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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

A Case for Bundle Protocol in Space

NASA, through the Advanced Exploration Systems (AES) project, is investing in the development and infusion of delay tolerant networking (DTN) protocols for use on future space flight missions. The cornerstone of the DTN suite is the Bundle Protocol which provides network layer addressing and routing of data blocks. In 2017, the Plankton, Aerosol, Cloud, and Ocean Ecosystem (PACE) mission was selected as the first in-house robotic science mission to implement the Bundle Protocol for downlink of housekeeping telemetry. One year into the design and incorporation of the Bundle Protocol on PACE, this presentation makes a case for using the Bundle Protocol for communication with future space assets. Specifically, the use of the Bundle Protocol (1) simplifies relaying data through store and forward routing and custody transfer; (2) simplifies downlink management through delivery guarantees; and (3) simplifies storage services through block level interactions with memory devices.

Swinski, J. P.↗
DOE OSTI2026

Knowledge-guided graph machine learning for spatially distributed prediction of daily discharge and nitrogen export dynamics

Spatially distributed prediction of streamflow and nitrogen export dynamics is essential for precision management of agricultural watersheds. While temporal deep learning models such as Long Short-Term Memory (LSTM) have shown strong performance at basin scales, their ability to generalize spatially is limited by insufficient representation of spatial dependencies and flow paths, particularly under data-scarce conditions. To address this gap, we propose HydroGraphNet, a knowledge-guided graph machine learning framework that integrates process-based knowledge and explicit spatial learning into temporal modeling. This framework incorporates directed graph topology to encode watershed connectivity and upstream inflows, with mass balance constraints to improve physical consistency. To enhance generalization in sparsely monitored regions, HydroGraphNet is pretrained on synthetic data generated by the SWAT+ (Soil and Water Assessment Tool Plus) model. We evaluated HydroGraphNet in the Upper Sangamon River Basin (44 HUC-12 subwatersheds, 2001–2020) against two LSTM baselines: a lumped basin-level model and a distributed variant. When benchmarked on SWAT+ simulations in pretraining, HydroGraphNet improved test NSEs by 8.9% (discharge) and 13.7% (NO₃–N load) in temporal extrapolation, and by 27.1% and 34.7% in spatial extrapolation, relative to the Lumped LSTM baseline. After fine-tuning with USGS monitoring data, the model achieved mean test NSE (KGE) scores of 0.768 (0.861) for discharge and 0.626 (0.664) for NO₃–N load, substantially outperforming baselines. Attribution analysis further highlighted the importance of upstream inflow representation and graph-based spatial learning in capturing cross-subwatershed dependencies. The model also reproduced seasonal hydrological and biogeochemical patterns consistent with known processes, demonstrating its robustness and process fidelity for spatially distributed prediction. Altogether, HydroGraphNet advances the integration of physical knowledge and spatially explicit learning in hydrological modeling, offering a generalizable framework for distributed modeling to support spatially targeted water quality management in data-scarce watersheds.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

EV Forecasting-Based Model Predictive Control for Distribution System Congestion Mitigation

The uncoordinated charging of electric vehicles (EVs) in time and space brings congestion issues to the distribution network. This paper proposes an EV charging demand forecasting-based model predictive control (MPC) method for distribution system congestion management. To effectively forecast the time-series EV station charging demand, a hybrid forecasting model that integrates the long short-term memory network (LSTM) and Transformer is proposed. The Transformer-LSTM model is trained using a one-year real historical charging dataset of EV stations to forecast future charging demand in 15-minute intervals. This informs the MPC for distribution network congestion management and minimization of PV curtailment. Numerical results carried out on the modified IEEE 123-bus distribution system demonstrate that the proposed method can effectively resolve line congestion issues through EV smart charging and PV curtailment while outperforming other benchmarks.

