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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

deadtrees.earth — An open-access and interactive database for centimeter-scale aerial imagery to uncover global tree mortality dynamics

Excessive tree mortality is a global concern and remains poorly understood as it is a complex phenomenon. We lack global and temporally continuous coverage on tree mortality data. Ground-based observations on tree mortality, e.g., derived from national inventories, are very sparse, and may not be standardized or spatially explicit. Earth observation data, combined with supervised machine learning, offer a promising approach to map overstory tree mortality in a consistent manner over space and time. However, global-scale machine learning requires broad training data covering a wide range of environmental settings and forest types. Low altitude observation platforms (e.g., drones or airplanes) provide a cost-effective source of training data by capturing high-resolution orthophotos of overstory tree mortality events at centimeter-scale resolution. Here, we introduce deadtrees.earth, an open-access platform hosting more than two thousand centimeter-resolution orthophotos, covering more than 1,000,000 ha, of which more than 58,000 ha are manually annotated with live/dead tree classifications. This community-sourced and rigorously curated dataset can serve as a comprehensive reference dataset to uncover tree mortality patterns from local to global scales using space-based Earth observation data and machine learning models. This will provide the basis to attribute tree mortality patterns to environmental changes or project tree mortality dynamics to the future. The open nature of deadtrees.earth, together with its curation of high-quality, spatially representative, and ecologically diverse data will continuously increase our capacity to uncover and understand tree mortality dynamics.

Citizen science↗
DOE OSTI2021

A non-intrusive optical approach to characterize heliostats in utility-scale power tower plants: Flight path generation/optimization of unmanned aerial systems

A newly developed in situ non-intrusive optical (NIO) approach has been developed to survey various types of heliostat optical errors for a concentrating solar power (CSP) tower plant. To measure mirror surface slope error, facet canting error, and heliostat tracking error at a sub-milliradian accuracy, NIO requires several reflection images scanned over each individual heliostat. For a utility-scale plant that typically includes more than 10,000 heliostats, an unmanned aerial system (UAS) is crucial for efficient implementation of the NIO method. Here, we develop a flight path generation/optimization algorithm to plan more efficient UAS paths to collect NIO data over a utility-scale heliostat field. The algorithm considers NIO data requirements, all potential constraints, optimization within each subfield, and operational flexibility. Case studies are presented to illustrate the feasibility and robustness of the developed flight path algorithm. The path planning algorithm may also find applications elsewhere, such as drone-driven imaging under extreme conditions.

14 SOLAR ENERGY↗
DOE OSTI2024

Unlocking Solutions: Innovative Approaches to Identifying and Mitigating the Environmental Impacts of Undocumented Orphan Wells in the United States

In the United States, hundreds of thousands of undocumented orphan wells have been abandoned, leaving the burden of managing environmental hazards to governmental agencies or the public. These wells, a result of over a century of fossil fuel extraction without adequate regulation, lack basic information like location and depth, emit greenhouse gases, and leak toxic substances into groundwater. For most of these wells, basic information such as well location and depth is unknown or unverified. Addressing this issue necessitates innovative and interdisciplinary approaches for locating, characterizing, and mitigating their environmental impacts. Our survey of the United States revealed the need for tools to identify well locations and assess conditions, prompting the development of technologies including machine learning to automatically extract information from old records (95%+ accuracy), remote sensing technologies like aero-magnetometers to find buried wells, and cost-effective methods for estimating methane emissions. Notably, fixed-wing drones equipped with magnetometers have emerged as cost-effective and efficient for discovering unknown wells, offering advantages over helicopters and quadcopters. Efforts also involved leveraging local knowledge through outreach to state and tribal governments as well as citizen science initiatives. These initiatives aim to significantly contribute to environmental sustainability by reducing greenhouse gases and improving air and water quality.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

A Hyphenated Preconcentrator-Infrared-Hollow-Waveguide Sensor System for N 2 O Sensing

