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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Simulations of the Large Surface Explosion Coupling Experiment (LSECE)

The Large Surface Explosion Coupling Experiment (LSECE) project will include two surface energetic events (chemical explosions) conducted as close as possible to ground zero (GZ) of the Source Physics Experiment (SPE) Phase II Dry Alluvium (DAG) site in Yucca Flat at the Nevada National Security Site (NNSS). Seismic and acoustic data from these events will complement DAG buried source data and improve interpretation of speed-of-sound signals from explosions for national security programs. A satellite image of the region near GZ along with some of the recording stations that we will use for the far- eld simulation is shown in Figure 1. The working design of the HE package is shown in the Figure 2 and is roughly a cube of C-4 bricks.

58 GEOSCIENCES↗
DOE OSTI2020

EMP, Attachment 3: Dose Assessment Guidance

This Dose Assessment Guidance (DAG) describes methods used to comply with the reporting requirements for dose to an individual member of the public receptor, collective dose, and biota dose for radionuclide air emissions under the U.S. Department of Energy, Office of Science, Pacific Northwest National Laboratory (PNNL) Environmental Monitoring Plan (EMP). The National Emission Standards for Hazardous Air Pollutants (40 CFR 61), Subpart H, is the greatest driver for the requirements. This DAG applies to public dose from radioactive material releases to the air from PNNL Richland Campus and PNNL Sequim Campus. Dose determinations from Richland Campus surveillance of ambient external dose and ambient air particulates is also briefly discussed. This guidance is Attachment 3 to PNNL’s EMP (PNNL-20919) and addresses a discrete, vital subject area of the EMP that is subject to revision independent of the main text of the EMP document.

40 CFR 61 Subpart H↗
DOE OSTI2021

EMP Attachment 3: Dose Assessment Guidance (Rev.3)

This Dose Assessment Guidance (DAG) describes methods used to comply with the reporting requirements for dose to an individual member of the public receptor, collective dose, and biota dose for radionuclide air emissions under the U.S. Department of Energy, Office of Science, Pacific Northwest National Laboratory (PNNL) Environmental Monitoring Plan (EMP). The National Emission Standards for Hazardous Air Pollutants (40 CFR 61), Subpart H, is the greatest driver for the requirements. This DAG applies to public dose from radioactive material releases to the air from PNNL-Richland Campus and PNNL Marine and Coastal Research Laboratory. Dose determinations from Richland Campus surveillance of ambient external dose and ambient air particulates is also briefly discussed. This guidance is Attachment 3 to PNNL’s EMP (PNNL-20919) and addresses a discrete, vital subject area of the EMP that is subject to revision independent of the main text of the EMP document.

40 CFR 61 Subpart H↗
DOE OSTI2024

EMP, Attachment 3: Dose Assessment Guidance (Rev.4)

This Dose Assessment Guidance (DAG) describes methods used to comply with the reporting requirements for dose to an individual member of the public receptor, collective dose, and biota dose for radionuclide air emissions under the U.S. Department of Energy, Office of Science, Pacific Northwest National Laboratory (PNNL) Environmental Monitoring Plan (EMP). The National Emission Standards for Hazardous Air Pollutants (40 CFR 61), Subpart H, is the greatest driver for the requirements. This DAG applies to public dose from radioactive material releases to the air from PNNL-Richland campus and PNNL-Sequim campus. Dose determinations from Richland campus surveillance of ambient external dose and ambient air particulates is also discussed. This guidance is Attachment 3 to PNNL’s EMP (PNNL-20919) and addresses a discrete, vital subject area of the EMP that is subject to revision independent of the main text of the EMP document.

40 CFR 61 Subpart H↗
DOE OSTI2023

Do betaine lipids replace phosphatidylcholine as fatty acid editing hubs in microalgae?

Acyl editing refers to a deacylation and reacylation cycle on a lipid, which allows for fatty acid desaturation and modification prior to being removed and incorporated into other pools. Acyl editing is an important determinant of glycerolipid synthesis and has been well-characterized in land plants, thus this review begins with an overview of acyl editing in plants. Much less is known about acyl editing in algae, including the extent to which acyl editing impacts lipid synthesis and on which lipid substrate(s) it occurs. This review compares what is known about acyl editing on its major hub phosphatidylcholine (PC) in land plants with the evidence for acyl editing of betaine lipids such as diacylglyceryltrimethylhomoserine (DGTS), the structural analog that replaces PC in several species of microalgae. In land plants, PC is also known to be a major source of fatty acids and diacylglycerol (DAG) for synthesis of the neutral lipid triacylglycerol (TAG). We review the evidence that DGTS contributes substantially to TAG accumulation in algae as a source of fatty acids, but not as a precursor to DAG. We conclude with evidence of acyl editing on other membrane lipid substrates in plants and algae apart from PC or DGTS, and discuss future analyses to elucidate the role of DGTS and other betaine lipids in acyl editing in microalgae.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series

