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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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405 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2011

A PENELOPE-based system for the automated Monte Carlo simulation of clinacs and voxelized geometries - application to far-from-axis fields

Purpose: Two new codes, PENEASY and PENEASYLINAC, which automate the Monte Carlo simulation of Varian Clinacs of the 600, 1800, 2100, and 2300 series, together with their electron applicators and multileaf collimators, are introduced. The challenging case of a relatively small and far-from-axis field has been studied with these tools. Methods: PENEASY is a modular, general-purpose main program for the PENELOPE Monte Carlo system that includes various source models, tallies and variance-reduction techniques (VRT). The code includes a new geometry model that allows the superposition of voxels and objects limited by quadric surfaces. A variant of the VRT known as particle splitting, called fan splitting, is also introduced. PENEASYLINAC, in turn, automatically generates detailed geometry and configuration files to simulate linacs with PENEASY. These tools are applied to the generation of phase-space files, and of the corresponding absorbed dose distributions in water, for two 6 MV photon beams from a Varian Clinac 2100 C/D: a 40 x 40 cm{sup 2} centered field; and a 3 x 5 cm{sup 2} field centered at (4.5, -11.5) cm from the beam central axis. This latter configuration implies the largest possible over-traveling values of two of the jaws. Simulation results for the depth dose and lateral profiles at various depths are compared, by using the gamma index, with experimental values obtained with a PTW 31002 ionization chamber. The contribution of several VRTs to the computing speed of the more demanding off-axis case is analyzed. Results: For the 40 x 40 cm{sup 2} field, the percentages {gamma}{sub 1} and {gamma}{sub 1.2} of voxels with gamma indices (using 0.2 cm and 2% criteria) larger than unity and larger than 1.2 are 0.2% and 0%, respectively. For the 3 x 5 cm{sup 2} field, {gamma}{sub 1} = 0%. These figures indicate an excellent agreement between simulation and experiment. The dose distribution for the off-axis case with voxels of 2.5 x 2.5 x 2.5 mm{sup 3} and an average standard statistical uncertainty of 2% (1{sigma}) is computed in 3.1 h on a single core of a 2.8 GHz Intel Core 2 Duo processor. This result is obtained with the optimal combination of the tested VRTs. In particular, fan splitting for the off-axis case accelerates execution by a factor of 240 with respect to standard particle splitting. Conclusions: PENEASY and PENEASYLINAC can simulate the considered Varian Clinacs both in an accurate and efficient manner. Fan splitting is crucial to achieve simulation results for the off-axis field in an affordable amount of CPU time. Work to include Elekta linacs and to develop a graphical interface that will facilitate user input is underway.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2017

CUDA Computation of the Feynman Distribution

In 2006, NVIDIA Corporation introduced the Computer Unified Device Architecture (CUDA) for C, C++, and Fortran programming languages. The CUDA parallel computing platform uses the Graphics Processing Unit (GPU) of NVIDIA graphics card, and it is designed for parallel computations. In these computations, the same algorithms are executed on the data in parallel. The GPU contains many Streaming Multiprocessors (SM) that can independently execute blocks of threads. A thread is a sequence of serial programmed instructions. The SM executes threads in groups of 32 and a group of 32 threads is called warp. The threads within a block can be synchronized and have access to a shared memory that is visible only to them. Conversely, blocks of threads cannot be synchronized and have access only to the global memory of the graphics card. This memory is called device memory and is independent of the host memory controlled by the Central Processing Unit (CPU). A single thread has access to a local memory (register) that is not visible to any other thread. The part of the program that is executed on the graphics card is called kernel. Each thread of the kernel has a unique identification index that is accessible by the in-built variable threadIdx; the latter is a 3-component vector. A threads block can contain up to 1024 threads. Blocks of threads have a unique identification index that is accessible by the in-built variable blockIdx; the latter is a 3-component vector. The in-built variable blockDim contains the number of threads per block; this variable is a 3-component vector. Threads and blocks represent different levels of parallelism; a kernel is executed as a grid of blocks of threads. Figure 1 illustrates a grid of thread blocks for a GPU with 2 SM. A SM can execute multiple blocks of threads in parallel; a block of threads is executed on a single SM. A GPU can execute multiple kernels concurrently. This work has used the Quadro M5000 graphics card with the following technical specifications: - 2048 cores with 861 MHz frequency, - 16 streaming multiprocessors, - 5.2 trillion transistors, - 8.2 Gb device memory, - 64 kb register memory (per thread), - 48 kb shared memory (per block) - 211.6 GB/s memory bandwidth, and - 3.5 GFLOPS floating point performance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

