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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Assessing and advancing the potential of quantum computing: A NASA case study

Quantum computing is one of the most enticing computational paradigms with the potential to revolutionize diverse areas of future-generation computational systems. While quantum computing hardware has advanced rapidly, from tiny laboratory experiments to quantum chips that can outperform even the largest supercomputers on specialized computational tasks, these noisy-intermediate scale quantum (NISQ) processors are still too small and non-robust to be directly useful for any real-world applications. In this paper, we describe NASA’s work in assessing and advancing the potential of quantum computing. We discuss advances in algorithms, both near- and longer-term, and the results of our explorations on current hardware as well as with simulations, including illustrating the benefits of algorithm-hardware co-design in the NISQ era. This work also includes physics-inspired classical algorithms that can be used at application scale today. We discuss innovative tools supporting the assessment and advancement of quantum computing and describe improved methods for simulating quantum systems of various types on high-performance computing systems that incorporate realistic error models. We provide an overview of recent methods for benchmarking, evaluating, and characterizing quantum hardware for error mitigation, as well as insights into fundamental quantum physics that can be harnessed for computational purposes.

Rieffel, Eleanor G.↗
DOE OSTI2026

Adaptable Standards for Discovery, Access, and Usability of Oak Ridge National Laboratory’s Data Portals and Catalogs

Oak Ridge National Laboratory (ORNL) is leveraging its established capabilities and subject matter expertise in data curation, governance, management, national security, and risk assessment and mitigation to support the US Department of Energy (DOE) Grid Modernization Initiative. Using standards modeled by the National Institute of Standards and Technology (NIST), the Data Curation Network (DCN), the Oak Ridge Leadership Computing Facility (OLCF), and other leading organizations in the fields of energy research, high-performance computing, and national and homeland security, ORNL seeks to provide a federated approach to research data discovery, use, and interoperability.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2025

One Million Open-source Cislunar Orbits

Cislunar space, encompassing the region from geosynchronous orbit to beyond the Moon, is poised to become a cornerstone for future exploration, scientific discovery, and national security. Missions in this region, spanning durations from weeks to decades, require robust infrastructure and reliable transit capabilities. The complex gravitational influences of the Moon, Sun, and planets, along with thermal radiation from Earth and the Sun, lead to significant trajectory deviations, resulting in kilometer-scale errors within days. Leveraging the high-performance computing resources at Lawrence Livermore National Laboratory (LLNL), we have simulated one million high-fidelity cislunar trajectories, now publicly available via LLNL’s Green Data Oasis and the Unified Data Library. Generated using the open-source Space Situational Awareness Python package, these trajectories match the precision of commercial tools such as AGI’s Systems Tool Kit and NASA’s General Mission Analysis Tool. This data set is a valuable resource for reference, statistical analysis of cislunar orbit populations, and training machine learning models for rapid orbit classification with minimal observational input. Preliminary analysis reveals stable bands in Keplerian element space, particularly around five geosynchronous radii across a range of inclinations and eccentricities. Beyond this threshold, the Moon’s influence disrupts most unassisted orbits, though co-orbiting L4/L5 Lunar Trojans persist throughout the six-year simulation.

Astronomy and AstroPhysics↗
NASA NTRS2003

A computational-grid based system for continental drainage network extraction using SRTM digital elevation models

We describe a new effort for the computation of elevation derivatives using the Shuttle Radar Topography Mission (SRTM) results. Jet Propulsion Laboratory's (JPL) SRTM has produced a near global database of highly accurate elevation data. The scope of this database enables computing precise stream drainage maps and other derivatives on Continental scales. We describe a computing architecture for this computationally very complex task based on NASA's Information Power Grid (IPG), a distributed high performance computing network based on the GLOBUS infrastructure. The SRTM data characteristics and unique problems they present are discussed. A new algorithm for organizing the conventional extraction algorithms [1] into a cooperating parallel grid is presented as an essential component to adapt to the IPG computing structure. Preliminary results are presented for a Southern California test area, established for comparing SRTM and its results against those produced using the USGS National Elevation Data (NED) model.

