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At least 1,009 records · Page 56

An Ensemble Recentering Kalman Filter with an Application to Argo Temperature Data Assimilation into the NASA GEOS-5 Coupled Model

A two-step ensemble recentering Kalman filter (ERKF) analysis scheme is introduced. The algorithm consists of a recentering step followed by an ensemble Kalman filter (EnKF) analysis step. The recentering step is formulated such as to adjust the prior distribution of an ensemble of model states so that the deviations of individual samples from the sample mean are unchanged but the original sample mean is shifted to the prior position of the most likely particle, where the likelihood of each particle is measured in terms of closeness to a chosen subset of the observations. The computational cost of the ERKF is essentially the same as that of a same size EnKF. The ERKF is applied to the assimilation of Argo temperature profiles into the OGCM component of an ensemble of NASA GEOS-5 coupled models. Unassimilated Argo salt data are used for validation. A surprisingly small number (16) of model trajectories is sufficient to significantly improve model estimates of salinity over estimates from an ensemble run without assimilation. The two-step algorithm also performs better than the EnKF although its performance is degraded in poorly observed regions.

Particle Filter↗

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↗

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↗

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↗

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↗

A numerical extension of the spatially-filtered Euler equations for contact discontinuities

Solving the spatially-filtered Euler equations with a kinetic energy-preserving centered discretization is an efficient framework for simulating shock-dominated flows. Here, the present work describes a numerical treatment of the spatially-filtered Euler equations to minimize oscillations when simulating contact discontinuities. To counteract dispersive errors inherent to centered schemes, a WENO-like correction term is applied to the enthalpy transport in the energy equation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Broadband bandpass Purcell filter for circuit quantum electrodynamics

In circuit quantum electrodynamics, qubits are typically measured using dispersively coupled readout resonators. Coupling between each readout resonator and its electrical environment, however, reduces the qubit lifetime via the Purcell effect. Inserting a Purcell filter counters this effect while maintaining high readout fidelity but reduces measurement bandwidth and, thus, limits multiplexing readout capacity. In this Letter, we develop and implement a multi-stage bandpass Purcell filter that yields better qubit protection while simultaneously increasing measurement bandwidth and multiplexed capacity. Here, we report on the experimental performance of our transmission-line-based implementation of this approach, a flexible design that can easily be integrated with current scaled-up, long coherence time superconducting quantum processors.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Structural, chemical, and electronic control in Co–SiNx granular metals for high-pass filter applications

Granular metals, consisting of nanoscale conducting and insulating regions, have been studied for more than 50 years for fundamental and applied research. Granular metals exhibit non-linear conductivity vs frequency behavior, consistent with the universal power law response, and have recently been suggested for high-pass filter applications. Here, we report that cobalt–silicon nitride (Co–SiNx) granular metals with optimized sputter conditions and post-growth annealing exhibit an exceptional 109 increase in conductivity at 1 MHz compared to the DC conductivity. The improved frequency response is correlated with structural and chemical improvements examined via scanning transmission electron microscopy and x-ray photoemission spectroscopy. While we focus on improvements for high-pass filter applications, the structural, chemical, and electronic control demonstrated here will benefit a variety of granular metal and nanoparticle applications.

Annealing↗

Identification of Distorted Gamma-Ray Signature Patterns Using Digital Filtering and Auto-Associative Memory Implemented with a Hopfield Neural Network

The detection and identification of radioactive sources in search applications involve analyzing passive gamma-ray emissions from high-level radioactive materials. This process uses a mobile detector-spectrometer in a complex field test environment. Recently, the use of artificial intelligence for gamma-ray spectrum analysis has shown promising results. However, challenges persist in identifying isotopic signatures from spectral measurements that may be distorted due to source shielding, random variations in natural radioactive background, or insufficient measurement time to obtain clear spectral lines. Here, this paper presents a novel intelligent signature recognition method that combines digital filtering techniques with an artificial Hopfield Neural Network (HNN). The HNN leverages auto-associative memory to store training sample patterns and match them with incoming gamma spectra from distorted sources. It restores the testing sources’ measurements by finding the closest matching signature patterns in the spectral library. Before HNN recognition, the measured spectrum undergoes preprocessing with a digital image filter to reduce fluctuations. Performance of the proposed method is evaluated using a set of gamma-ray spectra measured with a sodium iodide detector. The data collected include measurements from six pure samples: 241 Am, 60 Co, 137 Cs, 192 Ir, 239 Pu, and 235 U, which are used for training and validation (i.e. six cases). Additionally, the data set contains 24 distorted synthesized sources with various fluctuating backgrounds. Test results demonstrate the potential of the proposed method to accurately recognize the correct isotope with high precision, achieving an accuracy rate exceeding 85%. Furthermore, the proposed method exhibits superior performance compared to the conventional multiple regression fitting and simple feedforward neural network methods.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Jacobian sparsity detection using Bloom filters

