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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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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Joint state-parameter estimation for the reduced fracture model via the united filter

Here, in this paper, we introduce an effective United Filter method for jointly estimating the solution state and physical parameters in flow and transport problems within fractured porous media. Fluid flow and transport in fractured porous media are critical in subsurface hydrology, geophysics, and reservoir geomechanics. Reduced fracture models, which represent fractures as lower-dimensional interfaces, enable efficient multi-scale simulations. However, reduced fracture models also face accuracy challenges due to modeling errors and uncertainties in physical parameters such as permeability and fracture geometry. To address these challenges, we propose a United Filter method, which integrates the Ensemble Score Filter (EnSF) for state estimation with the Direct Filter for parameter estimation. EnSF, based on a score-based diffusion model framework, produces ensemble representations of the state distribution without deep learning. Meanwhile, the Direct Filter, a recursive Bayesian inference method, estimates parameters directly from state observations. The United Filter combines these methods iteratively: EnSF estimates are used to refine parameter values, which are then fed back to improve state estimation. Numerical experiments demonstrate that the United Filter method surpasses the state-of-the-art Augmented Ensemble Kalman Filter, delivering more accurate state and parameter estimation for reduced fracture models. This framework also provides a robust and efficient solution for PDE-constrained inverse problems with uncertainties and sparse observations.

Bayesian inference↗
NASA NTRS1985

Solar radiance models for determination of ERBE scanner filter factor

Shortwave spectral radiance models for use in the spectral correction algorithms for the ERBE Scanner Instrument are provided. The required data base was delivered to the ERBe Data Reduction Group in October 1984. It consisted of two sets of data files: (1) the spectral bidirectional angular models and (2) the spectral flux modes. The bidirectional models employ the angular characteristics of reflection by the Earth-atmosphere system and were derived from detailed radiance calculations using a finite difference model of the radiative transfer process. The spectral flux models were created through the use of a delta-Eddington model to economically simulate the effects of atmospheric variability. By combining these data sets, a wide range of radiances may be approximated for a number of scene types.

Arduini, R. F.↗
DOE OSTI2020

Evaluation of the Tenti S6 model for hydrocarbon fuels at elevated temperatures using filtered Rayleigh scattering measurements

This Letter targets the assessment of the well-known Tenti S6 model for predicting the Rayleigh–Brillouin scattering (RBS) spectra of select gas–phase hydrocarbon fuels (CH 4 , C 2 H 2 , C 2 H 4 , C 3 H 8 , and C 4 H 10 ) over a temperature range of 300 to 700 K. The Tenti S6 model is evaluated by comparing filtered Rayleigh scattering (FRS) measurements to synthetic FRS signals generated from the combination of the Tenti S6 output and an accurate iodine absorption filter model. Here, the experimental and synthetic FRS results agree very well (<3% difference) over the full temperature range for CH 4 , C 2 H 2 , and C 2 H 4 , indicating accurate calculation of the RBS spectra. For C 3 H 8 and C 4 H 10 , there are some large differences between the experimental and synthetic FRS results which cannot be resolved through tuning of bulk viscosity, internal heat capacity, or inclusion of vibrational degrees of freedom, suggesting the need for detailed measurements of the Rayleigh–Brillouin spectra.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2023

Many but not all deep neural network audio models capture brain responses and exhibit correspondence between model stages and brain regions

Models that predict brain responses to stimuli provide one measure of understanding of a sensory system and have many potential applications in science and engineering. Deep artificial neural networks have emerged as the leading such predictive models of the visual system but are less explored in audition. Prior work provided examples of audio-trained neural networks that produced good predictions of auditory cortical fMRI responses and exhibited correspondence between model stages and brain regions, but left it unclear whether these results generalize to other neural network models and, thus, how to further improve models in this domain. We evaluated model-brain correspondence for publicly available audio neural network models along with in-house models trained on 4 different tasks. Most tested models outpredicted standard spectromporal filter-bank models of auditory cortex and exhibited systematic model-brain correspondence: Middle stages best predicted primary auditory cortex, while deep stages best predicted non-primary cortex. However, some state-of-the-art models produced substantially worse brain predictions. Models trained to recognize speech in background noise produced better brain predictions than models trained to recognize speech in quiet, potentially because hearing in noise imposes constraints on biological auditory representations. The training task influenced the prediction quality for specific cortical tuning properties, with best overall predictions resulting from models trained on multiple tasks. The results generally support the promise of deep neural networks as models of audition, though they also indicate that current models do not explain auditory cortical responses in their entirety.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2020

