Lightweighting Impacts on Fuel Economy, Cost, and Component Losses
Abstract not provided
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
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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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Modeling of a chain of three plasma accelerator stages with the WarpX electromagnetic PIC code on GPUs
The fully electromagnetic particle-in-cell code WarpX is being developed by a team of the U.S. DOE Exascale Computing Project (with additional non-U.S. collaborators on part of the code) to enable the modeling of chains of tens to hundreds of plasma accelerator stages on exascale supercomputers, for future collider designs. The code is combining the latest algorithmic advances (e.g., Lorentz boosted frame and pseudo-spectral Maxwell solvers) with mesh refinement and runs on the latest computer processing unit and graphical processing unit (GPU) architectures. In this paper, we summarize the strategy that was adopted to port WarpX to GPUs, report on the weak parallel scaling of the pseudo-spectral electromagnetic solver, and then present solutions for decreasing the time spent in data exchanges from guard regions between subdomains. In Sec. IV, we demonstrate the simulations of a chain of three consecutive multi-GeV laser-driven plasma accelerator stages.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Reducing Disk Storage of Full-3D Seismic Waveform Tomography (F3DT) Through Lossy Online Compression
Full-3D seismic waveform tomography (F3DT) is the latest seismic tomography technique that can assimilate broadband, multi-component seismic waveform observations into high-resolution 3D subsurface seismic structure models. The main drawback in the current F3DT implementation, in particular the scattering-integral implementation (F3DT-SI), is the high disk storage cost and the associated I/O overhead of archiving the 4D space-time wavefields of the receiver- or source-side strain tensors. The strain tensor fields are needed for computing the data sensitivity kernels, which are used for constructing the Jacobian matrix in the Gauss-Newton optimization algorithm. In this study, we have successfully integrated a lossy compression algorithm into our F3DT SI workflow to significantly reduce the disk space for storing the strain tensor fields. The compressor supports a user-specified tolerance for bounding the error, and can be integrated into our finite-difference wave-propagation simulation code used for computing the strain fields. The decompressor can be integrated into the kernel calculation code that reads the strain fields from the disk and compute the data sensitivity kernels. During the wave-propagation simulations, we compress the strain fields before writing them to the disk. To compute the data sensitivity kernels, we read the compressed strain fields from the disk and decompress them before using them in kernel calculations. Experiments using a realistic dataset in our California statewide F3DT project have shown that we can reduce the strain-field disk storage by at least an order of magnitude with acceptable loss, and also improve the overall I/O performance of the entire F3DT-SI workflow significantly. The integration of the lossy online compressor may potentially open up the possibilities of the wide adoption of F3DT-SI in routine seismic tomography practices in the near future.
97 MATHEMATICS AND COMPUTING↗
Transition metal-substituted lead halide perovskite absorbers
Here, lead halide perovskites have proven to be a versatile class of visible light absorbers that allow rapid access to the long minority carrier lifetimes and diffusion lengths desirable for traditional single-junction photovoltaics. We explore the extent to which the attractive features of these semiconductors may be extended to include an intermediate density of states for future application in multi-level solar energy conversion systems capable of exceeding the Shockley–Queisser limit. We computationally and experimentally explore the substitution of transition metals on the Pb site of MAPbX 3 (MA = methylammonium, X = Br or Cl) to achieve a tunable density of states within the parent gap. Computational screening identified both Fe- and Co-substituted MAPbBr 3 as promising absorbers with a mid-gap density of states, and the later films were synthesized via conventional solution-based processing techniques. First-principles density functional theory (DFT) calculations support the existence of mid-gap states upon Co incorporation and enhanced sub-gap absorption, which are consistent with UV-visible-NIR absorption spectroscopy. Strikingly, steady state and time-resolved PL studies reveal no sign of self-quenching for Co-substitution up to 25%, which suggest this class of materials to be a worthy candidate for future application in intermediate band photovoltaics.
