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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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DOE OSTI2020

Projecting spatiotemporally explicit effects of climate change on stream temperature: A model comparison and implications for coldwater fishes

Conservation planners and resource managers seek information about how the availability and locations of cold-water habitats will change in the future and how these predictions vary among models. In this work, we used a physical process-based model to demonstrate the implications of climate change for streamflow and water temperature in two watersheds with distinctive flow regimes: the Snoqualmie watershed (WA) and Siletz watershed (OR), USA. Our model incorporated a downscaled ensemble of global climate model outputs and was calibrated with in situ and remotely sensed water temperatures. Furthermore, we compared predictions from our processed-based model to those from a publicly available and widely used statistical model. The process-based model projected greater changes in summer maximum water temperatures for the mixed-rain-snow Snoqualmie watershed than for the rain-dominated Siletz watershed as a result of the near-complete loss of winter snowpack and significant reduction in summer flow in the Snoqualmie watershed expected by the 2080s. Both models projected generally similar future spatial patterns of maximum water temperature in the two rivers, with cool reaches distributed farther upstream and fewer in number. However, the process-based model projected higher spatial heterogeneity in water temperature due to our spatially explicit simulation of streamflow and because we calibrated the model with spatially continuous remotely sensed water temperature data. We used stream temperature projections to assess the vulnerability of Pacific salmon and trout to changes in the spatial distribution of cold-water habitats during August by the 2080s. Results suggest that salmonids may have fewer summertime cold-water habitats in both watersheds. Projected stream warming may further limit particular species and life stages, especially in the Snoqualmie watershed. Our comparison of models highlights the importance of considering what might be gained by using a process-based model for evaluating and prioritizing management actions that mitigate climate impacts on cold-water habitats for stream fishes.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Data, model inputs, and analysis scripts associated with a manuscript on stream intermittency controls across spatial scales in Pacific Northwest watersheds

NOTE: The manuscript associated with this data package is currently in review. The data may be revised based on reviewer feedback. Upon manuscript acceptance, this data package will be updated with the final dataset and additional metadata. This data package is associated with the manuscript "Hydroclimatic Memory and Watershed Template Shape Stream Intermittency: Multi-scale Attribution Using Process-based Simulation and Explainable ML" by Niroula et al. (2026), submitted to Water Resources Research (WRR). The study investigates the dominant controls on stream intermittency across local, reach, and watershed scales using a coupled process-based simulation and explainable machine-learning framework. Long-term daily simulations from the Advanced Terrestrial Simulator (ATS) were used to generate wetness states and ponded-depth responses over river-corridor cells. These ATS outputs were then aggregated across scales and used to train XGBoost (eXtreme Gradient Boosting) models. SHAP (SHapley Additive exPlanations) was applied to quantify the relative importance of hydroclimatic forcings, watershed template attributes, and antecedent-memory effects in shaping intermittency behavior. The analysis is carried out for three contrasting Pacific Northwest watersheds: Oak Creek (OCW), American River Watershed (ARW), and H.J. Andrews (HJA). Across these testbeds, the package contains ATS-ready watershed inputs, ATS run configuration and selected output files, model-evaluation data products, intermittency-analysis datasets, machine-learning target-feature tables, SHAP outputs, and notebooks used to organize, analyze, and visualize results. At a high level, the package documents a workflow in which ATS provides the physically based simulation backbone and explainable machine learning is used as a post-processing attribution tool. The contents are intended to support interpretation of the manuscript figures and results, provide context for how intermittency metrics were generated at multiple scales, and preserve the key artifacts needed to understand and reuse the analysis workflow. The package contains a high-level directory summary file (`summary.txt`) and four main content folders (1) `evaluation_plots` contains evaluation figures and supporting evaluation datasets; (2) `intermittency_plots` contains intermittency-focused analysis notebook and prepared datasets; (3) `ml-training-and-shap_values_plots` contains ML training inputs, SHAP outputs, and figure-generation notebooks; and (4) `watershed_mesh_and_ats_input` contains ATS model setup materials, forcing inputs, geometry, and selected run files. More specifically, the `evaluation_plots` folder contains the notebook used for ATS evaluation plotting and site-specific evaluation datasets. These include evapotranspiration and water-balance products for three watersheds, as well as an Oak Creek field-measurement discharge file. The `intermittency_plots` folder contains the notebook used for intermittency analysis and the prepared datasets used to analyze intermittent and non-intermittent wetness behavior across the study watersheds. The `ml-training-and-shap_values_plots` folder contains notebooks and outputs for the machine-learning and explainability workflow. This includes the main XGBoost and SHAP notebook(s), a beeswarm plotting notebook, target-feature tables for machine-learning training, SHAP summary tables, and per-sample SHAP value archives. The `watershed_mesh_and_ats_input` folder contains ATS-related watershed inputs and supporting materials. This includes mesh and shape products, ATS-readable LAI and meteorological forcing inputs, selected ATS spinup and transient-run files, and a watershed workflow example notebook. Subdirectories are organized by watershed where applicable.All files are .cpg (codepage files), .csv (comma-separated values), .dbf (database files), .exo (Exodus mesh format), .h5 (HDF5 format), .ipynb (Jupyter notebooks), .pkl (Python pickle), .prj (projection files), .sh (shell scripts), .shp (shapefile geometry), .shx (shapefile index), .txt (text files), or .xml (markup data).