Determining Jacobian sparsity structure is an important step in the efficient computation of sparse Jacobians. We introduce a new method for determining Jacobian sparsity patterns by combining bit vector probing with Bloom filters. In conclusion, we further refine Bloom filter probing by combining it with hierarchical probing to yield a highly effective strategy for Jacobian sparsity pattern determination.

Bloom filter↗

Estimation of plasma properties using an extended Kalman filter with plasma global models

A physically-constrained extended Kalman filter (EKF) is applied to various zero-dimensional global models for the estimation of plasma properties using time-dependent experimental data such as the plasma density or ion flux. The capability of the EKF is demonstrated to estimate unknown system states simultaneously, such as reaction rate coefficients and the absorbed electron input power, which can be difficult, if not impossible, to measure experimentally. Global models accounting for pure argon reactions and argon-oxygen reactions are used in this work to demonstrate the ability of the filter to estimate dynamic and complex systems. Furthermore, the results obtained from the EKF plasma global model illustrate that model-data fusion techniques can be used to estimate plasma properties and processes for time-varying systems, such as pulsed discharges.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Quantitative Fit Evaluation of N95 Filtering Facepiece Respirators and Coronavirus Inactivation Following Heat Treatment

Abstract Reuse of filtering facepiece respirators (FFRs, commonly referred to as N95s) normally meant for single use has become common in healthcare facilities due to shortages caused by the COVID-19 pandemic. Here, we report that murine hepatitis coronavirus initially seeded on FFR filter material is inactivated (6 order of magnitude reduction as measured by median tissue culture infective dose, TCID50) after dry heating at 75°C for 30 min. 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 10 heating cycles. Previous studies have reported that the filtration efficiency of FFRs 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 reuse N95 FFRs in emergency situations where reusing FFRs is a necessity and broad-spectrum sterilization is unavailable. However, we also observe that a radiative heat source (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 investigating heat as a means to recycle FFRs. These differences suggest that while our repeated decontamination cycles do not affect FFR fit, overall wear time and the number of donning/doffing cycles are important factors that likely degrade FFR fit and must be investigated further.

60 APPLIED LIFE SCIENCES↗

Multicode Signaling in a Filter Bank Multicarrier Spread Spectrum System and Its Application to HF Communications

The use of filter banks for implementing multicarrier spread spectrum systems leads to a class of effective waveforms that are highly resilient to partial-band interferers. Such waveforms can be also designed to keep the peak-to-average power ratio (PAPR) of the resulting signal at a minimum level. The use of multiple spreading gain vectors (known as multicodes), on the other hand, is an effective method for increasing the data rate in spread spectrum systems, in general. This paper presents a detailed analysis of a class of filter bank multicarrier spread spectrum (FBMC-SS) waveforms and demonstrates an effective receiver implementation of them when multicodes are applied. Application of the developed multicode waveform for communications over high-frequency (HF) skywave channels is also explored, and the benefits that it provides are studied both numerically, through computer simulations, and experimentally, by examining the receivers performance over a variety of skywave links.

