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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Fuel-Flexible Combustion System for Refinery and Chemical Plant Process Heaters

This project culminated in the demonstration of a full-scale industrial burner which allows a broad range of “opportunity” gaseous fuels to be cost-effectively and efficiently utilized while generating minimal emissions of criteria air pollutants. The burner is capable of maintaining a stable flame when the fuel composition changes rapidly. This enhanced stability will contribute significantly to improving the safety and reliability of burner operation in manufacturing sites. Process heating in the refining and chemicals sectors is the primary application for this burner. The refining and chemical sectors account for more than 40% of total industrial natural gas use. Prior to the completion of this project, an enabling technology did not exist that would allow these energy-intensive industries to take full advantage of opportunity fuels and thereby reduce their natural gas consumption. Opportunity gaseous fuels include biogas (from animal and agricultural wastes, wastewater plants, and landfills) as well as syngas (from the gasification of biomass, municipal solid wastes, construction wastes, and refinery residuals). The primary challenge to using gaseous opportunity fuels is that their composition and combustion performance differ significantly from those of conventional fuels such as natural gas and refinery fuel gas. An effective fuel-flexible burner must accept fuels that range widely in quality and change in composition over time, often rapidly. In Phase 1 of this project, the team applied computational fluid dynamics analysis to optimize the prototype burner’s aerodynamic, combustion, heat transfer, and emissions performance. In Phase 2, full-scale testing and refinement of two prototype burners were conducted in test furnaces at Zeeco’s offices in Broken Arrow, OK. These tests demonstrated that the full range of conventional and opportunity fuels could be utilized by the project’s burner while achieving robust flame stability and very low levels of air pollutant emissions. In Phase 3, the team retrofitted three fuel-flexible burners into a fired heater at a Shell plant and demonstrated the project’s technology over a 6-month period. The project burners performed well during this period. They remain in commercial service at the Shell plant. Through this work, an improved understanding of flame stabilization mechanisms was gained. Also, methods for accommodating a wide range of fuel compositions were developed. This knowledge facilitated the commercialization of a new generation of burners that are suitable for the fuels of the future.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Unpinning the Lynchpin: Development of a system to introduce redundancy at Department of Energy laboratories

The United States Department of Energy (DOE) laboratory system employs over 78,000 people and had a budget of $8.1 billion for Fiscal Year 2023 (FY23). This diverse workforce includes employees at all levels from operational support staff to senior level management. Together, the labs take on incredibly complex and unique technical challenges in an attempt to address fundamental National and global needs. These technical challenges often require specialized training such that only a single person is truly capable of completing specific tasks. With the modern competitive environment, and an aging workforce, the risk of technical capability loss is highly probable with potentially severe impact. An online survey of Lawrence Livermore National Laboratories (LLNL) employees, a typical DOE lab, found that of responders with greater than 10 years of experience, 100% of respondents felt burnt out, 40% felt they were not paid fairly, and 50% believe that not enough is done by leadership to retain them as an employee. Couple that to an aging workforce, and the risk of losing institutional knowledge due to single point of failure, or lynchpin, is significant. There is a need for a system which reduces the risk of operational and technical losses due to lynchpin employees leaving the organization. A mitigation of this risk would lead to the successful transfer of knowledge from one generation to the next, improving the rate of development and minimizing general risk of mistakes.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2015

Elasticity of crystalline molecular explosives

Crystalline molecular explosives are key components of engineered explosive formulations. In precision applications a high degree of consistency and predictability is desired under a range of conditions to a variety of stimuli. Prediction of behaviors from mechanical response and failure to detonation initiation and detonation performance of the material is linked to accurate knowledge of the material structure and first stage of deformation: elasticity. The elastic response of pentaerythritol tetranitrate (PETN), cyclotrimethylene trinitramine (RDX), and cyclotetramethylene tetranitramine (HMX), including aspects of material and measurement variability, and computational methods are described in detail. Experimental determinations of elastic tensors are compared, and an evaluation of sources of error is presented. Furthermore, computed elastic constants are also compared for these materials and for triaminotrinitrobenzene (TATB), for which there are no measurements.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Kinetic modeling study of surrogate components for gasoline, jet and diesel fuels: C7-C11 methylated aromatics

