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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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DOE OSTI2020

Magnesium Technology 2020

Magnesium and its alloys are being investigated widely for applications that require extraordinarily high strength-to-weight ratio, vibration damping, electromagnetic shielding, reduction in carbon footprint, sustainability improvements, and low toxicity. In the past decade or so, this drive has led to increased adoption of magnesium and its alloys in everyday products and it is no longer regarded as an esoteric material. The overarching goal of its widespread adoption and replacement of conventional materials such as steel and aluminum alloys has been restricted because of the cost of the material, limited sources of primary material, alloying constraints, high chemical reactivity, and challenges associated with its plasticity. Researchers, scientists, engineers, and economists from industry, government agencies and laboratories, and academic institutions alike are actively developing roadmaps for next-generation products and addressing these challenges as quickly as possible through unique and innovative methods. The TMS Magnesium Committee has been actively involved in providing a platform to these entities for disseminating the latest information, developments, and cutting-edge research and development, and showcasing the latest research and development trends related to magnesium and its alloys through the Magnesium Technology Symposium, which takes place every year at the TMS Annual Meeting. This proceedings volume retains the essence of this longstanding tradition. The twenty-first volume in the series, Magnesium Technology 2020, is the proceedings of the Magnesium Technology Symposium held during the 149th TMS Annual Meeting & Exhibition in San Diego, CA, February 23–27, 2020. The volume captures commentaries and papers from 15 different countries. The papers have been categorized systematically based on topics pertaining to magnesium production, casting and solidification, thermomechanical processing, deformation mechanisms, modeling, corrosion, and applications. The symposium began with keynote sessions that featured several distinguished invited speakers from government organizations and academia, who provided their perspectives on the state of the art, goals, and opportunities in magnesium alloy research and development. Dr. Carlos Tomé from Los Alamos National Laboratory, U.S. Department of Energy, discussed 3D characterization of mechanical twins across grain boundaries. This was followed by a talk on hierarchically structured ultrafine-grained magnesium alloys by Dr. Rajiv Mishra from the University of North Texas, USA, who presented an example of using friction stir additive manufacturing for implementing such microstructures at a component level. Dr. Mark Horstemeyer from Liberty University, USA, presented an overview of fatigue modeling to capture the corrosion-fatigue behavior of magnesium alloys. The final talk in this session was presented by Dr. Bin Jiang from Chongqing University, China, who presented a novel extrusion approach for achieving high-strength magnesium alloy plates.

Jordon, J. Brian↗
DOE OSTI2020

Smoky Mountain Data Challenge 2020: An Open Call to Solve Data Problems in the Areas of Neutron Science, Material Science, Urban Modeling and Dynamics, Geophysics, and Biomedical Informatics

The 2020 Smoky Mountains Computational Sciences and Engineering Conference enlists research scientists from across Oak Ridge National Laboratory (ORNL) to be data sponsors and help create data analytics challenges for eminent data sets at the laboratory. This work describes the significance of each of the seven data sets and their as- sociated challenge questions. The challenge questions for each data set were required to cover multiple difficulty levels. An international call for participation was sent to students, and researchers asking them to form teams of up to four people to apply novel data analytics techniques to these data sets.

Parete-Koon, Suzanne↗
DOE OSTI2021

Mechanical Behavior of High-Entropy Alloys: A Review

High-entropy alloys (HEAs) are materials that consist of equimolar or near-equimolar multiple principal components but tend to form single phases, which is a new research topic in the field of metallurgy, and have attracted extensive attention in the past decade. The HEAs families contain the face-centered-cubic (fcc), body-centered-cubic (bcc), and hexagonal-close-packed (hcp)-structured HEAs. On the one hand, mechanical properties, e.g., hardness, strength, ductility, fatigue, and elastic moduli, are essential for practical applications of HEAs. Scientists have explored in this direction since the advent of HEAs. On the other hand, the pursuit of high strength and good plasticity is the critical research issue of materials. Hence, strengthening of HEAs is a crucial issue.In this chapter, we reviewed the recent work on the room-temperature elastic properties and mechanical behavior of HEAs, including the mechanisms behind the plastic deformation of HEAs at both low and high temperatures. Furthermore, the present work examined the strengthening strategies of HEAs, e.g., strain hardening, grain-boundary strengthening, solid-solution strengthening, and particle strengthening. The fatigue, creep, and fracture properties were briefly introduced. Lastly, the future scientific issues and challenges of HEAs were discussed.

