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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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At least 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

The critical importance of software for HEP

Particle physics has an ambitious and broad global experimental programme for the coming decades. Large investments in building new facilities are already underway or under consideration. Scaling the present processing power and data storage needs by the foreseen increase in data rates in the next decade for HL-LHC is not sustainable within the current budgets. As a result, a more efficient usage of computing resources is required in order to realise the physics potential of future experiments. Software and computing are an integral part of experimental design, trigger and data acquisition, simulation, reconstruction, and analysis, as well as related theoretical predictions. A significant investment in computing and software is therefore critical. Advances in software and computing, including artificial intelligence (AI) and machine learning (ML), will be key for solving these challenges. Making better use of new processing hardware such as graphical processing units (GPUs) or ARM chips is a growing trend. This forms part of a computing solution that makes efficient use of facilities and contributes to the reduction of the environmental footprint of HEP computing. The HEP community already provided a roadmap for software and computing for the last EPPSU, and this paper updates that, with a focus on the most resource critical parts of our data processing chain.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Roadmap for Optical Metasurfaces

Metasurfaces have recently risen to prominence in optical research, providing unique functionalities that can be used for imaging, beam forming, holography, polarimetry, and many more, while keeping device dimensions small. Despite the fact that a vast range of basic metasurface designs has already been thoroughly studied in the literature, the number of metasurface-related papers is still growing at a rapid pace, as metasurface research is now spreading to adjacent fields, including computational imaging, augmented and virtual reality, automotive, display, biosensing, nonlinear, quantum and topological optics, optical computing, and more. At the same time, the ability of metasurfaces to perform optical functions in much more compact optical systems has triggered strong and constantly growing interest from various industries that greatly benefit from the availability of miniaturized, highly functional, and efficient optical components that can be integrated in optoelectronic systems at low cost. This creates a truly unique opportunity for the field of metasurfaces to make both a scientific and an industrial impact. Furthermore, the goal of this Roadmap is to mark this “golden age” of metasurface research and define future directions to encourage scientists and engineers to drive research and development in the field of metasurfaces toward both scientific excellence and broad industrial adoption.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Battery metal recycling by flash Joule heating

The staggering accumulation of end-of-life lithium-ion batteries (LIBs) and the growing scarcity of battery metal sources have triggered an urgent call for an effective recycling strategy. However, it is challenging to reclaim these metals with both high efficiency and low environmental footprint. We use here a pulsed dc flash Joule heating (FJH) strategy that heats the black mass, the combined anode and cathode, to >2100 kelvin within seconds, leading to ~1000-fold increase in subsequent leaching kinetics. There are high recovery yields of all the battery metals, regardless of their chemistries, using even diluted acids like 0.01 M HCl, thereby lessening the secondary waste stream. The ultrafast high temperature achieves thermal decomposition of the passivated solid electrolyte interphase and valence state reduction of the hard-to-dissolve metal compounds while mitigating diffusional loss of volatile metals. Life cycle analysis versus present recycling methods shows that FJH significantly reduces the environmental footprint of spent LIB processing while turning it into an economically attractive process

36 MATERIALS SCIENCE↗
NASA NTRS2010

The Latest Space-Borne Observations of TGFs from Fermi-GBM

The Gamma-ray Burst Monitor (GBM) on the Fermi Gamma-ray Space Telescope Observatory (Fermi) is detecting about two TGFs per week. This rate has increased by a factor of approx.eight since launch when flight software was uploaded to the spacecraft in November 2009 in order to increase the sensitivity of GBM to TGFs. Weaker, un-triggered TGFs are now also being observed about once per day over selected low-latitude regions Americas. The high efficiency and time resolution (2 s) of GBM allows temporal features to be resolved so that some insight may be gained on the origin and transport of the gamma-ray photons through the atmosphere. TGFs are observed to be shorter than previously thought, with an average duration of approx.100 micro-s. The absolute times of TGFs are known to approx.10 micro-s, allowing accurate correlations of TGFs with lightning networks and other lightning-related phenomena. The events are observed in the thick bismuth germanate (BGO) scintillation detectors of GBM with photon energies above 40 MeV. Other new results on the temporal and spectral characteristics of TGFs will be presented, along with properties of several electron-positron TGF events that have been identified.

