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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 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Columbia Estuary Ecosystem Restoration Program: Restoration Design Challenges for Topographic Mounds, Channel Outlets, and Reed Canarygrass

The purpose of this study was to provide science-based information to practitioners and managers of restoration projects in the Columbia Estuary Ecosystem Restoration Program (CEERP) regarding aspects of restoration techniques that currently pose known challenges and uncertainties. The CEERP is a program of the Bonneville Power Administration (BPA) and the U.S. Army Corps of Engineers (Corps), Portland District, in collaboration with the National Marine Fisheries Service and five estuary sponsors implementing restoration. The estuary sponsors are Columbia Land Trust, Columbia River Estuary Study Taskforce, Cowlitz Tribe, Lower Columbia Estuary Partnership, and Washington Department of Fish and Wildlife. The scope of the research conducted during federal fiscal year 2015 included three aspects of hydrologic reconnection that were selected based on available scientific information and feedback from restoration practitioners during project reviews: the design of mounds (also called hummocks, peninsulas, or berms); the control of reed canarygrass (Phalaris arundinaceae); and aspects of channel network design related to habitat connectivity for juvenile salmonids.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Fluxes of CO 2 , CH 4 , CO, BVOCs, NOx, and O 3 in an Old Growth Amazonian Forest: Ecosystem Processes, Carbon Cycle, Atmospheric Chemistry, and Feedbacks on Climate

A comprehensive, merged data set of trace gases (NO, NO 2 , CO 2 , CH 4 and O 3 ) along with has been tabulated and subjected to meticulous quality assurance and quality control (QA/QC). The merged data set is being submitted to the ARM website dedicated to the Green Ocean Experiment: https://www.arm.gov/research/campaigns/amf2014goamazon Analysis using the final data set is in progress to determine the magnitudes of the fluxes for CH 4 , H 2 O, CO 2 , O 3 , NO x , sensible and latent heat, momentum, and their seasonal variations. Here are summary statements, from the discussion above: Total NO fluxes were calculated following Keller et al., 1986. A vertical gradient is established in the mixing ratio of NO because it is emitted at the soil surface and mixed upward in the atmosphere (see above). Once in the atmosphere, the NO reacts rapidly with O 3 to produce NO 2 (NO + O 3 → NO 2 + O 2 ). Therefore, if the vertical profiles of the mixing ratios of NO and O 3 are known, the surface flux of NO may be determined. If any other reaction removes NO (e.g., deposition on leaves), FNO should estimate the lower limit to the NO flux from the soil in this forest. Our preliminary results show fluxes of NO averaged 133 x 10 9 molecules cm -2 s -1 , a factor of 4 higher than fluxes previously observed in white sand soils in the Amazon, and a factor of 3 to 14 higher than fluxes observed for yellow clay soils (Bakwin et al., 1990 and references therein). The soil in the km 67 site is predominately oxisol with pockets of sandy ultisols, both having low reduced nutrient contents, mostly due to efficient microorganism decomposition and acid leaching by rain water. Oxisols contain both oxidized and reduced forms of nitrogen, of which concentrations vary independently of leaching (Jordan et al., 1982), with most part of the cycling processes occurring in the top layers. Methane fluxes showed no statistical difference between 2015 wet and dry seasons, and the forest at this site appear to be a methane sink throughout the year. The vertical profiles suggest that if a methane source exists in this forest, it might be in the canopy. Next steps include modeling and analysis using the Master Chemical Mechanism (Jenkin et al., 1997; Saunders et al., 2003 (A/B); http://mcm.leeds.ac.uk/MCM/) and the Ecosystem Demography-2 (ED-2) model. A final manuscript with the results from this work is in preparation and expected to be submitted for publication within the next several months. Publications to date are listed below.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Fechamento do balanço de energia em uma floresta tropical: contribuições da troca turbulenta e armazenamento de calor ecossistema [Energy balance closure in a tropical forest: contributions of turbulent exchange and ecosystem heat storage]

The surface energy balance is rarely closed using the common half-hourly averaging period for turbulent fluxes as eddies of greater characteristic time scales often provide a non-trivial contribution to energy exchange. Here, we briefly discuss previous efforts to improve surface energy balance closure of a tropical rainforest ecosystem – the K34 site - and describe how measurements from the GoAmazon campaign can be used to improve our understanding of energy flux and storage in tropical canopies. O balanço de energia da superfície raramente é fechado usando o período médio a cada meia hora comum para fluxos turbulentos como turbilhões de maior tempo característico escalas costumam oferecer uma contribuição não-trivial para troca de energia. Aqui, discutimos brevemente os esforços anteriores para melhorar o fechamento do balanço de energia da superfície de uma floresta tropical ecossistema tropical - o site K34 - e descrever como medições da campanha GoAmazon pode ser usado para melhorar a nossa compreensão do fluxo de energia e armazenamento em copas tropicais.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

