Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “sensor performance”

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.

At least 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Performance of a low-parasitic frequency-domain multiplexing readout

Frequency-domain multiplexing is a readout technique for transition-edge sensor bolometer arrays used on modern cosmic microwave background experiments, including the SPT-3G receiver. Here, we present design details and performance measurements for a low-parasitic frequency-domain multiplexing readout. Reducing the parasitic impedance of the connections between cryogenic components provides a path to improve both the crosstalk and noise performance of the readout. Reduced crosstalk will in turn allow higher-multiplexing factors. We have demonstrated a factor of two improvement in parasitic resistance compared to SPT-3G hardware. Reduced parasitics also permits operation of lower-resistance bolometers optimized for improved readout noise performance. We demonstrate that a module in the prototype system has comparable readout noise performance to an SPT-3G module when operated with dark TES bolometers in the laboratory.

frequency-domain multiplexing↗
DOE OSTI2023

Fully Distributed Acoustic and Magnetic Field Monitoring Via a Single Fiber Line for Optimized Production of Unconventional Resource Plays

This is the final technical report for the Virginia Tech Center for Photonics Technology (VT-CPT) research project entitled “Fully Distributed Acoustic and Magnetic Field Monitoring Via a Single Fiber Line for Optimized Production of Unconventional Resource Plays”. In the the four-year effort, VT-CPT collaborated with the Departments of Mathmatics at VT and Sentek Instrument, LLC (Sentek) to develop a fiber-optic sensing system capable of real-time simultaneous distributed measurement of multiple subsurface, drilling, and production parameters. The ultra-sensitive fiber optic distributed acoustic sensing technology, picoDAS, developed by Sentek was integrated with a novel multi-material optical sensing fiber fabricated by CPT-VT to obtain distributed acoustic and magnetic field measurements with ultrahigh sensitivity and high spatial resolution. The sensing technology successfully developed and demonstrated under this research program is truly unique has application beyond subsurface imaging to include carbon storage and electrical grid monitoring, as well as healthcare and nuclear fusion. The project, sponsored by the Advanced Technology Program, began in 2019 and had the original goal of developing the next generation of harsh environment sensing systems. Exhaustive theoretical modeling was performed to optimize the multi-material optical fiber design. VT developed the processing techniques employed to successfully fabricate single mode fibers with metal cladding wires. Sensing fibers with nickel and Metglas cladding wires were fully characterized, inscribed with sensors, and fully integrated with the picoDAS system. Laboratory scale testing was performed to demonstrate performance upon exposure to lateral and transverse magnetic fields. Preliminary field trials were performed for prototype magnetic and acoustic sensing systems upon near surface deployment at VT. The distributed acoustic and magnetic field sensing system was advanced from a (Technology Readiness Level) TRL=2 to a TRL=5 via laboratory scale system validation in relevant environments. The truly one-of-its kind distributed magnetic and acoustic sensing system is expected to find immediate applications that require subsurface monitoring, to include carbon storage site monitoring, and has the potential to be a distributive technology in healthcare and electric grid markets. The technologies developed by Virginia Tech’s Center for Photonics Technology will support the mission of the National Energy Technology Laboratory (NETL) to drive innovation and deliver solutions for an environmentally sustainable and prosperous energy future by ensuring affordable, abundant, and reliable energy that drives a robust economy and national security. Technical accomplishments during the program are summarized briefly here and described in detail in the remainder of the report.

02 PETROLEUM↗
DOE OSTI2022

Model Assumptions and Data Characteristics: Impacts on Domain Adaptation in Building Segmentation

