SEARCH · Search NASA
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.
Conference manuscript describing NETL’s development of drone methods to quickly survey large areas for the presence of abandoned wells and pipelines, which present risk to current and future land use.
The project provides appropriate software modules, which allow to represent a physical oven as a virtual oven model. For that a virtual oven model is developed, which defines oven parameters like temperature, power consumption but also the door status and the humidity. To connect a real oven to the virtual model for monitoring and control multiple sensors are used to collect data. Using a MQTT based data connection the measured parameters are send to the CESMII environment for further processing and monitoring. Within this project the CESMII model for the general oven was developed and provided. Further the sensors were connected to the actual oven. The sensor data is collected via means of a MQTT based data transfer protocol to a gateway module. The gateway module then translates and transfers the collected data to the actual CESMII oven model.
When a voltage is applied to a thin superconducting strip cooled just above its critical temperature, the superconductor becomes its own internal thermostat; an increase in temperature will cause the resistance to rise, which lowers the amount of current and drops the temperature. Likewise, a decrease in temperature causes the resistance to fall, increasing the current and raising the temperature. This change in current due to changes in energy makes the superconductor an ideal energy sensor. Due to the low noise, precise energy resolution, and time stamping of particles, these detectors have several potential applications, ranging from measuring cosmic microwave background, dark matter detection, or measuring the mass of the neutrino. SLAC is currently developing tools to readout data from an array of 10000 of these sensors.
We review the development of “single” nanoparticle-based inorganic and organic voltage sensors, which can eventually become a viable tool for “non-genetic optogenetics.” The voltage sensing is accomplished with optical imaging at the fast temporal response and high spatial resolutions in a large field of view. Inorganic voltage nanosensors utilize the Quantum Confined Stark Effect (QCSE) to sense local electric fields. Engineered nanoparticles achieve substantial single-particle voltage sensitivity (~2% Δλ spectral Stark shift up to ~30% ΔF/F per 160 mV) at room temperature due to enhanced charge separation. A dedicated home-built fluorescence microscope records spectrally resolved images to measure the QCSE induced spectral shift at the single-particle level. Biomaterial based surface ligands are designed and developed based on theoretical simulations. The hybrid nanobiomaterials satisfy anisotropic facet-selective coating, enabling effective compartmentalization beyond non-specific staining. Self-spiking- and patched-HEK293 cells and cortical neurons, when stained with hybrid nanobiomaterials, show clear photoluminescence intensity changes in response to membrane potential (MP) changes. Organic voltage nanosensors based on polystyrene beads and nanodisk technology utilize Fluorescence (Förster) Resonance Energy Transfer (FRET) to sense local electric fields. Voltage sensing FRET pairs achieve voltage sensitivity up to ~35% ΔF/F per 120 mV in cultures. Non-invasive MP recording from individual targeted sites (synapses and spines) with nanodisks has been realized. However, both of these QCSE- and FRET-based voltage nanosensors yet need to reach the milestone of recording individual action potentials from individual targeted sites.
Highlights: • Rutile phase TiO{sub 2} aggregates were prepared via thermal oxidation of pure Ti-discs, which is simple and inexpensive method. • TiO{sub 2} aggregates with submicron particles based sensor exhibited ultra-high response (R{sub A}/R{sub H{sub 2O}} > 10{sup 6}) to 90% RH. • Sensors based on TiO{sub 2} modified with GO exhibited improved sensitivity, stability and reproducibility. - Abstract: The development of cost-effective, robust, and reliable humidity monitoring system is nowadays in high demand. To this context, humidity sensors based on rutile TiO{sub 2} aggregates were prepared via thermal oxidation of pure Ti-discs at 1000 °C. The sensors based on only TiO{sub 2} aggregates exhibited the ultra-high response of about six orders of magnitude (R{sub A}/R{sub %RH} ∼ 10{sup 6}) to 90% RH with a response time of 28 s and excellent stability. However, these sensors showed no response to RH below 30%, which limits their use in practical applications for such RH level monitoring. Although with a reduced response in high RH, the TiO{sub 2} modified with GO showed potential to overcome this drawback. To understand their functionality, the TiO{sub 2} aggregates and TiO{sub 2}/GO were further characterized by XRD, SEM, TEM, Raman and FT-IR spectroscopy. The presented results are of high interest in terms of wide range humidity monitoring, low-cost and simple synthesis route, and high stability.