Anomaly detection is a widely studied task for a broad variety of data types; among them, multiple time series appear frequently in applications, including for example, power grids and traffic networks. Detecting anomalies for multiple time series, however, is a challenging subject, owing to the intricate interdependencies among the constituent series. We hypothesize that anomalies occur in low density regions of a distribution and explore the use of normalizing flows for unsupervised anomaly detection, because of their superior quality in density estimation. Moreover, we propose a novel flow model by imposing a Bayesian network among constituent series. A Bayesian network is a directed acyclic graph (DAG) that models causal relationships; it factorizes the joint probability of the series into the product of easy-to-evaluate conditional probabilities. We call such a graph-augmented normalizing flow approach GANF and propose joint estimation of the DAG with flow parameters. We conduct extensive experiments on real-world datasets and demonstrate the effectiveness of GANF for density estimation, anomaly detection, and identification of time series distribution drift.

Dai, Enyan↗
DOE OSTI2025

OpenSn: A massively parallel, open-source simulation environment for discrete ordinates radiation transport

OpenSn is an open-source, massively parallel deterministic radiation transport code for solving the discrete-ordinates ( S N ) form of the Boltzmann transport equation on unstructured, arbitrary polyhedral meshes. It supports high-fidelity simulations involving steady-state, eigenvalue, and adjoint problems for neutral particles (e.g., neutrons, photons, multi-particles), using the multigroup approximation in energy. OpenSn combines angular discretization via discrete ordinates with a discontinuous Galerkin finite element method (DGFEM) in space, enabling accurate resolution of transport physics on arbitrary polyhedral cells, included locally refined spatial grids. It includes multiple angular quadrature types, including locally refined angular quadratures. Written in modern C++ with a Python API, OpenSn runs efficiently on platforms ranging from laptops to supercomputers. The transport sweep algorithm is implemented using a task-based, directed-acyclic-graph (DAG) approach for each angle and supports asynchronous parallelism across thousands of MPI ranks. Group-set aggregation improves compute intensity, and synthetic acceleration techniques (e.g., diffusion synthetic acceleration, second-moment method) enhance solver convergence. OpenSn has been verified on reactor physics problems and demonstrated excellent weak and strong scaling performance on more than 32,768 processes, making it a versatile and robust platform for large-scale transport simulations in complex geometries.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Preparation of the Multi-Site Data Processing at the Vera C. Rubin Observatory

The Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST) Camera is scheduled to start taking data in the summer of 2025. The Data Release Production will run the LSST Science Pipe software at data facilities in the US, France and the UK. The LSST Science Pipeline consists of complex directed acyclic graphs (DAGs) of tasks. Rubin will use the Production and Distributed Analysis (PanDA) workflow and workload management system to orchestrate this complex workflow and the distribution of workloads to the data facilities. When run end-to-end by a team of data production staff, this processing (the Science Pipelines, distributed by the workflow and workload management system) is referred to as a 'campaign'. This paper describes the central services and data facility specific services that support this multi-site data process model, including the service deployment infrastructure, the workload and workflow system, the Campaign Management tools, and connection to Rubin Data Management. This paper will also mention the experience of processing the Rubin Commissioning Camera data. All these are part of the effort to scale up the processing capabilities for the expected very large data volume from the LSST Camera.

Yang, Wei [SLAC]↗
DOE OSTI2024

Seismic moment tensor classification using elliptical distribution functions on the hypersphere