stream extraction↗
DOE OSTI2024

Geospatial Data Platform for All

Spatiotemporal data has evolved in scale due to augmented use in cross-domain applications. Simultaneously, there is substantial growth in the availability of Geographic Information Systems (GIS) data provided by the United States Geological Survey (USGS) along with other federal, state, county, or local agencies through open-data portals and public access APIs. However, data availability does not equate with accessibility. Large-scale analyses and applications require robust, performant data management with co-location of data storage and computing. The insufficiency of data management infrastructure compels researchers to adopt ad hoc project- specific GIS data storage solutions (e.g., copying data to High-Performance computer file systems). As an ad hoc storage strategy does not scale, it hampers cross-domain analyses causing difficulty in data reuse and utilizing existing code bases. Furthermore, GIS data is complex and requires expertise to analyze and manipulate due to its intricate data structures and data-specific projection transformations. Despite the challenges, we recognize that derived GIS data products, e.g., satellite or LIDAR-based images, can be used in downstream applications such as AI by domain, but non-GIS experts. To address the data needs and overcome the challenges, we are working towards a GIS Data Platform focused on efficient data storage, data discovery and access, and an API to enable common workflows. We propose a knowledge-graph (KG) approach for data discovery, whereby datasets are semantically linked to higher- level constructs such as projects and research areas. The semantic data links enable researchers to explore datasets in a top-down approach by specifying relevant and meaningful terms (assists in finding hidden data). An advantage is that the nodes and edges in a knowledge graph create built-in semantic documentation. Deeper spatiotemporal connections between data sources can be encoded via Graph Neural Networks (GNN) (Zhang et al., 2021). The KG approach can be extended to integrate the data itself in a Virtual KG (VKG). Our work will derive inspiration from large-scale VKG efforts that have been undertaken or are currently underway as part of the OpenStreetMap project (Ding et al., 2021). For DOE Data Days, we share the proposed geospatial data platform hybrid (cloud/on-prem) architecture, our work-to-date on storing, retrieving, and transforming LiDAR and raster data relevant to two important NREL use-cases, including the Renewable Energy Potential (reV) Model, and present our proposal for a KG based data discovery engine.

data platform↗
NASA NTRS2004

Evaluating Performance of Components

Parallel Component Performance Benchmarks is a computer program developed to aid the evaluation of the Common Component Architecture (CCA) - a software architecture, based on a component model, that was conceived to foster high-performance computing, including parallel computing. More specifically, this program compares the performances (principally by measuring computing times) of componentized versus conventional versions of the Parallel Pyramid 2D Adaptive Mesh Refinement library - a software library that is used to generate computational meshes for solving physical problems and that is typical of software libraries in use at NASA s Jet Propulsion Laboratory.

Katz, Daniel↗
NASA NTRS1996

Finding New Math Identities by Computer

Recently a number of interesting new mathematical identities have been discovered by means of numerical searches on high performance computers, using some newly discovered algorithms. These include the following: pi = ((sup oo)(sub k=0))(Sigma) (1 / 16) (sup k) ((4 / 8k+1) - (2 / 8k+4) - (1 / 8k+5) - (1 / 8k+6)) and ((17 pi(exp 4)) / 360) = ((sup oo)(sub k=1))(Sigma) (1 + (1/2) + (1/3) + ... + (1/k))(exp 2) k(exp -2), zeta(3, 1, 3, 1, ..., 3, 1) = (2 pi(exp 4m) / (4m+2)! where m = number of (3,1) pairs. and where zeta(n1,n2,...,nr) = (sub k1 (is greater than) k2 (is greater than) ... (is greater than) kr)(Sigma) (1 / (k1 (sup n1) k2 (sup n2) ... kr (sup nr). The first identity is remarkable in that it permits one to compute the n-th binary or hexadecimal digit of pu directly, without computing any of the previous digits, and without using multiple precision arithmetic. Recently the ten billionth hexadecimal digit of pi was computed using this formula. The third identity has connections to quantum field theory. (The first and second of these been formally established; the third is affirmed by numerical evidence only.) The background and results of this work will be described, including an overview of the algorithms and computer techniques used in these studies.