Underlying Fundamentals of Kalman Filtering for River Network Modeling

The grand challenge of producing hydrometeorological estimates every time and everywhere has motivated the fusion of sparse observations with dense numerical models, with a particular interest on discharge in river modeling. Ensemble methods are largely preferred as they enable the estimation of error properties, but at the expense of computational load and generally with underestimations. These imperfect stochastic estimates motivate the use of correction methods, that is, error localization and inflation, although the physical justifications for their optimality are limited. The purpose of this study is to use one of the simplest forms of data assimilation when applied to river modeling and reveal the underlying mechanisms impacting its performance. Our framework based on assimilating daily averaged in situ discharge measurements to correct daily averaged runoff was tested over a 4-yr case study of two rivers in Texas. Results show that under optimal conditions of inflation and localization, discharge simulations are consistently improved such that the mean values of Nash–Sutcliffe efficiency are enhanced from211.32 to 0.55 at observed gauges and from212.24 to21.10 at validation gauges. Yet, parameters controlling the inflation and the localization have a large impact on the performance. Further investigations of these sensitivities showed that optimal inflation occurs when compensating exactly for discrepancies in the magnitude of errors while optimal localization matches the distance traveled during one assimilation window. These results may be applicable to more advanced data assimilation methods as well as for larger applications motivated by upcoming river-observing satellite missions, such as NASA’s Surface Water and Ocean Topography mission.

Streamflow↗
NASA NTRS2011

Compact Micromachined Infrared Bandpass Filters for Planetary Spectroscopy

The future needs of space based observational planetary and astronomy missions include low mass and small volume radiometric instruments that can operate in high radiation and low temperature environments. Here we focus on a central spectroscopic component, the bandpass filter. We model the bandpass response of the filters to target the wavelength of the resonance peaks at 20, 40, and 60 micrometers and report good agreement between the modeled and measured response. We present a technique of using common micromachining processes for semiconductor fabrication to make compact, free standing resonant metal mesh filter arrays with silicon support frames. The process can accommodate multiple detector array architectures and the silicon frame provides lightweight mechanical support with low form factor. We also present a conceptual hybridization of the filters with a detector array.

Merrell, Willie C., II↗
DOE OSTI2025

bmdrc: Python package for quantifying phenotypes from chemical exposures with benchmark dose modeling

Though chemical exposures are known to potentially have negative impacts on health, including contributing to chronic diseases such as cancer, the quantitative contribution of risk is not fully understood for every chemical. A commonly used approach to quantify levels of risk is to measure the proportion of organisms (such as a total number of zebrafish on a plate or mice in a cage) with abnormal behavioral responses or morphology at increasing concentrations of chemical exposure. A particular challenge with processing the proportional data from these assays is the appropriate estimation of chemical concentration levels that result in malformations or acute toxicity, as these values typically vary between experimental measurements. The recommended approach by the Environmental Protection Agency (EPA) is to fit benchmark dose curves with specific filters and model fitting steps, which are crucial to properly processing the proportional data. Several tools exist for the fitting of benchmark dose response curves, but none are standalone Python libraries built to process both morphological and behavioral data as proportions with all the EPA recommended filters, filter parameters, models, and model parameters. Thus, here we present the benchmark dose response curve (bmdrc) Python library, which was built to closely follow these EPA guidelines with helpful visualizations of filters and fitted model curves, and reports for reproducibility purposes. bmdrc is open-source and has demonstrated utility as a support package to an existing web portal for information on chemicals (https://srp.pnnl.gov). Our package will support any toxicology analysis where the response is a proportional value at increasing levels of a concentration of a chemical or chemical mixture.

