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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Multiphysics for nuclear energy applications using a cohesive computational framework

With the recent development of advanced numerical algorithms, software design, and low-cost high-performance computer hardware, reliance on coupled multiphysics to predict the behavior of complex physical systems is beginning to become standard practice. This is especially true in nuclear energy applications where strong nonlinear interdependencies exist between reactor physics, radiation transport, multi-scale nuclear fuels performance, thermal fluids, etc. Resolving these nonlinear dependencies requires choices in multiphysics software approaches. Two main multiphysics modeling and simulation approaches have emerged. The first is based upon "code coupling" where disparate physics codes of different software design, code languages, and spatial and temporal integration schemes are coupled together with relatively complex data passing interfaces. The second multiphysics software approach is to employ a "cohesive" framework where all physics applications are developed with a common software design, i.e., data structures, syntax, input format, integrated spatial and temporal discretization schemes, etc. In this paper we present the Multiphysics Object-Oriented Simulation Environment (MOOSE) development and runtime framework and describe the framework's cohesive modeling and simulation multiphysics approach. Then, a "cohesive-like" extension of the MOOSE framework is presented where MOOSE-based physics software applications are efficiently coupled to non-MOOSE (external) physics codes to form multiphysics applications using MOOSE's unique interface capabilities. Finally, several examples of MOOSE's cohesive and cohesive-like multiphysics applications will be demonstrated. These multiphysics demonstrations will incorporate both MOOSE-based applications and external codes, including Nek5000, RELAP-7, TRACE, BISON, and Pronghorn.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2019

Application of Machine Learning Techniques to Aviation Operations: Promises and Challenges

There is an increasing interest in applying methods based on Machine Learning Techniques (MLT) to problems in aviation operations. The current interest is based on developments in Cloud Computing, the availability of open software and the success of MLT in automation, consumer behavior and finance involving large database. This paper reviews the current-state-of-the art in applying MLT to aviation operations, its promises and challenges. Historically aviation operations have been analyzed using physics-based models and provide information for making operational decisions. This paper compares the methodology used in and issues to be addressed in applying either model-driven or data-driven methods. Some aviation operations involving many decision makers, multiple objectives, poor or unavailable physics-based models and a rich historical database are prime candidates for data-driven methods. The application of MLT to aviation operations falls into three categories: (a) based on the lack of a physics-based model, MLT is the favored approach, (b) marginal difference between regression methods using physics-based models and MLT and (c) better results using a blend of physics-based methods combined with MLT. As always, the best approach depends on the task, the physical understanding of the problem and the quality and quantity of the available data.

Sridhar, Banavar↗
NASA NTRS2020

Observations on the Application of Machine Learning Techniques to Aviation Operations

There is an increasing interest in applying methods based on Machine Learning Techniques (MLT) to problems in aviation operations. The current interest is based on developments in Cloud Computing, the availability of open software and the success of MLT in automation, consumer behavior and finance involving large database. Historically aviation operations have been analyzed using physics-based models and provide information for making operational decisions. This talk describes issues to be addressed in applying either model-driven or data-driven methods. Aviation operations involving many decision makers, multiple objectives, poor or unavailable physics-based models and a rich historical database are prime candidates for analysis using data-driven methods. The issues are illustrated by a detailed example and summary of current research in the area. The application of MLT to aviation operations falls into two categories: (a) based on the lack of a physics-based model, MLT is the favored approach and (b) marginal difference between regression methods using physics-based models and MLT. Further research is needed in the selection of MLT to critical aviation operations. As always, the best approach depends on the task, the physical understanding of the problem and the quality and quantity of the available data.

