Search NASA⌕ Search

SEARCH · Search NASA

Results for “mathematics and computing”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

Automation of closed environments in space for human comfort and safety

This report culminates the work accomplished during a three year design project on the automation of an Environmental Control and Life Support System (ECLSS) suitable for space travel and colonization. The system would provide a comfortable living environment in space that is fully functional with limited human supervision. A completely automated ECLSS would increase astronaut productivity while contributing to their safety and comfort. The first section of this report, section 1.0, briefly explains the project, its goals, and the scheduling used by the team in meeting these goals. Section 2.0 presents an in-depth look at each of the component subsystems. Each subsection describes the mathematical modeling and computer simulation used to represent that portion of the system. The individual models have been integrated into a complete computer simulation of the CO2 removal process. In section 3.0, the two simulation control schemes are described. The classical control approach uses traditional methods to control the mechanical equipment. The expert control system uses fuzzy logic and artificial intelligence to control the system. By integrating the two control systems with the mathematical computer simulation, the effectiveness of the two schemes can be compared. The results are then used as proof of concept in considering new control schemes for the entire ECLSS. Section 4.0 covers the results and trends observed when the model was subjected to different test situations. These results provide insight into the operating procedures of the model and the different control schemes. The appendix, section 5.0, contains summaries of lectures presented during the past year, homework assignments, and the completed source code used for the computer simulation and control system.

Source record↗
DOE OSTI2012

Final Reports from the Los Alamos National Laboratory Computational Physics Student Summer Workshop

The two primary purposes of LANL’s Computational Physics Student Summer Workshop are (1) To educate graduate and exceptional undergraduate students in the challenges and applications of computational physics of interest to LANL, and (2) Entice their interest toward those challenges. Computational physics is emerging as a discipline in its own right, combining expertise in mathematics, physics, and computer science. The mathematical aspects focus on numerical methods for solving equations on the computer as well as developing test problems with analytical solutions. The physics aspects are very broad, ranging from low-temperature material modeling to extremely high temperature plasma physics, radiation transport and neutron transport. The computer science issues are concerned with matching numerical algorithms to emerging architectures and maintaining the quality of extremely large codes built to achieve multi-physics calculations. Although graduate programs associated with computational physics are emerging, it is apparent that the pool of U.S. citizens in this multidisciplinary field is relatively small and is typically not focused on the aspects that are of primary interest to LANL. Furthermore, more structured foundations for LANL interaction with universities in computational physics is needed; currently interactions rely almost solely on individuals’ personalities and personal contacts. Thus a tertiary purpose of the Summer Workshop is to build an educational network of LANL researchers, university professors, and emerging students to advance the field and LANL’s involvement in it. This was the second year for the Summer Workshop. Like the previous year, the workshop’s goals were achieved by bringing into LANL a select group of students recruited from across the United States and immersing them for ten weeks in lectures and interesting research projects. The lectures provided an overview of the computational physics topics of interest this year along with some detailed instruction while the projects gave the students a positive experience accomplishing technical goals. Each team consisted of two students working under one or more mentors from LANL on specific research projects associated with predefined topics. This year, the topics were on verification of simulations in plasma and radiation-hydrodynamics codes, hypervelocity material deformation, plasma physics, graphical processor unit (“GPU”) programming, multi-material mixing, turbulence modeling, computational material science, and electromagnetic pulse simulations. The students’ growth was furthered by their participation on teams where their teammates were sometimes of different academic rank. It also developed them by requiring them to produce written and oral reports that they presented to peers, mentors, and management.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Selection and Assessment of Phenomenological Models of Tumor Growth

We address general approaches to the rational selection and validation of mathematical and computational models of tumor growth using methods of Bayesian inference. The model classes are derived from a general diffuse-interface, continuum mixture theory and focus on mass conservation of mixtures with up to four species. Synthetic data are generated using higher-order base models. We discuss general approaches to model calibration, validation, plausibility, and selection based on Bayesian-based methods, information theory, and maximum information entropy. We also address computational issues and provide numerical experiments based on Markov chain Monte Carlo algorithms and high performance computing implementations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

2HOT: An improved parallel hashed oct-tree N-body algorithm for cosmological simulation

Here we report on improvements made over the past two decades to our adaptive treecode N-body method (HOT). A mathematical and computational approach to the cosmological N-body problem is described, with performance and scalability measured up to 256k (218) processors. We present error analysis and scientific application results from a series of more than ten 69 billion (40963) particle cosmological simulations, accounting for 4×1020 floating point operations. These results include the first simulations using the new constraints on the standard model of cosmology from the Planck satellite. Our simulations set a new standard for accuracy and scientific throughput, while meeting or exceeding the computational efficiency of the latest generation of hybrid TreePM N-body methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Models and Methods for Sparse (Hyper)Network Science in Business, Industry, and Government

The authors are hosting an AMS sponsored Mathematics Research Community (MRC) focusing on two themes that have garnered intense attention in network models of complex relational data: (1) how to faithfully model multi-way relations in hypergraphs, rather than only pairwise interactions in graphs; and (2) challenges posed by modelling networks with extreme sparsity. Here we introduce and explore these two themes and their challenges. In this work, we hope to generate interest from researchers in pure and applied mathematics and computer science.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

