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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 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Lagrangian Characterization of Surface Transport From the Equatorial Atlantic to the Caribbean Sea Using Climatological Lagrangian Coherent Structures and Self‐Organizing Maps

Abstract This study presents an assessment of the transport of suspended material by surface ocean currents, which have a critical role in determining the connectivity and distribution of living and non‐living material. Lagrangian experiments reveal pathways from the Equatorial Atlantic to 10 strategic regions within the Caribbean Sea, determined by considering the space‐time variability of climatological Lagrangian Coherent Structures, which act as recurrent attracting pathways and transport barriers. Due to windage or Stokes drift, wind forcing is a significant factor in determining the spatial locations where particles cluster and the time needed to reach the Caribbean from the Equatorial Atlantic. Pathways shift westward within the Caribbean and take less time to arrive with increasing wind influence. Depending on the wind effect, the particles show higher confluence in different areas of the Caribbean. A case study is presented for the Mexican Caribbean nearshore area, isolated from ocean‐current trajectories. Here, wind weakens the transport barrier responsible for this isolation and causes particle confluence toward that region. Spatial patterns of the Eulerian velocity identified through Self‐Organizing Maps, with time dependence given by their best matching units, can reproduce the characteristic Lagrangian patterns of surface current climate variability. Our study demonstrates the application of tools from dynamical systems and unsupervised neural networks to understand Lagrangian patterns and identify the processes that drive them. These findings improve our understanding of transport mechanisms of suspended material by surface ocean currents in the Western Atlantic and the Caribbean Sea, which is essential for managing and conserving marine ecosystems.

Allende‐Arandía, Ma. Eugenia↗
DOE OSTI2025

Structural analysis of the NifL-NifA complex reveals the molecular basis of anti-activation of nitrogen fixation gene expression in Azotobacter vinelandii

Understanding the molecular basis of regulated nitrogen (N 2 ) fixation is essential for engineering N 2 -fixing bacteria that fulfill the demand of crop plants for fixed nitrogen, reducing our reliance on synthetic nitrogen fertilizers. In Azotobacter vinelandii and many other members of Proteobacteria, the two-component system comprising the anti-activator protein (NifL) and the Nif-specific transcriptional activator (NifA)controls the expression of nif genes, encoding the nitrogen fixation machinery. The NifL-NifA system evolved the ability to integrate several environmental cues, such as oxygen, nitrogen, and carbon availability. The nitrogen fixation machinery is thereby only activated under strictly favorable conditions, enabling diazotrophs to thrive in competitive environments. While genetic and biochemical studies have enlightened our understanding of how NifL represses NifA, the molecular basis of NifA sequestration by NifL depends on structural information on their interaction. Here, we present mechanistic insights into how nitrogen fixation is regulated by combining biochemical and genetic approaches with a low-resolution cryo-electron microscopy (cryo-EM) map of the oxidized NifL-NifA complex. Our findings define the interaction surface between NifL and NifA and reveal how this interaction can be manipulated to generate bacterial strains with increased nitrogen fixation rates able to secrete surplus nitrogen outside the cell, a crucial step in engineering improved nitrogen delivery to crop plants.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Exploring the role of biotic factors in regulating the spatial variability in land surface phenology across four temperate forest sites

Here, land surface phenology (LSP), the characterization of plant phenology with satellite data, is essential for understanding the effects of climate change on ecosystem functions. Considerable LSP variation is observed within local landscapes, and the role of biotic factors in regulating such variation remains underexplored. In this study, we selected four National Ecological Observatory Network terrestrial sites with minor topographic relief to investigate how biotic factors regulate intra-site LSP variability. We utilized plant functional type (PFT) maps, functional traits, and LSP data to assess the explanatory power of biotic factors for the start and end of season (SOS and EOS) variability. Our results indicate that PFTs alone explain only 0.8–23.4% of intra-site SOS and EOS variation, whereas including functional traits significantly improves explanatory power, with cross-validation correlations ranging from 0.50 to 0.85. While functional traits exhibited diverse effects on SOS and EOS across different sites, traits related to competitive ability and productivity were important for explaining both SOS and EOS variation at these sites. These findings reveal that plants exhibit diverse phenological responses to comparable environmental conditions, and functional traits significantly contribute to intra-site LSP variability, highlighting the importance of intrinsic biotic properties in regulating plant phenology.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Closure Report for the Project Shoal Area: Subsurface Corrective Action Unit 447 Shoal,Nevada, Site

