Search NASA⌕ Search

SEARCH · Search NASA

Results for “distributed temperature sensor”

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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Accurate Ultrasonic Thickness Measurement for Arbitrary Time-Variant Thermal Profile

Ultrasonic thickness measurement of mechanical structures is one of the most popular and commonly used nondestructive methods for various kinds of process control and corrosion monitoring. With ultrasonic propagation speed being temperature-dependent, the thickness measurement can be performed reliably only when the thermal profile is completely known. Most conventional techniques assume the temperature of the test structure is uniform and at room temperature across its thickness. Such assumptions may lead to large errors in the thickness measurement, especially when there are significant temperature variations across the thickness. State-of-the-art techniques use external temperature measurements or implement iterative methods to compensate for the unknown thermal profiles. However, such techniques produce unsatisfactory results when the heat distribution is complex or varies rapidly with time. In this work, we propose a two-sensors technique, using both compressive and shear excitations, with a non-iterative rapid data processing method for accurate thickness measurement under arbitrary time-variant thermal profile. The independent behavior of shear and compressive waves is used to formulate a real-time thickness estimation technique. The developed technique is experimentally validated on a steel plate with fixed acoustic sensors. Test results show that the error in thickness estimation can be reduced by up to 98% compared to conventional thickness gauging methods.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Room temperature multiplexed gas sensing using chemical-sensitive 3.5-nm-thin silicon transistors

There is great interest in developing a low-power gas sensing technology that can sensitively and selectively quantify the chemical composition of a target atmosphere. Nanomaterials have emerged as extremely promising candidates for this technology due to their inherent low-dimensional nature and high surface-to-volume ratio. Among these, nanoscale silicon is of great interest because pristine silicon is largely inert on its own in the context of gas sensing, unless functionalized with an appropriate gas-sensitive material. We report a chemical-sensitive field-effect transistor (CS-FET) platform based on 3.5-nm-thin silicon channel transistors. Using industry-compatible processing techniques, the conventional electrically active gate stack is replaced by an ultrathin chemical-sensitive layer that is electrically nonconducting and coupled to the 3.5-nm-thin silicon channel. We demonstrate a low-power, sensitive, and selective multiplexed gas sensing technology using this platform by detecting H 2 S, H 2 , and NO 2 at room temperature for environment, health, and safety in the oil and gas industry, offering significant advantages over existing technology. Moreover, the system described here can be readily integrated with mobile electronics for distributed sensor networks in environmental pollution mapping and personal air-quality monitors.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Mitigating urban heat island and enhancing indoor thermal comfort using terrace garden

The United Nations advocates for sustainable urban planning and design, emphasizing green infrastructure initiatives to mitigate urban heat island effects and enhance the resilience and livability of cities globally. To address urban heat challenges, a study was conducted in Chennai, India, from April to June 2023. The study focused on assessing temperature dynamics on a building's terrace by comparing a well-maintained garden area with an exposed region. Temperature and humidity sensors were deployed in both the garden and exposed areas of the terrace, as well as within rooms beneath it, to monitor hourly temperature fluctuations. The findings indicate a significant reduction in internal room temperatures in areas with rooftop gardens, ranging from 4 to 11 °C, depending on the time of year and sun's position, compared to rooms with fully exposed roof configurations. Additionally, simulation studies were performed to validate these findings, suggesting that optimizing the distribution of soil beds and plant density across the roof could yield an additional temperature reduction of 3–4 °C, resulting in an overall difference of up to 14–15 °C. The study highlights the efficacy of rooftop gardens in providing cooling effects during daylight hours and maintaining temperature parity post-sunset. Through analysis of sensor data, the research elucidates the intricate relationship between green infrastructure and thermal comfort, offering insights for energy-efficient building design and resilient urban planning. The findings underscore the potential of rooftop gardens in fostering a more comfortable, energy-efficient, and sustainable urban living environment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Applying Infrared Thermography as a Method for Online Monitoring of Turbine Blade Coolant Flow

