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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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Brochure featuring Fiscal Year 2019 accomplishments at the NREL High-Performance Computing User Facility and Data Center at the Energy Systems Integration Facility, including information on the facility's HPC systems as well as highlights from several modeling and simulation projects across the spectrum of energy efficiency and renewable energy technologies.
Existing methods of exchanging realtime data between the major control centers in the International Space Station program have resulted in a patchwork of local formats being imposed on each Mission Control Center. This puts the burden on a data customer to comply with the proprietary data formats of each data supplier. This has increased the cost and complexity for each participant, limited access to mission data and hampered the development of efficient and flexible operations concepts. Ideally, a universal format should be promoted in the industry to prevent the unnecessary burden of each center processing a different data format standard for every external interface with another center. With the broad acceptance of XML and other conventions used in other industries, it is now time for the Aerospace industry to fully engage and establish such a standard. This paper will briefly consider the components that would be required by such a standard (XML schema, data dictionaries, etc.) in order to accomplish the goal of a universal low-cost interface, and acquire broad industry acceptance. We will then examine current approaches being developed by standards bodies and other groups. The current state of CCSDS panel work will be reviewed, with a survey of the degree of industry acceptance. Other widely accepted commercial approaches will be considered, sometimes complimentary to the standards work, but sometimes not. The question is whether de facto industry standards are in concert with, or in conflict with the direction of the standards bodies. And given that state of affairs, the author will consider whether a new program establishing its Mission Control Center should implement a data interface based on those standards. The author proposes that broad industry support to unify the various efforts will enable collaboration between control centers and space programs to a wider degree than is currently available. This will reduce the cost for programs to provide realtime access to their data, hence reducing the cost of access to space, and benefiting the industry as a whole.
The global importance of data center water use is increasing with the rapid growth of digitalization and artificial intelligence. This study analyzes the factors influencing workload-level water use, measured in liters consumed per workload, to guide water-saving strategies in data centers. Our findings reveal workload-level water use variations exceeding 10,000-fold, driven by over 1000-fold differences in water consumption per kilowatt hour of server electricity consumed and approximately 10-fold differences in server workload efficiency. Key determinants are ranked as server efficiency, electrical grid water consumption factors, server utilization, cooling system type, infrastructure efficiency, climate zone, inactive server percentage, and server refresh cycle. Notably, there is no single recipe for minimizing water use; instead, optimal outcomes depend on tailored combinations of these factors. This analysis addresses critical knowledge gaps by identifying the determinants of data center water use and exploring their achievable minima under diverse site-specific constraints.
Liquid cooling is critical for thermal management in high-density data centers with the rising AI workloads. However, machine learning-based controllers are essential to unlock greater energy efficiency and reliability, promoting sustainability. We present LC-Opt, a Sustainable Liquid Cooling (LC) benchmark environment, for reinforcement learning (RL) control strategies in energy-efficient liquid cooling of high-performance computing (HPC) systems. Built on the baseline of a high-fidelity digital twin of Oak Ridge National Lab's Frontier Supercomputer cooling system, LC-Opt provides detailed Modelica-based end-to-end models spanning site-level cooling towers to data center cabinets and server blade groups. RL agents optimize critical thermal controls like liquid supply temperature, flow rate, and granular valve actuation at the IT cabinet level, as well as cooling tower (CT) setpoints through a Gymnasium interface, with dynamic changes in workloads. This environment creates a multi-objective real-time optimization challenge balancing local thermal regulation and global energy efficiency, and also supports additional components like a heat recovery unit (HRU). We benchmark centralized and decentralized multi-agent RL approaches, demonstrate policy distillation into decision and regression trees for interpretable control, and explore LLM-based methods that explain control actions in natural language through an agentic mesh architecture designed to foster user trust and simplify system management. LC-Opt democratizes access to detailed, customizable liquid cooling models, enabling the ML community, operators, and vendors to develop sustainable data center liquid cooling control solutions.
An all-digital high data rate parallel receiver architecture developed jointly by Goddard Space Flight Center and the Jet Propulsion Laboratory is presented.
Driven by the growth in digital services, cloud computing, AI, and manufacturing, data centers face rising energy demands that challenge traditional power sources and cooling efficiency. This study explores using nuclear power to meet these demands, focusing on accelerated reactor technology deployment and highlighting needs such as N+1/N+2 power supplies and integrated power-thermal management. A SWOT analysis addresses grid connectivity, reactors, and site selection, particularly DOE sites. Reactor technology demonstration and deployment could be accelerated by leveraging test facilities such as MARVEL, MAGNET, TED, FAS, DOME, LOTUS, ATR, Energy System Proving Grounds, and upcoming Energy Launch Pads, along with modeling and simulation tools such as RELAP5, MOOSE, VERA, RAVEN, and FORCE. The potential power and thermal management options, including various cooling technologies, waste-heat utilization, and an industrial-scale demonstration plan, aim to accelerate the integration of nuclear power and data centers in the USA, while emphasizing community and stakeholder engagement and synergistic efforts.