XVIS: Visualization for the Extreme-Scale Scientific-Computation Ecosystem Final Scientific/Technical Report

The XVis project brings together the key elements of research to enable scientific discovery at extreme scale. Scientific computing will no longer be purely about how fast computations can be performed. Energy constraints, processor changes, and I/O limitations necessitate significant changes in both the software applications used in scientific computation and the ways in which scientists use them. Components for modeling, simulation, analysis, and visualization must work together in a computational ecosystem, rather than working independently as they have in the past. The XVis project brought together collaborators from predominant DOE projects for visualization on accelerators and combining their respective features into a new visualization toolkit called VTK-m.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Understanding Litter Input Controls on Soil Organic Matter Turnover and Formation are Essential for Improving Carbon-Climate Feedback Predictions for Arctic, Tundra Ecosystems

The Arctic region stored vast amounts of carbon (C) in soils over thousands of years because decomposition has been limited by cold, wet conditions. Arctic soils now contain roughly as much C that is contained in all other soils across the globe combined. However, climate warming could unlock this oil C as decomposition accelerates and permafrost thaws. In addition to temperature-driven acceleration of decomposition, several additional processes could either counteract or augment warming-induced SOM losses. For example, increased plant growth under a warmer climate will increase organic matter inputs to soils, which could fuel further soil decomposition by microbes, but will also increase the production of new SOM. Whether Arctic ecosystems store or release carbon in the future depends in part on the balance between these two counteracting processes. By differentiating SOM decomposition and formation and understanding the drivers of these processes, we will better understand how these systems function. We did not find evidence of priming under current conditions, defined as an increase in the decomposition of native SOM stocks. This suggests that decomposition is unlikely to be further accelerated through this mechanism. We did find that decomposition of native SOM did occur when nitrogen was added to these soils, suggesting that nitrogen limits decomposition in these systems. Our results highlight the resilience and extraordinary C storage capacity of these soils, and suggest shrub expansion may partially mitigate C losses from decomposition of old SOM as Arctic soils warm.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Building Virtual Ecosystems: Computational Challenges for Mechanistic Modeling of Terrestrial Environments Workshop, Germantown, Maryland, March 26–27, 2014

The mission of the Climate and Environmental Sciences Division of the Office of Biological and Environmental Research (BER) within the U.S. Department of Energy’s (DOE) Office of Science is “to advance a robust, predictive understanding of Earth’s climate and environmental systems and to inform the development of sustainable solutions to the nation’s energy and environmental challenges.” This formidable challenge requires quantification of stocks and controls on states, fluxes, and residence times of water, carbon, and other key elements through all components of the terrestrial system. These components include vegetation, soils, the deep vadose zone, groundwater, and surface water. To achieve this level of predictive understanding, a new generation of multiscale, multiphysics models is needed for terrestrial systems—models that incorporate process couplings and feedbacks between various “pools” (i.e., vegetation, soils, subsurface aquifers, and surface waters) across wide ranges of spatial and temporal scales. To explore the potential of a new generation of multiscale, multiphysics models for revolutionizing the understanding of terrestrial ecosystem dynamics, BER held the Computational Challenges for Mechanistic Modeling of Terrestrial Environments workshop on March 26–27, 2014, in Germantown, Maryland. The workshop brought together 29 researchers with diverse expertise, including hydrologists, environmental scientists, ecologists, microbiologists, plant scientists, and computational scientists. Through a combination of invited talks, breakout sessions, and report-back discussions, workshop attendees identified challenges and research opportunities needed to develop a more seamless and continuous framework for mechanistic modeling of terrestrial environments extending from the bedrock to the atmospheric boundary layer and from single-plant systems to fields of crops to watersheds and river basins.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Grassland Ecosystem Management Plan for the Oak Ridge Reservation

Land managers at the Department of Energy’s (DOE’s) Oak Ridge National Laboratory (ORNL) in East Tennessee are restoring native warm-season grasses and wildflowers to various sites across the Oak Ridge Reservation (ORR). Some of the numerous benefits to planting native grasses and forbs include improved habitat quality for wildlife, improved aesthetic values, and lower long-term maintenance costs. Native grassland restorations also help meet the goals of two recent presidential memorandums: “Incorporating Ecosystem Services into Federal Decision Making” and “Creating a Federal Strategy to Promote the Health of Honey Bees and Other Pollinators.”

