SEARCH · Search NASA

Results for “Cluster Computing”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

SourceResearch connectionFrom yearThrough year

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

At least 1,099 records · Page 61

All sources NASA NTRS DOE OSTI

NASA NTRSDate not supplied

What Went Wrong: A Survey of Wildfire UAS Mishaps through Named Entity Recognition

Increasingly, unmanned aircraft systems (UAS) are being applied to wildfire incidents for tasks such as mapping, aerial ignition, and delivery. As a result, incident reporting systems for wildfires are beginning to accumulate data related to UAS mishaps in wildfire response. In this research, we apply state-of-the-art natural language processing (NLP) techniques to develop a custom Named Entity Recognition (NER) model which extracts a Failure Modes and Effects Analysis (FMEA)-style survey of wildfire UAS mishaps reported in SAFECOM. The custom NER model is built by fine-tuning an existing (BERT) model, resulting in a generalizable NER model that can extract engineering relevant entities including failure modes, causes, effects, control processes, and recommendations from any failure-relevant text. Similar mishaps are clustered and reported as single rows within the FMEA. For each cluster, frequency, severity, and overall risk are computed. The methodology can be applied as part of a broader safety management system to track trends in mishaps and discover knowledge that can be utilized to improve safety outcomes and system performance.

Machine Learning↗

NASA NTRSDate not supplied

What Went Wrong: A Survey of Wildfire UAS Mishaps through Named Entity Recognition

Machine Learning↗

DOE OSTI2017

Potential impacts of climate change on the built environment: ASHRAE climate zones, building codes and national energy efficiency

Statement of the Problem: ASHRAE releases updates to 90.1 “Energy Standard for Buildings except Low-Rise Residential Buildings” every three years resulting in a 3.7%-17.3% increase in energy efficiency for buildings with each release. This is adopted by or informs building codes in nations across the globe, is the National Standard for the US, and individual states elect which release year of the standard they will enforce. These codes are built upon Standard 169 “Climatic Data for Building Design Standards,” the latest 2017 release of which defines climate zones based on 8, 118 weather stations throughout the world and data from the past 8-25 years. This data may not be indicative of the weather that new buildings built today, will see during their upcoming 30-120 year lifespan. Methodology & Theoretical Orientation: Using more modern, high-resolution datasets from climate satellites, IPCC climate models (PCM and HadGCM), high performance computing resources (Titan) and new capabilities for clustering and optimization the authors briefly analyzed different methods for redefining climate zones. Using bottom-up analysis of multiple meteorological variables which were the subject matter, experts selected as being important to energy consumption, rather than the heating/cooling degree days currently used. Findings: We analyzed the accuracy of redefined climate zones, compared to current climate zones and how the climate zones moved under different climate change scenarios, and quantified the accuracy of these methods on a local level, at a national scale for the US. Conclusion & Significance: There is likely to be a significant annual, national energy and cost (billions USD) savings that could be realized by adjusting climate zones to take into account anticipated trends or scenarios in regional weather patterns.

New, Joshua Ryan↗

DOE OSTI2018

Two-level Clustering-based Target Detection Through Sensor Deployment and Data Fusion

Target detection is one fundamental problem in many sensor network-based applications, which is typically tackled in two separate stages for sensor deployment and data fusion. We propose an integrated solution, referred to as 2C-SSEM,which combines 2-level clustering-based sensor deployment and source strength estimate map-based data fusion for the detection of a single static or moving target. 2C-SSEM conducts the first level of clustering to determine a sensor deployment scheme and the second level of clustering to divide the deployed sensors into several subsets. For each sensor, the source strength is estimated at each grid point of the entire region based on a signal attenuation model, and for each subset of sensors, the target location is estimated using a distribution map-based statistical analysis method. A final detection decision is made by thresholding the clustering degree of the target location estimates computed by all subsets of sensors. Compared with traditional grid-based target detection methods, 2C-SSEM significantly reduces the computation complexity and improves the detection performance through an integrated optimization strategy. Extensive simulation results show the performance superiority of the proposed solution over several well-known methods for target detection.