Managing Waste Inventory and License Limits at the Perma-Fix Northwest Facility to Meet CH2M Hill Plateau Remediation Company (CHPRC) American Recovery and Reinvestment Act (ARRA) Deliverables - 12335

CH2M Hill Plateau Remediation Company (CHRPC) is a prime contractor to the U.S. Department of Energy (DOE) focused on the largest ongoing environmental remediation project in the world at the DOE Hanford Site Central Plateau, i.e. the DOE Hanford Plateau Remediation Contract. The East Tennessee Materials and Energy Corporation (M and EC); a wholly owned subsidiary of Perma-Fix Environmental Services, Inc. (PESI), is a small business team member to CHPRC. Our scope includes project management; operation and maintenance of on-site storage, repackaging, treatment, and disposal facilities; and on-site waste management including waste receipt from generators and delivery to on-site and off-site treatment, storage, and disposal facilities. As part of this scope, M and EC staffs the centralized Waste Support Services organization responsible for all waste characterization and acceptance required to support CHPRC and waste generators across the Hanford Site. At the time of the CHPRC contract award (August 2008) slightly more than 9,000 cubic meters (m{sup 3}) of legacy waste was defined as 'no-path-forward waste'. A significant portion of this waste (7,650 m{sup 3}) comprised wastes with up to 50 grams of special nuclear materials (SNM) in oversized packages recovered during retrieval operations and large glove boxes removed from the Plutonium Finishing Plant (PFP). Through a collaborative effort between the DOE, CHPRC, and Perma-Fix Environmental Services, Inc. (PESI), pathways for these problematic wastes were developed that took advantage of commercial treatment capabilities at a nearby vendor facility, Perma-Fix Northwest (PFNW). In the spring of 2009, CHPRC initiated a pilot program under which they began shipping large package, low gram suspect TRU (<15 g SNM per container), and large package contact and remote handled MLLW to the off-site PFNW facility for treatment. PFNW is restricted by the SNM limits set for the total quantity of SNM allowed at the facility in accordance with the facility's radioactive materials license(s) (RML). While both CHPRC and PFNW maintain waste databases to track all waste movements, it became evident early in the process that a tool was needed that married the two systems to better track SNM inventories and sequence waste from the point of generation, through the PFNW facility, and back to the Hanford site for final disposition. This tool, known as the Treatment Integration and Planning Tool (TIPT), has become a robust planning tool that provides real-time data to support compliant and efficient waste generation, transportation, treatment, and disposition. TIPT is developing into the next generation tool that will change the way in which legacy wastes, retrieval wastes and decontamination and decommissioning operations are conducted on the Plateau Remediation Contract (PRC). The real value of the TIPT is its predictive capability. It allows the W and FMP to map out optimal windows for processing waste through the PFNW facility, or through any process that is in some way resource limited. It allows project managers to identify and focus on problem areas before shipments are affected. It has been modified for use in broader applications to predict turnaround times and identify windows of opportunity for processing higher gram wastes through PFNW and to allow waste generators, site-wide, to accurately predict scope, cost, and schedule for waste generation to optimize processing and eliminate storage, double handling, and related costs and unnecessary safety risks. The TIPT addresses the years old problem of how to effectively predict not only what needs to be done, but when. 'When' is the key planning parameter that has been ignored by the generator and processor for many years, but has proven to be the most important parameter for both parties. While further refinement is a natural part of any development process, the current improvements on the TIPT have shown that prediction is a powerful consideration. Even in lean times expected for the foreseeable future, the improved TIPT continues to play a central role in managing our way through those times to assure facilities remain viable and available. It is recommended that other major remediation projects and waste processing facilities incorporate a tool such as TIPT to improve customer-commercial supplier communications and better optimization of resources. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2010