Discrimination of underground explosions from naturally occurring earthquakes and other anthropogenic sources is one of the fundamental challenges of nuclear explosion monitoring. In an operational setting, the number of events that can be thoroughly investigated by analysts is limited by available resources. The capability to rapidly screen out events that can be robustly identified as not being explosions is, therefore, of great potential benefit. Nevertheless, possible mis-classification of explosions as earthquakes currently limits the use of screening methods for verification of test-ban treaties. Moment tensors provide a physics-based classification tool for the characterization of different seismic sources and have enabled the advent of new techniques for discriminating between earthquakes and explosions. Following normalization and projection of their six-degree vectors onto the hypersphere, existing screening approaches use spherically symmetric metrics to determine whether any new moment tensor may have been an explosion. Here, we show that populations of moment tensors for both earthquakes and explosions are anisotropically distributed on the hypersphere. Distributions possessing elliptical symmetry, such as the scaled von Mises–Fisher distribution, therefore provide a better description of these populations than the existing spherically symmetric models. We describe a method that uses these elliptical distributions in combination with a Bayesian classifier to achieve successful classification rates of 99 per cent for explosions and 98 per cent for earthquakes using existing catalogues of events from the western United States. The 1983 May 5 Crowdie underground nuclear test and 2018 July 20 DAG-1 deep-borehole chemical explosion are the only two explosions out of 140 that are incorrectly classified. Application of the method to the 2006–2017 nuclear tests in the Democratic People’s Republic of Korea yields 100 per cent identification rates and we provide a simple routine MTid for general usage. The approach provides a means to rapidly assess the likelihood of an event being an explosion and can be built into monitoring workflows that rely on simultaneously assessing multiple different discrimination metrics.

58 GEOSCIENCES↗
DOE OSTI2022

Computational properties of finite PNmatrices

Abstract Recent compositionality results in logic have highlighted the advantages of enlarging the traditional notion of logical matrix semantics, namely by incorporating non-determinism and partiality. Still, several important properties which are known to be computable for finite logical matrices have not been studied in the wider context of partial non-deterministic matrices (PNmatrices). In this paper, we study how incorporating non-determinism and/or partiality in logical matrices impacts on the computational properties of some natural problems regarding their induced logics and concretely their sets of theorems. We show that, while for some of these problems there is no relevant computational impact, there are problems whose computational complexity increases and still other problems that simply become undecidable. In particular, we show that the problem of checking whether the logics characterized by two finite PNmatrices have the same set of theorems is not decidable. This undecidability result explores the connection between PNmatrices and term-DAG-automata, where the universality problem is known to be undecidable. This link also motivates a final contribution, in the form of a pumping-like lemma, which can be used, in some cases, to show that a given logic cannot be characterized by a finite PNmatrix.

Filipe, Pedro↗
DOE OSTI2026

Arabidopsis lipins mediate lipid droplet biogenesis to protect cells from lipotoxicity

Lipin proteins, a family of phosphatidic acid phosphatases (PAHs), are key regulators of lipid metabolism, storage, and homeostasis across eukaryotes. While Arabidopsis (Arabidopsis thaliana) lipins function in lipid biosynthesis and gene regulation, their roles in lipid droplet (LD) biogenesis and lipid homeostasis remain largely unknown. Here, we show that double knockout of two PAH genes (PAH1/2) results in impaired LD biogenesis, accelerated triacylglycerol (TAG) hydrolysis, and lipid imbalance. pah1/2 mutant leaves exhibited a marked reduction in TAG levels and a significant decrease in LD size, while the rates of TAG and diacylglycerol (DAG) synthesis remained largely unchanged. In seeds, PAH1/2 disruption minimally affected TAG content but significantly reduced LD size. Fatty acid feeding experiments demonstrated impaired LD formation and increased lipotoxicity in pah1/2 leaves and seedlings. Furthermore, knockout of PAH1/2 in mutants with enhanced fatty acid flux through phosphatidylcholine (PC) led to severe reductions in leaf TAG levels, despite increases in TAG synthesis rates, indicating accelerated TAG turnover. Phosphatidic acid, free fatty acids, and PC accumulated, leading to massive proliferation of endoplasmic reticulum membranes and severe growth and developmental defects. These findings demonstrate evolutionarily conserved roles for PAH1/2 in LD biogenesis, membrane lipid homeostasis, and cellular protection against lipotoxicity, particularly under conditions of elevated fatty acid flux.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

LibraryX: A Framework for Cross-Library-Call Optimization

Scientific applications utilize performance libraries as a software engineering concept: these libraries encapsulate important and well-understood (mathematical) operations, allow for reuse, and are implemented and tuned by experts. Domain scientists then implement complex algorithms based on these domainspecific libraries. While individual library calls are optimized, larger performance gains across sequences of calls—sometimes spanning multiple libraries—are often unrealized, forcing a trade-off between performance and implementation complexity.To overcome this issue, we propose LibraryX, an approach and a system that allows for cross-library-call optimization even when library calls stem from multiple performance libraries. LibraryX annotates library calls with semantic information and optimizes entire directed acyclic graphs (DAGs) of calls dynamically using the SPIRAL code generation system. We demonstrate its effectiveness across a range of memory bound workloads, achieving significant speedups on Nvidia, AMD, and Intel accelerators compared to code using native libraries without cross-call optimization.

