Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “filtering”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

The compressed state Kalman filter for nonlinear state estimation: Application to large-scale reservoir monitoring

Reservoir monitoring aims to provide snapshots of reservoir conditions and their uncertainties to assist operation management and risk analysis. These snapshots may contain millions of state variables, e.g., pressures and saturations, which can be estimated by assimilating data in real time using the Kalman filter (KF). However, the KF has a computational cost that scales quadratically with the number of unknowns, m, due to the cost of computing and storing the covariance and Jacobian matrices, along with their prod-ucts. The compressed state Kalman filter (CSKF) adapts the KF for solving large-scale monitoring problems. The CSKF uses N preselected orthogonal bases to compute an accurate rank-N approximation of the covari-ance that is close to the optimal spectral approximation given by SVD. The CSKF has a computational cost that scales linearly in m and uses an efficient matrix-free approach that propagates uncertainties using N + 1 forward model evaluations, where N << m. Here in this paper we present a generalized CSKF algorithm for nonlin-ear state estimation problems such as CO 2 monitoring. For simultaneous estimation of multiple types of state variables, the algorithm allows selecting bases that represent the variability of each state type. Through synthetic numerical experiments of CO 2 monitoring, we show that the CSKF can reproduce the Kal-man gain accurately even for large compression ratios (m/N). For a given computational cost, the CSKF uses a robust and flexible compression scheme that gives more reliable uncertainty estimates than the ensemble Kalman filter, which may display loss of ensemble variability leading to suboptimal uncertainty estimates.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Smoothing-based compressed state Kalman filter for joint state-parameter estimation: Applications in reservoir characterization and CO 2 storage monitoring

The operation of most engineered hydrogeological systems relies on simulating physical processes using numerical models with uncertain parameters and initial conditions. Predictions by such uncertain models can be greatly improved by Kalman-filter techniques that sequentially assimilate monitoring data. Each assimilation constitutes a nonlinear optimization, which is solved by linearizing an objective function about the model prediction and applying a linear correction to this prediction. However, if model parameters and initial conditions are uncertain, the optimization problem becomes strongly nonlinear and a linear correction may yield unphysical results. In this paper, we investigate the utility of one-step ahead smoothing, a variant of the traditional filtering process, to eliminate nonphysical results and reduce estimation artifacts caused by nonlinearities. Here, we present the smoothing-based compressed state Kalman filter (sCSKF), an algorithm that combines one step ahead smoothing, in which current observations are used to correct the state and parameters one step back in time, with a nonensemble covariance compression scheme, that reduces the computational cost by efficiently exploring the high-dimensional state and parameter space. Numerical experiments show that when model parameters are uncertain and the states exhibit hyperbolic behavior with sharp fronts, as in CO 2 storage applications, one-step ahead smoothing reduces overshooting errors and, by design, gives physically consistent state and parameter estimates. We compared sCSKF with commonly used data assimilation methods and showed that for the same computational cost, combining one step ahead smoothing and nonensemble compression is advantageous for real-time characterization and monitoring of large-scale hydrogeological systems with sharp moving fronts.

42 ENGINEERING↗
DOE OSTI2020

Filtering adaptive output feedback control for multivariable nonlinear systems with mismatched uncertainties and unmodeled dynamics

This article synthesizes a filtering adaptive output feedback controller for multivariable nonlinear systems with mismatched uncertainties and unmodeled dynamics. The multivariable nonlinear systems under consideration have both matched and mismatched uncertainties, which satisfy the semiglobal Lipschitz condition. The unmodeled dynamics are bounded-input bounded-output stable. By adopting an estimation/cancellation strategy, a piecewise constant adaptive law drives the estimation error to zero at every time instant, which yields the adaptive parameters; a disturbance rejection control law is designed to compensate the nonlinear uncertainties within the bandwidth of low-pass filters. The matched uncertainties are cancelled directly by adopting their opposite in the control signal, while a dynamic inversion of the system is required to eliminate the effect of the mismatched uncertainties on the output. A feedforward control law is designed to track a given command. Since the virtual reference system defines the best performance that can be achieved by the closed-loop system, the uniform performance bounds are derived for the states and control signals via comparison. Both numerical and practical examples are provided to illustrate the effectiveness of the proposed filtering adaptive output feedback control architecture, comparisons with the model reference adaptive control demonstrates the superiority of the proposed control method.

