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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Utah FORGE: Well 16B(78)-32 DTS, RFS DSS Strain, and Absolute Strain Circulation Test Fiber Optic Data - August 2024

This dataset contains processed fiber optic measurements collected during the extended cross-well circulation test at the Utah FORGE site in August 2024. The data was acquired from the 16B(78)-32 well using distributed fiber optic sensing (DFOS) technology, including distributed temperature sensing (DTS) on multimode fiber and distributed strain sensing (DSS) measurements on single-mode fiber. The dataset includes Brillouin absolute strain, Rayleigh frequency shift (RFS) DSS strain change, RFS DSS strain change rate, and temperature (DTS) data, all stored in HDF5 format. Time coordinates are provided in UTC, and depth measurements are given in measured depth relative to the rotary kelly bushing (MD RKB) in feet. The spatial sampling on the RFS DSS strain data and the Brillouin Absolute Total Strain data is 20 cm and the spatial sampling on the DTS data is 1m. The dataset is accompanied by a report from Neubrex, which provides further documentation.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

DTS data from SGP TBS

Distributed temperature-sensing data collected using a Silixa-XT DTS and 50/125 MM fiber. Data are calibrated using two water baths at the surface and an iMet aloft at the end of the fiber and tether. The data are then assigned an altitude for the TBS flight. Data collected prior to Febuary 11, 2020 were collected at the SGP Central Facility (CF). Data from 2/11/20 were collected with a Sensornet Oryx DTS at the E9 site, not at the CF. Data from May 2021 were collected with a Sensornet Oryx at the CF, and with a Silixa XT at the CF. Data from July 2021 were collected at the SGP CF, EF9, and EF36 with a Silixa XT. Data from October 2021 were collected at the SGP EF36 with a Silixa XT. Data from February 2022 were collected at the CF with a Silixa XT.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1994

DTS: Building custom, intelligent schedulers

DTS is a decision-theoretic scheduler, built on top of a flexible toolkit -- this paper focuses on how the toolkit might be reused in future NASA mission schedulers. The toolkit includes a user-customizable scheduling interface, and a 'Just-For-You' optimization engine. The customizable interface is built on two metaphors: objects and dynamic graphs. Objects help to structure problem specifications and related data, while dynamic graphs simplify the specification of graphical schedule editors (such as Gantt charts). The interface can be used with any 'back-end' scheduler, through dynamically-loaded code, interprocess communication, or a shared database. The 'Just-For-You' optimization engine includes user-specific utility functions, automatically compiled heuristic evaluations, and a postprocessing facility for enforcing scheduling policies. The optimization engine is based on BPS, the Bayesian Problem-Solver (1,2), which introduced a similar approach to solving single-agent and adversarial graph search problems.

Hansson, Othar↗
DOE OSTI2024

Laser-heated Pedestal Growth and Raman DTS for Harsh-environment Applications

Fibers composed of single crystal (SC) materials are mechanically, thermally, and chemically robust to harsh conditions, and thus attractive for sensing applications in harsh environments. However, SC fibers grown via the laser-heated pedestal growth (LHPG) technique do not intrinsically have a functional cladding layer. We investigate self-segregation of dopants in a crystal matrix within a SC fiber. Ti and Cr dopants within a sapphire matrix were grown via the LHPG method. Cross-sectional dopant concentrations were measured via electron-probe micro-analysis (EPMA). The cross-sectional constituent profiles and resulting transmissivities were characterized as a function of growth parameters.

Lander, Gary↗
DOE OSTI2026

Field and Model Data Associated with the Manuscript “Drivers of Streamflow Intermittency in Humid Regions: 2. Evaluating Controls on Flow Persistence in an Urbanized Catchment”