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Deep learning techniques to disentangle water use efficiency, climate change, and carbon sequestration across ecosystem scales

Focal Areas: We plan to use machine learning (ML) to disentangle the impact of climate change on plant water use efficiency (WUE) across the leaf level, structured canopy, and community ecosystem scales. Due to the complex behavior of modeling plant WUE’s connection to carbon storage (and hidden co-varying interactions) we suggest that physics-based deep learning must be utilized. The second topic we propose is, based on a better understanding of WUE response to climate change, there is a need to predict how WUE will limit or enhance the recycling of water from transpiration by vegetation back to the land surface worldwide, and thus impact water storage capacity (i.e., natural and reservoirs), using machine learning optimization techniques such as genetic algorithms.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Sandia National Laboratories Ecosystem for Open Science: Metadata Schema v0.2 Description.

The Ecosystem for Open Science (eOS) initiative was established in 2019. Its objective is improving openness and sharing of data and information across Defense Nuclear Nonproliferation (DNN) Research and Development (R&D) activities. To support this initiative, the eOS team at Sandia National Laboratories (SNL) developed metadata and data standards and proposed a machine-readable metadata schema. The nuclear explosion monitoring field was selected as a focus area due to its the wide range of pertinent phenomenologies.We developed the DCAT-eOS-AP metadata schema extending the Data Catalog Vocabulary version 2 (DCATv2) standard using an application profile (AP), to fit the needs of multi-disciplinary NA-22 projects. The DCAT-eOS-AP metadata schema describes data at different levels of granularity ranging from general descriptions to more domain-specific granular metadata. Its implementation and serialization is flexible with the ability to include new file or data types. Thus, it will scale with the ever-increasing data management needs of government research. Due to the multitude of phenomenologies represented in the DCAT-eOS-AP schema, we anticipate that it will be easily extensible to various projects across many DOE mission areas. This document describes data management challenges faced within the DNN R&D portfolio and provides insight on how metadata and data standards/guidelines combined with a comprehensive metadata schema can add value to programs throughout the Department of Energy (DOE). It reviews the importance of metadata standards, FAIR (Findability, Accessibility, Interoperability, and Reusability) data principles, and metadata schemas. Additionally, it summarizes input from subject matter experts (SME) at SNL and other National Laboratories that resulted in metadata and data standards/guidelines encompassing domains relevant to NA-22 projects. Finally, we discuss the DCAT-eOS-AP metadata development. Implementation recommendations and future development directions are included for those keen on adopting the DCAT-eOS-AP metadata schema.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2021

The Highly Integrated Vehicle Ecosystem (HIVE): A Platform for Managing the Operations of On-Demand Vehicle Fleets

This paper introduces the Highly Integrated Vehicle Ecosystem (HIVE), a transportation modeling tool developed by within the Center for Integrated Mobility Sciences (CIMS) group at the National Renewable Energy Laboratory (NREL). HIVE is an agent-based supply/demand model for Mobility on Demand (MoD) which mixes agent-based modeling and centralized dispatch for automated and human-driven fleets and ride hail passengers. Research questions using HIVE span multiple categories, including intelligent fleet planning (assessing fleet, battery, and infrastructure investment decisions), intelligent fleet control (charge management, vehicle dispatching) and strategic business model decision-making (depot-based full-time drivers versus gig-based drivers, human-driven versus automated). The components of the HIVE model are explained and then HIVE is demonstrated in a case study using demand data from the New York City Taxi & Limousine Commission data set.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Position Papers for the ASCR Workshop on Cybersecurity and Privacy for Scientific Computing Ecosystems

