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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 523 records · Page 29

All sources NASA NTRS DOE OSTI

DOE OSTI2020

Towards improved speed and accuracy of laser powder bed fusion simulations via multiscale spatial representations

Due to the growing popularity of laser powder bed fusion (LPBF) as a metal additive manufacturing technique, there is a strong need to be able to accurately predict build outcomes. Full fidelity simulations of this process are not feasible due to the vast range of length and time scales inherent to it. While part-scale codes for simulating residual stress and distortion have shown reasonable predictive capability, they often neglect many aspects of the process occurring over smaller length/time scales, and thus are unable to capture effects of process parameter adjustments or the behavior of fine features. One way of capturing aspects at more refined length scales is through the use of adaptive mesh refinement (AMR). AMR allows for the process to be simulated at scales approaching the physical spatial dimensions without drastically increasing the total degrees of freedom in the simulation. This manuscript describes the implementation of an AMR algorithm within a multiphysics, parallelized finite element code, and its application to the LPBF problem. In this work, part-scale examples are provided where the use of AMR has allowed for higher fidelity thermal and thermomechanical simulations, as compared to experimental measurements. Results from these higher resolution simulations show that while AMR is a necessary component for increased accuracy in a computationally efficient manner, other improvements are also necessary, including handling of the multiple time scales inherent to the problem and the need for improved AM-specific material models.

42 ENGINEERING↗

DOE OSTI2021

PRISMS-Fatigue computational framework for fatigue analysis in polycrystalline metals and alloys

Abstract The PRISMS-Fatigue open-source framework for simulation-based analysis of microstructural influences on fatigue resistance for polycrystalline metals and alloys is presented here. The framework uses the crystal plasticity finite element method as its microstructure analysis tool and provides a highly efficient, scalable, flexible, and easy-to-use ICME community platform. The PRISMS-Fatigue framework is linked to different open-source software to instantiate microstructures, compute the material response, and assess fatigue indicator parameters. The performance of PRISMS-Fatigue is benchmarked against a similar framework implemented using ABAQUS. Results indicate that the multilevel parallelism scheme of PRISMS-Fatigue is more efficient and scalable than ABAQUS for large-scale fatigue simulations. The performance and flexibility of this framework is demonstrated with various examples that assess the driving force for fatigue crack formation of microstructures with different crystallographic textures, grain morphologies, and grain numbers, and under different multiaxial strain states, strain magnitudes, and boundary conditions.

Chemistry↗

DOE OSTI2016

Rise of an argon bubble in liquid steel in the presence of a transverse magnetic field

The rise of gaseous bubbles in viscous liquids is a fundamental problem in fluid physics, and it is also a common phenomenon in many industrial applications such as materials processing, food processing, and fusion reactor cooling. In this work, the motion of a single argon gas bubble rising in quiescent liquid steel under an external magnetic field is studied numerically using a Volume-of-Fluid method. To mitigate spurious velocities normally generated during numerical simulation of multiphase flows with large density differences, an improved algorithm for surface tension modeling, originally proposed by Wang and Tong [“Deformation and oscillations of a single gas bubble rising in a narrow vertical tube,” Int. J. Therm. Sci. 47, 221–228 (2008)] is implemented, validated and used in the present computations. The governing equations are integrated by a second-order space and time accurate numerical scheme, and implemented on multiple Graphics Processing Units with high parallel efficiency. The motion and terminal velocities of the rising bubble under different magnetic fields are compared and a reduction in rise velocity is seen in cases with the magnetic field applied. The shape deformation and the path of the bubble are discussed. An elongation of the bubble along the field direction is seen, and the physics behind these phenomena is discussed. The wake structures behind the bubble are visualized and effects of the magnetic field on the wake structures are presented. A modified drag coefficient is obtained to include the additional resistance force caused by adding a transverse magnetic field.