filtering adaptive control, mismatched uncertainit↗
DOE OSTI2019

3D pore analysis of gasoline particulate filters using X-ray tomography: impact of coating and ash loading

Since particulate emissions control technologies are dependent on filtration technologies, development of porous materials with optimized pore structures is crucial to improve filtration efficiency and pressure drop across filters. Despite increasing attention to 3D measurements of porous materials, there are few reports of pore structure investigations of diesel/gasoline particulate filters (D/GPFs) using 3D visualization techniques. Here, GPF 3D pore structures were examined using X-ray tomography (XRT) to identify the impacts of catalyst coating or ash loading. Voxel resolution of 2.93 mu m made it impossible to distinguish coating or ash materials from the cordierite substrate or to recognize smaller pores than the voxel resolution. However, pores up to 200 mu m, which are responsible for the most pore volume, were successfully analyzed. Coating and ash loading resulted in lowering average pore diameter, total porosity, and open porosity, as the peak density of pore diameter at 60 mu m decreased, while pores below 20 mu m increased. Also, the visualized closed pores, which were homogeneously distributed throughout the bare filter, tended to get larger from inlet to outlet sides and more likely to be on the surface inlet due to coating and ash loading, indicating gas pathways originally existing in open pores were blocked due to coating and ash loading, leading to increased pressure drop. The ash impact was found to be more noticeable on mid and back positions than on front position. In addition, the investigation of areal porosity along the direction of gas flow suggested that while ash penetration could reach the outlet side as noted from increased closed pore population, most ash particles would be contained up to 150 mu m. Lastly, the 2D crosscut microscopic analysis that requires destructive procedures with the limited examination area could provide underestimation of ash penetration, whereas the 3D XRT analysis seems to provide more accurate information of pore structure changes due to ash loading at different locations.

36 MATERIALS SCIENCE↗
DOE OSTI2019

A High-Order L-Band HTS Filter for Sensitive Detecting

In this paper, a new high-temperature superconducting (HTS)-based microwave radiometer with an improved sensitivity is presented. The cryogenic receiver front end consists of an HTS filter and a cryogenic low noise amplifier (LNA). The cryogenic receiver front end shows an ultra-low noise figure and can suppress radio frequency interference (RFI) effectively. The proposed HTS filter works at a center frequency of 1.4135 GHz with a bandwidth of 25 MHz. The measured mid-band insertion loss, side band steepness, and out-of-band attenuation of the HTS filter are 0.14 dB, 35 dB/MHz, and 80 dB, respectively. The noise figure of the cryogenic LNA is about 0.27 dB at a temperature of 77 K. Compared with other total power radiometers, the proposed radiometer has a lower receiver noise temperature, which can improve the sensitivity with a short integration time of the satellite-based salinity meter. In addition, since the bandwidth of the salinity meter is fixed and the integration time of satellite-based equipment is limited, such a low receiver noise temperature can increase the flexibility of future satellite payload configuration program.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Uptake behavior of 73 As, 75 Se, 197m Hg, 212 Pb and 210 Po on Eichrom pre-filter resin from HCl and HNO 3

We report the extraction of 73 As, 75 Se, 197 mHg, 212 Pb and 210 Po on Eichrom pre-filter resin was studied in HCl and HNO 3 with batch uptake, kinetic and column experiments. There was significant extraction of mercury in HNO 3 and HCl, and polonium and selenium in HCl. The kinetics are sufficient to allow for retention on pre-filter columns, and separations of 73 As from 75 Se and 197 mHg, and 212 Pb from 210 Po are demonstrated. This retention is important for the development of chemical separations as pre-filter resin is commonly used alongside extraction chromatography resins but its potential for uptake of metal ions is not well characterized.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2019

