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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

A Further Study of the t{sub Burst} of GRBs: Rest-frame Properties, External Plateau Contributions, and Multiple Parameter Analysis

Zhang et al. propose to redefine the true γ-ray burst (GRB) central engine activity duration, t{sub burst}, by considering the contributions from the prompt γ-ray emission, X-ray flare, and internal plateau features. With a comprehensive study of a large sample of Swift GRBs, it is shown that the t{sub burst} distribution in the observer frame consists of a bimodal feature, suggesting the existence of a new population of ultra-long GRBs. In this work, we make a series of further studies on t{sub burst}: we update the Swift GRB sample up to 2016 June; we investigate the properties of t{sub burst} distribution in the rest frame; we redefine t{sub burst} by involving external plateau contributions; and we make a multiple parameter analysis to investigate whether the bursts within the ultra-long population are statistically different in the sense of other features besides the duration distribution. We find that for all situations, the distribution of t{sub burst} requires two normal distributions in logarithmic space to provide a good fit both in the observer frame and in the rest frame. Considering the observational gap effect would not completely erase the bimodal distribution feature. However, the bursts within the ultra-long population may have no statistical difference in the sense of other features besides the duration term. We thus suggest that if the ultra-long population of GRBs indeed exists, their central engine and radiation mechanisms should be similar to those of the normal population, but they have a longer central engine activity timescale.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Investigation of the role of deposition rate on optical, microstructure and ethanol sensing characteristics of nanostructured Sn doped In2O3 films

Highlights: • Deposition rate can play an important role in gas sensing of In{sub 2}O{sub 3}:Sn films. • The crystallite size and linear defects of the films is estimated by eCMWP method. • The correlation between the optical, microstructure and gas sensing properties is studied. • The size distribution behavior of samples is in agreement with the results of porosity. • Optimum deposition rate for In{sub 2}O{sub 3}:Sn films is 0.2 nm/s. - Abstract: In this paper, Sn doped In{sub 2}O{sub 3} (In{sub 2}O{sub 3}:Sn) nanostructured films as metal oxide semiconductor material which is very important in optoelectronics and gas sensing industries, were prepared by thermal evaporation technique. It is discussed how different deposition rate (0.1, 0.2, 0.3 and 0.4 nm/s) affects the optical, electrical, microstructural and ethanol gas sensing characteristics of In{sub 2}O{sub 3}:Sn films and must be precisely monitored. The gas sensing performance of the films was evaluated in terms of gas concentration and operating temperature. A correlation between the crystallite size, porosity, dislocation density, outer cut-off radius of dislocation, optical band gap, activation energy and sensitivity of the films was established. The results demonstrate that the maximum sensitivity to ethanol vapors can be obtained for the sample grown at deposition rate of 0.2 nm/s. The response and recovery times of this sensor exposed to 100 ppm of ethanol vapors were determined and found to be 9.0 s and 3.9 s, respectively.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Snow Distribution Patterns Revisited: A Physics-Based and Machine Learning Hybrid Approach to Snow Distribution Mapping in the Sub-Arctic

Snowpack distribution in Arctic and alpine landscapes often occurs in repeating, year-to-year patterns due to local topographic, weather, and vegetation characteristics. Previous studies have suggested that with years of observational data, these snow distribution patterns can be statistically integrated into a snow process modeling workflow. Recent advances in snow hydrology and machine learning (ML) have increased our ability to predict snowpack distribution using in-situ observations, remote sensing data sets, and simple landscape characteristics that can be easily obtained for most environments. Here, we propose a hybrid approach to couple a ML snow distribution pattern (MLSDP) map with a physics-based, snow process model. We trained a random forest ML algorithm on tens of thousands of snow survey observations from a subarctic study area on the Seward Peninsula, Alaska, collected during peak snow water equivalent (SWE). We validated hybrid model outputs using in-situ snow depth and SWE observations, as well as a light detection and ranging data set and a distributed temperature profiling sensor data set. When the hybrid results were compared with the physics-based method, the hybrid method more accurately depicted the spatial patterns of the snowpack, areas of drifting snow, and years when no in-situ observations were used in the random forest ML training data set. The hybrid method also showed improvements in root mean squared error at 61% of locations where time-series estimations of snow depth were observed. These results can be applied to any physics-based model to improve the snow distribution patterning to reflect observed conditions in high latitude and high elevation cold region environments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Characterizing and Understanding Aerosol Optical Properties: CARES - Final Report