As gas turbine engine manufacturers strive to implement condition-based operation and maintenance, there is a need for blade monitoring strategies capable of early fault detection and root-cause determination. Given the importance of blade cooling flows to turbine blade health and longevity, there is a distinct lack of methodologies for coolant flowrate monitoring. The present study addresses this identified opportunity by applying an infrared thermography system on an engine-representative research turbine to generate data-driven models for prediction of blade coolant flowrate. Thermal images were used as inputs to a linear regression and regularization algorithm to relate blade surface temperature distribution with blade coolant flowrate. Additionally, this study investigates how coolant flowrate prediction accuracy is influenced by the number and breadth of diagnostic measurements. Here, the results of this study indicate that a source of high-fidelity training data can be used to predict blade coolant flowrate within about six percent error. Furthermore, identification of prioritized sensor placement supports application of this technique across multiple sensor technologies capable of measuring blade surface temperature in operating gas turbine engines, including spatially resolved and point-based measurement techniques.

42 ENGINEERING↗
DOE OSTI2019

Application of Sapphire-Fiber-Bragg-Grating-Based Multi-Point Temperature Sensor in Boilers at a Commercial Power Plant

Readily available temperature sensing in boilers is necessary to improve efficiencies, minimize downtime, and reduce toxic emissions for a power plant. The current techniques are typically deployed as a single-point measurement and are primarily used for detection and prevention of catastrophic events due to the harsh environment. In this work, a multi-point temperature sensor based on wavelength-multiplexed sapphire fiber Bragg gratings (SFBGs) were fabricated via the point-by-point method with a femtosecond laser. The sensor was packaged and calibrated in the lab, including thermally equilibrating at 1200 °C, followed by a 110-h, 1000 °C stability test. After laboratory testing, the sensor system was deployed in both a commercial coal-fired and a gas-fired boiler for 42 days and 48 days, respectively. The performance of the sensor was consistent during the entire test duration, over the course of which it measured temperatures up to 950 °C (with some excursions over 1000 °C), showing the survivability of the sensor in a field environment. The sensor has a demonstrated measurement range from room temperature to 1200 °C, but the maximum temperature limit is expected to be up to 1900 °C, based on previous work with other sapphire based temperature sensors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Simulation and measurement of beam-induced heating of ceramic vacuum chambers

In this article, we summarize recent theoretical and experimental studies of the impedance and beam-induced heating of titanium-coated ceramic vacuum chambers used in the NSLS-II injection kickers. The impedance was calculated using the field matching theory assuming planar approximation and compared with the mpedanceake2 code. For the coating thickness of a few microns, we demonstrated that the beam-induced power is dissipated in the titanium coating and that the longitudinally averaged two-dimensional power density is approximated by an analytical expression, thus allowing the use of a simplified model of the power density as input for the code to simulate the temperature distribution with realistic nonuniform thickness of the Ti coating. For a few values of the NSLS-II beam current, we measured the beam-induced heating of two ceramic chambers using thermal sensors installed along the chamber and compared the measurement results with the simulations. Published by the American Physical Society 2024

36 MATERIALS SCIENCE↗
DOE OSTI2020

Integrating Radio-Over-Fiber Communication System and BOTDR Sensor System

In this paper, we propose and experimentally demonstrate for the first time, the integration of a radio-over-fiber (RoF) communication system and a Brillouin optical time-domain reflectometry (BOTDR) distributed sensor system using a single optical fiber link. In this proof-of-concept integrated system, the communication system is composed of three modulation formats of quadrature phase-shift keying (QPSK), 16-quadrature amplitude modulation (16-QAM) and 64-QAM, which are modulated onto an orthogonal frequency division multiplexing (OFDM) signal. Whereas, the BOTDR sensor system is used for strain and/or temperature monitoring over the fiber distance with a spatial resolution of 5 m using a 25 km single-mode silica fiber. The error vector magnitude (EVM) is analyzed in three modulation formats in the presence of various BOTDR input pump powers. Using QPSK modulation, optimized 18 dBm sensing and 10 dBm data power, the measured EVM values with and without bandpass filter are 3.5% and 14.5%, respectively. The proposed system demonstrates a low temperature measurement error (±0.49 °C at the end of 25 km) and acceptable EVM values, which were within the 3GPP requirements. The proposed integrated system can be effectively applied for practical applications, which significantly reduces the fiber infrastructure cost by effective usage of a single optical fiber link.

