Multi-Filter Radiometer (MFR) with 7 Narrowband Channels: upwelling irradiances at 3-meter height
Multi-Filter Radiometer (MFR) with 7 Narrowband Channels: upwelling irradiances at 3-meter height
3 m↗
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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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Multi-Filter Radiometer (MFR) with 7 Narrowband Channels: upwelling irradiances at 25-meter height
Multi-Filter Radiometer (MFR) with 7 Narrowband Channels: upwelling irradiances at 25-meter height
25 m↗
ARM Aerial Facility (AAF) - Unmanned Aircraft Systems, Filter Sampler with QC and lat/lon/alt
ARM Aerial Facility (AAF) - Unmanned Aircraft Systems, Filter Sampler with QC and lat/lon/alt
54 ENVIRONMENTAL SCIENCES↗
Normal Incidence Multi-Filter Radiometer (NIMFR) with 7 Narrowband Channels
Normal Incidence Multi-Filter Radiometer (NIMFR) with 7 Narrowband Channels
54 ENVIRONMENTAL SCIENCES↗
Near Optimality of Matched Filter Detection for Cyclic Prefix Direct Sequence Spread Spectrum
Abstract—Cyclic Prefix Direct Sequence Spread Spectrum -DSSS) has been presented as a potential solution for ultrareliable low latency communications (URLLC) and massive machine type communication (mMTC), where the CP-DSSS waveform would operate as a secondary network at the same frequencies as the primary network but at much lower SNR. In this paper, we show that when operating in the low SNR regime, CP-DSSS achieves near optimum performance when using a matched filter (MF) detector at the receiver. Time reversal (TR) precoding at the transmitter is also analyzed. These results also carry forward into multi-antenna scenarios where array gain is preserved. With nearly optimal performance of MF detection, CP-DSSS can be implemented with simple device transceiver structures, reducing per-unit cost for massively deployed networks.
5G and Beyond Communications↗
Birefringent Color Filter by Layered Metal‐Organic Chalcogenides: In‐Plane Anisotropy and Odd/Even Effect
Anisotropic 2D materials are gaining interest recently as building blocks for angular‐dependent optical/electrical devices. However, the fundamental understanding of their structure‐property‐relationship is limited, which hinders further modulation of their unique characteristics via structure tailoring. Here the in‐plane structural anisotropy and the tunable optical/electrical properties of a series of radiation‐sensitive (X‐ray, e‐beam) metal‐organic chalcogenide (MOC) single crystals are comprehensively revealed with ligands of variable length/parity. Their monoclinic crystallography is determined at atomic resolution by a simple method that couples X‐ray/electron diffraction with first‐principles calculations. The in‐plane inorganic backbone of the MOCs exhibits a strong lattice anisotropy with odd/even alternations, which originates from that of the out‐of‐plane organic motifs via organic/inorganic accommodation. Such structural anisotropy is implied mechanically by the preferred orientation of crystal cleavage. It triggers a maximum ≈8 × distinction of in‐plane electrical conductivity of the semiconducting MOCs, plus a distinct birefringence (maximum Δn ≈ 0.03) with a dispersive orientation of dielectric axes, which rotate up to 25.7° from UV to visible‐light regime, inspiring an emerging pathway for color filtering via single crystal rotation. Such in‐plane optical characteristics also exhibit odd/even alternation and can be flexibly tuned by the designable out‐of‐plane ligands.
birefringence↗
A graph signal processing‐based multiple model Kalman filter ( GSP‐MMKF ) tool for predictive analytics: An air separation unit process application
Abstract The industrial Air Separations Unit (ASU) is a complicated and tightly operated process. The use of dynamic process analytics is also a key element of safe and economic operation of these processes, with increasing focus on predictive analytics to take preemptive actions. With the availability of real‐time data from hundreds of sensors, the data analysis process should also consider the topology of the data, as seen in sensor networks. In this paper, a novel tool is presented that considers the complex connectivity patterns in the sensor network and uses local adaptive disturbance estimations to predict global network‐scale trends. The paper introduces the emerging field of Graph Signal Processing (GSP) and presents a rigorous derivation of the tool starting from the extraction of the sensor‐network (in a graph theoretical sense) from the data. This network, which is in the form of a matrix, is then used to derive a Kalman‐filter type of state‐space model driven by input disturbances. Multiple disturbance models (e.g., step, ramp, periodic) are included to allow the model to have different kinds of disturbance propagation. Each graph node (representing the sensors used) dynamically adapts to the most recent detected disturbance individually. These estimated disturbances are propagated to the global network using the graph. Modifications to ensure stability are also discussed. The fidelity of the tool is tested on certain downtime events and the paper concludes by discussing the advantages of the method and planned future improvements.