The Superconducting Quantum Materials and Systems Center, led by Fermi National Accelerator Laboratory, is one of five research centers funded by the U.S. Department of Energy as part of a national initiative to develop and deploy the world’s most powerful quantum computers and sensors. The SQMS Center uses world-record quality-factor superconducting radio-frequency, or SRF, cavities as ultra-sensitive quantum probes. Cavities combined with superconducting transmon qubits can be the building block of a quantum computer, and they can also be employed for fundamental physics searches. Within the quantum sensing thrust, researchers are developing experiments based on cavities and novel quantum devices to search for particles beyond the Standard Model, dark matter candidates, gravitational waves and fundamental material properties. The seminar will present a brief overview of the center and focus on the physics and sensing work conducted at Fermilab.
The Superconducting Quantum Materials and Systems Center, led by Fermi National Accelerator Laboratory, is one of five research centers funded by the U.S. Department of Energy as part of a national initiative to develop and deploy the world’s most powerful quantum computers and sensors. The SQMS Center uses world-record quality-factor superconducting radio-frequency, or SRF, cavities as ultra-sensitive quantum probes. Cavities combined with superconducting transmon qubits can be the building block of a quantum computer, and they can also be employed for fundamental physics searches. Within the quantum sensing thrust, researchers are developing experiments based on cavities and novel quantum devices to search for particles beyond the Standard Model, dark matter candidates, gravitational waves and fundamental material properties. The seminar will present a brief overview of the center and focus on the physics and sensing work conducted at Fermilab.
The Superconducting Quantum Materials and Systems Center, led by Fermi National Accelerator Laboratory, is one of five research centers funded by the U.S. Department of Energy as part of a national initiative to develop and deploy the world’s most powerful quantum computers and sensors. The SQMS Center uses world-record quality-factor superconducting radio-frequency, or SRF, cavities as ultra-sensitive quantum probes. Cavities combined with superconducting transmon qubits can be the building block of a quantum computer, and they can also be employed for fundamental physics searches. Within the quantum sensing thrust, researchers are developing experiments based on cavities and novel quantum devices to search for particles beyond the Standard Model, dark matter candidates, gravitational waves and fundamental material properties. The seminar will present a brief overview of the center and focus on the physics and sensing work conducted at Fermilab.
The U.S. Department of Energy (DOE) and the Hanford Site Tank Operations Contractor Washington River Protection Solutions, LLC (WRPS) are sponsoring a non-destructive evaluation (NDE) technology development program to identify and mature volumetric (non-visual) NDE technology that will enable the examination of Hanford double-shell tank (DST) bottoms. NDE technology for Hanford under-tank examination will be made possible through technology maturation that includes adaptation of NDE sensor technology to overcome access challenges presented by tank risers and refractory pad air-slots, followed by integration with robotic delivery systems, and ultimately qualification and deployment of the inspection systems. The stages of technology maturation and the testing and evaluation required to support the process are organized under three program phases: Phase I - NDE Capability Testing: Identify and down-select NDE technologies based on flaw detection and characterization abilities only; Phase 2 - NDE Delivery System Testing: Mature promising NDE technologies to adapt transducer hardware and robotic delivery system to address access challenges; Phase 3 - Full-scale Demonstration of Integrated NDE System: Demonstrate adapted NDE technologies in a cold test platform to challenge flaw detection and navigation abilities. Phase I of the program, which was initiated and completed in FY2017, focused on completing the following five key activities: 1. Identifying a broad cross section of potentially applicable NDE volumetric inspection technologies for under-tank examination. 2. Down-selecting the broad set of NDE technologies to a set of strong candidates based on initial flaw detection performance and potential to be adapted/matured to overcome primary tank access challenges posed by access risers and/or refractory pad air-slots. 3. Evaluating the flaw detection performance of the candidate sensors through testing. 4. Using the test results to baseline sensor performance against the flaw detection requirements established for high-level waste tanks. 5. Assessing the trade-offs and vulnerabilities of the candidate sensors that would negatively impact sensor performance reliability or complicate tank inspection operations. The completion of the first three key activities satisfied the first two Phase I test objectives, which were to complete Technology Screening and Sensor Effectiveness Testing. Completion of the fourth key activity satisfied the final test objective of Phase I, which was to determine the extent to which the sensors can satisfy high-level waste tank flaw detection requirements. Sensor flaw detection performance testing yielded several suitable sensor technology options, although each has specific risks or challenges that would need to be accepted or overcome. Thus, the fifth key activity was performed to evaluate sensor risks and challenges for consideration with flaw detection performance. The Phase I evaluation outcomes will be used to support the Phase I programmatic objective, which is to identify one or more NDE sensor technologies for adaptation and maturation for the DST environment. Selection of one or more sensors will mark the culmination of Phase I of the NDE Technology Development Program and allow progression to Phase II.