The scientific focus of this study was to use ambient measurements to develop new insights into the understanding of the direct radiative forcing by atmospheric aerosol particles. The study used data collected by the PI’s and others as part of both the 2010 U.S. Department of Energy (DOE) sponsored Carbonaceous Aerosols and Radiative Effects Study (CARES), which took place in and around Sacramento, CA, and the 2012 Clean Air for London (ClearfLo) study. We focus on measurements that were made of aerosol particle optical properties, namely the wavelength-dependent light absorption, scattering and extinction. Interpretation of these optical property measurements is facilitated through consideration of complementary measurements of the aerosol particle chemical composition and size distributions. With these measurements, we addressed the following general scientific questions: 1. How does light scattering and extinction by atmospheric aerosol particles depend on particle composition, water uptake, and size? 2. To what extent is light absorption by aerosol particles enhanced through the mixing of black carbon with other particulate components? 3. What relationships exist between intensive aerosol particle optical properties, and how do these depend on particle source and photochemical aging? 4. How well do spectral deconvolution methods, which are commonly used in remote sensing, retrieve information about particle size distributions?

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Femtosecond laser fabrication of nanograting-based distributed fiber sensors for extreme environmental applications

Abstract The femtosecond laser has emerged as a powerful tool for micro- and nanoscale device fabrication. Through nonlinear ionization processes, nanometer-sized material modifications can be inscribed in transparent materials for device fabrication. This paper describes femtosecond precision inscription of nanograting in silica fiber cores to form both distributed and point fiber sensors for sensing applications in extreme environmental conditions. Through the use of scanning electron microscope imaging and laser processing optimization, high-temperature stable, Type II femtosecond laser modifications were continuously inscribed, point by point, with only an insertion loss at 1 dB m −1 or 0.001 dB per point sensor device. High-temperature performance of fiber sensors was tested at 1000 °C, which showed a temperature fluctuation of ±5.5 °C over 5 days. The low laser-induced insertion loss in optical fibers enabled the fabrication of a 1.4 m, radiation-resilient distributed fiber sensor. The in-pile testing of the distributed fiber sensor further showed that fiber sensors can execute stable and distributed temperature measurements in extreme radiation environments. Overall, this paper demonstrates that femtosecond-laser-fabricated fiber sensors are suitable measurement devices for applications in extreme environments.

Wang, Mohan (ORCID:0000000166784993)↗
DOE OSTI2018

Statistical Theory on the Functional Form of Cloud Particle Size Distributions

Abstract Several functional forms of cloud particle size distributions (PSDs) have been used in numerical modeling and remote sensing retrieval studies of clouds and precipitation, including exponential, gamma, lognormal, and Weibull distributions. However, there is no satisfying theoretical explanation as to why certain distribution forms preferentially occur instead of others. Intuitively, the analytical form of a PSD can be derived by directly solving the general dynamic equation, but no analytical solutions have been found yet. Instead of a process-level approach, the use of the principle of maximum entropy (MaxEnt) for determining the theoretical form of PSDs from the perspective of system is examined here. MaxEnt theory states that the probability density function with the largest information entropy among a group satisfying the given properties of the variable should be chosen. Here, the issue of variability under coordinate transformations that arises using the Gibbs–Shannon definition of entropy is identified, and the use of the concept of relative entropy to avoid these problems is discussed. Focusing on cloud physics, the four-parameter generalized gamma distribution is proposed as the analytical form of a PSD using the principle of maximum (relative) entropy with assumptions on power-law relations among state variables, scale invariance, and a further constraint on the expectation of one state variable (e.g., bulk water mass). The four-parameter generalized gamma distribution is very flexible to accommodate various type of constraints that could be assumed for cloud PSDs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Multi-Functional Distributed Fiber Sensors for Pipeline Monitoring and Methane Detections. Final Report

