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At least 1,495 records · Page 83

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NASA NTRS1992

Novel approaches to the construction of miniaturized analytical instrumentation

This paper focuses on the design, construction, preliminary testing, and potential applications of three forms of miniaturized analytical instrumentation. The first is an optical fiber instrument for monitoring pH and other cations in aqueous solutions. The instrument couples chemically selective indicators that were immobilized at porous polymeric films with a hardware package that provides the excitation light source, required optical components, and detection and data processing hardware. The second is a new form of a piezoelectric mass sensor. The sensor was fabricated by the deposition of a thin (5.5 micron) film of piezoelectric aluminum nitride (AIN). The completed deposition process yields a thin film resonator (TFR) that is shaped as a 400 micron square and supports a standing bulk acoustic wave in a longitudinal mode at frequencies of approx. 1 GHz. Various deposition and vapor sorption studies indicate that the mass sensitivity of the TFR's rival those of the most sensitive mass sensors currently available, though offering such performance in a markedly smaller device. The third couples a novel form of liquid chromatography with microlithographic miniaturization techniques. The status of the miniaturization effort, the goal of which is to achieve chip-scale separations, is briefly discussed.

Porter, Marc D.↗

NASA NTRS1992

Novel approaches to the construction of miniaturized analytical instrumentation

Porter, Marc D.↗

NASA NTRS1996

Design of intelligent mesoscale periodic array structures utilizing smart hydrogel

Mesoscale Periodic Array Structures (MPAS, also known as crystalline colloidal arrays), composed of aqueous or nonaqueous dispersions of self-assembled submicron colloidal spheres are emerging toward the development of advanced optical devices for technological applications. This is because of their unique optical diffraction properties and the ease with which these intriguing properties can be modulated experimentally. Moreover our recent advancements in this area which include 'locking' the liquid MPAS into solid or semisolid polymer matrices for greater stability with longer life span, and incorporation of CdS quantum dots and laser dyes into colloidal spheres to obtain nonlinear optical (NLO) responses further corroborate the use of MPAS in optical technology. Our long term goal is fabrication of all-optical and electro-optical devices such as spatial light modulators for optical signal processing and flat panel display devices by utilizing intelligent nonlinear periodic array structural materials. Here we show further progress in the design of novel linear MPAS which have the ability to sense and respond to an external source such as temperature. This is achieved by combining the self-assembly properties of polymer colloidal spheres and thermoshrinking properties of smart polymer gels. At selected temperatures the periodic array efficiently Bragg diffracts light and transmits most of the light at other temperatures. Hence these intelligent systems are of potential use as fixed notch filters optical switches or limiters to protect delicate optical sensors from high intensity laser radiation.

Sunkara, H. B.↗

NASA NTRS1999

Is It Possible to Distinguish Between Dust and Salt Aerosol Over Waters with Unknown Chlorophyll Concentrations Using Spectral Remote Sensing?

Atmospheric aerosol has uncertain impacts on the global climate system, as well as on atmospheric and bio-geo-chemical processes of regional and local scales. EOS-MODIS is one example of a satellite sensor designed to improve understanding of the aerosols' type, size and distribution at all temporal and spatial scales. Ocean scientists also plan to use data from EOS-MODIS to assess the temporal and spatial coverage of in-water chlorophyll. MODIS is the first sensor planned to observe the combined ocean-atmosphere system with a wide spectral range (from 410 to 2200 nm). Dust aerosol and salt aerosol have similar spectral signals for wavelengths longer than 550 nm, but because dust selectively absorbs blue light, they have divergent signals in the blue wavelength regions (412 to 490 nm). Chlorophyll also selectively absorbs blue radiation, so that varying chlorophyll concentrations produces a highly varying signal in the blue regions, but less variability in the green, and almost no signal in the red to mid-infrared regions. Thus, theoretically, it may be difficult to differentiate dust and salt in the presence of unknown chlorophyll in the ocean. This study attempts to address the cases in which aerosol and chlorophyll signals can and cannot be separated. For the aerosol spectra, we use the aerosol lookup table from the operational MODIS aerosol-over-ocean algorithm, and for chlorophyll spectra, we use the SeaBAM data set (created for SeaWiFS). We compare the signals using Principal Component Analysis and attempt to retrieve both chlorophyll and aerosol properties using a variant of the operational MODIS aerosol-over-ocean algorithm. Results show that for small optical depths, less than 0.5, it is not possible to differentiate between dust and salt and to determine the chlorophyll concentration at the same time. For larger aerosol optical depths, the chlorophyll signals are comparatively insignificant, and we can hope to distinguish between dust and salt.