This package contains field data, modeling files, and scripts supporting the investigation of the drivers of streamflow intermittency in an urbanized catchment. It includes the field data collected from electrical resistivity tomography (ERT) surveys, distributed temperature sensing (DTS), continuous self-potential (SP) monitoring, groundwater and stilling well. In addition, it contains the data and results of the coupled water- and electrical-flow model developed using the COMSOL Multiphysics and Advanced Terrestrial Simulator (ATS), as well as software files and Jupyter notebooks used to process the data and generate figures in the manuscript submitted for peer review. The data archive is organized in the following directories: 1) Climate Includes hourly precipitation and daily evapotranspiration time series (2024 – 2025) provided as CSV files, alongside a text file detailing dataset units. 2) Coupled_model Field_Application subfolder contains the ATS XML input scripts, data files, output data for the SP site. It also contains the Jupyter notebook (Plot_final_calib.ipynb) to visualize the results of the modeled SP, stream-groundwater exchange and moisture content. The flow model simulation is executed using the ATS XML scripts and the included Python script (generate_data_set.py) to convert ATS output to COMSOL-ready input. COMSOL Multiphysics template (.m can only be used with COMSOL with MATLAB) is executed using the ATS output data to simulate the potential field. 3) Discharge Includes the electrical conductivity (EC) time series (provided as CSV files) from salt slug injections. It also includes the Jupyter notebook (Discharge_process.ipynyb) used to estimate discharge. All discharge measurements collated into rating_curve_processed.csv 4) DTS Contains collated DTS data including raw Stokes and anti-Stokes measurement (provided as .h5 file). It also includes DTS processing.ipynb, a Jupyter notebook for calibrating the DTS data using dts_calibration Python package. cooler_calibration.csv is the DTS calibration CSV used in the calibration sequence. 5) ERT Contains raw resistivity data (provided as CSV files), spatial location of each of the electrodes (provided as CSV files), and files used for the resistivity inversion. 6) Slug_test Includes the slug test data at all the groundwater wells provided as CSV files, as well as the Jupyter notebook (Slug_test.ipynb) for calculating hydraulic conductivity. 7) SP Contains the SP data collected in field at the SP sites (provided as CSV files). 8) Well_data Contains two subfolders: 1) Raw, which provides unprocessed pressure, electrical conductivity and temperature timeseries downloaded from the loggers in all the groundwater and stilling wells, and 2) Processed, which contains sorted, QA/QC timeseries data for each well. The data archive also contains data_process.ipynb, a Jupyter notebook used for field data analysis and generating figures (plotting well, SP, climate, and discharge data, as well as calculating head gradient at sites with nested groundwater wells). Note: Code files (.ipynb, .py, .xml) can be opened in any standard code editor, .exo file can be viewed using Paraview, .h5 files can be opened using HDFView software and h5py Python package, and .resipy file can be opened with the open-source ResIPy software.

ATS↗
DOE OSTI2025

Digital Twin Applications in the Water Sector: A Review

As cities develop and resource demands rise, the water sector faces crucial challenges to deliver reliable, sustainable, and efficient services. Digital Twins (DTs), virtual replicas of physical systems, offer a promising tool to transform how we manage water infrastructure. Originally developed in the aerospace industry, DTs are now gaining traction in the water sector, enabling real-time monitoring, simulation, and predictive control of water and wastewater treatment, collection and distribution networks, and water reclamation and reuse systems. While still emerging in the water sector, DTs have shown potential to enhance operational efficiency, reduce environmental impacts, and support smarter, more resilient water management. This review study provides a comprehensive overview of current DT applications in the water sector, highlighting successful case studies, technical challenges, and knowledge gaps. It also explores how DTs can help bridge the water–energy nexus by optimizing resources utilized across interconnected systems. By synthesizing recent advances and identifying future research directions, this paper illustrates how DTs can play a central role in building sustainable, adaptive, and digitally-enabled water infrastructure.

digital twin↗
NASA NTRS1988

Data Transport Subsystem - The SFOC glue

The design and operation of the Data Transport Subsystem (DTS) for the JPL Space Flight Operation Center (SFOC) are described. The SFOC is the ground data system under development to serve interplanetary space probes; in addition to the DTS, it comprises a ground interface facility, a telemetry-input subsystem, data monitor and display facilities, and a digital TV system. DTS links the other subsystems via an ISO OSI presentation layer and an LAN. Here, particular attention is given to the DTS services and service modes (virtual circuit, datagram, and broadcast), the DTS software architecture, the logical-name server, the role of the integrated AI library, and SFOC as a distributed system.