Particle filter-based modulation domain infrared targets tracking

Faced with problems of low contrast, poor SNR, and relatively complicated tracking environment, stable infrared target tracking is worth researching for its many potential applications. In this paper, instead of traditional target tracking in the pixel domain, we propose a sampling importance resampling (SIR) particle filter method with indirect velocity measurements to track infrared targets in the modulation domain. The dominant amplitude modulation (AM) features used for tracking is extracted by decomposing the input image using an 18-channel Gabor filter bank followed by the application of the dominant component analysis approach. The dominant AM modulation features provide a significant partial texture characteristic of the target which can be separated from background with better discrimination. To take advantage of observed kinematics, we utilize the augmented state vector with indirect velocity information via combining the measurements of velocity in adjacent frames to the SIR particle filter framework, which weakens weights of particles with bad velocity estimates but still having association with the cluttered background or other moving objects. A dynamic template update strategy is also provided to prevent the tracker from appearance model drift. Experiments indicate that the proposed method is effective for raising the tracking accuracy compared with other tracking methods.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Gapped-filtering for efficient Chebyshev expansion of the density projection operator

In this paper, we develop the gapped-filtering method, whereby a short Chebyshev expansion accurately represents the density-matrix operator. The method optimizes the Chebyshev coefficients to give the correct density matrix at all energies except within the gapped region where there are no eigenstates. Gapped filtering reduces the number of required terms in the Chebyshev expansion compared to traditional expansion methods, as long as one knows or can determine efficiently the HOMO and LUMO positions. The reduction is especially noticeable (factors of 2-3) when high accuracy is sought. To exemplify the method, we use gapped-filtering to increase the efficiency of stochastic-GW calculations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Ensemble Kalman filters for dynamical systems with unresolved turbulence

Ensemble Kalman filters are developed for turbulent dynamical systems where the forecast model does not resolve all the active scales of motion. Coarse-resolution models are intended to predict the large-scale part of the true dynamics, but observations invariably include contributions from both the resolved large scales and the unresolved small scales. The error due to the contribution of unresolved scales to the observations, called ‘representation’ or ‘representativeness’ error, is often included as part of the observation error, in addition to the raw measurement error, when estimating the large-scale part of the system. It is here shown how stochastic superparameterization (a multiscale method for subgridscale parameterization) can be used to provide estimates of the statistics of the unresolved scales. In addition, a new framework is developed wherein small-scale statistics can be used to estimate both the resolved and unresolved components of the solution. The one-dimensional test problem from dispersive wave turbulence used here is computationally tractable yet is particularly difficult for filtering because of the non-Gaussian extreme event statistics and substantial small scale turbulence: a shallow energy spectrum proportional to k{sup −5/6} (where k is the wavenumber) results in two-thirds of the climatological variance being carried by the unresolved small scales. Because the unresolved scales contain so much energy, filters that ignore the representation error fail utterly to provide meaningful estimates of the system state. Inclusion of a time-independent climatological estimate of the representation error in a standard framework leads to inaccurate estimates of the large-scale part of the signal; accurate estimates of the large scales are only achieved by using stochastic superparameterization to provide evolving, large-scale dependent predictions of the small-scale statistics. Again, because the unresolved scales contain so much energy, even an accurate estimate of the large-scale part of the system does not provide an accurate estimate of the true state. By providing simultaneous estimates of both the large- and small-scale parts of the solution, the new framework is able to provide accurate estimates of the true system state.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

Chebyshev-filtered subspace iteration method free of sparse diagonalization for solving the Kohn–Sham equation

First-principles density functional theory (DFT) calculations for the electronic structure problem require a solution of the Kohn–Sham equation, which requires one to solve a nonlinear eigenvalue problem. Solving the eigenvalue problem is usually the most expensive part in DFT calculations. Sparse iterative diagonalization methods that compute explicit eigenvectors can quickly become prohibitive for large scale problems. The Chebyshev-filtered subspace iteration (CheFSI) method avoids most of the explicit computation of eigenvectors and results in a significant speedup over iterative diagonalization methods for the DFT self-consistent field (SCF) calculations. However, the original formulation of the CheFSI method utilizes a sparse iterative diagonalization at the first SCF step to provide initial vectors for subspace filtering at latter SCF steps. This diagonalization is expensive for large scale problems. We develop a new initial filtering step to avoid completely this diagonalization, thus making the CheFSI method free of sparse iterative diagonalizations at all SCF steps. Our new approach saves memory usage and can be two to three times faster than the original CheFSI method.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Analytical closure to the spatially-filtered Euler equations for shock-dominated flows

