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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 1,081 records · Page 60
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Resilience for Advanced Distributed Wind Systems: Identifying the resilience benefits of advanced controls and hybrid systems for distributed wind

Under the Department of Energy (DOE) Wind Energy Technologies Office (WETO), Idaho National Laboratory (INL) has been tasked with defining the resilience benefits of distributed wind systems for the Microgrids, Infrastructure Resilience, and Advanced Controls Launchpad (MIRACL) project. This project is a collaboration between the National Renewable Energy Laboratory (NREL), Sandia National Laboratories (SNL) and Pacific Northwest National Laboratory (PNNL). In the final year of this project, INL is collaborating with the other labs to bring together key results from our previous work on resilience, cybersecurity and risk, distributed wind hybrid systems, and valuation of distributed wind.

17 WIND ENERGY↗
DOE OSTI2023

Identifying and Estimating Project Development Costs [Slides]

This presentation focuses on renewable energy project development and planning and highlights common project costs at each stage of a project's lifecycle. The presentation was produced as part of a series of trainings for awardees of the Energizing Rural Communities prize.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Identifying impacts of process, precursors, and defects in metal halide perovskite solar cells

With efficiencies of halide perovskite solar cells stagnating over the last few years at values above 25% and approaching thermodynamic limits, focus must shift to ensuring stability of these devices over the long term. The overarching goal of this project was to understand the root causes of instability in halide perovskite solar cells. Our approach was rooted in fundamental scientific pursuits, determining at the molecular level those processes responsible for degradation of the metal halide perovskite system, either in the precursor ink, or in the solid state. We then used this understanding to show that degradation leads to midgap states in halide perovskite semiconductors, explaining loss in performance; also, this in-depth understanding allowed us to develop specifically targeted solutions to prevent these degradation mechanisms. These include guidance about amines in perovskite inks, solvents to use (and not to use), and impacts of Cs incorporation to perovskite inks and solids. Finally, we achieved our end-of-project goal and demonstrated an effective lifetime of a halide perovskite solar cell of 5000 h, including measurement of devices over 3000 h, amongst the longest in the field. Overall, this project has considerably pushed forward the science of perovskite degradation and sets examples and groundwork for future studies to enable halide perovskite solar cells to be a successful solar energy conversion technology.

14 SOLAR ENERGY↗
DOE OSTI2024

Identifying Neutrino Final States and Energies in MicroBooNE with New Deep-Learning Based LArTPC Reconstruction Frameworks

MicroBooNE, a Liquid Argon Time Projection Chamber (LArTPC) located in the $\nu_{\mu}$-dominated Booster Neutrino Beam at Fermilab, has been studying $\nu_{e}$ charged-current (CC) interaction rates to shed light on the MiniBooNE low energy excess. The LArTPC technology employed by MicroBooNE provides the capability to image neutrino interactions with mm-scale precision. Computer vision and other machine learning techniques are promising tools for image processing that could boost efficiencies for selecting $\nu_{e}$-CC and other rare signals, reduce cosmic and beam-induced backgrounds, and improve the reconstruction of neutrino energies. The MicroBooNE experiment has been at the forefront of developing and testing such techniques for use in physics analyses. In this poster we overview deep-learning based reconstruction methods. We will showcase the use of a recurrent neural network to estimate neutrino energies and present a new reconstruction framework that uses convolutional neural networks to locate neutrino interaction vertices, tag pixels with track and shower labels, and perform particle identification on reconstructed clusters. We will present studies characterizing the performance of these new tools and demonstrate their effectiveness through their use in an inclusive $\nu_{e}$-CC event selection.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Mid-IR UAV-based sensing platform with deep learning to Identify and Quantify Gaseous Emission in Gas Flares

This report details the development and evaluation of a Mid-Infrared (Mid-IR) Unmanned Aerial Vehicle (UAV)-based sensing platform integrated with deep learning algorithms for the identification and quantification of gaseous emissions in gas flares. The project, spearheaded by Omega Optics, Inc., aimed to address environmental monitoring challenges by leveraging advanced photonic technologies and autonomous UAV operations. The research focused on designing, optimizing, and fabricating photonic crystal waveguides and grating couplers to enhance the sensitivity and accuracy of gas detection. A comprehensive drone-based system was developed, featuring a miniaturized sensor, GPS module, and microcontroller communication network for real-time gas concentration monitoring. The system's adaptive sampling algorithm, implemented using the Robot Operating System (ROS), enables autonomous detection and localization of gas emission sources. Preliminary results demonstrate the platform's capability to detect and monitor gas emissions with high precision, cost-effectiveness, and scalability. Future work will expand upon this foundation by introducing 3D wind model-based learning for dynamic environmental conditions and further enhancing the user interface and data processing algorithms to support broader environmental monitoring applications. Overall, this project represents a significant step forward in UAV-based environmental sensing technologies, offering robust solutions for detecting and mitigating the impacts of gaseous emissions on public health and safety.

47 OTHER INSTRUMENTATION↗