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At least 181 records · Page 10

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DOE OSTI2020

Quantitative form and fit of N95 filtering facepiece respirators are retained after dry and humid heat treatments for coronavirus deactivation

Re-use of filtering facepiece respirators (FFRs, commonly referred to as N95s) normally meant for single use only is becoming common in healthcare facilities due to shortages caused by the COVID19 pandemic. Here we report that mouse hepatitis virus (MHV) initially seeded on FFR filter material is inactivated (6 log reduction as measured by 50% tissue culture infective dose (TCID50)) after dry heating at 75 °C for 30 minutes. We also find that the quantitative fit of FFRs after heat treatment at this temperature, under dry conditions or at 90% relative humidity, is not affected by single or ten heating cycles. Previous studies have reported that the filtration efficiency of FFR filters is not negatively impacted by these heating conditions. These results suggest that thermal inactivation of coronaviruses is a potentially rapid and widely deployable method to re-use N95 FFRs in emergency situations where reusing FFRs is a necessity and broad-spectrum sterilization is unavailable. However, we also found that a heat source that emits radiation (e.g., an exposed heating element) results in rapid qualitative degradation of the FFR. Finally, we discuss differences in the results reported here and other recent studies investing heat as a means to recycle FFRs and suggest that overall wear time and donning/doffing cycles are important factors that need to be considered.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Assembly Operations Welding Filter(s) in Drum Calculation

This calculation documents the estimation of temperature and pressure rise inside a 55-gal TRU waste drum with a HEPA filter containing Titanium and Tantalum fume dust based on operational history. Actual Titanium values are believed to be less than 10g on any given filter. Actual Tantalum values are believed to be less than 1g on any given filter

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2024

Demonstrating the Potential of Adaptive LMS Filtering on FPGA-Based Qubit Control Platforms for Improved Qubit Readout in 2D and 3D Quantum Processing Units

Advancements in quantum computing underscore the critical need for sophisticated qubit readout techniques to accurately discern quantum states. This abstract presents our research intended for optimizing readout pulse fidelity for 2D and 3D Quantum Processing Units (QPUs), the latter coupled with Superconducting Radio Frequency (SRF) cavities. Focusing specifically on the application of the Least Mean Squares (LMS) adaptive filtering algorithm, we explore its integration into the FPGA-based control systems to enhance the accuracy and efficiency of qubit state detection by improving Signal-to-Noise Ratio (SNR). Implementing the LMS algorithm on the Zynq UltraScale+ RFSoC Gen 3 devices (RFSoC 4x2 FPGA and ZCU216 FPGA) using the Quantum Instrumentation Control Kit (QICK) open-source platform, we aim to dynamically test and adjust the filtering parameters in real-time to characterize and adapt to the noise profile presented in quantum computing readout signals. Our preliminary results demonstrate the LMS filter's capability to maintain high readout accuracy while efficiently managing FPGA resources. These findings are expected to contribute to developing more reliable and scalable quantum computing architectures, highlighting the pivotal role of adaptive signal processing in quantum technology advancements.

Johnson, Hans↗

DOE OSTI2024

Region of Interest Filter in DUNE DAQ

The Deep Underground Neutrino Experiment (DUNE) is the next generation neutrino experiment currently under construction. It consists of a broadband neutrino beam at Fermilab, a high precision near detector, and the largest liquid argon time projection chamber far detector ever designed at the Sanford Underground Research Facility (SURF). The Region of Interest (ROI) filter is designed for DUNE’s online Data Acquisition (DAQ) system to address data rate constraints and enable low energy physics in the <10 MeV range. The filter employs zero suppression on the detector signal, and by tuning the readout window and threshold the data rates can be reduced by >90%. Performance of the ROI is analyzed in LArsoft on MARLEY generated low energy MC events propagated through the detector simulation. Notably, the optimized ROI filter enables a lower trigger threshold for readout at ~5-10 MeV, allowing DUNE to explore low-energy physics, specifically focusing on solar boron 8 neutrinos which are relevant in this energy range. This advancement enhances DUNE's scientific capabilities, opening avenues for detailed analyses of previously inaccessible low-energy neutrino interactions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