42 ENGINEERING↗
DOE OSTI2015

Monitoring local heating around an interventional MRI antenna with RF radiometry

Purpose: Radiofrequency (RF) radiometry uses thermal noise detected by an antenna to measure the temperature of objects independent of medical imaging technologies such as magnetic resonance imaging (MRI). Here, an active interventional MRI antenna can be deployed as a RF radiometer to measure local heating, as a possible new method of monitoring device safety and thermal therapy. Methods: A 128 MHz radiometer receiver was fabricated to measure the RF noise voltage from an interventional 3 T MRI loopless antenna and calibrated for temperature in a uniformly heated bioanalogous gel phantom. Local heating (ΔT) was induced using the antenna for RF transmission and measured by RF radiometry, fiber-optic thermal sensors, and MRI thermometry. The spatial thermal sensitivity of the antenna radiometer was numerically computed using a method-of-moment electric field analyses. The gel’s thermal conductivity was measured by MRI thermometry, and the localized time-dependent ΔT distribution computed from the bioheat transfer equation and compared with radiometry measurements. A “H-factor” relating the 1 g-averaged ΔT to the radiometric temperature was introduced to estimate peak temperature rise in the antenna’s sensitive region. Results: The loopless antenna radiometer linearly tracked temperature inside a thermally equilibrated phantom up to 73 °C to within ±0.3 °C at a 2 Hz sample rate. Computed and MRI thermometric measures of peak ΔT agreed within 13%. The peak 1 g-average temperature was H = 1.36 ± 0.02 times higher than the radiometric temperature for any media with a thermal conductivity of 0.15–0.50 (W/m)/K, indicating that the radiometer can measure peak 1 g-averaged ΔT in physiologically relevant tissue within ±0.4 °C. Conclusions: Active internal MRI detectors can serve as RF radiometers at the MRI frequency to provide accurate independent measures of local and peak temperature without the artifacts that can accompany MRI thermometry or the extra space needed to accommodate alternative thermal transducers. A RF radiometer could be integrated in a MRI scanner to permit “self-monitoring” for assuring device safety and/or monitoring delivery of thermal therapy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Hierarchical TiO 2 :Cu 2 O Nanostructures for Gas/Vapor Sensing and CO 2 Sequestration

We investigate the effect of high-surface-area self-assembled TiO 2 :Cu 2 O nanostructures for CO 2 and relative humidity gravimetric detection using polyethylenimine (PEI), 1-ethyl-3-methylimidazolium (EMIM), and polyacrylamide (PAAm). Introduction of hierarchical TiO 2 :Cu 2 O nanostructures on the surface of quartz crystal microbalance sensors is found to significantly improve sensitivity to CO 2 and to H 2 O vapor. The response of EMIM to CO 2 increases fivefold for 100 nm-thick TiO 2 :Cu 2 O as compared to gold. At ambient CO 2 concentrations, the hierarchical assembly operates as a sensor with excellent reversibility, while at higher pressures, the CO 2 desorption rate decreases, suggesting possible application for CO 2 sequestration under these conditions. The gravimetric response of PEI to CO 2 increases by a factor of 3 upon introduction of a 50 nm TiO 2 :Cu 2 O layer. The PAAm gravimetric response to water vapor also increases by a factor of 3 and displays improved reversibility with the addition of 50 nm TiO 2 :Cu 2 O structures. We found that TiO 2 :Cu 2 O can be used to lower the detection limits for CO 2 sensing with EMIM and PEI and lower the detection limits for H 2 O sensing with PAAm by over a factor of 2. Coarse-grained and all-atom molecular dynamics simulations indicate the dissociative character of ionic liquid assembly on TiO 2 :Cu 2 O interfaces and different distributions of CO 2 and H 2 O molecules on bare and ionic liquid-coated surfaces, confirming experimental observations. Overall, our results show high potential of hierarchical assemblies of TiO 2 :Cu 2 O/room temperature ionic liquid and polymer films for sensors and CO 2 sequestration.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Explainable machine learning for incipient anomaly detection in compact molten salt heat exchanger with overlapping feature distributions