The data explosion in remote sensing is straining the ability of data centers to deliver the data to the user community, yet many large-volume users actually seek a relatively small information component within the data, which they extract at their sites using Knowledge Discovery in Databases (KDD) techniques. To improve the efficiency of this process, the Goddard Earth Sciences Distributed Active Archive Center (GES DAAC) has implemented a KDD subsystem that supports execution of the user's KDD algorithm at the data center, dramatically reducing the volume that is sent to the user. The data are extracted from the archive in a planned, organized "campaign"; the algorithms are executed, and the output products sent to the users over the network. The first campaign, now complete, has resulted in overall reductions in shipped volume from 3.3 TB to 0.4 TB.
The Federal Government is committed to following sustainable principles. At its heart, sustainability integrates environmental, societal and economic solutions for present needs without compromising the ability of future generations to meet their needs. Building upon its pledge towards environmental stewardship, the Administration generated a vision of sustainability spanning ten goals mandated within Executive Order (EO) 13693, Planning for Federal Sustainability in the Next Decade. In November 2015, the National Aeronautics and Space Administration (NASA) responded to this EO by incorporating it into a new release of the NASA Strategic Sustainability Performance Plan (SSPP). The SSPP recognizes the importance of aligning environmental practices in a manner that preserves, enhances and strengthens NASA's ability to perform its mission indefinitely. The Kennedy Space Center (KSC) is following suit with KSC's Sustainability Plan (SP) by promoting, maintaining and pioneering green practices in all aspects of our mission. KSC's SP recognizes that the best sustainable solutions use an interdisciplinary, collaborative approach spanning civil servant and contractor personnel from across the Center. This approach relies on the participation of all employees to develop and implement sustainability endeavors connected with the following ten goals: Reduce greenhouse gas (GHG) emissions. Design, build and maintain sustainable buildings, facilities and infrastructure. Leverage clean and renewable energy. Increase water conservation. Improve fleet and vehicle efficiency and management. Purchase sustainable products and services. Minimize waste and prevent pollution. Implement performance contracts for Federal buildings. Manage electronic equipment and data centers responsibly. Pursue climate change resilience. The KSC SP details the strategies and actions that address the following objectives: Reduce Center costs. center dot Increase energy and water efficiencies. Promote smart buying practices. Increase reuse and recycling while decreasing waste. Benefit the community. Meet or exceed the EO and NASA SSPP sustainability goals.
The new era of Big Data has opened doors for many new opportunities, as well as new challenges, for both Earth science research/application and data communities. As one of the twelve NASA data centers - Goddard Earth Sciences Data and Information Services Center (GES DISC), one of our great challenges has been how to help research/application community efficiently (quickly and properly) accessing, visualizing and analyzing the massive and diverse data in natural hazard research, management, or even prediction. GES DISC has archived over 2000 TB data on premises and distributed over 23,000 TB of data since 2010. Our data has been widely used in every phase of natural hazard management and research, i.e. long term risk assessment and reduction, forecasting and predicting, monitoring and detection, early warning, damage assessment and response. The big data challenge is not just about data storage, but also about data discoverability and accessibility, and even more, about data migration/mirroring in the cloud. This paper is going to demonstrate GES DISC’s efforts and approaches of evolving our overall Web services and powerful Giovanni (Geospatial Interactive Online Visualization ANd aNalysis Infrastructure) tool into further improving data discoverability and accessibility. Prototype works will also be presented.
This presentation focuses on the challenge of integrating AI-driven data centers with the power grid at scale. It examines the AI data center capacity challenge and the role of new Medium Voltage Direct Current (MVDC) and other grid-enhancing technologies in enabling efficient and reliable power delivery. The session will highlight the National Laboratory of the Rockies' ARIES capabilities and planning tools, along with collaborative examples involving Verrus, Compass, and Schneider through the Agora test bed for grid-friendly data center evaluations, and ON. Energy for UPS evaluation. It will showcase the NLR Stable Grid Platform for studying oscillations caused by large-scale data centers, along with planning tools to assess grid security and reliability. Additionally, the presentation covers reconductoring strategies to increase grid capacity and explores innovative data center architectures, including the Advanced DC Architectures with Power-electronic Transformers (ADAPT) platform, which enables testing of complete DC architectures for data centers.