At the request of the Department of Energy's (DOE) Office of Advanced Scientific Computing Research (ASCR), this program committee has been tasked with organizing a workshop to identify basic research needs in cybersecurity and privacy to better support DOE's science and energy mission. As part of the process, the program committee is soliciting community input in the form of position papers to help identify significant use cases, facility issues, and other barriers to enabling verifiably trustworthy computational science while preserving data confidentiality as appropriate for scientific workflows of interest to DOE. The program committee will review these position papers and based on the fit of their area of expertise and interest, selected contributors will have the opportunity to participate in the workshop currently planned as a virtual event November 3-5th, 2021. The thrust areas that will be explored by this workshop are the following: (1) Algorithms for secure, scalable, privacy-enhancing technologies and frameworks, including: Federated AI/ML, Differential privacy, Randomized algorithms, Adversarial modeling & simulation, Graph algorithms, and Formal methods; (2) Platforms to support the entire scientific-computing ecosystem, including edge computing for large-scale experiments, focusing on heterogeneous systems and distributed systems, including: Heterogeneous computing systems, Distributed computing systems, and Secure data architectures; and (3) Data workflows to allow agile use of data while preserving integrity and privacy, making the important properties verifiable either at runtime or post-computation, including: Integrity and provenance and Data management infrastructure. Topics that are out-of-scope for the workshop include discussing specific proposed solutions or areas that are clearly out of DOE's fundamental and applied-sciences mission scope, e.g., cryptography, enterprise security, and general-operations technology.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

ENIGMA: Ecosystems and Networks Integrated with Genes and Molecular Assemblies. Final Report

ENIGMA research is performed at a very large scale involving collaborations between multiple research groups to address two major driving concepts: For any microbe, it is possible to discover efficiently the genetic determinants of adaptation to life in dynamic environments in contact with other living members of its ecology and reveal hidden functional phenomena that are only possible by using a systems biology approach; and for any environment, it is possible to predict the temporal changes in geochemistry and accompanying system behavior, for some length of time, given biogeochemical and physical inputs. These driving concepts also map directly to grand challenges the ENIGMA team has outlined: “Define the levels of biological organization most relevant to scaling from single cells to ecosystems and global cycles; capture how that organization varies in time and space; and identify critical interactions that dictate rates of carbon, nutrient, and energy transformation in different environments.”

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Application of FARM to an IES scenario within the FORCE ecosystem

The FARM (Feasible Actuator Range Modifier) software module is a component of the RAVEN-based FORCE framework for analysis of Integrated Energy Systems (IES). FARM supports the HERON software module in the evaluation of the optimal dispatch by evaluating feasible set-points for the different IES unit components. Set-points are required to satisfy limits on both production variables (i.e., the variables to be optimized such as the electrical power, the hydrogen production rate, etc.) and process variables tied to the service life of equipment (e.g., steam flowrate, vessel pressure, turbine firing temperature, etc.). This problem is addressed by adopting a two-stage approach. First, the HERON power dispatcher determines set-points that meet the constraints on the former variables (e.g., power levels and power ramp rate limits). These constraints are called explicit constraints. Then, FARM adjusts these set-points to ensure the respect of the limits on the latter variables given the knowledge of the system physics acquired through machine learning algorithms. These constraints are called implicit constraints. From this standpoint, FARM constitutes a bridge between the HERON power dispatcher that adopts a simplified description of the IES unit (low-resolution physics) and the HYBRID high-fidelity models (high-resolution physics). The original version of the FARM software module (FARM-Alpha) was released by Argonne National Laboratory in January 2021. In the latest version of the code released in July 2022 (FARM-Delta), the Reference Governor (RG) algorithm was upgraded to a Multi-Input Multi-Output version from its original Single-Input Single-Output form. The RG algorithm acts to enforce constraints. With this improvement an IES unit is now treated as a single dynamic system from the standpoint of control. The crosstalk among components in an IES unit is now fully considered thereby ensuring a true optimization is obtained for those units that have multiple set-points. In this report, the capabilities of FARM-Delta operating within the FORCE ecosystem are demonstrated for an IES test case. The specific configuration of IES unit for this case was selected by the IES team with consultation from the Advanced Reactor IES Expert Group. A full TEA analysis that invoked HERON, HYBRID, FARM, and RAVEN was performed and serves to demonstrate how the latest modification to FARM algorithms (i.e., state variable selection, state-space matrices derivation, set-point verification) can shape setpoints that might otherwise compromise the health of equipment through accelerated wear and tear. In this specific test case, it was demonstrated that these algorithms ensure a more efficient utilization of steam resources to be shared by two different subsystems, namely Balance of Plant (BOP) and High-Temperature Steam Electrolysis (HTSE). Finally, some code improvements that can further enhance the user-friendliness are suggested.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Valuing Ecosystems in Equitable Energy Transition Planning