The U.S. Department of Energy (DOE) Office of Legacy Management (LM) has prepared this Closure Report for subsurface Corrective Action Unit (CAU) 447 at the Shoal, Nevada, Site, which was formerly called the Project Shoal Area. The Shoal site was the location of an underground nuclear test in 1963 that resulted in residual radionuclide contamination at the detonation depth of 1211 feet (ft) (DOE 2015a). The environmental restoration process and Corrective Action strategy are conducted in accordance with the Federal Facility Agreement and Consent Order (FFACO) (FFACO 1996, as amended) and all applicable Nevada Division of Environmental Protection (NDEP) statues and regulations. Responsibility for the environmental site restoration was transferred from the DOE, National Nuclear Security Administration, Nevada Field Office, to LM on October 1, 2006. The Shoal site is south of U.S. Highway 50, approximately 30 miles southeast of Fallon, in Churchill County, Nevada (Figure 1). The site was selected for underground nuclear testing in 1961, as part of the Vela Uniform Program. Vela was a research and development program directed toward locating, detecting, and identifying underground and high-altitude nuclear detonations. The Vela Uniform program was specific to underground detonations (AEC 1964). The objective of Project Shoal was to detonate a nuclear device underground in an active seismic area to improve the United States’ ability to detect, identify, and locate underground nuclear detonations. The underground nuclear test at Shoal was a joint effort between the U.S. Department of Defense (DOD) and the U.S. Atomic Energy Commission (AEC), which was the predecessor agency to DOE. Figure 1 is a map showing the site location.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Evaluating U.S. Natural Gas Environmental Performance

This work summarizes the U.S. Department of Energy National Energy Technology Laboratory's (NETL) modeling and analysis of natural gas environmental performance. Over the past year, NETL updated its U.S. natural gas supply chain life cycle baseline model to incorporate 2020 data, regionalize the transmission and distribution network into six regions, incorporate improved gathering and boosting stage and distribution stage emissions factors, and assign environmental burdens among the various co-products (i.e., crude oil, natural gas liquids, natural gas) of the natural gas supply chain. The updated natural gas model helps generate more comprehensive and regionalized natural gas profiles, mapping production basins to the relevant transmission and distribution regions downstream. The work incorporates data from multiple peer reviewed measurement-based studies to achieve the objective of providing a comprehensive understanding of emissions from the U.S. natural gas supply chain. This presentation also explores future work by NETL to develop a platform that enables stakeholders to customize key parameters within the natural gas LCA model and observe the corresponding effects on the carbon intensity of the overall supply chain. The platform aims to offer a convenient method for stakeholders to obtain relevant insights from the updated model, without diving deep into the intricacies of the NETL natural gas model with hundreds of parameters and linkages.

Khutal, Harshvardhan↗
DOE OSTI2025

Toward Drilling the Perfect Geothermal Well: An International Research Coordination Network for Geothermal Drilling Optimization Supported by Deep Machine Learning and Cloud Based Data Aggregation