Validation of the Wind-US flow solver against two sets of experimental data involving high-speed combustion is attempted. First, the well-known Burrows- Kurkov supersonic hydrogen-air combustion test case is simulated, and the sensitively of ignition location and combustion performance to key parameters is explored. Second, a numerical model is developed for simulation of an X-43B candidate, full-scale, JP-7-fueled, internal flowpath operating in ramjet mode. Numerical results using an ethylene-air chemical kinetics model are directly compared against previously existing pressure-distribution data along the entire flowpath, obtained in direct-connect testing conducted at NASA Langley Research Center. Comparison to derived quantities such as burn efficiency and thermal throat location are also made. Reasonable to excellent agreement with experimental data is demonstrated for key parameters in both simulation efforts. Additional Wind-US feature needed to improve simulation efforts are described herein, including maintaining stagnation conditions at inflow boundaries for multi-species flow. An open issue regarding the sensitivity of isolator unstart to key model parameters is briefly discussed.
Bidirectional wireless communication is employed in various smart grid components such as smart meters and control and monitoring applications where security is vital. The Trusted Third Party (TTP) and wireless connectivity between the smart meter and the third party in the key management-based encryption techniques for the smart grid are expected to be totally trustworthy and dependable. In a wired/wireless medium, however, a man-in-the-middle may seek to disrupt, monitor and manipulate the network, or simply execute a replay attack, revealing its vulnerability. Recognizing this, this study presents a novel authentication management (model) comprised of two layer security schema. The first layer implements an efficient novel encryption method for secure data exchange between meters and control center with the help of two partially trusted simple servers (constitutes the TTP). In this setting, one server handles the data encryption between the meter and control center/central database, and the other server administers the random sequence of data transmission. The second layer monitors and verifies exchanged data packets among smart meters. It detects abnormal packets from suspicious sources. To implement this node-to-node authentication, One class support vector machine algorithm is proposed which takes advantages of the location information as well as the data transmission history (node identification, packet size, and data transmission frequency). This schema secures data communication, and imposes a comprehensive privacy throughout the system without considerably extending the complexity of the conventional key management scheme.
The control of Flexible Structures (COFS) program has supported the development of an analysis capability at the Langley Research Center called the Integrated Multidisciplinary Analysis Tool (IMAT) which provides an efficient data storage and transfer capability among commercial computer codes to aid in the dynamic analysis of actively controlled structures. IMAT is a system of computer programs which transfers Computer-Aided-Design (CAD) configurations, structural finite element models, material property and stress information, structural and rigid-body dynamic model information, and linear system matrices for control law formulation among various commercial applications programs through a common database. Although general in its formulation, IMAT was developed specifically to aid in the evaluation of the structures. A description of the IMAT system and results of an application of the system are given.
The Modern-Era Retrospective analysis for Research and Applications, Version (MERRA-2) dataset is now available in the AWS West Region-2 cloud. The free data downloading policy remains unchanged. The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) offers cloud services such as OPeNDP subsetting, and time series services. This presentation will showcase use cases that testing the procedures and find the most efficiency workflows to access and process the data for finding climate extremes. The use cases include a) accessing and processing data directly in the cloud S3 bucket without downloading it, and b) downloading subset data to process on the user’s local system.
An all-digital high data rate parallel receiver architecture developed jointly by Goddard Space Flight Center and the Jet Propulsion Laboratory is presented. This receiver utilizes only a small number of high speed components along with a majority of lower speed components operating in a parallel frequency domain structure implementable in CMOS, and can currently process up to 600 Mbps with standard QPSK modulation. Performance results for this receiver for bandwidth efficient QPSK modulation schemes such as square-root raised cosine pulse shaped QPSK and Feher's patented QPSK are presented, demonstrating the flexibility of the receiver architecture.