Sridhar, Banavar↗
NASA NTRS2020

Application of Machine Learning Techniques to Aviation Operations: A Case Study

There is an increasing interest in applying methods based on Machine Learning Techniques (MLT) to problems in aviation operations. The current interest is based on developments in Cloud Computing, the availability of open software and the success of MLT in automation, consumer behavior and finance involving large database. Historically aviation operations have been analyzed using physics-based models and provide information for making operational decisions. This talk describes issues to be addressed in applying either model-driven or data-driven methods. Aviation operations involving many decision makers, multiple objectives, poor or unavailable physics-based models and a rich historical database are prime candidates for analysis using data-driven methods. The issues are illustrated by a detailed example and summary of current research in the area. The application of MLT to aviation operations falls into two categories 58; (a) based on the lack of a physics-based model, MLT is the favored approach and (b) marginal difference between regression methods using physics-based models and MLT. Further research is needed in the selection of MLT to critical aviation operations. As always, the best approach depends on the task, the physical understanding of the problem and the quality and quantity of the available data.

Sridhar, Banavar↗
NASA NTRS2020

Application of Machine Learning Techniques to Aviation Operations: NASA Case Studies

There is an increasing interest in applying methods based on Machine Learning Techniques(MLT) to problems in aviation operations. The current interest is based on developments in Cloud Computing, the availability of open software and the success of MLT in automation, consumer behavior and finance involving large database. Historically aviation operations have been analyzed using physics-based models and provide information for making operational decisions. This talk describes issues to be addressed in applying either model-driven or data-driven methods. Aviation operations involving many decision makers, multiple objectives, poor or unavailable physics-based models and a rich historical database are prime candidates for analysis using data-driven methods. The issues relating to data, feature selection and validation of the models are illustrated by examining case studies of the application of MLT to problems in air traffic management at NASA. Further research is needed in the application of MLT to critical aviation operations. As always, the best approach depends on the task, the physical understanding of the problem and the quality and quantity of the available data.

Sridhar, Banavar↗
NASA NTRSDate not supplied

Lessons Learned in the Application of Machine Learning Techniques to Air Traffic Management

There is an increasing interest in applying methods based on Machine Learning Techniques (MLT) to problems in Air Traffic Management (ATM). The current interest is based on developments in Cloud Computing, the availability of open software and the success of MLT in automation, consumer behavior and finance involving large databases. This paper reviews the current-state-of-the art in applying MLT to aviation operations, its promises and challenges. Historically aviation operations have been analyzed using physics-based models and provide information for making operational decisions. Aviation operations involving many decision makers, multiple objectives, poor or unavailable physics-based models and a rich historical database are prime candidates for analysis using data-driven methods. The promises and challenges in applying MLT to ATM is traced through three examples based on the authors’ experience, each separated by a decade, to show the influence of data and feature selection in the successful application of MLT to ATM. As always, the best approach depends on the task, the physical understanding of the problem and the quality and quantity of the available data.

Machine Learning Techniques↗
NASA NTRS2008

Virtual Satellite

Virtual Satellite (VirtualSat) is a computer program that creates an environment that facilitates the development, verification, and validation of flight software for a single spacecraft or for multiple spacecraft flying in formation. In this environment, enhanced functionality and autonomy of navigation, guidance, and control systems of a spacecraft are provided by a virtual satellite that is, a computational model that simulates the dynamic behavior of the spacecraft. Within this environment, it is possible to execute any associated software, the development of which could benefit from knowledge of, and possible interaction (typically, exchange of data) with, the virtual satellite. Examples of associated software include programs for simulating spacecraft power and thermal- management systems. This environment is independent of the flight hardware that will eventually host the flight software, making it possible to develop the software simultaneously with, or even before, the hardware is delivered. Optionally, by use of interfaces included in VirtualSat, hardware can be used instead of simulated. The flight software, coded in the C or C++ programming language, is compilable and loadable into VirtualSat without any special modifications. Thus, VirtualSat can serve as a relatively inexpensive software test-bed for development test, integration, and post-launch maintenance of spacecraft flight software.