Studies on domain adaptation (DA) for remote sensing (RS) imagery analysis lack consistency in selection and description of evaluation scenarios. Without properly characterizing datasets, model assumptions, and evaluation scenarios, it is difficult to objectively compare DA methods and reach conclusions about their suitability across different applications. With this motivation, this work seeks to empirically assess to which extent the interaction between data characteristics and model assumptions influences the effectiveness of DA methods. Using the widely explored task of building footprint segmentation as a case study, we perform a large-scale study across over 200 DA scenarios that include variations across view angles, areas observed, and sensors used for data acquisition. Rather than adopting different model architectures or optimization criteria, we contrast the performances of two DA methods based on adversarial learning that differ only in their assumptions about source and target domains. Informed by metadata and data characteristics unveiled using traditional computer vision (CV) techniques as well as pretrained deep models, we provide a detailed meta-analysis of experiments highlighting the importance of accurately considering data assumptions for DA in RS segmentation tasks. As demonstrated by a “cherry-picking” exercise, different claims regarding which model is best could be made by selecting different subsets of evaluation scenarios. While well-calibrated assumptions can be beneficial, mismatching assumptions can lead to negative biases in DA applications. Furthermore, this study intends to motivate the community toward more consistent evaluation protocols while providing recommendations and insights toward creating novel benchmark datasets, documenting data characteristics, application-specific knowledge, and model assumptions.

42 ENGINEERING↗
DOE OSTI2020

Thickness and strain dependence of piezoelectric coefficient in BaTiO 3 thin films

We explore the thickness dependence of the converse piezoelectric coefficient (d 33 ) in epitaxial thin films of BaTiO 3 (BTO) grown on (001) SrTiO 3 substrates. Piezoresponse force microscope was performed using an atomic force microscope equipped with an interferometric displacement sensor allowing direct quantification of electromechanical coupling coefficients in BTO free from unwanted background contributions. We find that 80-nm-thick films exhibit a d 33 of ~20.5pm/V, but as the thickness is reduced, the d 33 reduces to less than 2 pm/V for a 10 nm film. To explain the atomistic origin of the effect, we performed molecular dynamics simulations with a recently developed ab initio-derived reactive force field, constructed using the ReaxFF framework. Simulations predict that under applied electric fields thin films of BaTiO 3 show an increasing thickness, with compressive strain, of the region screening the depolarization-field. This study confirms quantitatively the drop in piezoelectric performance in BTO ultrathin films and again highlights the importance of the screening mechanisms when films approach the ultrathin limits in dictating the functional behaviors.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Achieving precise multiparameter measurements with distributed optical fiber sensor using wavelength diversity and deep neural networks

The development of advanced distributed optical fiber sensing systems that are capable of performing accurate and spatially resolved multiparameter measurements is of great interest to a wide range of scientific and industrial applications. Here, in this paper, we propose and experimentally demonstrate a wavelength diversity based advanced distributed optical fiber sensor system to accomplish multiparameter sensing while greatly enhancing measurement accuracy. A suite of deep neural network (DNN) algorithms are developed and verified for data denoising, rapid Brillouin frequency shift estimation, and vibration data event classification. As a proof-of-concept, we demonstrate the effectiveness of the proposed advanced wavelength diversity distributed fiber sensor system assisted by DNN for simultaneous, independent measurements of static strain, temperature, and acoustic vibrations over a 25 km long sensing fiber at 3 m spatial resolution. These results suggest the potential for an intelligent multiparameter monitoring system with enhanced performance in advanced structural health monitoring applications.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Assessing Resilience in Lane Detection Methods: Infrastructure-Based Sensors and Traditional Approaches for Autonomous Vehicles

Traditional autonomous vehicle perception subsystems that use onboard sensors have the drawbacks of high computational load and data duplication. Infrastructure-based sensors, which can provide high quality information without the computational burden and data duplication, are an alternative to traditional autonomous vehicle perception subsystems. However, these technologies are still in the early stages of development and have not been extensively evaluated for lane detection system performance. Therefore, there is a lack of quantitative data on their performance relative to traditional perception methods, especially during hazardous scenarios, such as lane line occlusion, sensor failure, and environmental obstructions. We address this need by evaluating the influence of hazards on the resilience of three different lane detection methods in simulation: (1) traditional camera detection using a U-Net algorithm, (2) radar detections using infrastructure-based radar retro-reflectors (RRs), and (3) direct communication of lane line information using chip-enabled raised pavement markers (CERPMs). The performance of each of these methods is assessed using resilience engineering metrics by simulating the individual methods for each sensor technology’s response to related hazards in the CARLA simulator. Using simulation techniques to replicate these methods and hazards acquires extensive datasets without lengthy time investments. Specifically, the resilience triangle was used to quantitatively measure the resilience of the lane detection system to obtain unique insights into each of the three lane detection methods; notably the infrastructure-based CERPMs and RRs had high resistance to hazards and were not as easily affected as the vision-based U-Net. However, while U-Net was able to recover the fastest from the disruption as compared to the other two methods, it also had the most performance loss. Overall, this study demonstrates that while infrastructure-based lane keeping technologies are still in early development, they have great potential as alternatives to traditional ones.

