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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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A simple code-generator to generate the low level code kernels used by the QPhiX Library for Lattice QCD. Generates Kernels for Wilson-Dslash, and Wilson-Clover kernels. Can be reused to write other optimized kernels for Intel Xeon Phi(tm), Intel Xeon(tm) and potentially other architectures.
X-ray and neutron optics both lack ray focusing capabilities. An x-ray source can be made small and powerful enough to facilitate high-resolution imaging while providing adequate flux. This is not yet possible for neutrons. One remedy is to employ a computational imaging technique such as magnified coded source imaging. The greatest challenge associated with successful reconstruction of high-resolution images from such radiographs is to precisely model the flux distribution for complex non-uniform neutron sources. We have developed a framework based on Monte Carlo simulation and iterative reconstruction that facilitates high- resolution coded source neutron imaging. In this paper, we define a methodology to empirically measure and approximate the flux profile of a non-uniform neutron source, and we show how to incorporate the result within the forward model of an iterative reconstruction algorithm. We assess improvement in image quality by comparing reconstructions based respectively on the new empirical forward model and our previous analytic models.
Code Analysis and Refactoring with Clang Tools is a small set of example code that demonstrates techniques for applying tools distributed with the open source Clang compiler. Examples include analyzing where variables are used and replacing old data structures with standard structures.
In 1974, Nelson, Kase and Svensson published an experimental investigation on muon shielding around SLAC high-energy electron accelerators [1]. They measured muon fluence and absorbed dose induced by 14 and 18 GeV electron beams hitting a copper/water beamdump and attenuated in a thick steel shielding. In their paper, they compared the results with the theoretical models available at that time. In order to compare their experimental results with present model calculations, we use the modern transport Monte Carlo codes MARS15, FLUKA2011 and GEANT4 to model the experimental setup and run simulations. The results are then compared between the codes, and with the SLAC data.
Here, we describe studies of the generation of electro-magnetic pulse (EMP) fields under the idealized conditions of a delta-function gamma pulse, a spherical cap of Compton electrons located at a common radius from the burst, and the absence of the conduction current. Various processes (geomagnetic rotation, drag, and scattering) contribute to the time derivative of the Compton current density, which appears as the source term in the Lienard–Wiechert expression for the radiation field. These processes are considered separately and in combination, and their influences on the waveform of the EMP assessed. Using a separate computational approach, different numerical models of multiple scattering are tested, in an effort to assess the validity of existing codes and earlier results. We also describe plans for and progress toward a comprehensive 3-D EMP code, which is to employ recently developed numerical methods and is to offer a variety of models, including the Lienard–Wiechert formulation described here.
EMPulse is a comprehensive and modern 3-D simulation code for electro-magnetic pulse (EMP) formation and propagation studies, being developed at LLNL under a Laboratory Directed R&D (LDRD) initiative that was launched in the fall of 2014. Furthermore, EMPulse builds upon the open-source Warp particle-in-cell code framework which was developed by members of this team and collaborators at other institutions. Our goal of this endeavor is a new tool, using methods different from those in common use, and enabling the detailed and self-consistent study of multidimensional effects in geometries that have typically been treated only approximately. We describe the underlying Warp framework and its capabilities; the models that have been or are being newly developed and incorporated into EMPulse, tests of these models, and number of approaches to increased computational efficiency that we are studying.
Computationally efficient algorithms may be applied for fast dictionary learning solving the convolutional sparse coding problem in the Fourier domain. More specifically, efficient convolutional sparse coding may be derived within an alternating direction method of multipliers (ADMM) framework that utilizes fast Fourier transforms (FFT) to solve the main linear system in the frequency domain. Such algorithms may enable a significant reduction in computational cost over conventional approaches by implementing a linear solver for the most critical and computationally expensive component of the conventional iterative algorithm. The theoretical computational cost of the algorithm may be reduced from O(M.sup.3N) to O(MN log N), where N is the dimensionality of the data and M is the number of elements in the dictionary. This significant improvement in efficiency may greatly increase the range of problems that can practically be addressed via convolutional sparse representations.
