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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 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

A Perspective on Scalable AI on High-Performance Computing and Leadership Class Supercomputing Facilities [Industrial and Governmental Activities]

Many scientific applications that support the mission of the US Department of Energy (US-DoE) require modeling complex engineering and/or physical systems. Here, examples of such complex systems arise from: (a) materials science to develop new compounds with exceptional mechanical and thermodynamical properties (e.g., resistance to mechanical stresses and high temperatures), (b) structural and nuclear engineering to model the temporal evolution of the structural damage of concrete shields exposed to continuous neutron and gamma radiations emitted by the nuclear reactor core, (c) urban sciences (e.g., transportation and smart buildings), and (d) power grid systems.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1986

Towards a science of expert systems

Developments in the field of AI are discussed. The components and applications of expert systems, which are computer systems designed to simulate the problem-solving behavior of a person expert in a narrow field, are examined. Two types of expert systems, shallow and deep, are described and examples are given. A logic programming system, rule-based system, and framed-based system are utilized as means of representing the expert system's data base. The limitations of expert systems are considered.

Denning, P. J.↗
DOE OSTI2026

The Impact of Time-Aware Design Choices in ICS Anomaly Detection

Industrial control systems (ICS) remain vulnerable to increasingly sophisticated cyberattacks, yet evaluating anomaly detection models in these environments is challenging due to temporal dependencies, missing-not-at-random patterns, and extremely imbalanced datasets. These factors make common practices—especially random data splits and na¨ıve imputation— prone to severe temporal leakage, which can inflate reported performance and obscure real-world limitations. In this work, we systematically examine classical machine learning models, temporal deep learning architecture, and tensordecomposition– based methods on a gas-pipeline dataset using a fully temporally separated evaluation pipeline designed to mimic realistic deployment conditions. Our findings show that proper temporal handling and MNAR-aware preprocessing significantly alter the relative performance of popular anomaly-detection methods, providing practical guidance for designing reliable, leakage-resistant ICS intrusion-detection systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Determination of Process Parameters for High-Density, Ti-6Al-4V Parts Using Additive Manufacturing

In our earlier work, we described an approach for determining the process parameters that re- sult in high-density parts manufactured using the additive-manufacturing process of selective laser melting (SLM). Our approach, which combines simple simulations and experiments, was demon- strated using 316L stainless steel. We have also used the approach successfully for several other materials. This short note summarizes the results of our work in determining process parameters for Ti-6Al-4V using a Concept Laser M2 system.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Workshop on Integrated Sensor Systems for Manufacturing Applications (Workshop Report)

On January 25th and 26th, the Department of Energy’s Advanced Manufacturing Office (AMO) held the first in a series of workshops on different topics related to semiconductor research and development (R&D). This workshop focused on integrated sensor system R&D for manufacturing applications. As AMO is housed within the office of Energy Efficiency and Renewable Energy (EERE), the workshop addressed not only industry needs and R&D opportunities, but also the impacts that improvements in sensor systems can have on energy efficiency and greenhouse gas production. The output of this workshop will inform AMO’s future portfolio of R&D investments, provide perspectives on trends, drivers, and challenges for next generation semiconductor sensor systems, and help the stakeholder community understand the opportunities on the horizon.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Designing Cellular Metal Structures for Thermal Insulation

This project focused on developing topology optimization software to design advanced metal thermal insulators. Initially, solid designs were created that matched the thermal performance of current baseline designs but were significantly heavier. To address this, cellular materials were incorporated, specifically the octet structure which is known for its high strength-to-weight ratio and thermal properties. By leveraging these cellular designs at various densities, superior thermal and mechanical performance was achieved without added weight. This novel approach enhances thermal management and structural integrity under extreme conditions, offering promising advancements for thermal protection systems.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Development of an Automated Software Reliability Tester for Digital I and C