Patil, Pritesh↗
DOE OSTI2022

Demonstration of VOC Fenceline Sensors and Canister Grab Sampling near Chemical Facilities in Louisville, Kentucky

Experimental fenceline sensor pods (SPods) fitted with 30 s duration canister grab sampling (CGS) systems were deployed at a site near chemical facilities in Louisville, KY, from 4 June 2018 to 5 January 2020. The objective of the study was to better understand lower cost 10.6 eV photoionization detector (PID)-based volatile organic compound (VOC) sensors and investigate their utility for near-source emissions detection applications. Prototype SPods containing PID sensor elements from two different manufacturers yielded between 78% and 86% valid data over the study, producing a dataset of over 120,000 collocated pair fenceline measurements averaged into 5-min datapoints. Ten-second time-resolved SPod data from an elevated fenceline sensor signal day are presented, illustrating source emission detections from the direction of a facility 500 m west of the monitoring site. An SPod-triggered CGS acquired in the emission plume on this day contained elevated concentrations of 1,3-butadiene and cyclohexane (36 parts per billion by volume (ppbv) and 637 ppbv, respectively), compounds known to be emitted by this facility. Elevated concentrations of these compounds were observed in a subset of the 61 manual and triggered CGS grab samples acquired during the study, with winds from the west. Using novel wind-resolved visualization and normalization approaches described herein, the collocated pair SPod datasets exhibited similarity in emission source signature. With winds from the west, approximately 50% of SPod readings were above our defined theoretical detection limit indicating persistent measurable VOC signal at this site. Overall, this 19-month study demonstrated reasonable prototype SPod operational performance indicating that improved commercial forms of lower cost PID sensors could be useful for select VOC fenceline monitoring applications.

1,3-butadiene↗
DOE OSTI2020

Cognition at the Point of Sensing

Over the last 15 years, compressive sensing techniques have been developed which have the potential to greatly reduce the amount of data collected by systems while preserving the amount of information obtained. A cost of this efficiency is that a computationally-intensive optimization routine must be used to put the sensed data into a form that a person can interpret. At the same time, machine learning techniques have experienced tremendous growth as well. Machines have demonstrated the ability learn how to effectively perform tasks such as detection and classification at speeds much faster than humanly possible. Our goal in this project was to study the feasibility of using compressive sensing systems "at the edge." That is, how can compressive sensing sensors be deployed such that information is created at the remote sensor rather than sending raw data to a central processing location? Studies were performed to analyze whether machine learning could be done on the compressively sensed data in its raw form. If a machine is performing the task, is it possible to do so without putting the data into a human interpretable form? We show that this is possible for some systems, in particular a compressive sensing snapshot imaging spectrometer. Machine learning tasks were demonstrated to be more effective and more robust to noise when the machine learning algorithm worked on data in its raw form. This system is shown to outperform a traditional spectrometer. Techniques for reducing the complexity of the reconstruction routine were also analyzed. Techniques for such as data regularization, deep neural networks, and matrix completion were studied and shown to have benefits over traditional reconstruction techniques. In this project we showed that compressive sensing sensors are indeed feasible at the edge. As always, sensors and algorithms must be carefully tuned to work in the constrained environment. In this project we developed tools and techniques to enable those analyses.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Hydrogen Mitigation Process Testing at Nevada Solar One

