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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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Contents include the following: 3 Listing of critical capabilities (knowledge, procedures, training, facilities) and metrics for validating that they are mission ready. Examples of critical capabilities and validation metrics: ground test and simulations. Flight testing to prove capabilities are mission ready. Issues and recommendations.
Task 4 of the SECARB-USA Project is designed to evaluate the regional needs and challenges for the development of a safe and environmentally sound CO 2 transport infrastructure. This infrastructure would transport CO 2 capture from power generating plants and/or industrial sources and distribute it to oil fields for associated storage in EOR operations, saline storage, and/or other suitable storage sites. The objective of this report on Sub-Basin Prospective Areas (Deliverable 4.1.a), is to provide the framework for understanding how regional infrastructure development could occur. The sub-basin prospective areas, in conjunction with the locations of CO 2 sources and pipeline infrastructure (for source-sink matching networks), form the basis for the analysis that will be performed in the Techno-Economic Analysis of Infrastructure Buildout Scenarios (Deliverable 4.1.b).
This slide presentation reviews the importance for the Mock LISA Data Challenges (MLDC). Laser Interferometer Space Antenna (LISA) is a gravitational wave (GW) observatory that will return data such that data analysis is integral to the measurement concept. Further rationale of the MLDC are to kickstart the development of a LISA data-analysis computational infrastructure, and to encourage, track, and compare progress in LISA data-analysis development in the open community. The MLDCs is a coordinated, voluntary effort in GW community, that will periodically issue datasets with synthetic noise and GW signals from sources of undisclosed parameters; increasing difficulty. The challenge participants return parameter estimates and descriptions of search methods. Some of the challenges and the resultant entries are reviewed. The aim is to show that LISA data analysis is possible, and to develop new techniques, using multiple international teams for the development of LISA core analysis tools
Prior to the public release of Kubernetes it was difficult to conduct joint development of elaborate analysis facilities due to the highly non-homogeneous nature of hardware and network topology across compute facilities. However, since the advent of systems like Kubernetes and OpenShift, which provide declarative interfaces for building fault-tolerant and self-healing deployments of networked software, it is possible for multiple institutes to collaborate more effectively since resource details are abstracted away through various forms of hardware and software virtualization. In this whitepaper we will outline the development of two analysis facilities: "Coffea-casa" at University of Nebraska Lincoln and the "Elastic Analysis Facility" at Fermilab, and how utilizing platform abstraction has improved the development of common software for each of these facilities, and future development plans made possible by this methodology.
High performance computers have for many years been on a trajectory that gives them extraordinary compute power with the addition of more and more compute cores. At the same time, other system parameters such as the amount of memory per core and bandwidth to storage have remained constant or have barely increased. This creates an imbalance in the computer, giving it the ability to compute a lot of data that it cannot reasonably save out due to time and storage constraints. While technologies have been invented to mitigate this problem (burst buffers, etc.), software has been adapting to employ in situ libraries which perform data analysis and visualization on simulation data while it is still resident in memory. This avoids the need to ever have to pay the costs of writing many terabytes of data files. Instead, in situ enables the creation of more concentrated data products such as statistics, plots, and data extracts, which are all far smaller than the full-sized volume data. With the increasing popularity of in situ, multiple in situ infrastructures have been created, each with its own mechanism for integrating with a simulation. To make it easier to instrument a simulation with multiple in situ infrastructures and include custom analysis algorithms, this project created the SENSEI framework.
The National Renewable Energy Laboratory's Scenario Evaluation and Regionalization Analysis (SERA) model estimates the number, size, and locations of hydrogen refueling stations that satisfy the fuel demand from fuel cell electric vehicles (FCEVs) and the hydrogen supply requirements to meet such a demand in terms of hydrogen production plants and delivery infrastructure. SERA simulates the systemwide cost-optimal evolution of hydrogen refueling infrastructure, providing insights that reduce the informational and financial risks associated with infrastructure investment decisions and helping accelerate the adoption of FCEVs. This report describes the SERA model's demand-side and refueling infrastructure buildout algorithms, and it also illustrates use of the model for simulating hydrogen refueling stations deployment scenarios using as an example the recent H2USA national hydrogen infrastructure report.