42 ENGINEERING↗

DOE OSTI2022

Dissecting protein tyrosine phosphatase signaling by engineered chemogenetic control of its activity

Protein tyrosine phosphatases (PTPases) are critical mediators of dynamic cell signaling. A tool capable of identifying transient signaling events downstream of PTPases is essential to understand phosphatase function on a physiological time scale. We report a broadly applicable protein engineering method for allosteric regulation of PTPases. This method enables dissection of transient events and reconstruction of individual signaling pathways. Implementation of this approach for Shp2 phosphatase revealed parallel MAPK and ROCK II dependent pathways downstream of Shp2, mediating transient cell spreading and migration. Furthermore, we show that the N-SH2 domain of Shp2 regulates MAPK-independent, ROCK II-dependent cell migration. Engineered targeting of Shp2 activity to different protein complexes revealed that Shp2-FAK signaling induces cell spreading whereas Shp2-Gab1 or Shp2-Gab2 mediates cell migration. We identified specific transient morphodynamic processes induced by Shp2 and determined the role of individual signaling pathways downstream of Shp2 in regulating these events. Broad application of this approach is demonstrated by regulating PTP1B and PTP-PEST phosphatases.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Quantum vacuum processes in the extremely intense light of relativistic plasma mirror sources

Abstract The advent of petawatt-class laser systems allows generating electromagnetic fields of unprecedented strength in a controlled environment, driving increasingly more efforts to probe yet unobserved processes through their interaction with the quantum vacuum. Still, the lowest intensity scale governing these effects lies orders of magnitude beyond foreseen capabilities, so that such endeavor is expected to remain extremely challenging. In recent years, however, plasma mirrors have emerged as a promising bridge across this gap, by enabling the conversion of intense infrared laser pulses into coherently focused Doppler harmonic beams lying in the X-UV range. In this work, we present predictions on the quantum vacuum signatures produced when such beams are focused to intensities between 10 24 and 10 28 W cm −2 , specifically photon–photon scattering and electron–positron pair creation. These signatures are computed via the stimulated vacuum formalism, combined with a model of perfectly focused beam built from PIC-generated harmonics spectra, and implemented on state-of-the-art massively parallel numerical tools. In view of identifying experimentally favorable configurations, we also consider the coupling of the focused harmonic beam with an auxiliary optical beam, and provide comparison with other established schemes. Our results show that a single coherently focused harmonic beam can produce as much scattered photons as two infrared pulses in head-on collision, and confirm that the coupling of the harmonic beam to an auxiliary beam gives rise to significant levels of inelastic scattering, and hence holds the potential to strongly improve the attainable signal to noise ratios in experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2017

Exploiting analytics techniques in CMS computing monitoring

The CMS experiment has collected an enormous volume of metadata about its computing operations in its monitoring systems, describing its experience in operating all of the CMS workflows on all of the Worldwide LHC Computing Grid Tiers. Data mining efforts into all these information have rarely been done, but are of crucial importance for a better understanding of how CMS did successful operations, and to reach an adequate and adaptive modelling of the CMS operations, in order to allow detailed optimizations and eventually a prediction of system behaviours. These data are now streamed into the CERN Hadoop data cluster for further analysis. Specific sets of information (e.g. data on how many replicas of datasets CMS wrote on disks at WLCG Tiers, data on which datasets were primarily requested for analysis, etc) were collected on Hadoop and processed with MapReduce applications profiting of the parallelization on the Hadoop cluster. We present the implementation of new monitoring applications on Hadoop, and discuss the new possibilities in CMS computing monitoring introduced with the ability to quickly process big data sets from mulltiple sources, looking forward to a predictive modeling of the system.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Scaling neural simulations in STACS