In this report we explore three areas of research and practice needed to scale clean energy sustainably: 1) advancing the predictive science to quantify positive and negative outcomes of energy development for the wellbeing of communities, environments, and disadvantaged groups; 2) scenario design using this science to shape renewable energy solutions for nation-wide decarbonization while delivering tangible, local benefits; and 3) participatory science-policy processes to understand what people want for the places where they live and work and how incorporating renewable energy can help or hinder their goals. Together these three components – quantifying social-ecological values, scenario design, and participatory processes – form the basis for a framework for valuing ecosystems to inform a more equitable energy transition.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

OLCF’s Advanced Computing Ecosystem (ACE): FY24 Efforts for the DOE Integrated Research Infrastructure (IRI) Program

This report highlights significant strides made by Oak Ridge National Laboratory’s Oak Ridge Leadership Computing Facility (OLCF) in advancing computational research and infrastructure. Through the Advanced Computing Ecosystem (ACE) strategic initiative, OLCF has been successfully integrated with Doe’s Integrated Research Infrastructure (IRI) program, establishing itself as a critical framework for enhancing scientific computing capabilities across various domains. This report outlines the activities, accomplishments, and future directions of ACE, emphasizing its role in developing cutting-edge technologies, supporting science pilots, and fostering collaborations that drive scientific innovation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Genomes to ecosystem function: Targeting critical knowledge gaps in methanogenesis and translation to updated global biogeochemical models

Natural freshwater temperate wetland systems currently represent the largest natural source of atmospheric methane, but are relatively understudied using systems biology tools (e.g. meta-omics) compared to other high producing methane systems (e.g. peat, tropical, or reconstructed wetlands). Using field investigations at the NOAA operated sentinel site on Lake Erie, methane producing activities and responses to geochemical conditions will be determined along seasonal and spatial gradients (Objective 1). Here, a combination of high-throughput activity and gas measurements, combined with high-resolution systems biology and analytical methods, will provide in depth knowledge of the microbiological, chemical, and physical constraints on methane production in wetlands. Using laboratory microcosms, the formation of anoxic microsites and their capacity to facilitate methane production in wetland soils will be simulated (Objective 2). This objective will validate the findings from the field investigations, offering a more controlled environment for teasing out the role of different yet interrelated variables. Lastly, these field and laboratory data will be used for multi-scale, process-level evaluation of an ecosystem biogeochemical model that accommodates these newly identified processes and parameterizes representation of these processes along relevant environmental gradients (Objective 3).This research will identify multiple interacting geochemical, ecological, and metabolic constraints that are poorly understood, oversimplified, or missing in global biogeochemical methane models. This proposal targets the role of oxygen limitation on methane processes in soil domains, to improve reactive transport models of microbial carbon cycling across terrestrial-aquatic soils and generate data on nutrient cycling activities in Great Lake wetlands. This information could provide new insights into the microbial controllers of Lake Erie eutrophication.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Software Tools Ecosystem Project (STEP) Midyear Report CY2025

This document provides a technical project report for the first six months of 2025 for the Software Tools Ecosystem Project (STEP). The mission of STEP is to enable critical software tools to proactively adapt to emerging platform technologies (such as new accelerators, storage devices, network technologies, and smart devices) and emerging application use cases (such as advanced machine learning and workflow frameworks) so that they continue to meet the needs of scientific computing and provide a strong foundation for future Advanced Scientific Computing Research activities. Our challenges include the wide breadth of our stakeholders and rapidly evolving platform technology dependencies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Deployment and Evaluation of SciStream on OLCF's Advanced Computing Ecosystem (ACE)

The growing demand for real-time analysis, experimental steering, and decision-making in scientific workflows has created a need for tightly coupled integrations between experimental facilities and high-performance computing (HPC) systems. The Department of Energy’s Integrated Research Infrastructure (IRI) initiative highlights data streaming as a key capability for enabling memory-to-memory data transfers, bypassing the limitations of traditional store-and-forward models. SciStream is a toolkit developed by researchers at Argonne National Laboratory (ANL) to support such streaming by addressing cross-domain security, delegated authentication, and application transparency. We deployed and evaluated SciStream on the Oak Ridge Leadership Computing Facility’s (OLCF) Advanced Computing Ecosystem (ACE) infrastructure, leveraging the Olivine OpenShift cluster and its high-bandwidth Data Streaming Nodes (DSNs) as gateway nodes. Our evaluation included synthetic streaming workloads derived from IRI science workflows, a streaming simulator, and integration with RabbitMQ to handle low-level messaging. This report documents the deployment process, performance evaluation, and challenges encountered, along with opportunities for future improvements.

97 MATHEMATICS AND COMPUTING↗