Following the Kyoto protocol, all signatory countries must provide an annual inventory of greenhouse-gas emission including N 2 O. This fact associated with the wide variety of sources for N 2 O emissions requires appropriate sensor technologies facilitating in-situ monitoring, compact dimensions, ease of operation, and sufficient sensitivity for addressing such emission scenarios. In this contribution, we therefore describe an innovative portable mid-infrared chemical sensor system for quantifying gaseous N 2 O via coupling a substrate-integrated hollow waveguide (iHWG) simultaneously serving as highly miniaturized mid-infrared photon conduit and gas cell to a custom-made preconcentrator. N 2 O was collected onto a solid sorbent material packed into the preconcentrator unit, and then released via thermal desorption into the iHWG-MIR sensor utilizing a compact Fourier transform infrared (FTIR) spectrometer for molecularly selective spectroscopic detection with a limit of detection (LOD) at 5 ppbv. Highlighting the device flexibility in terms of sampling time, flow-rate, and iHWG design facilitates tailoring the developed preconcentrator-iHWG device towards a wide variety of application scenarios ranging from soil and aquatic emission monitoring and drone- or unmanned aerial vehicle (UAV)-mounted monitoring systems to clinical/medical analysis scenarios.

Science & Technology - Other Topics↗
DOE OSTI2023

Assessing the impact of Byzantine attacks on coupled phase oscillators

Abstract For many coupled dynamical systems, the interaction is the outcome of the measurement that each unit has of the others as e.g. in modern inverter-based power grids, autonomous vehicular platoons or swarms of drones, or it is the result of physical flows. Synchronization among all the components of these systems is of primal importance to avoid failures. The overall operational state of these systems therefore crucially depends on the correct and reliable functioning of the individual elements as well as the information they transmit through the network. Here, we investigate the effect of Byzantine attacks where one unit does not behave as expected, but is controlled by an external attacker. For such attacks, we assess the impact on the global collective behavior of nonlinearly coupled phase oscillators. We relate the synchronization error induced by the input signal to the properties of the attacked node. This allows to anticipate the potential of an attacker and identify which network components to secure.

Tyloo, Melvyn (ORCID:0000000317614095)↗
DOE OSTI2024

Journey over Destination: Dynamic Sensor Placement Enhances Generalization

Reconstructing complex, high-dimensional global fields from limited data points is a challenge across various scientific and industrial domains. This is particularly important for recovering spatio-temporal fields using sensor data from, for example, laboratory-based scientific experiments, weather forecasting, or drone surveys. Given the prohibitive costs of specialized sensors and the inaccessibility of
certain regions of the domain, achieving full field coverage is typically not feasible. Therefore, the development of machine learning algorithms trained to reconstruct fields given a limited dataset is of critical importance. In this study, we introduce a general
approach that employs moving sensors to enhance data exploitation during the training of an attention based neural network, thereby improving field reconstruction. The training of sensor locations is accomplished using an end-to-end workflow, ensuring
differentiability in the interpolation of field values associated to the sensors, and is simple to implement using differentiable programming. Additionally, we have incorporated a correction mechanism to prevent sensors from entering invalid regions within the domain. We evaluated our method using two distinct datasets; the results show that our approach enhances learning, as evidenced by improved test scores.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Binary operations on neuromorphic hardware with application to linear algebraic operations and stochastic equations

Abstract Non-von Neumann computational hardware, based on neuron-inspired, non-linear elements connected via linear, weighted synapses—so-called neuromorphic systems—is a viable computational substrate. Since neuromorphic systems have been shown to use less power than CPUs for many applications, they are of potential use in autonomous systems such as robots, drones, and satellites, for which power resources are at a premium. The power used by neuromorphic systems is approximately proportional to the number of spiking events produced by neurons on-chip. However, typical information encoding on these chips is in the form of firing rates that unarily encode information. That is, the number of spikes generated by a neuron is meant to be proportional to an encoded value used in a computation or algorithm. Unary encoding is less efficient (produces more spikes) than binary encoding. For this reason, here we present neuromorphic computational mechanisms for implementing binary two’s complement operations. We use the mechanisms to construct a neuromorphic, binary matrix multiplication algorithm that may be used as a primitive for linear differential equation integration, deep networks, and other standard calculations. We also construct a random walk circuit and apply it in Brownian motion simulations. We study how both algorithms scale in circuit size and iteration time.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