Fishman, Gerald J.↗
DOE OSTI2021

Trigger-based Incremental Data Processing with Unified Sync and Async Model

In recent years, more and more applications in the cloud have needs to process large-scale on-line datasets, which evolve over time as new entries are added and existing entries are modified. Several programming frameworks, such as Percolator and Oolong, are proposed for such incremental data processing and can achieve efficient processing with an event-driven abstraction. However, these frameworks are inherently asynchronous, leaving the heavy burden of managing synchronization to applications' developers, which further significantly restricts their usabilities. In this study, we propose a trigger-based incremental computing framework in the cloud, called Domino, with both synchronous and asynchronous mechanisms to coordinate parallel triggers. With this new framework, both synchronous and asynchronous applications can be seamlessly developed. Use cases and extensive evaluation results confirm that it can deliver sufficient performance, and also is easy to use for incremental applications in large-scale distributed computing.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

A machine learning based approach to online electron reconstruction at CLAS12

Online reconstruction is key for monitoring purposes and real time analysis in High Energy and Nuclear Physics experiments. A necessary component of reconstruction algorithms is particle identification that combines information left by a particle passing through several detector components to identify the particle’s type. Of particular interest to electro-production Nuclear Physics experiments such as CLAS12 is electron identification which is used to trigger data recording. A machine learning approach was developed for CLAS12 to reconstruct and identify electrons by combining raw signals at the data acquisition level from several detector components. Here, this approach achieves an electron identification purity above 75% whilst retaining an efficiency close to 100%. The machine learning tools are capable of running at high rates exceeding the data acquisition rates and will allow electron reconstruction in real-time. This work enhances online analyses and monitoring and can contribute to improved triggering at CLAS12. This machine learning driven approach will also be crucial for experiments aiming to transition to streaming readout operations where online reconstruction will be a key component of the data taking paradigm.

Artificial intelligence↗
DOE OSTI2026

Real-Time Anomaly Detection for Searches Beyond the Standard Model in the ProtoDUNE Horizontal Drift Detector

This paper summarizes work conducted throughout a SULI internship at Fermi National Accelerator Laboratory focused on building an unsupervised machine learning model for real-time anomaly detection in ProtoDUNE Horizontal Drift. Using simulated data, we trained an autoencoder model on a pure cosmic dataset, and evaluated it on both cosmic and neutrino events—making the model an anomaly detector. The goal was to make a model which matches or exceeds the current ADC Simple Window trigger algorithm so that our model can perform at the same rate but provide sensitivity to potential beyond-the-Standard-Model (BSM) signatures. In the end, we were able to construct a model which slightly exceeds the capabilities of the ADC Simple Window while remaining completely unsupervised, achieving 31.9 ± 0.2% (26.6 ± 0.2%) ν efficiency at 5 Hz (2 Hz), a 3.6 (3.2) percentage point increase. Additionally, 17.5 ± 0.3% (18.3 ± 0.3%) of the events that passed the autoencoder at 5 Hz (2 Hz) were missed by the current trigger algorithm. Future work will investigate alternative normalization methods, including quantile transformation, and evaluate the model on ProtoDUNE-HD detector-glitch data if that data becomes available.

Wilson, C. [Cincinnati U., RWC]↗
DOE OSTI2021

Benefits and pathologies associated with the inflammatory response

Adult skeletal muscle regenerates completely after a damage, thanks to the satellite cells, or muscle stem cells (MuSCs), that implement the adult myogenic program. This program is sustained by both robust intrinsic mechanisms and extrinsic cues coming from the close neighborhood of MuSCs during muscle regeneration. Among the various cell types present in the regenerating muscle, immune cells, and particularly macrophages, exert numerous functions and provide sequential transient niches to support the myogenic program. The adequate orchestration of the delivery of these cues ensures efficient muscle regeneration and full functional recovery. The situation is very different in muscular dystrophies where asynchronous and permanent microinjuries occur, triggering contradictory regenerating cues at the same time in a specific area, that lead to chronic inflammation and fibrogenesis. Here we review the beneficial effects that leukocytes, and particularly macrophages, exert on their neighboring cells during skeletal muscle regeneration after an acute injury. Then, the more complicated (and less beneficial) roles of leukocytes during muscular dystrophies are presented. Finally, we discuss how the inflammatory compartment may be a target to improve muscle regeneration in both acute muscle injury and muscle diseases.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

