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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

NASA aerodynamics program

The annual accomplishments is reviewed for the Aerodynamics Division during FY 1991. The program includes both fundamental and applied research directed at the full spectrum of aerospace vehicles, from rotorcraft to planetary entry probes. A comprehensive review is presented of the following aerodynamics elements: computational methods and applications; CFD validation; transition and turbulence physics; numerical aerodynamic simulation; test techniques and instrumentation; configuration aerodynamics; aeroacoustics; aerothermodynamics; hypersonics; subsonics; fighter/attack aircraft and rotorcraft.

Williams, Louis J.↗
NASA NTRS1993

Particle bed reactor modeling

The topics are presented in viewgraph form and include the following: particle bed reactor (PBR) core cross section; PBR bleed cycle; fuel and moderator flow paths; PBR modeling requirements; characteristics of PBR and nuclear thermal propulsion (NTP) modeling; challenges for PBR and NTP modeling; thermal hydraulic computer codes; capabilities for PBR/reactor application; thermal/hydralic codes; limitations; physical correlations; comparison of predicted friction factor and experimental data; frit pressure drop testing; cold frit mask factor; decay heat flow rate; startup transient simulation; and philosophy of systems modeling.

Sapyta, Joe↗
NASA NTRS2001

Photochemical Phenomenology Model for the New Millennium

The "Photochemical Phenomenology Model for the New Millennium" project tackles the issue of reengineering and extension of validated physics-based modeling capabilities ("legacy" computer codes) to application-oriented software for use in science and science-support activities. While the design and architecture layouts are in terms of general particle distributions involved in scattering, impact, and reactive interactions, initial Photochemical Phenomenology Modeling Tool (PPMT) implementations are aimed at construction and evaluation of photochemical transport models with rapid execution for use in remote sensing data analysis activities in distributed systems. Current focus is on the Composite Infrared Spectrometer (CIRS) data acquired during the CASSINI flyby of Jupiter. Overall, the project has stayed on the development track outlined in the Year 1 annual report and most Year 2 goals have been met. The issues that have required the most attention are: implementation of the core photochemistry algorithms; implementation of a functional Java Graphical User Interface; completion of a functional CORBA Component Model framework; and assessment of performance issues. Specific accomplishments and the difficulties encountered are summarized in this report. Work to be carried out in the next year center on: completion of testing of the initial operational implementation; its application to analysis of the CASSINI/CIRS Jovian flyby data; extension of the PPMT to incorporate additional phenomenology algorithms; and delivery of a mature operational implementation.

Bishop, James↗
NASA NTRS2003

Support of Integrated Health Management (IHM) through Automated Analyses of Flowfield-Derived Spectrographic Data

Flow-field analysis techniques under continuing development at NASA's Marshall Space Flight Center are the foundation for a new type of health monitoring instrumentation for propulsion systems and a vast range of other applications. Physics, spectroscopy, mechanics, optics, and cutting-edge computer sciences merge to make recent developments in such instrumentation possible. Issues encountered in adaptation of such a system to future space vehicles, or retrofit in existing hardware, are central to the work. This paper is an overview of the collaborative efforts results, current efforts, and future plans.

Patrick, Marshall C.↗
NASA NTRSDate not supplied

Keeping Up With the Time(s): Advancing NASA’s Worldview to Facilitate Quick Access to Global Near Real-Time Imagery

Striving to keep up with the constantly changing environment, whether physical or computing, NASA Worldview’s interactive web mapping application continues to make improvements to ensure that avenues to near-real time imagery are quick, easy and accessible. This poster will explore recent additions to NASA Worldview and the Global Imagery Browse Services (GIBS) of near real-time imagery and imagery visualizations - from geostationary to flood mapping visualizations; improvements to access to imagery - from granule swath visualizations to vector interactions with fires and other types of vectors; and improvements to imagery discovery - from enhancements to the timeline to providing more ways to locate relevant imagery in the layer picker.

