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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.

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At least 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Using the Gerchberg-Saxton algorithm to reconstruct nonmodulated pyramid wavefront sensor measurements

Adaptive optics (AO) is a technique to improve the resolution of ground-based telescopes by correcting, in real-time, optical aberrations due to atmospheric turbulence and the telescope itself. With the rise of Giant Segmented Mirror Telescopes (GSMT), AO is needed more than ever to reach the full potential of these future observatories. One of the main performance drivers of an AO system is the wavefront sensing operation, consisting of measuring the shape of the above mentioned optical aberrations. Aims. The non-modulated pyramid wavefront sensor (nPWFS) is a wavefront sensor with high sensitivity, allowing the limits of AO systems to be pushed. The high sensitivity comes at the expense of its dynamic range, which makes it a highly non-linear sensor. We propose here a novel way to invert nPWFS signals by using the principle of reciprocity of light propagation and the Gerchberg-Saxton (GS) algorithm. We test the performance of this reconstructor in two steps: the technique is first implemented in simulations, where some of its basic properties are studied. Then, the GS reconstructor is tested on the Santa Cruz Extreme Adaptive optics Laboratory (SEAL) testbed located at the University of California Santa Cruz. This new way to invert the nPWFS measurements allows us to drastically increase the dynamic range of the reconstruction for the nPWFS, pushing the dynamics close to a modulated PWFS. The reconstructor is an iterative algorithm requiring heavy computational burden, which could be an issue for real-time purposes in its current implementation. However, this new reconstructor could still be helpful in the case of many wavefront control operations. In conclusion, this reconstruction technique has also been successfully tested on the Santa Cruz Extreme AO Laboratory (SEAL) bench where it is now used as the standard way to invert nPWFS signal.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Effects of head modeling errors on the spatial frequency representation of MEG

Abstract Objectives. We aim to investigate the effects of head model inaccuracies on signal and source reconstruction accuracies for various sensor array distances to the head. This allows for the assessment of the importance of head modeling for next-generation magnetoencephalography (MEG) sensors, optically-pumped magnetometers (OPM). Approach. A 1-shell boundary element method (BEM) spherical head model with 642 vertices of radius 9 cm and conductivity of 0.33 S m −1 was defined. The vertices were then randomly perturbed radially up to 2%, 4%, 6%, 8% and 10% of the radius. For each head perturbation case, the forward signal was calculated for dipolar sources located at 2 cm, 4 cm, 6 cm and 8 cm from the origin (center of the sphere), and for a 324 sensor array located at 10 cm to 15 cm from the origin. Equivalent current dipole (ECD) source localization was performed for each of these forward signals. The signal for each perturbed spherical head case was then analyzed in the spatial frequency domain, and the signal and ECD errors were quantified relative to the unperturbed case. Main results. In the noiseless and high signal-to-noise ratio (SNR) case of approximately ≥6 dB, inaccuracies in our spherical BEM head conductor models lead to increased signal and ECD inaccuracies when sensor arrays are placed closer to the head. This is true especially in the case of deep and superficial sources. In the noisy case however, the higher SNR for closer sensor arrays allows for an improved ECD fit and outweighs the effects of head geometry inaccuracies. Significance. OPMs may be placed directly on the head, as opposed to the more commonly used superconducting quantum interference device sensors which must be placed a few centimeters away from the head. OPMs thus allow for signals of higher spatial resolution to be captured, resulting in potentially more accurate source localizations. Our results suggest that an increased emphasis on accurate head modeling for OPMs may be necessary to fully realize its improved source localization potential.

