Search NASA⌕ Search

SEARCH · Search NASA

Results for “Computational methods”

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

Quantum Orbital Minimization Method for Excited States Calculation on a Quantum Computer

Herein we propose a quantum-classical hybrid variational algorithm, the quantum orbital minimization method (qOMM), for obtaining the ground state and low-lying excited states of a Hermitian operator. Given parametrized ansatz circuits representing eigenstates, qOMM implements quantum circuits to represent the objective function in the orbital minimization method and adopts a classical optimizer to minimize the objective function with respect to the parameters in ansatz circuits. The objective function has an orthogonality constraint implicitly embedded, which allows qOMM to apply a different ansatz circuit to each input reference state. We carry out numerical simulations that seek to find excited states of H 2 , LiH, and a toy model consisting of four hydrogen atoms arranged in a square lattice in the STO-3G basis with UCCSD ansatz circuits. Comparing the numerical results with existing excited states methods, qOMM is less prone to getting stuck in local minima and can achieve convergence with more shallow ansatz circuits.

97 MATHEMATICS AND COMPUTING↗

Toward reducing uncertainty quantification costs in DEM models of particulate flow: Testing simple, sensitivity-based, forward uncertainty propagation techniques

The performance of two conceptually-simple uncertainty quantification techniques are tested against the rigorous nested-loop sampling technique of Roy and Oberkampf (Comput Methods Appl Mech Eng, 200: 2131–2144, 2011) (herein called full-sampling) using two very small-scale DEM-based models of particulate flow (one gas-solid flow and one granular flow). The first simplified forward uncertainty propagation technique, reduced-sampling, uses a sensitivity analysis to eliminate uncertain inputs that have little impact on the model output prior to nested-loop sampling. The second technique, boundary-sampling, uses a sensitivity analysis to inform the selection of two bounding cases for each key model output. In conclusion, the uncertainties in the model outputs obtained via the reduced- and boundary-sampling methods agree well with those from full-sampling for both the gas-solid and granular flow models while yielding computational savings of 65–75% (reduced sampling) and 94–97% (boundary sampling).

42 ENGINEERING↗

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)↗

Debugging a high performance computing program

Methods, apparatus, and computer program products are disclosed for debugging a high performance computing program by gathering lists of addresses of calling instructions for a plurality of threads of execution of the program, assigning the threads to groups in dependence upon the addresses, and displaying the groups to identify defective threads.

97 MATHEMATICS AND COMPUTING↗

Debugging a high performance computing program

Methods, apparatus, and computer program products are disclosed for debugging a high performance computing program by gathering lists of addresses of calling instructions for a plurality of threads of execution of the program, assigning the threads to groups in dependence upon the addresses, and displaying the groups to identify defective threads.

97 MATHEMATICS AND COMPUTING↗

The Role of Mathematical Methods in Efficiency Calibration and Uncertainty Estimation in Gamma Based Non-Destructive Assay - 12311

Mathematical methods are being increasingly employed in the efficiency calibration of gamma based systems for non-destructive assay (NDA) of radioactive waste and for the estimation of the Total Measurement Uncertainty (TMU). Recently, ASTM (American Society for Testing and Materials) released a standard guide for use of modeling passive gamma measurements. This is a testimony to the common use and increasing acceptance of mathematical techniques in the calibration and characterization of NDA systems. Mathematical methods offer flexibility and cost savings in terms of rapidly incorporating calibrations for multiple container types, geometries, and matrix types in a new waste assay system or a system that may already be operational. Mathematical methods are also useful in modeling heterogeneous matrices and non-uniform activity distributions. In compliance with good practice, if a computational method is used in waste assay (or in any other radiological application), it must be validated or benchmarked using representative measurements. In this paper, applications involving mathematical methods in gamma based NDA systems are discussed with several examples. The application examples are from NDA systems that were recently calibrated and performance tested. Measurement based verification results are presented. Mathematical methods play an important role in the efficiency calibration of gamma based NDA systems. This is especially true when the measurement program involves a wide variety of complex item geometries and matrix combinations for which the development of physical standards may be impractical. Mathematical methods offer a cost effective means to perform TMU campaigns. Good practice demands that all mathematical estimates be benchmarked and validated using representative sets of measurements. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

On the characterization of biases arising from methods and approximations used for sensitivity analyses

