest National Laboratory (PNNL) has leveraged this capability at the Category II Nuclear Facility, the Radiochemical Processing Laboratory, to facilitate nuclear materials analysis and experimentation of used nuclear fuel. Micron-scale specimens of un-irradiated UO2 fuel pellets of various enrichments from depleted to 4.95% enriched, relative to 235U, were prepared at PNNL and shipped to the University of Missouri’s Research Reactor (MURR) for irradiation. Prior to shipping, samples were attached to commercially available Transmission Electron Microscopy half-grids via ion deposition, which were then housed in specially designed aluminum sample holders approved for use at the reactor facility. This minimized handling and preparation requirements prior to materials analysis after irradiation. Samples were irradiated to a burnup equivalent of 8 – 3,700 MWd/MTU. Additionally, by reducing the size of the specimens, and therefore their emitted radiation dose, the required cooling time after irradiation and prior to analysis was greatly diminished, and handling of these samples could proceed outside of a hot cell facility. Results from these experiments provided insight into the initial production of noble metal phase particles in used nuclear fuel at extremely low burnup levels. Some incompatibility with the container was observed at higher burnup. Micro-scale irradiation experiments were performed at roughly 1/7th of the time, effort and expense a similar bulk-scale study requiring a shielded hot cell facility would require. Conducting nuclear materials analysis at the micron-scale holds the promise of enhancing collaboration and material accessibility to the broader research community, improving accessibility to cutting edge research tools by the nuclear materials science community, lowering the risk to the scientist by reducing radiation dose and exposure, reducing facility and research costs associated with the requirement to conduct nuclear materials science within glovebox or remote handling facilities, and minimizing waste.

Schwantes, Jon M.↗
DOE OSTI2025

Weak lensing mass-richness relation of redMaPPer clusters in LSST DESC DC2 simulations

Cluster scaling relations are key ingredients in cluster abundance-based cosmological studies. In optical cluster cosmology, where clusters are detected through their richness, cluster-weak gravitational lensing has proven to be a powerful tool to constrain the cluster mass-richness relation. This work is conducted as part of the Dark Energy Science Collaboration (DESC), which aims to analyze the Legacy Survey of Space and Time (LSST) of the Vera C. Rubin Observatory, starting in 2026. Cluster properties inferred from weak lensing, such as mass, suffer from several sources of bias. In this paper, we aim to test the impact of modeling choices and observational systematics in cluster lensing on the inference of the mass-richness relation. We constrained the mass-richness relation of 3600 clusters detected by the redMaPPer algorithm in the cosmoDC2 extragalactic mock catalog of the LSST DESC DC2 simulation, covering 440 deg 2 , using number count measurements and either stacked weak lensing profiles or mean cluster masses in several intervals of richness (20 ≤ λ ≤ 200) and redshift (0.2 ≤ z ≤ 1). We provide the first constraints on the redMaPPer cluster mass-richness relation detected in cosmoDC2. We find that for an LSST-like source galaxy density, our constraints are robust to changes in the concentration-mass relation, as well as the dark matter density profile modeling choices, when source redshifts and shapes are perfectly known. We find that photometric redshift uncertainties can introduce bias at the 1σ level, which could be mitigated by an overall correction factor fitted jointly with the scaling parameters. We find that including positive shear-richness covariance in the fit shifts the results by up to 0.5σ. Our constraints also offer a fair comparison to a fiducial mass-richness relation, obtained from matching cosmoDC2 halo masses to redMaPPer-detected cluster richness results.

galaxy clusters↗
DOE OSTI2013

NREL Launches Collaborative Resource for Field Test Best Practices [Fact Sheet]

