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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Cosmic Reionization On Computers: Reionization Histories of Present-day Galaxies

We examine the reionization history of present-day galaxies by explicitly tracing the building blocks of halos from the Cosmic Reionization On Computers project. We track dark matter particles that belong to $z=0$ halos to trace the neutral fractions at corresponding positions during rapid global reionization. The resulting particle reionization histories allow us to explore different definitions of a halo's reionization redshift and to account for the neutral content of the interstellar medium. Consistent with previous work, we find a systematic trend of reionization redshift with mass - present day halos with higher masses have earlier reionization times. Finally, we quantify the spread of reionization times within each halo, which also has a mass dependence.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

The Evolution of Artificial Intelligence

Artificial intelligence is playing a significant role in defining new research methods that bring us to resolution or discovery more quickly than ever before. Yet decades ago, computers mainly helped us with calculations, storing and editing information. Today, Argonne is using or developing many of these technologies as AI extends to new branches of science and technology, sprouting new buds that represent various facets of human function and cognition — from learning to language processing to visual recognition. Read the story here www.anl.gov/article/welcome-to-the-machine ENIAC footage provided by the Computer History Museum.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Inventory of Public Key Cryptography in US Electric Vehicle Charging

Electric vehicles (EVs) and charging infrastructure are networked systems, which employ high-level communications in support of charging and grid service decisions. Public key cryptography (PKC) underlies much of the security and privacy protections of the information exchange. We are entering a new epoch where quantum computing threats must be seriously considered. A sufficiently large quantum computer, so named Cryptographically Relevant Quantum Computer (QRQC), will be able to perform the mathematical operations to efficiently attack the underpinnings of traditional PKC, thus jeopardizing the digital foundations for trust, communications security, and data security. Estimates suggest a QRQC can break public key encryption and digital signatures in the manner of tens to hundreds of hours, compared to traditional computing that would demand more than 10 18 years in a brute force-style attack. A consensus belief of quantum theorists, quantum experimenters, and cryptographers suggest that the quantum threat will be likely realized in the next twenty years. To address the threat, post-quantum cryptography, which is cryptosystems that are designed to be secure against both traditional and quantum computing threats, must be adopted. Migration from traditional PKC to quantum-resilient cryptography is a global undertaking and likely represents the largest transition in computing history. The nascent state of EV public key infrastructure, combined with limited adoption of the vehicle secure charging features, presents an opportunity to establish a preference for quantum-resistant cryptography as a step on the migration path. Delays will stunt the efforts as rapidly accelerating EVs sales and huge infrastructure investments will create large growing bases of long-lived vehicles and infrastructure. Migration preparations can commence while NIST continues the process to standardize post-quantum cryptography (PQC), which are quantum-resilient algorithms designed to be secure against traditional and quantum computing threats. The first step in preparing EV charging is to identify the presence of traditional public key cryptography algorithms and applications. With this objective in mind, this report is intended to advise the vehicle manufacturers, charging station manufacturers, charging station operators, charge network providers and other EV charging stakeholders with information on traditional PKC application and the potential risks when PKC becomes insecure. This report, the first in a series of reports discussing the topics existing at the confluence of post-quantum cryptography adoption and EV charging, identifies traditional public key applications employed and identifies potential consequences of leaving EV charging infrastructure vulnerable to quantum computing. The focus remains squarely on the of EV charging and infrastructure with respect to PKC and is believed by the authors to complement the NIST SP 1800-38 Migration to Post-Quantum Cryptography. While the report is centered on infrastructure, there are implications to vehicles.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

LeWRON: Agentic Analysis of Electroweak Phase Transitions

The electroweak phase transition (EWPT) is a central topic in particle physics and cosmology, connecting collider phenomenology, baryogenesis, and gravitational-wave observatories. Its analysis requires a technically demanding, convention-sensitive, and model-dependent pipeline, from constructing the finite-temperature effective potential to tracking thermal histories, computing bubble nucleation rates, and predicting gravitational-wave spectra. We present LeWRON (Learning ElectroWeak phase tRansitiON), an agentic framework that orchestrates this pipeline starting from an input Lagrangian. LeWRON combines audited toolbox construction with an Explorer module that uses the generated model-specific code for further analysis, including scans and plots. Intermediate analytic outputs are checked by auditor agents and stored as structured artifacts, enabling reproducible human inspection and downstream use through both a command-line interface and a public Python API. The framework supports a reproduction mode, which infers conventions from the literature and reproduces published results, and a discovery mode, which guides users through structured checkpoints for new models. We demonstrate LeWRON across representative beyond-the-Standard-Model scenarios and release the code on GitHub.