Superfund↗
NASA NTRS1980

Geomagnetic field modeling by optimal recursive filtering

Data sets selected for mini-batches and the software modifications required for processing these sets are described. Initial analysis was performed on minibatch field model recovery. Studies are being performed to examine the convergence of the solutions and the maximum expansion order the data will support in the constant and secular terms.

Source record↗
NASA NTRS1981

Sample data effects of high-pass filters

Four mathematical models of linear first and second order high pass washout filters were analyzed. These models were Euler's Integration, Zero-Order Hold, Bilinear Transformation, and Second-Order Adams-Bashforth Integration. Bode responses for each model at various sample rates were compared to the continuous filter response. Higher sample rates produced Bode responses approaching the continuous response and the Bilinear Transformation model produced the best responses over the frequency spectrum and sample rates. Pole location analysis of each model in the z-plane showed the Bilinear Transformation and Zero-Order Hold models gave stable poles regardless of time step size, whereas the other models did not display stable poles. A near constant gain error over the entire frequency spectrum was discovered in the Zero-Order Hold cases and a correction gain was calculated for the first-order high-pass filter case.

Chin, D. O.↗
NASA NTRS2002

Nanotechnology Infrared Optics for Astronomy Missions

The program "Nanotechnology Infrared Optics for Astronomy Missions" will design and develop new, nanotechnology techniques for infrared optical devices suitable for use in NASA space missions. The proposal combines expertise from the Smithsonian Astrophysical Observatory, the Naval Research Laboratory, the Goddard Space Flight Center, and the Physics Department at the Queen Mary and Westfield College in London, now relocated to the University of Cardiff, Cardiff, Wales. The method uses individually tailored metal grids, and layered stacks of metal mesh grids, both inductive (free-standing) and capacitive (substrate-mounted), to produce various kinds of filters. The program has the following goals: (1) Model FIR filter properties using electric-circuit analogs, and near-field, EM diffraction calculations; (2) Prototype fabrication of meshes on various substrates, with various materials, and of various dimensions; (3) Test of filter prototypes, and iterate with the modeling programs; (4) Travel to related sites, including trips to Washington, D.C. (location of NRL and GSFC), London (location of QMW), Cardiff, Wales, and Rome (location of ISO PMS project headquarters); (5) Produce ancillary science, including publication of both testing on mesh performance and infrared astronomical science.

Smith, Howard A.↗
NASA NTRS2003

Nanotechnology Infrared Optics for Astronomy Missions

The program "Nanotechnology Infrared Optics for Astronomy Missions" will design and develop new, nanotechnology techniques for infrared optical devices suitable for use in NASA space missions. The proposal combines expertise from the Smithsonian Astrophysical Observatory, the Naval Research Laboratory, the Goddard Space Flight Center, and the Physics Department at the Queen Mary and Westfield College in London, now relocated to the University of Cardiff, Cardiff, Wales. The method uses individually tailored metal grids and layered stacks of metal mesh grids, both inductive (freestanding) and capacitive (substrate-mounted), to produce various kinds of filters. The program has the following goals: 1) Model FIR filter properties using electric-circuit analogs and near-field, EM diffraction calculations. 2) Prototype fabrication of meshes on various substrates, with various materials, and of various dimensions. 3) Test filter prototypes and iterate with the modeling programs. 4) Travel to related sites, including trips to Washington, D.C. (location of NRL and GSFC), London (location of QMW), Cardiff, Wales, and Rome (location of ISO PMS project headquarters). 5) Produce ancillary science, including both publication of testing on mesh performance and infrared astronomical science.

Smith, Howard A.↗
NASA NTRS1989

Filtering information from human experts

The authors propose a model, or filter, for debiasing opinions from multiple experts and combining them into a single consistent estimate of some variable of interest. A distinguishing feature of the approach consists of making the calibration of experts an integral part of filtering. This enables the filter to learn from previous experience with the experts. The theoretical development takes a Bayesian perspective, using de Finetti's (1964) notion of exchangeability. Experimental results with a preliminary computer implementation of the filter show that its estimates are better than those from comparable filters that do not involve calibration.