Earth-Centered, Earth-Fixed Inertial Navigation System & Error-State Kalman Filter Reference Manual

This is a self-contained reference document that derives the equations necessary to build a combined inertial navigation system and error-state Kalman filter. Coordinate transform, linear time invariant system, inertial sensing, and error-state Kalman filtering theory is built up from first principles. This theory is then leveraged to derive the system equations for two combined inertial navigation system and error-state Kalman filters: (1) a 15-state system modeling white-noise-integrating accelerometer and gyroscope biases, and (2) a 39-state system modeling static and first-order Gauss-Markov accelerometer and gyroscope biases, scale factor errors, and cross-axis sensitivity errors.

42 ENGINEERING↗

DOE OSTI2025

Enhancing lifetime, forecasting, and economic benefits of photovoltaic technologies undergoing UV-induced degradation with optical filtering

Ultraviolet-induced degradation (UV-ID) of various PV cell types was analyzed under optical UV filters with different cutoff wavelengths. Cell types studied included interdigitated back contact (IBC), passivated emitter and rear totally diffused (PERT), and heterojunction technology (HJT) based on crystalline Si (c-Si), and metal halide perovskite (MHP) cells. Analyzing degradation rates in two distinct regimes proved beneficial for all cell types. We used empirical linearizing functions ln(t) for c-Si technologies and 2 √t for MHP samples where t is time. These were applied to extrapolate UV-induced degradation over the lifetime of PV modules under various levels of optical UV filtering and used to predict the relative economic benefits for PV power plants. Degradation rates for all technologies were generally faster under the long pass optical filters having shorter cutoff wavelengths transmitting more UV irradiation and at elevated temperatures when testing MHP samples in the range between 60 °C and 90 °C.

14 SOLAR ENERGY↗

DOE OSTI2020

Tunable graphene-based infrared reflectance filter having patterned nanoantenna layer and unpatterned graphene layer

An actively tunable optical filter can control the amplitude of reflected infrared light. The filter exploits the dependence of the excitation energy of plasmons in a continuous and unpatterned sheet of graphene, on the Fermi-level, which can be controlled by conventional electrostatic gating. An exemplary filter enables simultaneous modification of two distinct spectral bands whose positions are dictated by the device geometry and graphene plasmon dispersion. Within these bands, the reflected amplitude can be varied by over 15% and resonance positions can be shifted by over 90 cm−1. Electromagnetic simulations verify that tuning arises through coupling of incident light to graphene plasmons by a nanoantenna grating structure. Importantly, the tunable range is determined by a combination of graphene properties, device structure, and the surrounding dielectrics, which dictate the plasmon dispersion. Thus, the underlying design is applicable across a broad range of infrared frequencies.

Beechem, III, Thomas Edwin↗

DOE OSTI2021

Wien filter with integrated vacuum pump

An integral Wien filter and vacuum pump for separating charged particles or for orienting their spin direction while maintaining optimal beamline vacuum. The vacuum pump is an ion pump including one or more cylindrical Penning cells to trap and expel electrons. The Wien filter includes orthogonal electric and magnetic fields to direct particles with the desired speed through the device while deflecting particles at undesired speeds. The Wien filter includes two electrodes, one biased positive and one biased negative, a dipole magnet, and means for reversing polarity of the electrodes to flip the spin of the charged particles. Metal plates on either side of the Penning cells embed gas that is ionized by trapped electrons in the Penning cell thus creating vacuum by turning gas into solid. The two metal plates can be configured to obtain vacuum pumping via chemical gettering and for removal of noble gases.

Poelker, Bernard Matthew↗

DOE OSTI2022

Random filters for enriching the discriminatory power of topological representations

Topological representations of data are inherently coarse summaries which endows them with certain desirable properties like stability but also potentially inhibits their discriminatory power relative to fine-scale learned features. In this work we present a novel framework for enriching the discriminatory power of topological representations based on random filters and capturing ``interference topology'' rather than direct topology. We show that our random filters outperform previously explored structured image filters while requiring orders of magnitude less computational time. The approach is demonstrated on the MNIST dataset but is broadly applicable across data sets and modalities. This work is concluded with a discussion of the mathematical intuition underlying the approach and identification of future directions to enable deeper understanding and theoretical results.