The Tianlai dish pathfinder array: design, operation, and performance of a prototype transit radio interferometer

ABSTRACT The Tianlai Dish Pathfinder Array is a radio interferometer designed to test techniques for 21 cm intensity mapping in the post-reionization universe as a means for measuring large-scale cosmic structure. It performs drift scans of the sky at constant declination. We describe the design, calibration, noise level, and stability of this instrument based on the analysis of about 5% of 6200 h of on-sky observations through 2019 October. Beam pattern determinations using drones and the transit of bright sources are in good agreement, and compatible with electromagnetic simulations. Combining all the baselines, we make maps around bright sources and show that the array behaves as expected. A few hundred hours of observations at different declinations have been used to study the array geometry and pointing imperfections, as well as the instrument noise behaviour. We show that the system temperature is below 80 K for most feed antennas and that noise fluctuations decrease as expected with integration time, at least up to a few hundred seconds. Analysis of long integrations, from 10 nights of observations of the North Celestial Pole (NCP), yielded visibilities with amplitudes of 20–30 mK, consistent with the expected signal from the NCP radio sky with ${\lt}10\,$ mK precision for 1 MHz × 1 min binning. Hi-pass filtering the spectra to remove smooth spectrum signal yields a residual consistent with zero signal at the $0.5\,$ mK level.

(cosmology:) large-scale structure of the Universe↗
DOE OSTI2019

Single-Net Continual Learning with Progressive Segmented Training

There is an increasing need of continual learning in dynamic systems, such as the self-driving vehicle, the surveillance drone, and the robotic system. Such a system requires learning from the data stream, training the model to preserve previous information and adapt to a new task, and generating a single-headed vector for future inference. Different from previous approaches with dynamic structures, this work focuses on a single network and model segmentation to prevent catastrophic forgetting. Leveraging the redundant capacity of a single network, model parameters for each task are separated into two groups: one important group which is frozen to preserve current knowledge, and secondary group to be saved (not pruned) for a future learning. A fixed-size memory containing a small amount of previously seen data is further adopted to assist the training. Without additional regularization, the simple yet effective approach of Progressive Segmented Training (PST) successfully incorporates multiple tasks and achieves the state-of-the-art accuracy in the single-head evaluation on CIFAR-10 and CIFAR-100 datasets. Moreover, the segmented training significantly improves computation efficiency in continual learning at the edge.

Du, Xiaocong↗
DOE OSTI2024

Mission planning for photogrammetry-based autonomous 3D Mapping of Dams using a commercial UAV

The application of autonomous unmanned aerial vehicles (UAVs) for conducting inspections of dams represents an innovative approach aimed at enhancing safety, efficiency, and cost-effectiveness. In this context, this paper presents algorithms for UAV mission design in autonomous dam inspections that include the creation of a 3D map based on photogrammetry. The algorithms were systematically developed to incorporate a comprehensive set of parameters that account for the geometric characteristics of the dam and adhere to photogrammetry specifications. To validate the proposed methodology, we utilized a commercial programmable quadrotor, specifically the Parrot Anafi USA Gov drone, which is equipped with high-quality cameras and can be programmed with the help of a software development kit (SDK) provided by the manufacturer. Our results demonstrate the efficacy of our method, highlighting how the generated maps can be used for hazard detection in the downstream slope of dams.

42 ENGINEERING↗
DOE OSTI2021

Evolutionary NAS in Light of Model Stability for Accurate Continual Learning

Continual learning, the capability to learn new knowledge from streaming data without forgetting the previous knowledge, is a critical requirement for dynamic learning systems, especially for emerging edge devices such as self-driving cars and drones. However, continual learning is still facing the catastrophic forgetting problem. Previous work illustrate that model performance on continual learning is not only related to the learning algorithms but also strongly dependent on the inherited model, i.e., the model where continual learning starts. The better stability of the inherited model, the less catastrophic forgetting and thus, the inherited model should be elaborately selected. Inspired by this finding, we develop an evolutionary neural architecture search (ENAS) algorithm that emphasizes the Stability of the inherited model, namely ENAS-S. ENAS-S aims to find optimal architectures for accurate continual learning on edge devices. On CIFAR-10 and CIFAR-100, we present that ENAS-S achieves competitive architectures with lower catastrophic forgetting and smaller model size when learning from a data stream, as compared with handcrafted DNNs.