ADVANCED PROPULSION SYSTEMS,SOLAR ENERGY↗
DOE OSTI2024

BCSR on GPU: A Way Forward Extreme-scale Graph Processing on Accelerator-enabled Frontier Supercomputer

Handling large graphs in a distributed environment requires effective partitioning across processors and efficient management of local partitions. In 2D partitioning, local graphs often become too sparse, making memory-efficient data structures crucial. Using the Compressed Sparse Row (CSR) format wastes space, especially for > 83% of vertices with empty edges for the sparse graphs. This study explores bit-CSR (BCSR), a modified CSR representation, on GPUs to reduce memory usage in graph computations. We achieved 16.67% memory savings on a sparse rmat dataset with 268 million vertices and 357 million edges, without performance degradation, supported by both theoretical and experimental storage savings of 33%. However, we observed a 1.7× slowdown in degree lookup times due to bitwise operations on AMD CPUs. This analysis highlights the potential of BCSR on GPUs for improving Graph500 benchmark performance on GPU-accelerated systems, such as the Frontier supercomputer.

Sattar, Naw Safrin↗
NASA NTRS2008

Spaceborne Processor Array

A Spaceborne Processor Array in Multifunctional Structure (SPAMS) can lower the total mass of the electronic and structural overhead of spacecraft, resulting in reduced launch costs, while increasing the science return through dynamic onboard computing. SPAMS integrates the multifunctional structure (MFS) and the Gilgamesh Memory, Intelligence, and Network Device (MIND) multi-core in-memory computer architecture into a single-system super-architecture. This transforms every inch of a spacecraft into a sharable, interconnected, smart computing element to increase computing performance while simultaneously reducing mass. The MIND in-memory architecture provides a foundation for high-performance, low-power, and fault-tolerant computing. The MIND chip has an internal structure that includes memory, processing, and communication functionality. The Gilgamesh is a scalable system comprising multiple MIND chips interconnected to operate as a single, tightly coupled, parallel computer. The array of MIND components shares a global, virtual name space for program variables and tasks that are allocated at run time to the distributed physical memory and processing resources. Individual processor- memory nodes can be activated or powered down at run time to provide active power management and to configure around faults. A SPAMS system is comprised of a distributed Gilgamesh array built into MFS, interfaces into instrument and communication subsystems, a mass storage interface, and a radiation-hardened flight computer.

Chow, Edward T.↗
NASA NTRS2014

Ground Data System Analysis Tools to Track Flight System State Parameters for the Mars Science Laboratory (MSL) and Beyond

Flight software parameters enable space mission operators fine-tuned control over flight system configurations, enabling rapid and dynamic changes to ongoing science activities in a much more flexible manner than can be accomplished with (otherwise broadly used) configuration file based approaches. The Mars Science Laboratory (MSL), Curiosity, makes extensive use of parameters to support complex, daily activities via commanded changes to said parameters in memory. However, as the loss of Mars Global Surveyor (MGS) in 2006 demonstrated, flight system management by parameters brings with it risks, including the possibility of losing track of the flight system configuration and the threat of invalid command executions. To mitigate this risk a growing number of missions have funded efforts to implement parameter tracking parameter state software tools and services including MSL and the Soil Moisture Active Passive (SMAP) mission. This paper will discuss the engineering challenges and resulting software architecture of MSL's onboard parameter state tracking software and discuss the road forward to make parameter management tools suitable for use on multiple missions.

Allard, Dan↗
DOE OSTI2025

HDF5 in the exascale era: Delivering efficient and scalable parallel I/O for exascale applications

Accurately modeling real-world systems requires scientific applications at exascale to generate massive amounts of data and manage data storage efficiently. However, parallel input and output (I/O) faces challenges due to new application workflows and the state-of-the-art memory, interconnect, and storage architectures considered in exascale designs. The storage hierarchy has expanded with node-local persistent memory, solid-state storage, and traditional disk and tape-based storage, thus requiring efficiency at each layer and much more efficient data movement among these layers. This paper discusses how the ExaHDF5 project improved the I/O performance and data management for exascale architectures by enhancing HDF5, a widely used parallel I/O library. The team developed an Asynchronous I/O Virtual Object Layer (VOL) connector that allowed overlapping I/O with computation. They also created a Cache VOL to complement asynchronous I/O by incorporating fast storage layers, such as burst buffer and node-local storage, into the parallel I/O workflow through caching and staging data. Additionally, the team enabled data aggregation and I/O at the node level by using a Subfiling Virtual File Driver (VFD). To demonstrate superior I/O performance with HDF5 at exascale, the ExaHDF5 team collaborated with several exascale applications. In this paper, we show I/O performance improvements for three applications: Cabana (a particle-based simulation library), EQSIM (a regional earthquake simulation software), and E3SM (a climate system modeling library).