Wang, Isaac R. [Fermilab] (ORCID:000000030789218X)↗
DOE OSTI2018

A Future with Quantum Machine Learning

Could combining quantum computing and machine learning with Moore's law produce a true “rebooted computer”? Furthermore, this article posits that a three-technology hybrid-computing approach might yield sufficiently improved answers to a broad class of problems such that energy efficiency will no longer be the dominant concern.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Nuclear Weapons Computational Physics [Slides]

This presentation provides an overview of computational physics, the role LANL played in the history of computational physics, and how computational physics differs at LANL. Next, the presentation addresses computational physics as part of the LANL weapons program. Finally, the computational physics structure at LANL is addressed.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

GFiRe—Gauge Field integrator for Reheating

We present a new numerical algorithm and code, GFiRe, for solving the nonlinear evolution of Abelian gauge fields coupled to complex scalar fields in homogeneous and isotropic spacetimes. We adopt a hybrid approach to solving the system: the spatial derivatives are discretized using standard Lattice Gauge Field Theory techniques, whereas the time evolution of the fields and scale factor is implemented with explicit, composite, symplectic integrators. An important property of our compound algorithm is that the discretized Gauss constraint is respected exactly, regardless of the order of the symplectic integrator. This remains true even when the background expansion is computed “self-consistently”; that is, when the expansion history is computed using spatial averaged components of the energy momentum tensor in the simulation volume. Hence, our code can also be used in cases where the fields dominate the energy density of the universe, for example, during reheating after inflation. We test the algorithm in scenarios of reheating where the inflaton is a complex scalar field with a potential ∝(2|Φ| 2 - v 2 ) 2 and is coupled to an Abelian gauge field. Tracing the evolution of the system through complex dynamics (including resonant excitation of fields, backreaction, formation of solitons, and changes in the equation of state) in a self-consistently expanding universe, we find the energy conservation violation (<10 -4 ) to be very stable and the Gauss constraint violation (<10 -6 ) to be dominated by differencing noise.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Seventy Years of Computing in the Nuclear Weapons Program

Los Alamos has continuously been on the forefront of scientific computing since it helped found the field. This talk will explore the rich history of computing in the Los Alamos weapons program. The current status of computing will be discussed, as will the expectations for the near future.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2018

Coupled EM-PIC/Radiation Transport Simulations of HERMES Courtyard Experiments

A suite of coupled computational models for simulating the radiation, plasma, and electromagnetic (EM) environment in the High-Energy Radiation Megavolt Electron Source (HERMES) courtyard has been developed. In principle, this provides a predictive forward-simulation capability based solely on measured upstream anode and cathode current waveforms in the Magnetically Insulated Transmission Line (MITL). First, 2D R-Z ElectroMagnetic Particle-in-Cell (EM-PIC) simulations model the MITL and diode to compute a history of all electrons incident on the converter. Next, radiation transport simulations use these electrons as a source to compute the time-dependent dose rate and volumetric electron production in the courtyard. Finally, the radiation transport output is used as sources for EM-PIC simulations of the courtyard to com- pute electromagnetic responses. This suite has been applied to the November 2016 trials, shots 10268-10313. Modeling and experiment differ in significant ways. This is just the first iteration of a long process to improve the agreement, as outlined in the summary.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Differential formulation of the viscous history force on a particle for efficient and accurate computation

It is well known that the computation of the Basset-like history force is very demanding in terms of CPU and memory requirements, since it requires the evaluation of a history integral. We use the recent rational theory of Beylkin & Monzón ( Appl. Comput. Harmon. Anal. , vol. 19, 2005, pp. 17–48) to approximate the history kernel in the form of exponential sums to reformulate the viscous history force in a differential form. This theory allows us to approximate the history kernel in terms of exponential sums to any desired order of accuracy. This removes the need for long-time storage of the acceleration histories of the particle and the fluid. The proposed differential form approximation is applied to compute the history force on a spherical particle in a synthetic turbulent flow and a wall-bounded turbulent channel flow. Particles of various diameters are considered, and results obtained using the present technique are in reasonable agreement with those achieved using the full history integral.