36 MATERIALS SCIENCE↗
Probing the Local Site Disorder and Distortion in Pyrochlore High-Entropy Oxides
High-entropy oxides (HEOs) have attracted great interest in diverse fields because of their inherent opportunities to tailor and combine materials functionalities. The control of local order/disorder in the class is by extension a grand challenge toward realizing their vast potential. In this paper, we report the first examples of pyrochlore HEOs with five M-site cations, for Nd 2 M 2 O 7 , in which the local structure has been investigated by neutron diffraction and pair distribution function (PDF) analysis. The average structure of the pyrochlores is found to be orthorhombic Imma, in agreement with radius-ratio rules governing the structural archetype. The computed PDFs from density functional theory relaxed special quasirandom structure models are compared with real space PDFs in this work to evaluate M-site order/disorder. Reverse Monte Carlo combined with ab initio molecular dynamics and Metropolis Monte Carlo simulations demonstrates that Nd 2 (Ta 0.2 Sc 0.2 Sn 0.2 Hf 0.2 Zr 0.2 ) 2 O 7 is synthesized with its M-site local to nanoscale order highly randomized/disordered, while Nd 2 (Ti 0.2 Nb 0.2 Sn 0.2 Hf 0.2 Zr 0.2 ) 2 O 7+x exhibits a strong distortion of the TiO 6 octahedron and small degree of Ti chemical short-range order (SRO) on the subnanometer scale. Calculations suggest that this may be intrinsic, energetically favored SRO rather than due to sample processing. These results offer an important demonstration that the engineered variation of participating ions in HEOs, even among those with very similar radii, provides richly diverse opportunities to control local order/disorder motifs—and therefore materials properties for future designs. This work also hints at the exquisite level of detail that may be needed in computational and experimental data analysis to guide structure–property tuning in the emerging HEO materials class.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Testing NeuralGCM's capability to simulate future heatwaves based on the 2021 Pacific Northwest heatwave event
AI-based weather and climate models are emerging as accurate and computationally efficient tools. Beyond weather forecasting, they also show promise to accelerate storyline analyses. We evaluate NeuralGCM’s ability to simulate an extreme heatwave against the Energy Exascale Earth System Model (E3SM), a physics-based climate model. NeuralGCM accurately replicates the targeted event, and generates stable and realistic mid-century projections. However, due to the absence of land feedbacks, NeuralGCM underestimates the projected warming amplitude compared to physics-based model references.
54 ENVIRONMENTAL SCIENCES↗
Severe Accident Research Directions after Fukushima
The accidents at the Fukushima Daiichi nuclear power station in March of 2011 renewed interest globally in the nature, causes, and health and economic consequences of severe accidents involving core damage and containment impairment. As a result of these accidents, research has been conducted into the modeling and phenomenological understanding of the accidents at Fukushima and the response and effectiveness of mitigating measures such as emergency water injection, primary system depressurization and containment venting actions. An important example of this is the jointly conducted OECD-NEA organized Benchmark of the Fukushima Accidents (BSAF project) where accident reconstruction analyses using computer codes such as MELCOR and MAAP are ongoing. Even prior to the Fukushima accidents, the Sandia/NRC sponsored State of Art Reactor Consequence Analyses (SOARCA) foreshadowed the accidents in Japan where long-term and short-term station blackout accidents in boiling water reactors with Mark-1 containment were analyzed showing striking similarities to the accidents at Fukushima. These analyses also highlighted important differences in our prior understanding, in particular the importance of suppression pool thermal stratification and the extraordinary operation of the Fukushima Unit-2 reactor core isolation cooling (RCIC) system, prompting addition research into both of these areas. Additionally, the focus of the world's major computer codes on modeling the Fukushima accidents in the BSAF effort, led to several 'Crosswalk' comparisons between code predictions, highlighting subtle but important modeling differences between the codes and identifying areas where modeling improvements can be made. Collectively, these research areas are aimed at improving our understanding of severe accident progression and real-world response of safety related systems and components, and should lead to improved accident response and mitigation capabilities in the future. (authors)
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Adaptive local learning in sampling based motion planning for protein folding
Background: Simulating protein folding motions is an important problem in computational biology. Motion planning algorithms, such as Probabilistic Roadmap Methods, have been successful in modeling the folding landscape. Probabilistic Roadmap Methods and variants contain several phases (i.e., sampling, connection, and path extraction). Most of the time is spent in the connection phase and selecting which variant to employ is a difficult task. Global machine learning has been applied to the connection phase but is inefficient in situations with varying topology, such as those typical of folding landscapes. Results: We develop a local learning algorithm that exploits the past performance of methods within the neighborhood of the current connection attempts as a basis for learning. It is sensitive not only to different types of landscapes but also to differing regions in the landscape itself, removing the need to explicitly partition the landscape. We perform experiments on 23 proteins of varying secondary structure makeup with 52–114 residues. We compare the success rate when using our methods and other methods. We demonstrate a clear need for learning (i.e., only learning methods were able to validate against all available experimental data) and show that local learning is superior to global learning producing, in many cases, significantly higher quality results than the other methods. Conclusions: We present an algorithm that uses local learning to select appropriate connection methods in the context of roadmap construction for protein folding. Our method removes the burden of deciding which method to use, leverages the strengths of the individual input methods, and it is extendable to include other future connection methods.
