SEARCH · Engineering Papers
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
A two-mode squeezed microresonator-based frequency comb is demonstrated with CMOS-compatible silicon nitride integrated photonic circuits. Seventy quantum modes, in a span of 1.3 THz, are generated in an integrated Kerr microresonator at telecommunication wavelengths.
The ability for quantum and conventional networks to operate in the same optical fibers would aid the deployment of quantum network technology on a large scale. Quantum teleportation is a fundamental operation in quantum networking, but has yet to be demonstrated in fibers populated with high-power conventional optical signals. Here we report, to the best of our knowledge, the first demonstration of quantum teleportation over fibers carrying conventional telecommunications traffic. Quantum state transfer is achieved over a 30.2-km fiber carrying 400-Gbps C-band classical traffic with a Bell state measurement performed at the fiber’s midpoint. To protect quantum fidelity from spontaneous Raman scattering noise, we use optimal O-band quantum channels, narrow spectro-temporal filtering, and multi-photon coincidence detection. Fidelity is shown to be well maintained with an elevated C-band launch power of 18.7 dBm for the single-channel 400-Gbps signal, which we project could support multiple classical channels totaling many terabits/s aggregate data rates. These results show the feasibility of advanced quantum and classical network applications operating within a unified fiber infrastructure.
Two-dimensional materials have emerged as promising candidates to augment existing optical networks for metrology, sensing, and telecommunication, both in the classical and quantum mechanical regimes. Here, we review the development of several on-chip photonic components ranging from electro-optic modulators, photodetectors, bolometers, and light sources that are essential building blocks for a fully integrated nanophotonic and quantum photonic circuit.
The emergence of silicon photonics over the past two decades has established silicon as a preferred substrate platform for photonic integration. While most silicon-based photonic components have so far been realized in the near-infrared (near-IR) telecommunication bands, the mid-infrared (mid-IR, 2–20-μm wavelength) band presents a significant growth opportunity for integrated photonics. In this review, we offer our perspective on the burgeoning field of mid-IR integrated photonics on silicon. A comprehensive survey on the state-of-the-art of key photonic devices such as waveguides, light sources, modulators, and detectors is presented. Furthermore, on-chip spectroscopic chemical sensing is quantitatively analyzed as an example of mid-IR photonic system integration based on these basic building blocks, and the constituent component choices are discussed and contrasted in the context of system performance and integration technologies.
Integrating the large, subpicosecond nonlinear optical response of epsilon-near-zero (ENZ) materials with the broad design freedoms of plasmonic metasurfaces shows potential for creating rapidly modulated optical devices with possible applications in telecommunications, sensing, and reactive beam steering. In this work, we experimentally investigate a metasurface consisting of a plasmonic gradient array on a thin layer of indium tin oxide (ITO), characterize how incident probe pulses diffract from a system as it is being dynamically modulated by a pump pulse at wavelengths near the ENZ region. Angular shifts in the diffraction orders are observed and can be principally attributed to the adiabatic wavelength conversion of the probe as it witnesses the temporal change of index induced by the pump. Of note, the asymmetric gradient metasurface, considered to be a blazed diffraction grating, shows significantly different dynamic responses for different diffraction orders. The free-space wavelength shift to +1 and -1 diffraction orders is 6 and 12 nm, resulting in steering angle changes of 0.65 and 1.5°, respectively.