Yeo, Wan-Jin (ORCID:0000000241417123)↗
DOE OSTI2020

Porous Membranes for Moisture Probe Protection (SR18024)

In the presence of contaminants, moisture probes can give false moisture readings and have shorter lifetimes. Absolute humidity probes are composed of porous, hygroscopic metallic oxide thin films (Al 2 O 3 ) coupled to electrodes (e.g. Au and Al). While these materials provide absolute humidity readings at low water levels at a relatively low cost, they are sensitive to the presence of contaminants, especially ammonia, due to its high hygroscopicity and ability to bind to the probe. Without frequent recalibration, especially in the presence of contaminants, the humidity levels are inaccurately reported due to measurement drift that occurs when the pores close, changing the impedance of the probe. The goal of this project was to improve the stability and reliability of these moisture probes by incorporating a molecular sieve into the probe to allow only the water to reach the sensor. Molecular sieves contain pores that allow molecule specific permeation. Two dimensional inorganic nanosheets (e.g. BN, MoO 2 , MoS 2 ) have recently been shown to behave as molecular sieves for water. Compared to zeolites, nanoporous materials have higher flux rates through the pores, leading to faster water permeation. Graphene oxide sheets have also been successfully employed for unimpeded water permeation; however, at lower humidity levels, the structure is unstable and the pores shrink, preventing the sieving of molecules. Compared to graphene oxide, sheets based off of inorganic materials, such as molybdenum, have been found to be more stable at lower humidity levels and have faster permeation rates. By creating a barrier in before the Al 2 O 3 sensor chip to prevent the permeation of contaminants into the probe, the lifetime of the probe will be extended. While the molybdenum sheets are a promising material for this application, their performance against ammonia and tritiated compounds have not yet been evaluated. This technology can be applied to other applications, including gas and liquid purification for environmental remediation, single molecule sensors, field effect transistors, and catalysts.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Super-porous Pt/CuO/Pt hybrid platform for ultra-sensitive and selective H 2 O 2 detection

A super-porous Pt/CuO/Pt hybrid electrode is demonstrated with extremely high sensing performance parameters for the hydrogen peroxide (H 2 O 2 ) detection. A unique physiochemical approach is adapted for the fabrication of 3-D super-porous Pt/CuO/Pt sensing platform. Here, the super-porous Pt/CuO/Pt hybrid platform demonstrates a superior sensitivity of 16,694 µA mM -1 cm -2 with a limit of detection of 2.91 nM (S/N = 3). It also demonstrates an excellent selectivity against the interfering molecules such as NaCl, fructose, ascorbic acid, citric acid, dopamine and glucose with a wide linear range. The demonstrated performance ranks the Pt/CuO/Pt hybrid platform as one of the best H 2 O 2 sensors as summarized in the Table 2. The super-porous CuO layer is fabricated by a dynamic hydrogen bubbling technique of electrochemical deposition and metallic Pt NP decoration is achieved by the physical vapor deposition (PVD) and post-annealing. The super-porous CuO layer offers a drastically improved electrochemical active surface area, and the Pt NP decoration offers a significantly improved conductivity and improved charge accumulation for the H 2 O 2 reduction. The DFT simulations confirm the predominance of CuO over Cu 2 O and Pt over Pd for the H 2 O 2 detection based on the adsorption energy, density of states and charge accumulation calculations.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Solvent–Solvent Correlations across Graphene: The Effect of Image Charges

Wetting experiments show pure graphene to be weakly hydrophilic, but its contact angle (CA) also reflects the character of the supporting material. Measurements and Molecular Dynamics simulations on suspended and supported graphene often reveal a CA reduction due to the presence of the supporting substrate. A similar reduction is consistently observed when graphene is wetted from both sides. The effect has been attributed to transparency to molecular interactions across the graphene sheet, however, the possibility of substrate-induced graphene polarization has also been considered. Computer simulations of CA on graphene have so far been determined by ignoring the material’s conducting properties. We improve the graphene model by incorporating its conductivity according to the Constant Applied Potential Molecular Dynamics. Using this method, we compare the wettabilities of suspended graphene and graphene supported by water by measuring the CA of cylindrical water drops on the sheets. The inclusion of graphene conductivity and concomitant polarization effects lead to a lower CA on suspended graphene but the CA reduction is significantly bigger when the sheets are also wetted from the opposite side. The stronger adhesion is accompanied by a profound change in the correlations among water molecules across the sheet. While partial charges on water molecules interacting across an insulator sheet attract charges of the opposite sign, apparent attraction among like charges is manifested across the conducting graphene. The change is associated with graphene polarization, as the image charges inside the conductor attract equally signed partial charges of water molecules on both sides of the sheet. Additionally, by using a non-polar liquid (diiodomethane), we affirm a detectable wetting translucency when liquid-liquid forces are dominated by dispersive interactions. Furthermore, our findings are important for predictive modeling toward a variety of applications including sensors, fuel cell membranes, water filtration, and graphene-based electrode materials in high-performance supercapacitors.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2024

