Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “computational physics”

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 1,567 records · Page 87
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Influence of Ionization and Beam Quality on Interaction of TW-Peak CO2 Laser with Hydrogen Plasma

3D numerical simulations of the interaction of a powerful CO2 laser with hydrogen jets demonstrating the role of ionization and laser beam quality are presented. Simulations are performed in support of the plasma wakefield accelerator experiments being conducted at the BNL Accelerator Test Facility (ATF). The CO2 laser at BNL ATF has several potential advantages for laser wakefield acceleration compared to widely used solid-state lasers. SPACE, a parallel relativistic Particle-in-Cell code, developed at SBU and BNL, has been used in these studies. A novelty of the code is its set of efficient atomic physics algorithms that compute ionization and recombination rates on the grid and transfer them to particles. The primary goal of the initial BNL experiments was to characterize the plasma density by measuring the sidebands in the spectrum of the probe laser. Simulations, that resolve hydrogen ionization and laser spectra, help explain several trends that were observed in the experiments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

D-Wave for High Energy Physics

aWe analyze different problems in high-energy physics where quantum computing, and namely quantum annealers, may be able to provide an edge in coming years: 1) The training of Restricted Boltzmann Machines (RBM) is a natural problem for quantum annealers. We explore RBM trained on a D-Wave machine for classification of galaxy morphologies, including a discussion of how to obtain a Boltzmann distribution from the annealer. 2) For this study, we cast the problem of explaining and predicting the production of particles in high energy collisions (color recombination) as an optimization problem and employ classical and quantum computing techniques to find solutions. In particular, we present a quadratic unconstrained binary optimization (QUBO) formulation for quantum annealers. In parallel to these efforts, we also present the cross-platform XACC package for quantum compilation and execution.

Caldeira, J.↗
DOE OSTI2020

Computational Fluid Dynamics of Bioreactors with Micro-Aeration

Recent work has probed the potential for using Zymomonas mobilis as a flexible platform for the production of advanced fuels and intermediates. One set of recombinant organisms requires a micro-aerobic environment to promote the production of the desired product (2,3-BDO), but the economics and process-control implications of this approach are uncertain at industrial scale. Computational multi-physics models have the potential to describe the fermentation in detail at multiple scales, reducing the risk of scale-up. A two-phase Euler-Euler CFD model is used alongside a simple model of oxygen uptake to describe this bioreaction. A few reactor vessels, including airlift and CSTR bioreactors, are analyzed at multiple scales, regions of oxygen-depletion and over-oxygenation are characterized, and the bioreactors are compared. We show that it is difficult to achieve homogenous oxygen concentrations in industrial-scale CSTR reactors and that advanced bioreactor designs are needed for economical operation.

09 BIOMASS FUELS↗
DOE OSTI2021

Hands-on with OWL: the Oak–Ridge Wang–Landau Monte Carlo software suite

The Oak–Ridge Wang–Landau (OWL) package is an open-source scientific software specialized for large-scale, Monte Carlo simulations for the study of materials properties at finite temperature. In this paper, we discuss the main features and capabilities of OWL, followed by detailed descriptions of building and running the code. The readers will be guided through the usage and functionality of the code with a few hands-on examples. This paper is based on a tutorial on OWL given at the 32nd Center for Simulational Physics Workshop on Recent Developments in Computer Simulation Studies in Condensed Matter Physics.

Li, Ying Wai↗
DOE OSTI2024

Performance Improvements of Poincaré Analysis for Exascale Fusion Simulations

Understanding the time-varying magnetic field in a fusion device is critical for the successful design and construction of clean-burning fusion power plants. Poincaré analysis provides a powerful method for the visualization of magnetic fields in fusion devices. However, Poincaré plots can be very computationally expensive making it impractical, for example, to generate these plots in situ during a simulation. In this short paper, we describe a collaboration among computer science and physics researchers to develop a new Poincaré tool that provides a significant reduction in the time to generate analysis results.