Shang, Yuanyuan↗
DOE OSTI2021

Morphology and Dynamics of Bottlebrush Polymers

This thesis makes significant advances to the understanding of bottlebrush polymers. While bottlebrushes have received much attention due to the recent discovery of their unprecedented properties, including supersoftness, ultra-low viscosity, and hyperelasticity, this thesis is the first fundamental investigation at the molecular level that comprises structure and dynamics. Neutron scattering experiments, detailed within, reveal spherical or cylindrical shapes, instead of a random coil conformation. Another highlight is the analysis of the fast dynamics at the sub nm-length scale. The combination of three neutron spectrometers and the development of a new analysis technique enabled the calculation of the mean-square displacement over seven orders of magnitude in time scale. This unprecedented result can be applied to a broad class of samples, including polymers and other materials. The thesis is accessible to scientists from other fields, provides the reader with easily understandable guidelines for applying this analysis to other materials, and has the potential to make a significant impact on the analysis of neutron scattering data.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Smoky Mountain Data Challenge 2021: An Open Call to Solve Scientific Data Challenges Using Advanced Data Analytics and Edge Computing

The 2021 Smoky Mountains Computational Sciences and Engineering Conference enlists scientists from across Oak Ridge National Laboratory (ORNL) and industry to be data sponsors and help create data analytics and edge computing challenges for eminent datasets in a variety of scientific domains. This work describes the significance of each of the eight datasets and their associated challenge questions. The challenge questions for each dataset were required to cover multiple difficulty levels. An international call for participation was sent to students, asking them to form teams of up to six people and apply novel data analytics and edge computing methods to solve these challenges.

Devineni, Pravallika↗
DOE OSTI2022

Understanding and Leveraging the I/O Patterns of Emerging Machine Learning Analytics

The scientific community is currently experiencing unprecedented amounts of data generated by cutting-edge science facilities. Soon facilities will be producing up to 1 PB/s which will force scientist to use more autonomous techniques to learn from the data. The adoption of machine learning methods, like deep learning techniques, in large-scale workflows comes with a shift in the workflow’s computational and I/O patterns. These changes often include iterative processes and model architecture searches, in which datasets are analyzed multiple times in different formats with different model configurations in order to find accurate, reliable and efficient learning models. This shift in behavior brings changes in I/O patterns at the application level as well at the system level. These changes also bring new challenges for the HPC I/O teams, since these patterns contain more complex I/O workloads. In this paper we discuss the I/O patterns experienced by emerging analytical codes that rely on machine learning algorithms and highlight the challenges in designing efficient I/O transfers for such workflows. We comment on how to leverage the data access patterns in order to fetch in a more efficient way the required input data in the format and order given by the needs of the application and how to optimize the data path between collaborative processes. We will motivate our work and show performance gains with a study case of medical applications.

Gainaru, Ana↗
DOE OSTI2023

Toward an Autonomous Workflow for Single Crystal Neutron Diffraction

The operation of the neutron facility relies heavily on beamline scientists. Some experiments can take one or two days with experts making decisions along the way. Leveraging the computing power of HPC platforms and AI advances in image analyses, here we demonstrate an autonomous workflow for the single-crystal neutron diffraction experiments. The workflow consists of three components: an inference service that provides real-time AI segmentation on the image stream from the experiments conducted at the neutron facility, a continuous integration service that launches distributed training jobs on Summit to update the AI model on newly collected images, and a frontend web service to display the AI tagged images to the expert. Ultimately, the feedback can be directly fed to the equipment at the edge in deciding the next-step experiment without requiring an expert in the loop. With the analyses of the requirements and benchmarks of the performance for each component, this effort serves as the first step toward an autonomous workflow for real-time experiment steering at ORNL neutron facilities.