Rao, Nageswara↗

DOE OSTI2025

Experiments to validate Thermodynamics and Transport models of Strongly Coupled Dusty Plasma Matter (Final Technical Report for DE-SC0023416)

The goal of this two-year grant is to provide access to the PI to dusty plasma experimental facilities at the DOE-funded Collaborative Research Facility Magnetized Plasma Research Laboratory, Auburn University to become a user of that facility, to obtain experimental data to support another ongoing grant DE-SC0021146 (an Early Career Award to the PI that is focused on modeling of dusty plasma thermodynamics and transport processes), to generate experimental data for funding proposals, and to provide exposure to University of Memphis students to advanced experimental techniques. The following technical accomplishments were made: 1. Development of a novel Bidirectional Electrode Control Arms Assembly (BECAA) for producing perfect 2D grain layers for complex plasma experimentation. BECAA uses movable electrode arms to tilt or move the electrode in a RF discharge from outside the chamber, allowing for the manipulation of grain clouds without needing to change the plasma parameters or gas pressure. This work addresses a longstanding gap in the literature for a method to produce clusters of selectable number of grains and that are perfectly two dimensional as opposed to being only quasi-2D. 2. Experimental investigation of the structural properties of finite-N clusters with N=2 to 50. Individual particle behavior in clusters could vary from grain to grain and this study measured systematically produced clusters for two different grain sizes. Analysis (funded by another grant DE-SC0021146) is currently underway to quantify the differences between grains that are found on the surface vs. the interior of clusters, the shell structure, and the decay of correlations in position, velocity, and kinetic energy. 3. An experimental method to measure the structural entropy of clusters was developed by observing the self-induced structural transitions between various possible arrangements. In a series of heating and cooling cycles, the number of times each possible arrangement was attained was experimentally observed and used to compute the probability of existence of that arrangement, and subsequently the configurational entropy of the cluster. Analysis (funded by another grant DE-SC0021146) is currently underway to produce the entropy of clusters as a function of the number of grains and use the same to compute thermodynamic state variables for 2D complex plasma/grain clusters. 4. A preliminary experimental study of multibody collisions between N grains (N=2 – 10) was conducted. The clusters were produced using the BECAA technique and velocities were imparted to the grains using manipulation laser pulses. Analysis (funded by another grant DE-SC0021146) is currently underway to develop a theoretical framework to describe multibody collisions analogous to classical two-body interactions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2014

Spatially dependent cluster dynamics model of He plasma surface interaction in tungsten for fusion relevant conditions

In fusion reactors, plasma facing components (PFC) and, in particular, the divertor will be irradiated with high fluxes of low-energy (similar to 100 eV) helium and hydrogen ions. Tungsten is one of the leading candidate divertor materials for ITER and DEMO fusion reactors. However, the behaviour of tungsten under high dose, coupled helium/hydrogen exposure remains to be fully understood. The PFC response and performance changes are intimately related to microstructural changes, such as the formation of point defect clusters, helium and hydrogen bubbles or dislocation loops. Computational materials' modelling results are described here that investigate the mechanisms controlling microstructural evolution in tungsten. The aim of this study is to understand and predict sub-surface helium bubble growth under high flux helium ion implantation (similar to 10 22 m -2 s -1 ) at high temperatures (>1000 K). Here, we report results from a spatially dependent cluster dynamics model based on reaction-diffusion rate theory to describe the evolution of the microstructure under these conditions. The key input parameters to the model (diffusion coefficients, migration and binding energies, initial defect production) are determined from a combination of atomistic modelling and available experimental data. The results are in good agreement with results of an analytical model that is presented in a separate paper. In particular, it is found that the sub-surface evolution with respect to bubble size and concentration of the helium bubbles strongly depends on the flux and temperature.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2019