Bailey, David H.↗
NASA NTRSDate not supplied

Large-Scale Computational Fluid Dynamics Simulations of Aerospace Configurations on the Frontier Exascale System

Over the past fifteen years, the high performance computing landscape has undergone a seismic shift in both hardware and software paradigms, which has been necessary to realize a 1000× leap in computational performance while meeting stringent constraints on power consumption. A historical overview of a long-term research effort aimed at addressing these challenges within the context of a commonly-used aerospace computational fluid dynamics (CFD) application is presented. Details of the current implementation as they relate to the new era of exascale-relevant hardware architectures and programming models are described. Two large-scale simulations of aerospace configurations are performed using the entire Frontier exascale system, currently ranked as the most powerful supercomputing system in the world. The effort serves to address a 2024 milestone posed a decade ago by the seminal CFD Vision 2030 Study.

Eric J Nielsen↗
NASA NTRS1996

Experiences From NASA/Langley's DMSS Project

There is a trend in institutions with high performance computing and data management requirements to explore mass storage systems with peripherals directly attached to a high speed network. The Distributed Mass Storage System (DMSS) Project at the NASA Langley Research Center (LaRC) has placed such a system into production use. This paper will present the experiences, both good and bad, we have had with this system since putting it into production usage. The system is comprised of: 1) National Storage Laboratory (NSL)/UniTree 2.1, 2) IBM 9570 HIPPI attached disk arrays (both RAID 3 and RAID 5), 3) IBM RS6000 server, 4) HIPPI/IPI3 third party transfers between the disk array systems and the supercomputer clients, a CRAY Y-MP and a CRAY 2, 5) a "warm spare" file server, 6) transition software to convert from CRAY's Data Migration Facility (DMF) based system to DMSS, 7) an NSC PS32 HIPPI switch, and 8) a STK 4490 robotic library accessed from the IBM RS6000 block mux interface. This paper will cover: the performance of the DMSS in the following areas: file transfer rates, migration and recall, and file manipulation (listing, deleting, etc.); the appropriateness of a workstation class of file server for NSL/UniTree with LaRC's present storage requirements in mind the role of the third party transfers between the supercomputers and the DMSS disk array systems in DMSS; a detailed comparison (both in performance and functionality) between the DMF and DMSS systems LaRC's enhancements to the NSL/UniTree system administration environment the mechanism for DMSS to provide file server redundancy the statistics on the availability of DMSS the design and experiences with the locally developed transparent transition software which allowed us to make over 1.5 million DMF files available to NSL/UniTree with minimal system outage

Source record↗
DOE OSTI2025

Basic Research Needs for Inverse Methods for Complex Systems under Uncertainty [Brochure]

The four priority research directions outlined in this brochure represent a cohesive vision for advancing the science of inverse problems for complex systems under uncertainty. Together, they address the critical challenges of: discovering, exploiting, and preserving physical and problem structure; overcoming model limitations; integrating disparate, multimodal, and/or dynamic data; and tailoring the solution of inverse problems to downstream tasks. While each PRD focuses on a distinct aspect of inverse-problem research, their interconnected nature highlights the importance of a holistic approach that leverages progress across all areas to achieve transformative solutions. This agenda calls for research across mathematics, statistics, and computer science disciplines, which are guided and complemented by rapid advances in artificial intelligence, high-performance computing, and experimental facilities, to unlock new capabilities, maximize scientific impact, and meet the growing demands of inverse problems that arise across applications that are critical to DOE's mission.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1994

Tuning CAS Application using AIMS: An Automated Instrumentation and Monitoring System

To bring together NASA's scientists and engineers and their counterparts in industry, other government agencies, and academia working in the Computational AeroSciences (CAS) field. This workshop is part of the technology transfer plan of the High Performance Computing and Communications Program (HPCCP). Specific objectives of this Workshop are to: (1) communicate the goals and objectives of HPCCP in the area of CAS; (2) promote and disseminate CAS technology within the appropriate technical communities, including NASA, industry, academia, and other government labs; (3) help promote synergy among CAS scientists; and (4) permit feedback from peer researchers in issues pacing the CAS field in general and the HPCCP CAS program in particular.