The EDGE project, supported by the U.S. Department of Energy Geothermal Technologies Office under award DE-EE0008793, established a data-driven framework for improving the efficiency, cost-effectiveness, and reliability of geothermal well drilling. The project focused on developing scalable data infrastructure, advanced machine learning and probabilistic models, and integrated analytics tools to support continuous drilling optimization. A central objective was to reduce geothermal drilling costs by up to seventy percent while minimizing the risk of well failure through predictive diagnostics and adaptive planning. Over the project period, a comprehensive data repository was designed and deployed, incorporating records from over one hundred geothermal wells across varied geological settings. This repository supported both structured and unstructured data and adhered to FAIR data principles, enabling provenance tracking, quality control, and standardized metadata. The project introduced automated ingestion pipelines and a cloud-hosted platform that facilitated access to raw, processed, and derived datasets. This infrastructure served as the foundation for model development and analysis. Machine learning workflows were developed to predict key drilling metrics including rate of penetration, non-productive time, and total drilling costs. Self-organizing maps and dimensionality reduction methods were used to uncover operational patterns and outliers, while supervised learning algorithms such as random forests and deep neural networks were applied to forecast performance outcomes. The models were validated on heterogeneous datasets from both U.S. and Icelandic fields, demonstrating variable but significant predictive accuracy. The results indicated that finer temporal resolution, inclusion of lithological data, and consistency in operational annotations could substantially improve model performance. The project also implemented process mining techniques to reconstruct state-transition models from drilling event logs. These models enabled the identification of deviations from optimal workflows and provided insights into recurring failure modes. Analysis of non-productive time highlighted the impact of equipment failures, geological challenges, and human factors, offering opportunities for targeted mitigation strategies. The EDGE Dashboard was developed as a web-based expert system integrating data visualization, model outputs, and user-driven queries. It provided an accessible interface for operators to explore historical data, evaluate predicted outcomes, and compare drilling scenarios. Initial feedback from project partners suggested that the dashboard could serve as a foundation for more advanced advisory and optimization tools. Overall, the EDGE project demonstrated the feasibility and value of applying modern data science techniques to geothermal drilling. It delivered a set of interoperable tools and models that can support more efficient, lower-risk well development. The findings point toward a viable path for transitioning from advisory analytics to semi-autonomous drilling systems, contingent on continued collaboration, expanded datasets, and field validation. The project results have immediate relevance for drilling operations, data management practices, and future geothermal R&D efforts aimed at achieving reliable, cost-competitive geothermal energy at scale.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Mapping Dark Matter on Small Scales with the Cosmic Microwave Background (Final Report)

Sehgal was funded by DOE Grant DE-SC0020441 over the period from 11/1/2019 - 04/30/2024 (no remaining funds are anticipated). Most recently, Sehgal and her group completed a publication forecasting cosmological parameter constraints for a CMB-HD survey, in addition to SO and CMB-S4 (1). One focus of this work was determining the improvement in parameter constraints when removing the gravitational lensing effect from the primordial CMB (a process called delensing). This work also explored the bias to parameters from neglecting baryonic effects, and ways to mitigate that. In addition, this work highlighted that a CMB-HD N eff measurement could tightly constrain the QCD axion in a modelindependent way (see left panel of Figure 1). Sehgal also developed a novel way to probe inflation via CMB experiments by measuring inflationary magnetic fields (IMFs) (2). IMFs are thought to seed the large magnetic fields we observe in galaxies today, and can be measured by looking for anisotropic rotation of the CMB polarization vectors across the sky (an effect called cosmic birefringence). The cosmic birefringence from IMFs has a unique frequency dependence, allowing it to be separated from other sources of cosmic birefringence. In (2), Sehgal and her postdoc also presented a novel way to remove foreground contamination from Galactic magnetic fields using measurements of the polarization of nearby radio sources. The removal of this Galactic contamination is necessary when measuring IMFs at the level of 0.1 nG; IMFs with a strength of at least 0.1 nG are needed to seed the magnetic fields in galaxies we observe today. Since only inflation can generate such a strong magnetic field, measuring such a signal would be a “smoking gun” signature that inflation occurred. Sehgal showed in (2) that CMB-HD could detect such IMFs with at least 3σ significance (see right panel of Figure 1).