The preliminary assessment of the performance and safety of advanced reactors, as well as the identification of research and development needs, rely on computer simulations due to the lack of operational experience. Those simulations should be based on verified and validated computational tools, calculation schemes, and nuclear data libraries, and should be accompanied of a proper characterization of the involved uncertainties, providing confidence in the computational-based predictions. Thus, sensitivity and uncertainty studies, together with integral experiments, play an essential role in that process. Sensitivities are then a critical element and guidelines about how to produce accurate enough sensitivities depending on the subsequent analyses to be performed are of interest. The present study addresses the sensitivities biases arising from the use of different nuclear data libraries, computational methods and the assumption of modelling simplifications. Not only sensitivities for multiplication factor but also for safety-relevant reactivity responses have been analyzed. In the study, propagated uncertainties are also included for the considered parameters to illustrate the impact of the sensitivity profiles obtained for each case on the final uncertainty. Then, relevant recommendations are given for sensitivity analyses of highly complex systems. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Computational Science at the National Laboratory of the Rockies

Computational methods underpin advancing the science and engineering of energy technologies and developing a knowledge base to optimize energy systems. NLR's Computational Science Center (CSC) proudly focuses on providing the service of computing, advancing the science of computing, and enabling DOE missions.

97 MATHEMATICS AND COMPUTING↗

Characterizing the Thermal Column Beam in the University of Massachusetts Lowell Research Reactor

The University of Massachusetts Lowell operates a 1 MW, light water cooled and moderated Research Reactor (UMLRR) fueled by low-enriched uranium to generate neutron flux that is used at a variety of in-core and beam port facilities within the reactor to perform irradiation experiments for academic and industrial research. One of the irradiation sites is a graphite-moderated thermal column designed to deliver a collimated beam of thermal neutrons to a neutron radiography imaging system. A detailed characterization of the neutron beam is essential for the design and interpretation of experiments at the facility and for radiation safety. This work aimed to characterize the neutron flux and energy spectrum in the UMLRR thermal column beam by employing experimental and computational methods known as the foil activation and spectrum unfolding technique. A previously existing Monte Carlo N-Particle transport (MCNP) model of the UMLRR reactor core has been extended to the thermal column where the neutron flux and energy spectrum have been computed and experimentally validated. This work employed the foil activation and spectrum unfolding technique along with neutron transport computation to extend the knowledge of the neutron fluence and energy spectrum from the UMLRR core to the adjacent thermal column beam. Radiography systems are used for materials characterization, studying dynamic behavior of engineering systems, and for nuclear forensics applications. Foil activation is an experimental method where thin foils of highly pure materials are placed in the beam where interactions between the neutrons and the foil material activate radioisotopes in the foils whose activities can be measured after the irradiation. Downstream computation with MCNP and the spectrum unfolding code SAND can deconvolute the neutron fluence and energy spectrum that must have been necessary to induce the production rates of radioisotopes that were obtained through foil activation. The necessary ingredients for successful spectrum unfolding are the experimentally measured production rates and some a priori (initial guess) knowledge of the flux spectrum. The thermal column is equipped with a neutron radiography imaging system, so any experimenters who desire to use neutron radiography at UMass Lowell will benefit significantly from knowing the characteristics of the neutron beam. Comprehensive knowledge of these characteristics of the beam is essential to compare and benchmark results obtained for similar experiments at other facilities. The Integrated Nuclear Security and Safeguards Laboratory (INSSL) is pursuing the investigation of the performance of self-healing materials under stress-strain environments and the characterization of radiation detectors and special containers for storing and transporting nuclear material. This work contributes to the improvement of the current radiography system, expands the utility of the system for nuclear safeguards and security application and provides information that will enhance the general safety of the UMLRR experimental facilities. The main objective of this work has been achieved; an appropriate neutron flux spectrum for the UMLRR thermal column beam has been determined using the foil activation method followed by spectrum unfolding. There now exists an experimentally validated computational model of the neutron flux spectrum and the thermal column's neutron environment is characterized. We can conclude that the thermal column is well characterized; an appropriate neutron flux spectrum has been determined through an experimentally validated computational model. Future experimenters interested in the thermal column may now expand on the computational model or use the spectral information for their own purposes. The knowledge gained herein will be helpful for the INSSL's future work using neutron radiography to analyze the effectiveness of self-healing materials and special containers. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Packing microstructure and local density variations of experimental and computational pebble beds

In pebble bed type nuclear reactors the fuel is contained in graphite pebbles, which form a randomly stacked bed with a non-uniform packing density. These variations can influence local coolant flow and power density and are a possible cause of hotspots. To analyse local density variations computational methods are needed that can generate randomly stacked pebble beds with a realistic packing structure on a pebble-to-pebble level. We first compare various properties of the local packing structure of a computed bed with those of an image made using computer aided X-ray tomography, looking at properties in the bulk of the bed and near the wall separately. Especially for the bulk of the bed, properties of the computed bed show good comparison with the scanned bed and with literature, giving confidence our method generates beds with realistic packing microstructure. Results also show the packing structure is different near the wall than in the bulk of the bed, with pebbles near the wall forming ordered layers similar to hexagonal close packing. Next, variations in the local packing density are investigated by comparing probability density functions of the packing fraction of small clusters of pebbles throughout the bed. Especially near the wall large variations in local packing fractions exists, with a higher probability for both clusters of pebbles with low (<0.6) and high (>0.65) packing fraction, which could significantly affect flow rates and, together with higher power densities, could result in hotspots. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Magnetism of new metastable cobalt-nitride compounds