Flash healing of laser-induced graphene

The advancement of laser-induced graphene (LIG) technology has streamlined the fabrications of flexible graphene devices. However, the ultrafast kinetics triggered by laser irradiation generates intrinsic amorphous characteristics, leading to high resistivity and compromised performance in electronic devices. Healing graphene defects in specific patterns is technologically challenging by conventional methods. Herein, we report the rapid rectification of LIG’s topological defects by flash Joule heating in milliseconds (referred to as F-LIG), whilst preserving its overall structure and porosity. The F-LIG exhibits a decreased I D /I G ratio from 0.84 – 0.33 and increased crystalline domain from Raman analysis, coupled with a 5-fold surge in conductivity. Pair distribution function and atomic-resolution imaging delineate a broader-range order of F-LIG with a shorter C-C bond of 1.425 Å. The improved crystallinity and conductivity of F-LIG with excellent flexibility enables its utilization in high-performance soft electronics and low-voltage disinfections. Notably, our F-LIG/polydimethylsiloxane strain sensor exhibits a gauge factor of 129.3 within 10% strain, which outperforms pristine LIG by 800%, showcasing significant potential for human-machine interfaces.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Multi-amplifier Sensing Charge-coupled Devices for Next Generation Spectroscopy

We present characterization results and performance of a prototype Multiple-Amplifier Sensing (MAS) silicon charge-coupled device (CCD) sensor with 16 channels potentially suitable for faint object astronomical spectroscopy and low-signal, photon-limited imaging. The MAS CCD is designed to reach sub-electron readout noise by repeatedly measuring charge through a line of amplifiers during the serial transfer shifts. Using synchronized readout electronics based on the Dark Energy Spectroscopic Instrument CCD controller, we report a read noise of 1.03 e$^{−}$ rms pix$^{−1}$ at a speed of 26 μs pix$^{−1}$ with a single-sample readout scheme where charge in a pixel is measured only once for each output stage. At these operating parameters, we find the amplifier-to-amplifier charge transfer efficiency (ACTE) to be >0.9995 at low counts for all amplifiers but one for which the ACTE is 0.997. This charge transfer efficiency falls above 50,000 electrons for the read-noise optimized voltage configuration we chose for the serial clocks and gates. The amplifier linearity across a broad dynamic range from ∼300 to 35,000 e$^{−}$ was also measured to be ±2.5%. We describe key operating parameters to optimize on these characteristics and describe the specific applications for which the MAS CCD may be a suitable detector candidate.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Output-weighted sampling for multi-armed bandits with extreme payoffs

We present a new type of acquisition function for online decision-making in multi-armed and contextual bandit problems with extreme payoffs. Specifically, we model the payoff function as a Gaussian process and formulate a novel type of upper confidence bound acquisition function that guides exploration towards the bandits that are deemed most relevant according to the variability of the observed rewards. This is achieved by computing a tractable likelihood ratio that quantifies the importance of the output relative to the inputs and essentially acts as an attention mechanism that promotes exploration of extreme rewards. Our formulation is supported by asymptotic zero-regret guarantees, and its performance is demonstrated across several synthetic benchmarks, as well as two realistic examples involving noisy sensor network data. Finally, we provide a JAX library for efficient bandit optimization using Gaussian processes.