Kinetic mechanisms for aromatics are needed to successfully simulate the autoignition of transportation fuels using the surrogate fuel approach. An aromatic detailed kinetic mechanism that describes kinetics of C7-C11 methylated aromatics, including toluene, o-xylene, p-xylene, 1,2,4-trimethylbenzene, 1,3,5, trimethyl-benzene and alpha-methylnaphthalene has been developed in the current study. The kinetic mechanism was built hierarchically using similar set of reaction pathways and reaction rate rules. In the mechanism developed, special emphasis is put on describing the detailed low-temperature ignition chemistry of o-xylene and 1,2,4-trimethylbenzene and, to our knowledge, this is the first attempt to do so in a detailed kinetic mechanism. In addition to kinetic modeling, new experimental data were acquired for toluene, o-xylene, and 1,2,4-trimethylbenzene using a rapid compression machine at low-to-intermediate temperatures and engine relevant pressures. In addition, the mechanism has been compared against data sets from the literature covering ignition delay times, flame speeds, and speciation profiles measured in a jet-stirred reactor and flow reactor. Good agreement is observed between the mechanism predictions and the experimental data. Kinetic analysis demonstrated the importance of including the low temperature chemistry of the benzylperoxy radicals to accurately capture the ignition propensity of o-xylene and 1,2,4-trimethylbenzene at low-to-intermediate temperatures and high pressures. The kinetic mechanism developed in the current study can be used for surrogate modeling of gasoline, jet and diesel fuels. (C) 2018 The Combustion Institute. Published by Elsevier Inc. All rights reserved.

02 PETROLEUM↗
DOE OSTI2020

Emerging materials intelligence ecosystems propelled by machine learning

We report that the age of cognitive computing and artificial intelligence (AI) is just dawning. Inspired by its successes and promises, several AI ecosystems are blossoming, many of them within the domain of materials science and engineering. These materials intelligence ecosystems are being shaped by several independent developments. Machine learning (ML) algorithms and extant materials data are utilized to create surrogate models of materials properties and performance predictions. Materials data repositories, which fuel such surrogate model development, are mushrooming. Automated data and knowledge capture from the literature (to populate data repositories) using natural language processing approaches is being explored. The design of materials that meet target property requirements and of synthesis steps to create target materials appear to be within reach, either by closed-loop active-learning strategies or by inverting the prediction pipeline using advanced generative algorithms. AI and ML concepts are also transforming the computational and physical laboratory infrastructural landscapes used to create materials data in the first place. Surrogate models that can outstrip physics-based simulations (on which they are trained) by several orders of magnitude in speed while preserving accuracy are being actively developed. Automation, autonomy and guided high-throughput techniques are imparting enormous efficiencies and eliminating redundancies in materials synthesis and characterization. The integration of the various parts of the burgeoning ML landscape may lead to materials-savvy digital assistants and to a human-machine partnership that could enable dramatic efficiencies, accelerated discoveries and increased productivity. Here, we review these emergent materials intelligence ecosystems and discuss the imminent challenges and opportunities. The materials research landscape is being transformed by the infusion of approaches based on machine learning. This Review discusses the emerging materials intelligence ecosystems and the potential of human-machine partnerships for fast and efficient virtual materials screening, development and discovery.