Asynchronous I/Ol↗
DOE OSTI2025

Binder-benchmarking

SAND2025-07593O Binder-benchmarking evaluates the speed and memory impacts of C++, Python, and Matlab code binders. As a repository, it provides a way to locally run computation-based and memory-based benchmark suites on pybind11 and nanobind-based code in a Docker image. The software runs simple-speed and memory benchmarks on primitive navigation and integration exemplar algorithms. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Walker II, Michael [Sandia National Lab. (SNL-CA),↗
NASA NTRS1982

Geology and image processing

Digital image processing for geological applications will be integrated with geographic information systems and data base management systems. While multiband data sets from radar and multispectral scanners will make extreme demands on memory, bus and processor architectures, it is expected that array processors and VLSI/VHSIC dedicated function chips will allow the use of fast Fourier transform and classification algorithms. It is anticipted that, as processor power increases, the weakest link of a processing system will become the analyst who uses it. Human engineering of systems is therefore recommended for the most effective utilization of remotely sensed geologic data.

Daily, M.↗
NASA NTRS1986

Spaceborne optical disk controller development

The current status and potential applications of an optical-disk buffer (ODB) memory system being developed by an interagency consortium including NASA and the USAF are reviewed. The design goals for the ODB include usable capacity 1 Tb, maximum data rate 1.6 Gb/s, read error rate less than 10 to the -12th, time to initial access less than 100 ms, and unlimited read/write cycles. Present efforts focus on a brassboard ODB which employs 12 14-inch magnetooptic disks and 24 nine-diode read/write heads. A typical space application of an optical disk mass memory system (ODMMS) is discussed: as communications buffer, temporary storage, and/or multiuser I/O buffer for data management on the Space Station Earth Observing System. Environmental, operational, system-architecture, and functional-separation factors; critical design issues; and standardization questions for spaceborne ODMMSs are examined in detail.

Shull, Thomas A.↗
NASA NTRS2015

Apparatus and Method for Low-Temperature Training of Shape Memory Alloys

An apparatus and method for the low-temperature thermo-mechanical training of shape memory alloys (SMA) has been developed. The experimental SMA materials are being evaluated as prototypes for applicability in novel thermal management systems for future cryogenic applications. Alloys providing two-way actuation at cryogenic temperatures are the chief target. The mechanical training regimen was focused on the controlled movement of rectangular strips, with S-bend configurations, at temperatures as low as 30 K. The custom holding fixture included temperature sensors and a low heat-leak linear actuator with a magnetic coupling. The fixture was mounted to a Gifford-McMahon cryocooler providing up to 25 W of cooling power at 20 K and housed within a custom vacuum chamber. Operations included both training cycles and verification of shape memory movement. The system design and operation are discussed. Results of the training for select prototype alloys are presented.

Training↗
DOE OSTI2024

MDLoader: A Hybrid Model-Driven Data Loader for Distributed Graph Neural Network Training

Scalable data management is essential for processing large scientific dataset on HPC platforms for distributed deep learning. In-memory distributed storage is preferred for its speed, enabling rapid, random, and frequent data access required by stochastic optimizers. Processes use one-sided or collective communication to fetch remote data, with optimal performance depending on (i) dataset characteristics, (ii) training scale, and (iii) interconnection network. Empirical analysis shows collective communication excels with larger mini-batch sizes and/or fewer processes, whereas one-sided communication outperforms at larger scales. We propose MDLoader, a hybrid in-memory data loader for distributed graph neural network training. MDLoader features a model-driven performance estimator that dynamically selects between one-sided and collective communication at the beginning of training using Tree of Parzen Estimators (TPE). Evaluations on NERSC Perlmutter and OLCF Summit show MDLoader outperforms single-backend loaders by up to 2.83 × and predicts the suitable communication method with 96.3% (Perlmutter) and 94.3% (Summit) success rate.