Du, Xiaocong↗
DOE OSTI2021

FPV Video Adaptation for UAV Collision Avoidance

First person view (FPV) technology for unmanned aerial vehicles (UAVs) provides an immersive experience for pilots and enables various personal and commercial applications such as aerial photography, drone racing, search and rescue operations, agricultural surveillance, and structural inspection. While real time video streaming from a UAV and vision-based collision avoidance strategies have been studied in literature as separate topics, in this paper we tackle collision avoidance in FPV scenarios, taking into account network delays and real time video parameters. We present a theoretical model for obstacle collisions that considers the current communication channel conditions, the real time video parameters, and the UAV's position relative to the closest obstacle. A video adaptation algorithm is then designed, using this metric, to tune the FPV video resolution, number of re-transmission attempts, and the modulation scheme to maximize the probability of avoiding collisions. This algorithm also takes into account specific latency constraints of the application. This video algorithm was evaluated in various scenarios and its ability to respond to both distances to the obstacle as well as the communication channel conditions was demonstrated. It was found that, for the considered scenarios, the performance of the proposed adaptive algorithm was, on an average, 58.63% higher than the closest non-adaptive one in terms of maximizing the probability of avoiding collision. Such collision avoidance strategies could be used to make UAV FPV applications safer and more reliable.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Canopy tree mortality depends on the proportion of crown exposed to sunlight, but this effect varies with species' wood density

Abstract Understanding what drives changes in tree mortality as well as the covariates influencing trees' response is a research priority to predict forest responses to global change. Here, we combined drone photogrammetry and ground‐based data to assess the influence of crown exposure to light (relative to total crown area), growth deviations (relative to conspecifics), tree size, and species' wood density (as a surrogate for light‐demanding and shade‐tolerant life‐history strategies) on the mortality of 984 canopy trees in an Amazonterra firmeforest. Trees with lower wood density were less prone to die when their proportion of crown was more exposed to sunlight, but this relationship with relative crown exposure weakened and slightly reversed as wood density increased. Trees growing less than their species average had higher mortality, especially when the species' wood density decreased. The role of wood density in determining the survival of canopy trees under varying light conditions indicates differential responses of light‐demanding versus shade‐tolerant species. Our results highlight the importance of accounting for life‐history strategies, via plant functional types, in vegetation dynamic models aiming to predict forest demography under a rapidly changing climate. Abstract in Spanish is available with online material.

Environmental Sciences & Ecology↗
DOE OSTI2014

Amplify scientific discovery with artificial intelligence

Computing innovations have fundamentally changed many aspects of scientific inquiry. For example, advances in robotics, high-end computing, networking, and databases now underlie much of what we do in science such as gene sequencing, general number crunching, sharing information between scientists, and analyzing large amounts of data. As computing has evolved at a rapid pace, so too has its impact in science, with the most recent computing innovations repeatedly being brought to bear to facilitate new forms of inquiry. Recently, advances in Artificial Intelligence (AI) have deeply penetrated many consumer sectors, including for example Apple’s Siri™ speech recognition system, real-time automated language translation services, and a new generation of self-driving cars and self-navigating drones. However, AI has yet to achieve comparable levels of penetration in scientific inquiry, despite its tremendous potential in aiding computers to help scientists tackle tasks that require scientific reasoning. We contend that advances in AI will transform the practice of science as we are increasingly able to effectively and jointly harness human and machine intelligence in the pursuit of major scientific challenges.