Ghosh, Sambit↗
Crack detection in fuel cell electrodes using a spatial filtering technique for overcoming noisy backgrounds
Image processing is a powerful tool that allows for rapid and automated data parsing in settings that occupy large variable spaces and require large data sets. Feature detection on difficultly discerned backgrounds is a subset of image processing that facilitates the extraction of quantitative metrics from otherwise subjective data. Crack detection and quantification is an important capability in polymer electrolyte membrane fuel cell quality control, failure analysis, and optimization. This work presents a technique to perform crack detection and quantification which overcomes challenges faced by commonly used image segmentation techniques. We demonstrate the use of a geometrically filtered noise‐level detection technique to select a binary threshold value from which we then quantify how cracked a sample is. Furthermore, we demonstrate the accuracy of our technique using programmatically generated test images of known crack amounts and their performance on real‐world fuel cell catalyst layer samples.
30 DIRECT ENERGY CONVERSION↗
Ensemble Kalman filter for data assimilation coupled with low-resolution computations techniques applied in fluid dynamics
This paper presents an innovative Reduced-order model (ROM) for merging experimental and simulation data using data assimilation (DA) to estimate the "True" state of a fluid dynamics system, leading to more accurate predictions. Our methodology introduces a novel approach by implementing the ensemble Kalman filter (EnKF) within a reduced-dimensional framework, grounded in a robust theoretical foundation and applied to fluid dynamics. To address the substantial computational demands of DA, the proposed ROM employs low-resolution (LR) techniques to drastically reduce computational costs. This innovative approach involves downsampling datasets for DA computations, followed by an advanced reconstruction technique based on low-cost singular value decomposition (lcSVD). The lcSVD method, a key innovation in this paper, has never been applied to DA before and offers a highly efficient way to enhance resolution with minimal computational resources. Our results demonstrate significant reductions in both computation time and RAM usage through these LR techniques without compromising the accuracy of the estimations. For instance, in a turbulent test case, for a data compression rate of 15.9, the LR approach can achieve a speed-up of 13.7 and a RAM compression of 90.9% while maintaining a low relative root mean square error (RRMSE) of 2.6%, compared to 0.8% in the high-resolution (HR) reference. Furthermore, we highlight the effectiveness of the EnKF in estimating and predicting the state of fluid flow systems based on limited observations and given low-fidelity numerical data. This paper highlights the potential of the proposed DA method in fluid dynamics applications, particularly for improving computational efficiency in CFD and related fields. Its ability to balance accuracy with low computational and memory costs makes it especially suitable for large-scale and real-time applications, such as environmental monitoring or engineering design. This method will be incorporated into ModelFLOWs-app.
Data Assimilation↗
Real-time cure monitoring of fiber-reinforced polymer composites using infrared thermography and recursive Bayesian filtering
Despite the desirable characteristics of fiber-reinforced polymer (FRP) composites, their utilization in high volume production industries is limited by a lack of efficient manufacturing techniques. Monitoring the curing process of these composites can help with improving the quality and efficiency of the manufacturing process. This article discusses a method that uses Kalman-filter-based fusion of the information obtained from infrared thermography (surface temperature measurements) and a heat conduction model to estimate degrees of cure and internal temperatures of curing FRP composite parts in real time. The effectiveness of the methodology is demonstrated by successfully monitoring the curing of an FRP composite part in a laboratory experiment. Finally, the proposed methodology is a crucial step towards identifying anomalies in the curing process that negatively impact the quality of FRP composite parts.