Jorgenson, Grayson S.↗

DOE OSTI2022

Random Filters for Enriching the Discriminatory Power of Topological Representations

Topological representations of data are inherently coarse summaries which endows them with certain desirable properties like stability but also potentially inhibits their discriminatory power relative to fine-scale learned features. In this work we present a novel framework for enriching the discriminatory power of topological representations based on random filters and capturing "interference topology'' rather than direct topology. We show that our random filters outperform previously explored structured image filters while requiring orders of magnitude less computational time. The approach is demonstrated on the MNIST dataset but is broadly applicable across data sets and modalities. This work is concluded with a discussion of the mathematical intuition underlying the approach and identification of future directions to enable deeper understanding and theoretical results.

Jorgenson, Grayson S.↗

DOE OSTI2024

Demonstrating the Potential of Adaptive LMS Filtering on FPGA-Based Qubit Control Platforms for Improved Qubit Readout in 2D and 3D Quantum Processing Units

Johnson, Hans↗

DOE OSTI2024

Mechanically stacked tandem photovoltaic cells with intermediate optical filters

A method of fabricating a multi-junction photosensitive device is provided. The method may include fabricating at least two photoactive layers, wherein at least one photoactive layer is fabricated on a transparent substrate, and at least one photoactive layer is fabricated on a reflective substrate, patterning at least one optical filter layer on at least one photoactive layer fabricated on a transparent substrate, and bonding the at least two photoactive layers using cold weld or van der Waals bonding. A multi-junction photosensitive device is also provided. The device may have at least two photoactive layers, and at least one optical filter layer, wherein at least two layers are bonded using cold weld or van der Waals bonding. The optical filter layer may be a Distributed Bragg Reflector.

Forrest, Stephen R.↗

DOE OSTI2025

A dynamic likelihood approach to filtering transport processes: advection-diffusion dynamics

A Bayesian data assimilation scheme is formulated for advection-dominated advective and diffusive evolutionary problems, based upon the Dynamic Likelihood (DLF) approach to filtering. The DLF was developed specifically for hyperbolic problems –waves–, and in this paper, it is extended via a split step formulation, to handle advection-diffusion problems. In the dynamic likelihood approach, observations and their statistics are used to propagate probabilities along characteristics, evolving the likelihood in time. The estimate posterior thus inherits phase information. For advection-diffusion the advective part of the time evolution is handled on the basis of observations alone, while the diffusive part is informed through the model as well as observations. We expect, and indeed show here, that in advection-dominated problems, the DLF approach produces better estimates than other assimilation approaches, particularly when the observations are sparse and have low uncertainty. The added computational expense of the method is cubic in the total number of observations over time, which is on the same order of magnitude as a standard Kalman filter and can be mitigated by bounding the number of forward propagated observations, discarding the least informative data.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Filtered Multi-Tone Spread Spectrum With Overlapping Subbands

A new form of the filter bank multi-carrier spread spectrum (FBMC-SS) waveform is presented. This new waveform modifies the filtered multi-tone spread spectrum (FMT-SS) system, and is intended to whiten the power spectral density (PSD) of the transmit signal. In the conventional FMT-SS, subcarrier bands are non-overlapping, leaving a spectral null between the adjacent subcarrier bands. To make FMT-SS more appealing for a broader set of applications than those studied in the past, we propose adding additional subcarriers centered at these nulls and thoroughly explore the impact of the added subcarriers on the system performance. This modified form of FMT-SS is referred to as overlapped FMT-SS (OFMT-SS). We explore the conditions required for maximally flattening the PSD of the synthesized OFMT-SS signal and for cancelling the interference caused by overlapping subbands. We also explore the choices of spreading gains that result in a low peak-to-average power ratio (PAPR) for a number of different scenarios. Further reduction of the PAPR of the synthesized signal through clipping methods is also explored. Additionally, we propose methods of multi-coding for increasing the data rate of the OFMT-SS waveform, while minimally impacting its PAPR.