36 MATERIALS SCIENCE↗
DOE OSTI2014

TH-E-BRD-01: Innovation in (gold) Nanoparticle-Enhanced Therapy

Radiation therapy relies on the concept of delivering high dose to tumor volumes whilst simultaneously aiming to minimize irradiation of healthy tissue. Gold and other metallic nanoparticles (GNPs) have the potential to greatly enhance dose depositions in their close proximity. While it was originally thought that this effect would only be significant for kV photon beams, it has been shown that GNPs also enhance dose and increase cell killing and survival fraction for MV photons as well as protons. GNPs have been shown to be preferentially taken up in tumors, depending on the GNP properties either internalized in the tumor cells or clustering in the tumor vasculature. Therefore GNPs offer an intriguing additional option to target the tumor while sparing healthy tissue. While a growing amount of research shows GNP induced enhancement factors in the order of 1.5 and higher, GNPs have not yet entered into clinical routine. In this symposium we will have three presentations discussing the current status of GNP based research, the potential to include GNPs in radiation therapy and the limitations and problems to use GNPs in the clinic. Physical and biological underpinnings of radiosensitization with gold nano particles An evolving body of recent literature alludes to the potential to sensitize tumors to radiation therapy using metallic nanoparticles. In preclinical studies, the techniques that hold promise for eventual clinical deployment are nanoparticle-assisted radiation dose enhancement and hyperthermic radiosensitization. To understand the underlying nanoparticle-radiation interactions, computational techniques offer an explanation for and predict the biophysical consequences at a nano-/meso-scopic scale. Nonetheless, there are persisting gaps in knowledge relating to the molecular mechanism of action of these radiosensitization approaches — some of these issues will be addressed. Since the literature relating to the diverse disciplines involved in these efforts spans across multiple specialties (clinical radiation oncology, radiation physics, radiation biology, nanotechnology, material science, biomedical engineering, pharmacology, chemistry, and tumor biology) and numerous specialty journals, there is no single compilation of extant research in this arena or forum for merging analogous concepts and paradigms. This symposium will provide such a venue — my presentation will start with familiarizing the audience with the potential applications of metallic nanoparticles in radiation therapy using specific illustrative examples and begin to explore ways to understand the underlying mechanisms of the effects observed. Biological effects of Gold nanoparticles in radiation therapy Gold nanoparticles (GNP) have been investigated as platforms to carry drugs or radio-sensitizing agents to tumors due to the biocompatibility of gold and relative ease of conjugation with therapeutic and targeting moieties. Recently, there has been interest in exploiting the physical properties of gold, specifically the high atomic number, to enhance radiation therapy. When irradiated, gold atoms will produce low energy electrons, depositing energy within a short distance. The ratio of dose deposited in the presence of the GNP to the dose deposited in the absence of GNP is referred to as the dose enhancement factor (DEF). This factor has been shown to depend on the concentration of GNP and the energy of the incident photons. The physics of this process, preliminary in vitro and in vivo experiments and future directions for this nascent field are described in this presentation. Gold Nanoparticles for improved therapeutic outcome in radiation therapy The application of nanoparticles (NPs) for improved therapeutics is at the forefront of cancer nanotechnology. Among other NP systems, gold nanoparticles (GNPs) are extensively used due to its impressive ability to act as both an anticancer drug carrier in chemotherapy and as a dose enhancer in radiotherapy. Cellular uptake of GNPs was dependent on their size. Among GNPs of diameter between 14–74 nm, GNPs of size 50 nm has the highest uptake. Radiosensitization was dependent on the size of the GNPs as well. GNPs of size 50-nm showed the highest radiosensitization enhancement factor compared to GNPs of 14 and 74 nm for lower- (105 kVp) and higher- (6 MVp) energy photons. GNPs used in those studies were predominantly localized in the cell cytoplasm. However, the therapeutic response can be further enhanced if NPs can be effectively targeted into the nucleus. Here, we present an effective strategy for designing a GNP-peptide complex for nuclear targeting. Two peptides were conjugated onto a GNP: One peptide enhanced the uptake while the other peptide enhanced the nuclear delivery. The nuclear-targeted cells displayed a fourfold increase in the therapeutic response when treated with radiation as compared to untargeted ones. DNA double-strand breaks were quantified using radiation-induced foci of γ- H2AX and 53BP1, and a modest increase in the number of foci per nucleus was observed in irradiated cell populations with internalized GNPs. This research will establish a more aggressive NP-based treatment approach for improved outcome in cancer therapy. Learning Objectives: Introduce radiosensitization concepts of metallic nanoparticle and provide the theoretical basis Provide an overview over the size and coating dependence for GNP uptake in cells Provide a compilation of the extant, multi-discipline research on metallic nanoparticles Understand the prospects for future studies and innovations and the potential for applications of metallic nanoparticles in radiation therapy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Characterization and identification of HPC applications at leadership computing facility