Inclusion of Scatter in HADES: Final Report

Covert nuclear attack is one of the foremost threats facing the United States and is a primary focus of the War on Terror. The Domestic Nuclear Detection Office (DNDO), within the Department of Homeland Security (DHS), is chartered to develop, and improve domestic systems to detect and interdict smuggling for the illicit use of a nuclear explosive device, fissile material or radiologica1 material. The CAARS (Cargo Advanced Automated Radiography System) program is a major part of the DHS effort to enhance US security by harnessing cutting-edge technologies to detect radiological and nuclear threats at points of entry to the United States. DNDO has selected vendors to develop complete radiographic systems. It is crucial that the initial design and testing concepts for the systems be validated and compared prior to the substantial efforts to build and deploy prototypes and subsequent large-scale production. An important aspect of these systems is the scatter which interferes with imaging. Monte Carlo codes, such as MCNP (X-5 Monte Carlo Team, 2005 Revision) allow scatter to be calculatied, but these calculations are very time consuming. It would be useful to have a fast scatter estimation algorithm in a fast ray tracing code. We have been extending the HADES ray-tracing radiographic simulation code to model vendor systems in a flexible and quick fashion and to use this tool to study a variety of questions involving system performance and the comparative value of surrogates. To enable this work, HADES has been linked to the BRL-CAD library (BRL-CAD Open Source Project, 2010), in order to enable the inclusion of complex CAD geometries in simulations, scanner geometries have been implemented in HADES, and the novel detector responses have been included in HADES. A major extension of HADES which has been required by this effort is the inclusion of scatter in these radiographic simulations. Ray tracing codes generally do not easily allow the inclusion of scatter, because these codes define a source and a grid of detector pixels and only compute the attenuation along rays between these points. Scatter is an extremely complex set of processes which can involve rays which change directions many times between the source and detector. Scatter from outside the field of view of the imaging system, as well as within the field of view, can have an important role in image formation. In this report, we will describe how we implemented a treatment of scatter in HADES. We begin with a discussion of how we define scatter in Section 2, followed by a description of how single Compton scatter is now included in HADES in Section 3. In Section 4 we report a set of verification tests against MCNP and tests of how the technique scales with image size, number of scatters allowed and number of processors used in the calculations. In Section 5, we describe how we plan to extend this approach to other forms of scatter and conclude in Section 6. It should be emphasized that the purpose of this report is to show that a form of scatter has been implemented in HADES and has been verified against MCNP. Validation, the process of comparing simulation and experiment, is a future task.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2017

Preface to the Special Issue on TOUGH Symposium 2015

The TOUGH Symposium 2015 was held in Berkeley, California, September 28-30, 2015. The TOUGH family of codes, developed at the Energy Geosciences Division of Lawrence Berkeley National Laboratory (LBNL), is a suite of computer programs for the simulation of multiphase and multicomponent fluid and heat flows in porous and fractured media with applications in many geosciences fields, such as geothermal reservoir engineering, nuclear waste disposal, geological carbon sequestration, oil and gas reservoirs, gas hydrate research, vadose zone hydrology and environmental remediation. Since the first release in the 1980s, many modifications and enhancements have been continuously made to TOUGH and its various descendants (iTOUGH2, TOUGH+, TOUGH-MP, TOUGHREACT, TOUGH+HYDRATE, TMVOC...), at LBNL and elsewhere. Today, these codes are used worldwide in academia, government organizations and private companies in problems involving coupled hydrological, thermal, biogeochemical and geomechanical processes. The Symposia, organized every 2-3 years, bring together developers and users for an open exchange on recent code enhancements and applications. In 2015, the Symposium was attended by one hundred participants, representing thirty-four nationalities. This Special Issue in Computers & Geosciences gathers extended versions of selected Symposium proceedings related to (i) recent enhancements to the TOUGH family of codes and (ii) coupled flow and geomechanics processes modeling. In this special issue, a paper by Jung et al. provides a new base version of TOUGH, TOUGH3. This latest version addresses the computational robustness and efficiency needed for the increasing complexity of the simulated processes and the growing size of model domains. Moreover, it consolidates the serial and parallel versions into a single code, hence, inconsistencies between different versions are less likely to occur, and code maintenance and development become more efficient. Finsterle et al. present a review of the capabilities of iTOUGH2 and illustrate the workflow of an integrated simulation-optimization framework. Liu et al. present an approach to reduce the computational cost of performing Bayesian inverse modeling and demonstrate their approach using a synthetic experiment simulated with TOUGH2. Regarding the mesh generation process, the paper by Doughty presents a generalization of the grid-generation algorithm embedded in TOUGH2 (MESHMAKER) to create one-column grids with fractal dimension, useful for representing flow through fractures or highly heterogeneous media with fractal geometry, and also for inverse methods. Sentís and Gable present a module to interface the open source code Lagrit with TOUGH2, that assists in the processes of mesh generation and model setup, and that can be particularly useful for complex geometries and large models. Bonduà et al. present a pre-processing tool (VORO2MESH) that features a full 3D Voronoi approach for structured and unstructured grids, and an enhanced post-processor (TOUGH2Viewer) for the visualization of 3D Voronoi grids. The literature review shows that coupled fluid flow and geomechanics has gained increasing attention in the last years in many geosciences fields. In this context, several codes of the TOUGH family have been successfully coupled with geomechanics software. The paper by Rutqvist presents a review of some of these TOUGH-based geomechanics models, of which the TOUGH-FLAC simulator – presented for the first time in the TOUGH Symposium 2003 – is well-known worldwide and has been used in a large variety of applications, encompassing the application domains of the TOUGH codes. Blanco-Martín et al. present an extension of TOUGH-FLAC to the finite strain framework, motivated by the existence of large strains associated with rock salt deformation in the context of heat-generating nuclear waste disposal. Following a paper published in the Special Issue of the TOUGH Symposium 2012 in this journal, Kim et al. present the current status of the TOUGH-RSBN simulator for coupled hydro-mechanical modeling of hydraulic fracturing using a discrete fracture network approach, and present some validation and demonstration cases. Rinaldi and Nespoli present TOUGH-SEED, an injection-induced seismicity modeling approach using TOUGH2 and a geomechanics stochastic model, and apply it to the Basel EGS project. Finally, Rinaldi et al. use the PEST protocol implemented in iTOUGH2, combined with TOUGH-FLAC as the forward model, to perform inverse modeling for the In Salah CO2 demonstration site in Algeria. Using pore pressures and InSAR displacements as observations, this approach allows for a multi-physics joint inversion and takes advantage of the inverse capabilities of iTOUGH2 (parameter estimation, sensitivity analysis, uncertainty estimation …), applied to coupled fluid flow and geomechanics. On behalf of the members of the TOUGH Symposium 2015 Organizing Committee, I hope that this collection of papers will provide the readers of Computers & Geosciences with a recent view of the capabilities of the TOUGH family of codes, as well as with current modeling research domains in the geosciences. Last but not least, I would like to express my gratitude to all the reviewers who kindly contributed to this Special Issue, as well as the Journal staff who have made this publication possible.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