Dynamic portal documents and shares state-of-the-art residential field test tools and techniques. Field testing is a science and an art—a tricky process that develops through a lot of trial and error. Researchers in the Advanced Residential Buildings group at the National Renewable Energy Laboratory (NREL) regularly conduct field experiments and long-term monitoring in occupied and unoccupied houses throughout the United States. The goal is to capture real-world performance of energy-efficient systems, in support of the U.S. Department of Energy’s Building America program. In addition to the technical challenges of making accurate field measurements, NREL researchers realized another problem: the vast body of field test know-how based on years of collective experience is currently scattered throughout the building science community. To address this need, NREL developed the Field Test Best Practices website, a dynamic, collaborative resource that documents practical “lessons from the field” for building scientists nationwide.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Nuclear Physics Network Requirements Review Report

The Energy Sciences Network (ESnet) is the Office of Science’s high-performance network user facility, delivering highly reliable data transport capabilities optimized for the requirements of data-intensive science. In essence, ESnet is the circulatory system that enables the U.S. Department of Energy (DOE) science mission by connecting each and every DOE lab and its user facilities. ESnet is funded and stewarded by the Advanced Scientific Computing Research (ASCR) Program, and managed and operated by the Scientific Networking Division at Lawrence Berkeley National Laboratory (LBNL). ESnet is widely regarded as a global leader in the research and education networking community. ESnet connects DOE national laboratories, user facilities, and major experiments so scientists can use remote instruments and computing resources as well as share data with collaborators, transfer large data sets, and access distributed data repositories. While ESnet provides network connectivity, it cannot be characterized as an internet service provider as it is specifically built to provide a range of network services that are tailored to meet the unique requirements of DOE’s data-intensive science. In short, ESnet’s mission is to enable and accelerate scientific discovery by delivering unparalleled network infrastructure, capabilities, and tools. ESnet’s vision is summarized by these three points. 1. Scientific progress will be completely unconstrained by the physical location of instruments, people, computational resources, or data. 2. Collaborations at every scale, in every domain, will have the information and tools they need to achieve maximum benefit from scientific facilities, global networks, and emerging network capabilities. 3. ESnet will foster the partnerships and pioneer the technologies necessary to ensure that these transformations occur. In May 2019, ESnet and the Office of Nuclear Physics (NP) of the DOE Office of Science organized an ESnet requirements review of NP-supported activities. Preparation for this event included identification of key stakeholders to the process: program and facility management, research groups, technology providers, and a number of external observers. These individuals were asked to prepare formal case study documents about their relationship to the NP program to build a complete understanding of the current, near-term, and long-term status, expectations, and processes that will support the science going forward. A series of pre-planning meetings better prepared case study authors for this task and facilitated a smooth transition to the in-person review.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2019

BisQue for 3D Materials Science in the Cloud: Microstructure–Property Linkages

Accelerating the design and development of new advanced materials is one of the priorities in modern materials science. These efforts are critically dependent on the development of comprehensive materials cyber-infrastructures which enable effcient data storage, management, sharing, and collaboration as well as integration of computational tools that help establish processing{structure{property relationships. In this contribution, we present implementation of such computational tools into a cloud-based platform called BisQue (Kvilekval et al., 2010). We first describe the current state of BisQue as an open-source platform for multidisciplinary research in the cloud and its potential for 3D materials science. We then demonstrate how new computational tools, primarily aimed at processing{structure{property relationships, can be implemented into the system. Speci cally, in this work, we develop a module for BisQue that enables microstructure-sensitive predictions of effective yield strength of two-phase materials. Towards this end, we present an implementation of a computationally effcient data-driven model into the BisQue platform. The new module is made available online and can be used from a web browser without any special software and with minimal computational requirements on the user end. The capabilities of the module for rapid property screening are demonstrated in case studies with two different methodologies based on datasets containing 3D microstructure information from (i) synthetic generation and (ii) sampling large 3D volumes obtained in experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2016

The Monarch Initiative: an integrative data and analytic platform connecting phenotypes to genotypes across species