Mehra, P.↗
NASA NTRS1997

Petaflops Computing: The Key Algorithmic Challenges

The prospect of petaflops-class computers brings to the fore some important algorithmic issues that have been considered in the high performance computing community for several years. Key among them are (1) concurrency (whether the fundamental concurrency of an algorithm is sufficient to keep thousands of processors productively busy); (2) data locality; (3) latency tolerance; and (4) memory and operation count scaling. This introductory presentation will give an overview of these issues.

Bailey, David H.↗
NASA NTRS2001

EngineSim: Turbojet Engine Simulator Adapted for High School Classroom Use

EngineSim is an interactive educational computer program that allows users to explore the effect of engine operation on total aircraft performance. The software is supported by a basic propulsion web site called the Beginner's Guide to Propulsion, which includes educator-created, web-based activities for the classroom use of EngineSim. In addition, educators can schedule videoconferencing workshops in which EngineSim's creator demonstrates the software and discusses its use in the educational setting. This software is a product of NASA Glenn Research Center's Learning Technologies Project, an educational outreach initiative within the High Performance Computing and Communications Program.

Petersen, Ruth A.↗
DOE OSTI2024

ECP libraries and tools: An overview

The Exascale Computing Project (ECP) Software Technology and Co-Design teams addressed the growing complexities in high-performance computing (HPC) by developing scalable software libraries and tools that leverage exascale system capabilities. As we enter the exascale era, the need for reusable, optimized software solutions that can handle the unique challenges posed by these systems becomes increasingly important. The primary challenges the ECP teams faced were to create software libraries and tools that are performant on exascale architectures and portable and usable across diverse hardware platforms. Efforts addressed issues related to concurrent execution, memory management, and the integration of heterogeneous computing resources, such as GPUs from multiple vendors. The ECP’s strategy involved a structured development process encompassing the creation, optimization, and deployment of software in collaboration with industry, academia, and national laboratories. The project was organized into several technical areas: co-design of domain-specific suites with target applications, programming models and runtimes, development tools, mathematical libraries, data and visualization tools, and software ecosystem and delivery mechanisms. ECP has successfully developed a large portfolio of software libraries and tools that demonstrate significant improvements in performance and scalability on exascale systems. These products have been integrated into the Department of Energy’s computing facilities, supporting various scientific applications and ensuring robust performance across different hardware setups. ECP advancements in software development for exascale computing highlight the importance of a collaborative and adaptive approach to handling next-generation HPC systems complexities. The lessons learned emphasize the need for continuous engagement with end-users and vendors, and the importance of maintaining a balance between innovation and practical implementation. Future efforts will focus on ensuring scalability, keeping pace with rapid hardware advancements, and further enhancing the interoperability and usability of the software ecosystem. In conclusion, subsequent articles in this special issue provide in-depth discussions and case studies into specific library and tool efforts.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1995

Towards Distributed Fluids/Controls Simulations

Computational prototyping of aircraft in nonlinear regimes will require the coupling of high-order disciplines on high performance computers. This effort investigates the coupling of the Reynolds-averaged Navier-Stokes equations with six degree-of-freedom nonlinear body dynamics and controls on distributed architectures. Towards this goal, solutions obtained on a wide-node IBM SP-2, an Ethernet-linked cluster of workstations, and a Cray C90 are compared in terms of accuracy and throughput.

Atwood, Christopher A.↗
NASA NTRS2019

AladynPi – Adaptive Neural Network Molecular Dynamics Simulation Code with Physically Informed Potential: Computational Materials Mini-Application