36 MATERIALS SCIENCE↗
Investigating the application of Kalman Filters for real-time accountancy in fusion fuel cycles
Here tritium accountancy in the fusion fuel cycle is a significant concern for the operation of commercial devices. It is expected that a limit on the maximum amount of tritium inventory in the system will be implemented, meaning that accountancy of the tritium inventory in the fuel cycle will need to be as accurate as possible. This is difficult since not all locations along the fuel cycle can benefit from the implementation of a tritium accountancy sensor and measurements will inherently contain error. Commercial fusion plants will also operate continuously, challenging current accountancy techniques that rely on static processes in well-controlled environments. In this paper a simple fusion fuel cycle concept is defined and the tritium inventory over time of each component is modelled using the Euler Approximation of a series of differential Equations in Python. This information is then used to simulate sensor measurements at specific points on the fuel cycle and then passed through a Kalman Filter (KF) to improve the accuracy of the true measurement of the sensors.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Effective antiviral coatings for deactivating SARS-CoV-2 virus on N95 respirator masks or filters
The application of antiviral coatings to masks and respirators is a potential mitigating step toward reducing viral transmission during the SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) pandemic. The use of appropriate masks, social distancing, and vaccines is the immediate solution for limiting the viral spread and protecting people from this virus. N95 respirator masks are effective in filtering the virus particles, but they cannot kill or deactivate the virus. We report a possible approach to deactivating SARS-CoV-2 by applying an antimicrobial coating (Goldshield 75) to masks and respirators, rendering them suitable for repeated use. Masks coated with Goldshield 75 demonstrated continuous inactivation of the Alpha and Beta variants of the SARS-CoV-2 over a 3-day period and no loss of inactivation when stored at temperatures at 50 °C.
36 MATERIALS SCIENCE↗
The use of a digital micromirror array as a temporal gate and spatial-filtering device for laser-induced breakdown spectroscopy and laser ablation molecular isotopic spectrometry
A digital micromirror array (DMMA) is used in a simple and inexpensive approach for spatially resolved temporal gating of atomic emission detection in laser-induced breakdown spectroscopy (LIBS). Selected mirrors in the array are actuated at a time delayed from the primary laser pulse, permitting optical gating with a response time of 160 ns while using a conventional CCD detector. Detector gating is shown to decrease noise from short-lived spectroscopic background (both continuum and N II from air), improving signal-to-background ratios by 22-times for selected samples and emission lines. The utility of rapid temporal gating is also demonstrated for the optical detection of isotopes using the laser-ablation molecular isotope spectrometry (LAMIS) experiment. Additionally, optical temporal gating and spatial filtering are used to isolate regions of the laser-induced plasma, enhancing signal from molecular emission. The potential of the simple technique for use in field-portable LIBS and LAMIS experiments is explored.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Three-Dimensional Analysis of Particle Distribution on Filter Layers inside N95 Respirators by Deep Learning
The global COVID-19 pandemic has changed many aspects of daily lives. Wearing personal protective equipment, especially respirators (face masks), has become common for both the public and medical professionals, proving to be effective in preventing spread of the virus. Nevertheless, a detailed understanding of respirator filtration-layer internal structures and their physical configurations is lacking. Here, we report three-dimensional (3D) internal analysis of N95 filtration layers via X-ray tomography. Using deep learning methods, we uncover how the distribution and diameters of fibers within these layers directly affect contaminant particle filtration. The average porosity of the filter layers is found to be 89.1%. Contaminants are more efficiently captured by denser fiber regions, with fibers <1.8 μm in diameter being particularly effective, presumably because of the stronger electric field gradient on smaller diameter fibers. This study provides critical information for further development of N95-type respirators that combine high efficiency with good breathability.
42 ENGINEERING↗
Polymer, Additives, and Processing Effects on N95 Filter Performance
The current severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) pandemic has highlighted the need for personal protective equipment, specifically filtering facepiece respirators like N95 masks. While it is common knowledge that polypropylene (PP) is the industry standard material for filtration media, trial and error is often required to identify suitable commercial precursors for filtration media production. This work aims to identify differences between several commercial grades of PP and demonstrate the development of N95 filtration media with the intent that the industry partners can pivot and help address N95 shortages. Three commercial grades of high melt flow index PP were melt blown at Oak Ridge National Laboratory and broadly characterized by several methods including differential scanning calorimetry (DSC), X-ray diffraction (XRD), and neutron scattering. Despite the apparent similarities (high melt flow and isotacticity) between PP feedstocks, the application of corona charging and charge enhancing additives improve each material to widely varying degrees. From the analysis performed here, the most differentiating factor appears to be related to crystallization of the polymer and the resulting electret formation. Materials with higher crystallization onset temperatures, slower crystallization rates, and larger number of crystallites form a stronger electret and are more effective at filtration.