Yin, Junqi↗
DOE OSTI2016

Probing Perovskite Interfaces and Superlattices with X-ray Photoemission Spectroscopy

Complex oxides exhibit an exceedingly wide range of electronic, magnetic and optical properties, giving these materials considerable potential for interesting basic science investigations, as well as possible utility in a range of technological applications. Complex oxides have been vigorously investigated as epitaxial films over the past two decades, in part because of the discovery of high-Tc superconductivity in cuprates . A second reason for this interest is the rich and varied property set, including colossal magnetoresistance , exhibited by the doped manganites, AExRE1-xMnO3, where AE and RE represent alkaline earth and rare earth metals, respectively. The observations of many interesting and often unexpected phenomena have captured the attention and imagination of a growing community of condensed matter scientists who wants to explore the fundamental physics of these materials, as well as novel technological concepts.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2017

Performance Evaluation of NWChem Ab-Initio Molecular Dynamics (AIMD) Simulations on the Intel® Xeon Phi™ Processor

Ab-initio Molecular Dynamics (AIMD) methods are an important class of algorithms, as they enable scientists to understand the chemistry and dynamics of molecular and condensed phase systems while retaining a first-principles-based description of their interactions. Many-core architectures such as the Intel® Xeon Phi™ processor are an interesting and promising target for these algorithms, as they can provide the computational power that is needed to solve interesting problems in chemistry. In this paper, we describe the efforts of refactoring the existing AIMD plane-wave method of NWChem from an MPI-only implementation to a scalable, hybrid code that employs MPI and OpenMP to exploit the capabilities of current and future many-core architectures. We describe the optimizations required to get close to optimal performance for the multiplication of the tall-and-skinny matrices that form the core of the computational algorithm. We present strong scaling results on the complete AIMD simulation for a test case that simulates 256 water molecules and that strong-scales well on a cluster of 1024 nodes of Intel Xeon Phi processors. We compare the performance obtained with a cluster of dual-socket Intel® Xeon® E5–2698v3 processors.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2018

Scalable Data Management of the Uintah Simulation Framework for Next-Generation Engineering Problems with Radiation

The need to scale next-generation industrial engineering problems to the largest computational platforms presents unique challenges. This paper focuses on data management related problems faced by the Uintah simulation framework at a production scale of 260K processes. Uintah provides a highly scalable asynchronous many-task runtime system, which in this work is used for the modeling of a 1000 megawatt electric (MWe) ultra-supercritical (USC) coal boiler. At 260K processes, we faced both parallel I/O and visualization related challenges, e.g., the default file-per-process I/O approach of Uintah did not scale on Mira. In this paper we present a simple to implement, restructuring based parallel I/O technique. We impose a restructuring step that alters the distribution of data among processes. The goal is to distribute the dataset such that each process holds a larger chunk of data, which is then written to a file independently. This approach finds a middle ground between two of the most common parallel I/O schemes–file per process I/O and shared file I/O–in terms of both the total number of generated files, and the extent of communication involved during the data aggregation phase. To address scalability issues when visualizing the simulation data, we developed a lightweight renderer using OSPRay, which allows scientists to visualize the data interactively at high quality and make production movies. Finally, this work presents a highly efficient and scalable radiation model based on the sweeping method, which significantly outperforms previous approaches in Uintah, like discrete ordinates. The integrated approach allowed the USC boiler problem to run on 260K CPU cores on Mira.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Querying Large Scientific Data Sets with Adaptable IO System ADIOS

When working with a large dataset, a relatively small fraction of data records are of interest in each analysis operation. For example, while examining a billion-particle dataset from an accelerator model, the scientists might focus on a few thousand fastest particles, or on the particle farthest from the beam center. In general, this type of selective data access is challenging because the selected data records could be anywhere in the dataset and require a significant amount of time to locate and retrieve. In this paper, we report our experience of addressing this data access challenge with the Adaptable IO System ADIOS. More specifically, we design a query interface for ADIOS to allow arbitrary combinations of range conditions on known variables, implement a number of different mechanisms for resolving these selection conditions, and devise strategies to reduce the time needed to retrieve the scattered data records. In many cases, the query mechanism can retrieve the selected data records orders of magnitude faster than the brute-force approach.Our work relies heavily on the in situ data processing feature of ADIOS to allow user functions to be executed in the data transport pipeline. This feature allows us to build indexes for efficient query processing, and to perform other intricate analyses while the data is in memory.