High Performance Computing (HPC) is an important method for scientific discovery via large-scale simulation, data analysis, or artificial intelligence. Leadership-class supercomputers are expensive, but essential to run large HPC applications. The Petascale era of supercomputers began in 2008, with the first machines achieving performance in excess of one petaflops, and with the advent of new supercomputers in 2021 (e.g., Aurora, Frontier), the Exascale era will soon begin. However, the high theoretical computing capability (i.e., peak FLOPS) of a machine is not the only meaningful target when designing a supercomputer, as the resources demand of applications varies. A deep understanding of the characterization of applications that run on a leadership supercomputer is one of the most important ways for planning its design, development and operation. In order to improve our understanding of HPC applications, user demands and resource usage characteristics, we perform correlative analysis of various logs for different subsystems of a leadership supercomputer. This analysis reveals surprising, sometimes counter-intuitive patterns, which, in some cases, conflicts with existing assumptions, and have important implications for future system designs as well as supercomputer operations. For example, our analysis shows that while the applications spend significant time on MPI, most applications spend very little time on file I/O. Combined analysis of hardware event logs and task failure logs show that the probability of a hardware FATAL event causing task failure is low. Combined analysis of control system logs and file I/O logs reveals that pure POSIX I/O is used more widely than higher level parallel I/O. Based on holistic insights of the application gained through combined and co-analysis of multiple logs from different perspectives and general intuition, we engineer features to "fingerprint" HPC applications. We use t-SNE (a machine learning technique for dimensionality reduction) to validate the explainability of our features and finally train machine learning models to identify HPC applications or group those with similar characteristic. To the best of our knowledge, this is the first work that combines logs on file I/O, computing, and inter-node communication for insightful analysis of HPC applications in production.

Liu, Zhengchun↗
DOE OSTI2026

SEED: Semantic Energy Exploration and Discovery

The Bioenergy Knowledge Discovery Framework (KDF) hosts a vast repository of specialized data, yet traditional keyword-based search methods often struggle to provide direct answers, requiring significant domain expertise and manual effort to filter through raw documents. To overcome these barriers, this software introduces a semantic search engine that enables both specialists and non-specialists to query the KDF using natural language. By shifting from rigid keyword matching to intent-based retrieval, the tool automatically identifies and ranks the most relevant sources within the database. The system functions by processing natural language queries to extract the most pertinent information, delivering an AI-generated plain-language summary alongside exact supporting quotes from retrieved documents. This integrated approach provides users with immediate, evidence-based answers while eliminating the need for exhaustive manual review. By surfacing direct insights and contextual evidence, the software enhances the usability of existing KDF resources and democratizes access to complex bioenergy data. Ultimately, this semantic search solution accelerates the discovery process and supports faster, more informed decision-making across the bioenergy sector.

Pan, Meiyu (Melrose) [Oak Ridge National Laborator↗
DOE OSTI2020

Characterizing Thunderstorm Gust Fronts near Complex Terrain

Fire safety, aviation, wind energy, and structural-engineering operations are impacted by thunderstorm outflow boundaries or gust fronts (GFs) particularly when they occur in mountainous terrain. For example, during the 2013 Arizona Yarnell Hill Fire, 19 firefighters were killed as a result of sudden changes in fire behavior triggered by a passing GF. Knowledge of GF behavior in complex terrain also determines departure and landing operations at nearby airports, and GFs can induce exceptional structural loads on wind turbines. Furthermore, while most examinations of GF characteristics focus on well-organized convection in areas such as the Great Plains, here the investigation is broadened to explore GF characteristics that evolve near the complex terrain of the Colorado Rocky Mountains. Using in situ observations from meteorological towers, as well as data from wind-profiling lidars and a microwave radiometer, 24 GF events are assessed to quantify changes in wind, temperature, humidity, and turbulence in the lowest 300 m AGL as these GFs passed over the instruments. The changes in magnitude for all variables are on average weaker in the Colorado Front Range than those typically observed from organized, severe storms in flatter regions. Most events from this study experience an increase in wind speed from 1 to 8 m s -1 , relative humidity from 1% to 8%, and weak vertical motion from 0.3 to 3.6 m s -1 during GF passage while temperature drops by 0.2°–3°C and turbulent kinetic energy peaks at >4 m2 s -2 . Vertical profiles reveal that these changes vary little with height in the lowest 300 m.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Chapter 24: The Compound Middle Lamella as a Target for Improved Deconstruction of Woody Biomass

