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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 1,081 records · Page 60
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Robust highly durable solid oxide fuel cell cathodes – Improved materials compatibility & self-regulating surface chemistry

Solid oxide fuel cells (SOFCs) are electrochemical conversion devices that directly transform hydrogen or hydrocarbon fuels to electricity, with energy efficiencies as high as 90%, coupled with reduced emissions. Several factors, however, remain to be addressed when considering scale-up of SOFC technology, including the need to overcome decreased performance due to sluggish rates of the oxygen reduction reaction (ORR) at the cathode under reduced temperatures and susceptibility to degradation in performance from surface poisoning e.g. from chromia, while limiting the use of critical raw materials (lanthanides and transition metals) present in high performing mixed ionic electronic conducting electrodes like (La,Sr)CoO 3 (LSC). In this project we explored the key descriptors for determining ORR activity in SOFC electrodes and tried to recover performance degradation by applying them to SOFC electrodes. In order to do this, we first selected a model mixed ionic electronic conducting (MIEC) oxide, Pr-doped CeO 2 (Pr 0.1 Ce 0.9 O 2-δ , PCO), which is a chemically stable fluorite and free of inherent poison sources (e.g. Sr segregation in LSC) that potentially react with external impurities such as Cr-species vaporized from the interconnect. The three approaches originally planned in this project are as follows: 1) evaluation of scavenger exsolution characteristics, 2) study of scavengers gettering efficacy following Cr and Si poisoning and 3) integration of new compositions into porous electrodes. Among them, exceptional progress has been made in 2) and 3), especially understanding the role of surface infiltrants in impacting electrode performance and degradation of PCO materials. We found that the Smith acidity scale for binary oxides serves as a powerful descriptor for tuning and predicting the oxygen exchange kinetics on MIEC PCO surfaces. As a result, with infiltration with binary oxides, ranging from strongly basic (Li 2 O) to strongly acidic (SiO 2 ) onto the surface of porous PCO, it was possible to systematically vary the chemical surface exchange coefficient (k chem ) by 6 orders of magnitude! L i2 O increased k chem by nearly 1,000 times over that of pristine PCO, while SiO 2 decreased k chem by nearly the same factor. Strikingly, although the pre-exponential of k chem scales linearly with the acidity of the infiltrated binary oxide, there is nearly no change in the activation energy. With this insight, we attributed the origin of these dramatic changes in k chem values to the systematic increase and decrease in the surface electron density induced by infiltrated binary oxides. More interestingly, although both Cr 2 O 3 and SiO 2 were determined to be acidic by Smith, suggesting that this feature could likely be the primary reason that these compounds serve to poison the ORR on SOFC cathodes, the effect of poisoning could be subsequently tuned by adding multiple infiltrants and controlling their relative surface acidities. We also systematically examined the effect of serial infiltration of basic and acidic oxides. It turned out that serial infiltration of Li not only recovers approximately 20-fold degraded k chem of PCO by acidic Cr 2 O 3 but its k chem is enhanced even beyond that of the non-infiltrated PCO by more than three orders of magnitude. This was further verified with a screen-printing PCO symmetric cell in terms of the electrode performance (area-specific resistance, ASR) related to approach 3). These observations point to acidity as a key descriptor not only in tuning and predicting the ORR activity of SOFC cathodes that largely determines the overall performance of SOFC, but in mitigating and reactivating poisoned electrode performance. This work provides novel guidelines for making the electrode performance much more active and robust in SOFCs, which can further be applied to all applications requiring oxygen exchange reaction, such as electrolyzers, permeation membranes and gas sensors.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2024

Aerosol and Gas Transport in Ventilation Ducts in Nonreactor Nuclear Facilities

This document summarizes outcomes and finding in FY 2022 from a project sponsored by the Nuclear Safety Research and Development Program, which is managed by the Office of Nuclear Safety, within the Office of Environment, Health, Safety and Security. Literature survey and data collection are discussed in Sections 1 and 2, respectively. Numerical modeling of particulate transports in ventilation systems performed for standard geometries and a full-scale ventilation system is described in Section 3, and Section 4 summarizes the development of proof-of-concept sensors featuring ultrasound technology for particle deposition removal. Conclusions and recommendations are outlined in Section 5.

