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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Assessing Machine Learning as a Tool to Explain Variance in Deployed Photovoltaic (PV) System Degradation

Degradation remains a large uncertainty in forecasting production for PV plants, creating significant risk for developers and financiers. This study aims to quantify the distribution and drivers of degradation across 10,000 PV systems deployed for distributed or utility generation by training a machine learning model to predict year-over-year degradation rates from metadata characteristics. A combination of K-Means clustering and random forest regressor were found to associate multiple metadata features as potential drivers of degradation, including module characteristics, system design, and climate features. From this, it is inferred that if machine learning is able to find complex patterns between metadata features and system performance loss, such methods can be employed to help developers and financiers make data-informed decisions when estimating long-term energy production forecasts in financial models.

Dunn, Jimmy C.↗
DOE OSTI2021

Illinois Express Quantum Network (IEQNET): metropolitan-scale experimental quantum networking over deployed optical fiber

The Illinois Express Quantum Network (IEQNET) is a program to realize metro-scale quantum networking over deployed optical fiber using currently available technology. IEQNET consists of multiple sites that are geographically dispersed in the Chicago metropolitan area. Each site has one or more quantum nodes (Qnodes) representing the communication parties in a quantum network. Q-nodes generate or measure quantum signals such as entangled photons and communicate the results via standard, classical, means. The entangled photons in IEQNET nodes are generated at multiple wavelengths, and are selectively distributed to the desired users via optical switches. Here we describe the network architecture of IEQNET, including the Internet-inspired layered hierarchy that leverages software-defined-networking (SDN) technology to perform traditional wavelength routing and assignment between the Q-nodes. Specifically, SDN decouples the control and data planes, with the control plane being entirely classical. Issues associated with synchronization, calibration, network monitoring, and scheduling will be discussed. An important goal of IEQNET is demonstrating the extent to which the control plane can coexist with the data plane using the same fiber lines. This goal is furthered by the use of tunable narrow-band optical filtering at the receivers and, at least in some cases, a wide wavelength separation between the quantum and classical channels. We envision IEQNET to aid in developing robust and practical quantum networks by demonstrating metro-scale quantum communication tasks such as entanglement distribution and quantum-state teleportation.

Chung, Joaquin↗
DOE OSTI2021

Medium and Heavy-Duty Electric Vehicle Infrastructure - Load Operations and Deployment (HEVI-LOAD) v1.0

In order to support the California state goals to reduce the carbon and pollution emissions (AB2127), The goal of this project (HEVI-Pro) is to project the charging infrastructure needs by future medium- and heavy- duty electric vehicles, and assess optimal deployment the infrastructure in California and provide grid impacts analysis. The MHDV projections will also consider transportation system and electric system interaction with light duty vehicles. The project consists of 2 approaches in general: top-down approach that takes aggregated MHDV adoptions as the inputs and provide the county-level projections of charging load profile and infrastructure need, and the bottom-up approach that that incorporates more granular (temporal, spatial and duty-cycle-specific) behaviors of a variety of MHDVs into activity simulations/optimizations for further analysis.

Wang, Bin↗
DOE OSTI2023

Python Urban Deployment Model (PyUDM) v1.0.0

The Python Urban Deployment Model (PyUDM) is a simulation tool used to investigate networks of static radiation detectors in urban environments. PyUDM simulates traffic, stationary NaI gamma-ray detectors, and moving radioactive sources on simulated vehicles. The analyzed output of the simulations contain valuable insights into the performance of different configurations of urban radiological detector configurations such as their ability to detect sources moving through the environment. To accurately simulate radioactive material moving through urban environments, PyUDM combines Monte Carlo simulation tools, publicly available map and traffic data, and measured gamma-ray spectra from urban environments.

Rofors, Emil↗
DOE OSTI2024

Code Description for "Brief Communication: Monitoring snow depth using small, cheap, and easy-to-deploy ground surface temperature sensors"

Temporally continuous snow depth estimates are vital for understanding changing snow patterns and impacts on permafrost in the Arctic. We train a random forest machine learning model to predict snow depth from variability in ground surface temperature. To our knowledge, this is the first time that small ground surface temperature sensors have been used to estimate snow depth. The model performs well at sites where the model was trained and at pan-arctic evaluation sites (RMSE <= 0.15 m). Small temperature sensors are cheap and easy-to-deploy, so this technique enables spatially distributed and temporally continuous snowpack monitoring to an extent previously infeasible. The model is flexible and can be applied to datasets retroactively to retrieve snow depth estimates at additional sites. This code package includes a *.joblib file of the trained random forest model and a *.ipynb file showing how to clean input data, train the random forest model, and apply the model.