The development of nodal methods for hexagonal geometry reactors at National Tsing-Hua University is reviewed. In particular, the development of the Hybrid Nodal Green's Function Method in hexagonal geometry is re-cast into the Direct-Coarse Mesh Finite Difference Method for efficient implementation with residual solution techniques such as GMRES. The method is further modified to include a rigorous treatment of non-physical terms that arise from the transverse integration procedure. The treatment follows the conformal mapping approach first developed by Y-A Chao and his co-workers. The overall development is implemented into a code written in C++ and tested on a number of well known benchmarks. The new code is shown to be very highly accurate while extremely efficient computationally.
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.
Hydrogen has potential applications that require larger-scale storage, use, and handling systems than currently are employed in emerging-market fuel cell applications. These potential applications include hydrogen generation and storage systems that would support electrical grid systems. There has been extensive work evaluating regulations, codes, and standards (RCS) for the emerging fuel cell market, such as the infrastructure required to support fuel cell electric vehicles. However, there has not been a similar RCS evaluation and development process for these larger systems. This paper presents an evaluation of the existing RCS in the United States for large-scale systems and identifies potential RCS gaps. This analysis considers large-scale hydrogen technologies that are currently being employed in limited use but may be more widely used as large-scale applications expand. The paper also identifies areas of potential safety research that would need to be conducted to fill the RCS gaps. U.S. codes define bulk hydrogen storage systems but do not define large-scale systems. This paper evaluates potential applications to define a large-scale hydrogen system relative to the systems employed in emerging technologies such as hydrogen fuelling stations. These large-scale systems would likely be of similar size to or larger than industrial hydrogen systems.
The behavior of fission gases (Xe, Kr) is of critical importance to nuclear fuel performance, because it determines both the amount of fuel swelling and the quantity of fission gas released to the fuel rod plenum. It follows that accurate models of fission gas behavior need to be developed for the calculation of fission gas swelling and release in fuel performance codes, such as the BISON code developed at Idaho National Laboratory (INL). To this end, the fission gas behavior model included in BISON is being continually extended and improved. In the present contribution, we give an account of the most recent developments and results on modeling fission gas behavior in uranium dioxide fuel with BISON.
New versions of the USNRC fuel performance codes, FRAPCON-3 and FRAPTRAN are available to members of the code users group. FRAPCON-3.5 and FRAPTRAN-1.5 include model improvements to various material property models such as gaseous swelling, primary creep, pellet relocation, cladding models, cladding ballooning, and ID oxidation. Various new capabilities have been added such as increased numbers of time steps, axial nodes, and radial nodes. The ability to model pellet chamfers has been added. The ability to specify variable axial node lengths and specify different enrichment, gadolinia content, and central hole size in each axial node has been added. Finally the capability to model refabrication of a rodlet following irradiation of a full length rod has been added to FRAPCON-3.5. This paper will describe the changes made in the new versions of FRAPCON-3 and FRAPTRAN.
In the described embodiments, a processor core (e.g., a GPU core) receives a section of program code to be executed in a transaction from another entity in a computing device. The processor core sends the section of program code to one or more compute units in the processor core to be executed in a first transaction and concurrently executed in a second transaction, thereby creating a “redundant transaction pair.” When the first transaction and the second transaction are completed, the processor core compares a read-set of the first transaction to a read-set of the second transaction and compares a write-set of the first transaction to a write-set of the second transaction. When the read-sets and the write-sets match and no transactional error condition has occurred, the processor core allows results from the first transaction to be committed to an architectural state of the computing device.
In recent years there has been an increase in flooding concern to nuclear power plants; thus, tools to reduce the cost of mitigation efforts are valuable to industry. Simulating flooding events is achieved through different methods and at varied levels of detail. However, these simulations must be able to match application conditions, which requires code validation as well as knowledge of which parameters affect the accuracy of the simulation. To validate and determine the significant parameters for these tools, a sloshing experiment was conducted at the George Washington University and then simulated with the smoothed particle hydrodynamic code Neutrino. The simulation setup was constructed to match the experiment as closely as possible, and the end-wall pressure results were analysed. The 90% and 50% end-wall pressure bounds were compared along with the pressure impulse. The simulation bounds fell mostly within the experimental bounds, with exceptions in insignificant low-pressure areas, such as at the beginning (~40% difference) and the end of the cycle (~40% difference). Another exception was seen at a point in the peak pressure (~75% difference). The simulated pressure impulse matched the experiments well and was within 10%. Additionally, seven parameters were qualitatively assessed and then investigated to determine their significance. The parameters’ significance was determined by sampling five values across the parameters’ range using the Risk Analysis Virtual Environment, then comparing the average pressure for each run. All seven parameters were identified as having some significance. However, the particle size, interaction-radius to particle-size ratio, and fluid settling seem to have a greater effect due to the larger fluctuation of the results. Now that the significant parameters are identified, further research is being done to quantify the significance of each parameter.