The National Renewable Energy Laboratory (NREL) and Acciona Solar Power (ASP) developed and installed a process that addresses the issue of hydrogen buildup in Acciona's Nevada Solar One power plant. Our method selectively removes hydrogen from the expansion tanks of the power plant to control hydrogen levels in the circulating heat-transfer fluid (HTF). During previous work, we developed a sensor that measures hydrogen partial pressure in the expansion-tank headspace gas. We demonstrated that our sensor measures hydrogen levels over a wide range of partial pressure from 10 mbar down to 0.003 mbar. More recently, we conceived and developed an integrated process module that performs both hydrogen sensing and separating functions. The sensor/separator measures hydrogen partial pressure in the headspace gas in the same way as our original sensor design. Additionally, the integrated module separates hydrogen from the headspace gas to reduce hydrogen to the level needed to maintain the performance of receivers in the collector field. Laboratory testing at NREL showed that the sensor function had an accuracy of +/-7%, and the hydrogen extraction rate for separator function was consistent with our modeling predictions. The primary benefit of this module is its simple design, both in terms of function and incorporation into the HTF subsystem of the power plant. Most recently, NREL and ASP completed installation and initial testing of a mitigation process at ASP's Nevada Solar One power plant in Boulder City, Nevada. In this paper, we report on the completed installation, initial testing, and plans to bring the process to full automation, so that it can be operated unattended on a daily schedule.

chemical elements↗
DOE OSTI2022

Use of diamond sensors for a high-flux, high-rate X-ray pass-through diagnostic

X-ray free-electron lasers (XFELs) deliver pulses of coherent X-rays on the femtosecond time scale, with potentially high repetition rates. While XFELs provide high peak intensities, both the intensity and the centroid of the beam fluctuate strongly on a pulse-to-pulse basis, motivating high-rate beam diagnostics that operate over a large dynamic range. The fast drift velocity, low X-ray absorption and high radiation tolerance properties of chemical vapour deposition diamonds make these crystals a promising candidate material for developing a fast (multi-GHz) pass-through diagnostic for the next generation of XFELs. A new approach to the design of a diamond sensor signal path is presented, along with associated characterization studies performed in the XPP endstation of the LINAC Coherent Light Source (LCLS) at SLAC. Qualitative charge collection profiles (collected charge versus time) are presented and compared with those from a commercially available detector. Quantitative results on the charge collection efficiency and signal collection times are presented over a range of approximately four orders of magnitude in the generated electron–hole plasma density.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Evaluation of Three Types of Electric Field Sensors

On June 3-4, 2025, we evaluated three types of electric-field (E-field) sensors: capacitive electrodes, stainless-steel stakes, and porous pots - at two electromagnetic (EM) stations (PWBVI and PWAOF) on Aqueduct Mesa at the Nevada National Security Site. We compared calibrated time series and derived metrics including power spectral density, root-mean-square (RMS) amplitude, signal-to-noise ratio (SNR), and detectability of power line harmonics and Schumann resonance. The objective was to identify an alternative E field sensor with higher SNR than the existing capacitive electrodes. Our data demonstrates that stainless-steel electrodes perform best, with the highest SNR and the most consistent detection of power-line harmonics and Schumann resonance among the three sensor types; they may be used as a supplement to, or replacement for, the existing capacitive electrodes.

58 GEOSCIENCES↗
DOE OSTI2024

Characterization of Intrinsic Fiber Optic Temperature Sensors by In-core and Furnace Testing

Increased research and development in nuclear technology has raised the demand for novel sensors and instrumentation to meet data objectives, and survive in different conditions and environments, beyond conventional light water reactor (LWR) environments. Expediting the deployment of advanced nuclear technologies by developing, demonstrating, and qualifying advanced reactor fuel forms necessitates a deeper understanding of how irradiation affects fuels and materials' performance. To achieve a more comprehensive understanding of fuels and materials performance, researchers require more specialized experiments and measurements. The demand for innovative sensors to support nuclear fuel development arises from the complexity of materials' behavior under irradiation and the challenges of deploying instrumentation in Material Test Reactors (MTRs) for irradiation tests. Additionally, measurements of material properties require integrated measurement systems to characterize thermal properties, mechanical properties, chemistry, and microstructure [1]. Among the potential measurement techniques, optical fiber-based sensors have been identified as potential sensors to measure different physical phenomena such as temperature, strain, pressure, and fluid level. Optical fiber sensors have the capability to provide multi-sensing and multiplexing instrumentation, allowing the measurement of different physical parameters within a single sensor configuration, and transmitting data collected at multiple locations through a single fiber. They offer immunity to electromagnetic interference, electrical passivity, compatibility with various sensing methodologies, and cost-effectiveness. Beyond their widespread use in telecommunications, silica fiber-based instruments are utilized in industrial applications, even at temperatures reaching 300?400°C, such as distributed temperature sensing in oil and gas recovery. The Department of Energy (DOE) is interested in using fiber optics to support fuel cycle development [2]. Fiber optics are an excellent candidate for harsh environment sensing, including sensing at very high temperatures (1900oC for sapphire optical fibers). Distributed fiber optic sensing has been deployed in other harsh environments like coal gasification plants [3]. Distributed strain sensing, which operates similarly to distributed temperature sensing, has been deployed to monitor underground mines and fibers have been imbedded in soil to monitor sink-hole development [4][5]. The application of fiber optic sensors to advanced reactor development is a promising area of research.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Characterizing TES Power Noise for Future Single Optical-Phonon and Infrared-Photon Detectors