Bachand, Claire↗
DOE OSTI2023

Performance of optical sensors for cloud measurements deployed by the ARM Aerial Facility during ACE-ENA

During the Aerosol and Cloud Experiment in the Eastern North Atlantic (ACE-ENA), a variety of in situ optical sensors using shadow imaging, scattering and holography were deployed by the Atmospheric Radiation Measurement (ARM) Aerial Facility to determine cloud properties. Taking advantage of the wide, overlapping range of instrumentation, we compare in situ cloud data from several different measurement methods for droplets up to 100 µm. Further, data processing was tailored to the encountered conditions, leading to good agreement. Improvements include noise reduction for holography and better out-of-focus correction for shadow imaging. Comparison between direct liquid water content measurements and optical sensors showed better agreement at higher droplet number concentrations (>120/cm 3 ).

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Characterizing thermal-induced temporal drift in deployed quantum fiber networks

We study temperature-induced fiber-delay drifts affecting entanglement fidelity in deployed quantum systems. Using field data and simulations, we model timing uncertainties and evaluate their impact on coincidence detection, revealing critical challenges for robust quantum networks.

Alshowkan, Muneer [ORNL] (ORCID:0000000264293450)↗
DOE OSTI2025

Ancilla-assisted process tomography of a deployed fiber link

We perform ancilla-assisted process tomography over a deployed 1.6 km fiber-optic link using Bayesian inference, confirming a stable, high-fidelity polarization channel. Overall, our approach should offer value for in situ analyses of entanglement networks.

Rahman, Arefur [Arizona State University]↗
DOE OSTI2024

Entanglement-based quantum digital signatures over a deployed campus network

The quantum digital signature protocol offers a replacement for most aspects of public-key digital signatures ubiquitous in today’s digital world. A major advantage of a quantum-digital-signatures protocol is that it can have information-theoretic security, whereas public-key cryptography cannot. Here we demonstrate and characterize hardware to implement entanglement-based quantum digital signatures over our campus network. Over 25 hours, we collect measurements on our campus network, where we measure sufficiently low quantum bit error rates (<5% in most cases) which in principle enable quantum digital signatures at over 50 km as shown through rigorous simulation accompanied by a noise model developed specifically for our implementation. These results show quantum digital signatures can be successfully employed over deployed fiber. Moreover, our reported method provides great flexibility in the number of users, but with reduced entanglement rate per user. Finally, while the current implementation of our entanglement-based approach has a low signature rate, feasible upgrades would significantly increase the signature rate.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Contact-dependent growth inhibition (CDI) systems deploy a large family of polymorphic ionophoric toxins for inter-bacterial competition

Contact-dependent growth inhibition (CDI) is a widespread form of inter-bacterial competition mediated by CdiA effector proteins. CdiA is presented on the inhibitor cell surface and delivers its toxic C-terminal region (CdiA-CT) into neighboring bacteria upon contact. Inhibitor cells also produce CdiI immunity proteins, which neutralize CdiA-CT toxins to prevent auto-inhibition. Here, we describe a diverse group of CDI ionophore toxins that dissipate the transmembrane potential in target bacteria. These CdiA-CT toxins are composed of two distinct domains based on AlphaFold2 modeling. The C-terminal ionophore domains are all predicted to form five-helix bundles capable of spanning the cell membrane. The N-terminal "entry" domains are variable in structure and appear to hijack different integral membrane proteins to promote toxin assembly into the lipid bilayer. The CDI ionophores deployed by E. coli isolates partition into six major groups based on their entry domain structures. Comparative sequence analyses led to the identification of receptor proteins for ionophore toxins from groups 1 & 3 (AcrB), group 2 (SecY) and groups 4 (YciB). Using forward genetic approaches, we identify novel receptors for the group 5 and 6 ionophores. Group 5 exploits homologous putrescine import proteins encoded by puuP and plaP, and group 6 toxins recognize di/tripeptide transporters encoded by paralogous dtpA and dtpB genes. Finally, we find that the ionophore domains exhibit significant intra-group sequence variation, particularly at positions that are predicted to interact with CdiI. Accordingly, the corresponding immunity proteins are also highly polymorphic, typically sharing only ~30% sequence identity with members of the same group. Competition experiments confirm that the immunity proteins are specific for their cognate ionophores and provide no protection against other toxins from the same group. The specificity of this protein interaction network provides a mechanism for self/nonself discrimination between E. coli isolates.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