The search for new magnetic materials with high magnetization and magnetocrystalline anisotropy is important for a wide range of applications including information and energy processing. There is only a limited number of naturally occurring magnetic compounds that are suitable. This situation stimulates an exploration of new phases that occur far from thermal-equilibrium conditions, but their stabilization is generally inhibited due to high positive formation energies. Here a nanocluster-deposition method has enabled the discovery of a set of new non-equilibrium Co–N intermetallic compounds. The experimental search was assisted by computational methods including adaptive-genetic-algorithm and electronic-structure calculations. Conventional wisdom is that the interstitial or substitutional solubility of N in Co is much lower than that in Fe and that N in Co in equilibrium alloys does not produce materials with significant magnetization and anisotropy. By contrast, our experiments identify new Co–N compounds with favorable magnetic properties including hexagonal Co 3 N nanoparticles with a high saturation magnetic polarization ($J_s$ = 1.28 T or 12.8 kG) and an appreciable uniaxial magnetocrystalline anisotropy ($K_1$ = 1.01 MJ m -3 or 10.1 Mergs per cm 3 ). This research provides a pathway for uncovering new magnetic compounds with computational efficiency beyond the existing materials database, which is significant for future technologies.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

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↗

Efficient Discovery of Novel Multicomponent Mixtures for Hydrogen Storage: A Combined Computational/Experimental Approach

The objective of the proposed program is to discover novel mixed hydrides for hydrogen storage, which enable the DOE 2010 system-level goals. Our goal is to find a material that desorbs 8.5 wt.% H 2 or more at temperatures below 85°C. The research program will combine first-principles calculations of reaction thermodynamics and kinetics with material and catalyst synthesis, testing, and characterization. We will combine materials from distinct categories (e.g., chemical and complex hydrides) to form novel multicomponent reactions. Systems to be studied include mixtures of complex hydrides and chemical hydrides [e.g. LiNH 2+ NH 3 BH 3 ] and nitrogen-hydrogen based borohydrides [e.g. Al(BH 4 ) 3 (NH 3 ) 3 ]. The 2010 and 2015 FreedomCAR/DOE targets for hydrogen storage systems are very challenging, and cannot be met with existing materials. The vast majority of the work to date has delineated materials into various classes, e.g., complex and metal hydrides, chemical hydrides, and sorbents. However, very recent studies indicate that mixtures of storage materials, particularly mixtures between various classes, hold promise to achieve technological attributes that materials within an individual class cannot reach. Our project involves a systematic, rational approach to designing novel multicomponent mixtures of materials with fast hydrogenation/dehydrogenation kinetics and favorable thermodynamics using a combination of state-of-the-art scientific computing and experimentation. We will use the accurate predictive power of first-principles modeling to understand the thermodynamic and microscopic kinetic processes involved in hydrogen release and uptake and to design new material/catalyst systems with improved properties. Detailed characterization and atomic-scale catalysis experiments will elucidate the effect of dopants and nanoscale catalysts in achieving fast kinetics and reversibility. And, state-of-the-art storage experiments will give key storage attributes of the investigated reactions, validate computational predictions, and help guide and improve computational methods. In sum, our approach involves a powerful blend of: 1) H2 Storage measurements and characterization, 2) State-of-the-art computational modeling, 3) Detailed catalysis experiments, 4) In-depth automotive perspective.

08 HYDROGEN↗

Final Technical Report for Predictive Computing for Condensed Matter

This grant supported research into the development of computational methods to predict the properties of materials to high precision. The overarching purpose of such tools is to aid materials discovery by computation, without the need for costly materials synthesis or experimental measurements. A wide variety of computational tools were developed during this grant. Using these tools, we demonstrated that we could achieve the stated goal of computational modeling with predictive accuracy in several prototypical materials: a molecular crystal of benzene, a solid composed entirely of hydrogen, and a model solid intended to mimic the high temperature superconductors.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Leveraging Artificial Intelligence to Expedite Antibody Design and Enhance Antibody–Antigen Interactions