To ensure numerical stability in the vicinity of shocks, a variety of methods have been used, including shock-capturing schemes such as weighted essentially non-oscillatory schemes, as well as the addition of artificial diffusivities to the governing equations. Centered finite difference schemes are often avoided near discontinuities due to the tendency for significant oscillations. However, such schemes have desirable conservation properties compared to many shock-capturing schemes. The objective of this work is to derive all necessary viscous/diffusion terms from first principles and then demonstrate the performance of these analytical terms within a centered differencing framework. The physical Euler equations are spatially-filtered with a Gaussian-like filter. Sub-filter scale (SFS) terms arise in the momentum and energy equations. Analytical closure is provided for each of them by leveraging the jump conditions for a shock. No SFS terms are present in the continuity or species equations. Here, this approach is tested for several problems involving shocks in one and two dimensions. Implemented within a centered difference code, the SFS terms perform well for a range of flow conditions without introducing excessive diffusion.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Parallel processing of filtered queries in attributed semantic graphs

Execution of complex analytic queries on massive semantic graphs is a challenging problem in big-data analytics that requires high-performance parallel computing. In a semantic graph, vertices and edges carry attributes of various types and the analytic queries typically depend on the values of these attributes. Thus, the computation must view the graph through a filter that passes only those individual vertices and edges of interest. Previous investigations have developed Knowledge Discovery Toolbox (KDT), a sophisticated Python library for parallel graph computations. In KDT, the user can write custom graph algorithms by specifying operations between edges and vertices ( semiring operations ). The user can also customize existing graph algorithms by writing filters. While the high-level language for this customization enables domain scientists to productively express their graph analytics requirements, the customized queries perform poorly due to the overhead of having to call into the Python virtual machine for each vertex and edge. In this work, we use the Selective Embedded Just-In-Time Specialization (SEJITS) approach to automatically translate semiring operations and filters defined by programmers into a lower-level efficiency language, bypassing the upcall into Python. We evaluate our approach by comparing it with the high-performance Combinatorial BLAS engine and show that our approach combines the benefits of programming in a high-level language with executing in a low-level parallel environment. We increase the system's flexibility by developing techniques that provide users with the ability to define new vertex and edge types from Python. We also present a new Roofline model for graph traversals and show that we achieve performance that is significantly closer to the bounds suggested by the Roofline. Finally, to further understand the complex interaction with the underlying architecture, we present an analysis using performance counters that quantifies the improvement in hardware behavior in the context our SEJITS methodology. Comprehensively, we demonstrate the first known solution to the problem of obtaining high performance from a productivity language when applying graph algorithms selectively on semantic graphs with hundreds of millions of edges and scaling to thousands of processors for graphs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

A new efficient grain growth model using a random Gaussian-sampled mode filter

This paper presents the use of a Gaussian neighborhood mode filter for predicting grain growth in a manner similar to the solutions obtained by a Monte Carlo Potts model. This flexible grain growth model can quickly utilize modern, computationally optimized data science strategies on graphics processing units to simulate grain growth up to 100 times faster than the state-of-the-art, publicly available Monte Carlo Potts model. We show that, given the correct neighborhood, the mode filter can replicate normal grain growth in two or three dimensions. In addition, the paper briefly demonstrates the ability to model limited anisotropic in grain boundary energy and mobility. Anisotropic grain boundary energy is modeled by defining a weighted mode filter operation. Anisotropic grain boundary mobility is modeled by scaling and orienting the Gaussian neighborhood in a particular direction.

Anisotropy↗
DOE OSTI2018

Olivine, dolomite and ceramic filters in one vessel to produce clean gas from biomass

Highlights: • Syngas free of particles and with very low amount of tar is produced from biomass gasification. • Combination is tested of primary catalyst in the gasifier fluidized bed with catalytic ceramic candles in its freeboard. • Remarkable process intensification and thermal integration is obtained. • The adoption of catalytic filter candles doesn’t increase the required standard filtration surface in practical applications. - Abstract: Heavy organic compounds produced during almond shells gasification in a steam and/or air atmosphere, usually called tar, are drastically reduced in the product gas by using simultaneously in one vessel a ceramic filter placed in the freeboard and a mixture of olivine and dolomite particles in the fluidized bed of the gasifier. The content of tar in the product gas during a reference gasification test with air, in presence of fresh olivine particles only, was 8600 mg/Nm{sup 3} of dry gas. By gasifying biomass with steam at the same temperature level of 820 °C in a bed of olivine and dolomite (20% by weight), and in the presence of a catalytic ceramic filter inserted in the freeboard of the fluidized bed gasifier, the level of tar was brought down to 57 mg/Nm{sup 3} of dry producct gas, with a decrease of more than two orders of magnitude.