MBARI WEC 2021 deployment

This dataset includes data from the Monterey Bay Aquarium Research Institute (MBARI) wave energy converter (WEC) and a nearby located Sofar Spotter buoy. The Monterey Bay Aquarium Research Institute has developed and deployed a small two-body point absorber wave energy device suitable to autonomous underwater vehicle, sensor system, and even aquaculture farm needs. For more information on the MBARI WEC see the research journal attached in the submission.

16 TIDAL AND WAVE POWER↗
DOE OSTI2022

HERO WEC V1.0 - Electrical PTO Deployment Data and Analysis

This submission includes the processed and raw field electrical data from NREL's August 2022 HERO WEC (hydraulic and electric reverse osmosis wave energy converter) deployment at Jennette's Pier for the electrical PTO (power take-off).

16 TIDAL AND WAVE POWER↗
DOE OSTI2026

Pilot Heavy-Duty Electric Vehicle Deployment for Anchorage, Alaska, Municipal Solid Waste Collection

Through a grant awarded by the U.S. Department of Energy, the Municipality of Anchorage initiated a pilot program in their Solid Waste Services (SWS) department to add heavy-duty electric trucks to its vehicle fleet. The project involves the purchase and deployment of a Peterbilt 220EV electric box truck and two Peterbilt 520EV heavy-duty electric refuse trucks. The Alaska Center for Energy and Power at the University of Alaska Fairbanks performed data analysis. Data collected include telemetry data from both types of electric trucks, charging data from the 520EV telemetry data and a Level 2 charger, and facility-level electric use data.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Pilot Heavy-Duty Electric Vehicle Deployment for Anchorage, Alaska, Municipal Solid Waste Collection

Through a grant awarded by the U.S. Department of Energy, the Municipality of Anchorage initiated a pilot program in their Solid Waste Services (SWS) department to add heavy-duty electric trucks to its vehicle fleet. The project involves the purchase and deployment of a Peterbilt 220EV electric box truck and two Peterbilt 520EV heavy-duty electric refuse trucks. The Alaska Center for Energy and Power at the University of Alaska Fairbanks performed data analysis. Data collected include telemetry data from both types of electric trucks, charging data from the 520EV telemetry data and a Level 2 charger, and facility-level electric use data.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

CROCUS 3-D wind data from Argonne Deployable Mast during Urban Canyon IOP July 2024

During the Community Research on Climate and Urban Science (CROCUS) Urban Integrated Field Laboratory (UIFL) project, led by Argonne National Laboratory, this dataset was collected by METEK uSonic-3 Class A MP sonic anemometer at 10 meter height of the Argonne Deployable Mast (ADM) from July 26 to July 28, 2024, .As part of the CROCUS 2024 Urban Canyon Intensive Observation Period (IOP), 3-D sonic anemometer on the ADM was set up to provide continuous measurements of wind and temperature in Chicago during July 2024. These measurements cover winds in X-, Y- and Z- direction and sonic temperature at 30-Hz resolution. The data are formatted as NetCDF (.nc) files, making them easily accessible using common software such as MATLAB, R, and Python.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

PCOR Partnership: A Catalyst for Commercial CCUS Deployment

Conference paper presented at 17th International Conference on Greenhouse Gas Control Technologies (GHGT-17), Calgary, Alberta, Canada, October 20–24, 2024. This paper presents an overview of the Plains CO 2 Reduction (PCOR) Partnership Initiative and the current commercial carbon capture, utilization, and storage (CCUS) activity in the region, discusses challenges facing CCUS in the region, and highlights important challenges that have recently been overcome to enable CCUS deployment in the region.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2020

Regional Energy Deployment System (ReEDS) Model Documentation: Version 2019

The Regional Energy Deployment System (ReEDS) model is a capacity expansion and dispatch model that is primarily used for the contiguous U.S. electric power sector. The model relies on system-wide least cost optimization to estimate the type and location of future generation and transmission capacity. This document describes details of how the model is formulated, how it functions, and many of the key inputs.

24 POWER TRANSMISSION AND DISTRIBUTION↗