Bae, Jonghyun↗
DOE OSTI2024

Odor exposure during imprinting periods increases odorant-specific sensitivity and receptor gene expression in coho salmon ( Oncorhynchus kisutch )

ABSTRACT Pacific salmon are well known for their homing migrations; juvenile salmon learn odors associated with their natal streams prior to seaward migration, and then use these retained odor memories to guide them back from oceanic feeding grounds to their river of origin to spawn several years later. This memory formation, termed olfactory imprinting, involves (at least in part) sensitization of the peripheral olfactory epithelium to specific odorants. We hypothesized that this change in peripheral sensitivity is due to exposure-dependent increases in the expression of odorant receptor (OR) proteins that are activated by specific odorants experienced during imprinting. To test this hypothesis, we exposed juvenile coho salmon, Oncorhynchus kisutch, to the basic amino acid odorant l-arginine during the parr–smolt transformation (PST), when imprinting occurs, and assessed sensitivity of the olfactory epithelium to this and other odorants. We then identified the coho salmon ortholog of a basic amino acid odorant receptor (BAAR) and determined the mRNA expression levels of this receptor and other transcripts representing different classes of OR families. Exposure to l-arginine during the PST resulted in increased sensitivity to that odorant and a specific increase in BAAR mRNA expression in the olfactory epithelium relative to other ORs. These results suggest that specific increases in ORs activated during imprinting may be an important component of home stream memory formation and this phenomenon may ultimately be useful as a marker of successful imprinting to assess management strategies and hatchery practices that may influence straying in salmon.

Dittman, Andrew H. (ORCID:000000016482359X)↗
NASA NTRS1997

Computational Models of Human Performance: Validation of Memory and Procedural Representation in Advanced Air/Ground Simulation

The Man-Machine Interaction Design and Analysis System (MIDAS) under joint U.S. Army and NASA cooperative is intended to assist designers of complex human/automation systems in successfully incorporating human performance capabilities and limitations into decision and action support systems. MIDAS is a computational representation of multiple human operators, selected perceptual, cognitive, and physical functions of those operators, and the physical/functional representation of the equipment with which they operate. MIDAS has been used as an integrated predictive framework for the investigation of human/machine systems, particularly in situations with high demands on the operators. We have extended the human performance models to include representation of both human operators and intelligent aiding systems in flight management, and air traffic service. The focus of this development is to predict human performance in response to aiding system developed to identify aircraft conflict and to assist in the shared authority for resolution. The demands of this application requires representation of many intelligent agents sharing world-models, coordinating action/intention, and cooperative scheduling of goals and action in an somewhat unpredictable world of operations. In recent applications to airborne systems development, MIDAS has demonstrated an ability to predict flight crew decision-making and procedural behavior when interacting with automated flight management systems and Air Traffic Control. In this paper, we describe two enhancements to MIDAS. The first involves the addition of working memory in the form of an articulatory buffer for verbal communication protocols and a visuo-spatial buffer for communications via digital datalink. The second enhancement is a representation of multiple operators working as a team. This enhanced model was used to predict the performance of human flight crews and their level of compliance with commercial aviation communication procedures. We show how the data produced by MIDAS compares with flight crew performance data from full mission simulations. Finally, we discuss the use of these features to study communication issues connected with aircraft-based separation assurance.