Hammrs, Stephan R.↗
DOE OSTI2024

Exploring the Feasibility of INCONEL® ALLOY 740H® for Power Plant Headers: Integrating Machine Learning with Computational Fluid Dynamics (CFD)

This keynote presentation explores the behavior of headers—essential components of pipeline systems—using ANSYS simulation software and machine learning techniques. The study aims to predict the thermal and mechanical performance of headers under diverse conditions through both steady-state and transient simulations. We investigate critical parameters such as heat transfer coefficient, fluid velocity, and temperature to optimize header design. Conducted as part of a DOE project led by NCAT in collaboration with UNC Charlotte, this research encompasses multiple key topics. The initial section focuses on the behavior of header systems under steady-state conditions using ANSYS simulation. It underscores the importance of headers in industrial infrastructure, especially in the energy sector, and examines the implications of material selection and flow direction on heat transfer dynamics. Methodologically, we employ Computational Fluid Dynamics (CFD) analysis through ANSYS, detailing the development of models, material properties, geometry specifications, boundary conditions, and meshing strategies. Our simulations explore various operational parameters, including temperature and mass flow rates, crucial for predicting heat transfer coefficients and enhancing header design. Results from the study include parametric investigations into mesh sensitivity, viscosity model evaluations, and the effects of heat transfer locations, all validated against theoretical calculations. We conclude with insights on mesh optimization, the suitability of viscosity models, and recommendations for future research aimed at improving header system efficiency and sustainability in industrial applications.

20 FOSSIL-FUELED POWER PLANTS↗
NASA NTRS2002

Human performance cognitive-behavioral modeling: a benefit for occupational safety

Human Performance Modeling (HPM) is a computer-aided job analysis software methodology used to generate predictions of complex human-automation integration and system flow patterns with the goal of improving operator and system safety. The use of HPM tools has recently been increasing due to reductions in computational cost, augmentations in the tools' fidelity, and usefulness in the generated output. An examination of an Air Man-machine Integration Design and Analysis System (Air MIDAS) model evaluating complex human-automation integration currently underway at NASA Ames Research Center will highlight the importance to occupational safety of considering both cognitive and physical aspects of performance when researching human error.

Occupational Health↗
DOE OSTI2014

Data Movement Dominates: Advanced Memory Technology to Address the Real Exascale Power Problem

Energy is the fundamental barrier to Exascale supercomputing and is dominated by the cost of moving data from one point to another, not computation. Similarly, performance is dominated by data movement, not computation. The solution to this problem requires three critical technologies: 3D integration, optical chip-to-chip communication, and a new communication model. The central goal of the Sandia led "Data Movement Dominates" project aimed to develop memory systems and new architectures based on these technologies that have the potential to lower the cost of local memory accesses by orders of magnitude and provide substantially more bandwidth. Only through these transformational advances can future systems reach the goals of Exascale computing with a manageable power budgets. The Sandia led team included co-PIs from Columbia University, Lawrence Berkeley Lab, and the University of Maryland. The Columbia effort of Data Movement Dominates focused on developing a physically accurate simulation environment and experimental verification for optically-connected memory (OCM) systems that can enable continued performance scaling through high-bandwidth capacity, energy-efficient bit-rate transparency, and time-of-flight latency. With OCM, memory device parallelism and total capacity can scale to match future high-performance computing requirements without sacrificing data-movement efficiency. When we consider systems with integrated photonics, links to memory can be seamlessly integrated with the interconnection network-in a sense, memory becomes a primary aspect of the interconnection network. At the core of the Columbia effort, toward expanding our understanding of OCM enabled computing we have created an integrated modeling and simulation environment that uniquely integrates the physical behavior of the optical layer. The PhoenxSim suite of design and software tools developed under this effort has enabled the co-design of and performance evaluation photonics-enabled OCM architectures on Exascale computing systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

BMX: Biological modelling and interface exchange

Abstract High performance computing has a great potential to provide a range of significant benefits for investigating biological systems. These systems often present large modelling problems with many coupled subsystems, such as when studying colonies of bacteria cells. The aim to understand cell colonies has generated substantial interest as they can have strong economic and societal impacts through their roles in in industrial bioreactors and complex community structures, called biofilms, found in clinical settings. Investigating these communities through realistic models can rapidly exceed the capabilities of current serial software. Here, we introduce BMX, a software system developed for the high performance modelling of large cell communities by utilising GPU acceleration. BMX builds upon the AMRex adaptive mesh refinement package to efficiently model cell colony formation under realistic laboratory conditions. Using simple test scenarios with varying nutrient availability, we show that BMX is capable of correctly reproducing observed behavior of bacterial colonies on realistic time scales demonstrating a potential application of high performance computing to colony modelling. The open source software is available from the zenodo repository https://doi.org/10.5281/zenodo.8084270 under the BSD-2-Clause licence.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Numerical simulation projects in micromagnetics with Jupyter