Min Minnie Wong↗
DOE OSTI2023

2022 American Conference on Neutron Scattering (ACNS 2022)

The 11th American Conference on Neutron Scattering (ACNS 2022) will be held on June 5-9, 2022, in Boulder, CO. The Conference will provide essential information on the breadth and depth of current neutron-related research worldwide. Hosted by the Neutron Scattering Society of America, this year’s Conference will feature a combination of invited and contributed talks, poster sessions, and tutorials. Topics of the conference are: Advances in Neutron Facilities, Instrumentation and Software: Developments in sources, instrumentation, sample environments and control software. Hard Condensed Matter: Magnetism, correlated metals, quantum/topological materials, superconductors, ferroelectrics, multiferroics, glasses, and disorder phenomena. Submissions outlining examples of neutron scattering in industrial and engineering applications involving hard condensed matter systems are also encouraged. Soft Matter: Neutron studies of soft materials and related fields including in situ and in operando studies. Polymers, surfactants, emulsions, gels, nanoparticles, colloidal suspensions and more. Submissions of computational studies or applications of machine learning beneficial to neutron scattering experiments, as well as examples of neutron scattering in industrial and engineering applications are strongly encouraged. Biology, Biophysics and Biotechnology: Neutron studies of biological and biologically relevant systems. Proteins, bio membranes, biological assemblies, natural materials, nucleic acids, drug-delivery platforms and biomedical systems. Submissions of computational studies or applications of machine learning beneficial to biological neutron scattering experiments, as well as examples of neutron scattering in applied research involving biological systems, are strongly encouraged. Materials Chemistry and Energy: Neutron-based studies of functional materials and materials for energy applications. Examples include porous materials such as metal organic frameworks (MOFs), zeolites; phosphors; novel pigments; electrolytes; catalysts; ionic conductors/cathode materials; photovoltaic materials (hybrid perovskites); thermoelectrics; magnetocalorics/electrocalorics. Structural Materials and Engineering: Neutron scattering studies of materials and engineering processes including structural materials, concrete and metals, as well as engineering processes including combustion, corrosion, additive manufacturing, and others. Neutron Physics: Fundamental physical studies of the neutron and related areas. Emerging Applications in Neutron Scattering: Machine Learning and Data Science: Advances in computing power have contributed to rapidly evolving machine learning and data science fields that can be leveraged to the benefit of the neutron scattering community. The purpose of this session is to highlight recent advances in machine learning and data science and to serve as the foundation of a parallel data and computation track highlighting computation advances and applications in neutron scattering throughout the conference.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A Brief Overview of Reactive Transport Codes Used in CO 2 Applications

Reactive transport modelling consists of computational and numerical models that describe the coupled physical, chemical, mechanical, and biological processes interacting with each other over a broad range of spatial and temporal scales. This report reviews some of the reactive transport codes available in the literature for carbon dioxide (CO 2 ) applications and can assist the scientific community with the applicability of each code to solve a specific problem. Approaches implemented in these codes can be categorized in three groups: (i) continuum-scale, (ii) pore-scale, and (iii) hybrid-scale approaches. Theoretical foundations, numerical implementations, and application examples using the models are described. In the continuum approach, flow and transport are formulated in terms of a representative control volume of the medium and coupled with geochemical reactions. The medium is characterized by bulk parameters such as porosity, permeability, or reactive surface area. In the pore-scale approach, each point of space in the pore network is occupied by either a fluid or solid phase. The pore-scale approach requires an exact knowledge of the spatial and temporal phase distribution. In the hybrid-scale approach, media are described by multiple characteristic length-scales, with some regions using a pore-scale approach while others used a continuum approach.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2021

Quantum Algorithm for High Energy Physics Simulations

Simulating quantum field theories is a flagship application of quantum computing. However, calculating experimentally relevant high energy scattering amplitudes entirely on a quantum computer is prohibitively difficult. It is well known that such high energy scattering processes can be factored into pieces that can be computed using well established perturbative techniques, and pieces which currently have to be simulated using classical Markov chain algorithms. These classical Markov chain simulation approaches work well to capture many of the salient features, but cannot capture all quantum effects. To exploit quantum resources in the most efficient way, we introduce a new paradigm for quantum algorithms in field theories. This approach uses quantum computers only for those parts of the problem which are not computable using existing techniques. In particular, we develop a polynomial time quantum final state shower that accurately models the effects of intermediate spin states similar to those present in high energy electroweak showers with a global evolution variable. The algorithm is explicitly demonstrated for a simplified quantum field theory on a quantum computer.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRSDate not supplied

Vision 2030 Aircraft Propulsion Grand Challenge Problem: Full-engine CFD Simulations with High Geometric Fidelity and Physics Accuracy