Science & Technology - Other Topics↗
DOE OSTI2024

Data Visualization: Augmented Reality

In recent years, there has been an increasing interest in developing new technologies for automated characterization and visualization of condition monitoring data. Augmented Reality (AR) is a technology that is being developed to improve such data visualization. Augmented reality has been defined as a technology that merges virtual and physical components in real-time, and in three dimensions. Wearable, commercially-available AR devices allow onsite engineers and technicians to perform inspection tasks with significantly more available information such as comparisons of past and present sensor and imager data, onsite data analysis and result displays, and various forms of metadata including technical drawings, previous inspection reports and maintenance histories, operation manuals, codes and standards, and holograms representing data analysis results superimposed onto the in situ monitored system.

42 ENGINEERING↗
DOE OSTI2024

Progress and Application of Halide Perovskite Materials for Solar Cells and Light Emitting Devices

Halide perovskite materials have attracted worldwide attention in the photovoltaic area due to the rapid improvement in efficiency, from less than 4% in 2009 to 26.1% in 2023 with only a nanometer lever photo-active layer. Meanwhile, this nova star found applications in many other areas, such as light emitting, sensor, etc. This review started with the fundamentals of physics and chemistry behind the excellent performance of halide perovskite materials for photovoltaic/light emitting and the methods for preparing them. Then, it described the basic principles for solar cells and light emitting devices. It summarized the strategies including nanotechnology to improve the performance and the application of halide perovskite materials in these two areas: from structure–property relation to how each component in the devices affects the overall performance. Moreover, this review listed the challenges for the future applications of halide perovskite materials.

36 MATERIALS SCIENCE↗
DOE OSTI2026

X-ray Response of the Fully-Depleted, p-Channel SiSeRO-CCD

We present an X-ray characterization of a fully depleted, 725 $μ$m thick p-channel SiSeRO CCD. Measurements with a $^{55}$Fe source yield an energy resolution of $54 \pm 0.9$ eV ($14.6 \pm 0.25 e^{-}$) at 5.9 keV for single-pixel events, demonstrating that the SiSeRO amplifier preserves the intrinsic charge resolution of the CCD under multi-sample non-destructive readout. Characterization with a $^{241}$Am source extends the response to higher-energy photons, with reconstructed spectral features observed between 9-26 keV and the 59.5 keV $γ$ emission. These measurements, together with a muon-derived diffusion calibration, show that charge transport and diffusion are consistent with interactions spanning the full sensor depth. These results demonstrate that the SiSeRO CCD simultaneously achieves sub-electron noise performance and efficient charge collection in a thick, fully depleted silicon detector. This combination enables X-ray spectroscopy across a broad energy range while maintaining sensitivity to faint signals.

Cuevas-Zepeda, Julian↗
DOE OSTI2022

Flow Loop Study of ECT-Based Volume Fraction Monitoring in Oil–Water Two-Phase Flows

Measurement of phase volume fractions in water-containing multiphase flows is necessary for the optimization of a host of industrial flow processes. Many water-containing multiphase flows can be classified as either water-dispersed or water-continuous mixtures. A recently developed approach based on Hanai’s mixture formula and utilizing electrical capacitance tomography (ECT) sensors have shown good potential for obtaining water volume fraction estimates in two-phase water-containing flows with different water salinity levels. However, the proposed approach was investigated via controlled experiments restricted to static configurations while, in practice, multiphase flows can be dynamic and unpredictable. In this work, we perform a flow loop study of the proposed ECT-based method for volume fraction estimation in oil–water two-phase flows. Here, we evaluate the performance of the proposed method in both water-dispersed and water-continuous flow regimes by employing different types of capacitive sensors in cylindrical arrangements and parallel-plate rectangular arrangements.