Levy, R. C.↗

NASA NTRS2007

North American Vegetation Dynamics Observed with Multi-Resolution Satellite Data

North American vegetation has been discovered to be a net carbon sink, with atypical behavior of drawing down more carbon from the atmosphere during the past century. It has been suggested that the Northern Hemisphere will respond favorably to climate warming by enhancing productivity and reducing the impact of fossil fuel emissions into the atmosphere. Many investigations are currently underway to understand and identify mechanisms of storage so they might be actively managed to offset carbon emissions which have detrimental consequences to the functioning of ecosystems and human well being. This paper used a time series of satellite data from multiple sensors at multiple resolutions over the past thlrty years to identify and understand mechanisms of change to vegetation productivity throughout North America. We found that humans had a marked impact to vegetation growth in half of the six selected study regions which cover greater than two million km2. We found climatic influences of increasing temperatures, and longer growing seasons with reduced snow cover in the northern regions of North America with forest fire recovery in the Northern coniferous forests of Canada. The Mid-latitudes had more direct land cover changes induced by humans coupled with climatic influences such as severe drought and altered production strategies of rain-fed agriculture in the upper Midwest, expansion of irrigated agriculture in the lower Midwest, and insect outbreaks followed by subsequent logging in the upper Northeast. Vegetation growth over long time periods (20+ years) in North America appears to be associated with long term climate change but most of the marked changes appear to be associated with climate variability on decadal and shorter time scales along with direct human land cover conversions. Our results document regional land cover land use change and climatic influences that have altered continental scale vegetation dynamics in North America.

Neigh, Christopher S. R.↗

DOE OSTI2017

Biopolymers Containing Unnatural Amino Acids

Although the main chain structure of polymers has a profound effect on their materials properties, the side groups can also have dramatic effects on their properties including conductivity, liquid crystallinity, hydrophobicity, elasticity and biodegradability. Unfortunately control over the side chain structure of polymers remains a challenge – it is difficult to control the sequence of chain elongation when mixtures of monomers are polymerized, and postpolymerization side chain modification is made difficult by polymer effects on side chain reactivity. In contrast, the mRNA templated synthesis of polypeptides on the ribosome affords absolute control over the primary sequence of the twenty amino acid monomers. Moreover, the length of the biopolymer is precisely controlled as are sites of crosslinking. However, whereas synthetic polymers can be synthesized from monomers with a wide range of chemically defined structures, ribosomal biosynthesis is largely limited to the 20 canonical amino acids. For many applications in material sciences, additional building blocks would be desirable, for example, amino acids containing metallocene, photoactive, and halogenated side chains. To overcome this natural constraint we have developed a method that allows unnatural amino acids, beyond the common twenty, to be genetically encoded in response to nonsense or frameshift codons in bacteria, yeast and mammalian cells with high fidelity and good yields. Here we have developed methods that allow identical or distinct noncanonical amino acids to be incorporated at multiple sites in a polypeptide chain, potentially leading to a new class of templated biopolymers. We have also developed improved methods for genetically encoding unnatural amino acids. In addition, we have genetically encoded new amino acids with novel physical and chemical properties that allow selective modification of proteins with synthetic agents. Finally, we have evolved new metal-ion binding sites in proteins using a novel metal-ion binding amino acid, which may facilitate our ability to generate new protein based sensors and catalysts.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Metal-Organic Framework-based Luminescent Sensors for Rare Earth Elements