This perspective sheds light on the transformative impact of recent computational advancements in the field of protein therapeutics, with a particular focus on the design and development of antibodies. Cutting-edge computational methods have revolutionized our understanding of protein–protein interactions (PPIs), enhancing the efficacy of protein therapeutics in preclinical and clinical settings. Central to these advancements is the application of machine learning and deep learning, which offers unprecedented insights into the intricate mechanisms of PPIs and facilitates precise control over protein functions. Despite these advancements, the complex structural nuances of antibodies pose ongoing challenges in their design and optimization. Our review provides a comprehensive exploration of the latest deep learning approaches, including language models and diffusion techniques, and their role in surmounting these challenges. We also present a critical analysis of these methods, offering insights to drive further progress in this rapidly evolving field. The paper includes practical recommendations for the application of these computational techniques, supplemented with independent benchmark studies. These studies focus on key performance metrics such as accuracy and the ease of program execution, providing a valuable resource for researchers engaged in antibody design and development. Through this detailed perspective, we aim to contribute to the advancement of antibody design, equipping researchers with the tools and knowledge to navigate the complexities of this field.

59 BASIC BIOLOGICAL SCIENCES↗

Predicting crystal structures and properties of matter under extreme conditions via quantum mechanics: The pressure is on

Experimental studies of compressed matter are now routinely conducted at pressures exceeding 1 mln atm (100 GPa) and occasionally they even surpass 10 mln atm (1 TPa). The structure and properties of solids that have been so significantly squeezed differ considerably from those know at ambient pressures (1 atm), often times leading to new and unexpected physics. Chemical reactivity is also substantially altered in the extreme pressure regime. In this feature paper we describe how synergy between theory and experiment can pave the road towards new experimental discoveries. Because chemical rules-of-thumb established at 1 atm often fail to predict the structures of solids under high pressure, automated crystal structure prediction (CSP) methods have been increasingly employed. After outlining the most important CSP techniques, we showcase a few examples from the recent literature that exemplify just how useful theory can be as an aid in the interpretation of experimental data, describe exciting theoretical predictions that are guiding experiment, and discuss when the computational methods that are currently routinely employed fail. Lastly, we forecast important problems that will be targeted by theory as theoretical methods undergo rapid development, along with the simultaneous increase of computational power.

36 MATERIALS SCIENCE↗

Sweep-tracing algorithm: in silico slip crystallography and tension-compression asymmetry in BCC metals

Abstract Direct Molecular Dynamics (MD) simulations are being increasingly employed to model dislocation-mediated crystal plasticity with atomic resolution. Thanks to the dislocation extraction algorithm (DXA), dislocation lines can be now accurately detected and positioned in space and their Burgers vector unambiguously identified in silico, while the simulation is being performed. However, DXA extracts static snapshots of dislocation configurations that by themselves present no information on dislocation motion. Referred to as a sweep-tracing algorithm (STA), here we introduce a practical computational method to observe dislocation motion and to accurately quantify its important characteristics such as preferential slip planes (slip crystallography). STA reconnects pairs of successive snapshots extracted by DXA and computes elementary slip facets thus precisely tracing the motion of dislocation segments from one snapshot to the next. As a testbed for our new method, we apply STA to the analysis of dislocation motion in large-scale MD simulations of single crystal plasticity in BCC metals. We observe that, when the crystal is subjected to uniaxial deformation along its [001] axis, dislocation slip predominantly occurs on the {112} maximum resolved shear stress plane under tension, while in compression slip is non-crystallographic (pencil) resulting in asymmetric mechanical response. The marked contrast in the observed slip crystallography is attributed to the twinning/anti-twinning asymmetry of shears in the {112} planes relatively favoring dislocation motion in the twinning sense while hindering dislocations from moving in the anti-twinning directions.

36 MATERIALS SCIENCE↗

Mathematical methods for optimal polynomial recovery of high-dimensional systems from noisy data

The goal of our Early Career Research Project (ECRP) is to establish a modern mathematical foundation that will enable next-generation computational methods for polynomial approximation of high-dimensional systems, having a certain set of constraints, from a limited amount of noisy data. Such a foundation is critical to realizing the future potential of the DOE user facilities, and will ultimately empower scientists to address a fundamental question, namely, “how many realizations of a nonlinear manifold are required to recover the entire high-dimensional solution map, with optimal approximation guarantees and minimal computational cost?” The central theme of this effort aims to conquer this challenge by pioneering the development of extraordinarily innovative theoretical analysis and transformational non-intrusive computational methodologies. Such approaches will enable the reconstruction of the entire high-dimensional solution map, with accuracy comparable to the best approximation, while utilizing an optimal number of samples. During this reporting period we have made significant progress on four thrusts.

97 MATHEMATICS AND COMPUTING↗