02 PETROLEUM↗
DOE OSTI2024

A globally sampled high-resolution hand-labeled validation dataset for evaluating surface water extent maps

Effective monitoring of global water resources is increasingly critical due to climate change and population growth. Advancements in remote sensing technology, specifically in spatial, spectral, and temporal resolutions, are revolutionizing water resource monitoring, leading to more frequent and high-quality surface water extent maps using various techniques such as traditional image processing and machine learning algorithms. However, satellite imagery datasets contain trade-offs that result in inconsistencies in performance, such as disparities in measurement principles between optical (e.g., Sentinel-2) and radar (e.g., Sentinel-1) sensors and differences in spatial and spectral resolutions among optical sensors. Therefore, developing accurate and robust surface water mapping solutions requires independent validations from multiple datasets to identify potential biases within the imagery and algorithms. However, high-quality validation datasets are expensive to build, and few contain information on water resources. For this purpose, we introduce a globally sampled, high-spatial-resolution dataset labeled using 3 m PlanetScope imagery. Our surface water extent dataset comprises 100 images, each with a size of 1024×1024 pixels, which were sampled using a stratified random sampling strategy covering all 14 biomes. We highlighted urban and rural regions, lakes, and rivers, including braided rivers and coastal regions. We evaluated two surface water extent mapping methods using our dataset – Dynamic World, based on Sentinel-2, and the NASA IMPACT model, based on Sentinel-1. Dynamic World achieved a mean intersection over union (IoU) of 72.16 % and F1 score of 79.70 %, while the NASA IMPACT model had a mean IoU of 57.61 % and F1 score of 65.79 %. Performance varied substantially across biomes, highlighting the importance of evaluating models on diverse landscapes to assess their generalizability and robustness. Our dataset can be used to analyze satellite products and methods, providing insights into their advantages and drawbacks. Our dataset offers a unique tool for analyzing satellite products, aiding the development of more accurate and robust surface water monitoring solutions. The dataset can be accessed via https://doi.org/10.25739/03nt-4f29.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Stresses within rare-earth doped yttria-stabilized zirconia thermal barrier coatings from in-situ synchrotron X-ray diffraction at high temperatures

There is a growing interest for smart coatings that can be integrated into turbine engines for in-situ temperature measurements or health monitoring. The addition of rare-earth dopants into standard thermal barrier ceramic top coat materials is used to obtain luminescent coatings that enable spectral measurements, for real-time temperature or health monitoring. The thermomechanical performance and durability of such novel coating compositions in extreme environments still remains to be evaluated. Consequently, the ability to manufacture sensor coatings which present suitable thermal properties needs to be demonstrated. For this study, highly luminescent erbium and europium doped yttria-stabilized zirconia and state-of-the-art yttria-stabilized zirconia coatings manufactured by air plasma spray were characterized to determine the effects of the embedded rareearth dopants on coating internal strain and stress and to quantify and compare their high temperature response using synchrotron X-ray diffraction. In-situ depth-resolved strain measurements were performed at 15 μm intervals along the depth of the coatings to evaluate materials response at key locations, specifically at layer interfaces. In-plane stress was calculated for the coatings and a finite element model was implemented to supplement the results and enable further predictions. The results show that the sensor coatings that were manufactured in this work revealed only minor variations in the strain response of sensor coatings under a typical thermal cycle and especially at temperatures closer to that of gas turbine operating conditions, compared to state-of-the-art coatings. This work demonstrates the viability of manufacturing rare-earth doped yttria-stabilized zirconia coatings that provide beneficial spectroscopic monitoring capabilities while having minimal impact on the thermomechanical response of the thermal barrier coatings.

Air plasma spray↗
DOE OSTI2024

Leveraging Calibration Transfer Techniques for Remote Monitoring of Samarium and Europium in LiCl Using Laser-Induced Florescence Spectroscopy for Radioisotope Production Applications