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

High-quality Acinetobacter genomes recovered from combat wounds via metagenomic sequencing resemble cultured isolate genomes

The ability to accurately characterize wound pathogens is critical to informing clinical decisions for wound infections with complex treatment requirements. Acinetobacter baumannii is an impactful nosocomial pathogen in combat wounds and civilian hospital-acquired infections. An informed understanding of the phylogenetics and epidemiology of A. baumannii infections in military and civilian environments could guide approaches that improve antibiotic treatment regimens for both military and civilian patients. Whole-genome data for bacterial strains can be difficult to obtain due to challenges in culturing isolates from preserved military specimens. Metagenomic sequencing and assembly create opportunities for genomic analysis of pathogens directly from clinical specimens. The ability to perform comparative analyses between metagenome-derived genomes and culture-derived genomes would support a range of comparative bacterial genomic studies. Wound tissue biopsy and effluent samples from combat injuries were subjected to metagenomic sequencing and assembly. In total, 42 microbial metagenome-assembled genomes (MAGs) were obtained directly from metagenomic sequence data, 36 of which were designated “high” quality. Thirty of these genomes corresponded to Acinetobacter, with 29 mapping specifically to A. baumannii. Other observed genera included Bordetella, Citrobacter, Escherichia, and Pseudomonas. Single-copy and multi-copy orthologs were identified across Acinetobacter MAGs and publicly available isolate genomes derived from military and civilian sources. Both MAG and military isolate genomes were annotated with antimicrobial resistance data, and MAG genomes were statistically comparable to genomes obtained from isolates. Our results highlight the potential of de novo metagenome assembly for enabling high-resolution characterization directly from clinical specimens, thereby improving diagnostic precision, guiding antimicrobial stewardship, and enhancing understanding of pathogen evolution across diverse healthcare and battlefield environments.

Acinetobacter baumannii↗
DOE OSTI2021

Regional suitability of heating tower heat pumps based on technical and economic performance maps

The heating tower heat pump (HTHP) technology is evolving rapidly due to its high performance in summer and frost-free characteristic in winter. However, lacking comprehensive comparisons between the HTHP and conventional methods, such as air-source heat pump (ASHP), and chiller and boiler, greatly limits its applications. To address this problem, this paper carries out technological and economic performance analyses of the three systems in 353 locations over China by the processes of building load calculation, system sizing, annual simulation, performance comparison, and demonstration. Energy saving principle and potential of the HTHP are presented and explained. Based on that, economic performance comparison is conducted by considering the energy conversion efficiency and prices. The results show that the HTHP has advantage over the ASHP in most locations in China. The improvement in annual coefficient of performance (COP) is about 10% to 20%. For the locations in southwest China like Chongqing, the improvement is also less than 10% due to the large regeneration penalization in winter and small difference between dry-bulb and wet-bulb temperature in summer. The annual COP of the HTHP is 10% to 23% higher than that of the chiller and boiler in hot summer and cold winter zone, and cold zone. However, the HTHP shows no advantage over the chiller and boiler in severe cold zone because of the low efficiency of heat pump in winter, neither in hot summer and warm winter zone due to the small heating load. Based on the results, technical and economic performance maps are developed to guide the application of the HTHP in different climate regions.

heating tower heat pump, air-source heat pump, chi↗
DOE OSTI2022

Integrating data types to estimate spatial patterns of avian migration across the Western Hemisphere