Gu, Junmin↗
DOE OSTI2022

Domain-Specific Type-Safe APIs for Hierarchical Scientific Data with Modern C++

General-purpose library application programming interfaces (APIs) for self-describing hierarchical scientific data storage, such as the HDF5 and NetCDF libraries, are traditionally of runtime nature. Runtime errors for entry existence and data types are typically caught later in the development process of higher-level application-specific APIs. In this paper, we propose exploiting modern C++ metaprogramming features to add compile-time type-safety to improve the interaction with a well-defined metadata-rich scientific schema in domain-specific hierarchical datasets. We tackle two aspects of common use: (i) direct data access, (ii) flexible “in-memory” index models for efficient search and data processing. The proposed APIs use C++17’s template type auto deduction features, C++11’s enum class for type-safety and C-style preprocessor macros for generative templated code. We showcase the pros and cons of our initial work on the standard NeXus schema used for annotating and storing experimental neutron scattering data at several facilities around the world on top of HDF5. Extendable compile-time type-safe APIs are a desirable feature that could be indexed by any modern integrated development environment (IDE). Hence, such APIs can help ease the learning curve for domain scientists using a less error-prone software interaction to enhance the findability of their data without resorting to a domain-specific language (DSL).

Godoy, William↗
DOE OSTI2025

Machine Learning Techniques for Data Reduction of Climate Applications

Scientists conduct large-scale simulations to compute derived quantities-of-interest (QoI) from primary data. Often, QoI are linked to specific features, regions, or time intervals, such that data can be adaptively reduced without compromising the integrity of QoI. For many spatiotemporal applications, these QoI are binary in nature and represent presence or absence of a physical phenomenon. We present a pipelined compression approach that first uses neural-network-based techniques to derive regions where QoI are highly likely to be present. Then, we employ a Guaranteed Autoencoder (GAE) to compress data with differential error bounds. GAE uses QoI information to apply low-error compression to only these regions. This results in overall high compression ratios while still achieving downstream goals of simulation or data collections. Experimental results are presented for climate data generated from the E3SM Simulation model for downstream quantities such as tropical cyclone and atmospheric river detection and tracking. These results show that our approach is superior to comparable methods in the literature.

Li, Xiao [University of Florida]↗
DOE OSTI2018

Fueling Peter’s Mill: Mikhail Lomonosov’s Educational Training in Russia and Germany, 1731–1741

This article, the second in a series about the Russian scientist Mikhail Lomonosov (1711–1765), traces his education from his arrival in Moscow in 1731 to study at the Slavo- Greco-Latin Academy, through his admission to the St. Petersburg Academy of Sciences in 1736, to his trip abroad to complete his educational studies from 1736 to 1741. Lomonosov’s story during this time opens a vista on the introduction of modern physics and modern science into Russia. Michael D. Gordin has argued that Peter the Great’s plans to Westernize Russia were more broadly conceived than he is usually credited, with ambitions that exceeded mere utilitarian and pragmatic goals. Lomonosov’s career trajectory is a good example, illustrating how different aspects of the Petrine vision intersected with and reinforced each other. Here, the article ends with Lomonosov’s return to Russia from Germany in 1741, an important landmark in the growth of the Academy and of Russian science.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2014

A multi-scale spatial approach to address environmental effects of small hydropower development

Hydropower development continues to grow worldwide in developed and developing countries. While the ecological and physical responses to dam construction have been well documented, translating this information into planning for hydropower development is extremely difficult. Very few studies have conducted environmental assessments to guide site-specific or widespread hydropower development. Herein, we propose a spatial approach for estimating environmental effects of hydropower development at multiple scales, as opposed to individual site-by-site assessments (e.g., environmental impact assessment). Because the complex, process-driven effects of future hydropower development may be uncertain or, at best, limited by available information, we invested considerable effort in describing novel approaches to represent environmental concerns using spatial data and in developing the spatial footprint of hydropower infrastructure. Furthermore, we then use two case studies in the US, one at the scale of the conterminous US and another within two adjoining rivers basins, to examine how environmental concerns can be identified and related to areas of varying energy capacity. We use combinations of reserve-design planning and multi-metric ranking to visualize tradeoffs among environmental concerns and potential energy capacity. Spatial frameworks, like the one presented, are not meant to replace more in-depth environmental assessments, but to identify information gaps and measure the sustainability of multi-development scenarios as to inform policy decisions at the basin or national level. Most importantly, the approach should foster discussions among environmental scientists and stakeholders regarding solutions to optimize energy development and environmental sustainability.