The compound middle lamella (CML) of woody angiosperm species, such as poplar (Populus spp.), birch (Betula spp.), and alder (Alnus spp.), and gymnosperm species, such as pine (Pinus spp.), Douglas fir (Pseudotsuga menziesii), and spruce (Picea spp.), is an amalgam of lignin, hemicellulosic xylans, and pectic polysaccharides that is the major site of cell-cell adhesion of fibre cells and tracheary elements. In grass species, such as sorghum (Sorghum bicolor), switchgrass (Panicum virgatum), and sugarcane (Saccharum officinarum), a phenylpropanoid network deposited in xylan-rich primary walls extends into the lignified CML. This review traces the early history of discovery of the molecular components of the CML and presents the current state of knowledge on the development of the CML in bioenergy-relevant woody species and grasses. This chapter emphasises the role of the CML in the recalcitrance of biomass to deconstruction by mechanical and enzymatic means for conversion to biofuels and valuable bioproducts. The use of genetic engineering to alter CML composition and structure to facilitate biomass deconstruction and to redesign wood to generate materials with novel properties is discussed.

BIOMASS FUELS↗
DOE OSTI2020

Physics Flash Spring 2020

Published by the Physical Sciences Directorate at LANL, the Spring 2020 issue of this newsletter contains an update from the division leader, Tanja Pietraß, Physics Division news roundaup, welcome to new hires, snapshots of Physics managers' work-from-home offices, and service celebrations.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Analysis into Asymptotic Convergence to Full Nonlinear Solutions and Exploration of the Implication of Numerical Operator Mutation of Differential Systems

A robust, sufficiently accurate and practical hydrodynamic simulation toolset is required as a key component of the modeling and simulation of air-gap electrostatic discharge events. This work was performed to complement these ongoing efforts. In particular, hydrodynamic simulations must be vetted to ensure they are robust and sufficiently accurate over relevant characteristic scales. Verification models were generated in order to cultivate the technical knowledge and expertise needed to properly create, implement and execute numerical simulations. Furthermore, this effort was utilized extensively to educate students on the mathematical and numerical principles underlying hydrodynamic simulations. This education opportunity, provided in a holistic and rigorous manner, has greatly benefited developing scientists and engineers with the necessary understandings and toolsets required to excel at accomplishing the task at hand, and, more generally, it has enabled them to generate key programmatic deliverables. This report articulates several subtilties; specifically, how perturbations, nonlinear behavior, and dissipative mechanisms influence numerical stability, how to properly structure mathematical and numerical solutions, and how to properly generate error estimation/assignment. A more rigorous discussion of the consequences of such topics can be found in the body of this report in Chapters 2 and 3 with qualitative findings discussed in Chapter 4.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Physics-informed KNN milling stability model with process damping effects

This paper describes a k-nearest neighbors, or KNN, model for milling stability including process damping effects. A physics-based, frequency domain milling stability solution is used to generate the training data, but does not incorporate process damping effects. The data set is then updated using limited tests to capture the process damping behavior. A “stair step” approach is used to select the test points, where a first spindle speed-axial depth combination is selected based on the physics-based stability map, subsequent tests are defined using the previous test result, and data points are updated by knowledge of process damping behavior and the test results. Furthermore, the KNN modeling approach demonstrates the ability to predict both stable and unstable results, including process damping behavior.