A reduced model for edge localized mode control by supersonic molecular beam injection and pellet injection

We develop a diffusive, bistable, tri-unstable cellular automata (CA) model to study the dynamics of H-mode pedestal with edge localized modes (ELMs) and their control by supersonic molecular beam injection (SMBI) and pellet injection (PI). It is shown that the new CA model can reproduce the key features of H-mode pedestals with various types of ELM, including Type-I ELM. SMBI and PI are modeled as additional grain injections into pedestal with varying degrees of injected materials and profiles. It is found that H-mode pedestal responds to SMBI differently depending on the baseline fueling. If the baseline fueling is large enough to allow Type-I ELM, SMBI enhances large transport avalanches caused by ballooning instabilities. These avalanches prevent the total pedestal current from reaching the boundary for peeling instability. On the other hand, if the baseline fueling is low to avoid Type-I ELM, SMBI enhances small scale avalanches, which prevent the pedestal from growing to profiles globally vulnerable to ballooning instabilities. These imply that SMBI can mitigate different types of ELM by converting them to more benign types. From CA modeling of pellet injection, it is shown that Type-I ELM can be triggered by pellet injection with sufficient strength and depth. Scanning the frequency of pellet injection, it is found that a maximum efficiency of pellet pacing is achieved when the injection frequency is approximately ten times the natural frequency of Type-I ELM.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Discrimination between individual dust and bioparticles using aerosol time-of-flight mass spectrometry

Ice nucleating particles (INPs) impact cloud properties and precipitation processes through their ability to trigger cloud glaciation. Dust and bioparticles are two important sources of INPs that have markedly different atmospheric loadings and ice nucleating efficiencies. In situ identification of the sources of INPs in clouds has been accomplished using single particle mass spectrometry (SPMS). However, external mixtures of dust and bioparticles present a unique challenge as they have overlapping mass spectral ion signatures, complicating their unambiguous identification. This study presents a detailed discussion of dust and bioparticle SPMS signatures, uniting data from a broad array of studies. As emphasized, the ion signals from both dust and bioparticles are highly sensitive to ionization conditions. To understand the observed variations, we characterize the mass spectral dependence of distinct dust and bioparticle samples using total positive ion intensity (TPII) as an indicator of the laser pulse energy each particle encountered. Through this analysis, a broad range of characteristic biogenic low intensity ion peaks that may be useful to distinguish bioparticles from dust became apparent and are highlighted. Insights informed by this analysis were utilized to identify bioparticles in ambient SPMS data. Ambient particles exhibiting both dust and characteristic biogenic spectral fingerprints were excluded from the bioparticle classification. Although bioparticles only made up 0.2% of all sampled particles, their abundance was moderately correlated with INP concentrations measured at 15 °C.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Expansion of the Fast Neutron Hodoscope at TREAT to Support Fuel Safety Experiments

In 2024 the Fuel Motion Monitoring System (FMMS), or Hodoscope, at the Transient Reactor Test Facility, is being expanded from 96 viewing channels to 192 viewing channels, effectively doubling the FMMS field of view. Here, this increase in capability will allow the FMMS to support larger scale fuel tests, encompassing height-of-core test devices, test devices with multi-pin fuel assemblies, and test devices with recirculating coolant flow. Work supporting the expansion included refurbishing 96 additional proton recoil scintillator (PRS) detectors, doubling the data acquisition system (DAS) installed architecture, improving time synchronization in the DAS, and new research to measure the PRS detector energy-dependent, fast-neutron detection efficiency. In addition, laboratory activities have produced an improved benchtop testing capability for assessing the DAS, time synchronization, and external start triggering, along with an updated capability to scan PRS detectors to develop a preliminary flat-field normalization prior to deployment to TREAT.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
DOE OSTI2025

Intelligent experiments through real-time AI: Fast Data Processing and Autonomous Detector Control for sPHENIX and future EIC detectors

