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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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Researchers learned machine learning methods during a boot camp, then applied their new knowledge to real-world research problems during a hackathon.
In the Anthropocene, in which we now live, climate change is impacting most life on Earth. Microorganisms support the existence of all higher trophic life forms. To understand how humans and other life forms on Earth (including those we are yet to discover) can withstand anthropogenic climate change, it is vital to incorporate knowledge of the microbial ‘unseen majority’. We must learn not just how microorganisms affect climate change (including production and consumption of greenhouse gases) but also how they will be affected by climate change and other human activities. This Consensus Statement documents the central role and global importance of microorganisms in climate change biology. It also puts humanity on notice that the impact of climate change will depend heavily on responses of microorganisms, which are essential for achieving an environmentally sustainable future.
Education, computing, experiments, and facilities jostle for position in a grassroots exercise that seeks to maintain a nuclear-physics workforce and scientific leadership.
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Abstract not provided.
Large-scale simulations or scientific experiments produce petabytes of data per run. This poses massive challenges for I/O and storage when scientific analysis workflows are run manually offline. Unsupervised deep learning-based techniques to extract patterns and non-linear relations from these large amounts of data provide a way to build scientific understanding from raw data, reducing the need for manual pre-selection of analysis steps, but require exascale compute and memory to process the full dataset available. In this paper, we demonstrate a heterogeneous streaming workflow in which plasma simulation data is streamed directly to a Machine Learning (ML) application training a model on the simulation data in-transit, completely circumventing the capacity-constrained filesystem bottleneck. This workflow employs openPMD to provide a high level interface to describe scientific data and also uses ADIOS2, to transfer volumes of data that exceed the capabilities of the filesystem. We employ experience replay to avoid catastrophic forgetting in learning from this non-steady state process in a continual manner and adapt it to improve model convergence while learning in-transit. As a proof-of-concept, we approach the ill-posed inverse problem of predicting particle dynamics from radiation in a particle-incell (PIConGPU) simulation of the Kelvin-Helmholtz instability (KHI). We detail hardware-software co-design challenges as we scale PIConGPU to full Frontier, the Top-1 system as of June 2024 Top500 list.
In situ processing produces reduced size persistent representations of a simulations state while the simulation is running. The need for in situ visualization and data analysis is often described in terms of supercomputer size and performance in relation to available storage size.
Two recent projects have used the Cray XK7 Titan supercomputer at the US Department of Energy's Oak Ridge National Laboratory to study Earth's dynamics, specifically seismology-related studies. Using Titan, one team of researchers modeled Earth's interior using earthquake data, while a second team developed physics-based earthquake simulations for disaster management applications. Together, the projects demonstrate how leadership-class supercomputing provides context to scientific discussions and informs decision making among civic leaders.
This special issue celebrates the scientific legacy of Dr. Malcolm J. Andrews, former Editor of the ASME Journal of Fluids Engineering (2010–2018). Malcolm devoted his career to the development and applications of computational, experimental, and analytical tools to investigate and solve diverse, complex fluid dynamics, and heat transfer problems. Malcolm’s most notable research accomplishments include: (i) contributions to innovative experiments, simulations, and theory to understand buoyancydriven turbulent mixing due to Rayleigh–Taylor instability; (ii) development of the multiphase particle-in-cell method for understanding fragmentation in spray jets, coal-water slurry, and other dense particulate flows; (iii) mixing in nano- and biomaterials in applications ranging from protein glues to carbon nanotube composites; (iv) determining dispersion in proteins to improve therapeutic drug delivery via micro-encapsulation; (v) innovative methods for storing information using nanopinned dipole magnetic vortices in superconducting materials; and (vi) modeling and design modifications for shell-and-tube heat exchangers, as well as heat transfer in rotating systems such as turbine blades. The results of his research have been published in a long series of trend-setting journal and conference papers, book chapters, and patents.
Accelerator science and technology is inherently an integrative discipline that combines aspects of physics, computational science, electrical and mechanical engineering. As few universities offer full academic programs, the education of accelerator physicists and engineers for the future has primarily relied on a combination of on-the-job training supplemented with intense courses at regional accelerator schools. This paper describes the approaches being used to satisfy the educational interests of a growing number of interested physicists and engineers.
An AI-powered system for generating, reviewing, ranking, and evolving research hypotheses. It leverages Large Language Models (LLMs) for various tasks, including hypothesis generation, reflection, and comparison. The system is designed to assist researchers in exploring a research space and identifying promising hypotheses.
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Use of Earth-abundant materials in solar absorber films is critical for expanding the reach of photovoltaic (PV) technologies.
Research provides insight for exploring use of earth-abundant quaternary semiconductors for large-scale solar cell applications.
Research reveals active role of cluster symmetries on the size-sensitive, diverse melting behaviors of metallic nanoclusters, providing insight to understanding phase changes of nanoparticles for thermal energy storage.
On the evening of October 24, 2011 a geomagnetic storm raged across Europe and the United States. Aurora was visible as far south as Alabama in the US, and social media networks helped transmit the visibility of the lights. The purpose of this proposal is a proof of concept demonstration for collecting, analyzing, and interpreting data on the dynamics and evolution of auroral events via real-time social media, which acts as a sensor array distributed in both time and space. We will validate the efficacy of this observational framework by comparison to measurements and predictions of geomagnetic activity as provided by the NOAA Space Weather Prediction Center. We hypothesize that time-domain volunteered information (VI) from the Twitter social network can be derived with sufficient integrity to track events as they unfold and possibly predict events and their impending severity. By comparing the derived information with other space-based and ground-based measurements of an event, we further hypothesize that the derived information can be used as a complementary data source for scientific research of the event.