We report a case study where an existing materials science course was modified to include numerical simulation projects on the micromagnetic behavior of materials. The Ubermag micromagnetic simulation software package is used in order to solve problems computationally. The simulation software is controlled through the Python code in Jupyter notebooks. Our experience is that the self-paced problem-solving nature of the project work can facilitate a better in-depth exploration of the course contents. We discuss which aspects of the Ubermag and the project Jupyter ecosystem have been beneficial for the students' learning experience and which could be transferred to similar teaching activities in other subject areas.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1994

Safety aspects of spacecraft commanding

The commanding of spacecraft is a potentially hazardous activity for the safety of the spacecraft. Present day control systems contain safety features in their commanding subsystem and in addition, strict procedures are also followed by operations staff. However, problems have occurred on a number of missions as a result of erroneous commanding leading in some cases to spacecraft contingencies and even to near loss of the spacecraft. The problems of checking commands in advance are increased by the tendency in modern spacecraft to use blocked/time-tagged commands and the increased usage of on-board computers, for which commands changing on-board software tables can radically change spacecraft or subsystem behavior. This paper reports on an on-going study. The study aims to improve the approach to safety of spacecraft commanding. It will show how ensuring 'safe' commanding can be carried out more efficiently, and with greater reliability, with the help of knowledge based systems and/or fast simulators. The whole concept will be developed based on the Object-Oriented approach.

Peccia, N.↗
DOE OSTI2022

Traveler: Navigating Task Parallel Traces for Performance Analysis

Understanding the behavior of software in execution is a key step in identifying and fixing performance issues. This is especially important in high performance computing contexts where even minor performance tweaks can translate into large savings in terms of computational resource use. To aid performance analysis, developers may collect an execution trace —a chronological log of program activity during execution. As traces represent the full history, developers can discover a wide array of possibly previously unknown performance issues, making them an important artifact for exploratory performance analysis. However, interactive trace visualization is difficult due to issues of data size and complexity of meaning. Traces represent nanosecond-level events across many parallel processes, meaning the collected data is often large and difficult to explore. The rise of asynchronous task parallel programming paradigms complicates the relation between events and their probable cause. Here, to address these challenges, we conduct a continuing design study in collaboration with high performance computing researchers. We develop diverse and hierarchical ways to navigate and represent execution trace data in support of their trace analysis tasks. Through an iterative design process, we developed Traveler , an integrated visualization platform for task parallel traces. Traveler provides multiple linked interfaces to help navigate trace data from multiple contexts. We evaluate the utility of Traveler through feedback from users and a case study, finding that integrating multiple modes of navigation in our design supported performance analysis tasks and led to the discovery of previously unknown behavior in a distributed array library.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1993

Improved inhalation technology for setting safe exposure levels for workplace chemicals

Threshold Limit Values recommended as allowable air concentrations of a chemical in the workplace are often based upon a no-observable-effect-level (NOEL) determined by experimental inhalation studies using rodents. A 'safe level' for human exposure must then be estimated by the use of generalized safety factors in attempts to extrapolate from experimental rodents to man. The recent development of chemical-specific physiologically-based toxicokinetics makes use of measured physiological, biochemical, and metabolic parameters to construct a validated model that is able to 'scale-up' rodent response data to predict the behavior of the chemical in man. This procedure is made possible by recent advances in personal computer software and the emergence of appropriate biological data, and provides an analytical tool for much more reliable risk evaluation and airborne chemical exposure level setting for humans.

Stuart, Bruce O.↗