Demand for abundant and diverse resources in the oceans is growing, necessitating marine spatial planning. To inform development of Marine Hydrokinetic (MHK) and Offshore Wind (OSW) resources, the Department of Energy (DOE) has asked NREL to identify the competing uses areas between promising MHK/OSW sites and submarine power and telecommunications cables. The first step in this work is to identify and quantify the overlap between the MHK/OSW resource availability and existing cable routes. The analysis is done in terms of resource area because the task of quantifying actual impacts on available resource is a non-trivial undertaking that involves subjective decisions of identifying resource opportunities. Quantifying overlap in-terms of resource area?on the other hand?is significantly more straight forward, and useful to marine spatial planners. This dataset of polygons describes recommended setback areas from submarine cables for marine renewable energy Development. Two archive files are provided: 1. NREL Cables Setback-Polygons.zip contains the following GeoJSON files: 1.a. cables_100ft.geojson: The Code of Federal Regulations (CFR 585.301) specifies that the legal right of way for submarine cables is 100 ft (~30 meters) to either side of the cable (i.e., 200 ft wide). 1.b. cables_2z.geojson: The International Cable Protection Committee (ICPC) of the North American Submarine Cable Association (NASCA) recommends setback distances for new facilities as the maximum of 500 m or twice the bottom depth (2z), per ICPC Recommendation 13 No. 2 (Communications Security, Reliability and Interoperability Council IV 2014). For depths 250 m, 2 * depth is to be used. 1.c. cables_3z.geojson: The ICPC of NASCA recommends setback distances for new cables as the maximum of 500 m or three times the bottom depth (3z), per ICPC Recommendation 2 No. 10 (Communications Security, Reliability and Interoperability Council IV 2014). So for depths 167 m, 3 * depth is to be used. 2. NREL Submarine Cable Analysis Code Data Presentations.zip contains all files (code, data, README, etc) from release 0.1 of this Github repository for possibly regenerating the product as submarine cable configurations change: https://github.com/ecoquants/nrel-cables. For more details, please see the link to the full report for which this data product is a supplemental output: Submarine Cable Analysis for U.S. Marine Renewable Energy Development by Ben Best and Levi Kilcher National Renewable Energy Laboratory
Cryosphere/Ocean Distributed Acoustic Sensing (CODAS) data collected from the Beaufort Sea, Alaska, using ~37.4 km of dark telecommunications fiber located at Oliktok Point, Alaska. Data were collected with a Silixa iDAS, using 10 m gauge length, 2 m spatial resolution, and 1000 Hz sample rate. Provided here are the DAS-recorded time series for the rapid refreeze event described in Baker & Abbott (2022) (see link below). This covers a date range of 2021-11-10 15:00 UTC to 2021-11-11 17:00 UTC. Data have been decimated to 100 Hz and 20 m (i.e., every 10th channel for 1831 channels, total), as used in Baker & Abbott (2022). Data have been extracted from raw format into 1-hour long .sac* files and organized into directories by channel number, spanning channels 100 to 18400. Time series units are nano-strainrate (nm/m/s). For distribution, data have been compressed into .zip files containing all time series files for 100 channels. *For information on the Seismic Analysis Code (SAC) file format: https://seiscode.iris.washington.edu/projects/sac
Abstract There is growing interest in floating offshore wind turbine (FOWT) technology, where turbines are installed on floating structures anchored to the seabed, allowing wind energy development in areas unsuitable for traditional fixed-platform turbines. Responsible development requires monitoring the impact of FOWTs on marine wildlife, such as whales, throughout the operational lifecycle of the turbines. Distributed acoustic sensing (DAS)—a technology that transforms fiber-optic cables into vibration sensor arrays—has been demonstrated for acoustic monitoring of whales using seafloor telecommunications cables. However, no studies have yet evaluated DAS performance in dynamic, engineered environments, such as floating platforms or moving vessels with complex, dynamic strain loads, despite their relevance to FOWT settings. This study addresses that gap by deploying DAS aboard a boat in Monterey Bay, California, where a fiber-optic cable was lowered using a weighted and suspended mooring line, enabling vertical deployment. Humpback whale vocalizations were captured and identified in the DAS data, noise sources were identified, and DAS data were compared to audio captured by a standalone hydrophone attached to the mooring line and a nearby hydrophone on a cabled observatory. This study is unique in: (1) deploying DAS in a vertical deployment mode, where noise from turbulence, cable vibrations, and other sources posed additional challenges compared to seafloor DAS applications; (2) demonstrating DAS in a dynamic, nonstationary setup, which is uncommon for DAS interrogators typically used in more stable environments; and (3) leveraging looped sections of the cable to reduce the noise floor and mitigate the effects of excessive cable vibrations and strain. This research demonstrates DAS’s ability to capture whale vocalizations in challenging environments, highlighting its potential to enhance underwater acoustic monitoring, particularly in the context of renewable energy development in offshore environments.
Distributed acoustic sensing (DAS) systems, which use existing telecommunication fibers, offer high‐resolution capabilities ideal for recording anthropogenic sources. However, the complexity of urban environments and the large amount of data recorded by DAS require automated methods to efficiently detect and categorize anthropogenic sources. Here, we evaluate how well three machine learning models (k‐nearest neighbor [k‐NN], convolutional neural networks, and recurrent‐convolutional neural networks) can identify various anthropogenic sources recorded by DAS. Our findings reveal that both k‐NN and neural network methods perform well in high signal‐to‐noise ratio (SNR) settings. However, their accuracy decreases at SNRs <4. We also use Kalman filtering, a form of Bayesian inference, on backprojected locations of these sources to recover locations that generally fall within standard smartphone Global Positioning System errors. By combining machine learning and Kalman filter results, we calculate a multidimensional model of moving anthropogenic sources. These results demonstrate the potential of DAS data in urban seismology for accurately identifying and locating such sources. Depending on the research objectives, these sources can be further studied or filtered out to improve the quality of seismic data for earthquake studies. Such methods provide a valuable tool for urban seismology and seismic hazard analysis.