Rare earth elements (REEs) are crucial for many applications and are particularly important in wind power and electric vehicle technologies, spurring projected demand increases for REEs in coming decades. However, the REE supply is restricted by a monopolistic global marketplace, tedious and costly processing steps for REE production, and environmental hazards associated with mining and processing. Coal and coal-utilization byproducts have emerged as potential domestic sources for REEs, however REE production from these sources is currently hindered by slow and expensive characterization techniques. Here, we demonstrate that metal-organic framework (MOF)-based sensitizers can effectively serve as luminescent sensors capable of rapidly detecting and distinguishing part-per-billion concentration levels of emissive REEs, including terbium, europium, dysprosium, samarium, neodymium, and ytterbium. Importantly, we demonstrate that the MOF structure plays a significant role in tuning the selectivity of the MOF for REEs in acidic environments, an essential step towards deployment in various REE-rich process streams. As a proof-of-concept, we demonstrate sensitive and reversible detection of REEs using a portable fiber-optic spectrometer

Crawford, Scott↗

DOE OSTI2023

Opportunities and Challenges for Remote Microreactor Operations

The nuclear industry is developing new advanced reactor technologies, and many companies are embracing this advancement by pursuing the development of microreactors. The term microreactor generally refers to a nuclear reactor with an operating power of 20 MW or less. The power range of microreactors makes them appealing for many use cases, such as powering remote communities, mining sites, and military bases. Most of the microreactor designs being pursued will incorporate remote facility operations into the final product. However, no framework has yet been developed to determine what remote operations systems require for reliable, resilient, and secure operation of a microreactor. This work identifies the challenges unique to remote operations and monitoring for microreactors specifically regarding instrumentation and control, communication methods, regulatory requirements, and operational policies. The types of commands and sensor measurements that must be transmitted between the facilities as well as methods for verifying the trustworthiness of these signals are assessed. This work evaluates the security, reliability, and performance requirements that must be met when considering the selection of communication hardware and protocols for use in remote operations. Also, an assessment was performed to study how remote operations fit within current regulatory requirements and what may need to be updated in regulatory policy to allow for remote operation. Finally, the operational contingencies unique to remote operations that must be in place for response to abnormal events are identified. This paper details these challenges and research opportunities to provide a foundation for the design of remote operation systems.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2017

Framework for structural online health monitoring of aging and degradation of secondary piping systems due to some aspects of erosion - 330

This paper describes the current state of research related to critical aspects of erosion and selected aspects of degradation of secondary piping systems in nuclear power plants (NPPs). The paper also proposes a framework for online health monitoring of aging and degradation of secondary piping. The framework consists of an integrated multi-sensor modality system, which can be used to monitor different piping configurations under different degradation conditions. The paper analyses the currently known degradation mechanisms and available monitoring techniques and practices. Based on this analysis, the structural health monitoring framework is proposed. There is unanimous agreement between industry experts and academic researchers that identifying and prioritizing inspection locations in secondary piping systems would eliminate many excessive in-service inspections. The proposed structural health monitoring framework takes aim at answering this challenge by combining long range guided wave technologies with other monitoring techniques, which can significantly increase the inspection length and pinpoint the locations that degraded the most. More widely, the paper suggests research efforts aimed at developing, validating, and deploying online corrosion monitoring techniques for complex geometries, which are pervasive in NPPs This transition to condition-based, risk-informed automated maintenance will contribute to a significant reduction of operations and maintenance costs that account for 80% of nuclear power generation costs. To address the issue of rising operating costs and economic viability, in 2017, companies that operate the national nuclear energy fleet started the Delivering the Nuclear Promise Initiative, which is a 3-year program aimed at maintaining operational focus and safety, increasing value, and improving efficiency. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2023