Parr, Stephen J.↗
NASA NTRS1991

Autonomous docking ground demonstration (category 3)

The NASA Johnson Space Center (JSC) is involved in the development of an autonomous docking ground demonstration. The demonstration combines the technologies, expertise and facilities of the JSC Tracking and Communications Division (EE), Structures and Mechanics Division (ES), and the Navigation, Guidance and Control Division (EG) and their supporting contractors. The autonomous docking ground demonstration is an evaluation of the capabilities of the laser sensor system to support the docking phase (12ft to contact) when operated in conjunction with the Guidance, Navigation and Control Software. The docking mechanism being used was developed for the Apollo Soyuz Test Program. This demonstration will be conducted using the Six-Degrees of Freedom (6-DOF) Dynamic Test System (DTS). The DTS environment simulates the Space Station Freedom as the stationary or target vehicle and the Orbiter as the active or chase vehicle. For this demonstration the laser sensor will be mounted on the target vehicle and the retroreflectors on the chase vehicle. This arrangement was used to prevent potential damage to the laser. The sensor system. GN&C and 6-DOF DTS will be operated closed-loop. Initial condition to simulate vehicle misalignments, translational and rotational, will be introduced within the constraints of the systems involved. Detailed description of each of the demonstration components (e.g., Sensor System, GN&C, 6-DOF DTS and supporting computer configuration) including their capabilities and limitations will be discussed. A demonstration architecture drawing and photographs of the test configuration will be presented.

Lamkin, Steve L.↗
DOE OSTI2024

Utah FORGE: Well 16B(78)-32 Distributed Temperature Sensing Data from April and May 2024

This dataset includes Neubrex Energy Services fiber optic distributed temperature sensing (DTS) data from well 16B(78)-32 during stimulation and circulation, including interaction with well 16A(78)-32, during April and May 2024. The DTS data are stored in HDF5 file format and are accompanied by a PowerPoint report on the study. All times in this dataset are in UTC. Depths are in MD relative to Kelly Bushing Height, and temperatures are in degrees Fahrenheit. All DTS measurements were made using a Yokogawa 3000DTSX Distributed Temperature Sensing Interrogator Unit, with a spatial sampling interval of 3.28 feet and a temporal sampling rate of 129 seconds. The third-party Pressure-Temperature Gauge data should be used with caution after April 20, 2024, as its performance is not considered reliable beyond this date.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Guidance for Developing Digital Twins for Online Condition Monitoring of Nuclear Power Plant Components

Online condition monitoring is an area of active research that may enable optimized scheduling, maintenance, and safety of nuclear power plant components, reducing unnecessary derates while simultaneously improving operational capacity. Digital twins (DTs) are one avenue to conduct online condition monitoring and are currently being explored by national laboratories and universities alike. DTs for online condition monitoring are, in essence, state concurrent models that emulate a physical process which predicts a parameter and compares it against a measured value. The promise of DT is that they may provide additional insights by combining and interpreting various sources of information and may be used for preventative maintenance scheduling optimization or early fault detection. DTs for condition monitoring are projected to be valuable for meeting requirements under 10 CFR 50.55a and 10 CFR 50.65. However, DT technologies are still under significant development and the process for developing a DT for condition monitoring has not been formalized. Therefore, in this work, we present an initial framework for developing a DT, discuss and review the various challenges and considerations for DT deployment, and identify the opportunities that a DT can improve. Here, the presented framework is intended to help developers formulate a strategy when approaching DT development for condition monitoring. A DT use case for a reactor coolant pump is presented to demonstrate the proposed framework.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

From Edge to HPC: Investigating Cross-Facility Data Streaming Architectures

In this paper, we investigate three cross-facility data streaming architectures, Direct Streaming (DTS), Proxied Streaming (PRS), and Managed Service Streaming (MSS). We examine their architectural variations in data flow paths and deployment feasibility, and detail their implementation using the Data Streaming to HPC (DS2HPC) architectural framework and the SciStream memory-to-memory streaming toolkit on the production-grade Advanced Computing Ecosystem (ACE) infrastructure at Oak Ridge Leadership Computing Facility (OLCF). We present a workflow-specific evaluation of these architectures using three synthetic workloads derived from the streaming characteristics of scientific workflows. Through simulated experiments, we measure streaming throughput, round-trip time, and overhead under work sharing, work sharing with feedback, and broadcast and gather messaging patterns commonly found in AI-HPC communication motifs. Our study shows that DTS offers a minimal-hop path, resulting in higher throughput and lower latency, whereas MSS provides greater deployment feasibility and scalability across multiple users but incurs significant overhead. PRS lies in between, offering a scalable architecture whose performance matches DTS in most cases.