Pugmire, Dave↗
DOE OSTI2025

Simulating Alpha Particles Incident on MKID Chips for Quantum Sensitivity Analysis

Superconducting quantum devices, such as microwave kinetic inductance detectors (MKIDs), are highly sensitive instruments used in quantum computing and advanced sensing technologies. However, their extreme sensitivity also makes them vulnerable to background noise from natural sources like radiation. One significant contributor to this noise is alpha particles emitted by 210Po, a radon decay daughter that accumulates on surfaces near the detector. This project investigates how alpha particles emitted from 210Po interact with MKID chips. These particles can deposit energy on the detector surface, disrupting its operation and generating false signals. Understanding the energy and behavior of these particles is crucial for improving the design and reliability of quantum devices. To explore this, we first modeled the decay chain starting from 210Pb to 210Po using differential equations. This allowed us to predict how the activity of alpha-emitting isotopes changes over time, reaching a steady state after about two years. Next, we simulated alpha particle interactions with the MKID chip using the Geant4 software toolkit. We built a detailed computer model of the detector housing, including the copper lid where alpha particles originate, the silicon chip, and a thin aluminum sensor layer. Alpha particles were emitted isotropically from just beneath the copper lid’s surface, mimicking natural decay conditions. The simulation tracked how these particles deposit energy on the chip, generating electron-hole pairs and phonons. The results provide insight into the behavior of the resultant electron-hole pairs and phonons, giving us a clear understanding of the energy deposition distribution on the chip. This work supports efforts to mitigate background noise in superconducting sensors, advancing their use in quantum computing and sensitive physics experiments.

Hall, Matthew [Fermilab; UCLA]↗
DOE OSTI2026

No Quantum Utility from Hadron Masses? No, Quantum Utility from Hadron Masses!

Is there a quantum utility in establishing the masses of hadrons? Here we show that the best response is that of Farrés, Capó, and Davis: perhaps, perhaps, perhaps. This is interesting given the general case that particle physics demands quantum computers. For stable hadrons, classical LQCD has achieved sub-percent precision with no sign problem, and quantum computers offer no advantage. For resonances, the Maiani-Testa theorem is an obstruction that quantum simulation is immune to. For nuclei, Wick contractions and signal-to-noise are genuine classical barriers. Underlying these cases is a unified picture connecting the sign problem to Wigner negativity and T gate cost. This manuscript was drafted from extensive interaction with \textsc{Claude}.

Lamm, Henry [Fermilab] (ORCID:0000000330330791)↗
DOE OSTI2026

CoLoRe-2LPT: Lyman-$α$ mock catalogues for the validation of DESI cosmological analyses

The Lyman-$α$ (Ly$α$) forest has become a crucial probe for studying the large-scale structure of the universe at high redshift ($z > 2$), providing powerful constraints on Baryon Acoustic Oscillations (BAO) and the full-shape (FS) clustering of matter. As a key ingredient for upcoming BAO and FS analyses, we present a new generation of fast cosmological Ly$α$ mocks based on second-order Lagrangian perturbation theory (2LPT). These new mocks significantly improve upon previous log-normal approaches, both at accurately capturing small scale clustering and at recovering the non-linear broadening of the BAO peak. They are able to reproduce Ly$α$ statistics within $10\%$ of the latest DESI measurement; including the Ly$α$ bias and the redshift-space distortion $β$ parameter, mean transmitted flux, and 1D power spectrum. The corresponding quasar (QSO) clustering is also improved with respect to previous approaches, calibrated against high-resolution Abacus simulations, recovering the observational QSO linear bias to less than $5\%$ and improving redshift-space distortions via 2LPT velocities and the addition of Fingers-of-God effects. Furthermore, these mocks incorporate high column density systems and metal lines, allowing us to explore the effects and systematics induced by these astrophysical contaminants. This new set of mocks has been key for enhancing the modeling and validation of the DESI DR2 Ly$α$ full shape cosmological analysis. This work provides a physically motivated and computationally efficient tool for simulating current and next-generation Ly$α$ surveys and validating FS and BAO analysis.