42 - ENGINEERING↗

DOE OSTI2020

An augmented Lagrangian filter method

Here, we introduce a filter mechanism to enforce convergence for augmented Lagrangian methods for nonlinear programming. In contrast to traditional augmented Lagrangian methods, our approach does not require the use of forcing sequences that drive the first-order error to zero. Instead, we employ a filter to drive the optimality measures to zero. Our algorithm is flexible in the sense that it allows for equality-constrained quadratic programming steps to accelerate local convergence. We also include a feasibility restoration phase that allows fast detection of infeasible problems. We provide a convergence proof that shows that our algorithm converges to first-order stationary points. We provide preliminary numerical results that demonstrate the effectiveness of our proposed method.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Preserving nonlinear constraints in variational flow filtering data assimilation

Data assimilation aims to estimate the states of a dynamical system by optimally combining sparse and noisy observations of the physical system with uncertain forecasts produced by a computational model. The states of many dynamical systems of interest obey nonlinear physical constraints, and the corresponding dynamics is confined to a certain sub-manifold of the state space. Standard data assimilation techniques applied to such systems yield posterior states lying outside the manifold, violating the physical constraints. This work focuses on particle flow filters which use stochastic differential equations to evolve state samples from a prior distribution to samples from an observation-informed posterior distribution. The variational Fokker-Planck (VFP)—a generic particle flow filtering framework—is extended to incorporate non-linear, equality state constraints in the analysis. To this end, two algorithmic approaches that modify the VFP stochastic differential equation are discussed: (i) VFPSTAB, to inexactly preserve constraints with the addition of a stabilizing drift term, and (ii) VFPDAE, to exactly preserve constraints by treating the VFP dynamics as a stochastic differential-algebraic equation (SDAE). Additionally, an implicit-explicit time integrator is developed to evolve the VFPDAE dynamics. The strength of the proposed approach for constraint preservation in data assimilation is demonstrated on three test problems: the double pendulum, Korteweg-de-Vries, and the incompressible Navier-Stokes equations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Using Filter Methods to Guide Convergence for ADMM, with Applications to Nonnegative Matrix Factorization Problems

Nonconvex, nonlinear optimization problems arise naturally in parameter fitting and machine learning. While augmented Lagrangian methods have demonstrated robust convergence for classes of these problems, their convergence for block updates has been relatively unexplored outside of the context of the alternating direction method of multipliers (ADMM). ADMM has seen extensive use in these applications, but may exhibit uncertain convergence behavior in many practical nonconvex settings, and struggles with general nonlinear constraints. In contrast, filter methods have proved effective in enforcing convergence for sequential quadratic programming methods and interior point methods with feasibility criteria. We develop an ADMM-filter method for highly nonlinear and nonconvex problems. Here, we show convergence under mild assumptions for several types of coordinate descent schemes, and demonstrate our algorithm on nonnegative matrix factorization and completion problems in imaging and chemical spectrum analysis.

Nonconvex optimization↗

DOE OSTI2021

Decentralized Data-Driven Estimation of Generator Rotor Speed and Inertia Constant Based on Adaptive Unscented Kalman Filter

This paper proposes an online decentralized and data-driven method for synchronous generator rotor speed and inertia estimation. First, an analytical relationship between the generator terminal voltage/current phasors and its internal rotor speed is derived using the Thevenin equivalent. The parameters of the latter are obtained via a recursive estimator, which allows us to estimate the generator's internal rotor speed from voltage/current phasors without the need for any generator information. Second, we reformulate the equivalent swing equation into the Kalman filter form, which enables us to use a priori-estimated internal rotor speed along with measured real and reactive power injections to obtain an estimate of the inertia for each online synchronous generator. This is achieved through an adaptive unscented Kalman filter. We demonstrate that the use of terminal bus frequency to approximate the rotor speed for inertia estimation by existing methods leads to large estimation biases/errors. Numerical simulations carried out on the IEEE 39-bus and Texas 2000-bus systems reveal that our proposed method is consistently more accurate than existing methods. Moreover, our proposed method is insensitive to both the type and location of system disturbances, and it is robust to different levels of measurement noise.

bus frequency↗