Evaluation of Graph Analytics Frameworks Using the GAP Benchmark Suite

The analysis of connected data is an increasingly important application in high-performance computing. Such analyses can reveal fraudulent patterns in financial transactions, optimize telecommunications networks, predict information flow in social networks, etc. However, the landscape of graph analytics is highly diverse. Graph algorithms stress processor architectures differently, and no one graph can represent all topologies. Consequently, no single approach or framework is expected to be optimal for all graph analytics problems. To help make sense of this diverse landscape, we evaluated four approaches to graph analytics: GraphBLAS, Galois, BGL17, GraphIt; and compare them against hand-tuned implementations that take advantage of hardware features on our test platform. Graph- BLAS formulates graph analytics as sparse linear algebra. Galois provides syntactic constructs for data parallelism over irregular data structures. BGL17 is a generic C++ template library for implementing graph algorithms. GraphIt provides a domain- specific language to describe and optimize graph algorithms. We use the GAP Benchmark Suite to establish baseline performance and guide the side-by-side evaluation of each framework. GAP consists of 30 tests: six graph analytics algorithms (breadth- first search, single-source shortest path, PageRank, betweenness centrality, connected components, and triangle counting) run on five graphs, each with different topological characteristics (e.g., high diameter, skewed degree distribution, high average degree). High-performance reference implementations are included for each benchmark algorithm. Because a graph can be loaded into memory a number of ways (e.g., flat file on disk, compressed sparse format, data frames, retrieved from SQL or NoSQL databases), our evaluation focused on computational performance rather than I/O. Our results show the relative strengths of each framework.

Graph algorithms, Benchmarking, shared-memory prog↗
DOE OSTI2011

Skel: Generative Software for Producing Skeletal I/O Applications

Massively parallel computations consist of a mixture of computation, communication, and I/O. As part of the co-design for the inevitable progress towards exascale computing, we must apply lessons learned from past work to succeed in this new age of computing. Of the three components listed above, implementing an effective parallel I/O solution has often been overlooked by application scientists and was usually added to large scale simulations only when existing serial techniques had failed. As scientists teams scaled their codes to run on hundreds of processors, it was common to call on an I/O expert to implement a set of more scalable I/O routines. These routines were easily separated from the calculations and communication, and in many cases, an I/O kernel was derived from the application which could be used for testing I/O performance independent of the application. These I/O kernels developed a life of their own used as a broad measure for comparing different I/O techniques. Unfortunately, as years passed and computation and communication changes required changes to the I/O, the separate I/O kernel used for benchmarking remained static no longer providing an accurate indicator of the I/O performance of the simulation making I/O research less relevant for the application scientists. In this paper we describe a new approach to this problem where I/O kernels are replaced with skeletal I/O applications automatically generated from an abstract set of simulation I/O parameters. We realize this abstraction by leveraging the ADIOS middleware's XML I/O specification with additional runtime parameters. Skeletal applications offer all of the benefits of I/O kernels including allowing I/O optimizations to focus on useful I/O patterns. Moreover, since they are automatically generated, it is easy to produce an updated I/O skeleton whenever the simulation's I/O changes. In this paper we analyze the performance of automatically generated I/O skeletal applications for the S3D and GTS codes. We show that these skeletal applications achieve performance comparable to that of the production applications. We wrap up the paper with a discussion of future changes to make the skeletal application better approximate the actual I/O performed in the simulation.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2026

