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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.
No abstract prepared.
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.
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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Under this grant our team contributed scientific support to the Department of Energy Atmospheric Radiation Program’s (DOE-ARM) Infrastructure team to maintain high quality research data at the DOE-ARM North Slope of Alaska with special emphasis on the radars. Under our guidance two major field campaigns focusing on mixed-phase Arctic clouds were conducted that greatly increased the community’s understanding of the many processes working together to control the evolution of single-layer cloud mixed-phase clouds. A series of modeling and observational studies revealed that the longevity of the radiatively important liquid phase is strongly dependent on how the ice phase develops in mixed-phase clouds. A new ice microphysics parameterization was developed to capture better the natural evolution of ice particle growth in evolving environments. An ice particle scattering database was developed for all ARM radar frequencies. This database was used in a radar simulator (Doppler spectrum and polarimetric variables) to aid in the interpretation of the advanced ARM radars. At the conclusion of this project our team was poised to develop a complete radar simulator consistent with the new microphysical parameterization, taking advantage of parameterization’s advanced characterization of the ice shape and ice density.
These note aim to set forth, systematically and from elementary beginnings, some constructs that arise from study of the three-dimensional rotation group.
To be prepared for a response to a complex radiological or nuclear emergency, the first responder community must incorporate radiation protection principles, and be able to conduct tactical operational measurements, assess data, and determine associated health risks. While these actions must occur promptly in the first hours of an unfolding crisis, the on-scene responders must do so with no scientific support from radiation protection experts, such as from the central government. To further the challenges to effectively perform these actions, local hazardous material (HAZMAT) response teams, which are usually the source of technical expertise at the scene of a hazardous materials release, rarely encounter radiological materials or respond to large-scale radiological or nuclear emergencies.
No abstract provided.
Particle transport codes read in physical data e.g. ACE tables. ACE tables are hard to reason about as illustrated below.
Abstract not provided.
He was a willing subject in countless scientific experiments, a superstar of health physics, as well as a favorite photo prop -- and not just around Halloween time. He was Plastic Man. Beginning in the mid-1950s, the Lab (then called Los Alamos Scientific Laboratory) often relied on several life-sized synthetic test subjects to avoid experimenting on people. In the early days of the Laboratory, not much was known about how different radioisotopes interacted with the human body and how much it absorbed from different radiation sources. Answering queries like these is where Plastic Man came in. For example, during the 1950s and 1960s, the Lab was tasked with developing nuclear rockets for space exploration under the Rover Program. One of the concerns was how astronauts might be affected when exposed to radiation from these rocket engines. So, the Lab’s health group set up an experiment in which Plastic Man was placed in close proximity to an experimental Rover nuclear rocket engine. Running experiments like this using Plastic Man kept humans safer. How life-like was he? There were actually two variations of Plastic Man. Both had hard plastic exteriors, but one of the models encased a real human skeleton. They also had movable joints and simulated organs, such as a liver, a spleen, kidneys, lungs and a thyroid. Although Plastic Man in its entirety was used in experiments, much of the data collected were from research conducted inside on these replicated organs. In time, technology advanced and Plastic Man was able to retire from the Lab. He found a new home in 1964 as a display at the Laboratory’s science museum, and is sometimes displayed today in the Bradbury Science Museum in downtown Los Alamos.