Abstract As modern neuroscience tools acquire more details about the brain, the need to move towards biological-scale neural simulations continues to grow. However, effective simulations at scale remain a challenge. Beyond just the tooling required to enable parallel execution, there is also the unique structure of the synaptic interconnectivity, which is globally sparse but has relatively high connection density and non-local interactions per neuron. There are also various practicalities to consider in high performance computing applications, such as the need for serializing neural networks to support potentially long-running simulations that require checkpoint-restart. Although acceleration on neuromorphic hardware is also a possibility, development in this space can be difficult as hardware support tends to vary between platforms and software support for larger scale models also tends to be limited. In this paper, we focus our attention on Simulation Tool for Asynchronous Cortical Streams (STACS), a spiking neural network simulator that leverages the Charm++ parallel programming framework, with the goal of supporting biological-scale simulations as well as interoperability between platforms. Central to these goals is the implementation of scalable data structures suitable for efficiently distributing a network across parallel partitions. Here, we discuss a straightforward extension of a parallel data format with a history of use in graph partitioners, which also serves as a portable intermediate representation for different neuromorphic backends. We perform scaling studies on the Summit supercomputer, examining the capabilities of STACS in terms of network build and storage, partitioning, and execution. We highlight how a suitably partitioned, spatially dependent synaptic structure introduces a communication workload well-suited to the multicast communication supported by Charm++. We evaluate the strong and weak scaling behavior for networks on the order of millions of neurons and billions of synapses, and show that STACS achieves competitive levels of parallel efficiency.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2018

Umbrella sampling: a powerful method to sample tails of distributions

We present the umbrella sampling (US) technique and show that it can be used to sample extremely low-probability areas of the posterior distribution that may be required in statistical analyses of data. In this approach sampling of the target likelihood is split into sampling of multiple biased likelihoods confined within individual umbrella windows. We show that the US algorithm is efficient and highly parallel and that it can be easily used with other existing Markov Chain Monte Carlo (MCMC) samplers. The method allows the user to capitalize on their intuition and define umbrella windows and increase sampling accuracy along specific directions in the parameter space. Alternatively, one can define umbrella windows using an approach similar to parallel tempering. We provide a public code that implements US as a standalone PYTHON package. We present a number of tests illustrating the power of the US method in sampling low-probability areas of the posterior and show that this ability allows a considerably more robust sampling of multimodal distributions compared to standard sampling methods. We also present an application of the method in a real world example of deriving cosmological constraints using the supernova type Ia data. We show that US can sample the posterior accurately down to the approximate to 15 sigma credible region in the Omega(m) - Omega(Lambda) plane, while for the same computational effort the affine-invariant MCMC sampling implemented in the emcee code samples the posterior reliably only to approximate to 3 sigma.

cosmology: cosmological parameters↗

DOE OSTI2016

Mini-apps for high performance data analysis

Scaling-up scientific data analysis and machine learning algorithms for data-driven discovery is a grand challenge that we face today. Despite the growing need for analysis from science domains that are generating `Big Data' from instruments and simulations, building high-performance analytical workflows of data-intensive algorithms have been daunting because: (i) the `Big Data' hardware and software architecture landscape is constantly evolving, (ii) newer architectures impose new programming models, and (iii) data-parallel kernels of analysis algorithms and their performance facets on different architectures are poorly understood. To address these problems, we have: (i) identified scalable data-parallel kernels of popular data analysis algorithms, (ii) implemented `Mini-Apps' of those kernels using different programming models (e.g. Map Reduce, MPI, etc.), (iii) benchmarked and validated the performance of the kernels in diverse architectures. In this paper, we discuss two of those Mini-Apps and show the execution of principal component analysis built as a workflow of the Mini-Apps. We show that Mini-Apps enable scientists to (i) write domain-specific data analysis code that scales on most HPC hardware and (ii) and offers the ability (most times with over a 10x speed-up) to analyze data sizes 100 times the size of what off-the-shelf desktop/workstations of today can handle.

Sukumar, Sreenivas R.↗

DOE OSTI2016

On-demand data analytics in HPC environments at leadership computing facilities: Challenges and experiences

Harney, John↗

DOE OSTI2021

WIRE: Resource-efficient Scaling with Online Prediction for DAG-based Workflows

This paper introduces WIRE that manages resources for the DAG-based workflows on IaaS clouds. WIRE predicts and plans resources over the MAPE (Monitor-Analyze-Plan-Execute) loops to: 1) Estimate task performance with online data, 2) Conduct simulations to predict the upcoming loads based on online estimates and workflow DAGs, 3) Apply a resource-steering policy to size cloud instance pools for the maximal parallelism that is consistent with low cost. We implement WIRE on Pegasus WMS/HTCondor and evaluate its performance on the ExoGENI network cloud. The results show that WIRE attains low resource cost with the performance that is typically within a factor of two of optimal.