09 BIOMASS FUELS↗
DOE OSTI2021

Development of a Filtered CFD-DEM Drag Model with Multiscale Markers Using an Artificial Neural Network and Nonlinear Regression

Here, the accuracy of coarse-grained Euler-Lagrangian simulations of fluidized beds heavily depends on the mesoscale drag models to account for the influences of the unresolved sub-grid structures. Traditional filtered drag models are regressed with mesoscale markers such as voidage and slip velocities. In this research, a filtered drag was regressed with both mesoscale and macro-scale markers using fine grid Computational Fluid Dynamics - Discrete Element Method (CFD-DEM) simulations. The traditional non-linear regression method was compared with machine learning regression using an Artificial Neural Network (ANN) implemented in PyTorch and coupled with MFiX. The new drag showed higher accuracy than the Wen-Yu drag and another filtered drag derived from the two-fluid model. The nonlinear regression shows slightly better results than ANN regression in cases with similar R 2 values. The utilization of the gas inlet velocity as an additional macro-scale marker reduced the errors by up to 55.3% in the tested cases.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Optical Fiber Sensor with a Hydrophobic Filter Layer for Monitoring Hydrogen under Humid Conditions

Real-time and remote monitoring of hydrogen concentration in underground hydrogen storage reservoirs is crucial to maintaining the integrity and safety of the storage facilities. High humidity in the underground deposits interferes with hydrogen sensors, introducing inaccuracy into the hydrogen sensing measurements. A hydrophobic filter layer over a hydrogen sensing layer on an optical fiber hydrogen sensor was devised to minimize the impact of the humidity on the sensor. The hydrogen sensor coated with a hydrophobic filter layer demonstrated a significant improvement in reliable hydrogen sensing under high humidity conditions (99% RH) without severe baseline drift and reduction of transmission intensity. Finally, the optical fiber hydrogen sensor revamped with the filter layer would enable the reliable measurement of hydrogen concentration under the humid conditions expected in subsurface hydrogen storage facilities.

08 HYDROGEN↗
DOE OSTI2013

An energy-filtering device coupled to a quadrupole mass spectrometer for soft-landing molecular ions on surfaces with controlled energy

We have developed an energy-filtering device coupled to a quadrupole mass spectrometer to deposit ionized molecules on surfaces with controlled energy in ultra high vacuum environment. Extensive numerical simulations as well as direct measurements show that the ion beam flying out of a quadrupole exhibits a high-energy tail decreasing slowly up to several hundred eV. This energy distribution renders impossible any direct soft-landing deposition of molecular ions. To remove this high-energy tail by energy filtering, a 127° electrostatic sector and a specific triplet lenses were designed and added after the last quadrupole of a triple quadrupole mass spectrometer. The results obtained with this energy-filtering device show clearly the elimination of the high-energy tail. The ion beam that impinges on the sample surface satisfies now the soft-landing criterion for molecular ions, opening new research opportunities in the numerous scientific domains involving charges adsorbed on insulating surfaces.

43 PARTICLE ACCELERATORS↗
DOE OSTI2013

Coupled double-layer Fano resonance photonic crystal filters with lattice-displacement

We present here ultra-compact high-Q Fano resonance filters with displaced lattices between two coupled photonic crystal slabs, fabricated with crystalline silicon nanomembrane transfer printing and aligned e-beam lithography techniques. Theoretically, with the control of lattice displacement between two coupled photonic crystal slabs layers, optical filter Q factors can approach 211 000 000 for the design considered here. Experimentally, Q factors up to 80 000 have been demonstrated for a filter design with target Q factor of 130 000.

36 MATERIALS SCIENCE↗