Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “deconvolution”

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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Multi-Scale Modeling of Hydrogen Transport in a Porous Fuel Cell Anode

Proton-exchange-membrane fuel cells (PEMFC) are a clean energy conversion alternative to traditional fossil-fuel combustion; however, transport resistances in the electrode pose a lower-limit to catalyst loading and commercialization of PEMFCs. PEMFCs consist of simultaneous hydrogen (H 2 ) oxidation and oxygen (O 2 ) reduction at the anode and cathode, respectively. Here, while oxygen transport resistances in PEMFCs have been widely studied both experimentally and analytically, hydrogen transport resistances are less understood. Herein, we present a physics-based model that encompasses multi-scale transport within the anode side of the PEMFC. The O 2 in the cathode here is omitted and replaced with H 2 to deconvolute O 2 transport resistance contributions, similar to that of a hydrogen pump. Replication of the hydrogen pump setup allows for comparison of the model against experimental analysis of H 2 gas-transport resistance in H 2 limiting-current experiments, which can also inform gas transport (including oxygen) in general. Herein, we present a multi-scale analytical model of the porous anode catalyst layers and individual catalyst agglomerates that enables determination of the effects of electrode morphology such as agglomerate size, catalyst loading, etc. on H 2 transport resistance through the porous electrode to complement and better understand H 2 limiting current experiments and deconvolute local H 2 transport resistances.

Zhang, Rosa↗
DOE OSTI2024

Environmental Molecular Network (ENVnet) v1

Here, we present an approach that integrates mass difference based deconvolution with molecular networking to build a static reference network from all publicly available organic matter metabolomics datasets. This is accomplished using MS/MS deconvolution coupled with both recently reported (BUDDY) and novel machine learning algorithms to determine chemical formulas and perform MS/MS alignments (REM-BLINK).

Bowen, Benjamin↗
DOE OSTI2026

STM/S Grid LDOS Data and Analysis Code for Deciphering Majorana Zero Modes in Topological Superconductor

This dataset provides raw millikelvin scanning tunneling microscopy/spectroscopy (STM/S) grid spectroscopy data and Python analysis scripts supporting the manuscript “Deciphering Majorana Zero Modes in Topological Superconductor FeTe0.55Se0.45 with Machine-Learning-Assisted Spectral Deconvolution.” The dataset includes a raw grid spectroscopy file acquired on FeTe0.55Se0.45 at 40 mK under magnetic field, together with Python/Jupytext analysis scripts used for STM/S data processing, visualization, spectral deconvolution, Lorentzian peak fitting, feature extraction, machine-learning-assisted clustering, and figure generation. These files support the analysis of vortex-core local density of states and the identification of zero-bias-peak-related spectral components from complex in-gap states. The dataset is intended to provide a citable archival record of the data and analysis code associated with the published manuscript and to support transparency and reproducibility of the reported STM/S and machine-learning workflow.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Super-resolution computational saturated absorption microscopy

Imaging beyond the diffraction limit barrier has attracted wide attention due to the ability to resolve previously hidden image features. Of the various super-resolution microscopy techniques available, a particularly simple method called saturated excitation microscopy (SAX) requires only simple modification of a laser scanning microscope: The illumination beam power is sinusoidally modulated and driven into saturation. SAX images are extracted from the harmonics of the modulation frequency and exhibit improved spatial resolution. Unfortunately, this elegant strategy is hindered by the incursion of shot noise that prevents high-resolution imaging in many realistic scenarios. Here, we demonstrate a technique for super-resolution imaging that we call computational saturated absorption (CSA) in which a joint deconvolution is applied to a set of images with diversity in spatial frequency support among the point spread functions (PSFs) used in the image formation with saturated laser scanning fluorescence microscopy. CSA microscopy allows access to the high spatial frequency diversity in a set of saturated effective PSFs, while avoiding image degradation from shot noise.