High-temperature molten salt-cooled reactors (MSCRs) are a promising next-generation nuclear technology option, offering efficient power conversion and inherent safety features. However, the reliability of these systems depends on the robust operation of heat exchangers (HXs), which are susceptible to failure due to temperature gradients and channel plugging caused by fluid freezing. Conventional monitoring methods, relying on inlet and outlet measurements, lack the spatial resolution needed to detect early-stage faults. We propose a novel design of a compact salt-to-salt matrix-type HX design consisting of interleaved arrays of parallel tubes, with integrated synthetic fiber optic distributed temperature sensing (DTS) to enable localized detection of incipient faults. To evaluate performance of this design, we generate high-fidelity synthetic data using heat transfer computational modeling to simulate channel plugging, and introduce sensor noise for realistic modeling of measurements. The dataset comprises of 97% normal operation and 3% anomaly cases, with each anomaly class representing 1% of the data. These early anomalies result in overlapping temperature profiles between normal and faulty channels, producing a non-separable dataset that challenges traditional classification techniques. We benchmark eight supervised machine learning (ML) models and demonstrate that XGBoost achieves the highest performance. To improve transparency, we develop an explainability framework combining Shapley values and partially ordered sets (POSETs) to quantify and structurally analyze feature importance. This approach identifies both dominant predictors and ambiguous feature relationships, enhancing trust and interpretability. Our results highlight the potential of combining DTS and explainable ML with intelligent feature selection to improve predictive maintenance and ensure operational resilience in advanced nuclear systems.

Prantikos, Konstantinos [Argonne National Laborato↗
DOE OSTI2011

Hydrostatic Microextrusion of Steel and Copper

The paper presents an experimental investigation based on hydrostatic micro extrusion of billets in low carbon steel and commercially pure copper, and the relevant results. The starting billets have a diameter of 0.3 mm and are 5 mm long; a high pressure generator consisting of a manually operated piston screw pump is used to pressurize the fluid up to 4200 bar, the screw pump is connected through a 3-way distribution block to the extrusion die and to a strain gauge high pressure sensor. The sensor has a full scale of 5000 bar and the extrusion pressure is acquired at a sampling rate of 2 kHz by means of an acquisition program written in the LabVIEW environment. Tests have been conducted at room temperature and a lubricant for wire drawing (Chemetall Gardolube DO 338) acts both as the pressurizing fluid and lubricant too. In addition, billets were graphite coated. Different fluid pressures and process durations have been adopted, resulting in different extrusion lengths. The required extrusion pressure is much higher than in non-micro forming operations (this effect is more evident for steel). On the cross section of the extruded parts, hardness and grain size distribution have been measured, the former through Vickers micro hardness (10 g load) tests. In the case of the extrusion of copper, the material behaves as in microdrawing process. In the case of the extrusion of steel, the hardness increases from the core to the surface as in the drawing process, but with lower values. The analysis evidenced the presence of the external layer, but its thickness is about 1/3 of the external layer in the drawn wire and the grains appear smaller than in the layer of the drawn wire. The extruding force required along the extruding direction is higher (22-24 N) than the drawing force along the same direction (12 N): being the material, the reduction ratio, the die sliding length the same in both cases, the higher extrusion force should be caused by a higher tangential friction force and/or a higher redundant work of deformation and/or a different material behaviour. Which is the real mechanism is not clear at present, but surface layer grains in extrusion are more deformed than in wire drawing. For this reason the deformation inhomogeneity increases in extrusion and the material under the highly deformed surface layer should be subjected to lower strains, strain hardening and finally resulting in lower hardness.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Acoustic modal analysis of unique fused-silica fibers for passive non-destructive structural monitoring

