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We summarize the specifications for the UVIS filters derived through interaction with the Science Oversight Committee (SOC). A detailed characterization of the 48 UVIS filters will be presented.
A new technology for infrared optical filters is presented. We have produced a bandpass filter consisting of a thin Si wafer with cross-shaped metal patterns deposited on both sides.
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.
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.
We have studied the features of noncollinear acousto-optic filtering of quasi-nondiffracting Bessel light beams in uniaxial crystals. We have obtained coupled wave equations for the vector amplitudes of an incident ordinary (o-type) Bessel beam and an extraordinary (e-type) diffracted beam for different modes. We have found an expression for the diffraction efficiency as a function of the parameters of interacting ordinary and extraordinary Bessel beams, and also as a function of the values of the overlap integrals. Using the overlap integral method, we have shown that regardless of the mode order for the Bessel light beam, under transverse phase matching conditions for ordinary and extraordinary diffracted Bessel beams in the optical spectral range 0.4–1.1 μm we can achieve a filter transmission bandwidth of ~0.6 nm in paratellurite crystals .
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.
The dust generated from the basic oxygen steelmaking (BOS) process is a waste material mainly containing iron that cannot be recycled owing to its high zinc content. In this study, the leaching effects of different organic acids are compared, with the aim of determining an acid that selectively leaches and removes zinc from BOS dust, so that the waste material can be recycled into ironmaking and steelmaking processes in order to substitute part of the raw materials of steelmaking. The dust used in this study was scrubbed and collected in the form of a filter cake. The acids tested were oxalic, citric, acetic, propionic, butyric, and valeric acids. Butyric acid was found to be the most effective, with a high zinc extraction level of 49.7 pct and a low iron level of only 2.5 pct. Oxalic acid was the least effective leaching reagent for both zinc and iron extractions, owing to the formation of zinc and iron oxalate precipitates following metal dissolution. The filter cake and leached residues were characterized by chemical analysis, X-ray diffraction, X-ray photoelectron spectroscopy, and scanning electron microscopy with energy dispersive spectroscpopy.
With advances in the precision of digital electronics, waveform generation technology has progressed to a state that enables the creation of m/z filters that are purely digitally driven. These advances present new methods of performing mass analyses that provide information from a chemical system that are inherently difficult to achieve by other means. One notable characteristic of digitally driven mass filters is the capacity to transmit ions at m/z ratios that vastly exceed the capabilities of traditional resonant systems. However, the capacity to probe ion m/z ratios that span multiple orders of magnitudes across multiple orders of magnitude presents a new set of issues requiring a solution. In the present work, when probing multiply charged protein species beyond m/z 2000 using a gentle atmospheric pressure interface, the presence of solvent adducts and poorly resolved multimers can severely degrade spectral fidelity. Increasing energy imparted into a target ion population is one approach minimizing these clusters; however, the use of digital waveform technology provides an alternative that maximizes ion transport efficiency and simultaneously minimizes solvent clustering. In addition to the frequency of the applied waveform, digital manipulation also provides control over the duty cycle of the target waveform. This work examines the conditions and approach leading to optimal digital waveform operation to minimize solvent clustering. .
Polymers are a common component of chemical background which complicates data analysis and can impair interpretation. Undesired chemical background cannot always be addressed via pre-analytical methods, chromatography, or existing data processing methods. The Kendrick mass filter (KMF) is presented for the computational removal of undesired signals present in MS{sup 1} spectra. The KMF is analogous to mass defect filtering but utilizes homology information via Kendrick mass scaling in combination with chromatographic retention time and the number of observed signals. The KMF is intended to assist in situations in which current data processing methods to remove background, e.g., blank subtraction, are either not possible or effective. The major parameters affecting KMF were investigated using PEG 400 and NIST standard reference material 1950 (metabolites in human plasma). Further exploration of the KMF performance was tested using an extract of a swab known to contain polymers. An illustrative real-world example of skin analysis with polymeric signal is discussed. The KMF is also able to provide a high-level view of the compositionality of data regarding the presence of signals with repeat units and indicate the presence of different polymers. .
Although particulate filters (PFs) have been a key component of the emission control system for modern diesel engines, there remain significant questions about the basic regeneration behavior of the filters and how it changes with accumulation of increasing soot layers. This effort describes a systematic deposition and regeneration of particulate matter in 25-mm diameter × 76-mm long wall-flow PFs composed of silicon carbide (SiC) material. The initial soot distributions were analyzed for soot cake thickness using a nondestructive neutron imaging technique. With the PFs intact, it was then possible to sequentially regenerate the samples and reanalyze them, which was performed after nominal 20, 50, and 70 % regenerations. The loaded samples show a relatively uniform distribution of particulate with an increasing soot cake thickness and nearly identical initial density of 70 mg/cm 3 . Throughout regeneration, the soot cake thickness initially decreases significantly while the density increases to 80–90 mg/cm 3 . After ~50 % regeneration, the soot cake thickness stays relatively constant, but instead, the density decreases as pores open up in the layer (~35 mg/cm 3 at 70 % regeneration). Here, complete regeneration initially occurs at the rear of the PF channels. With this information, a conceptual model of the regeneration is proposed.
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.