Murray, Gabe (ORCID:0000000295655485)↗
DOE OSTI2021

Universal dwell time optimization for deterministic optics fabrication

Computer-Controlled Optical Surfacing (CCOS) has been greatly developed and widely used for precision optical fabrication in the past three decades. It relies on robust dwell time solutions to determine how long the polishing tools must dwell at certain points over the surfaces to achieve the expected forms. However, as dwell time calculations are modeled as ill-posed deconvolution, it is always non-trivial to reach a reliable solution that 1) is non-negative, since CCOS systems are not capable of adding materials, 2) minimizes the residual in the clear aperture 3) minimizes the total dwell time to guarantee the stability and efficiency of CCOS processes, 4) can be flexibly adapted to different tool paths, 5) the parameter tuning of the algorithm is simple, and 6) the computational cost is reasonable. In this study, we propose a novel Universal Dwell time Optimization (UDO) model that universally satisfies these criteria. First, the matrix-based discretization of the convolutional polishing model is employed so that dwell time can be flexibly calculated for arbitrary dwell points. Second, UDO simplifies the inverse deconvolution as a forward scalar optimization for the first time, which drastically increases the solution stability and the computational efficiency. Finally, the dwell time solution is improved by a robust iterative refinement and a total dwell time reduction scheme. The superiority and general applicability of the proposed algorithm are verified on the simulations of different CCOS processes. A real application of UDO in improving a synchrotron X-ray mirror using Ion Beam Figuring (IBF) is then demonstrated. The simulation indicates that the estimated residual in the 92.3 mm × 15.7 mm CA can be reduced from 6.32 nm Root Mean Square (RMS) to 0.20 nm RMS in 3.37 min. After one IBF process, the measured residual in the CA converges to 0.19 nm RMS, which coincides with the simulation.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Developing Accelerated Test Protocols and Tuning Microstructures of the Common Materials to Improve Robustness, Reliability, and Endurance of SOFC Cells

This work is built on our prior work where we found that phase transformation in praseodymium nickelates, e.g. Pr 2 NiO 4 (PNO) and (Pr 1-x Nd x ) 2 NiO 4 (PNNO), can be electrochemically driven, and is substantially faster when compared to thermal annealing studies. The first task aims at an attempt to further accelerate the phase transformation in the oxygen electrode by alternating the current input in the cells, which lead to the development of accelerated test protocols (ATPs). ATPs showed up to 60x faster phase transformation and up to 10x faster performance degradation in (Pr 0.50 Nd 0.50 ) 2 NiO 4 electrodes, when compared to long-term operation under constant current density. Furthermore, the phase stable Nd 2 NiO 4 and (La 0.6 Sr 0.4 )(Co 0.8 Fe 0.2 )O 3 (LSCF6482) electrodes were tested in full cells under ATPs, and showed up to 10x faster performance degradation within 1,100 hours in a comparison with long-term thermal annealing studies and electrochemical operation under constant current density. The second task aims at the quantification of the contributions of cell components to the total impedance of a solid oxide fuel cell (SOFC) using electrochemical impedance spectroscopy (EIS) and distribution of relaxation times (DRT). Specifically, the role of gas composition at both anode and cathode was systematically studied, aiming at deconvoluting, identifying and quantifying the contributions of different electrode processes. This was achieved by first tuning the partial pressure of H 2 at the anode and subsequently varying the partial pressure of O 2 at the cathode. The results suggest that, while DRT offers a viable way of deconvoluting different times distributions, additional attention is needed before assigning a peak to a specific electrode process due to the significant overlap of the contributions from the cathode and the anode. Density function theory studies show that both Pr-vacancies and O-defects play a key role on the activity and stability for nickelates towards oxygen reduction reaction. The resident O-interstitials and oxygen ions in the PrO layer form peroxide (O 2 2- ) nearby Pr vacancies. The O 2 2- limits oxygen-ion transport due to the required additional energy to break its O-O bond. We further calculated the formation and segregation energies for different Ln ions (La, Pr, Nd, Pm, Sm, Gd, Tb, Dy, and Ho) in PNO and CeO 2 (111) surfaces. In addition to Nd, Pm and La are suggested as potential dopants in PNO to enhance it stability without decomposition due to their more negative formation energies, lower diffusion energies, and positive separation energies.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2024

Temporal Explosion Source Processes of Declared Nuclear Tests in the Democratic People’s Republic of Korea

In this work we highlight a preliminary temporal source analysis of the six declared Democratic People's Republic of Korea (DPRK) nuclear tests. We use regional seismic data to estimate relative source time functions (RSTFs) via iterative time-domain deconvolution (Ammon, 2006; Pippin, 2022) of vertical-component ground motions recorded within 2000 km of the source region. Since RSTFs are ideally independent of site and propagation effects, their amplitude spectrum is equivalent to the source spectral ratio, but they also retain phase information. We compare observed RSTFs (in the time and frequency domains) with synthetic RSTFs derived from the Mueller & Murphy (1971) explosion source model. The resolution of these time functions varies, however, we generally obtain high-quality results within the limitations of the recording broadband instrumentation. The results indicate that this method effectively preserves source time-history information that can be used for temporal analysis of remote nuclear explosions. This preliminary analysis is intended to assess the viability of using time-domain deconvolution methods for extracting temporal source information.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2024