Quantifying uncertainty for deep learning based forecasting and flow-reconstruction using neural architecture search ensembles

Classical problems in computational physics such as data-driven forecasting and signal reconstruction from sparse sensors have recently seen an explosion in deep neural network (DNN) based algorithmic approaches. However, most DNN models do not provide uncertainty estimates, which are crucial for establishing the trustworthiness of these techniques in downstream decision making tasks and scenarios. In recent years, ensemble-based methods have achieved significant success for the uncertainty quantification in DNNs on a number of benchmark problems. However, their performance on real-world applications remains under-explored. In this work, we present an automated approach to DNN discovery and demonstrate how this may also be utilized for ensemble-based uncertainty quantification. Specifically, we propose the use of a scalable neural and hyperparameter architecture search for discovering an ensemble of DNN models for complex dynamical systems. We highlight how the proposed method not only discovers high-performing neural network ensembles for our tasks, but also quantifies uncertainty seamlessly. This is achieved by using genetic algorithms and Bayesian optimization for sampling the search space of neural network architectures and hyperparameters. Subsequently, a model selection approach is used to identify candidate models for an ensemble set construction. Afterwards, a variance decomposition approach is used to estimate the uncertainty of the predictions from the ensemble. We demonstrate the feasibility of this framework for two tasks — forecasting from historical data and flow reconstruction from sparse sensors for the sea-surface temperature. In conclusion, we demonstrate superior performance from the ensemble in contrast with individual high-performing models and other benchmarks.

Deep ensembles↗

DOE OSTI2017

Sensor for Measuring Hydrogen Partial Pressure in Parabolic Trough Power Plant Expansion Tanks

The National Renewable Energy Laboratory and Acciona Energy North America are working together to design and implement a process system that provides a permanent solution to the issue of hydrogen buildup at parabolic trough power plants. We are pursuing a method that selectively removes hydrogen from the expansion tanks that serve as reservoirs for the heat transfer fluid (HTF) that circulates in the collector field and power block components. Our modeling shows that removing hydrogen from the expansion tanks at a design rate reduces and maintains dissolved hydrogen in the circulating HTF to a selected target level. Our collaborative work consists of several tasks that are needed to advance this process concept to a development stage, where it is ready for implementation at a commercial power plant. Our main effort is to design and evaluate likely process-unit operations that remove hydrogen from the expansion tanks at a specified rate. Additionally, we designed and demonstrated a method and instrumentation to measure hydrogen partial pressure and concentration in the expansion-tank headspace gas. We measured hydrogen partial pressure in the headspace gas mixture using a palladium-alloy membrane, which is permeable exclusively to hydrogen. The membrane establishes a pure hydrogen gas phase that is in equilibrium with the hydrogen in the gas mixture. We designed and fabricated instrumentation, and demonstrated its effectiveness in measuring hydrogen partial pressures over a range of three orders of magnitude. Our goal is to install this instrument at the Nevada Solar One power plant and to demonstrate its effectiveness in measuring hydrogen levels in the expansion tanks under normal plant operating conditions.