The CT-PPS detector will be installed close to the beam line on both sides of CMS, 200 m downstream the interaction point. This detector will measure forward scattered protons, allowing detailed studies of diffractive hadron physics and Central Exclusive Production. The main components of the CT-PPS detector are a silicon tracking system and a timing system. In this contribution we present the proposal of an innovative solution for the timing system, based on Ultra-Fast Silicon Detectors (UFSD). UFSD are a novel concept of silicon detectors potentially able to obtain the necessary time resolution (~20 ps on the proton arrival time). The use of UFSD has also other attractive features as its material budget is small and the pixel geometries can be tailored to the precise physics distribution of protons. UFSD prototypes for CT-PPS have been designed by CNM (Barcelona) and FBK (Trento): we will present the status of the sensor productions and of the low-noise front-end electronics currently under development and test.
In many non-canonical data science scenarios, obtaining, detecting, attributing, and annotating enough high-quality training data is the primary barrier to developing highly effective models. Moreover, in many problems that are not sufficiently defined or constrained, manually developing a training dataset can often overlook interesting phenomena that should be included. To this end, we have developed and demonstrated an iterative self-supervised learning procedure, whereby models are successfully trained and applied to new data to extract new training examples that are added to the corpus of training data. Successive generations of classifiers are then trained on this augmented corpus. Using low-frequency acoustic data collected by a network of infrasound sensors deployed around the High Flux Isotope Reactor and Radiochemical Engineering Development Center at Oak Ridge National Laboratory, we test the viability of our proposed approach to develop a powerful classifier with the goal of identifying vehicles from continuously streamed data and differentiating these from other sources of noise such as tools, people, airplanes, and wind. Using a small collection of exhaustively manually labeled data, we test several implementation details of the procedure and demonstrate its success regardless of the fidelity of the initial model used to seed the iterative procedure. Finally, we demonstrate the method’s ability to update a model to accommodate changes in the data-generating distribution encountered during long-term persistent data collection.
Fracture is an important component of nuclear fuel behavior, and significant efforts have been invested into developing fuel performance models that are capable of accurately representing fracture. Usable data on the process of fracture propagation in nuclear fuel under realistic service conditions are very limited. To address this need, a series of separate-effects experiments were developed and performed at Idaho National Laboratory's Transient Reactor Test (TREAT) facility. These experiments employ a heat sink to radially remove heat from the fuel in a manner that approximates the effect of coolant in an operating light-water reactor (LWR). The test holder for these experiments is known as the Dry In-pile Fracture Test (DRIFT). A series of experiments employing DRIFT and TREAT were performed to provide data on the extent and nature of fracture in fresh fuel at various points during a ramp to full power. Novel aspects of these experiments include the way they employ a heat sink to replicate steady-state LWR conditions, as well as the use of fiber optic sensors for in-reactor thermal instrumentation. Details on the development of this experiment, experimental conditions, and resulting data (including in situ thermal measurements and post-irradiation imaging of fracture) are provided in this work. LWR-equivalent powers ranging from 10 to 25 kW/m were tested using this apparatus. Cracking was visible at all power levels, with increasing cracking extent as the power level increased, although there was little difference in the cracking between the two highest-power tests, which had LWR-equivalent powers of 20 and 25 kW/m.
Abstract A major challenge in biotechnology and biomanufacturing is the identification of a set of biomarkers for perturbations and metabolites of interest. Here, we develop a data-driven, transcriptome-wide approach to rank perturbation-inducible genes from time-series RNA sequencing data for the discovery of analyte-responsive promoters. This provides a set of biomarkers that act as a proxy for the transcriptional state referred to as cell state. We construct low-dimensional models of gene expression dynamics and rank genes by their ability to capture the perturbation-specific cell state using a novel observability analysis. Using this ranking, we extract 15 analyte-responsive promoters for the organophosphate malathion in the underutilized host organism Pseudomonas fluorescens SBW25. We develop synthetic genetic reporters from each analyte-responsive promoter and characterize their response to malathion. Furthermore, we enhance malathion reporting through the aggregation of the response of individual reporters with a synthetic consortium approach, and we exemplify the library’s ability to be useful outside the lab by detecting malathion in the environment. The engineered host cell, a living malathion sensor, can be optimized for use in environmental diagnostics while the developed machine learning tool can be applied to discover perturbation-inducible gene expression systems in the compendium of host organisms.
This study reports on the development and testing of a passive wireless device designed to track temperature and corrosion behavior in SS304H stainless steel under elevated temperatures. The ANSYS HFSS software was utilized to model and optimize the design of an inductor-capacitor (LC) resonator passive wireless sensors fabricated using platinum designs printed onto an aluminum oxide support operating at frequencies between (50–190 MHz). The optimal LC wireless sensor designs were then fabricated using screen-printing and sintering methods. Here, the sensors were tested by placing the sensor onto polished SS304H flat substrates and heated to 900–1050 °C in air. Wireless acquisition of sensor data during heating, cooling, and isothermal stages was achieved through a Pt loop antenna connected to an RF signal generator and a network analyzer.