In this letter, we present the performance of a 100 μm × 400 μm × 40 nm W Transition-Edge Sensor with a critical temperature of 40 mK. This device has a noise equivalent power of 1.5×10-18 W/Hz, in a bandwidth of 2.6 kHz, indicating a resolution for Dirac delta energy depositions of 40 ± 5 meV (rms). The performance demonstrated by this device is a critical step toward developing a O(100) meV threshold athermal phonon detector for low-mass dark matter searches.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

The DAQ system of the 12,000 channel CMS high granularity calorimeter prototype

The CMS experiment at the CERN LHC will be upgraded to accommodate the 5-fold increase in the instantaneous luminosity expected at the High-Luminosity LHC (HL-LHC). Concomitant with this increase will be an increase in the number of interactions in each bunch crossing and a significant increase in the total ionising dose and fluence. One part of this upgrade is the replacement of the current endcap calorimeters with a high granularity sampling calorimeter equipped with silicon sensors, designed to manage the high collision rates. As part of the development of this calorimeter, a series of beam tests have been conducted with different sampling configurations using prototype segmented silicon detectors. In the most recent of these tests, conducted in late 2018 at the CERN SPS, the performance of a prototype calorimeter equipped with ≈12,000 channels of silicon sensors was studied with beams of high-energy electrons, pions and muons. This paper describes the custom-built scalable data acquisition system that was built with readily available FPGA mezzanines and low-cost Raspberry Pi computers.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Test Information Management System (Report FY 2020 and FY 2021)

Delivery Environments supported work in Fiscal Years 2020 and 2021 to develop the Test Information Management System (TIMS). This effort involved creating a suite of tools consisting of a database and the uploading and downloading scripts. It has been demonstrated that the TIMS database is suitable for archiving raw measurements performed on various measured test articles. The TIMS database has been populated with data from the TRUST projects in FY 20 and FY 21. This included the functional data in the Sensors records as well as metadata stored in records in several other tables. The performed work demonstrated that a database archiving validation test data and integrating it with engineering analysis simulations has a great potential to increase the efficiency, responsiveness, and confidence in the modeling, simulation, and validation efforts for future (and current) weapon systems and assemblies in normal and abnormal environments. In this report, we also discuss how the records belonging to the same project should be arranged, what are the minimum requirements for linking the records using the tabular links, and we provide several recommendations to the projects engineers and the database administrator to improve the TIMS database.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Advanced Sensors for In-Situ Amine Degradation Monitoring in Post-Combustion Carbon Capture

In order to improve the long-term performance of post-combustion carbon capture technologies, improvements to the detection of solvent degradation or reduction in CO2 capture efficiency are necessary. A pair of sensors were deployed to the National Carbon Capture Center’s (NCCC) to monitor CO2 capture and solvent degradation. The CO2 sensors were located to measure before capture and after capture concentrations of CO2 to measure the capture efficiency of the solvent system. Amine degradation sensors were located at four locations along the main solvent lines corresponding to CO2 loading and temperature: hot rich, cold rich, hot lean, and cold lean. Experimental results from laboratory tests using the sensors are shown, along with prototype designs that were deployed to the field site.

Brister, Matthew↗