This R&D project, initiated by the DOE Nuclear Physics AI-Machine Learning initiative in 2022, leverages AI to address data processing challenges in high-energy nuclear experiments (RHIC, LHC, and future EIC). Our focus is on developing a demonstrator for real-time processing of high-rate data streams from sPHENIX experiment tracking detectors. The limitations of a 15 kHz maximum trigger rate imposed by the calorimeters can be negated by intelligent use of streaming technology in the tracking system. The approach efficiently identifies low momentum rare heavy flavor events in high-rate p+p collisions (3MHz), using Graph Neural Network (GNN) and High Level Synthesis for Machine Learning (hls4ml). Success at sPHENIX promises immediate benefits, minimizing resources and accelerating the heavy-flavor measurements. The approach is transferable to other fields. For the EIC, we develop a DIS-electron tagger using Artificial Intelligence - Machine Learning (AI-ML) algorithms for real-time identification, showcasing the transformative potential of AI and FPGA technologies in high-energy nuclear and particle experiments real-time data processing pipelines.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Magnetogenesis in a Collisionless Plasma: From Weibel Instability to Turbulent Dynamo

We report on a first-principles numerical and theoretical study of plasma dynamo in a fully kinetic framework. By applying an external mechanical force to an initially unmagnetized plasma, we develop a self-consistent treatment of the generation of “seed” magnetic fields, the formation of turbulence, and the inductive amplification of fields by the fluctuation dynamo. Driven large-scale motions in an unmagnetized, weakly collisional plasma are subject to strong phase mixing, which leads to the development of thermal pressure anisotropy. This anisotropy triggers the Weibel instability, which produces filamentary “seed” magnetic fields on plasma-kinetic scales. The plasma is thereby magnetized, enabling efficient stretching and folding of the fields by the plasma motions and the development of Larmor-scale kinetic instabilities such as the firehose and mirror. The scattering of particles off the associated microscale magnetic fluctuations provides an effective viscosity, regulating the field morphology and turbulence. During this process, the seed field is further amplified by the fluctuation dynamo until energy equipartition with the turbulent flow is reached. By demonstrating that equipartition magnetic fields can be generated from an initially unmagnetized plasma through large-scale turbulent flows, this work has important implications for the origin and amplification of magnetic fields in the intracluster and intergalactic mediums.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1986

Antarctic O3 - Chemical mechanisms for the spring decrease

Chemical explanations for the spring decline of Antarctic O3 involve reactions of ClO or interactions between BrO and ClO. Reaction schemes involving Br radicals have the highest efficiency for removal of O3. The chemical mechanisms require low levels of NO(x). Recent cooling of the stratosphere may have triggered formation of HNO3 monohydrate, removing NO(x) and setting the stage for enhanced loss of ozone over Antarctica.

Mcelroy, Michael B.↗
DOE OSTI2022

Adaptive elasticity policies for staging-based in situ visualization

In situ processing aims to alleviate the growing gap between computation and I/O capabilities by performing data processing close to the data source. In situ processing is widely used to process data generated by multiple data sources, including observation data from edge devices or scientific observational facilities and the simulation data generated by scientific computation on a high-performance computing (HPC) platform. For a scientific workflow that is run on an HPC platform and composed of a simulation program and an in situ data analytics or visualization (abbreviated as ana/vis) task, there is an implicit assumption that the computing resources assigned to the workflow keep static during the workflow execution. However, with the converging trend between the HPC and cloud computing platform, running the in situ ana/vis task in an elastic way is promising to decrease its overhead and improve its resource utilization rate. Resource elasticity represents the ability to change resource configurations such as the number of computing nodes/processes during workflow execution. An elastic job may dynamically adjust resource configurations; it may use a few resources at the beginning and more resources toward the end of the job when interesting data appear. However, it is hard to predict a priori how many computing nodes/processes need to be added/removed during the workflow execution to adapt to changing workflow needs. How to efficiently guide elasticity operations, such as growing or shrinking the number of processes used for in situ analysis during workflow execution, is an open-ended research question. In this article, we present adaptive elasticity policies that adopt workflow runtime information collected during workflow execution to predict how to trigger the addition/removal of processes in order to minimize in situ processing overhead. Taking in situ visualization tasks as an example, we integrate the presented elasticity policies into a staging-based elastic workflow and evaluate its efficiency in multiple elasticity scenarios. Compared with the situation without elasticity or with a static elasticity policy that uses a fixed number of processes for each rescaling operation, the adaptive elasticity policy can save overhead in finding a proper resource configuration and improve resource utilization efficiency. Furthermore, one experiment illustrates that the adaptive elasticity policy saves 41% of core-hours compared with the situation without the resource elasticity.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Real-Time Anomaly Detection for Beyond Standard Model Searches in ProtoDUNE Horizontal Drift