13 HYDRO ENERGY↗
DOE OSTI2022

GeoSoilEnviroCARS (Sector 13) at the Advanced Photon Source: a comprehensive synchrotron radiation facility for Earth science research at ambient and extreme conditions

GeoSoilEnviroCARS (GSECARS) is a comprehensive analytical laboratory for Earth and environmental science research using X-ray beams from the Advanced Photon Source, Argonne National Laboratory. State-of-the-art instruments are available for (1) high-pressure/high- or low-temperature diffraction, total scattering, and spectroscopy (Brillouin, Raman, and VIS-IR) using the laser heated diamond anvil cell (DAC); (2) high-pressure/high-temperature diffraction, scattering, and imaging as well as acoustic emission (AE) and ultrasonics using the large-volume press (LVP); (3) powder, single crystal, and surface/interface diffraction; (4) X-ray absorption fine structure spectroscopy; (5) X-ray fluorescence microprobe analysis; and (6) microtomography. As shown here, experiments are facilitated by senior level staff who collaborate on all aspects of the analytical work including experiment design, sample preparation, data collection, data interpretation, and publication preparation. Both technical and scientific synergies occur as a result of the intimate association of the various techniques and scientists experienced in the applications of synchrotron radiation to Earth, environmental, and planetary science problems. The facility includes state-of-the-art instrumentation designed and built in-house, including custom X-ray optics, online and offline laser-based systems, specialized sample environments and positioning systems, as well as pixel-array and multi-crystal energy dispersive X-ray detectors, which are available to be shared among the experimental stations.

36 MATERIALS SCIENCE↗
DOE OSTI2024

In situ X-ray and IR probes relevant to Earth science at the Advanced Light Source at Lawrence Berkeley Laboratory

Access to synchrotron X-ray facilities has become an important aspect for many disciplines in experimental Earth science. This is especially important for studies that rely on probing samples in situ under natural conditions different from the ones found at the surface of the Earth. The non-ambient condition Earth science program at the Advanced Light Source (ALS), Lawrence Berkeley National Laboratory, offers a variety of tools utilizing the infra-red and hard X-ray spectrum that allow Earth scientists to probe Earth and environmental materials at variable conditions of pressure, stress, temperature, atmospheric composition, and humidity. These facilities are important tools for the user community in that they offer not only considerable capacity (non-ambient condition diffraction) but also complementary (IR spectroscopy, microtomography), and in some cases unique (Laue microdiffraction) instruments. The availability of the ALS’ in situ probes to the Earth science community grows especially critical during the ongoing dark time of the Advanced Photon Source in Chicago, which massively reduces available in situ synchrotron user time in North America.

58 GEOSCIENCES↗
DOE OSTI2015

ND 2 AV: N-dimensional data analysis and visualization analysis for the National Ignition Campaign

Here, one of the biggest challenges in high-energy physics is to analyze a complex mix of experimental and simulation data to gain new insights into the underlying physics. Currently, this analysis relies primarily on the intuition of trained experts often using nothing more sophisticated than default scatter plots. Many advanced analysis techniques are not easily accessible to scientists and not flexible enough to explore the potentially interesting hypotheses in an intuitive manner. Furthermore, results from individual techniques are often difficult to integrate, leading to a confusing patchwork of analysis snippets too cumbersome for data exploration. This paper presents a case study on how a combination of techniques from statistics, machine learning, topology, and visualization can have a significant impact in the field of inertial confinement fusion. We present the $\mathrm{ND}^2\mathrm{AV}$: N-dimensional data analysis and visualization framework, a user-friendly tool aimed at exploiting the intuition and current workflow of the target users. The system integrates traditional analysis approaches such as dimension reduction and clustering with state-of-the-art techniques such as neighborhood graphs and topological analysis, and custom capabilities such as defining combined metrics on the fly. All components are linked into an interactive environment that enables an intuitive exploration of a wide variety of hypotheses while relating the results to concepts familiar to the users, such as scatter plots. $\mathrm{ND}^2\mathrm{AV}$ uses a modular design providing easy extensibility and customization for different applications. $\mathrm{ND}^2\mathrm{AV}$ is being actively used in the National Ignition Campaign and has already led to a number of unexpected discoveries.

97 MATHEMATICS AND COMPUTING↗