A central issue in contemporary science is the development of nonlinear data driven statistical–dynamical models for time series of noisy partial observations from nature or a complex model. It has been established recently that ad-hoc quadratic multi-level regression models can have finite-time blow-up of statistical solutions and/or pathological behavior of their invariant measure. Recently, a new class of physics constrained nonlinear regression models were developed to ameliorate this pathological behavior. Here a new finite ensemble Kalman filtering algorithm is developed for estimating the state, the linear and nonlinear model coefficients, the model and the observation noise covariances from available partial noisy observations of the state. Several stringent tests and applications of the method are developed here. In the most complex application, the perfect model has 57 degrees of freedom involving a zonal (east–west) jet, two topographic Rossby waves, and 54 nonlinearly interacting Rossby waves; the perfect model has significant non-Gaussian statistics in the zonal jet with blocked and unblocked regimes and a non-Gaussian skewed distribution due to interaction with the other 56 modes. We only observe the zonal jet contaminated by noise and apply the ensemble filter algorithm for estimation. Numerically, we find that a three dimensional nonlinear stochastic model with one level of memory mimics the statistical effect of the other 56 modes on the zonal jet in an accurate fashion, including the skew non-Gaussian distribution and autocorrelation decay. On the other hand, a similar stochastic model with zero memory levels fails to capture the crucial non-Gaussian behavior of the zonal jet from the perfect 57-mode model.
In this paper, we introduce an algebraic method to construct stable and consistent univariate autoregressive (AR) models of low order for filtering and predicting nonlinear turbulent signals with memory depth. By stable, we refer to the classical stability condition for the AR model. By consistent, we refer to the classical consistency constraints of Adams–Bashforth methods of order-two. One attractive feature of this algebraic method is that the model parameters can be obtained without directly knowing any training data set as opposed to many standard, regression-based parameterization methods. It takes only long-time average statistics as inputs. The proposed method provides a discretization time step interval which guarantees the existence of stable and consistent AR model and simultaneously produces the parameters for the AR models. In our numerical examples with two chaotic time series with different characteristics of decaying time scales, we find that the proposed AR models produce significantly more accurate short-term predictive skill and comparable filtering skill relative to the linear regression-based AR models. These encouraging results are robust across wide ranges of discretization times, observation times, and observation noise variances. Finally, we also find that the proposed model produces an improved short-time prediction relative to the linear regression-based AR-models in forecasting a data set that characterizes the variability of the Madden–Julian Oscillation, a dominant tropical atmospheric wave pattern.
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.
Nuclear waste cleanup is challenged by the handling of feed stocks that are both unknown and complex. Plasma filtering, operating on dissociated elements, offers advantages over chemical methods in processing such wastes. The costs incurred by plasma mass filtering for nuclear waste pretreatment, before ultimate disposal, are similar to those for chemical pretreatment. However, significant savings might be achieved in minimizing the waste mass. As a result, this advantage may be realized over a large range of chemical waste compositions, thereby addressing the heterogeneity of legacy nuclear waste.
Highlights: • Facilesynthesis of inorganic Bi(OH){sub 3} nanoparticles. • High biocompatibility in human skin and dog kidney cells in vitro. • Ultraviolet filter capable of reducing the photocatalytic activity of TiO{sub 2} and ZnO. • Preparation of TiO{sub 2}/Bi(OH){sub 3} and classical TiO{sub 2}/ZnO sunscreen formulations. • Significantly improved photoprotection and photostability compared to TiO{sub 2}/ZnO. - Abstract: In this study we investigate readily synthesised Bi(OH){sub 3} nanoparticles as a novel, multifunctional ultraviolet filter for sunscreen. The absorbance of Bi(OH){sub 3} NPs in the ultraviolet region is comparable to that of both ZnO and TiO{sub 2} NPs used in commercial sunscreens. In vitro photoprotection results show that the combination of TiO{sub 2}/Bi(OH){sub 3} is more efficient than TiO{sub 2}/ZnO over the whole UV range, with an increase in sun protection factor of 28%. The emulsions show rheological properties comparable to those of commercial sunscreens. The combination of TiO{sub 2}/Bi(OH){sub 3} led to insignificant damage on pre-painted steel panels after exterior exposure for twelve weeks. We also demonstrate that the addition of Bi(OH){sub 3} NPs reduces the degradation of crystal violet by photocatalytically active TiO{sub 2} or ZnO NPs under ultraviolet exposure. Finally, assessment of the biocompatibility of Bi(OH){sub 3} with HaCaT keratinocytes and Madin-Darby Canine Kidney (MDCK) cells in vitro is described.
In this study, artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS) are investigated to predict the thickness of the compensator filter in radiation therapy. In the proposed models, the input parameters are field size (S), off-axis distance, and relative dose (D/D{sub 0}), and the output is the thickness of the compensator. The obtained results show that the proposed ANN and ANFIS models are useful, reliable, and cheap tools to predict the thickness of the compensator filter in intensity-modulated radiation therapy.
This research describes the development and implementation of high-fidelity neutron imaging and the associated analysis of the images. This advanced capability allows the non-destructive, non-invasive imaging of particulate filters (PFs) and how the deposition of particulate and catalytic washcoat occurs within the filter. The majority of the efforts described here were performed at the High Flux Isotope Reactor (HFIR) CG-1D neutron imaging beamline at Oak Ridge National Laboratory; the current spatial resolution is approximately 50 μm. The sample holder is equipped with a high-precision rotation stage that allows 3D imaging (i.e., computed tomography) of the sample when combined with computerized reconstruction tools. What enables the neutron-based image is the ability of some elements to absorb or scatter neutrons where other elements allow the neutron to pass through them with negligible interaction. Of particular interest in this study is the scattering of neutrons by hydrogen-containing molecules, such as hydrocarbons (HCs) and/or water, which are adsorbed to the surface of soot, ash and catalytic washcoat. Even so, the interactions with this adsorbed water/HC is low and computational techniques were required to enhance the contrast, primarily a modified simultaneous iterative reconstruction technique (SIRT). Lastly, this effort describes the following systems: particulate randomly distributed in a PF, ash deposition in PFs, a catalyzed washcoat layer in a PF, and three particulate loadings in a SiC PF.