For many avian species, spatial migration patterns remain largely undescribed, especially across hemispheric extents. Recent advancements in tracking technologies and high-resolution species distribution models (i.e., eBird Status and Trends products) provide new insights into migratory bird movements and offer a promising opportunity for integrating independent data sources to describe avian migration. Here, we present a three-stage modeling framework for estimating spatial patterns of avian migration. First, we integrate tracking and band re-encounter data to quantify migratory connectivity, defined as the relative proportions of individuals migrating between breeding and nonbreeding regions. Next, we use estimated connectivity proportions along with eBird occurrence probabilities to produce probabilistic least-cost path (LCP) indices. In a final step, we use generalized additive mixed models (GAMMs) both to evaluate the ability of LCP indices to accurately predict (i.e., as a covariate) observed locations derived from tracking and band re-encounter data sets versus pseudo-absence locations during migratory periods and to create a fully integrated (i.e., eBird occurrence, LCP, and tracking/band re-encounter data) spatial prediction index for mapping species-specific seasonal migrations. To illustrate this approach, we apply this framework to describe seasonal migrations of 12 bird species across the Western Hemisphere during pre- and postbreeding migratory periods (i.e., spring and fall, respectively). We found that including LCP indices with eBird occurrence in GAMMs generally improved the ability to accurately predict observed migratory locations compared to models with eBird occurrence alone. Using three performance metrics, the eBird + LCP model demonstrated equivalent or superior fit relative to the eBird-only model for 22 of 24 species–season GAMMs. In particular, the integrated index filled in spatial gaps for species with over-water movements and those that migrated over land where there were few eBird sightings and, thus, low predictive ability of eBird occurrence probabilities (e.g., Amazonian rainforest in South America). This methodology of combining individual-based seasonal movement data with temporally dynamic species distribution models provides a comprehensive approach to integrating multiple data types to describe broad-scale spatial patterns of animal movement. Further development and customization of this approach will continue to advance knowledge about the full annual cycle and conservation of migratory birds.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Decarbonizing nitrogen fertilizer production via the electrochemical nitrogen oxidation reaction

Nitric acid is an important commodity chemical with extensive applications in both agricultural and industrial sectors. However, current production methods involve a combination of the Haber–Bosch and Ostwald processes, which are both energy and carbon emission intensive. The electrochemical nitrogen oxidation reaction (NOR) to produce nitric acid or nitrates shows great potential as an environmentally friendly method for producing fertilizers under mild conditions. The key to progress in this field lies in understanding the fundamental mechanistic insights and establishing robust experimental methods, which is essential for the efficient design and synthesis of electrocatalysts for NOR. Additionally, poor gas mass transport in conventional electrochemical reactors at present lead to lower NOR activity, thereby limiting the progress in this field. In this work, we present a synergistic computational and experimental approach to map out the potential chemical and electrochemical steps and determine the energetics on PtO 2 catalyst to gain mechanistic insights into NOR. Here, this study marks the first attempt to perform NOR in a vapor-fed reactor designed using advanced (additive) manufacturing. The vapor-fed reactor significantly improved the N 2 mass transport to the catalyst, allowing us to report the highest rate for nitrate production to date at 3.3 μmol cm -2 h -1 at 2.01 V vs RHE.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2023

Learning subgrid-scale models with neural ordinary differential equations

We propose a new approach to learning the subgrid-scale model when simulating partial differential equations (PDEs) solved by the method of lines and their representation in chaotic ordinary differential equations, based on neural ordinary differential equations (NODEs). Solving systems with fine temporal and spatial grid scales is an ongoing computational challenge, and closure models are generally difficult to tune. Machine learning approaches have increased the accuracy and efficiency of computational fluid dynamics solvers. In this approach neural networks are used to learn the coarse- to fine-grid map, which can be viewed as subgrid-scale parameterization. We propose a strategy that uses the NODE and partial knowledge to learn the source dynamics at a continuous level. Our method inherits the advantages of NODEs and can be used to parameterize subgrid scales, approximate coupling operators, and improve the efficiency of low-order solvers. Numerical results with the two-scale Lorenz 96 ODE, the convection-diffusion PDE, and the viscous Burgers' PDE are used to illustrate this approach.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Network-medicine framework for studying disease trajectories in U.S. veterans

A better understanding of the sequential and temporal aspects in which diseases occur in patient’s lives is essential for developing improved intervention strategies that reduce burden and increase the quality of health services. Here we present a network-based framework to study disease relationships using Electronic Health Records from > 9 million patients in the United States Veterans Health Administration (VHA) system. We create the Temporal Disease Network, which maps the sequential aspects of disease co-occurrence among patients and demonstrate that network properties reflect clinical aspects of the respective diseases. We use the Temporal Disease Network to identify disease groups that reflect patterns of disease co-occurrence and the flow of patients among diagnoses. Finally, we define a strategy for the identification of trajectories that lead from one disease to another. The framework presented here has the potential to offer new insights for disease treatment and prevention in large health care systems.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