Existing nuclear facilities are in the process of upgrading analog systems to new, software- based digital components; however, the qualification of these new systems has been a significant hurdle for their implementation in the United States. This paper describes the development of a Software Reliability Tester (SRT) that will provide a means to quantify the reliability and fault tolerance of digital instrumentation and control (I and C) systems used in nuclear facilities. The SRT is comprised of hardware and software that exercise digital equipment inputs and compare the outputs to expected values to quantify system reliability. Incorporation of fault injection techniques enables the SRT to evaluate the robustness of digital I and C in the presence of abnormal conditions. The integration of both quantitative reliability and fault tolerance measures will provide the nuclear industry with an automated and systematic approach to digital I and C qualification. This will enable safer, more reliable, and more cost effective implementations of a broad spectrum of digital I and C equipment in existing and next generation nuclear facilities. This paper includes a description of the design of the SRT, including software modules and the hardware interface. In addition, an example of using the SRT to automate functional testing of a digital timer relay is presented. The SRT described in this paper provides a means to conduct efficient and robust testing for a variety of digital devices. Future plans for the SRT include integrating fault injection testing so that devices can be evaluated in the presence of off-normal conditions. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Urban Energy Systems: Research at Oak Ridge National Laboratory

In the coming decades, our planet will witness unprecedented urban population growth in both established and emerging communities. The development and maintenance of urban infrastructures are highly energy-intensive. Urban areas are dictated by complex intersections among physical, engineered, and human dimensions that have significant implications for traffic congestion, emissions, and energy usage. In this chapter, we highlight recent research and development efforts at Oak Ridge National Laboratory (ORNL), the largest multipurpose science laboratory within the U.S. Department of Energy’s (DOE) national laboratory system, that characterizes the interactions between the human dynamics and critical infrastructures in conjunction with the integration of four distinct components: data, critical infrastructure models, and scalable computation and visualization, all within the context of physical and social systems. Discussions focus on four key topical themes: population and land use, sustainable mobility, the energy-water nexus, and urban resiliency, that are mutually aligned with DOE’s mission and ORNL’s signature science and technology capabilities. Using scalable computing, data visualization, and unique datasets from a variety of sources, the institute fosters innovative interdisciplinary research that integrates ORNL expertise in critical infrastructures including energy, water, transportation, and cyber, and their interactions with the human population.

Bhaduri, Budhu↗
DOE OSTI2022

LANL Contributions to pyiron: An integrated development environment for computational materials science

To support and accelerate the development of simulation protocols in atomistic modelling, we introduce an integrated development environment (IDE) for computational materials science called pyiron (http://pyiron.org). The pyiron IDE combines a web based source code editor, a job management system for build automation, and a hierarchical data management solution. The core components of the pyiron IDE are pyiron objects based on an abstract class, which links application structures such as atomistic structures, projects, jobs, simulation protocols and computing resources with persistent storage and an interactive user environment. The simulation protocols within the pyiron IDE are constructed using the Python programming language. To highlight key concepts of this tool as well as to demonstrate its ability to simplify the implementation and testing of simulation protocols we discuss two applications. In these examples we show how pyiron supports the whole life cycle of a typical simulation, seamlessly combines ab initio with empirical potential calculations, and how complex feedback loops can be implemented. While originally developed with focus on ab initio thermodynamics simulations, the concepts and implementation of pyiron are general thus allowing to employ it for a wide range of simulation topics.

Janssen, Jan↗
DOE OSTI2017

Task-based Parallel Computation of the Density Matrix in Quantum-based Molecular Dynamics using Graph Partitioning

Quantum-based molecular dynamics (QMD) is a highly accurate and transferable method for material science simulations. However, the time scales and system sizes accessible to QMD are typically limited to picoseconds and a few hundred atoms. These constraints arise due to expensive self-consistent ground-state electronic structure calculations that can often scale cubically with the number of atoms. Linearly scaling methods depend on computing the density matrix P from the Hamiltonian matrix H by exploiting the sparsity in both matrices. The second-order spectral projection (SP2) algorithm is an O(N) algorithm that computes P with a sequence of 40 - 50 matrix-matrix multiplications. In this paper, we present task-based implementations of a recently developed data-parallel graph-based approach to the SP2 algorithm, G-SP2. We represent the density matrix P as an undirected graph and use graph partitioning techniques to divide the computation into smaller independent tasks. The partitions thus obtained are generally not of equal size and give rise to undesirable load imbalances in standard MPI-based implementations. This load-balancing challenge can be mitigated by dynamically scheduling parallel computations at runtime using task-based programming models. We develop task-based implementations of the data-parallel G-SP2 algorithm using both Intel's Concurrent Collections (CnC) as well as the Charm++ programming model and evaluate these implementations for future use. Scaling and performance results of our implementations are investigated for representative segments of QMD simulations for solvated protein systems containing more than 10,000 atoms.

97 MATHEMATICS AND COMPUTING↗