Corker, Kevin M.↗
NASA NTRS1996

Man-Machine Interaction Design and Analysis System (MIDAS): Memory Representation and Procedural Implications for Airborne Communication Modalities

The Man-Machine Interaction Design and Analysis System (MIDAS) has been under development for the past ten years through a joint US Army and NASA cooperative agreement. MIDAS represents multiple human operators and selected perceptual, cognitive, and physical functions of those operators as they interact with simulated systems. MIDAS has been used as an integrated predictive framework for the investigation of human/machine systems, particularly in situations with high demands on the operators. Specific examples include: nuclear power plant crew simulation, military helicopter flight crew response, and police force emergency dispatch. In recent applications to airborne systems development, MIDAS has demonstrated an ability to predict flight crew decision-making and procedural behavior when interacting with automated flight management systems and Air Traffic Control. In this paper we describe two enhancements to MIDAS. The first involves the addition of working memory in the form of an articulatory buffer for verbal communication protocols and a visuo-spatial buffer for communications via digital datalink. The second enhancement is a representation of multiple operators working as a team. This enhanced model was used to predict the performance of human flight crews and their level of compliance with commercial aviation communication procedures. We show how the data produced by MIDAS compares with flight crew performance data from full mission simulations. Finally, we discuss the use of these features to study communications issues connected with aircraft-based separation assurance.

Corker, Kevin M.↗
NASA NTRSDate not supplied

Medium-Range River Flood Forecasts Using a Long Short-Term Memory Network

River flooding and the impacts are a concern for decision makers throughout the United States. Accurate medium-range forecasts (~3-7 days) are critical for providing advanced outlooks to emergency management officials. Unfortunately, accurately forecasting rainfall-runoff and the subsequent rise and fall within rivers remain a challenge in hydrological modeling. While complex physical modeling systems are the standard for representing the hydrological processes, they are computationally demanding and can require extensive calibration. Further, uncertainties remain in the model parameters and input data. The use of machine learning can reduce some of the computational demand while maintaining high accuracy. Therefore, this project makes use of a Long Short-Term Memory (LSTM) network which explicitly accounts for the time-dependent nature of rainfall-runoff modeling. The developed LSTM was trained to predict river gauge height, or stage height, based on time-lagged input features which include: gauge height to initialize the model, the NASA Short-term Prediction Research and Transition Center’s instance of the Land Information System (SPoRT-LIS) relative soil moisture to describe the rainfall infiltration rate, and 6-hr Multi-Radar Multi-Sensor quantitative precipitation estimate (MRMS QPE). The developed LSTM based system is then used to produce 7-day forecasts with a 6-hr temporal resolution using three different quantitative precipitation forecasts (QPF) from the NWS’s Weather Prediction Center (WPC), the NCEP Global Forecast System (GFS) model and the National Blend of Models (NBM). This trained modeling system has been implemented as an experimental product at over 100 different rivers in collaboration with at multiple National Weather Service (NWS) Forecast Offices and River Forecast Centers (RFC) across the eastern half of the United States. The developed LSTM model achieved average Nash-Sutcliffe efficiency (NSE) 0.89 higher than the equivalent medium-range National Water Model ensemble member forecast over a 7-day forecast. In addition to the initial development and evaluation, this project has continued to expand. While the initial model was developed for precipitation dominated basins, expansion of the project has taken it to basins effected by snow melt. This presentation will provide an overview of the project with focus on recent developments on incorporating snow melt processes into the model.

Andrew T. White↗
NASA NTRS1977

SPAR: Structural-performance analysis and redesign

System of processor programs performs stress, buckling, and vibrational analysis of large linear finite element systems in excess of 50,000 degrees of freedom, while minimizing processing cost, execution time, central memory storage, and secondary data storage requirements. Programs use sparse matrix solution techniques and other computational and data management procedures.

Whetstone, W. D.↗
NASA NTRS1980

Optical mass memory system (AMM-13). AMM/DBMS interface control document

The baseline for external interfaces of a 10 to the 13th power bit, optical archival mass memory system (AMM-13) is established. The types of interfaces addressed include data transfer; AMM-13, Data Base Management System, NASA End-to-End Data System computer interconnect; data/control input and output interfaces; test input data source; file management; and facilities interface.

Bailey, G. A.↗