pyRMG: A framework for high-throughput, large-cell DFT calculations on supercomputers

Exascale computing delivers the raw power to simulate ever larger and more chemically realistic systems, but realizing this potential requires codes that can efficiently use thousands of processors. Our real-space multigrid (RMG) density functional theory (DFT) code’s grid-decomposition approach scales nearly linearly with the number of graphics processing units (GPUs), even for simulations exceeding thousands of atoms. This scalability makes RMG a compelling tool for high-throughput DFT studies of materials that would otherwise be bottlenecked in other codes (for example, by global fast Fourier transforms in plane-wave DFT). However, the limited workflow infrastructure for RMG has thus far constrained its adoption to a small user community. In this work, we present pyRMG, a Python package designed to streamline the setup and execution of RMG DFT calculations. Built on the pymatgen and ASE (Atomic Simulation Environment) computational materials science Python packages, pyRMG automates input generation and convergence checking, and it integrates with modern job schedulers (e.g., Flux) on leadership-class platforms such as Frontier and Perlmutter. Here, we demonstrate pyRMG for a high-throughput study of strain effects in 2D 2L-Bi 2 Se 3 /2L-NbSe 2 heterostructures, which offers chemical insights into this system and shows that RMG-based workflows can converge with limited user intervention.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2011

Development of a GPU-based high-performance radiative transfer model for the Infrared Atmospheric Sounding Interferometer (IASI)

Satellite-observed radiance is a nonlinear functional of surface properties and atmospheric temperature and absorbing gas profiles as described by the radiative transfer equation (RTE). In the era of hyperspectral sounders with thousands of high-resolution channels, the computation of the radiative transfer model becomes more time-consuming. The radiative transfer model performance in operational numerical weather prediction systems still limits the number of channels we can use in hyperspectral sounders to only a few hundreds. To take the full advantage of such high-resolution infrared observations, a computationally efficient radiative transfer model is needed to facilitate satellite data assimilation. In recent years the programmable commodity graphics processing unit (GPU) has evolved into a highly parallel, multi-threaded, many-core processor with tremendous computational speed and very high memory bandwidth. The radiative transfer model is very suitable for the GPU implementation to take advantage of the hardware's efficiency and parallelism where radiances of many channels can be calculated in parallel in GPUs. In this paper, we develop a GPU-based high-performance radiative transfer model for the Infrared Atmospheric Sounding Interferometer (IASI) launched in 2006 onboard the first European meteorological polar-orbiting satellites, METOP-A. Each IASI spectrum has 8461 spectral channels. The IASI radiative transfer model consists of three modules. The first module for computing the regression predictors takes less than 0.004% of CPU time, while the second module for transmittance computation and the third module for radiance computation take approximately 92.5% and 7.5%, respectively. Our GPU-based IASI radiative transfer model is developed to run on a low-cost personal supercomputer with four GPUs with total 960 compute cores, delivering near 4 TFlops theoretical peak performance. By massively parallelizing the second and third modules, we reached 364x speedup for 1 GPU and 1455x speedup for all 4 GPUs, both with respect to the original CPU-based single-threaded Fortran code with the -O{sub 2} compiling optimization. The significant 1455x speedup using a computer with four GPUs means that the proposed GPU-based high-performance forward model is able to compute one day's amount of 1,296,000 IASI spectra within nearly 10 min, whereas the original single CPU-based version will impractically take more than 10 days. This model runs over 80% of the theoretical memory bandwidth with asynchronous data transfer. A novel CPU-GPU pipeline implementation of the IASI radiative transfer model is proposed. The GPU-based high-performance IASI radiative transfer model is suitable for the assimilation of the IASI radiance observations into the operational numerical weather forecast model.

97 MATHEMATICS AND COMPUTING↗