Radioisotope production relies on complex chemical processes that must be performed in radiological hot cells or glove boxes because of the radioactive and otherwise hazardous materials being used. In these situations, optical sensors can provide real-time monitoring to users, which is unobtainable by more traditional methods. This study explores the use of calibration transfer methods to train a model on one instrument and date and then transfer it to another instrument of the same or different configuration on a different date. By performing laser-induced fluorescence measurements of Eu(III) and Sm(III) in 10 M LiCl over the course of 6 months using two disparate spectrometers and two different training sets, a strategy for calibrating and deploying models for online monitoring was established. Three transfer techniques were compared: direct standardization (DS), piecewise direct standardization (PDS), and external parameter orthogonalization (EPO). DS and PDS outperformed EPO for day-to-day transfers, and EPO was not effective for instrument-to-instrument transfers. Transferring the initial date’s full factorial model provided better prediction performance compared with retraining models the day of measurements using a D-optimal designed calibration set. For both day-to-day and instrument-to-instrument transfers, five Kennard–Stone selected samples were sufficient. Based on this choice, the initial-date, high-resolution spectrometer model was transferred to a lower-resolution, compact spectrometer 6 months later to monitor a simulated, real-time demonstration. Here, the combined predictions of the DS and PDS transferred models were able to accurately track the anticipated concentration profiles, maintaining root-mean-square error of prediction values below 10 ppm.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Automatic Calibration and Health Monitoring of Infrastructure Sensors

Smart transportation infrastructure relies on networks of heterogeneous sensors - cameras, radars, and lidars - continuously monitoring traffic conditions. However, executing the initial spatial calibration of multiple sensors and the subsequent health monitoring presents significant operational challenges. Environmental factors, mechanical vibrations, and gradual drift cause spatial misalignment, degrading fusion performance and tracking accuracy. Traditional calibration approaches require manual intervention with specialized targets or survey equipment, resulting in service interruptions and high maintenance costs. This work presents an automated framework for initial calibration and continuous health monitoring without human intervention or service disruption. Our approach addresses two critical problems: (1) detecting when sensors become miscalibrated during operation, and (2) automatically re-establishing spatial alignment using only operational traffic data. The health monitoring component analyzes measurement innovations - differences between sensor observations and predicted object states - to detect systematic biases indicative of calibration drift. By computing bias magnitude, directional consistency, and rejection rates, the system identifies miscalibrations as small as 0.5 meters. Unlike traditional methods requiring known calibration targets, our diagnostic operates continuously on live traffic observations, enabling early detection before fusion quality degrades. The automatic recalibration algorithm leverages overlapping sensor fields-of-view and temporal correlation of vehicle observations. Using graph-based optimization, the system automatically discovers which sensor pairs observe common regions, estimates pairwise spatial transformations using RANSAC-based robust estimation, and jointly optimizes all sensor poses through bundle adjustment. The framework handles practical deployment challenges, including different sensor sampling rates (1-10 Hz), varying installation positions, unknown orientations, and limited overlap regions (>10%). When approximate sensor positions are available from installation surveys (+/-1m accuracy), the algorithm additionally estimates sensor orientations, refining both position and rotation to sub-meter and sub-degree accuracy. We validate the framework on multi-hour traffic datasets from six heterogeneous sensors with sampling rates ranging from 1 Hz to 10 Hz. Results demonstrate successful calibration even with sparse overlap (<20%) and automatic detection of miscalibrations exceeding 0.8 meters. This work enables a "deploy-and-forget" sensor infrastructure that maintains calibration autonomously, reducing maintenance costs while improving tracking accuracy. The techniques generalize beyond transportation to any multi-sensor monitoring application requiring robust spatial alignment, including smart cities, industrial monitoring, and surveillance systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Hydrogen Leak Modeling for Development of Smart Distributed Monitoring Under Unintended Releases