Verification and validation results of the SCALE 6.2 lattice physics codes TRITON/NEWT and Polaris are presented. The SCALE/KENO continuous energy Monte-Carlo code is used as a reference to compare results of more than 2,000 combinations of pressurized water reactor and boiling water reactor lattice designs and state points. Radiochemical assay measurements were also used to assess the accuracy of depletion calculations. Results show that Polaris and TRITON/NEWT using ENDF/B_VII.1 produce accurate lattice physics calculations and cross section data for core simulators.
It has been a long-lasting aspiration of the nuclear power community to apply artificial intelligence (AI) technologies to nuclear power plants (NPPs), as AI is expected to substantially improve the efficiency, reliability, and continuity of NPPs. Along with the recent rise of AI featured with machining learning, there is an ever-increasing interest in utilizing the data-orientated technologies for nuclear power. In this chapter, we provide a brief summary of the history of AI and the previous efforts on introducing AI to nuclear reactor systems in 1980s. These histories suggest that although with a promising future, AI in NPPs also faces unique challenges, which are still not fully addressed and need special attention. We also introduced some recent studies that apply machine learning techniques to nuclear code development.Keywords: Nuclear power plants, code for nuclear power, artificial intelligence, expert system, machine learning
In this chapter, two state-of-the-art and well-used subchannel codes are presented: CTF and VIPRE-01. As part of the Consortium for Advanced Simulation of Light Water Reactors, CTF is being used for single- and two-phase flow analysis under light-water reactor normal and anticipated transient operating conditions. Accurate determination of flow distribution, pressure drop, and void content is crucial for predicting margins to thermal crisis and ensuring more efficient nuclear power plant performance. In preparation for the intended applications, CTF is extensively validated against data from experimental facilities. Meanwhile, the licensed commercial code VIPRE-01 is used to generate a baseline set of simulations for the targeted tests and the solution parameters are compared to CTF results. The closure models in CTF that are in the greatest need of improvement are highlighted and discussed.
Today's high-performance computing (HPC) applications are producing vast volumes of data, which are challenging to store and transfer efficiently during the execution, such that data compression is becoming a critical technique to mitigate the storage burden and data movement cost. Huffman coding is arguably the most efficient Entropy coding algorithm in information theory, such that it could be found as a fundamental step in many modern compression algorithms such as DEFLATE. On the other hand, today's HPC applications are more and more relying on the accelerators such as GPU on supercomputers, while Huffman encoding suffers from low throughput on GPUs, resulting in a significant bottleneck in the entire data processing. In this paper, we propose and implement an efficient Huffman encoding approach based on modern GPU architectures, which addresses two key challenges: (1) how to parallelize the entire Huffman encoding algorithm, including codebook construction, and (2) how to fully utilize the high memory-bandwidth feature of modern GPU architectures. The detailed contribution is fourfold. (1) We develop an efficient parallel codebook construction on GPUs that scales effectively with the number of input symbols. (2) We propose a novel reduction based encoding scheme that can efficiently merge the codewords on GPUs. (3) We optimize the overall GPU performance by leveraging the state-of-the-art CUDA APIs such as Cooperative Groups. (4) We evaluate our Huffman encoder thoroughly using six real-world application datasets on two advanced GPUs and compare with our implemented multithreaded Huffman encoder. Experiments show that our solution can improve the encoding throughput by up to 5.0× and 6.8× on NVIDIA RTX 5000 and V100, respectively, over the state-of-the-art GPU Huffman encoder, and by up to 3.3× over the multithread encoder on two 28-core Xeon Platinum 8280 CPUs.