Short-Range Structure of Amorphous Calcium Hydrogen Phosphate

Calcium orthophosphates (CaPs) are the hard constituents of bones and teeth, and thus of ultimate importance to humankind, while amorphous CaPs (ACPs) may play crucial roles in CaP biomineralization. Among the various ACPs with Ca/P atomic ratios between 1.0–1.5, an established structural model exists for basic ACP (Ca/P = 1.5), while those of other ACPs remain unclear. In this work, the structure of amorphous calcium hydrogen phosphate (ACHP; Ca/P = 1.0) obtained via aqueous routes at near-neutral pH values, without stabilizers, was studied by experiments (mainly, TEM with ED, XRD, IR, and NMR spectroscopies, as well as XAS) and computer simulation. Our results globally show that ACHP has a distinct short-range structure, and we propose calcium hydrogen phosphate clusters (CHPCs) as its basic unit. This model is consistent with both computer simulations and the experimental results, where CHPCs are arranged together with water molecules to build up ACHP. We demonstrate that Posner’s clusters, which are conventionally accepted to be the building unit of basic ACPs, do not represent the short-range structure of ACHP, as Posner’s clusters and CHPCs are structurally distinct. This finding is important not only for the determination of the structures of diverse ACPs with varying Ca/P atomic ratios but also for fundamental understanding of a major mineral class that is central to biomineralization in vertebrates and, thus, humans, in particular.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2018

Cloud Quantum Computing of an Atomic Nucleus

Here, we report a quantum simulation of the deuteron binding energy on quantum processors accessed via cloud servers. We use a Hamiltonian from pionless effective field theory at leading order. We design a low-depth version of the unitary coupled-cluster ansatz, use the variational quantum eigensolver algorithm, and compute the binding energy to within a few percent. Our work is the first step towards scalable nuclear structure computations on a quantum processor via the cloud, and it sheds light on how to map scientific computing applications onto nascent quantum devices.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Edge-Mediated Annihilation of Vacancy Clusters in Monolayer Molybdenum Diselenide (MoSe 2 ) under Electron Beam Irradiation

Here annihilation of vacancy clusters in monolayer molybdenum diselenide (MoSe2) under electron beam irradiation is reported. In situ high-resolution transmission electron microscopy observation reveals that the annihilation is achieved by diffusion of vacancies to the free edge near the vacancy clusters. Monte Carlo simulations confirm that it is energetically favorable for the vacancies to locate at the free edge. By computing the minimum energy path for the annihilation of one vacancy cluster as a case study, it is further shown that electron beam irradiation and pre-stress in the suspended MoSe2 monolayer are necessary for the vacancies to overcome the energy barriers for diffusion. The findings suggest a new mechanism of vacancy healing in 2D materials and broaden the capability of electron beam for defect engineering of 2D materials, a promising way of tuning their properties for engineering applications.

2D materials↗

DOE OSTI2016

A Brownian dynamics study on ferrofluid colloidal dispersions using an iterative constraint method to satisfy Maxwell’s equations

Ferrofluids are often favored for their ability to be remotely positioned via external magnetic fields. The behavior of particles in ferromagnetic clusters under uniformly applied magnetic fields has been computationally simulated using the Brownian dynamics, Stokesian dynamics, and Monte Carlo methods. However, few methods have been established that effectively handle the basic principles of magnetic materials, namely, Maxwell’s equations. An iterative constraint method was developed to satisfy Maxwell’s equations when a uniform magnetic field is imposed on ferrofluids in a heterogeneous Brownian dynamics simulation that examines the impact of ferromagnetic clusters in a mesoscale particle collection. This was accomplished by allowing a particulate system in a simple shear flow to advance by a time step under a uniformly applied magnetic field, then adjusting the ferroparticles via an iterative constraint method applied over sub-volume length scales until Maxwell’s equations were satisfied. The resultant ferrofluid model with constraints demonstrates that the magnetoviscosity contribution is not as substantial when compared to homogeneous simulations that assume the material’s magnetism is a direct response to the external magnetic field. This was detected across varying intensities of particle-particle interaction, Brownian motion, and shear flow. Ferroparticle aggregation was still extensively present but less so than typically observed.