Bernal, M.F. Ruiz-Herrera [Madrid, CIEMAT] (ORCID:↗
DOE OSTI1993

Nuclear Safety [Vol. 34, No. 1, January-March 1993]

Nuclear Safety is a review journal that covers significant developments in the field of nuclear safety. Its scope includes the analysis and control of hazards associated with nuclear energy, operations involving fissionable materials, and the products of nuclear fission and their effects on the environment. Primary emphasis is on safety in reactor design, construction, and operation; however, the safety aspects of the entire fuel cycle, including fuel fabrication, spent-fuel processing, nuclear waste disposal, handling of radioisotopes, and environmental effects of these operations, are also treated. Table of Contents for this issue follows. GENERAL SAFETY CONSIDERATIONS: 1 An Integrated Regional Approach To Risk Management of Industrial Systems, S. Chakraborty and R. Stratton; 9 Annual Technical Meeting of the NRC Cooperative Severe Accident Research Program, E. G. Silver; ACCIDENT ANALYSIS: 13 Deterministic Severe Accident Criteria as Severe Accident Design Criteria and Policy for the New Production Reactor-Heavy Water Reactor, P. T. Rhoads; 20 Proposed Deterministic Severe Accident Criteria for the Heavy Water Reactor-New Production Reactor Containment, K. D. Bergeron, S. E. Slezak, and C. E. Leach; 33 Quasi-Static Core Liquid Level Depression and Long-Term Core Uncovery During a PWR LOCA, Y. Kukita, R. R. Schultz, H. Nakamura, and J. Katayama; 49 LIRA: An Advanced Containment System to Minimize the Accidental Radioactivity Releases, A. Turricchia; 63 Errata to “A Review of Hydrogen Production During Melt/Water Interaction in LWRs,” Vol. 33, No. 4; CONTROL AND INSTRUMENTATION: 64 The Nuplex 80+™ Advanced Control Complex from ABB Combustion Engineering, F. Ridolfo, D. Harmon, and K. Scarola; DESIGN FEATURES: 76 A Scheme for Passive Isolation of the Containment of a Reactor, A. K. Ghosh, V. V. Raj, and K. Kakodkar; 84 Utility Requirements for Safety in the Passive Advanced Light-Water Reactor, T. U. Marston, W. H. Layman, and G. Bockhold, Jr.; WASTE AND SPENT FUEL MANAGEMENT: 91 Activities Related to Waste and Spent Fuel Management, Compiled by M. D. Muhlheim and E. G. Silver; OPERATING EXPERIENCES: 103 Testing Deficiencies in Auxiliary Systems Feedwater Systems, J. D. Kueck; 110 Reactor Shutdown Experience, Compiled by J. W. Cletcher; 113 Selected Safety-Related Events, Compiled by G. A. Murphy; 115 Operating U.S. Power Reactors, Compiled by M. D. Muhlheim and E. G. Silver; RECENT DEVELOPMENTS: 133 General Administrative Activities, Compiled by M. D. Muhlheim and E. G. Silver; 145 Reports, Standards, and Safety Guides, D. S. Queener; 150 Proposed Rule Changes as of Sept. 30, 1992; ANNOUNCEMENTS: 75 Harvard Short Course on Risk: Science, Assessment, and Management; 102 International Conference on Reactor Physics and Reactor Computations; 102 The Fourth International Conference on Nuclear Waste Reprocessing and Waste Management [RECOD ’94]; 132 International Symposium on the Radiological Exposure of the Population of the European Community from Radioactivity in the Mediterranean Sea [MARINA-MED]; 162 International High-Level Radioactive Waste Conference ‘Technology Integration—Above and Below”; 154 The Authors; 158 Indexes to Nuclear Safety, Volume 33.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Physics Community Needs, Tools, and Resources for Machine Learning

Machine learning (ML) is becoming an increasingly important component of cutting-edge physics research, but its computational requirements present significant challenges. In this white paper, we discuss the needs of the physics community regarding ML across latency and throughput regimes, the tools and resources that offer the possibility of addressing these needs, and how these can be best utilized and accessed in the coming years.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Physics-Guided, Physics-Informed, and Physics-Encoded Neural Networks and Operators in Scientific Computing: Fluid and Solid Mechanics

Abstract Advancements in computing power have recently made it possible to utilize machine learning and deep learning to push scientific computing forward in a range of disciplines, such as fluid mechanics, solid mechanics, materials science, etc. The incorporation of neural networks is particularly crucial in this hybridization process. Due to their intrinsic architecture, conventional neural networks cannot be successfully trained and scoped when data are sparse, which is the case in many scientific and engineering domains. Nonetheless, neural networks provide a solid foundation to respect physics-driven or knowledge-based constraints during training. Generally speaking, there are three distinct neural network frameworks to enforce the underlying physics: (i) physics-guided neural networks (PgNNs), (ii) physics-informed neural networks (PiNNs), and (iii) physics-encoded neural networks (PeNNs). These methods provide distinct advantages for accelerating the numerical modeling of complex multiscale multiphysics phenomena. In addition, the recent developments in neural operators (NOs) add another dimension to these new simulation paradigms, especially when the real-time prediction of complex multiphysics systems is required. All these models also come with their own unique drawbacks and limitations that call for further fundamental research. This study aims to present a review of the four neural network frameworks (i.e., PgNNs, PiNNs, PeNNs, and NOs) used in scientific computing research. The state-of-the-art architectures and their applications are reviewed, limitations are discussed, and future research opportunities are presented in terms of improving algorithms, considering causalities, expanding applications, and coupling scientific and deep learning solvers.