42 ENGINEERING↗
DOE OSTI2024

2023 Reftek Colt Seismometer Exploratory Evaluation

Sandia National Laboratories has tested and evaluated three Colt broadband seismometers designed and manufactured by Reftek. The purpose of this seismometer evaluation is to measure performance characteristics in areas such as power consumption, sensitivity, frequency response, full scale, self-noise, dynamic range, calibration system response, and passband. The Colt model of sensors are being evaluated to explore the potential for a future seismometer Type Approval process in the International Monitoring System (IMS) of the Comprehensive Nuclear-Test-Ban Treaty (CTBT).

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Digital Miniature Cathode Ray Magnetometer

In this study, we introduce the concept and construction of an innovative Digital Miniature Cathode Ray Magnetometer designed for the precise detection of magnetic fields. This device addresses several limitations inherent to magnetic probes such as D.C. offset, nonlinearity, temperature drift, sensor aging, and the need for frequent recalibration, while capable of operating in a wide range of magnetic fields. The core principle of this device involves the utilization of a charged particle beam as the sensitivity medium. The system leverages the interaction of an electron beam with a scintillator material, which then emits visible light that is captured by an imager. The emitted scintillation light is captured by a CMOS sensor. This sensor not only records the scintillation light but also accurately determines the position of the electron beam, providing invaluable spatial information crucial for magnetic field mapping. The key innovation lies in the combination of electron beam projection, CMOS imager scintillation-based detection, and digital image signal processing. By employing this synergy, the magnetometer achieves remarkable accuracy, sensitivity and dynamic range. The precise position registration enabled by the CMOS sensor further enhances the device’s utility in capturing complex magnetic field patterns, allowing for 2D field mapping. In this work, the optimization of the probe’s performance is tailored for applications related to the characterization of insertion devices in light sources, including undulators.

Turqueti, Marcos↗
DOE OSTI2021

Real-time Data Analytics for Condition Monitoring of Complex Industrial Systems

Modern industrial systems are now fitted with several sensors for condition monitoring. This is advantageous because these sensors can provide mass amounts of data that have the potential for aiding in tasks such as fault detection, diagnosis, and prognostics. However, the information valuable for performing these tasks is often clouded in noise and must be mined from high-dimensional data structures. Therefore, this dissertation presents a data analytics framework for performing these condition monitoring tasks using high-dimensional data. Demonstrations of this framework are detailed for challenges related to power generation systems in automobiles, power plants, and aircraft engines. These implementations leverage data collected from state-of-the-art, industry class test-rigs. Results indicate the ability of this framework to develop effective methodologies for condition monitoring of complex systems.

Peters, Benjamin↗
DOE OSTI2022

Multifunctional fiber-reinforced composites for passive sensing and energy harvesting with enhanced mechanical performance

Fiber-reinforced polymer composites (FRPCs) are valid alternatives to metals, especially in the aerospace industry, for their superior strength-to-weight ratio. FRPCs are generally manufactured by stacking layers of fibers and infusing them with epoxy-based adhesives to result in laminates. Such layer-by-layer structure often ensues a poor interlaminar property of the composites. Additionally, FRPCs can sustain complex damage modes because of their inherent anisotropic characteristics. For example, delamination can occur due to low-velocity impact at a subsurface level, which can result in premature catastrophic failures if undetected. Thus, structural monitoring is crucial to identify such damage. Point-based sensors (e.g., strain gauges, accelerometers, among many) can be embedded in the FRPCs for structural monitoring. However, their electrical power demand often embroils their usage. Therefore, the main objective of this research is to design and implement multifunctional composites that can simultaneously perform passive sensing and energy harvesting while exhibiting better mechanical performance. Previous research efforts have demonstrated that a continuous feed-through deposition of functional materials on fiber surfaces simultaneously enhanced the mechanical and sensing properties of the FRPCs. This study explores a similar approach to encode passive sensing and energy harvesting properties in the FRPCs by integrating ferroelectric microparticles on the fiber surfaces. Upon ensuring their superior interlaminar shear strength, sensing and energy harvesting properties were characterized through experimental studies. The outcome is a multifunctional composite fabricated by coating the fibers with functional microparticles through a high throughput, scalable, and low-cost approach that enables passive sensing, energy harvesting, and improved mechanical performance.