BEYONDPLANCK XIII. Intensity foreground sampling, degeneracies, and priors

We present the intensity foreground algorithms and model employed within the BEYONDPLANCK analysis framework. The BEYONDPLANCK analysis is aimed at integrating component separation and instrumental parameter sampling within a global framework, leading to complete end-to-end error propagation in the Planck Low Frequency Instrument (LFI) data analysis. Given the scope of the BEYONDPLANCK analysis, a limited set of data is included in the component separation process, leading to foreground parameter degeneracies. In order to properly constrain the Galactic foreground parameters, we improve upon the previous Commander component separation implementation by adding a suite of algorithmic techniques. These algorithms are designed to improve the stability and computational efficiency for weakly constrained posterior distributions. These are: (1) joint foreground spectral parameter and amplitude sampling, building on ideas from MIRAMARE; (2) component-based monopole determination; (3) joint spectral parameter and monopole sampling; and (4) application of informative spatial priors for component amplitude maps. We find that the only spectral parameter with a significant signal-to-noise ratio using the current BEYONDPLANCK data set is the peak frequency of the anomalous microwave emission component, for which we find $ν$ p = 25.3 ± 0.5 GHz; all others must be constrained through external priors. Future works will be aimed at integrating many more data sets into this analysis, both map and time-ordered based, thereby gradually eliminating the currently observed degeneracies in a controlled manner with respect to both instrumental systematic effects and astrophysical degeneracies. When this happens, the simple LFI-oriented data model employed in the current work will need to be generalized to account for both a richer astrophysical model and additional instrumental effects. This work will be organized within the Open Science-based COSMOGLOBE community effort.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Taking aim at the wino-Higgsino plane with the LHC

In this work we explore multiple search strategies for Higgsinos and mixed Higgsino-wino states in the minimal supersymmetric Standard Model (MSSM) and project the results onto the ( μ , M 2 ) plane. Assuming associated production of Higgsino-like pairs with a W / Z boson, we develop a search in a channel characterized by a hadronically tagged vector boson accompanied by missing energy. We use as our template an ATLAS search for dark matter produced in association with a hadronically decaying vector boson, but upgrade the search by implementing a joint-likelihood analysis, binning the missing transverse energy distribution, which greatly improves the search sensitivity. For Higgsino-like states (more than 96% admixture) we find sensitivity to masses up to 550 GeV. For well-mixed Higgsino-wino states (70–30% Higgsino) we still find sensitivities above 300 GeV. Using this newly proposed search, we draw a phenomenological map of the wino-Higgsino parameter space, recasting several complementary searches for disappearing tracks, soft leptons, trileptons, and hadronic diboson events in order to predict LHC coverage of the ( μ , M 2 ) mass plane at integrated luminosities of up to 3 ab − 1 . Altogether, the full run of the HL-LHC can exclude much of the “natural” ( μ , M 2 < 500 GeV ) wino-Higgsino parameter space. Published by the American Physical Society 2024

Astronomy & Astrophysics↗
DOE OSTI2021

Improved Constraints on Primordial Gravitational Waves using Planck , WMAP, and BICEP/ Keck Observations through the 2018 Observing Season