George, Anjus [ORNL] (ORCID:0000000179737061)↗
DOE OSTI2024

Digital-Twin-Enabling Technologies for Online Condition Monitoring of Nuclear Power Plant Components

Online condition monitoring is an area of active research that may enable optimized scheduling, maintenance, and safety of nuclear power plant components, reducing unnecessary derates while simultaneously improving operational capacity. Digital twins (DTs) are one avenue to conduct online condition monitoring and are currently being explored by national laboratories and universities alike. DTs for online condition monitoring are, in essence, state concurrent models that emulate a physical process which predicts a parameter and compares it against a measured value. A DT’s goal is to provide additional insights by combining and interpreting various sources of information for preventative maintenance scheduling optimization or early fault detection. DTs for condition monitoring are projected to be valuable for meeting requirements under 10 CFR 50.55a, “Codes and Standards,” and 10 CFR 50.65, “Requirements for Monitoring the Effectiveness of Maintenance at Nuclear Power Plants”. However, DT technologies are still under significant development, and the process for developing a DT for condition monitoring has not been formalized. Therefore, in this work, we present an initial framework for developing a DT, discuss and review the various challenges and considerations for DT deployment, and identify the opportunities that a DT can improve. The presented framework is intended to help developers formulate a strategy when approaching DT development for condition monitoring. In conclusion, a DT use case for a reactor coolant pump is presented to demonstrate the proposed framework.

advanced sensor instrumentation↗
NASA NTRS1991

Autonomous docking ground demonstration

The Autonomous Docking Ground Demonstration is an evaluation of the laser sensor system to support the docking phase (12 ft to contact) when operated in conjunction with the guidance, navigation, and control (GN&C) software. The docking mechanism being used was developed for the Apollo/Soyuz Test Program. This demonstration will be conducted using the 6-DOF Dynamic Test System (DTS). The DTS simulates the Space Station Freedom as the stationary or target vehicle and the Orbiter as the active or chase vehicle. For this demonstration, the laser sensor will be mounted on the target vehicle and the retroflectors will be on the chase vehicle. This arrangement was chosen to prevent potential damage to the laser. The laser sensor system, GN&C, and 6-DOF DTS will be operated closed-loop. Initial conditions to simulate vehicle misalignments, translational and rotational, will be introduced within the constraints of the systems involved.

Lamkin, Steve L.↗
DOE OSTI2025

A Risk-Informed Approach to Trustworthiness Assessment in Digital Twins-Based Autonomous Control

In autonomous control systems, digital twins (DTs) are used to perform diagnostic and prognostic functions. The trustworthiness of these DTs is dependent on quality and coverage of the training data, model accuracy and integrity of sensor data. This work introduces a methodology to determine the trustworthiness of a DT system given faulty sensor data using a risk informed approach. Bayesian Belief Networks (BBNs) are used to propagate uncertainties and determine the probability of trustable recommendations. The decision to trust the control action provided by the DT is based on the DT output, expert opinion, and severity of problems. The performance of DTs is reliant on the data they are trained on. When they encounter out of distribution data, the trustworthiness of the recommendations decreases. To address this issue, we include an expert component that provides input on sensor degradation. For this, we utilize a generative artificial intelligence (AI) model, such as Generative Pretrained Transformer (GPT). The GPT functions as an expert with broad knowledge. The GPT is fine-tuned to understand and discriminate sensor degradation scenarios using manufactured data. This methodology is demonstrated through a case study on a Nearly Autonomous Management and Control System (NAMAC) during a steady state scenario. Various sensor degradation types with different severity levels are considered. Degraded sensor data is processed by the DT system and the fine-tuned GPT. Finally, using the BBN, we combine the GPT information and the DT output with its sources of uncertainty. This provides an output regarding the trustworthiness of the DT recommendation.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