Mechanics↗
DOE OSTI2020

ASCR@40: Four Decades of Department of Energy Leadership in Advanced Scientific Computing Research

Throughout its long history, the Office of Advanced Scientific Computing Research (ASCR) has built the critical technologies to ensure U.S. leadership in energy science and national security. It has made its parent agency, the Department of Energy (DOE) and its Office of Science, the world’s recognized leader in computational science. ASCR’s stated mission is “to discover, develop, and deploy computational and networking capabilities to analyze, model, simulate, and predict complex phenomena important to the DOE.” To accomplish this goal, ASCR oversees a large complex of computing and networking facilities and is responsible for procuring, deploying and operating high-performance computing (HPC), networking and storage resources; conducting basic research in mathematics and computer science; developing and sustaining a large body of software; and collaborating with other Office of Science programs, academia and industry. ASCR’s computational science leadership has a long history, predating even DOE’s inception. Applied mathematics and advanced computing were both elements of the Manhattan Project’s Theoretical Division. In the 1950s, DOE’s predecessor, the Atomic Energy Commission, created a mathematics program to develop and apply digital computing by supporting researchers at universities and AEC laboratories. Several organizational and name changes later, this program would grow and become ASCR.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Differentiation of hepatic abscess from metastasis on contrast-enhanced dynamic computed tomography in patients with a history of extrahepatic malignancy: emphasis on dynamic change of arterial rim enhancement

Objectives: The objective of the study is to identify computed tomography (CT) findings that differentiate hepatic abscess from hepatic metastasis in a patient with a history of extrahepatic malignancy. Materials and methods: This retrospective study included 30 patients with 93 hepatic abscesses and 40 patients with 125 hepatic metastases who had a history of extrahepatic malignancy and underwent contrast-enhanced dynamic CT with arterial phase (AP) and portal venous phase (PVP). The diagnosis of hepatic abscess and hepatic metastasis was made using pathological confirmation or clinical diagnosis. Margin, patchy parenchymal enhancement, arterial rim enhancement, dynamic change of arterial rim enhancement, size discrepancy of lesions between arterial and portal phases, bile duct dilatation, perilesional hyperemia, and perilesional low density were evaluated by two radiologists independently. Significant findings for differentiating two groups were identified at univariate and multivariate analysis with nomogram for predicting hepatic abscess. Interobserver agreement was also analyzed for each variable. Results: Multivariate analysis revealed that patchy parenchymal enhancement (P < 0.001), arterial rim enhancement persistent through PVP (P < 0.001), and perilesional hyperemia (P = 0.013) were independent significant findings to predict hepatic abscess than metastasis. Among them, arterial rim enhancement persistent through PVP showed a highest odds ratio (OR 33.73) on multivariate analysis and a highest predictor point on a nomogram for predicting hepatic abscess. When two of these three criteria were combined, 80.7% (75/93) of hepatic abscess were correctly identified, with a specificity of 85.6% (107/125). When all three criteria were satisfied, specificity was up to 100% (125/125). Conclusions: At contrast-enhanced dynamic CT, patchy parenchymal enhancement, arterial rim enhancement persistent through PVP, perilesional hyperemia, and their combinations may be reliable CT features for differentiating hepatic abscess from metastasis in patients with a history of primary extrahepatic malignancy.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