Xie, Bing↗

DOE OSTI2023

Julia as a unifying end-to-end workflow language on the Frontier exascale system

We evaluate Julia as a single language and ecosystem paradigm powered by LLVM to develop workflow components for high-performance computing. We run a Gray-Scott, 2-variable diffusion-reaction application using a memory-bound, 7-point stencil kernel on Frontier, the US Department of Energy’s first exascale supercomputer. We evaluate the performance, scaling, and trade-offs of (i) the computational kernel on AMD’s MI250x GPUs, (ii) weak scaling up to 4,096 MPI processes/GPUs or 512 nodes, (iii) parallel I/O writes using the ADIOS2 library bindings, and (iv) Jupyter Notebooks for interactive analysis. Results suggest that although Julia generates a reasonable LLVM-IR, a nearly 50% performance difference exists vs. native AMD HIP stencil codes when running on the GPUs. As expected, we observed near-zero overhead when using MPI and parallel I/O bindings for system-wide installed implementations. Consequently, Julia emerges as a compelling high-performance and high-productivity workflow composition language, as measured on the fastest supercomputer in the world.

Godoy, William↗

DOE OSTI2025

Determining Levels of Detail for Simulators of Parallel and Distributed Computing Systems via Automated Calibration

There are two sources of inaccuracy when simulating parallel and distributed computing systems: (i) a simulator implemented at an insufficient level of detail; and (ii) incorrectly calibrated simulation parameter values. Increasing the simulator’s level of detail can improve accuracy, but at the cost of higher space, time, and/or software complexity. Furthermore, evaluating the intrinsic accuracy of a simulator requires that its parameters be well-calibrated. Making decisions regarding the level of detail is thus challenging. We propose a methodology for instantiating the simulation calibration process and a framework for automating this process, which makes it possible to pick appropriate levels of detail for any simulator. We demonstrate the usefulness of our approach via two case studies for two different domains.

McDonald, Jessie [University of Hawaii at Manoa, H↗

DOE OSTI2017

LLVM Infrastructure and Tools Project Summary

This project works with the open source LLVM Compiler Infrastructure (http://llvm.org) to provide tools and capabilities that address needs and challenges faced by ECP community (applications, libraries, and other components of the software stack). Our focus is on providing a more productive development environment that enables (i) improved compilation times and code generation for parallelism, (ii) additional features/capabilities within the design and implementations of LLVM components for improved platform/performance portability and (iii) improved aspects related to composition of the underlying implementation details of the programming environment, capturing resource utilization, overheads, etc. -- including runtime systems that are often not easily addressed by application and library developers.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

PETSc Users Manual Revision 3.8

This manual describes the use of PETSc for the numerical solution of partial differential equations and related problems on high-performance computers. The Portable, Extensible Toolkit for Scientific Computation (PETSc) is a suite of data structures and routines that provide the building blocks for the implementation of large-scale application codes on parallel (and serial) computers. PETSc uses the MPI standard for all message-passing communication.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

PETSc Users Manual: Revision 3.10

This manual describes the use of PETSc for the numerical solution of partial differential equa-tions and related problems on high-performance computers. The Portable, Extensible Toolkit forScientific Computation (PETSc) is a suite of data structures and routines that provide the buildingblocks for the implementation of large-scale application codes on parallel (and serial) computers.PETSc uses the MPI standard for all message-passing communication.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

PETSc Users Manual (Rev. 3.11)

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

PETSc Users Manual (Revision 3.15)

This manual describes the use of the Portable, Extensible Toolkit for Scientific Computation (PETSc) for the numerical solution of partial differential equations and related problems on high-performance computers. PETSc is a suite of data structures and routines that provide the building blocks for the implementation of large-scale application codes on parallel (and serial) computers. PETSc uses the MPI standard for all distributed memory communication.

97 MATHEMATICS AND COMPUTING↗