Beryllium is a strategic metal, indispensable for national defense programs in aerospace, telecommunications, electronics, and weaponry. Exposure to beryllium is an extensively documented occupational hazard that causes irreversible, debilitating granulomatous lung disease in as much as 3 - 5% of exposed workers. Mechanistic research on beryllium exposure-disease relationships has been severely limited by a general lack of a sufficient CBD animal model. We have now developed and tested an animal model which can be used for dissecting dose-response relationships and pathogenic mechanisms and for testing new diagnostic and treatment paradigms. We have created 3 strains of transgenic mice in which the human antigen-presenting moiety, HLA-DP, was inserted into the mouse genome. Each mouse strain contains HLA-DPB1 alleles that confer different magnitude of risk for chronic beryllium disease (CBD): HLA-DPB1*0401 (odds ratio = 0.2), HLA-DPB1*0201 (odds ratio = 15), HLA-DPB1*1701 (odds ratio = 240). Our preliminary work has demonstrated that the *1701 allele, as predicted by human studies, results in the greatest degree of sensitization in a mouse ear swelling test. We have also completed dose-response experiments examining beryllium-induced lung granulomas and identified susceptible and resistant inbred strains of mice (without the human transgenes) as well as quantitative trait loci that may contain gene(s) that modify the immune response to beryllium. In this grant application, we propose to use the transgenic and normal inbred strains of mice to identify biomarkers for the progression of beryllium sensitization and CBD. To achieve this goal, we propose to compare the sensitivity and accuracy of the lymphocyte proliferation test (blood and bronchoalveolar lavage fluid) with the ELISPOT test in the three HLA-DP transgenic mice strains throughout a 6 month treatment with beryllium particles. Because of the availability of high-throughput proteomics, we will also identify changes in potential protein biomarkers in beryllium-treated mice. We will correlate these findings with the ability of the transgenic mice to develop a beryllium-specific adaptive immune response in blood and bronchoalveolar lavage (BAL) fluid. We will also determine whether beryllium-responsive CD4+ T cells in blood and BAL correlate with the onset of granuloma formation. Thus, we will provide the scientific community with biomarkers of sensitization and disease progression for CBD. These biomarkers will serve as critical tools for development of improved industrial hygiene and therapeutic interventions.
The purpose of the Institutional Computing Urban EMP project is to simulate NUDET (nuclear detonation) in an urban environment, and determine what forensic information can be obtained from the resulting EMP (electromagnetic pulse) produced by the detonation. The effect of EMP on telecommunications and power infrastructure is also being studied.
The goal of this project was to study robust connectivity and flow patterns of complex multi-scale systems modeled as networks. Networks provide effective ways to study global, system level properties, as well as local, multi-scale interactions at a component level. Numerous applications from power systems, telecommunication, transportation, biology, social science, and other areas have benefited from novel network-based models and their analysis. Modeling and optimization techniques that employ appropriate measures of risk for identifying robust clusters and resilient network designs in networks subject to uncertain failures were investigated in this collaborative multi-university project. In many practical situations one has to deal with uncertainties associated with possible failures of network components, thereby affecting the overall efficiency and performance of the system (e.g., every node/connection has a probability of partial or complete failure). Some extreme examples include power grid component failures, airline hub failures due to weather, or freeway closures due to emergencies. These are also situations in which people, materials, or other resources need to be managed efficiently. Important practical examples include rerouting flow through power grids, adjusting flight plans, and identifying routes for emergency services and supplies, in the event network elements fail unexpectedly. Solutions that are robust under uncertainty, in addition to being economically efficient, are needed. This project has led to the development of novel models and methodologies that can tackle the optimization problems arising in such situations. A number of new concepts, which have not been previously applied in this setting, were investigated in the framework of the project. The results can potentially help decision-makers to better control and identify robust or risk-averse decisions in such situations. Formulations and optimal solutions of the considered problems need to capture uncertainty and risk using appropriate probabilistic, statistical and optimization concepts. The main difficulty arising in addressing these issues is the dramatic increase in the computational complexity of the resulting optimization problems. This project studied novel models and methodologies for risk-averse network optimization- specifically, network design, network flows and cluster detection problems under uncertainty. The approach taken was to incorporate a quantitative risk measure known as conditional value-at-risk that is widely used in financial applications. This approach presents a viable alternate modeling and optimization framework to chance-constrained optimization and mean-variance optimization, one that also facilitates the detection of risk-averse solutions.