Gupta, Sumit↗
DOE OSTI2024

Quantum sensing effect of electron tunneling in DQD/analyte complex

We investigate electron tunneling between quantum dots and molecules to propose a quantum sensor. This sensor consists of double quantum dots (DQD) with energy levels specifically tailored to mirror those of the target analyte. By analyzing the spectral distribution of electron localizations in the DQD system, we can delineate the analyte’s spectrum and deduce its composition by comparing it with a reference sample. To understand electron tunneling dynamics within the DQD/analyte complex, we performed three-dimensional computational modeling applying the effective potential approach to the InAs/GaAs heterostructure. In this modeling, we mimicked the analyte spectrum by utilizing a quantum well characterized by a quasi-discrete spectrum. Our calculations reveal the inherent potential of utilizing this method as a highly sensitive and selective sensor.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2023

Developing Enzyme Immobilization with Fibrous Membranes: Longevity and Characterization Considerations

Fibrous membranes offer broad opportunities to deploy immobilized enzymes in new reactor and application designs, including multiphase continuous flow-through reactions. Enzyme immobilization is a technology strategy that simplifies the separation of otherwise soluble catalytic proteins from liquid reaction media and imparts stabilization and performance enhancement. Flexible immobilization matrices made from fibers have versatile physical attributes, such as high surface area, light weight, and controllable porosity, which give them membrane-like characteristics, while simultaneously providing good mechanical properties for creating functional filters, sensors, scaffolds, and other interface-active biocatalytic materials. This review examines immobilization strategies for enzymes on fibrous membrane-like polymeric supports involving all three fundamental mechanisms of post-immobilization, incorporation, and coating. Post-immobilization offers an infinite selection of matrix materials, but may encounter loading and durability issues, while incorporation offers longevity but has more limited material options and may present mass transfer obstacles. Coating techniques on fibrous materials at different geometric scales are a growing trend in making membranes that integrate biocatalytic functionality with versatile physical supports. Biocatalytic performance parameters and characterization techniques for immobilized enzymes are described, including several emerging techniques of special relevance for fibrous immobilized enzymes. Diverse application examples from the literature, focusing on fibrous matrices, are summarized, and biocatalyst longevity is emphasized as a critical performance parameter that needs increased attention to advance concepts from lab scale to broader utilization. This consolidation of fabrication, performance measurement, and characterization techniques, with guiding examples highlighted, is intended to inspire future innovations in enzyme immobilization with fibrous membranes and expand their uses in novel reactors and processes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Novel Membrane-Based Corrosion Sensors For Operating In Natural Gas Pipelines

Membrane-based electrochemical sensors (MBES) provide an attractive option for real-time continuous humidity and corrosion rate measurements inside of natural gas pipelines. The MBES, can use electrochemical techniques such as linear polarization resistance and electrochemical impedance spectroscopy even when a conductive aqueous phase is intermittent or unavailable. We now present field-test performance data of our MBES operated inside a multiphase water and natural gas flow facility. The MBES measured internal corrosion and conductivity at the top, bottom, and side of the pipe spool. Tests were carried out with flow rates of 4.6, 9.1, and 18.3 m/s, temperatures of 21, 29, and 35 °C, and a pressure of 1000 psia. All sensors followed similar trends in open circuit potential, corrosion rate, and conductivity over time. Neither flow rate, temperature, nor build-up of metal-based solids on the sensor surfaces impacted the quality of the electrochemical measurements. The field-test results confirm that the membrane-based electrochemical sensors are suitable for monitoring internal corrosion of operational natural-gas pipelines.