The goal of non-destructive acoustic sensing is to passively monitor innate structural parameters such as temperature, strain, and pressure. Intended for use in harsh structural environments, a distributed acoustic sensing system has been created using a single mode, radiation hardened, fused-silica core rod and a high-resolution acoustic sensor. This study aims to enhance the capabilities of the acoustic sensor by manipulating the fused-silica core rod, geometrically, to induce and control additional acoustic reflections. Here, two geometries are demonstrated to markedly improve upon previous fiber designs and result in a consistent acoustic profile.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Minimization of Measurement Uncertainty in Optical Frequency Domain Reflectometry

Optical frequency domain reflectometry (OFDR) is a technique for interrogating optical fiber sensors to generate relative, quasi-distributed measurements. Although Optical frequency domain reflectometry (OFDR) is increasingly being adopted for aerospace, energy production, and structural monitoring applications, the quantification of uncertainty for OFDR measurements has not been developed beyond sparse empirical relationships. To address this knowledge gap, an uncertainty metric for OFDR measurements was developed. This uncertainty metric was applied to weight the edges between OFDR measurements on directed correlation graphs and analyzed to minimize the cumulative uncertainty. In conclusion, this work is the first to propose an uncertainty metric for OFDR and provides a generalized mathematical framework for optimizing OFDR hardware selection, optical fiber sensor selection, and postprocessing strategy.

42 ENGINEERING↗
DOE OSTI2020

Elevation distributed micro-climatology data in a coastal glaciated watershed

Air temperature, ground temperature and relative humidity data were collected in a longitudinal transect of the Nooksack watershed at varying elevations from 500 to 1800 m above sea level. Data were collected by anchoring sensors from trees above winter snow levels and shaded from direct solar radiation. Paired sensors were also buried 3 cm under ground near each air temperature sensor to determine snow absence or presence. Select sites included relative humidity sensors to indicate whether precipitation was occurring. Data were collected every 3-4 h from December 2015 to Sept 2018 (with ongoing collection). Code for analysis of daily mean, minimum, maximum, and temperature change with elevation (lapse rates) are available on Github (https://doi.org/10.5281/zenodo.3239539). The sensor download and intermediate data products are available on HydroShare at (http://www.hydroshare.org/resource/222e832d3df24dea9bae9bbeb6f4219d) with publicly accessible visualization available from the Nooksack Observatory at data.cuahsi.org. Hydrologic models are generally structured with a single annual average lapse rate parameter which assumes a linear temperature gradient with elevation. The daily data (2016-2018) is used as part of ongoing studies on the non-linear dynamics and temporal variability of temperature with elevation to improve assessments of watershed function and salmon habitat.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Date Release Report for Large Surface Explosion Coupling Experiment (LSECE) Nevada National Security Site