Highly segmented, position-sensitive germanium detector systems are being developed for nuclear physics research where traditional electronic signal processing with mixed analog and digital function blocks would be enormously complex and costly. Future systems will be constructed using pipelined processing of high-speed digitized signals as is done in the telecommunications industry. Techniques which provide rapid algorithm and system development for future systems are desirable. This project has used digital signal processing concepts and existing graphical system design tools to develop a set of re-usable modular functions and libraries targeted for the nuclear physics community. Researchers working with complex nuclear detector arrays such as the Gamma-Ray Energy Tracking Array (GRETA) have been able to construct advanced data processing algorithms for implementation in field programmable gate arrays (FPGAs) through application of these library functions using intuitive graphical interfaces.
The Hydrogen Safety Panel brings a broad cross-section of expertise from the industrial, government, and academic sectors to help advise the U.S. Department of Energy’s (DOE) Fuel Cell Technologies Office through its work in hydrogen safety, codes, and standards. The Panel’s initiatives in reviewing safety plans, conducting safety evaluations, identifying safety-related technical data gaps, and supporting safety knowledge tools and databases cover the gamut from research and development to demonstration and deployment. The Panel’s recent work has focused on the safe deployment of hydrogen and fuel cell systems in support of DOE efforts to accelerate fuel cell commercialization in early market applications: vehicle refueling, material handling equipment, backup power for warehouses and telecommunication sites, and portable power devices. This paper resulted from observations and considerations stemming from the Panel’s work on early market applications. This paper focuses on hydrogen system components that are installed in outdoor enclosures. These enclosures might alternatively be called “cabinets,” but for simplicity, they are all referred to as “enclosures” in this paper. These enclosures can provide a space where a flammable mixture of hydrogen and air might accumulate, creating the potential for a fire or explosion should an ignition occur. If the enclosure is large enough for a person to enter, and ventilation is inadequate, the hydrogen concentration could be high enough to asphyxiate a person who entered the space. Manufacturers, users, and government authorities rely on requirements described in codes to guide safe design and installation of such systems. Except for small enclosures used for hydrogen gas cylinders (gas cabinets), fuel cell power systems, and the enclosures that most people would describe as buildings, there are no hydrogen safety requirements for these enclosures, leaving gaps that must be addressed. This paper proposes that a technical basis be developed to enable code bodies to write requirements for the range of enclosures from the smallest to the largest.
Demand for electricity is rapidly increasing thereby applying pressure to expand generation and distribution capacity worldwide. The expansion of traditional services not only imposes burdens on financial resources but also encounters many challenges from community residents who oppose the construction of new power generation plants or transmission facilities in their backyard. From the integrated resource planning point of view, a feasible and economical remedy is, therefore, to utilize existing dispersed generation capacity known as distributed generation (DG) and/or renewable energy resources that may exist in the vicinity of the load centers. The main criteria for selecting the type of fuel source for a DG is its local availability, conversion system technological advancement, impact on the environment, and operating cost. DG’s have existed in the market for many years. Large diesel or gas powered generation sets are used in stand-by mode to power up vital services such as hospitals, financial and commercial compounds, telecommunication centers and industrial premises. Wind turbines, photovoltaic energy sources, and fuel cells are new comers that are now competing in size and efficiency with many standard generating sets. With this in mind, they are being used in many places not only as stand-by units but as the prime source of power particularly when the $\$$/kWhr rate is high.