Duffy, Timothy↗
DOE OSTI2023

Performance of the CMS High Granularity Calorimeter prototype to charged pion beams of 20–300 GeV/c

The upgrade of the CMS experiment for the high luminosity operation of the LHC comprises the replacement of the current endcap calorimeter by a high granularity sampling calorimeter (HGCAL). The electromagnetic section of the HGCAL is based on silicon sensors interspersed between lead and copper (or copper tungsten) absorbers. The hadronic section uses layers of stainless steel as an absorbing medium and silicon sensors as an active medium in the regions of high radiation exposure, and scintillator tiles directly read out by silicon photomultipliers in the remaining regions. As part of the development of the detector and its readout electronic components, a section of a silicon-based HGCAL prototype detector along with a section of the CALICE AHCAL prototype was exposed to muons, electrons and charged pions in beam test experiments at the H2 beamline at the CERN SPS in October 2018. The AHCAL uses the same technology as foreseen for the HGCAL but with much finer longitudinal segmentation. The performance of the calorimeters in terms of energy response and resolution, longitudinal and transverse shower profiles is studied using negatively charged pions, and is compared to GEANT4 predictions. This is the first report summarizing results of hadronic showers measured by the HGCAL prototype using beam test data.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

In-flight performance of S PIDER'S 280-GHz receivers

S PIDER is a balloon-borne instrument designed to map the cosmic microwave background at degree-angular scales in the presence of Galactic foregrounds. S PIDER has mapped a large sky area in the Southern Hemisphere using more than 2000 transition-edge sensors (TESs) during two NASA Long Duration Balloon flights above the Antarctic continent. During its first flight in January 2015, S PIDER observed in the 95 GHz and 150 GHz frequency bands, setting constraints on the B-mode signature of primordial gravitational waves. Its second flight in the 2022-23 season added new receivers at 280 GHz, each using an array of TESs coupled to the sky through feedhorns formed from stacks of silicon wafers. Here, these receivers are optimized to produce deep maps of polarized Galactic dust emission over a large sky area, providing a unique data set with lasting value to the field. In this work, we describe the instrument’s performance during S PIDER'S second flight.

280 GHz cosmology↗
DOE OSTI2020

Urban Seismic Site Characterization by Fiber-Optic Seismology

ccurate ground motion prediction requires detailed site effect assessment, but in urban areas where such assessments are most important, geotechnical surveys are difficult to perform, limiting their availability. Distributed acoustic sensing (DAS) offers an appealing alternative by repurposing existing fiber-optic cables, normally employed for telecommunication, as an array of seismic sensors. We present a proof-of-concept demonstration by using DAS to produce high-resolution maps of the shallow subsurface with the Stanford DAS array, California. We describe new methods and their assumptions to assess H/V spectral ratio—a technique widely used to estimate the natural frequency of the soil—and to extract Rayleigh wave dispersion curves from ambient seismic field. These measurements are jointly inverted to provide models of shallow seismic velocities and sediment thicknesses above bedrock in central campus. The good agreement with an independent survey validates the methodology and demonstrates the power of DAS for microzonation.