36 MATERIALS SCIENCE↗
Large Ensemble Particle Filter for Spatial Climate Reconstructions Using a Linear Inverse Model
Proxy records that document the last 2000 years of climate provide evidence for the wide range of the natural climate variability from inter-annual to secular timescales not captured by the short window of recent direct observations. Assessing climate models ability to reproduce such natural variations is crucial to understand climate sensitivity and impacts of future climate change. Paleoclimate data assimilation (PDA) offers a powerful way to extend the short instrumental period by optimally combining the physics described by General Circulation Climate Models (GCMs) with information from available proxy records while taking into account their uncertainties. Here we present a new PDA approach based on a sequential importance resampling (SIR) Particle filter (PF) that uses Linear Inverse Modeling (LIM) as an emulator of several CMIP-class GCMs. We examine in a perfect-model framework the skill of the various LIMs to forecast the dynamics of the surface temperatures and provide spatial field reconstructions over the last millennium in a SIR PF. Our results show that the LIMs allow for skillful ensemble forecasts at 1-year lead-time based on GCMs dynamical knowledge with best prediction in the tropics and the North Atlantic. The PDA further provides a set of physically consistent spatial fields allowing robust uncertainty quantification related to climate models biases and proxy spatial sampling. Our results indicate that the LIM yields dynamical memory improving climate variability reconstructions and support the use of the LIM as a GCM-emulator in real reconstruction to propagate large ensembles of particles at low cost in SIR PF.
54 ENVIRONMENTAL SCIENCES↗
Polar coupling enabled nonlinear optical filtering at MoS 2 /ferroelectric heterointerfaces
Complex oxide heterointerfaces and van der Waals heterostructures present two versatile but intrinsically different platforms for exploring emergent quantum phenomena and designing new functionalities. The rich opportunity offered by the synergy between these two classes of materials, however, is yet to be charted. Here, we report an unconventional nonlinear optical filtering effect resulting from the interfacial polar alignment between monolayer MoS 2 and a neighboring ferroelectric oxide thin film. The second harmonic generation response at the heterointerface is either substantially enhanced or almost entirely quenched by an underlying ferroelectric domain wall depending on its chirality, and can be further tailored by the polar domains. Unlike the extensively studied coupling mechanisms driven by charge, spin, and lattice, the interfacial tailoring effect is solely mediated by the polar symmetry, as well explained via our density functional theory calculations, pointing to a new material strategy for the functional design of nanoscale reconfigurable optical applications.
36 MATERIALS SCIENCE↗
Thermal stability and diffusion characteristics of ultrathin amorphous carbon films grown on crystalline and nitrogenated silicon substrates by filtered cathodic vacuum arc deposition
Amorphous carbon (a-C) films are widely used as protective overcoats in many technology sectors, principally due to their excellent thermophysical properties and chemical inertness. The growth and thermal stability of sub-5-nm-thick a-C films synthesized by filtered cathodic vacuum arc on pure (crystalline) and nitrogenated (amorphous) silicon substrate surfaces were investigated in this study. Samples of a-C/Si and a-C/SiN x /Si stacks were thermally annealed for various durations and subsequently characterized by high-resolution transmission electron microscopy (TEM) and electron energy loss spectroscopy (EELS). The TEM images confirmed the continuity and uniformity of the a-C films and the 5-nm-thick SiN x underlayer formed by silicon nitrogenation using radio-frequency sputtering. The EELS analysis of cross-sectional samples revealed the thermal stability of the a-C films and the efficacy of the SiNx underlayer to prevent carbon migration into the silicon substrate, even after prolonged heating. The obtained results provide insight into the important attributes of an underlayer in heated multilayered media for preventing elemental intermixing with the substrate, while preserving the structural stability of the a-C film at the stack surface. An important contribution of this investigation is the establishment of an experimental framework for accurately assessing the thermal stability and elemental diffusion in layered microstructures exposed to elevated temperatures.
42 ENGINEERING↗