Mathematical & Computational Biology↗
Advancing the science and impact of blockchain technology at Oak Ridge National Laboratory
Over the last decade, blockchain technology has experienced unprecedented growth from its humble beginnings as the decentralized currency Bitcoin in 2009 to a $500 billion industry today with applications including decentralized cloud computing, peer-to-peer lending, governance, social media, cybersecurity and the development of decentralized, trustless smart contracts and applications. Although it has not yet reached maturity, the blockchain technology industry (sometimes hailed as the “Web 3.0”) is experiencing exponential growth and adoption: new projects and partnerships are announced almost daily, and top research institutions are beginning to develop novel initiatives in this space. In this white paper, I will outline a vision for the future of blockchain technology at Oak Ridge National Laboratory (ORNL). Specifically, I will (1) introduce the fundamental concepts of blockchain technology and provide a survey of the current state-of-the-art, (2) identify the wide-ranging applications that blockchain technology can have across groups, divisions, and directorates at ORNL, (3) summarize the newly initiated programs in blockchain technology at ORNL’s core partnership universities that may lead to future collaboration opportunities, (4) suggest critical ways that blockchain technology can advance ORNL’s strategic objectives for excellence in science and technology, laboratory operations, and community engagement, and (5) outline the most likely funding strategies to support new blockchain initiatives and partnerships at the Lab. Finally, I will propose a vision for the future of a Blockchain Institute at ORNL.
97 MATHEMATICS AND COMPUTING↗
The Dynamics of Ions on Phased Radio-frequency Carpets in High Pressure Gases and Application for Barium Tagging in Xenon Gas Time Projection Chambers
Radio-frequency (RF) carpets with ultra-fine pitches are examined for ion transport in gases at atmospheric pressures and above. We develop new analytic and computational methods for modeling RF ion transport at densities where dynamics are strongly influenced by buffer gas collisions. An analytic description of levitating and sweeping forces from phased arrays is obtained, then thermodynamic and kinetic principles are used to calculate ion loss rates in the presence of collisions. This methodology is validated against detailed microscopic SIMION simulations. We then explore a parameter space of special interest for neutrinoless double beta decay experiments: transport of barium ions in xenon at pressures from 1 to 10 bar. Our computations account for molecular ion formation and pressure dependent mobility as well as finite temperature effects. We discuss the challenges associated with achieving suitable operating conditions, which lie beyond the capabilities of existing devices, using presently available or near-future manufacturing techniques.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
TECA: Petascale pattern recognition for climate science
Climate Change is one of the most pressing challenges facing humanity in the 21st century. Climate simulations provide us with a unique opportunity to examine effects of anthropogenic emissions. Highresolution climate simulations produce “Big Data”: contemporary climate archives are ≈ 5PB in size and we expect future archives to measure on the order of Exa-Bytes. In this work, we present the successful application of TECA (Toolkit for Extreme Climate Analysis) framework, for extracting extreme weather patterns such as Tropical Cyclones, Atmospheric Rivers and Extra-Tropical Cyclones from TB-sized simulation datasets. TECA has been run at full-scale on Cray XE6 and IBM BG/Q systems, and has reduced the runtime for pattern detection tasks from years to hours. TECA has been utilized to evaluate the performance of various computational models in reproducing the statistics of extreme weather events, and for characterizing the change in frequency of storm systems in the future.