42 ENGINEERING↗

DOE OSTI2020

Unexpected Hydroxide Ion Structure and Properties at Low Hydration

Understanding the behavior of hydroxide ions in aqueous and non-aqueous media is fundamental to many chemical, biological, and electrochemical processes. Research has primarily focused on a single fully solvated hydroxide ion, either as an isolated cluster or in bulk. This work presents the first computational study to consider hydroxide under low hydration levels in detail, where the anion may not be fully solvated. Under such conditions, we find that the anions are predominantly present as unique water-bridged hydroxide pair complexes, distinct from previously reported structures under fully hydrated conditions. Although similar hydroxide pair structures were previously reported, we analyze these structures for the first time in the disordered liquid state where they are found to be unusually stable in the presence of bulky quaternary ammonium cations. Our findings help explain the unusual diffusion behavior as well as the higher reactivity of hydroxide anions observed under low hydration conditions.

computational studies↗

DOE OSTI2025

Quantifying uncertainties in α -nucleus reaction dynamics informed from first principles

The ab initio symmetry-adapted no-core shell model is a microscopic many-body method which naturally describes challenging collective and clustering features of atomic nuclei. Wave functions and observables computed with realistic nucleon-nucleon forces in this framework are tied to first principles, and are hence well-suited for rigorous uncertainty quantification. We discuss α-deuteron and α- 12 C cluster potentials informed by symmetry-adapted calculations, and propagate uncertainties in the effective binary cluster method as well as those in the nuclear interaction to reaction observables, namely scattering phase shifts, cross sections, partial widths and resonance energies. Here, we find that the overall uncertainties are dominated by those originating in the underlying nuclear force, speaking to the need for tighter constraints on realistic nucleon-nucleon interactions.

Ab initio↗

DOE OSTI2016

Automated property optimization via ab initio O(N) elongation method: Application to (hyper-)polarizability in DNA

An automated property optimization method was developed based on the ab initio O(N) elongation (ELG) method and applied to the optimization of nonlinear optical (NLO) properties in DNA as a first test. The ELG method mimics a polymerization reaction on a computer, and the reaction terminal of a starting cluster is attacked by monomers sequentially to elongate the electronic structure of the system by solving in each step a limited space including the terminal (localized molecular orbitals at the terminal) and monomer. The ELG-finite field (ELG-FF) method for calculating (hyper-)polarizabilities was used as the engine program of the optimization method, and it was found to show linear scaling efficiency while maintaining high computational accuracy for a random sequenced DNA model. Furthermore, the self-consistent field convergence was significantly improved by using the ELG-FF method compared with a conventional method, and it can lead to more feasible NLO property values in the FF treatment. The automated optimization method successfully chose an appropriate base pair from four base pairs (A, T, G, and C) for each elongation step according to an evaluation function. From test optimizations for the first order hyper-polarizability (β) in DNA, a substantial difference was observed depending on optimization conditions between “choose-maximum” (choose a base pair giving the maximum β for each step) and “choose-minimum” (choose a base pair giving the minimum β). In contrast, there was an ambiguous difference between these conditions for optimizing the second order hyper-polarizability (γ) because of the small absolute value of γ and the limitation of numerical differential calculations in the FF method. It can be concluded that the ab initio level property optimization method introduced here can be an effective step towards an advanced computer aided material design method as long as the numerical limitation of the FF method is taken into account.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2006