Advanced Terrestrial Simulator↗

NASA NTRS1978

Large-scale Gulf Stream frontal study using GEOS 3 radar altimeter data

From data obtained by the GEOS 3 radar altimeter, sea surface heights are found by both editing and filtering the raw sea surface height measurements and then referencing these processed data to a 5 foot by 5 foot geoid. Any trend between the processed data and the geoid is removed by subtracting out a linear fit to the residuals in the open ocean. Data from individual passes are further processed by applying a minimum variance technique at the subsatellite crossing points to produce surface topography maps for the 6 months and an overall mean map which reveal important details about the Gulf Stream system. The differences between the monthly mean and the overall mean are calculated for each of the 6 months to show the temporal and spatial changes of the Gulf Stream front and spawned eddies. The standard deviation map is even more informative and shows preferred locations of Gulf Stream meanders.

Huang, N. E.↗

DOE OSTI2019

A Comparison of Phasor Communication Protocols

In this paper, we compare three approaches used for continuous transfer of real-time synchrophasor data: IEEE C37.118.2-2011, IEC TR 61850-90-5 and a new protocol being developed under the Department of Energy (DOE) project DOE-OE-859 called the Streaming Telemetry Transport Protocol (STTP). STTP is currently being advanced as a potential third standard protocol via the IEEE Power Engineering Society STTP P10 Work Group (P2664). Each of these three synchrophasor protocols is described in detail in this paper along with the basis for their operating characteristics using Internet Protocol (IP) transport. The dominant protocol for the exchange of synchrophasor data is IEEE C37.118, both in the U.S. and internationally. The most recent IEEE C37.118 standard is broken into two parts where IEEE C37.118.1-2011 (Part 1) defines the normative synchrophasor measurement requirements and IEEE C37.118.2-2011 (Part 2) defines the protocol’s data transmission format. Both the IEC TR 61850-90-5 and the emergent STTP specifications only address synchrophasor data transmission; therefore, this paper focuses on comparing the data transmission protocol elements of these standards. The primary dimensions of comparison of these three protocols explored in this paper are: structure, efficiency, susceptibility to data loss, scalability, security, and other operability functionality. Additionally, though not specifically relevant to synchrophasor data telemetry, the three protocols are evaluated with respect to flexibility for transporting non-synchrophasor precise-time data streams.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS1993

A data distribution strategy for the 1990s (files are not enough)

Virtually all of the data distribution strategies being contemplated for the EOSDIS era revolve around the use of files. Most, if not all, mass storage technologies are based around the file model. However, files may be the wrong primary abstraction for supporting scientific users in the 1990s and beyond. Other abstractions more closely matching the respective scientific discipline of the end user may be more appropriate. JPL has built a unique multimission data distribution system based on a strategy of telemetry stream emulation to match the responsibilities of spacecraft team and ground data system operators supporting our nations suite of planetary probes. The current system, operational since 1989 and the launch of the Magellan spacecraft, is supporting over 200 users at 15 remote sites. This stream-oriented data distribution model can provide important lessons learned to builders of future data systems.