2014 NASA published the outcome of the 2030 CFD (Computational Fluid Dynamics) Vision study: “CFD Vision 2030: A path to Revolutionary Computational Aerosciences” . The study provided a comprehensive review of the state of the art of CFD in 2014 for aerospace applications including, but not limited to, numerical algorithms, physics models, MDAO (Multidisciplinary Design Analysis and Optimization) and HPC (High Performance Computing) hardware. The study also proposed four conceptual ideas of Grand Challenge problems that would build on and benefit from advances outlined in the roadmap. The proposed challenges were meant to foster more detailed descriptions of grand challenge problems for specific disciplines. One of the proposed challenges was in the gas turbine propulsion area, focusing on transient full engine simulations. The current paper addresses detailed technical aspects of that challenge, and proposes a plan to approach it in a gradual manner, which includes high fidelity modeling of components, component coupling, and targeted experimental campaigns relying on common research models.

turbine engine↗
DOE OSTI2021

Towards Predictive Plasma Science and Engineering through Revolutionary Multi-Scale Algorithms and Models (Final Report)

This report describes the high-level accomplishments from the Plasma Science and Engineering Grand Challenge LDRD at Sandia National Laboratories. The Laboratory has a need to demonstrate predictive capabilities to model plasma phenomena in order to rapidly accelerate engineering development in several mission areas. The purpose of this Grand Challenge LDRD was to advance the fundamental models, methods, and algorithms along with supporting electrode science foundation to enable a revolutionary shift towards predictive plasma engineering design principles. This project integrated the SNL knowledge base in computer science, plasma physics, materials science, applied mathematics, and relevant application engineering to establish new cross-laboratory collaborations on these topics. As an initial exemplar, this project focused efforts on improving multi-scale modeling capabilities that are utilized to predict the electrical power delivery on large-scale pulsed power accelerators. Specifically, this LDRD was structured into three primary research thrusts that, when integrated, enable complex simulations of these devices: (1) the exploration of multi-scale models describing the desorption of contaminants from pulsed power electrodes, (2) the development of improved algorithms and code technologies to treat the multi-physics phenomena required to predict device performance, and (3) the creation of a rigorous verification and validation infrastructure to evaluate the codes and models across a range of challenge problems. These components were integrated into initial demonstrations of the largest simulations of multi-level vacuum power flow completed to-date, executed on the leading HPC computing machines available in the NNSA complex today. These preliminary studies indicate relevant pulsed power engineering design simulations can now be completed in (of order) several days, a significant improvement over pre-LDRD levels of performance.

42 ENGINEERING↗
NASA NTRS2006

Time Triggered Protocol (TTP) for Integrated Modular Avionics

Traditional avionics computing systems are federated, with each system provided on a number of dedicated hardware units. Federated applications are physically separated from one another and analysis of the systems is undertaken individually. Integrated Modular Avionics (IMA) takes these federated functions and integrates them on a common computing platform in a tightly deterministic distributed real-time network of computing modules in which the different applications can run. IMA supports different levels of criticality in the same computing resource and provides a platform for implementation of fault tolerance through hardware and application redundancy. Modular implementation has distinct benefits in design, testing and system maintainability. This paper covers the requirements for fault tolerant bus systems used to provide reliable communication between IMA computing modules. An overview of the Time Triggered Protocol (TTP) specification and implementation as a reliable solution for IMA systems is presented. Application examples in aircraft avionics and a development system for future space application are covered. The commercially available TTP controller can be also be implemented in an FPGA and the results from implementation studies are covered. Finally future direction for the application of TTP and related development activities are presented.

Motzet, Guenter↗
DOE OSTI2020

Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors

Pattern recognition problems in high energy physics are notably different from traditional machine learning applications in computer vision. Reconstruction algorithms identify and measure the kinematic properties of particles produced in high energy collisions and recorded with complex detector systems. Two critical applications are the reconstruction of charged particle trajectories in tracking detectors and the reconstruction of particle showers in calorimeters. These two problems have unique challenges and characteristics, but both have high dimensionality, high degree of sparsity, and complex geometric layouts. Graph Neural Networks (GNNs) are a relatively new class of deep learning architectures which can deal with such data effectively, allowing scientists to incorporate domain knowledge in a graph structure and learn powerful representations leveraging that structure to identify patterns of interest. In this work we demonstrate the applicability of GNNs to these two diverse particle reconstruction problems.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2005