The correlation of phenotypic outcomes with genetic variation and environmental factors is a core pursuit in biology and biomedicine. Numerous challenges impede our progress: patient phenotypes may not match known diseases, candidate variants may be in genes that have not been characterized, model organisms may not recapitulate human or veterinary diseases, filling evolutionary gaps is difficult, and many resources must be queried to find potentially significant genotype-phenotype associations. Nonhuman organisms have proven instrumental in revealing biological mechanisms. Advanced informatics tools can identify phenotypically relevant disease models in research and diagnostic contexts. Large-scale integration of model organism and clinical research data can provide a breadth of knowledge not available from individual sources and can provide contextualization of data back to these sources. The Monarch Initiative (monarchinitiative.org) is a collaborative, open science effort that aims to semantically integrate genotype-phenotype data from many species and sources in order to support precision medicine, disease modeling, and mechanistic exploration. Our integrated knowledge graph, analytic tools, and web services enable diverse users to explore relationships between phenotypes and genotypes across species.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Understanding and Constraining the Midlatitude Cloud Optical Depth Feedback in Climate Models (Final Report)

The objectives of this investigation are to provide a deeper understanding of the brightening of middle and high latitude clouds that occurs in many climate models as the climate warms, and to better understand why models give varying estimates of the strength of this negative feedback. The investigation may also provide new information on why models are deficient in their simulation of the present day clouds in these regions. The analysis will reveal which models simulate current clouds most realistically in the regions of interest, and relate this to the strength of the cloud feedbacks in the models. This will improve the uncertainty quantification of climate simulations and will provide theoretical understanding potentially leading to increased reliability of climate change projections. This will enhance the predictive understanding of the Earth system by analyzing the natural and anthropogenic components of global climate change in models. The primary methods involve the study of the clouds in the suite of CMIP5 model climate simulations and comparison of clouds in climate simulations with data from satellite measurement systems, with a particular emphasis on mixed-phase cloud physics, for which ice and water are present simultaneously. State-of-the-art climate model feedback analysis will be combined with new and more comprehensive observations of clouds available from modern measurement systems. Novel use of model simulations in combination with observations enables a deeper understanding of the role of clouds in climate change. Metrics that facilitate model-to-model and model-to-observation comparisons related to mixed-phase cloud physics will be developed and used to highlight challenges and opportunities for improving the state-of-the-science models. This investigation is a collaboration between the University of Washington, where the data work is concentrated and LLNL where the model data and the tools and expertise to do cloud feedback analysis are located. The grant supported the work of 3 different graduate students at different times (McCoy, Wall and Berry) Post-doctoral researcher Blaž Gasparini was also partially supported on this grant and led our collaboration with PNNL scientists. PNNL scientists also collaborated in the work of graduate student Wall (Po-Lun Ma). The main results of our research efforts are reported in 6 papers and 1 book chapter that were supported by this grant.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Transforming Energy Through Computational Excellence: A View From NREL

At the National Renewable Energy Laboratory (NREL)—a U.S. Department of Energy laboratory—computational science, high-performance computing, applied mathematics, advanced computer science, visualization, and data play a pivotal role in advancing energy abundance, affordability, security, and reliability. From fundamental scientifc discovery to systems engineering and analysis, NREL researchers tackle market-relevant challenges to develop solutions for an independent energy system that is reliable, resilient and secure. Collaborative partnerships with industry, government, and academia ensure that our research remains cutting edge, impactful, applicable, and aligned with real-world energy needs. This special issue of Computing in Science & Engineering highlights exemplary NREL projects where computational tools and methodologies drive discovery and accelerate innovation in scalable and integrated energy systems. The featured articles explore the role of computational modeling, high-performance computing, generative AI, and adaptive computing in advancing independent energy solutions, optimizing sustainability research, and enhancing decision-making for energy solutions using a broad mix of energy technologies. Here, these contributions demonstrate how NREL’s computational research bridges the gap between theoretical advancements and practical implementation, emphasizing interdisciplinary collaboration and a commitment to innovation, with a focus on translating computational excellence into real-world impact, thus accelerate progress toward national energy goals. By showcasing cutting-edge research at the intersection of computational science and energy systems, this issue aims to inspire and inform researchers, practitioners, and policymakers dedicated to shaping a more reliable energy future.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Better together: Elements of successful scientific software development in a distributed collaborative community

