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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 1,531 records · Page 85
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Science-integrated Artificial-intelligence for Flooding and precipitation Extremes (SAFE)

A grand challenge in hydrologic science is to understand why signals of climate change and variability, which are often visible in precipitation extremes at aggregate scales, are not consistently observed in the case of extreme flooding. However, a solution to this challenge may prove elusive unless the water cycle is viewed in an integrative manner. Thus, for riverine flooding, while Hortonian (infiltration excess) runoff may have stronger correlation with precipitation extremes and hence perhaps to warming trends or climate oscillators, Dunne (saturation excess) runoff may have a more complex relationships with time series of precipitation and with evaporation and transpiration, but rain-on-snow and snowmelt events may depend on land-surface and atmospheric temperatures. Atmospheric rivers and tropical cyclones lead to precipitation or flooding and are impacted by climate. Flooding assessments need to consider long-term baselines, evolving risk factors, coupled natural-human systems, and novel adaptation such as nature-inspired design.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Semi-automated Design of Artificial Intelligence Earth Systems Models

Prediction and observation of water cycles at various scales involve not only patterns isolated in space and time, but also modeling of complex spatio-temporal relationships across multiple domains. For instance, evapotranspiration (ET) and leaf area indexes (LAI) are two parameters that are needed to accurately model and understand land-atmosphere processes. Accurate assessments of ET and LAI are critical for understanding hydrological processes, deforestation, crop yield, and irrigation impacts. However, ET estimates for global simulations are available at very coarse spatial resolution. They are usually derived from satellite data based on broad plant functional types (PFTs), which fail to capture fine-scale variations because of changes in vegetation type across the globe. Similarly LAI estimates have typically been derived from vegetation indices at global scales or estimated locally using physical models, both of which suffer from a range of uncertainties that impact model sensitivity. The new era of AI model development for Earth systems (ES) calls for data-driven methods that provide domain scientists with uncertainty-aware estimations of biophysical parameters such as ET and LAI in a generalizable, interpretable, and discoverable manner.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

In Situ Machine Learning for Intelligent Data Capture on Exascale Platforms. Final Report

In many dynamic systems, interesting events occur locally in time and space. Examples of such systems include ignition events in combustion simulations, material fractures in mechanics simulations, and extreme weather events in climate simulations. Due to memory constraints and data I/O costs, current simulation workflows save data at regularly spaced time-steps, at a fixed rate determined before the start of the simulation. Often this mode of operation results in missed events of interest, necessitating a simulation restart from before an event occurred with more frequent data saves. This data saving workflow is grossly inefficient and is already a bottleneck in the computing process. We propose to develop machine learning algorithms that can detect when interesting dynamical events are occurring, triggering data saves. These machine learning algorithms will perform in situ anomaly detection to flag regions with different dynamical properties than those previously recorded. The adaptive data saves would be local in time and space to match the event of interest, thereby enabling a much more efficient workflow that will reduce data I/O costs and data storage memory requirements. The algorithms will be tested on two applications: auto-ignition simulations and climate simulations. A critical component of this project will be developing machine learning algorithms that can be deployed efficiently in situ on HPC platforms with out-of-the-box functionality. The development of in situ machine learning methods to detect anomalous events would enable a more efficient and effective workflow, in which all the relevant data are saved in a single simulation run, without re-starts or scientist intervention.

42 ENGINEERING↗
DOE OSTI2021

Represent precipitation-induced geological hazards in Earth system models using artificial intelligence

Precipitation-induced geological hazards, such as debris flow, landslides, mudflow and rockfalls (hereafter referred to as landslides), pose serious threats to public safety in many areas through the world. As residential properties and infrastructure in the US have increasingly expanded into landslide-prone areas and the drivers of landslides (e.g., wildfires and hurricanes) are predicted to intensify under climate warming, losses and fatalities from landslides are likely to increase in the future. Although our understanding of geoenvironmental factors and mechanisms contributing to landslides has greatly improved, only moderate progress has been made in predicting landslides out of well-studied watersheds. Furthermore, explicitly representing various landslide-related processes in Earth system models (ESMs), from the buckling of local bearing elements in granular materials, to frictional sliding between grains, formation of microcracks in the soil matrix, rupture of capillary bridges, or breakage of plant roots3 is still very unlikely within the next decade, even with the help of exascale computers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Artificial Intelligence Design of Nuclear Systems

Advanced manufacturing applications in the nuclear industry have the potential to revolutionize nuclear design. While current engineering design objectives may be consistent with objectives from the past, such as those intended to minimize the fuel cost or to limit the maximum fuel temperature for a given power level, the possible geometries which can be considered as candidate designs have changed. Optimizations applied to current designs in nuclear engineering are limited by traditional geometric shapes that are easy to manufacture with traditional processes: slabs as fuel plates, cylinders as fuel rods, spheres as fuel pebbles, axis-aligned coolant channels, and more. Advanced manufacturing allows for an approach to the nuclear design problem with arbitrary geometries that can enable previously unrealized optimizations in performance and cost. This work explores the application of arbitrary geometry to the nuclear engineering design process.