Proceedings of the 2004 NASA/ONR Circulation Control Workshop, Part 2

This conference proceeding is comprised of papers that were presented at the NASA/ONR Circulation Control Workshop held 16-17 March 2004 at the Radisson-Hampton in Hampton, VA. Over two full days, 30 papers and 4 posters were presented with 110 scientists and engineers in attendance, representing 3 countries. As technological advances influence the efficiency and effectiveness of aerodynamic and hydrodynamic applications, designs, and operations, this workshop was intended to address the technologies, systems, challenges and successes specific to Coanda driven circulation control in aerodynamics and hydrodynamics. A major goal of this workshop was to determine the state-of-the-art in circulation control and to assess the future directions and applications for circulation control. The 2004 workshop addressed applications, experiments, computations, and theories related to circulation control, emphasizing fundamental physics, systems analysis, and applied research. The workshop consisted of single session oral presentations, posters, and written papers that are documented in this unclassified conference proceeding. The format of this written proceeding follows the agenda of the workshop. Each paper is followed with the presentation given at the workshop. the editors compiled brief summaries for each effort that is at the end of this proceeding. These summaries include the paper, oral presentation, and questions or comments that occurred during the workshop. The 2004 Circulation Control Workshop focused on applications including Naval vehicles (Surface and Underwater vehicles), Fixed Wing Aviation (general aviation, commercial, cargo, and business aircraft); V/STOL platforms (helicopters, military aircraft, tilt rotors); propulsion systems (propellers, jet engines, gas turbines), and ground vehicles (automotive, trucks, and other); wind turbines, and other nontraditional applications (e.g., vacuum cleaner, ceiling fan). As part of the CFD focus area of the 2004 CC Workshop, CFD practitioners were invited to compute a two-dimensional benchmark problem for which geometry, flow conditions, grids, and experimental data were available before the workshop. The purpose was to accumulate a database of simulations for a single problem using a range of CFD codes, turbulence models, and grid strategies so as to expand knowledge of model performance/requirements and guide simulation of practical CC configurations.

Jones, Gregory S.↗
DOE OSTI2022

Unsupervised quantum circuit learning in high energy physics

Unsupervised training of generative models is a machine learning task that has many applications in scientific computing. Here, in this work, we evaluate the efficacy of using quantum circuit-based generative models to generate synthetic data of high energy physics processes. We use nonadversarial, gradient-based training of quantum circuit Born machines to generate joint distributions over two and three variables.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Physics Flash (Summer 2021)

The Summer 2021 issue of Physics Flash includes a note from Physics Division Leader Tanja Pietraß, Physics Division staff in the news, LANSCE neutron beam transport, novel application of 'computer vision' techniques and using pRad to visualize electromagnetic fields.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Emergence of p+ip superconductivity in two-dimensional doped Dirac systems

Searching for the $p + ip$ superconducting (SC) state has become a fascinating subject in condensed matter physics recently, as a dream application awaiting in topological quantum computation. Unfortunately, so far there is no universal principle for realizing $p + ip$ in generic solid-state systems. Here we report a theoretical discovery of a $p + ip$ SC ground state (coexisting with ferromagnetic order) in the honeycomb lattice Hubbard model in the extremely strong-coupling limit (e.g., infinite U) at low doping $(δ < 0.2)$, by using both the state-of-art Grassmann tensor product state approach and a continuum quantum field theory approach. Our discovery suggests a mechanism for the $p + ip$ SC state in generic strongly correlated systems based on spin-charge separation and the charge current–current coupling scenario, which opens a door towards experimental realization. The $p + ip$ SC state has an instability towards a potential non-Fermi liquid with a large but finite U . Nevertheless, by applying an in-plane Zeeman field, such a $p + ip$ SC state can be stabilized with finite U in a very wide range of doping. Relevant realistic materials are also proposed.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS1996

Computational Modeling of Multi-Phase/Multi-Species Flows with Applications to Liquid Rocket Engines

Accurate prediction of all physical phenomena in a combustion chamber is essential for better understanding of the system performance. Atomization, evaporation, combustion, chemical kinetics, and turbulence are those processes of great importance that need to be well understood. Processes involving the liquid phase in a combustion chamber will be further complicated under supercritical conditions. More advanced and accurate numerical techniques are required to extend our understanding of the above phenomena. A computer program for multi-species/multi-phase flow was developed for NASA/MSFC in 1992. This code, called Liquid Thrust Chamber Performance (LTCP) program takes an Eulerian- Eulerian approach and is based on the Total Variation Diminishing (TVD) technique with Lax-Friedrichs upwind method. Under the NASA/ASEE SFFP the LTCP code was used to predict the performance characteristics of several engines that were of particular interest to NASA. This code was also successful in a combustion detonation study. Converting the program to the PC platform was accomplished which extends usability and makes it available to a wider range of users. The Eulerian formulation of the liquid phase provides a suitable model that can be extended to include combustion modeling under supercritical conditions. The results have been compared against the ones of other codes and available measured data. The algorithm proved to be robust and efficient for problems with stiff source terms.

Navaz, Homayun K.↗