artificial intelligence↗
DOE OSTI2024

Fracture length data for geothermal applications

Fracture lengths govern permeability and are unknowns in geothermal assessment. Along their lengths, fracture widths vary due to growth by linkage. Under the influence of diagenesis, narrow widths seal, breaking porosity continuity and reducing open length. The largest range of widths and thus susceptibility to fill occurs where fractures are linked by narrow segments. Outcrops of a geothermal target, Cambrian Potsdam quartz arenite, contain opening-mode fractures having lengths spanning five orders of magnitude from 0.082 mm to 17.9 m. Combined lengths measured at a range of scales can be described by power laws, but at a given image resolution, lengths are best fit by exponential functions. Owing to preferential sealing of small fractures, open fractures follow exponential functions, but values depend on rules for designating fractures as continuous. En échelon segments, offset 10 mm, are connected by narrow fractures or microfractures (hard linked) not evident on outcrop 1 m-elevation LiDAR or 30 m-height drone images. A rule that identifies where narrow and likely connected segments are located can yield lengths meaningful for flow simulation. Depending on diagenesis, continuity rules can halve or double average and maximum length values. Length values from outcrop for geothermal applications should be adjusted based on wellsite-specific diagenesis information.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

WOMBAT (Windfarm Operations and Maintenance cost-Benefit Analysis Tool) WISDEM® [SWR-21-68]

The windfarm operations and maintenance cost-benefit analysis tool (WOMBAT) is software to simulate the operations and maintenance phase of either onshore or offshore windfarms. WOMBAT is a medium-fidelity tool that is designed to model turbine, cable, and substation failures at the subassembly level, but is flexible enough that individual component or asset-level modeling can also be performed. WOMBAT enables users to model technological improvements, the use of a wide range of service equipment from drone repairs and remote resets to heavy lift vessels and crawler cranes, to analyze cost trends with varying trade-offs in conjunction with energy production, availability and a growing range of metrics. This library provides a tool to simulate the operation and maintenance phase (O&M) of distributed, land-based, and offshore windfarms using a discrete event simulation framework. WOMBAT is written around the SimPy discrete event simulation framework. Additionally, this is supported using a flexible and modular object-oriented code base, which enables the modeling of arbitrarily large (or small) windfarms with as many or as few failure and maintenance tasks that can be encoded. Please note that this is still heavily under development, so you may find some functionality to be incomplete at the current moment, but rest assured the functionality is expanding. With that said, it would be greatly appreciated for issues or PRs to be submitted for any improvements at all, from fixing typos (guaranteed to be a few) to features to testing. Also available at: https://pypi.org/project/wombat/

Cooperman, Aubryn↗
DOE OSTI2022

Distant Observer™ [SWR-12-09]

Distant Observer™ (DO) is an optical measurement tool, designed for parabolic trough solar collectors, that determines reflector slope error, absorber position error, and the combined errors based on images of the receiver-tube reflection taken from different angles. We have demonstrated that DO can provide a measurement accuracy of 0.25 mrad for the slope and receiver position errors. DO has two implementation versions: ground-based and drone-driven. As we pursue efforts to lower the capital and installation costs of parabolic trough solar collectors, it is essential to maintain high optical performance. The Distant Observer™ (DO) tool, developed by engineers at NREL, is a fast and highly accurate tool that provides complete characterization of the performance of the optical components which include the mirror panels and thermal receiver. This tool is very useful for testing both prototype and operational modules. The Distant Observer™ is an optical measurement tool for parabolic solar collectors that measures reflector slope error, absorber position error and the combined errors. Reflector slope errors occur for many reasons including imperfection in structural frame design, manufacturing and assembly. Absorber position error can be caused by poor structural design, poor installation, sag from the absorber weight, or change in the structure over time.

Ihas, Benjamin↗
DOE OSTI2023

OpenCSP v.1.0

SAND2024-08987O OpenCSP analyzes and improves the performance of optical mirrors that reflect sunlight and concentrate it onto a receiver. The algorithms in this library analyze the flow of light from an original source—such as the sun—reflecting off one or more mirrors, and onto a receiver, optical screen, or camera. OpenCSP also analyzes optical systems using other techniques, such as shape or slope comparison. The software calculates aggregate properties of the optical system, such as its efficiency, manufacturing error, pointing control error, expected performance, and other useful parameters. OpenCSP also includes high-level tools, such as Open SOFAST, a program that obtains high-resolution measurement of CSP mirrors, full heliostats, and other CSP collectors. OpenCSP supports producing meaningful output data to help CSP designers and field operators improve the performance of their systems. OpenCSP also includes classes and functions that support these calculations, including input utilities, analysis output libraries, geometric constraint analysis for drone flight planning, and more. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Brost, Randolph↗