Geochemistry & Geophysics↗
DOE OSTI2022

Temporal Characterization and Filtering of Sensor Data to Support Anomaly Detection

Here, we present an approach for characterizing complex temporal behavior in the sensor measurements of a system in order to support detection of anomalies in that system. We first characterize typical behavior by extending a hidden Markov model-based approach to time series alignment. We then use a trace of that learned behavior to develop a particle filter that enables efficient estimation of the filtering distribution on the state space. This produces filtered residuals that can then be used in an anomaly detection framework. Our motivating example is the daily behavior of a building’s heating, ventilation, and air conditioning (HVAC) system, using sensor measurements that arrive every minute and induce a state space with 15,120 states. We provide an end-to-end demonstration of our approach showing improved performance of anomaly detection after application of alignment and filtering compared to the unaligned data. The proposed model is implemented as a computationally efficient R package alignts (align time series) built with R and Fortran 95 with OpenMP support.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

High-Performance Piezoelectric MEMS Manufacturing & Application on Micro Power Generators (CRADA Final Report)

This project aimed to develop and characterize a micro-fabrication technology for wafer-scale heterogeneous integration of bulk piezoelectric materials on silicon substrates that could enable manufacturing of high-performance micro transducers. Typical technical challenges for piezoelectric microfabrication technologies are high-temperature processing, non-uniform film quality, and low process repeatability, which will be addressed during this project. Progress was made on preliminary research towards development of a technology platform that will enable a new micro-fabrication process to provide the highest electromechanical coupling among any other thin/thick-film deposition methods available today. This work has tested and characterized samples that we hope will further our goal of refining and enabling existing micro transducers to achieve unprecedentedly high performance. This project focused solely on the characterization of the proposed microfabrication process technology, and it did not consist of design or manufacturing of new transducers, such as sensors, actuators or energy harvesters based on this fabrication process. Future work will be aimed at the research and development of process steps specifically to enable integration of on-chip electronics with piezoelectric materials.

36 MATERIALS SCIENCE↗
DOE OSTI2022

AN EXPERIMENTAL STUDY OF FUEL SELECTION FOR A GASOLINE MULTI-MODE

In this study of fuels of different reactivities and their performance in a low-temperature combustion (LTC) engine. The engine was a 2.2L CRDI engine code D4HB provided by industry partner Hyundai America Technical Center inc. (HATCI). The engine was instrumented with in-cylinder pressure sensors allowing for monitoring of the combustion process. First, the engine was operated with standard US pump grade gasoline, Research Octane number (RON) 91 E10, to find initial operation conditions as well as control points of stable operation for daily checks on system conditions. Tested were 8 different fuel blends, the four base blends were provided by Phillips 66 (P66) with a RON value ranging from 60 to 91, and three other variations of these fuels were formulated by splash blending alcohols, iso-butanol, and ethanol, with the RON60 fuel and the final blend, was US RON91 E10 pump gasoline. Using these 8 fuels, the low load performance was analyzed allowing a selection of one of the fuels to be made to proceed with experimentation. To study the low load performance of each fuel, the load was decreased until combustion became too unstable to continue. This was done at 1500 rpm with all of the fuels. At 1200 rpm, the same methodology was applied, though all of the fuels were not tested, only the fuels which showed promise at 1500 rpm. Meaning that the lowest targeted load was achieved without any aid to the combustion process other than advancing combustion, increasing intake air temperature (IAT) for example was not done to maintain consistency across all fuels. The same methodology was applied at 800 rpm based upon the 1200 rpm results to see the ability of the engine to idle with the given fuel. It was found that fuel with a RON of 80 showed good low load performance while allowing for high load performance to be maintained. A blend of 25% Iso-Butanol and 75% RON 60 equated to a RON of 80 and showed good low load performance and showed peak indicated thermal efficiency (ITE) numbers of 46% at 17 bar indicated mean effective pressure (IMEP) and 2200 rpm.