42 ENGINEERING↗

Photonic ring resonator notch filters for astronomical OH line suppression

Photonic ring resonator arrays used as notch filters are a promising novel solution to improve the signal-to-noise ratio of ground-based astronomical observations by suppressing OH emission lines in the near-infrared (NIR) wavelength range (0.9-2.5 μm). We aim to fabricate a series of ring resonators connected by a waveguide, each with its resonance wavelength and full-width-half-maximum (FWHM) matched with one of the OH emission lines. The fundamental structure of ring resonator-based notch filters is a ring resonator evanescently coupled to a waveguide. Due to the optical loss of the ring resonator, the transmission of light through the waveguide will be suppressed at the resonance wavelength of the ring resonator. The degree of suppression can be controlled by varying the coupling strength between the waveguide and the ring resonator, while the resonance wavelength can be controlled by the geometry and size of the ring. However, since a single ring resonator has multiple modes each with a different wavelength separated by its free spectral range (FSR), the additional suppression at other wavelengths may undermine the signal. Therefore, the ideal FSR off the ring resonators needs to be larger than the measurement window. For this application, we aim the FSR to be 1 larger than 30 nm . Our initial measurement results revealed two major challenges for this application. First, the FSR of the ring resonators is limited by the bending loss of ring resonators. In a standard 220 nm silicon-on-insulator material platform, the FSR of ring resonators cannot exceed 30 nm; Second, due to the wafer thickness variation and fabrication inaccuracy, the achievable accuracy in the resonance wavelength of a fabricated single ring resonator is only ~1.5 nm. In this work, we report our novel solutions to the above two challenges. For the first one, we have developed a novel structure based on the Vernier effect to greatly raise the FSR of ring resonators. Serially coupled Vernier ring structures is a typical approach to increase the FSR without encountering bending loss, but it requires restrictive matching of the coupling and optical loss of the rings and any imperfect matching can result in even noisier signals. On the contrary, our structure, as shown in shown in Fig. 1, does not require the restrictive matching. The signals of two output ports in our structure marked in Fig. 1 will be added together to form the final output signal, and Fig. 2 shows the finite-difference time-domain (FDTD) simulation results of the final output signal. As can be seen, the FSR of the entire structure is well-beyond 60 nm with little noise at other wavelengths. For the second one, we have developed an easy-to-implement method to permanently tune the resonance wavelengths of ring resonators within 0.1 nm accuracy. The tuning mechanism is based on the property of hydrogen silsesquioxane (HSQ) resist. As a negative tone electron beam resist, HSQ upon exposure will cross-link and eventually form SiO2. The refractive 2 index of HSQ resist and formed SiO2 are slightly different , enabling the tuning. By having a thin layer of HSQ resist on top of ring resonator structures followed by SiO2 cladding, the resonance wavelengths of ring resonators can be post-tuned Advances in Optical Astronomical Instrumentation 2019, edited by Simon Ellis, Céline d'Orgeville, Downloaded From: https://www.spiedigitallibrary.org/conference-proceedings-of-spie on 23 Sep 2020 Terms of Use: https://www.spiedigitallibrary.org/terms-of-use through electron beam exposure. We have found that, within 5 nm tuning range, the resonance wavelength shift and dosage applied obeys a linear relationship. So a simple calibration beforehand can result in resonance wavelength tuning within 0.1 nm accuracy. The transmission spectra of a 3-ring device before and after tuning is shown in Fig. 3.

Astronomical optics↗

CFD Analysis of RLUOB Zone 1 HEPA Filter Plenum and Testing Manifolds

The purpose of this computational fluid dynamics (CFD) analysis is to ensure that Camfil Farr’s upstream and downstream injection and sampling manifolds can meet or exceed the requirements outlined in the ASME AG-1 1997 a(2000) Code on Nuclear Air and Gas Treatment for the testing of HEPA and adsorbent filters. This paper will present a numerical simulation of airflow in the Radiological Laboratory Utility Office Building (RLUOB) zone 1 HEPA filter plenum and testing manifolds using the commercial CFD software ANSYS FLUENT 2020R1. The CFD analysis focuses on the investigation of the air flow distribution and air-aerosol mixing uniformity. The evaluation was done for all steps of the modeling process: grid generation, physics setup, simulation, and post-processing. The mass flow rate in each section of the zone 1 injection and sampling manifolds is also reported.

42 ENGINEERING↗

Earth Filter Mathematica Toolbox V4 User’s Guide: Simplifying, Visualizing and Quick Modeling of All Fission Products from 60 Nuclear Sources

The Earth Filter Mathematica toolbox is a set of user-friendly routines that accurately calculate the ingrowth and decay of any of the 908 fission products for an infinite combination of sources over a large dynamic range and large time scales. This toolbox was developed by Dr. W. P. Brug at Los Alamos National Laboratory and eliminates the need for the user to define and solve differential equations by automatically scanning the ENDF-349 dataset and creating a complete set of differential equations (including delayed neutron branches to both the ground and metastable states) for every fission product. Additionally, this code allows the user to choose the data source for half-lives from several key references (i.e., TORI, IAEA, ENDF or NUDAT.) The complete set of ENDF-349 independent and cumulative fission yields for all 60 isotopic targets and 4 different neutron spectrums are included. This allows nearly any nuclear fission source to be accurately modeled as a linear combination of endpoint sources. The Earth Filter Toolbox has no limit as to the length of split list, and split lists of 5 or more endpoint sources can be quite common.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