42 ENGINEERING↗
DOE OSTI2021

Machine Learning Guided Operational Intelligence from Synchrophasors (Final Report)

Schweitzer Engineering Laboratories (SEL) and Oregon State University (OSU) received over 27 terabytes of electrical power system phasor measurement unit (PMU) data for the Eastern, Western, and ERCOT interconnections. The dataset includes measurements spread across 446 PMUs from early 2016 to mid 2018 depending on the interconnect. The full dataset was split into a training and test (holdout) dataset by PNNL. All data was received in the Apache Parquet format. The overarching goal of this project is to develop and execute a strategy to mitigate data anomalies, perform analysis on the dataset, and detect anomalous events in the data.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Intelligent Monitoring System for Real-time Geologic Storage, Optimization, and Reservoir Management

The objective of the subtask was to develop a near-real-time monitoring system for seismic data at the Decatur, IL, geologic carbon sequestration (GCS) site and specifically include fiber-optic cable derived distributed acoustic signal (DAS) data in the process. Owing to the large volumes of data, we opted to utilize existing deep borehole conventional seismic sensors for detection and pull DAS and shallow borehole seismic data once a detection has been made. Unfortunately, the horizontal fiber-optic cables did not yield microseismic signals for use in locating events near the GCS site. Various stacking and filtering approaches were tested without any coherent detection becoming apparent. We attribute the insensitivity to local microseismic events to a lack of coupling in downhole, vertical cable and the non-ideal alignment of the horizontal fiber-optic cable to the vertically polarized seismic energy. Despite the inability to detect and utilize the DAS data from the fiber-optic cables, we developed a general processing framework that enables easy adaptation for future deployment of fiber-optic cables with better suited alignment at Decatur or elsewhere.

Kaven, J. Ole↗
DOE OSTI2022

IMoFi - Intelligent Model Fidelity: Physics-Based Data-Driven Grid Modeling to Accelerate Accurate PV Integration (Final Report)

This report summarizes the work performed under a project funded by U.S. DOE Solar Energy Technologies Office (SETO) to use grid edge measurements to calibrate distribution system models for improved planning and grid integration of solar PV. Several physics-based data-driven algorithms are developed to identify inaccuracies in models and to bring increased visibility into distribution system planning. This includes phase identification, secondary system topology and parameter estimation, meter-to-transformer pairing, medium-voltage reconfiguration detection, determination of regulator and capacitor settings, PV system detection, PV parameter and setting estimation, PV dynamic models, and improved load modeling. Each of the algorithms is tested using simulation data and demonstrated on real feeders with our utility partners. The final algorithms demonstrate the potential for future planning and operations of the electric power grid to be more automated and data-driven, with more granularity, higher accuracy, and more comprehensive visibility into the system.

14 SOLAR ENERGY↗
DOE OSTI2023

Research in Advanced Artificial Intelligence Techniques for Modern Accelerator Control

The major goals for the project were to remain in contact with accelerator stewardship customers, develop and test new techniques based to further improve accelerator and peripheral systems, and train early career persons to participate on the project and teach them aspects of the field of accelerator science and technology.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

IMoFi (Intelligent Model Fidelity): Physics-Based Data-Driven Grid Modeling to Accelerate Accurate PV Integration Updated Accomplishments

This report summarizes the work performed under a project funded by U.S. DOE Solar Energy Technologies Office (SETO), including some updates from the previous report SAND2022-0215, to use grid edge measurements to calibrate distribution system models for improved planning and grid integration of solar PV. Several physics-based data-driven algorithms are developed to identify inaccuracies in models and to bring increased visibility into distribution system planning. This includes phase identification, secondary system topology and parameter estimation, meter-to-transformer pairing, medium-voltage reconfiguration detection, determination of regulator and capacitor settings, PV system detection, PV parameter and setting estimation, PV dynamic models, and improved load modeling. Each of the algorithms is tested using simulation data and demonstrated on real feeders with our utility partners. The final algorithms demonstrate the potential for future planning and operations of the electric power grid to be more automated and data-driven, with more granularity, higher accuracy, and more comprehensive visibility into the system.

14 SOLAR ENERGY↗
DOE OSTI2022

A Demonstration of Intelligent Container Surveillance Using Stationary and Mobile Camera Platforms

Surveillance of nuclear material storage containers is required to ensure that the container safety boundary is maintained during the service life of the container. As many of these container types are the first and only containment barrier to release protecting the worker, public, and environment, it is imperative to develop robust surveillance tools to characterize container degradation. Machine learning (ML) techniques have matured quickly in the past decade and are slowly becoming a routine application in data analysis. While more popular architectures have been developed with different applications in mind, these can be easily translated to container surveillance requirements. In this study, we developed a ML model based on the Detectron2 framework to identify slip lid containers and common exterior container defects, primarily dents. Commercial-off-the-shelf cameras are combined into a stationary camera array or are mounted to a robotic arm to provide more mobility for positioning needs. Results from the model evaluations on the images indicate that the stationary camera arrays outperform the accuracy of the mobile camera systems for both individual camera and composite image detections. Overall, however, the detection accuracies of the systems fell short of 50% and were less than satisfactory. Future efforts to improve the system involve focusing on a single camera deployable solution that combines a controllable light source that eliminates one of the biggest environmental factors that influence ML model detection performance. Additional training set collection is planned to build a more robust model that enables accurate detection from a wider range of imaging conditions.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