14 SOLAR ENERGY↗

NASA NTRSDate not supplied

Towards Autonomous Lunar Resource Excavation via Reinforcement Learning

To continue on a sustainable and flexible path, NASA needs to address the challenge of collecting and moving large amounts of regolith at the destination. NASA’s Regolith Advanced Surface Systems Operations Robot (RASSOR) is principally designed to mine and deliver regolith for In-Situ Resource Utilization (ISRU) processing. RASSOR’s design enables it to efficiently collect and deposit regolith, return collected material for processing, and myriad related ISRU activities. To reliably perform these operations on the lunar surface, RASSOR software and sensory systems need to be robust and maximize the information extracted from a reduced sensor payload. Herein, we present preliminary findings from the Intelligent Capabilities Enhanced RASSOR project. We created reduced-order simulation environments to develop autonomous trenching controllers via reinforcement learning and prototype state estimation architectures. The goal of reinforcement learning is for an agent to learn a policy (task strategy) through interactions with an environment. When the agent performs an action, a change occurs in environment state and a numerical reward is received which informs the agent whether the action performed was good or not. Since reinforcement learning algorithms learn through trial-and-error, a simulation is a desirable first environment for development and learning. We developed two simulations, the first is a 2D excavation simulation developed to facilitate parameter selection, and a 3D simulation developed using a game physics engine, to simulate simplified soil interactions and increase the fidelity of the dynamic models of the robotic agents. The development of this 3D simulation has enabled the training of additional sensing capabilities and research both at the granular mechanics and operations levels. We experimented with various virtual sensor payloads to identify a combination that enabled efficient excavation operation and learning. Our reward function is based on how much material is excavated per step. A penalty is also received for leaving the dig site and to smooth the acceleration of the drum arms. We implemented pseudo time-of-flight sensors to report distance from each drum to ground and the height above ground which was found to be more efficient than existing solutions. Our findings suggest that reinforcement learning for autonomous operations has learned viable trenching strategies within 3000 training episodes in our simplified 2D environment and helped identify desirable sensing capabilities, arrangements, and considerations such as the positioning of time-of-flight sensors. Future work includes expanding our simulation to more complex environments and scenarios, and transfer learning from simulation to RASSOR 2.0 hardware for deployment in the Regolith Test Bin at NASA's Kennedy Space Center.

rassor↗

DOE OSTI2022

Functional fiber-optic sensors embedded in stainless steel components using ultrasonic additive manufacturing for distributed temperature and strain measurements

The nuclear industry is progressing toward microreactors that can be factory assembled and deployed to remote regions for reliable, scalable power generation. However, the reduced power output requires enhanced monitoring capabilities to reduce staffing and eventually move toward autonomous control to improve the economics of microreactors. The ability to embed sensors such as fiber-optics, which can provide spatially distributed temperature and strain measurements, within microreactor components for real-time health monitoring would be particularly advantageous. Recent advances in ultrasonic additive manufacturing (UAM) have demonstrated the successful embedding of fiber-optics in soft materials such as Al and Cu by placing the sensors in machined cavities and ultrasonically welding over the top with thin foils. This article reports the first successful embedment of fiber-optic sensors and thermocouples within a common nuclear reactor material, SS304, via UAM. UAM parameters were first explored with simple plate geometries before moving to more complex geometries, such as pipes and other test articles for heat pipe–based microreactors. Select samples were sectioned for microscopy to evaluate the sensor/foil and foil/foil interfaces from samples fabricated by using 100% SS304 foils vs. foils plated with a Ni coating to improve UAM bonding. Furthermore, embedding metal-coated, low-bend loss fibers resulted in greatly reduced signal attenuation and adequate compressive residual strain in the embedded region to ensure successful strain coupling. Pipe specimen functional testing was performed by monitoring temperature and strain during transient and steady-state thermal testing.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Measuring the Elemental Composition of Phobos: The Mars-moon Exploration with GAmma rays and NEutrons (MEGANE) Investigation for the Martian Moons eXploration (MMX) Mission

The Mars-moon Exploration with GAmma rays and NEutrons (MEGANE) investigation will use gamma-ray and neutron spectroscopy to measure the elemental composition of Mars' moon Phobos. MEGANE is part of the Japanese Martian Moons eXploration (MMX) mission that will make comprehensive remote sensing measurements of both of Mars' moons Phobos and Deimos. MMX will also return to Earth regolith samples of Phobos. The science goals of the MEGANE investigation mirror those of the MMX mission. MEGANE will use elemental composition measurements to determine if Phobos is a captured asteroid or the end result of a giant impact event on Mars, study Phobos surface processes, provide reconnaissance to support the sample site selection, and supply compositional context for the returned samples. To accomplish its measurements, MEGANE will use a high-purity Ge gamma-ray spectrometer (GRS), and a neutron spectrometer (NS) that consists of two 3He gas proportional neutron sensors. The GRS derives heritage from similar instruments from NASA's MErcury Surface, Space ENvironment, GEochemistry, and Ranging mission and the Psyche mission that is currently in development; the NS is based on similar instruments used for NASA's Lunar Prospector and Psyche missions.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1976