Electric vehicle (EV) adoption in the U.S. will be accelerated by the historic $7.5 billion public investments in EV charging infrastructure. Careful analysis of EV charging demands plays a vital role in understanding the energy requirements, power grid impact, and smart charging management opportunities of EVs. To this end, this paper develops a data-driven trip-chaining-based modeling framework including five steps: Trip data acquisition and preprocessing, EV adoption modeling, travel itinerary synthesis, EV charging demand simulation and EV load profile generation. The developed analysis framework was demonstrated using real-world data for one region in Virginia, U.S. The results show that the proposed modeling framework can work effectively. For the study region in 2040, the predicted number of plug-in EVs is 470,114, resulting in a weekly charging demand of 38,078,127 kWh (55% home, 9% work, and 36% public) in September and 45,920,358 kWh (61% home, 9% work, and 30% public) in February.
NREL analysis will inform VTO on costs/benefits of DOE investment in an array of electric vehicle charging infrastructure technologies, trade-offs between residential and non-residential solutions, and cost of charging to consumers. Additional analysis will focus on vehicle sales, energy consumption, and emission impacts of meeting DOE technology target goals ahead of or behind schedule (timing assessment).
The primary challenge motivating this project is the widening gap between the ability to compute information and to store it for subsequent analysis. This gap adversely impacts science code teams, who can perform analysis only on a small fraction of the data they calculate, resulting in the substantial likelihood of lost or missed science, when results are computed but not analyzed. Our approach is to perform as much analysis or visualization processing on data while it is still resident in memory, which is known as in situ processing. The idea in situ processing was not new at the time of the start of this effort in 2014, but efforts in that space were largely ad hoc, and there was no concerted effort within the research community that aimed to foster production-quality software tools suitable for use by Department of Energy (DOE) science projects. Our objective was to produce and enable the use of production-quality in situ methods and infrastructure, at scale, on DOE high-performance computing (HPC) facilities, though we expected to have an impact beyond DOE due to the widespread nature of the challenges, which affect virtually all large-scale computational science efforts. To achieve this objective, we engaged in software technology research and development (R&D), in close partnerships with DOE science code teams, to produce software technologies that were shown to run efficiently at scale on DOE HPC platforms.
The Biofuel Infrastructure, Logistics, and Transportation (BILT) model for supply chain analysis developed by Oak Ridge National Laboratory for the US Department of Energy is an accounting and optimization model that tracks the entire biomass-to-end-use supply chain (e.g., biomass production, transportation, and processing) for potential feedstocks, pathways, and end uses. This report summarizes the development, function, and operation of the BILT model.
The SECARB-USA region refers to the states in the southeastern portion of the United States including Alabama, Arkansas, Florida, Georgia, Kentucky, Louisiana, Mississippi, Missouri, North Carolina, eastern Oklahoma, South Carolina, Tennessee, eastern Texas, Virginia, and West Virginia. This region is a hub for economic activity and accordingly emits almost 1 Gtpa of CO 2 which is the motivation for the development of Carbon Capture and Storage (CCS). This preliminary commercialization plan reviews the status of commercialization of CCS within the region, outlines the infrastructure buildout to achieve CO 2 reduction goals by 2050 described by the Los Alamos National Lab in Task 4.1.b (see the Task 4.1.b “Technoeconomic Analysis of Infrastructure Buildout Scenarios” report), and provides an early estimate of the economic activity and employment creation resulting from the increase in CCS activity. Additionally, this report addresses the challenges associated with commercializing CCS within the region at a large scale.