2019 Computing Sciences Strategic Plan

Computing has transformed nearly every aspect of scientific inquiry — across disciplines and across scales — from the behavior of subatomic particles to the formation of structures in the early universe, from the assembly of the human genome to the evolution of earth systems. Over the past two decades, computing has become an integral part of how Berkeley Lab is “Bringing Science Solutions to the World.” Advances in computing and mathematics have been key, with new mathematical models of complex physical phenomena, new methods for analyzing complex data, new algorithms for accuracy and scaling and sophisticated software systems that encapsulate these techniques into open, reusable tools. The performance of NERSC computers and the ESnet network have grown by several orders of magnitude, along with our understanding of how to map scientific computations and workflows onto these systems. From research to facility operations, the passion, talent and dedication of the Computing Sciences Area staff has been the cornerstone of our success. The plan outlined in this document describes the next step in a journey to expand the influence and impact of our efforts, building an increasingly connected global enterprise for science that places more powerful instruments in the hands of scientists, along with more powerful methods and tools for modeling, analysis and prediction.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

SIERRA Multimechanics Module: Aria Thermal Theory Manual (Version 4.46)

This document describes the theoretical foundation of thermal analysis in Sierra Mechanics. The SIERRA Multimechanics Module: Aria, henceforth referred to as Aria for brevity, was developed at Sandia National Laboratories under the ASC program, and approximates linear and nonlinear continuum models of heat transfer. Aria uses the SIERRA Framework, which provides data management services commonly required by computational mechanics software, and facilitates the development of coupled, multi-mechanics applications for massively parallel computers. The mathematical models in Aria are based heavily on those of COYOTE, a well-established thermal analysis program that was also developed at Sandia and its ASC code predecessor, Calore. Aria, Calore and COYOTE share a significant body of numerical methods, which are described in detail by Reddy and Gartling. Throughout this document, the terms software and implementation are synonymous with the Aria thermal-fluid analysis computer program.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Scalar Field Comparison with Topological Descriptors: Properties and Applications for Scientific Visualization

In topological data analysis and visualization, topological descriptors such as persistence diagrams, merge trees, contour trees, Reeb graphs, and Morse–Smale complexes play an essential role in capturing the shape of scalar field data. Herein we present a state–of–the–art report on scalar field comparison using topological descriptors. We provide a taxonomy of existing approaches based on visualization tasks associated with three categories of data: single fields, time–varying fields, and ensembles. These tasks include symmetry detection, periodicity detection, key event/feature detection, feature tracking, clustering, and structure statistics. Our main contributions include the formulation of a set of desirable mathematical and computational properties of comparative measures, and the classification of visualization tasks and applications that are enabled by these measures.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

SciDAC Visualization and Analytics Center for Enabling Technologies

This project focuses on leveraging scientific visualization and analytics software technology as an enabling technology for increasing scientific productivity and insight. Advances in computational technology have resulted in an "information big bang," which in turn has created a significant data understanding challenge. This challenge is widely acknowledged to be one of the primary bottlenecks in contemporary science. The vision for our Center is to respond directly to that challenge by adapting, extending, creating when necessary and deploying visualization and data understanding technologies for our science stakeholders. Using an organizational model as a Visualization and Analytics Center for Enabling Technologies (VACET), we are well positioned to be responsive to the needs of a diverse set of scientific stakeholders in a coordinated fashion using a range of visualization, mathematics, statistics, computer and computational science and data management technologies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

AI-based design of a nuclear reactor core

The authors developed an artificial intelligence (AI)-based algorithm for the design and optimization of a nuclear reactor core based on a flexible geometry and demonstrated a 3× improvement in the selected performance metric: temperature peaking factor. The rapid development of advanced, and specifically, additive manufacturing (3-D printing) and its introduction into advanced nuclear core design through the Transformational Challenge Reactor program have presented the opportunity to explore the arbitrary geometry design of nuclear-heated structures. The primary challenge is that the arbitrary geometry design space is vast and requires the computational evaluation of many candidate designs, and the multiphysics simulation of nuclear systems is very time-intensive. Therefore, the authors developed a machine learning-based multiphysics emulator and evaluated thousands of candidate geometries on Summit, Oak Ridge National Laboratory’s leadership class supercomputer. The results presented in this work demonstrate temperature distribution smoothing in a nuclear reactor core through the manipulation of the geometry, which is traditionally achieved in light water reactors through variable assembly loading in the axial direction and fuel shuffling during refueling in the radial direction. The conclusions discuss the future implications for nuclear systems design with arbitrary geometry and the potential for AI-based autonomous design algorithms.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2019

[Office of Basic Energy Sciences (BES)] Roundtable on Producing and Managing Large Scientific Data with Artificial Intelligence and Machine Learning

To identify specific Priority Research Opportunities (PROs) for artificial intelligence and machine learning (AI/ML) at the US Department of Energy’s scientific user facilities, the Office of Basic Energy Sciences (BES) convened a roundtable of facility experts encompassing the fields of physics, chemistry, materials synthesis science, computational science, detector and accelerator technology, theory, modeling, simulation, and atomic-scale characterization techniques. The roundtable met on October 22–23, 2019, to identify coordinated, long-term AI/ML research efforts that will drive major advances in neutron, photon, and nanoscale sciences. This report describes the four PROs identified at the roundtable: PRO 1 on how AI/ML can extract high-value information from the large datasets; PRO 2 on how AI/ML can use such information in real time to maximize the facilities’ scientific output; PRO 3 on using AI/ML virtual laboratories (i.e., computational models of experimental facilities) to aid the facilities and user community in design and control of machine parameters and the design and execution of experiments, including training AI/ML models for PROs 1 and 2; and PRO 4 on how a shared scientific data infrastructure can provide tools to assemble and analyze the totality of data coming from user facilities. A section of the end of the report provides a summary on computer science and mathematics that highlights where enhanced AI/ML capabilities could be particularly impactful for BES user facilities.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