Computer Science↗
DOE OSTI2016

RIKEN/RBRC Scientific Review Committee Meeting April 5-8, 2016. Proceedings of RIKEN BNL Research Center, Volume 126

Considerable progress in lattice gauge computing has been made by the RBRC Computing Group over the last 5 years. The lattice gauge community has recognized some of this work by awarding two Wilson prizes, in 2012 and in 2015. The Computing Group, in collaboration with RIKEN, BNL and the UKQCD collaboration, has focused on both physics simulations and technical developments, in both software and hardware. The physics output has been primarily in the area of fundamental physical quantities, e.g., in K and B mesons, nucleon form factors, g-2 of the muon, electric dipole moments and physics beyond the Standard Model. The software effort has produced new and/or more efficient algorithms, such as the all-mode averaging (AMA), and ensemble generation techniques. This proceedings records the 14th scientific evaluation of the RIKEN BNL Research Center (RBRC). In order to illustrate the breadth and scope of the RBRC program, each member of the Center made a presentation on his/her research efforts. This encompassed three major areas of investigation: theoretical, experimental and computational physics. In addition, the committee met privately with the fellows and postdocs to ascertain their opinions and concerns. Although the main purpose of this review is a report to RIKEN management on the health, scientific value, management and future prospects of the Center, the RBRC management felt that a compendium of the scientific presentations are of sufficient quality and interest that they warrant a wider distribution. Therefore we have made this compilation and present it to the community for its information and enlightenment.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2016

Computational homogenization at extreme scales

Multi-scale simulations at extreme scales in terms of both physical length scales and computational resources are presented. In this letter, we introduce a hierarchically parallel computational homogenization solver that employs hundreds of thousands of computing cores and resolves in material length scales (from to ). Simulations of this kind are important in understanding the multi-scale essence of many natural and synthetically made materials. Thus, we present a simulation consisting of 53.8 Billion finite elements with 28.1 Billion nonlinear equations that is solved on 393,216 computing cores (786,432 threads). The excellent parallel performance of the computational homogenization solver is demonstrated by a strong scaling test from 4,096 to 262,144 cores. A fully coupled multi-scale damage simulation shows a complex crack profile at the micro-scale and the macroscopic crack tunneling phenomenon. Such large and predictive simulations are an important step towards Virtual Materials Testing and can aid in development of new material formulations with extreme properties. Furthermore, the high computational efficiency of our computational homogenization solver holds great promise for utilizing the next generation of exascale parallel computing platforms that are expected to accelerate computations through orders of magnitude increase in parallelism rather than speed of each processor.

Engineering↗
NASA NTRS1987

Development and application of unified algorithms for problems in computational science

A framework is presented for developing computationally unified numerical algorithms for solving nonlinear equations that arise in modeling various problems in mathematical physics. The concept of computational unification is an attempt to encompass efficient solution procedures for computing various nonlinear phenomena that may occur in a given problem. For example, in Computational Fluid Dynamics (CFD), a unified algorithm will be one that allows for solutions to subsonic (elliptic), transonic (mixed elliptic-hyperbolic), and supersonic (hyperbolic) flows for both steady and unsteady problems. The objectives are: development of superior unified algorithms emphasizing accuracy and efficiency aspects; development of codes based on selected algorithms leading to validation; application of mature codes to realistic problems; and extension/application of CFD-based algorithms to problems in other areas of mathematical physics. The ultimate objective is to achieve integration of multidisciplinary technologies to enhance synergism in the design process through computational simulation. Specific unified algorithms for a hierarchy of gas dynamics equations and their applications to two other areas: electromagnetic scattering, and laser-materials interaction accounting for melting.