Many scientific disciplines rely on computational methods for data analysis, model generation, and prediction. Implementing these methods is often accomplished by researchers with domain expertise but without formal training in software engineering or computer science. This arrangement has led to underappreciation of sustainability and maintainability of scientific software tools developed in academic environments. Some software tools have avoided this fate, including the scientific library Rosetta. We use this software and its community as a case study to show how modern software development can be accomplished successfully, irrespective of subject area. Rosetta is one of the largest software suites for macromolecular modeling, with 3.1 million lines of code and many state-of-the-art applications. Since the mid 1990s, the software has been developed collaboratively by the RosettaCommons, a community of academics from over 60 institutions worldwide with diverse backgrounds including chemistry, biology, physiology, physics, engineering, mathematics, and computer science. Developing this software suite has provided us with more than two decades of experience in how to effectively develop advanced scientific software in a global community with hundreds of contributors. Here we illustrate the functioning of this development community by addressing technical aspects (like version control, testing, and maintenance), community-building strategies, diversity efforts, software dissemination, and user support. We demonstrate how modern computational research can thrive in a distributed collaborative community. The practices described here are independent of subject area and can be readily adopted by other software development communities

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Enabling FAIR data in Earth and environmental science with community-centric (meta)data reporting formats

Abstract Research can be more transparent and collaborative by using Findable, Accessible, Interoperable, and Reusable (FAIR) principles to publish Earth and environmental science data. Reporting formats—instructions, templates, and tools for consistently formatting data within a discipline—can help make data more accessible and reusable. However, the immense diversity of data types across Earth science disciplines makes development and adoption challenging. Here, we describe 11 community reporting formats for a diverse set of Earth science (meta)data including cross-domain metadata (dataset metadata, location metadata, sample metadata), file-formatting guidelines (file-level metadata, CSV files, terrestrial model data archiving), and domain-specific reporting formats for some biological, geochemical, and hydrological data (amplicon abundance tables, leaf-level gas exchange, soil respiration, water and sediment chemistry, sensor-based hydrologic measurements). More broadly, we provide guidelines that communities can use to create new (meta)data formats that integrate with their scientific workflows. Such reporting formats have the potential to accelerate scientific discovery and predictions by making it easier for data contributors to provide (meta)data that are more interoperable and reusable.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

Estimating Species Distributions Across Space Through Time and with Features of the Environment

Complete guidance for mastering the tools and techniques of the digital revolution With the digital revolution opening up tremendous opportunities in many fields, there is a growing need for skilled professionals who can develop data-intensive systems and extract information and knowledge from them. This book frames for the first time a new systematic approach for tackling the challenges of data-intensive computing, providing decision makers and technical experts alike with practical tools for dealing with our exploding data collections. Emphasizing data-intensive thinking and interdisciplinary collaboration, The Data Bonanza: Improving Knowledge Discovery in Science, Engineering, and Business examines the essential components of knowledge discovery, surveys many of the current research efforts worldwide, and points to new areas for innovation. Complete with a wealth of examples and DISPEL-based methods demonstrating how to gain more from data in real-world systems, the book: Outlines the concepts and rationale for implementing data-intensive computing in organizations Covers from the ground up problem-solving strategies for data analysis in a data-rich world Introduces techniques for data-intensive engineering using the Data-Intensive Systems Process Engineering Language DISPEL Features in-depth case studies in customer relations, environmental hazards, seismology, and more Showcases successful applications in areas ranging from astronomy and the humanities to transport engineering Includes sample program snippets throughout the text as well as additional materials on a companion website The Data Bonanza is a must-have guide for information strategists, data analysts, and engineers in business, research, and government, and for anyone wishing to be on the cutting edge of data mining, machine learning, databases, distributed systems, or large-scale computing.

Kelling, S.↗