Across the United States (U.S.) grid expansion and modernization is underway, paving the way for accelerated load growth and intelligent resource management. Digitization of the grid is supported by several state and federal programs, providing support for utilities installing advanced metering infrastructure (AMI), AI-powered analytics systems, battery energy storage systems (BESS), and distributed energy resource management systems (DERMS) to transform the grid from a one-way power delivery system into an intelligent, responsive network that will enable faster load growth and power expansion of data centers for advanced artificial intelligence (AI) applications. The digital transformation of America's grid presents opportunity for increased efficiency and resiliency but also introduces new digital risks that require careful management. Digital equipment often contains several vulnerabilities such as unencrypted communication protocols, and persistent remote access capabilities that could be exploited to manipulate device settings, coordinate service disruptions, or inject false data into grid operations. These digital risks become particularly important as the grid must rapidly scale to support AI-driven data centers, which the administration has identified as essential for maintaining U.S. technological leadership and economic competitiveness. These vulnerabilities are compounded by supply chain realities: Chinese manufacturers currently produce 70-90% of essential grid components including inverters, batteries, and control systems, with the U.S. lacking domestic manufacturing capacity for critical assets like extra-high voltage transformers. Recent federal legislation has established Foreign Entity of Concern (FEOC) restrictions to address these risks, requiring projects to achieve escalating thresholds of non-FEOC content to receive tax credits while utilities work to expand sourcing channels for their supply chains and strengthen security measures. These restrictions arrive precisely when utilities face unprecedented electricity demand growth driven by the rapid growth in data centers, creating a considerable challenge: rapidly expanding infrastructure while navigating complex compliance requirements while lacking viable alternatives for many critical components. Idaho National Laboratory (INL) and its partners have developed practical approaches to help utilities navigate these intersecting challenges as they leverage federal investment to strengthen and grow the grid. These solutions include Cyber-Informed Engineering (CIE) principles that build resilience directly into systems, the Cirrus tool for secure cloud migration, and enhanced procurement guidance that embeds security requirements throughout equipment lifecycles. Federal initiatives, such as the Technical Assistance for Digital Assurance (TADA) project, provide direct support to utilities implementing these approaches while facilitating knowledge sharing across the industry. While these tools and frameworks cannot eliminate all risks inherent in foreign supply chain dependencies, they offer pragmatic pathways for strengthening security posture without sacrificing the deployment momentum essential to meeting surging electricity demand. Ultimately, securing America's digital energy infrastructure demands dedicated coordination across multiple fronts: building domestic supply chains, implementing robust digital assurance practices, and maintaining the aggressive modernization timeline necessary for reliability, resilience, and energy independence.
Numerical Weather Prediction (NWP) centers assimilate cloud-free infrared (IR) radiances because the assimilation of all-sky IR radiances is not yet operationally achievable. The cloud-clearing procedure offers a simpler, but effective strategy that produces cloud-affected radiances suitable for assimilation in partially cloudy regions. Several studies conducted by this team have demonstrated that IR Cloud-Cleared Radiances (CCRs), if thinned more aggressively than clear-sky radiances, can improve analysis and forecasts, particularly in meteorologically active areas. However, CCRs are not used by operational centers due partly to the thought that the process of cloud-clearing may affect latency and introduce difficult-to-control external dependencies. This study presents the results of implementing an Atmospheric Infrared Sounder (AIRS) and Cross-Track Infrared Sounder (CrIS) cloud-clearing procedure into the NASA Goddard Earth Observing System (GEOS) to demonstrate the portability of the procedure. The AIRS and CrIS cloud-clearing algorithms have been deprived of external dependencies, made customizable to any specific model, and the computational efficiency has been improved via parallelization. The revised AIRS and CrIS cloud-clearing algorithms allow a customized choice of channel selection, the use of a user-specified model's fields as first guess, and can perform in real time. Data assimilation experiments with the hybrid 4DEnVar GEOS system were successfully performed for the 2017 tropical cyclones (TC) season with a focus on three major hurricanes (Harvey, Irma, and Maria). This study shows that assimilation of locally-generated CCRs have a positive impact on both global skill and TC representation, compared to the assimilation of AIRS and CrIS clear-sky radiances, and a comparable or slightly improved impact compared to assimilation of CCRs produced by external sources, such as NASA's Distributed Active Archive Centers and NOAA’s Comprehensive Large Array-data Stewardship System. The customization and computational efficiency of the revised procedure would enable its usability in a real-time forecast context.
The Transportation Secure Data Center is a centralized repository for high-resolution transportation data from hundreds of travel and transit surveys and studies. It makes vital transportation data broadly available to users while preserving the privacy of survey participants. It houses surveys and studies conducted by state departments of transportation, metropolitan planning organizations, transit agencies, cities, and other public agencies. Meanwhile, the Livewire Data Platform empowers research, industry, and academic partners to easily and securely preserve, maintain, share, discover, and gain access to transportation and mobility data. Livewire accommodates a range of datasets, including behavioral, experimental, model, analytical, and raw data at the vehicle, traveler, and system levels. Datasets support mobility research and planning spanning urban science, connected and automated vehicles, fueling and charging infrastructure, mobility decision science, multimodal transportation, vehicle efficiency, and more.