42 ENGINEERING↗
DOE OSTI2015

Knowledge management information tool Web analytics - 15182

The Knowledge Management Information Tool (KM-IT) is a web-based [01] system developed to maintain and preserve the Department of Energy's (DOE's) D and D knowledge base. The system was developed by Florida International University's Applied Research Center (FIU-ARC) with the support of the D and D community including the DOE Office of Environmental Management, and with the active collaboration and support of the DOE's Energy Facility Contractors Group (EFCOG). The KM-IT system is a D and D community driven system tailored to serve the technical issues faced by the D and D workforce across the DOE Complex. The D and D KM-IT is an interactive web-based system operational and available for free access at www.dndkm.org. [02] The D and D KM-IT is currently composed of the following tailored D and D modules: Web Crawler, Hotline, Technology, Document Library (including Hanford's and Savannah River Sites' ALARA Center Reports), Specialist Directory, Lessons Learned, Best Practices, Video/Picture Library, Vendors, Collaboration Tools, Training, and Mobile Systems. The Web Crawler dynamically searches through the D and D KM-IT repository as well as the web and displays search results based on the search criteria. The Hotline allows registered users to post questions/problems related to D and D and to receive solutions from a subject-matter specialist. The Technology module serves as repository of D and D technologies and contains technology/ demonstration fact sheets and vendor information. D and D community members can add Lessons Learned, Best Practices, Documents, and Pictures/Videos to the D and D KM-IT repository, after a formal approval process. The Vendor module provides a directory of commercial vendors who provide D and D related technologies, supplies, and services. D and D KM-IT makes excellent use of the knowledge that exists within the D and D community by allowing D and D project managers around the DOE complex to collaborate by sharing innovative ideas, past experiences, and practices and by maintaining a directory of subject matter specialists. The mobile version of the KM-IT can be accessed from m.dndkm.org [03] and currently has six active modules: Vendor, Technology, Picture/Video Library, Hotline, Lessons Learned and Specialist Directory. Web analytics is a key tool being implemented by the D and D KM-IT project team in an effort to enhance the system to better meet users' needs. The D and D KM-IT project team incorporates web analytics as part of its operational elements to analyze information and key data points on how the system is being used. Web analytics is the measurement, collection, analysis and reporting of internet data for the purposes of understanding and optimizing web usage. The use of web analytics retains or attracts D and D community members to access and share knowledge through virtual collaboration on the KM-IT platform. D and D KM-IT web analytics measures details on the overall usage of the system without tracking personal information or specific user behavior: how many users visited the application, how many of those users were unique visitors, how visitors come to the system (i.e., by following a link to the system or by using the web site URL via bookmark or directly typing the address), what keywords were used to perform searches with the search engine, how long the average user stayed on a given screen or on the entire system and what links were clicked on. This information is used to identify which areas of the system are more popular than the others and provides information that can be used to streamline the application for a better user experience. (authors)

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2015

Training practices for CEA engineers qualified in criticality safety - 14598

Soon after the Tokai Mura criticality accident, the French nuclear authority addressed nuclear operators to take measures regarding criticality safety. In CEA in particular after a thorough assessment of the late 1990's - early 2000's practices, the decision was taken to put up a training program in criticality safety for young (and 'old') nuclear safety specialists. The evolution of the training practices for the IQC (Ingenieurs Qualifies en Criticite - Engineers Qualified in Criticality safety) are presented hereafter, beginning with the first training session set up in October 2001 until now. This primarily deals with the kind of audience this training addresses (professional background), what is taught, learned, used later on and how one becomes an IQC. Furthermore, a discussion involves who recognizes the qualification, how long does one need to become a real criticality safety specialist on an operational basis, how the staff uses (or not) the knowledge and how this ensemble contributes to the professional careers advancement (criticality safety is a strong 'card'). The methodological documents in support of the IQC are also presented, particularly the last version of the 'criticality guide fiches (files, cards, sheets)' collection which summarizes the main aspects and principles of criticality safety (including the rudiments of neutron physics, criticality control modes, double contingency principle, etc.). (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

MenuFramework

The Menu Framework is a reusable Extended Reality multi-level menu system for the Unity Development Platform. The menu structure is architected for reusability to suit whatever need the developer may have for a multi-level, reusable menu framework. Menu Framework's architecture, GUI, and functionality are based on the paper "Extended Reality for Enhanced Training and Knowledge Capture" (SAND2020-2361C). This tool uses the Microsoft Mixed Reality ToolKit (MRTK) built in functionality to provide a suite of functionalities including bounding box, follow me, minimize, maximize, settings, and help commands. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. SAND2021-1507 O

Klein, BrandonThorin↗
DOE OSTI2013

Complex Systems Research at LANL [Slides]

Mathematics, computer science, and technologies required to extract information, knowledge, and insight from data. Thrust leverages advances in theory, algorithms, and the exponential growth of high–performance computing to accelerate the integrative and predictive capability of the scientific method. Thrust focuses on the integration of LANL capabilities for understanding, prediction, and design of complex, natural and engineered systems.

97 MATHEMATICS AND COMPUTING↗