This report provides an overview and description of commands used in the Computational Materials mini-application, AladynPi. AladynPi is an extension of a previously released mini-application, Aladyn (https://github.com/nasa/aladyn; Yamakov, V.I., and Glaessgen, E.H., NASA/TM-2018-220104). Aladyn and AladynPi are basic molecular dynamics codes written in FORTRAN 2003, which are designed to demonstrate the use of adaptive neural networks (ANNs) in atomistic simulations. The role of ANNs is to efficiently reproduce the very complex energy landscape resulting from the atomic interactions in materials with the accuracy of the more expensive quantum mechanics-based calculations. The ANN is trained on a large set of atomic structures calculated using the density functional theory method. An input for the ANN is a set of structure coefficients, characterizing the local atomic environment of each atom, for which the atomic energy is obtained in the ANN inference process. In Aladyn, the ANN gives directly the energy of interatomic interactions. In AladynPi, the ANN gives optimized parameters for a predefined empirical function, known as bond-order-potential (BOP). The parameterized BOP function is then used to calculate the energy. AladynPi code is being released to serve as a training testbed for students and professors in academia to explore possible optimization algorithms for parallel computing on multicore central processing unit (CPU) computers or computers utilizing manycore architectures based on graphic processing units (GPUs). The effort is supported by the High Performance Computing incubator (HPCi) project at NASA Langley Research Center.

Yamakov, Vesselin I.↗
NASA NTRS2000

Testing New Programming Paradigms with NAS Parallel Benchmarks

Over the past decade, high performance computing has evolved rapidly, not only in hardware architectures but also with increasing complexity of real applications. Technologies have been developing to aim at scaling up to thousands of processors on both distributed and shared memory systems. Development of parallel programs on these computers is always a challenging task. Today, writing parallel programs with message passing (e.g. MPI) is the most popular way of achieving scalability and high performance. However, writing message passing programs is difficult and error prone. Recent years new effort has been made in defining new parallel programming paradigms. The best examples are: HPF (based on data parallelism) and OpenMP (based on shared memory parallelism). Both provide simple and clear extensions to sequential programs, thus greatly simplify the tedious tasks encountered in writing message passing programs. HPF is independent of memory hierarchy, however, due to the immaturity of compiler technology its performance is still questionable. Although use of parallel compiler directives is not new, OpenMP offers a portable solution in the shared-memory domain. Another important development involves the tremendous progress in the internet and its associated technology. Although still in its infancy, Java promisses portability in a heterogeneous environment and offers possibility to "compile once and run anywhere." In light of testing these new technologies, we implemented new parallel versions of the NAS Parallel Benchmarks (NPBs) with HPF and OpenMP directives, and extended the work with Java and Java-threads. The purpose of this study is to examine the effectiveness of alternative programming paradigms. NPBs consist of five kernels and three simulated applications that mimic the computation and data movement of large scale computational fluid dynamics (CFD) applications. We started with the serial version included in NPB2.3. Optimization of memory and cache usage was applied to several benchmarks, noticeably BT and SP, resulting in better sequential performance. In order to overcome the lack of an HPF performance model and guide the development of the HPF codes, we employed an empirical performance model for several primitives found in the benchmarks. We encountered a few limitations of HPF, such as lack of supporting the "REDISTRIBUTION" directive and no easy way to handle irregular computation. The parallelization with OpenMP directives was done at the outer-most loop level to achieve the largest granularity. The performance of six HPF and OpenMP benchmarks is compared with their MPI counterparts for the Class-A problem size in the figure in next page. These results were obtained on an SGI Origin2000 (195MHz) with MIPSpro-f77 compiler 7.2.1 for OpenMP and MPI codes and PGI pghpf-2.4.3 compiler with MPI interface for HPF programs.

Jin, H.↗
DOE OSTI2024

Towards Cross-Facility Workflows Orchestration through Distributed Automation

Modern science relies on end-to-end workflows that incorporate experimental instruments and utilize edge, cloud, or high-performance computing and storage resources. These components are geographically dispersed across various user facilities and interconnected through high-speed networks. In this paper, we present Zambeze, an automated distributed framework designed to facilitate this new class of cross-facility workflows. Utilizing swarm intelligence principles, Zambeze orchestrates science campaigns by managing distributed autonomous agents. These agents can offer a suite of services, including computing, storage, and data management. We demonstrate the feasibility of Zambeze through a real-world application involving electron microscopy, enhanced with Artificial Intelligence capabilities.

Skluzacek, Tyler↗