HILARRI is a database of links between major datasets of operational hydropower dams and powerplants, and inland water bodies. These connections are critical for conducting large-scale analysis of hydropower infrastructure and their associated natural and engineered water systems. Features include: – Dams from the National Inventory of Dams (2025) and the Global Reservoir and Dam Database (GRanD v1.3) – Hydropower plants from the Existing Hydropower Assets dataset (EHA 2025) – Power plants that are listed in the 2025 U.S. Hydropower Development Pipeline Data or were listed in previous versions of the dataset These hydropower infrastructure features are linked to several major datasets that provide hydrologic and hydraulic information relevant for analysis of hydropower systems that includes the integral water resources. That information comes from: – Products from the National Hydrography Dataset (NHD) – NHDPlusV2 Medium Resolution river network flowlines, – NHD waterbodies (limited to lakes and reservoirs), – NHD Watershed Boundary Dataset (HUC12-level for the Conterminous United States (CONUS)) – NHD High Resolution waterbodies – HydroLAKES water bodies (lakes and reservoirs) – LAGOS-US lakes and reservoirs – EPA National Lakes Assessment (2007, 2012, 2017, and 2022) – The Reservoir Sedimentation Database (RESSED) – EPA SuRGE sampling locations Unique identifiers are used to facilitate joining to the original full datasets. For example, characteristics of NHD flowlines such as estimated average flow rate can be joined from the NHDPlusV2 dataset to a dam or power plant listed in HILARRI based on the ID field, “COMID”, that is common to both datasets. HILARRI only includes basic information about identifiers, location, and data quality or usage notes. It does not contain the attributes or time series data associated with these sites. The HILARRI dataset incorporates information from several datasets to facilitate more effective and accurate analysis of hydropower infrastructure and their associated waterbodies. For example, dams were checked against the most recent American Rivers Dam Removal Database to identify and flag facilities that may no longer exist. Additionally, dams that are listed multiple times in the NID are identified and flagged to avoid double-counting when analyzing and summarizing information. Other quality flags include certainty of operational hydropower (i.e., if one or more datasets indicates hydropower at a particular location), whether an associated water body is accurate or composed of multiple polygons, or whether there is a known issue with reported characteristics in one of the underlying datasets. These additional data flags are designed to increase confidence in data usage for individual to large-scale analyses.
“Resilience” is fast becoming a key word for describing the desired result of the activities we group under the heading of “homeland security.” Department of Homeland Security (DHS) Secretary Napolitano has made resilience a key theme in her speeches, highlighting the efforts of DHS to “strengthen the resilience of ... infrastructure, computer networks, and of ... communities and citizens.” She has talked about resilience as “strengthening the ability for our systems, our cities, our towns, and our country to bounce back quickly from an attack or a natural disaster, and to be even stronger than before. This paper first provides an overview of resilience concepts from U.S. government and academic research sources. It then discusses resilience concepts and metrics from the academic literature in more detail, focusing on three types of resilience: engineering resilience, ecological resilience, and community and regional resilience. Next, it discusses system characteristics that have been associated with increased resilience. Based on this background, we propose a model of the infrastructure resilience analysis process. We then review LANL capabilities for each process element and indicate areas where capability development opportunities exist. Appendices provide additional information on funding sources and key players in infrastructure resilience, and a more detailed technical explanation of ecological resilience.
This presentation summarizes the results of the National Renewable Energy Laboratory (NREL) report, 'Charging Electric Vehicles in Smart Cities: An EVI-Pro Analysis of Columbus, Ohio.' As part of the Smart Columbus Initiative, the city has set specific goals for annual plug-in electric vehicle (PEV) sales. NREL used its Electric Vehicle Infrastructure Projection (EVI-Pro) model to analyze charging behavior and infrastructure requirements to support PEV adoption in Columbus, including estimating PEV supply equipment counts, location, use, and resulting hourly load profiles.