As an abundant and cheap fossil energy source, natural gas has become a significant energy supply to support the United States’ economy. However, the large-scale extraction and utilization of natural gas also impose significant challenges on methane leakage. This problem is exacerbated by aging gas utility delivery systems, including interstate high-pressure pipelines, storage, and transmission facilities. This project aims to develop a cost-effective fiber optical sensing method that can perform multi-parameter real-time measurements of natural gas pipelines across long interrogation distances up to 100 km with 1-meter spatial resolution. This sensing tool can evaluate overall pipeline efficiency and reduce methane emissions for mid-stream methane infrastructures. To accomplish this objective, research and development efforts funded by this project have resulted in the following accomplishments: This project successfully has developed new functional sensory polymer materials using Metal-Organic Frameworks (MOFs) that can be coated on optical fiber through the reel-to-reel coating process. Functional polymer-coated optical fibers can perform sensitive methane detection through evanescence wave interaction and strain-based measurements to achieve 1% detection sensitivities. The new sensors fibers support both distributed measurements and multiplexed fiber sensors array for multi-point measurements. This project developed and optimized a new multi-core optical fiber that supports simultaneous and distributed measurements of strain and temperatures with 1-meter spatial resolutions across up to 100-km interrogation distance. This new fiber, combined with sensory polymercoated fiber, could perform both distributed temperature and methane detections. This project developed a new artificial intelligence big data algorithm approach that can effectively analyze high-resolution data harnessed by distributed fiber sensors to protect natural gas pipelines against external threats and detect internal defects induced by corrosion. Working with our industry partner, this project developed new optical fibers that support fiber sensor fabrications through polymer coating after the fibers are drawn. These new fibers eliminate the need for direct sensor fabrication when the fiber is fabricated on a fiber draw tower, which drastically expands fiber sensors' applicability. This research project has significantly advanced the distributed fiber sensing technology. It will dramatically increase the applicability and adaptability of distributed fiber sensors for a wide array of applications in energy, sustainability, and environmental science, including structural health monitoring of natural gas pipelines, oil infrastructures, hydrogen facilities, and environmental monitoring of carbon storage sites, water supply systems, and others.

03 NATURAL GAS↗
DOE OSTI2021

Survey on stochastic distribution systems: A full probability density function control theory with potential applications

Complex systems seen either in general engineering practice or economics are subjected to ever increased uncertainties that are mostly represented as random variables or parameters, and the characteristics of random variables are represented by their probability density functions (PDFs). Controlling their PDFs means to shape their stochastic distributions and in general it would provide a full treatment for system analysis and operational control and optimization. This leads to the development of stochastic distribution control (SDC) systems theory in the past decades, where the original aim of the controller design is to realize a shape control of the distributions of certain random variables in their PDFs sense for some engineering processes. Indeed, once the PDFs of these random variables or parameters are used to describe their distribution characters, the control task is to obtain control signals so that the output PDFs of stochastic systems are made to follow their target PDFs. The subject of SDC was initially originated for non-Gaussian stochastic control systems design but has found a wide spectrum of applications in general systems in terms of data-driven modeling, analysis, signal processing (filtering), data mining via multivariable statistics, decision-making (optimization) for systems subjected to uncertainties and even in economics. In this context, SDC constitutes an effective primer tool for complex system analysis, control and operational optimizations. In this review paper, a detailed survey of the developments on the research of SDC systems will be made together with their wide spectrum applications and future perspectives.

42 ENGINEERING↗
DOE OSTI2023

Utah FORGE 3-2417: Fiber-Optic Geophysical Monitoring of Reservoir Evolution - Workshop Presentation

This is a presentation on the Fiber-Optic Geophysical Monitoring of Reservoir Evolution at the Utah FORGE Milford Site project by Rice University, presented by Prof. Jonathan Ajo-Franklin. The project's objective was to develop an end-to-end fiber-optic sensing approach for EGS to track the initial zone of fracture creation, zones of connected mechanically compliant fractures, and zones of flowing fractures linking the injection/production well pair. This approach would also be used to integrate data into an improved thermo-hydro-mechanical model (THM). This presentation was featured in the Utah FORGE R&D Annual Workshop on September 8, 2023. The workshop provided a valuable opportunity to explore the progress made in each of the 17 Research and Development projects funded under Solicitation 2020-1 which aim to enhance our understanding of the crucial factors influencing the development of Enhanced Geothermal Systems (EGS) reservoirs and resources.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Deep learning models map rapid plant species changes from citizen science and remote sensing data

Anthropogenic habitat destruction and climate change are reshaping the geographic distribution of plants worldwide. However, we are still unable to map species shifts at high spatial, temporal, and taxonomic resolution. Here, we develop a deep learning model trained using remote sensing images from California paired with half a million citizen science observations that can map the distribution of over 2,000 plant species. Our model— Deepbiosphere— not only outperforms many common species distribution modeling approaches (AUC 0.95 vs. 0.88) but can map species at up to a few meters resolution and finely delineate plant communities with high accuracy, including the pristine and clear-cut forests of Redwood National Park. These fine-scale predictions can further be used to map the intensity of habitat fragmentation and sharp ecosystem transitions across human-altered landscapes. In addition, from frequent collections of remote sensing data, Deepbiosphere can detect the rapid effects of severe wildfire on plant community composition across a 2-y time period. These findings demonstrate that integrating public earth observations and citizen science with deep learning can pave the way toward automated systems for monitoring biodiversity change in real-time worldwide.