Architecture Adaptive Computing Environment

Architecture Adaptive Computing Environment (aCe) is a software system that includes a language, compiler, and run-time library for parallel computing. aCe was developed to enable programmers to write programs, more easily than was previously possible, for a variety of parallel computing architectures. Heretofore, it has been perceived to be difficult to write parallel programs for parallel computers and more difficult to port the programs to different parallel computing architectures. In contrast, aCe is supportable on all high-performance computing architectures. Currently, it is supported on LINUX clusters. aCe uses parallel programming constructs that facilitate writing of parallel programs. Such constructs were used in single-instruction/multiple-data (SIMD) programming languages of the 1980s, including Parallel Pascal, Parallel Forth, C*, *LISP, and MasPar MPL. In aCe, these constructs are extended and implemented for both SIMD and multiple- instruction/multiple-data (MIMD) architectures. Two new constructs incorporated in aCe are those of (1) scalar and virtual variables and (2) pre-computed paths. The scalar-and-virtual-variables construct increases flexibility in optimizing memory utilization in various architectures. The pre-computed-paths construct enables the compiler to pre-compute part of a communication operation once, rather than computing it every time the communication operation is performed.

Dorband, John E.↗

DOE OSTI2022

Assessing MP2 frozen natural orbitals in relativistic correlated electronic structure calculations

The high computational scaling with the basis set size and the number of correlated electrons is a bottleneck limiting applications of coupled cluster algorithms, in particular for calculations based on two- or four-component relativistic Hamiltonians, which often employ uncontracted basis sets. This problem may be alleviated by replacing canonical Hartree–Fock virtual orbitals by natural orbitals (NOs). Here, in this paper, we describe the implementation of a module for generating NOs for correlated wavefunctions and, in particular, second order Møller–Plesset perturbation frozen natural orbitals (MP2FNOs) as a component of our novel implementation of relativistic coupled cluster theory for massively parallel architectures [Pototschnig et al. J. Chem. Theory Comput. 17, 5509, (2021)]. Our implementation can manipulate complex or quaternion density matrices, thus allowing for the generation of both Kramers-restricted and Kramers-unrestricted MP2FNOs. Furthermore, NOs are re-expressed in the parent atomic orbital (AO) basis, allowing for generating coupled cluster singles and doubles NOs in the AO basis for further analysis. By investigating the truncation errors of MP2FNOs for both the correlation energy and molecular properties—electric field gradients at the nuclei, electric dipole and quadrupole moments for hydrogen halides HX (X = F–Ts), and parity-violating energy differences for H 2 Z 2 (Z = O–Se)—we find MP2FNOs accelerate the convergence of the correlation energy in a roughly uniform manner across the Periodic Table. It is possible to obtain reliable estimates for both energies and the molecular properties considered with virtual molecular orbital spaces truncated to about half the size of the full spaces.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2015

Computational design of nanoclusters by property-based genetic algorithms: Tuning the electronic properties of ( TiO 2 ) n clusters

Not Available

Bhattacharya, Saswata↗

DOE OSTI2026

NuLattice: Ab initio computations of atomic nuclei on lattices

Here, we introduce NuLattice, a Python software package for ab initio computations of atomic nuclei on lattices. The computational tools consist of Hartree Fock, the coupled cluster method, the in-medium similarity renormalization group, and full configuration interaction. At present, the employed interactions are from pion-less effective field theory at leading order and consist of two-body and three-body contacts. We present results for light nuclei 2 H, 3,4 He, 8 Be, 12 C, and 16 O. NuLattice algorithms exploit the sparsity and locality of lattice interactions, and as a result computations can be run on laptops.

Rothman, Maxwell [Univ. of Tennessee, Knoxville, T↗

NASA NTRS1998

Workload Characterization of CFD Applications Using Partial Differential Equation Solvers

Workload characterization is used for modeling and evaluating of computing systems at different levels of detail. We present workload characterization for a class of Computational Fluid Dynamics (CFD) applications that solve Partial Differential Equations (PDEs). This workload characterization focuses on three high performance computing platforms: SGI Origin2000, EBM SP-2, a cluster of Intel Pentium Pro bases PCs. We execute extensive measurement-based experiments on these platforms to gather statistics of system resource usage, which results in workload characterization. Our workload characterization approach yields a coarse-grain resource utilization behavior that is being applied for performance modeling and evaluation of distributed high performance metacomputing systems. In addition, this study enhances our understanding of interactions between PDE solver workloads and high performance computing platforms and is useful for tuning these applications.

Waheed, Abdul↗