54 ENVIRONMENTAL SCIENCES↗
Precision gamma-ray constraints for sub-GeV dark matter models
The indirect detection of dark matter particles with mass below the GeV scale has recently received significant attention. Future space-borne gamma-ray telescopes, including All-Sky-ASTROGAM, AMEGO, and GECCO, will probe the MeV gamma-ray sky with unprecedented precision, offering an exciting test of particle dark matter in the MeV-GeV mass range. While it is typically assumed that dark matter annihilates into only one Standard Model final state, this is not the case for realistic dark matter models. Here we analyze existing indirect detection constraints and the discovery reach of future detectors for the well-motivated Higgs and vector-portal models using our publicly-available code Hazma. In particular, we show how to leverage chiral perturbation theory to compute the dark matter self-annihilation cross sections into final states containing mesons, the strongly-interacting Standard Model dynamical degrees of freedom below the GeV scale. We find that future telescopes could probe dark matter self-annihilation cross sections orders of magnitude smaller than those presently constrained by cosmic microwave background, gamma-ray and terrestrial observations.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
The method of modular characteristic direction probabilities in MPACT
The method of characteristic direction probabilities (CDP) is based on a modular ray tracing technique which combines the benefits of the collision probability method (CPM) and the method of characteristics (MOC). This past year CDP was implemented in the transport code MPACT for 2-D and 3-D transport calculations. By only coupling the fine mesh regions passed by the characteristic rays in the particular direction, the scale of the probabilities matrix is much smaller compared to the CPM. At the same time, the CDP has the same capacity of dealing with the complicated geometries with the MOC, because the same modular ray tracing techniques are used. Results from the C5G7 benchmark problems are given for different cases to show the accuracy and efficiency of the CDP compared to MOC. For the cases examined, the CDP and MOC methods were seen to differ in k{sub eff} by about 1-20 pcm, and the computational efficiency of the CDP appears to be better than the MOC for some problems. However, in other problems, particularly when the CDP matrices have to be recomputed from changing cross sections, the CDP does not perform as well. This indicates an area of future work. (authors)
97 MATHEMATICS AND COMPUTING↗
Strength can be controlled by edge dislocations in refractory high-entropy alloys
Energy efficiency is motivating the search for new high-temperature (high-T) metals. Some new body-centered-cubic (BCC) random multicomponent “high-entropy alloys (HEAs)” based on refractory elements (Cr-Mo-Nb-Ta-V-W-Hf-Ti-Zr) possess exceptional strengths at high temperatures but the physical origins of this outstanding behavior are not known. Here we show, using integrated in-situ neutron-diffraction (ND), high-resolution transmission electron microscopy (HRTEM), and recent theory, that the high strength and strength retention of a NbTaTiV alloy and a high-strength/low-density CrMoNbV alloy are attributable to edge dislocations. This finding is surprising because plastic flows in BCC elemental metals and dilute alloys are generally controlled by screw dislocations. We use the insight and theory to perform a computationally-guided search over 10 7 BCC HEAs and identify over 10 6 possible ultra-strong high-T alloy compositions for future exploration.