Mendel, Max B.↗
NASA NTRS2001

Design and Implementation of a Parallel Multivariate Ensemble Kalman Filter for the Poseidon Ocean General Circulation Model

A multivariate ensemble Kalman filter (MvEnKF) implemented on a massively parallel computer architecture has been implemented for the Poseidon ocean circulation model and tested with a Pacific Basin model configuration. There are about two million prognostic state-vector variables. Parallelism for the data assimilation step is achieved by regionalization of the background-error covariances that are calculated from the phase-space distribution of the ensemble. Each processing element (PE) collects elements of a matrix measurement functional from nearby PEs. To avoid the introduction of spurious long-range covariances associated with finite ensemble sizes, the background-error covariances are given compact support by means of a Hadamard (element by element) product with a three-dimensional canonical correlation function. The methodology and the MvEnKF configuration are discussed. It is shown that the regionalization of the background covariances; has a negligible impact on the quality of the analyses. The parallel algorithm is very efficient for large numbers of observations but does not scale well beyond 100 PEs at the current model resolution. On a platform with distributed memory, memory rather than speed is the limiting factor.

Keppenne, Christian L.↗
DOE OSTI2020

Prediction for Pressure Differential Across HEPA Filter Media Based on Media Characteristics and Particle Size Distribution

A new method for predicting the pressure drop across High Efficiency Particulate Air (HEPA) filter media is proposed based upon mass deposited onto the filter and known physical characteristics of the filter media. Detailed are the methods used in conjunction with current filter loading models to predict the pressure drop, as well as tests conducted to validate the methods. The benefit of a prediction model for practical use lies in the manufacturing and service life of nuclear grade HEPA filters. HEPA filters for use in nuclear facilities have a prescribed expiration date and a maximum allowable operating pressure drop. Therefore, the ability to predict the pressure drop across a filter and relate it to an expected length of service time can enable a reduction in wasted filters. This will allow for more informed decisions to be made based upon the dictated life cycle of the filters. Also, understanding and predicting how the pressure drop of HEPA filter media behaves as a function of physical characteristics and loaded mass can assist in future design and manufacturing of filter media. Currently, most existing pressure drop models are either computationally based or analytical methods relying on data gathered during tests. Neither are practical for prediction; the computational methods are difficult to implement, and the current analytical methods are more useful as tools for analysis. The current analytical model developed by Bergman et al. is based upon the pressure drop from each media fiber and modeling the deposited particles as newly formed fibers. Using Bergman's model and the media properties, air properties, and known aerosol particle size distribution, a pressure drop curve as a function of loaded mass can be created. This curve implies initial loading in the depth of the filter media with a transition to pure surface loading on the filter face, and a sensitivity to an evolving particle size distribution as mass continues to load onto the filter. To implement the predictive model, knowledge of the mean filter fiber diameter and porosity of the filter media is required. Traditionally, the mean fiber diameter is calculated as an effective diameter from prior media testing, however, in this study a Scanning Electron Microscope (SEM) technique is used to acquire this variable. Validation of the predictive model is provided by flat sheet media tests under a controlled environment with a measured particle size distribution of the challenge aerosol and shows reasonable preliminary agreement.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS1975

Solid state electro-optic color filter and iris

A pair of solid state electro-optic filters (SSEF) in a binocular holder were designed and fabricated for evaluation of field sequential stereo TV applications. The electronic circuitry for use with the stereo goggles was designed and fabricated, requiring only an external video input. A polarizing screen suitable for attachment to various size TV monitors for use in conjunction with the stereo goggles was designed and fabricated. An improved engineering model 2 filter was fabricated using the bonded holder technique developed previously and integrated to a GCTA color TV camera. An engineering model color filter was fabricated and assembled using PLZT control elements. In addition, a ruggedized holder assembly was designed, fabricated and tested. This assembly provides electrical contacts, high voltage protection, and support for the fragile PLZT disk, and also permits mounting and optical alignment of the associated polarizers.

Source record↗