Here, we present results from an analysis of all data taken by the BICEP2, Keck Array, and BICEP3 CMB polarization experiments up to and including the 2018 observing season. We add additional Keck Array observations at 220 GHz and BICEP3 observations at 95 GHz to the previous 95/150/220 GHz dataset. The Q/U maps now reach depths of 2.8, 2.8, and 8.8 μK CMB arcmin at 95,150, and 220 GHz, respectively, over an effective area of ≈ 600 square degrees at 95 GHz and ≈ 400 square degrees at 150 and 220 GHz. The 220 GHz maps now achieve a signal-to-noise ratio on polarized dust emission exceeding that of Planck at 353 GHz. We take auto- and cross-spectra between these maps and publicly available WMAP and Planck maps at frequencies from 23 to 353 GHz and evaluate the joint likelihood of the spectra versus a multicomponent model of lensed ΛCDM + r + dust + synchrotron + noise . The foreground model has seven parameters, and no longer requires a prior on the frequency spectral index of the dust emission taken from measurements on other regions of the sky. This model is an adequate description of the data at the current noise levels. The likelihood analysis yields the constraint r 0.05 < 0.036 at 95% confidence. Running maximum likelihood search on simulations we obtain unbiased results and find that σ(r) = 0.009 . These are the strongest constraints to date on primordial gravitational waves.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Report on ISR-1 High-Altitude Balloon Flight

To test small technologies at lower cost for space science applications, LANL has developed a small high altitude balloon payload that could, in the future, be regularly and inexpensively launched from LANL. A neutron detector, NEMO, was integrated to evaluate its performance in a space-like mixed-radiation environment and collect neutron data in the atmosphere. In collaboration with EES-14, a high-altitude balloon payload was launched from LANL Technical Area 51 on February 27, 2023 and April 17, 2023. For real-time geolocation, a SAM-M8Q M8 GNSS module was used to get position and time, and an Iridium RockBLOCK 9603 was used to communicate with the ground using the Iridium satellite fleet. These modules were all controlled using an Iteaduino Mega microcontroller board. Finally, a High Altitude Science Eagle Flight Computer with a temperature pressure sensor ran independently, writing data to an SD card. All of these modules were powered by a 5 mAh lithium polymer battery. The battery was attached to the bottom of the payload while the remaining electronics were embedded in the underside of the top of the payload. These modules were wired as seen in Figure 1-2. The Iteaduino Mega microcontroller board was programmed to use the RockBLOCK to send a message once every 10 minutes containing neutron and GPS data read off the NEMO and SAM M8Q, respectively. Once the message send attempt finished, the RockBLOCK would be slept for the rest of the 10 minute interval. The Eagle Flight Computer ran continuously throughout the flight, taking data every 6 seconds. The RockBLOCK message data was set up to be delivered from the Iridium satellite fleet to a website, where it was stored and parsed to create live maps and plots for analysis and balloon retrieval. The RockBLOCK message data was additionally configured to be sent to an email as a fail-safe. The payload was ground-tested successfully for over 50 hours, with multiple revisions occurring to best prepare for conditions at altitude and improve the software and firmware to fix any issues that cropped up with the data pipeline. Additional to the balloon payload, the flight had an attached iMet-4 radiosonde and Garmin T5 GPS Dog Collar. The radiosonde provided GPS and meteorological data. The T5 dog collar is used along with a Garmin Astro 430 to track the balloon at a range of up to 9 miles for retrieval. The balloon itself was initially a 1600 g meteorological balloon with an attached High Altitude Science parachute, both of which can be seen in Figure 1-3. After the first flight, the EES team swapped to a Rocketman parachute.

42 ENGINEERING↗
DOE OSTI2020

Improving Performance of M-to-N Processing and Data Redistribution in In Transit Analysis and Visualization

In an in transit setting, a parallel data producer, such as a numerical simulation, runs on one set of ranks M, while a data consumer, such as a parallel visualization application, runs on a different set of ranks N. One of the central challenges in this in transit setting is to determine the mapping of data from the set of M producer ranks to the set of N consumer ranks. This is a challenging problem for several reasons, such as the producer and consumer codes potentially having different scaling characteristics and different data models. The resulting mapping from M to N ranks can have a significant impact on aggregate application performance. In this work, we present an approach for performing this M-to-N mapping in a way that has broad applicability across a diversity of data producer and consumer applications. We evaluate its design and performance with a study that runs at high concurrency on a modern HPC platform. By leveraging design characteristics, which facilitate an “intelligent” mapping from M-to-N, we observe significant performance gains are possible in terms of several different metrics, including time-to-solution and amount of data moved.

Loring, Burlen↗