Gillespie, Lauren E.↗
DOE OSTI2025

Brillouin Sensing with PCA, and PCA-Based Neural Networks for Efficient Temperature Monitoring

This work explores peak estimation techniques in Brillouin Optical Time Domain Analysis (BOTDA), emphasizing both accuracy and efficiency. Euclidean distance measurement method is applied to principal components derived from Brillouin Gain Spectrum data. It offers a major speed advantage being 180 170 times faster than traditional curve fitting methods such as Lorentzian curve fitting, while maintaining similar accuracy. Additionally, a PCA- based neural network model shows significant reduction of peak estimation time compared to Lorentzian fitting. Results show Brillouin frequency shift errors lie under 0.75 MHz in both Euclidean distance-based and neural network-based methods, both of which utilize PCA components. For large data sets and long length fibers, PCA- assisted neural network for peak estimation would be an efficient solution.

Distributed optical fiber sensing↗
DOE OSTI2025

Deep Neural Network Assisted Distributed Strain and Temperature Fiber Sensor System for Natural Gas Pipeline Monitoring

Natural gas pipeline integrity monitoring is crucial to detect potential leaks, find structural issues, and prevent environmental damage. This article presents a system of natural gas pipeline monitoring that uses a specialized double Brillouin peak sensing fiber along with the Brillouin optical time domain analysis (BOTDAs) technique. The calibrated sensing fiber coefficients for strain and temperature are 41.8 kHz/ με and 0.9 MHz/°C for peak 1; and 47.2 kHz/ με , and 1.11 MHz/°C for peak 2, respectively. Initially, lab tests were performed by installing a short section of double Brillouin peak fiber (DBPF) on a 1-in steel pipe under pressure up to 1000 per square inch (psi) at elevated temperatures. Simultaneous distributed measurements of temperature and pressure-induced hoop strain were successfully measured. Considering the long processing speed to extract Brillouin frequency shift (BFS), we employ a novel probabilistic deep neural network (PDNN) framework for rapid BFS prediction. Additionally, using the Finite Element Method, the effects of the pipeline pressure on hoop strain were modeled and compared to the experimental hoop strain under the same set of pipeline conditions. Finally, an actual 4-in outer diameter steel natural gas pipeline was used for pilot-scale tests, where hoop strain was measured at various pressure levels. Leaks were simulated to demonstrate accurate pipeline integrity monitoring. At an internal pipe pressure of 1000 psi, hoop strain of approximately 300 με was observed, and the sensitivity was calculated as 0.28 με /psi. The results of this pilot-scale study demonstrated that the system is capable of performing distributed monitoring sufficient to detect pipeline pressure and the presence of leaks to ensure the safe operation of gas pipelines in the field.

03 NATURAL GAS↗
DOE OSTI2011

Convective Signals from Surface Measurements at ARM Tropical Western Pacific Site: Manus

The Madden-Julian Oscillation (MJO) signal has been detected using observations from the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility (ACRF) Tropical Western Pacific (TWP). With downwelling shortwave radiative fluxes and fractional sky cover from the ACRF TWP Manus site, and the statistical tools of wavelet and spectrum power, we report finding major convective signals from surface observations spanning the period from 1996 to 2006. Our findings are confirmed with the satellite-retrieved values of precipitation from the Global Precipitation Climatology Project (GPCP), and interpolated outgoing longwave radiation (OLR) satellite measurements from the National Oceanic and Atmospheric Administration (NOAA) for the same location. Our results indicate that the MJO convective signal has a strong seasonal-to-interannual evolution that is likely correlated with the interannual variability of El Ni ˜no Southern Oscillation (ENSO).

Wang, Yi↗
DOE OSTI2022

Real-Time Distribution System State Estimation with Asynchronous Measurements

We report state estimation is a fundamental task in power systems. Although distribution systems are increasingly equipped with sensing devices and smart meters, measurements are typically reported at different rates and asynchronously; these aspects pose severe strains on workhorse state estimation algorithms, which are designed to process batches of data collected in a synchronous manner from all the measurement units. In this paper, we develop a novel state estimation algorithm to continuously update the estimate of the state based on measurements received in an asynchronous manner from measurement units. The synthesis of the algorithm hinges on a proximal-point type method, implemented in an online fashion, and capable of processing measurements received sequentially from sensors. A performance analysis is presented by providing bounds on the estimation error in terms of the mean and variance that hold at each iteration and asymptotically. The scheme is also compared with a more traditional Weighted Least Squares estimator that compensates for the lack of measurement data by using, as pseudo measurements, the measurement retrieved during a certain time window. Numerical simulations on the IEEE 37-bus feeder corroborate the analytical findings.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Voltage regulation in distribution grids: A survey