Tankenson, Mike↗

DOE OSTI2022

Effects of structure and volcanic stratigraphy on groundwater and surface water flow: Hat Creek basin, California, USA

Abstract Hydrogeologic systems in the southern Cascade Range in California (USA) develop in volcanic rocks where morphology, stratigraphy, extensional structures, and attendant basin geometry play a central role in groundwater flow paths, groundwater/surface-water interactions, and spring discharge locations. High-volume springs (greater than 3 m 3 /s) flow from basin-filling (<800 ka) volcanic rocks in the Hat Creek and Fall River tributaries and contribute approximately half of the average annual flow of the Pit River, the largest tributary to Shasta Lake. A hydrogeologic conceptual framework is constructed for the Hat Creek basin combining new geologic mapping, water-well lithologic logs, a database of active faults, LiDAR mapping of faults and volcanic landforms, streamflow measurements and airborne thermal infrared remote sensing of stream temperature. These data are used to integrate the geologic structure and the volcanic and volcaniclastic stratigraphy to create a three-dimensional interpretation of the hydrogeology in the basin. Two large streamflow gains from focused groundwater discharge near Big Spring and north of Sugarloaf Peak result from geologic barriers that restrict lateral groundwater flow and force water into Hat Creek. The inferred groundwater-flow barriers divide the aquifer system into at least three leaky compartments. The two downstream compartments lose streamflow in the upstream reaches (immediately downstream of the groundwater-flow barriers) and gain in downstream reaches with the greatest inflows immediately upstream of the barriers.

Geology↗

DOE OSTI2024

Dual particle imaging: Applications in security and environmental imaging

Radiation imaging detectors have been applied in a variety of fields for detection, localization and characterization of radioactive sources. In this work, we describe a handheld detection system (H2DPI) that can simultaneously image neutrons and gamma rays, and reconstruct the energy spectra of the incident particles. This system was applied to the detection and characterization of a plutonium metal source in experiments performed at the National Criticality Experiments Research Center, where the 4.5 kg plutonium source was placed approximately 60 cm from the H2DPI. Neutron and gamma-ray images were measured that show good agreement with the known location of the source. Furthermore, the H2DPI was also used to measure the neutron spectrum, which shows the expected shape of a Watt fission spectrum, and the gamma-ray energy spectrum, which shows the energies common to plutonium metal. Finally, we describe the application of the H2DPI in augmented reality, whereby real-time neutron and gamma-ray imaging data can be streamed to the Microsoft HoloLens to allow the user to visualize the source locations.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2021

Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators

It is widely known that neural networks (NNs) are universal approximators of continuous functions. However, a less known but powerful result is that a NN with a single hidden layer can accurately approximate any nonlinear continuous operator. This universal approximation theorem of operators is suggestive of the structure and potential of deep neural networks (DNNs) in learning continuous operators or complex systems from streams of scattered data. Here, in this work, we thus extend this theorem to DNNs. We design a new network with small generalization error, the deep operator network (DeepONet), which consists of a DNN for encoding the discrete input function space (branch net) and another DNN for encoding the domain of the output functions (trunk net). We demonstrate that DeepONet can learn various explicit operators, such as integrals and fractional Laplacians, as well as implicit operators that represent deterministic and stochastic differential equations. We study different formulations of the input function space and its effect on the generalization error for 16 different diverse applications.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

A lightweight, user-configurable detector ASIC digital architecture with on-chip data compression for MHz X-ray coherent diffraction imaging

Today, most X-ray pixel detectors used at light sources transmit raw pixel data off the detector ASIC. With the availability of more advanced ASIC technology nodes for scientific application, more digital functionalities from the computing domains (e.g., compression) can be integrated directly into a detector ASIC to increase data velocity. In this paper, we describe a lightweight, user-configurable detector ASIC digital architecture with on-chip compression which can be implemented in 130 nm technologies in a reasonable area on the ASIC periphery. In addition, we present a design to efficiently handle the variable data from the stream of parallel compressors. The architecture includes user-selectable lossy and lossless compression blocks. The impact of lossy compression algorithms is evaluated on simulated and experimental X-ray ptychography datasets. This architecture is a practical approach to increase pixel detector frame rates towards the continuous 1 MHz regime for not only coherent imaging techniques such as ptychography, but also for other diffraction techniques at X-ray light sources.

47 OTHER INSTRUMENTATION↗

DOE OSTI2016

Observing Ocean Ecosystems with Sonar

We present a real-time processing system for sonar to detect and track animals, and to extract water column biomass statistics in order to facilitate continuous monitoring of an underwater environment. The Nekton Interaction Monitoring System (NIMS) is built to connect to an instrumentation network, where it consumes a real-time stream of sonar data and archives tracking and biomass data.

ocean energy↗

DOE OSTI2019