Shankar, Vijaya↗
DOE OSTI2026

Stochastic tensor contraction for quantum chemistry

Many computational methods in ab initio quantum chemistry are formulated in terms of high-order tensor contractions, whose cost determines the size of system that can be studied. We introduce stochastic tensor contraction to perform such operations with greatly reduced cost, and present its application to the gold-standard quantum chemistry method, coupled cluster theory with up to perturbative triples. For total energy errors more stringent than chemical accuracy, we reduce the computational scaling to that of mean-field theory, while starting to approach the mean-field absolute cost, thereby challenging the existing cost-to-accuracy landscape. Benchmarks against state-of-the-art local correlation approximations further show that we achieve an order-of-magnitude improvement in both total computation time and error, with significantly reduced sensitivity to system dimensionality and electron delocalization. We conclude that stochastic tensor contraction is a powerful computational primitive to accelerate a wide range of quantum chemistry.

Chemical Physics (physics.chem-ph)↗
DOE OSTI2018

HEP Software Foundation Community White Paper Working Group - Detector Simulation

A working group on detector simulation was formed as part of the high-energy physics (HEP) Software Foundation's initiative to prepare a Community White Paper that describes the main software challenges and opportunities to be faced in the HEP field over the next decade. The working group met over a period of several months in order to review the current status of the Full and Fast simulation applications of HEP experiments and the improvements that will need to be made in order to meet the goals of future HEP experimental programmes. The scope of the topics covered includes the main components of a HEP simulation application, such as MC truth handling, geometry modeling, particle propagation in materials and fields, physics modeling of the interactions of particles with matter, the treatment of pileup and other backgrounds, as well as signal processing and digitisation. The resulting work programme described in this document focuses on the need to improve both the software performance and the physics of detector simulation. The goals are to increase the accuracy of the physics models and expand their applicability to future physics programmes, while achieving large factors in computing performance gains consistent with projections on available computing resources.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

GAHLS: an optimized graph analytics based high level synthesis framework

The urgent need for low latency, high-compute and low power on-board intelligence in autonomous systems, cyber-physical systems, robotics, edge computing, evolvable computing, and complex data science calls for determining the optimal amount and type of specialized hardware together with reconfigurability capabilities. With these goals in mind, we propose a novel comprehensive graph analytics based high level synthesis (GAHLS) framework that efficiently analyzes complex high level programs through a combined compiler-based approach and graph theoretic optimization and synthesizes them into message passing domain-specific accelerators. This GAHLS framework first constructs a compiler-assisted dependency graph (CaDG) from low level virtual machine (LLVM) intermediate representation (IR) of high level programs and converts it into a hardware friendly description representation. Next, the GAHLS framework performs a memory design space exploration while account for the identified computational properties from the CaDG and optimizing the system performance for higher bandwidth. The GAHLS framework also performs a robust optimization to identify the CaDG subgraphs with similar computational structures and aggregate them into intelligent processing clusters in order to optimize the usage of underlying hardware resources. Finally, the GAHLS framework synthesizes this compressed specialized CaDG into processing elements while optimizing the system performance and area metrics. Evaluations of the GAHLS framework on several real-life applications (e.g., deep learning, brain machine interfaces) demonstrate that it provides 14.27× performance improvements compared to state-of-the-art approaches such as LegUp 6.2.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1991

A Large-Grain Mapping Approach for Multiprocessor Systems Through Data Flow Model Ph.D. Thesis

A large-grain level mapping method is presented of numerical oriented applications onto multiprocessor systems. The method is based on the large-grain data flow representation of the input application and it assumes a general interconnection topology of the multiprocessor system. The large-grain data flow model was used because such representation best exhibits inherited parallelism in many important applications, e.g., CFD models based on partial differential equations can be presented in large-grain data flow format, very effectively. A generalized interconnection topology of the multiprocessor architecture is considered, including such architectural issues as interprocessor communication cost, with the aim to identify the 'best matching' between the application and the multiprocessor structure. The objective is to minimize the total execution time of the input algorithm running on the target system. The mapping strategy consists of the following: (1) large-grain data flow graph generation from the input application using compilation techniques; (2) data flow graph partitioning into basic computation blocks; and (3) physical mapping onto the target multiprocessor using a priority allocation scheme for the computation blocks.

Kim, Hwa-Soo↗