Diurnal measurements with prototype CMOS Omega receivers

Diurnal signals from eight omega channels have been monitored at 10.2 KHz for selected station pairs. All eight Omega stations have been received at least 50 percent of the time over a 24 hour period during the month of October 1976. The data presented confirm the expected performance of the CMOS omega sensor processor in being able to digsignals out of a noisy environment. Of particular interest are possibilities for use of antipodal reception phenomena and a need for some ways of correcting for multi-modal propagation effects.

Burhans, R. W.↗

NASA NTRS1988

Fiber optic sensors with internal referencing

The main problem with amplitude modulating type sensors is that any variation in the intensity of the optical signal which occurs throughout the sensing system is interpreted by the photodetector as resulting from the sensor itself and is reflected as an error in the sensed parameter. To account for these errors, a referencing technique with the signal and reference channels separated in the time domain over the same fiber link can be used. Selected sensing and signal processing techniques involving temporally separated signal and referencing channels are described. A transition from the time into the frequency domain is also discussed. Experimental data are presented.

Adamovsky, Grigory↗

NASA NTRS1986

Ada and knowledge-based systems: A prototype combining the best of both worlds

A software architecture is described which facilitates the construction of distributed expert systems using Ada and selected knowledge based systems. This architecture was utilized in the development of a Knowledge-based Maintenance Expert System (KNOMES) prototype for the Space Station Mobile Service Center (MSC). The KNOMES prototype monitors a simulated data stream from MSC sensors and built-in test equipment. It detects anomalies in the data and performs diagnosis to determine the cause. The software architecture which supports the KNOMES prototype allows for the monitoring and diagnosis tasks to be performed concurrently. The basic concept of this software architecture is named ACTOR (Ada Cognitive Task ORganization Scheme). An individual ACTOR is a modular software unit which contains both standard data processing and artificial intelligence components. A generic ACTOR module contains Ada packages for communicating with other ACTORs and accessing various data sources. The knowledge based component of an ACTOR determines the role it will play in a system. In this prototype, an ACTOR will monitor the MSC data stream.

Brauer, David C.↗

NASA NTRS1994

Architecture and evolution of Goddard Space Flight Center Distributed Active Archive Center

The Goddard Space Flight Center (GSFC) Distributed Active Archive Center (DAAC) has been developed to enhance Earth Science research by improved access to remote sensor earth science data. Building and operating an archive, even one of a moderate size (a few Terabytes), is a challenging task. One of the critical components of this system is Unitree, the Hierarchical File Storage Management System. Unitree, selected two years ago as the best available solution, requires constant system administrative support. It is not always suitable as an archive and distribution data center, and has moderate performance. The Data Archive and Distribution System (DADS) software developed to monitor, manage, and automate the ingestion, archive, and distribution functions turned out to be more challenging than anticipated. Having the software and tools is not sufficient to succeed. Human interaction within the system must be fully understood to improve efficiency to improve efficiency and ensure that the right tools are developed. One of the lessons learned is that the operability, reliability, and performance aspects should be thoroughly addressed in the initial design. However, the GSFC DAAC has demonstrated that it is capable of distributing over 40 GB per day. A backup system to archive a second copy of all data ingested is under development. This backup system will be used not only for disaster recovery but will also replace the main archive when it is unavailable during maintenance or hardware replacement. The GSFC DAAC has put a strong emphasis on quality at all level of its organization. A Quality team has also been formed to identify quality issues and to propose improvements. The DAAC has conducted numerous tests to benchmark the performance of the system. These tests proved to be extremely useful in identifying bottlenecks and deficiencies in operational procedures.

Bedet, Jean-Jacques↗