At Sandia National Laboratories in New Mexico (SNL/NM), the design, construction, operation, and maintenance of facilities is guided by industry standards, a graded approach, and the systematic analysis of life cycle benefits received for costs incurred. The design of the physical plant must ensure that the facilities are "fit for use," and provide conditions that effectively, efficiently, and safely support current and future mission needs. In addition, SNL/NM applies sustainable design principles, using an integrated whole-building design approach, from site planning to facility design, construction, and operation to ensure building resource efficiency and the health and productivity of occupants. The safety and health of the workforce and the public, any possible effects on the environment, and compliance with building codes take precedence over project issues, such as performance, cost, and schedule. These design standards generally apply to all disciplines on all SNL/NM projects. Architectural and engineering design must be both functional and cost-effective. Facility design must be tailored to fit its intended function, while emphasizing low-maintenance, energy-efficient, and energy-conscious design. Design facilities that can be maintained easily, with readily accessible equipment areas, low maintenance, and quality systems. To promote an orderly and efficient appearance, architectural features of new facilities must complement and enhance the existing architecture at the site. As an Architectural and Engineering (A/E) professional, you must advise the Project Manager when this approach is prohibitively expensive. You are encouraged to use professional judgment and ingenuity to produce a coordinated interdisciplinary design that is cost-effective, easily contractible or buildable, high-performing, aesthetically pleasing, and compliant with applicable building codes. Close coordination and development of civil, landscape, structural, architectural, fire protection, mechanical, electrical, telecommunications, and security features is expected to ensure compatibility with planned functional equipment and to facilitate constructability. If portions of the design are subcontracted to specialists, delivery of the finished design documents must not be considered complete until the subcontracted portions are also submitted for review. You must, along with support consultants, perform functional analyses and programming in developing design solutions. These solutions must reflect coordination of the competing functional, budgetary, and physical requirements for the project. During design phases, meetings between you and the SNL/NM Project Team to discuss and resolve design issues are required. These meetings are a normal part of the design process. For specific design-review requirements, see the project-specific Design Criteria. In addition to the design requirements described in this manual, instructive information is provided to explain the sustainable building practice goals for design, construction, operation, and maintenance of SNL/NM facilities. Please notify SNL/NM personnel of design best practices not included in this manual, so they can be incorporated in future updates.
Car ownership in India is expected to skyrocket in the coming decades, strongly driven by rising incomes. This phenomenon provides unprecedented opportunities for automakers and equally unprecedented social and environmental challenges. Policymakers, urban planners and civil society see this car boom leading to an explosion in problems related to congestion, infrastructure, air pollution, safety, higher oil imports and climate change. For all these stakeholders to take effective action, good data on how people use their cars, their demand for mobility and their behavior in mobility is essential. Unfortunately, there is very little data on the Indian transport sector as a whole and virtually none on real-world vehicle performance and use. The rapid development of high quality mobile telecommunications infrastructure provides India with the opportunity to leapfrog the West in cheaply collecting vast amounts of useful data from transportation. In this paper, we describe a pilot project in which we use commercial smart phone apps to collect per second car driving data from the city of Pune, instantly upload it through 3G and prepare it for analysis using advanced noise filtering algorithms for less than $1 per day per car. We then use our data in an Autonomie simulation to show that India’s currently planned fuel economy test procedures will result in over-estimates of fuel economy of approximately 35% for a typical Indian car when it is operated in real world conditions. Supporting better driving cycle development is just one of many applications for smart phone derived data in Indian transportation.
This final technical report details research works performed supported by a Department of Energy grant (DE-FE0003859), which was awarded under the University Coal Research Program administrated by National Energy Technology Laboratory. This research program studied high temperature fiber sensor for harsh environment applications. It developed two fiber optical sensor platform technology including regenerative fiber Bragg grating sensors and distributed fiber optical sensing based on Rayleigh backscattering optical frequency domain reflectometry. Through the studies of chemical and thermal regenerative techniques for fiber Bragg grating (FBG) fabrication, high-temperature stable FBG sensors were successfully developed and fabricated in air-hole microstructured fibers, high-attenuation fibers, rare-earth doped fibers, and standard telecommunication fibers. By optimizing the laser processing and thermal annealing procedures, fiber grating sensors with stable performance up to 1100°C have been developed. Using these temperature-stable FBG gratings as sensor platform, fiber optical flow, temperature, pressure, and chemical sensors have been developed to operate at high temperatures up to 800°C. Through the integration of on-fiber functional coating, the use of application-specific air-hole microstructural fiber, and application of active fiber sensing scheme, distributed fiber sensor for temperature, pressure, flow, liquid level, and chemical sensing have been demonstrated with high spatial resolution (1-cm or better) with wide temperature ranges. These include the demonstration of 1) liquid level sensing from 77K to the room temperature, pressure/temperature sensing from the room temperature to 800C and from the 15psi to 2000 psi, and hydrogen concentration measurement from 0.2% to 10% with temperature ranges from the room temperature to 700°C. Optical sensors developed by this program has broken several technical records including flow sensors with the highest operation temperature up to 750°C, first distributed chemical measurements at the record high temperature up to 700°C, first distributed pressure measurement at the record high temperature up to 800°C, and the fiber laser sensors with the record high operation temperature up to 700°C. The research performed by this program dramatically expand the functionality, adaptability, and applicability of distributed fiber optical sensors with potential applications in a number of high-temperature energy systems such as fossil-fuel power generation, high-temperature fuel cell applications, and potential for nuclear energy systems.