Environmental and sustainability concerns have caused a recent surge in the penetration of distributed energy resources into the power grid. This may lead to voltage violations in the distribution systems making voltage regulation more relevant than ever. Owing to this and rapid advancements in sensing, communication, and computation technologies, the literature on voltage control techniques is growing at a rapid pace in distribution networks. In particular, there is a paradigm shift from traditional offline centralized approaches to distributed ones leveraging increased and varied types of actuators, real-time sensing, fast and efficient computations, and an overall distributed situational awareness. This paper reviews state-of-the-art voltage control algorithms, summarizes the underlying methods, and classifies their coordination mechanisms into local, centralized, distributed, and decentralized. The underlying solution methodologies are further classified into two categories, open-loop and feedback-based. Two specific example workflows are provided to illustrate these solutions for voltage regulation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Performance analysis of functionally graded multifunctional piezoelectric energy harvesting microgyroscopes

Functionally graded materials (FGMs) are composite materials with varying material properties in one or more directions. These materials possess distinct characteristics compared to their constituent components. The ability to control material distribution and compositions in FGMs offers improved constraints over the natural frequencies of the system, making them highly suitable for energy harvesting applications. In this study, we focus on a piezoelectric FGM composed of Platinum (Pt) and Lead Titanate Zirconate (PZT). By utilizing FGMs for energy harvesting, we simplify the system from a multilayer structure to a single-layer system, thereby increasing power density by reducing volume. In this work, a power law distribution is used to model the material variation throughout the thickness of the single-layered beam. The governing equations of motion and boundary conditions for FG energy harvesting microgyroscope are derived using Euler-Bernoulli beam theory, the constitutive piezoelectric principle, and the extended Hamilton's principle. To simulate the nonlinear motion of the FG energy harvester, we employ the differential quadrature method (DQM). Subsequently, an investigation is carried out to examine the effects of various factors including material distribution, platinum percentage, base rotation, DC voltage, and electrical load resistance on the energy harvesting microgyroscope with functionally graded materials. Here, the findings suggest that functionally graded energy harvesting gyroscopes can be adjusted to obtain effective energy harvesting and sensing capabilities by carefully selecting the material distribution, input voltage, and electrical load resistance.

42 ENGINEERING↗
DOE OSTI2018

Global gridded anthropogenic emissions inventory of carbonyl sulfide

Atmospheric carbonyl sulfide (COS or OCS) is the most abundant sulfur containing gas in the troposphere and is an atmospheric tracer for the carbon cycle. Gridded inventories of global anthropogenic COS are used for interpreting global COS measurements. However, previous gridded anthropogenic data are a climatological estimate based on input data that is over three decades old and are not representative of current conditions. Here we develop a new gridded data set of global anthropogenic COS sources that includes more source sectors than previously available and uses the most current emissions factors and industry activity data as input. Additionally, the inventory is provided as annually varying estimates from years 1980–2012 and employs a source specific spatial scaling procedure. We estimate a global source in year 2012 of 406 Gg S y -1 (range of 223–586 Gg S y -1 ), which is highly concentrated in China and is twice as large as the previous gridded inventory. Our large upward revision in the bottom-up estimate of the source is consistent with a recent top-down estimate based on air-monitoring and Antarctic firn data. Furthermore, our inventory time trends, including a decline in the 1990's and growth after the year 2000, are qualitatively consistent with trends in atmospheric data. Lastly, similarities between the spatial distribution in this inventory and remote sensing data suggest that the anthropogenic source could potentially play a role in explaining a missing source in the global COS budget.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Typical occupancy profiles and behaviors in residential buildings in the United States

The energy performance of a residential building is highly dependent on occupant's presence or non-presence in a building and their interactions with energy-consuming appliances. Typical occupancy schedules for residential buildings must be defined for applications such as building energy modeling as well as for assessing energy savings associated with the use of occupancy sensing technologies and occupancy-dependent controls. Currently, commonly used simulation programs assume a typical occupancy schedule, however, there is significant opportunity for improvement to these schedules as this is generally based on engineering judgement. This research uses 12 years of the American Time Use Survey (ATUS) data to develop typical occupancy schedules for a range of household types and occupant age ranges. This is compared to currently utilized residential occupancy schedules. In many cases the developed schedules exhibit similar patterns, however, differences are also found to be as high as 41% for certain periods of time. For occupancy sensing applications, the spatial-temporal distribution of occupants in residential buildings is also evaluated. These locations vary based on temporal factors as well as demographic factors such as age and number of occupants. Finally, the results of this research work towards improved occupancy schedule development can benefit both industry professionals and researchers.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