36 MATERIALS SCIENCE↗
Dynamic Non-Hierarchical File Systems for Exascale Storage
This constitutes the final report for “Dynamic Non-Hierarchical File Systems for Exascale Storage”. The ultimate goal of this project was to improve data management in scientific computing and high-end computing (HEC) applications, and to achieve this goal we proposed: to develop the first, HEC-targeted, file system featuring rich metadata and provenance collection, extreme scalability, and future storage hardware integration as core design goals, and to evaluate and develop a flexible non-hierarchical file system interface suitable for providing more powerful and intuitive data management interfaces to HEC and scientific computing users. Data management is swiftly becoming a serious problem in the scientific community – while copious amounts of data are good for obtaining results, finding the right data is often daunting and sometimes impossible. Scientists participating in a Department of Energy workshop noted that most of their time was spent “...finding, processing, organizing, and moving data and it’s going to get much worse”. Scientists should not be forced to become data mining experts in order to retrieve the data they want, nor should they be expected to remember the naming convention they used several years ago for a set of experiments they now wish to revisit. Ideally, locating the data you need would be as easy as browsing the web. Unfortunately, existing data management approaches are usually based on hierarchical naming, a 40 year-old technology designed to manage thousands of files, not exabytes of data. Today’s systems do not take advantage of the rich array of metadata that current high-end computing (HEC) file systems can gather, including content-based metadata and provenance1 information. As a result, current metadata search approaches are typically ad hoc and often work by providing a parallel management system to the “main” file system, as is done in Linux (the locate utility), personal computers, and enterprise search appliances. These search applications are often optimized for a single file system, making it difficult to move files and their metadata between file systems. Users have tried to solve this problem in several ways, including the use of separate databases to index file properties, the encoding of file properties into file names, and separately gathering and managing provenance data, but none of these approaches has worked well, either due to limited usefulness or scalability, or both. Our research addressed several key issues: High-performance, real-time metadata harvesting: extracting important attributes from files dynamically and immediately updating indexes used to improve search; Transparent, automatic, and secure provenance capture: recording the data inputs and processing steps used in the production of each file in the system; Scalable indexing: indexes that are optimized for integration with the file system; Dynamic file system structure: our approach provides dynamic directories similar to those in semantic file systems, but these are the native organization rather than a feature grafted onto a conventional system. In addition to these goals, our research effort will include evaluating the impact of new storage technologies on the file system design and performance. In particular, the indexing and metadata harvesting functions can potentially benefit from the performance improvements promised by new storage class memories.
42 ENGINEERING↗
The Matrix Element Method: Past, Present, and Future
The increasing use of multivariate methods, and in particular the Matrix Element Method (MEM), represents a revolution in experimental particle physics. With continued exponential growth in computing capabilities, the use of sophisticated multivariate methods-- already common-- will soon become ubiquitous and ultimately almost compulsory. While the existence of sophisticated algorithms for disentangling signal and background might naively suggest a diminished role for theorists, the use of the MEM, with its inherent connection to the calculation of differential cross sections will benefit from collaboration between theorists and experimentalists. In this white paper, we will briefly describe the MEM and some of its recent uses, note some current issues and potential resolutions, and speculate about exciting future opportunities.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Regularized Differentiation for Bioburden Density Estimation in Planetary Protection
In this paper, we propose and investigate the performance of two novel shrinkage estimators for bioburden density estimation in planetary protection. The estimators are based on the regularized differentiation of a cumulative count of colony forming units collected throughout the data collecting session or the life cycle of the entire mission. The regularized differentiation recasts the problem of bioburden density estimation as a linear least squares problem. The least squares problem is then solved through regularization techniques, such as truncated singular value decomposition and penalized least squares. The regularization is necessary to avoid noise amplification during the differentiation of noisy data. The two regularization estimators are compared with four other commonly used estimators to simultaneously evaluate the means of multivariable independent Poisson distributions: the maximum likelihood, noninformative Bayes estimator with Jeffreys prior, Empirical Bayes using conjugate gamma-Poisson model with gamma parameters selected by method of moments, and the Clevenson-Zidek estimator. It is shown through computer-simulated data that the regularized differentiation based on ridge regression has the smallest mean-squared error among all estimators. The analysis of shrinkage mechanism implemented by regularized differentiation is performed, and it is shown that the regularized differentiation amounts to performing a weighted averaging of all the samples. The weights are determined by the regularization parameter automatically selected by the L-curve technique. Since the method of least squares makes no distributional assumptions about the data, it presents an attractive technique for bioburden density estimation when there are concerns about the misspecification of the distributional model. The paper concludes with the analysis of the bioburden data collected during InSight mission and directions for future work.
97 - MATHEMATICS AND COMPUTING↗
Feasibility Study of Non-Potable Reuse Water for Cooling of Future Supercomputer Infrastructure
No abstract provided.
97 MATHEMATICS AND COMPUTING↗