Hydrogen is a versatile and clean energy carrier that can be produced from various renewable sources such as wind, solar, and hydropower. Hydrogen has the potential to play a crucial role in decarbonizing industrial processes that are currently reliant on fossil fuels and provide long-duration and/or seasonal energy storage to enable electricity decarbonization. Hydrogen can also be used as a fuel for fuel cell vehicles, providing a zero-emission alternative to traditional internal combustion engines. DOE launched the Hydrogen Energy Earthshot (Hydrogen Shot) in June 2021 to reduce the cost of clean hydrogen by 80% to $1 per 1 kilogram in 1 decade ("1 1 1"). While promising, Hydrogen is highly-flammable, and in the presence of oxygen, it can form explosive mixtures. . Therefore, understanding leak scenarios is essential to evaluate and mitigate the safety risks associated with potential hydrogen leaks. An increased understanding of leak behavior, and having tools to model leaks, can help assess how hydrogen would disperse in different environments, influencing emergency response plans and safety measures, and identify potential issues with materials and design systems that can withstand the challenges posed by hydrogen. Recently, researchers have attempted to study hydrogen leaks for development of risk management strategies. However, the focus has been on closed or semi-closed spaces like storage rooms, vehicles, garages, and fueling stations - all promising locations for future hydrogen infrastructure. In this presentation, the modeling environment extends the span of research further by modeling hydrogen leak in an outdoor, open space. We will present the key challenges with modeling hydrogen leaks in an uncontrollable environment, how they were handled, and how modeling results informed sensor selection and placement. A Hydrogen research facility at the National Renewable Energy Laboratory (NREL) was used as a case study to model hydrogen leaks. In the future, Hydrogen wide area detection methodologies will be developed and tested at this site to monitor for unintended and operational hydrogen releases. The data generated from modeling will be used to develop a predictive model to detect hydrogen leak location based on concentration measured by sensors in this open space. Furthermore, the facility was also chosen because controlled hydrogen releases can be performed. A computational fluid dynamics (CFD) based modeling approach was taken to model hydrogen leak. The full-scale hydrogen facility was modeled with a large ambient domain. The electrolyzer at the facility can produce a controlled release rate of 27 kg-H2/hr. Site-specific atmospheric and weather condition data such as wind direction, wind speed at various altitudes, and temperature were used as inputs to the model. To capture the variability of weather conditions, a subset of the weather conditions experienced during daytime hours without precipitation over the course of three months was generated; using established data clustering techniques, a total of 100 condition sets were chosen. The results show statistical distributions and ranges of hydrogen concentrations at locations throughout the domain. These distributions are compared to experimental data from a constant mass flow, controlled hydrogen release at the facility. The stochastic wind conditions of the release make direct validation difficult, therefore, statistical comparison approaches were used. Wind conditions are found to significantly impact the release behavior, including direction and concentration. Sensor selection and placement is proposed for the facility and is now based on release behavior predicted for the facility given its weather patterns; this is much more informed than without the modeling results. The methodology and analysis procedure can be translated to other facilities using modified geometries and site-specific weather conditions. Hydrogen holds great promise as a renewable energy fuel, but ensuring safety in its production, storage, and use is paramount. Studying potential leak scenarios in an open space will help develop sensors to detect hydrogen on a large spectrum of concentration and eventually build a smart distributed monitoring system.

CFD↗
DOE OSTI2021

A study of mechanoelectrical transduction behavior in polyvinyl chloride (PVC) gel as smart sensors

Abstract Polyvinyl chloride (PVC) gels are soft electroactive polymers (EAPs) being researched for soft robotic applications. Sensing properties of these EAPs have not been investigated in detail in regard to fundamental mechanoelectrical transduction behavior, but this smart material has been shown to exhibit a detectable response to external stimuli. This study shows PVC gels to be an extremely sensitive material when undergoing mechanoelectrical transduction and explores some response dependencies and proposes a theoretical framework for mechanoelectrical transduction within the gel. The work presented here also uncovers a very interesting phenomena under extremely low compressive loads during the initial contact with the gel. This phenomenon is attributed to a surface tension creeping motion onto the loading surface with an accompanying polarity inversion in the sensing signal relative to fully loaded gels in compression. Experimental work on hysteresis was also completed showing very little memory in steady state mechanoelectrical response to repeated stepped loading cycles. This study demonstrates the mechanoelectric ability of PVC gels to perform in sensing experiments and acts as a fundamental framework to further broaden the applications of PVC gel sensors.

Instruments & Instrumentation↗