Recent years have seen renewed interest and activities in developing SRF cavity materials based on thin film technologies. In this framework, considerable progress has been achieved in the development of high quality films and layered structures along with associated deposition techniques such as ECR, HiPIMS, and ALD. Beyond cavity applications, the developments in SRF thin film technologies find a variety of applications in the fields of superconducting metamaterials, electronics, sensors and quantum devices. High quality Nb films with high RRR open the way for enhanced coherence times for quantum qbits. Other SRF thin films such as NbTiN are developed for superconducting backend processes for future generations of computing hardware and radiation-hard sensors for nuclear and high-energy physics. The unifying theme across these technologies is that the same physics and material properties such as extreme low loss, stability, and manufacturability are required. This talk will present an overview of the emerging applications of SRF films and structures beyond accelerator cavities.
Abstract. Wind turbines in a wind farm typically operate individually to maximize their own performance and do not take into account information from nearby turbines. To enable cooperation to achieve farm-level objectives, turbines will need to use information from nearby turbines to optimize performance, ensure resiliency when other sensors fail, and adapt to changing local conditions. A key element of achieving a more efficient wind farm is to develop algorithms that ensure reliable, robust, real-time, and efficient operation of wind turbines in a wind farm using local sensor information that is already being collected, such as supervisory control and data acquisition (SCADA) data, local meteorological stations, and nearby radars/sodars/lidars. This article presents a framework for developing a cooperative wind farm that incorporates information from nearby turbines in real time to better align turbines in a wind farm. SCADA data from multiple turbines can be used to make better estimates of the local inflow conditions at each individual turbine. By incorporating measurements from multiple nearby turbines, a more reliable estimate of the wind direction can be obtained at an individual turbine. The consensus-based approach presented in this paper uses information from nearby turbines to estimate wind direction in an iterative way rather than aggregating all the data in a wind farm at once. Results indicate that this estimate of the wind direction can be used to improve the turbine's knowledge of the wind direction. This estimated wind direction signal has implications for potentially decreasing dynamic yaw misalignment, decreasing the amount of time a turbine spends yawing due to a more reliable input to the yaw controller, increasing resiliency to faulty wind-vane measurements, and increasing the potential for wind farm control strategies such as wake steering.
Data Challenge 1 (DC1) is the first synthetic data set produced by the Rubin Observatory Legacy Survey of Space and Time (LSST) Dark Energy Science Collaboration (DESC). DC1 is designed to develop and validate data reduction and analysis and to study the impact of systematic effects that will affect the LSST data set. DC1 is comprised of r -band observations of 40 deg 2 to 10 yr LSST depth. In this paper, we present each stage of the simulation and analysis process: (a) generation, by synthesizing sources from cosmological N -body simulations in individual sensor-visit images with different observing conditions; (b) reduction using a development version of the LSST Science Pipelines; and (c) matching to the input cosmological catalogue for validation and testing. We verify that testable LSST requirements pass within the fidelity of DC1. We establish a selection procedure that produces a sufficiently clean extragalactic sample for clustering analyses and we discuss residual sample contamination, including contributions from inefficiency in star–galaxy separation and imperfect deblending. We compute the galaxy power spectrum on the simulated field and conclude that: (i) survey properties have an impact of 50 per cent of the statistical uncertainty for the scales and models used in DC1; (ii) a selection to eliminate artefacts in the catalogues is necessary to avoid biases in the measured clustering; and (iii) the presence of bright objects has a significant impact (2σ–6σ) in the estimated power spectra at small scales (ℓ > 1200), highlighting the impact of blending in studies at small angular scales in LSST.
To support efficiency, productivity, yield improvements, and future industry 4.0 and SMART manufacturing objectives for a competitive and prosperous steel industry, this program has embarked on an effort to develop, demonstrate and deploy several novel sensing technologies based on fiber optics for use in production steel facilities. Over the duration of this program, our team has successfully demonstrated three of these technologies: (1) near continuous Rayliegh Scattering optical frequency domain reflectometry (OFDR) sensing with single mode silica fibers for temperature sensing to 700 C, (2) semi-distributed fiber Bragg grating (FBG) sensing with multimode sapphire fibers for temperature sensing to >1600 C, and (3) remote in-situ Raman analysis for slag and flux chemistry analysis at steelmaking temperatures >1550 C. Each of these sensor and interrogation systems was developed, refined, and tested in our labs at Missouri S&T and then successfully deployed at SSAB’s production facilities at two sites, one in Montpelier, IA and one in Mobile, AL.