02 PETROLEUM↗
DOE OSTI2024

Implications of stop-and-go traffic on training learning-based car-following control

Learning-based car-following control (LCC) of connected and autonomous vehicles (CAVs) is gaining significant attention with the advancement of computing power and data accessibility. While the flexibility and large model capacity of model-free architecture enable LCC to potentially outperform the model-based car-following (CF) model in improving traffic efficiency and mitigating congestion, the generalizability of LCC for traffic conditions different from the training environment/dataset is not well-understood. Herein, this study seeks to explore the impact of stop-and-go traffic in the training dataset on the generalizability of LCC. It uses the characteristics of lead vehicle trajectories to describe stop-and-go traffic, and links the theory of identifiability (i.e., obtaining a unique parameter estimation result using sensor measurements) to the generalizability of behavior cloning (BC) and policy-based deep reinforcement learning (DRL). Correspondingly, the study shows theoretically that: (i) stop-and-go traffic can enable the property of identifiability and enhance the control performance of BC-based LCC in different traffic conditions; (ii) stop-and-go traffic is not necessary for DRL-based LCC to generalize to different traffic conditions; (iii) DRL-based LCC trained with only constant-speed lead vehicle trajectories (not sufficient to ensure identifiability) can be generalized to different traffic conditions; and (iv) stop-and-go traffic increases variance in the training dataset, which improves the convergence of parameter estimation while negatively impacting the convergence of DRL to the optimal control policy. Numerical experiments validate the above findings, illustrating that BC-based LCC entails comprehensive training datasets for generalizing to different traffic conditions, while DRL-based LCC can achieve generalization with simple free-flow traffic training environments. This further suggests DRL as a more promising and cost-effective LCC approach to reduce operational costs, mitigate traffic congestion, and enhance safety and mobility, which can accelerate the deployment and acceptance of CAVs.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

X-ray free-electron laser wavefront sensing using the fractional Talbot effect

Wavefront sensing at X-ray free-electron lasers is important for quantitatively understanding the fundamental properties of the laser, for aligning X-ray instruments and for conducting scientific experimental analysis. A fractional Talbot wavefront sensor has been developed. This wavefront sensor enables measurements over a wide range of energies, as is common on X-ray instruments, with simplified mechanical requirements and is compatible with the high average power pulses expected in upcoming X-ray free-electron laser upgrades. Single-shot measurements were performed at 500 eV, 1000 eV and 1500 eV at the Linac Coherent Light Source. These measurements were applied to study both mirror alignment and the effects of undulator tapering schemes on source properties. The beamline focal plane position was tracked to an uncertainty of 0.12 mm, and the source location for various undulator tapering schemes to an uncertainty of 1m, demonstrating excellent sensitivity. These findings pave the way to use the fractional Talbot wavefront sensor as a routine, robust and sensitive tool at X-ray free-electron lasers as well as other high-brightness X-ray sources.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

IoT-Based Comfort Control and Fault Diagnostics System for Energy-Efficient Homes

This project studies an Internet of Things (IoT)-based comfort control and fault diagnostics system (referred as iComfort in this report) for energy-efficient homes. The system delivers an occupant-comfort-oriented thermal environment adaptive to fault scenarios and achieves HVAC energy savings in a cost-effective and straightforward way. This smart iComfort home system consists of the following key features. 1) Cost-effectiveness and scalability of the entire hardware and software system: The system includes low-cost temperature, humidity, and airflow sensors, and a Raspberry Pi-based local hub that interfaces with the cloud and IoT-enabled devices. The cost is low, not only for sensors, but also the costs associated with sensor installation, system setup and commissioning, data communication and storage, and data analytics (e.g., the development of automated fault detection and diagnosis (AFDD), as well as adaptive control strategies that are both computationally efficient and practical to implement). 2) Energy performance and user satisfaction: The system delivers user satisfaction and energy savings. This includes a) ease of use, b) optimal occupant thermal comfort, and c) accurate system feedback (e.g., low false alarm of AFDD strategies). 3) Favorable demonstrated prototype performance: The prototype tested at the Pacific Northwest National Laboratory (PNNL) Lab Homes demonstrates the accuracy of fault detections and diagnoses and shows thermal comfort improvement and energy savings through adaptive and optimal HVAC operations.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