This paper summarizes work conducted throughout a SULI internship at Fermi National Accelerator Laboratory focused on building an unsupervised machine learning model for real-time anomaly detection in ProtoDUNE Horizontal Drift. Using simulated data, we trained an autoencoder model on a pure cosmic dataset, and evaluated it on both cosmic and neutrino events---making the model an anomaly detector. The goal was to make a model which matches or exceeds the current ADC Simple Window trigger algorithm so that our model can perform at the same rate but provide sensitivity to potential beyond-the-Standard-Model (BSM) signatures. In the end, we were able to construct a model which slightly exceeds the capabilities of the ADC Simple Window while remaining completely unsupervised, achieving $31.9 \pm 0.2$\% ($26.6 \pm 0.2$\%) $\nu$ efficiency at 5 Hz (2 Hz), a 3.6 (3.2) percentage point increase. Additionally, $17.5 \pm 0.3$\% ($18.3 \pm 0.3$\%) of the events that passed the autoencoder at 5 Hz (2 Hz) were missed by the current trigger algorithm. Future work will investigate alternative normalization methods, including quantile transformation, and evaluate the model on ProtoDUNE-HD detector-glitch data if that data becomes available.

Wilson, Cameron C. [Cincinnati U., RWC]↗
DOE OSTI2026

Real-Time Anomaly Detection for Beyond Standard Model Searches in ProtoDUNE Horizontal Drift

This paper summarizes work conducted throughout a SULI internship at Fermi National Accelerator Laboratory focused on building an unsupervised machine learning model for real-time anomaly detection in ProtoDUNE Horizontal Drift. Using simulated data, we trained an autoencoder model on a pure cosmic dataset, and evaluated it on both cosmic and neutrino events---making the model an anomaly detector. The goal was to make a model which matches or exceeds the current ADC Simple Window trigger algorithm so that our model can perform at the same rate but provide sensitivity to potential beyond-the-Standard-Model (BSM) signatures. In the end, we were able to construct a model which slightly exceeds the capabilities of the ADC Simple Window while remaining completely unsupervised, achieving $31.9 \pm 0.2$\% ($26.6 \pm 0.2$\%) $\nu$ efficiency at 5 Hz (2 Hz), a 3.6 (3.2) percentage point increase. Additionally, $17.5 \pm 0.3$\% ($18.3 \pm 0.3$\%) of the events that passed the autoencoder at 5 Hz (2 Hz) were missed by the current trigger algorithm. Future work will investigate alternative normalization methods, including quantile transformation, and evaluate the model on ProtoDUNE-HD detector-glitch data if that data becomes available.

Wilson, Cameron C. [Cincinnati U., RWC]↗
DOE OSTI2023

Metal–Organic Framework as a New Type of Magnetothermally‐Triggered On‐Demand Release Carrier

The development of external stimuli‐controlled payload systems has been sought after with increasing interest toward magnetothermally‐triggered drug release (MTDR) carriers due to their non‐invasive features. However, current MTDR carriers present several limitations, such as poor heating efficiency caused by the aggregation of iron oxide nanoparticles (IONPs) or the presence of antiferromagnetic phases which affect their efficiency. Herein, a novel MTDR carrier is developed using a controlled encapsulation method that fully fixes and confines IONPs of various sizes within the metal–organic frameworks (MOFs). This novel carrier preserves the MOF's morphology, porosity, and IONP segregation, while enhances heating efficiency through the oxidation of antiferromagnetic phases in IONPs during encapsulation. It also features a magnetothermally‐responsive nanobrush that is stimulated by an alternating magnetic field to enable on‐demand drug release. The novel carrier shows improved heating, which has potential applications as contrast agents and for combined chemo and magnetic hyperthermia therapy. It holds a great promise for magneto‐thermally modulated drug dosing at tumor sites, making it an exciting avenue for cancer treatment.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