This analysis was conducted under the first round of the Solar Energy Innovation Network (SEIN), a program led by the National Renewable Energy Laboratory (NREL) that assembles diverse teams of stakeholders to research solutions to real-world challenges associated with solar energy adoption. As part of the Solar Energy Innovation Network, NREL used a tool called REopt to evaluate the impact on utility costs of light duty electric vehicle (EV) charging stations in Minnesota. The analysis models EV chargers at medium offices in Minneapolis, Minnesota. Specifically, the analysis explores three questions: (a) How can photovoltaics (PV) and stationary storage be co-deployed with EV charging infrastructure to lower the cost of purchasing grid electricity? (b) What are the potential savings of co-locating EV charging infrastructure with (behind the meter of) a commercial building? and (c) What savings can be gained from optimizing the times at which the EVs are charged to decrease lifecycle costs? Note that this presentation is not indented to be presented widely, but is just to provide ReOpt outputs for the partner's use.
The database, known as the Nuclear Energy Infrastructure Database (NEID), is accessed through a web-based application (at nsuf-infrastructure.inl.gov) and built specifically to track research and development (R&D) facilities and associated equipment throughout the DOE-NE complex. The NEID serves internal users (such as DOE personnel and national laboratory staff and researchers) and potential infrastructure users who will be able to view the inventory of DOE-NE-compatible R&D capabilities potentially available to them. NSUF utilizes the NEID to support the annual nuclear energy gap analysis process. Infrastructure needs are gathered from DOE-NE R&D programs and researchers at U.S. national laboratories, universities, and the U.S. nuclear industry. The fiscal year (FY) 2019 Nuclear Energy Infrastructure Database (NEID) Accomplishments report captures all the additions and improvements made to the database in FY 2019.
The use of biogenic and waste feedstocks is a promising strategy to improve the chemical sector's supply chain resiliency and carbon intensity. To help inform research efforts that transform these feedstocks into industrial chemicals, we used a systematic analysis framework to consistently evaluate the economics and environmental impacts of >200 alternative production pathways for 51 organic commodity chemicals in the United States under an optimistic future scenario that reflects the potential upper bounds of process scalability, energy availability, and carbon uptake. Lower-impact and lower-cost alternative pathways were identified for all but three chemicals, with 75% using thermochemical routes and half leveraging existing manufacturing infrastructure. Scenario analysis shows that the ranking of these pathways for half of the assessed chemicals is particularly sensitive to carbon uptake assumptions and criteria prioritization (i.e., cost only, environmental impact only, or both), with changes in electricity grid mix, hydrogen source, and underlying mass and energy flow data proving less influential. Implementing alternative pathways for just 11 chemicals could support a transition to net-zero greenhouse gas emissions from chemical production by 2050, with 11% lower cost than business as usual, similar water requirements, quadrupled electricity demand, and the use of most available woody biomass. These findings provide an exploratory guide toward a future chemical industry that harnesses alternative feedstocks.
Although more established for light-duty vehicles (LDVs), advancements in electric vehicle (EV) charging technology are being made in the medium- and heavy-duty (MD/HD) sector. Progress is also being made with the electrification of MD/HD vehicles, including transit buses, school buses, MD trucks, and HD trucks. The diverse set of operational requirements and duty cycles for each vocation, as well as the range in the size of fleets, present unique charging and infrastructure requirements. This report focuses on charging requirements for MD/HD vehicles and synergies with LDV infrastructure. This analysis leans toward the qualitative rather than quantitative because relevant model inputs are in development and will not be established for a few years, as EV deployments are more mature in the LDV sectors than MD/HD. The report begins with an overview of MD/HD vehicle classes and types of charging, including depot and residential charging, among others (Section 2). Section 3 analyzes the home bases (overnight dwell locations) of existing MD/HD vehicles, with an emphasis on depot and residential home bases, and discusses implications for charging infrastructure. Section 4 discusses the key characteristics for determining if, when, and where MD/HD vehicles can leverage LDV charging infrastructure rather than requiring dedicated chargers. These considerations include electricity demand, connectors, physical space requirements, payment considerations, and impacts on the grid. Section 5 summarizes shared characteristics for MD/HD vehicles that are appropriate for near-term electrification and includes a summary of the outlook of the electric MD/HD vehicle market. The conclusion (Section 6) summarizes the report's findings and outlines areas for future research.