Deep Learning for Enhancing Fault Tolerant Capabilities of Scientific Workflows

Deep Learning for Enhancing Fault TolerantCapabilities of Scientific WorkflowsAlok Singh, Ilkay AltintasSan Diego Supercomputer Center, UCSDLa Jolla, CA, USA{a1singh, ialtintas}@ucsd.eduMalachi Schram, Nathan TallentPacific Northwestern National LaboratoryRichland, WA, USA{malachi.schram, nathan.tallent}@pnnl.govAbstract-In the history of Computer Science, the act of ‘delegation’ has been the greatest multiplier of society’s problem-solving ability. A scientist working on detecting anomalies in a phenomenon, does not need to re-invent matrix multiplication techniques to solve her problem. Scientific workflows provide ultimate ‘delegation’ mechanism -where a domain scientist can completely forget the specifics of ‘how’ her program will execute on a large cluster in an efficient and cost-effective manner and can instead focus on the mathematical formulation and theoretical robustness of her solution. We present here an approach that directly aims to make the execution of Scientific Workflows more reliable, robust and efficient. We aim that the work presented in this paper will propel the larger effort, from the scientific workflow community, of making scientific workflow execution as simple, efficient and robust as a JOIN operation in a modern database. Specifically, we apply Deep Learning techniques to develop a mechanism that forecasts the final state (success or failure) of a dynamic job in a large-scale particle physics experiment, with minimal data gathering, and as early as possible in job’s life cycle. The key advantage of having a predictive mechanism to identify and anticipate failure-prone jobs is the potential for designing intelligent Fault Tolerance mechanisms to handle anomalous events. We achieve a 14% improvement in computational resources utilization, and an overall classification accuracy of 85% on real tasks executed in a High Energy Physics Computing workflow.To the best of our knowledge, this is the most exhaustive and first of its kind study of neural network architectures in context of a real-dataset profiled from a large-scalescientific workflow.

deep learning↗
DOE OSTI2021

The Los Alamos Computing Facility During the Manhattan Project

This paper describes the history of the computing facility at Los Alamos during the Manhattan Project, 1944 to 1946. While the hand computations are briefly discussed, the paper’s main focus is on the IBM Punch-Card Accounting Machines (PCAMs). During World War II, the Los Alamos facility was one of the most advanced PCAM facilities, both in the machines and in the problems being solved.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Green Computing Catalyzer Kickoff: Green Artificial Intelligence (AI) Directions

For the 2022 JISEA annual meeting, JISEA introduces in this presentation the new Green Computing Catalyzer. The new catalyzer looks to establish green computing as a salient research domain at NREL, and crucially, to begin to address the looming computing energy crisis. The presentation provides some basic numbers, establishing energy use and source or carbon emissions for computing. The presentation also includes a brief history of green computing at NREL, empirical deep learning efficiency study, and research directions.

AI efficiency↗
DOE OSTI2026

Computationally inexpensive part-scale thermal history of additive friction-stir deposition

This study presents an analytical model for steady-state power generation and tool heat loss in additive friction-stir deposition (AFSD), developed to enable part-scale thermal simulation while remaining computationally inexpensive. The model predicts total generated power, yielding 3.7–4.7 kW across deposition temperature setpoints of 400–460 °C for the deposition of AA6061 with a Be-Cu tool. This corresponds to 90–95% of the reported spindle power. Tool heat loss is experimentally determined by calibrating a steady-state energy balance between the generated power, the substrate-deposition thermal gradient, and a temperature dependent tool heat loss term: q tool (T) = a + b (T - 400°C) with a = 2.7 x 10 6 Wm -2 and b = 9.5 x 10 3 Wm -2 K -1 . The calibration indicates that about 69% of the generated heat is conducted into the tool for this configuration, which is much higher than previously reported. The calibrated heat-source is implemented in finite element software (Adamantine) to simulate the transient thermal history of a 100 cm 3 representative build in 8 min on a standard desktop (at 0.635 mm build-height resolution). For the first three layers, the substrate temperatures between simulation and experiment are within 10% mean absolute percentage error. Sensitivity analysis indicates that uncertainties in average deposition temperature and deformation localization (stir-zone geometry, depth, and spatial dependance of strain-rate and flow stress) dominate model variance, motivating additional experimental verification.

Additive Friction-Stir Deposition↗