Rao, Sanil [Carnegie Mellon University,Department ↗
DOE OSTI2026

iDDS: intelligent distributed dispatch and scheduling for workflow orchestration

The intelligent distributed dispatch and scheduling (iDDS) service is a versatile workflow orchestration system designed for large-scale, distributed scientific computing. iDDS extends traditional workload and data management by integrating data-aware execution, conditional logic, and programmable workflows, enabling automation of complex and dynamic processing pipelines. Originally developed for the ATLAS experiment at the large hadron collider, iDDS has evolved into an experiment-agnostic platform that supports both template-driven workflows and a Function-as-a-Task model for Python-based orchestration. This paper presents the architecture and core components of iDDS, highlighting its scalability, modular message-driven design, and integration with systems such as PanDA and Rucio. We demonstrate its versatility through real-world use cases: fine-grained tape resource optimization for ATLAS, orchestration of large Directed Acyclic Graph (DAG) workflows for the Rubin Observatory, distributed hyperparameter optimization for machine learning applications, active learning for physics analyses, and AI-assisted detector design at the electron–ion collider. By unifying workload scheduling, data movement, and adaptive decision-making, iDDS reduces operational overhead and enables reproducible, high-throughput workflows across heterogeneous infrastructures. We conclude with current challenges and future directions, including interactive, cloud-native, and serverless workflow support.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Deeplynx Airflow Provider Package

The DeepLynx Airflow Provider Package is a python package used to interact with the data warehouse DeepLynx when using the workflow orchestration tool Apache Airflow. This python package is packaged together using the airflow package standard so that it can be easily installed and used in any Apache Airflow environment. This package is meant to encapsulate the DeepLynx API for use in Airflow so that any interactions with DeepLynx that a user may want to use in their Airflow workflow can be easily accomplished using this provider package. This allows us to develop, implement, and test our DeepLynx-Airflow interactions in one provider package repository, and then easily install and use this package in any airflow instance. This DeepLynx Airflow Provider Package will be used extensively by the DeepLynx DAG repository.

Cavaluzzi, JackM↗
DOE OSTI2024

interactEM v1.0

An interactive, container-based workflow tool for creating and spawning directed acyclic graphs (DAGs) of operators in a distributed environment. It has a microservices architecture, and flow-based programming model. Current tools like this do not enable streaming of data directly between operators.

Welborn, Sam [Lawrence Berkeley National Laborator↗
DOE OSTI2024

Batched sparse direct solver design and evaluation in SuperLU_DIST

Over the course of interactions with various application teams, the need for batched sparse linear algebra functions has emerged in order to make more efficient use of the GPUs for many small and sparse linear algebra problems. In this paper, we present our recent work on a batched sparse direct solver for GPUs. The sparse LU factorization is computed by the levels of the elimination tree, leveraging the batched dense operations at each level and a new batched Scatter GPU kernel. The sparse triangular solve is computed by the level sets of the directed acyclic graph (DAG) of the triangular matrix. Batched operations overcome the large overhead associated with launching many small kernels. For medium sized matrix batches with not-so-small bandwidth, using an NVIDIA A100 GPU, our new batched sparse direct solver is orders of magnitude faster than a batched banded solver and uses less than one-tenth of the memory.

Boukaram, Wajih↗
DOE OSTI2023

A Multimodal Event Catalog and Waveform Data Set That Supports Explosion Monitoring from Nevada, U.S.A.

Multimodal, curated data sets and nuisance event catalogs remain rare in the explosion monitoring community relative to curated seismic data sets. The source of this relative absence is the difficultly in deploying multimodal receivers that sense the seismic, acoustic, and other modalities from multiphysics sources. We provide such a data set in this study that delivers seismic, infrasound, and electromagnetic (magnetometer) sensor records collected over a two–week period, within 255 km of a 10 ton buried chemical explosion called DAG–4 that was located at 37.1146°, –116.0693° on 22 June 2019 21:06:19.88 UTC. This catalog includes 485 seismic, seismoacoustic, and infrasound–only events that an expert analyst manually built by reviewing waveforms from 29 seismic and infrasound sensors. Our data release includes waveforms from these 29 seismic, infrasound, and seismoacoustic stations and two magnetometer stations and their station metadata. We deliver these waveforms in NNSA KB Core CSS.w format (i4) with a corresponding wfdisc table that provides the header information. Here, we expect that this data set will provide a valuable, benchmark resource to develop signal processing algorithms and explosion monitoring methods against manual, human observations.

58 GEOSCIENCES↗