The DTRA sponsored 2020-2022 Large Surface Explosion Coupling Experiment (LSECE) consists of two large ground surface chemical explosions, data collection, analysis and modeling carried out in 2020-2022. The LSECE chemical explosions were carried out at the site of prior NNSA sponsored buried chemical explosions that were part of the Source Physics Experiment (SPE) Phase II in Dry Alluvium Geology (DAG). This report described the data collected under LSECE that is being made publicly available. The prior buried explosion DAG data are described in a separate report (https://www.osti.gov/biblio/1825534) and are also publicly available. The LSECE explosions, data analysis and numerical modeling work sponsored by DTRA were intended to address three objectives: 1) Generate seismo-acoustic data to test and improve numerical models of explosion energy coupling in dry alluvium geology; 2) To improve seismo-acoustic yield estimation techniques as a function of depth and medium properties; 3) To study and improve acoustic propagation modeling under two different atmospheric conditions. The LSECE data that were collected have expanded the prior DAG buried explosion dataset to include surface chemical explosions recorded on a common set of seismo-acoustic stations. They have allowed a more detailed study of above and below surface seismo-acoustic energy coupling with applications to explosion monitoring and assessment. The two approximately 1-ton TNT equivalent yield LSECE explosions were detonated at different times of the day to explore the effects of the different atmospheric conditions. The first chemical explosion “Artemis” was conducted before dawn when temperature inversions were present. The second chemical explosion “Apollo” was conducted on a sunny afternoon when the temperature gradient was more linear. The LSECE chemical explosion data were also collected across a variety of different sensor types to allow evaluation of their effectiveness in recovering useful information. The LSECE instrumentation included fiber optic or Distributed Acoustic Sensing (DAS), a dense array or Large-N array of seismometers, borehole accelerometers, DAS and a velocity meter, airborne acoustic instruments and a variety of visual and remote sensing data.

58 GEOSCIENCES↗
DOE OSTI2019

Large-Scale Thermoset Pick and Place Testing and Implementation

Oak Ridge National Laboratory is developing the first commercially available medium to large-scale thermoset additive manufacturing (AM) system with Magnum Venus Products (MVP). This 3D printer is capable of fabricating large-scale thermoset components at room temperature with a build volume of 16’ x 8’ x 42’’. The thermoset extrusion process uses irreversible exothermic chemical reactions to form a cross-linked polymer. Printing thermosets at such large scales with highly customizable materials at room temperature provides huge opportunities for complex and smart tooling applications. Integrating a pick and place actuator into this system will allow for the placement of heating/cooling channels as well as sensors to monitor tooling health and heat distribution. The pneumatically-driven pick and place actuator is integrated into the existing electrical and mechanical design and is controlled using custom M-code commands. The system is comprised mostly of commercially available components, providing easy adoptability by future thermoset systems.

Boulger, Alex↗
DOE OSTI2024

Shrubs Strongly Influence Snow Properties in Two Subarctic Watersheds

Understanding changes in snow distribution in permafrost ecosystems is fundamental to predicting their response to future climate change. The expansion of tall shrubs into tundra ecosystems can trap snow and insulate permafrost ecosystems during the winter, but the overall insulation effect is dependent upon many ecosystem properties. To study shrub–snow–ground interactions, small temperature sensors were deployed at two research sites on the Seward Peninsula of Alaska, USA, during the 2019–2020 winter. Snow temperatures were used to extrapolate multiple metrics, including freezing n-factors, the snow insulation effect, snow cover duration, and the length of the snowmelt period. Statistical and spatial analysis showed that shrub patches were a dominant control on all snow metrics. Within shrub patches, average ground temperatures were 2.1°C warmer, snow persisted 50 days longer, snow insulation was double, and a longer, later spring snowmelt period occurred compared to nonshrubby areas. Site-level differences contributed relatively little to variation in snow metrics, indicating that shrub presence is an overarching driver of snow–ground interactions at the locations examined. Shrub expansion, which is anticipated under climate change, will strongly impact future permafrost distribution and Arctic energy, water, and carbon cycles through snow–shrub–ground feedbacks.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

HVAC control fine-grained occupancy pattern estimation

A thermal state of a plurality of zones of the building is updated according to a building thermal model and information received from temperature sensors of the building. Predicted occupant counts for an upcoming plurality of time slots for each of the plurality of zones are updated using actual occupancy counts for each of the plurality of zones. A misprediction type distribution for the upcoming plurality of time slots for each of the plurality of zones is updated, the misprediction type distribution indicating misprediction for true negatives, false positives, false negatives, and true positives. A total misprediction cost expectation is updated according to the predicted occupant counts and the misprediction type distribution. HVAC power for each of the plurality of zones is determined to optimize occupant thermal comfort weighted according to the predicted occupant counts while minimizing the total misprediction cost expectation. HVAC operation is controlled per the HVAC power.

Lin, Shan↗