Super Resolving Unrolled Neural Networks for Remote Sensing

In remote sensing systems, the capabilities of the system are constrained by the complex interactions between size, weight, and power (SWAP) of potential designs. In electro-optical (EO) systems, examples of these critical parameters include the system’s sensitivity and resolution. Those parameters can be increased by ever larger optical apertures and focal planes but at the cost of more SWAP. Multi-image super resolution (MISR) techniques allow resolution to be enhanced via computation rather than more sophisticated optical hardware. These algorithms combine multiple images together into a single, higher resolution image, trading temporal resolution and computation for spatial resolution. Fielded MISR techniques, such as Drizzle, can require several hundred images to create a single super resolved image, implying reduced temporal resolution, increased data acquisition load, and limiting mission applications. Iterative techniques, such as model-based image reconstruction and compressive sensing, have been shown to create super resolved images using fewer images than Drizzle. They do this by posing an optimization problem that balances accuracy between a highly accurate physical model and an image model. In the case of super resolution, the physical model is defined by the relation between low resolution input images and the desired high resolution output image. The image model encodes some assumptions about the super resolved image. These assumptions are meant to suppress reconstruction artifacts that arise due to deterministic physical model error, stochastic measurement noise, and potential undersampling. In practice, the performance of iterative methods are limited by imaging models compatible with optimization. Deep learning-based methods can effectively learn image models of arbitrary complexity, but lack the theoretical explainability and robustness of iterative techniques. Consensus equilibrium (CE) generalizes the iterative techniques beyond optimization, enabling blackbox algorithms such as traditional and neural image denoisers to be used as the image model. CE-based approaches retain much of the explainability and robustness of iterative techniques while allowing the expressiveness of machine learning image models to be used. Additionally, by unrolling iterations of CE with an embedded image denoiser, the image denoiser can be further trained and specialized to the specific application with potentially higher quality reconstructions. Under this project, we demonstrated the feasibility of training an unrolled neural network based upon CE. While we didn’t train one, we showed that the CE process is differentiable and its gradient can be tractably computed. We also explored the usage of a variants of CE akin to generative neural works. Most importantly, we applied the CE framework to a number of problems including non-blind deconvolution, upsampling, single-image super resolution, MISR, event-based sensing, and saturated deconvolution. Our MISR prototype creates high quality reconstructions with an order of magnitude fewer images than previous approaches and, critically, produces these reconstructions fast enough for practical usage.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Regularizing the linearly extrapolated BDF2 scheme for incompressible flows with time relaxation

This paper presents a highly-efficient finite element scheme for the time relaxation model (TRM). The efficiency is achieved through the second-order BDF2 time-stepping scheme with linear extrapolation (BDF2LE). The accuracy of the scheme is also greatly enhanced through the use of the divergence-free Scott-Vogeulis finite elements, and van Cittert approximate deconvolution. A complete finite element analysis is provided, which includes rigorous proofs for the stability, well-possessedness, and convergence of both velocity and pressure solutions. Furthermore, we also demonstrate that the inclusion of the linear time relaxation term preserves the long-time stability of the unregularized BDF2LE scheme. Finally, numerical experiments are presented that demonstrate the added stability and accuracy that time relaxation can provide.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Synthetic aromatic kerosene property prediction improvements with isomer specific characterization via GCxGC and vacuum ultraviolet spectroscopy

This research explores an advanced method of fuel composition determination and builds upon typical hydrocarbon group type analyses performed with two-dimensional gas chromatography (GCxGC). In this study, structural information of individual species within Virent’s Synthetic Aromatic Kerosene (SAK) is identified by vacuum ultraviolet (VUV) spectroscopy. By mass, 71.3% of the components elute within six peaks of the chromatogram, from which 12 unique species are identified through a novel deconvolution method. Overall, the identification of 93.6%m across 26 structural isomers is made by the methods described in this work. With 93.6%m ascribed to specific isomers, the precision of fuel property predictions improves dramatically. For example, the absolute error of the viscosity prediction is reduced by 90% because of this advancement in diagnostic capability, and its 95-percentile confidence interval (precision only) is reduced by 93%. Additionally, the properties of SAK, blended with hydro processed esters fatty acids (HEFA), are demonstrated to have blended properties consistent with conventional jet fuel.

10 SYNTHETIC FUELS↗
DOE OSTI2021

Adversarial sampling of unknown and high-dimensional conditional distributions

Many engineering problems require the prediction of realization-to-realization variability or a refined description of modeled quantities. In that case, it is necessary to sample elements from unknown high-dimensional spaces with possibly millions of degrees of freedom. While there exist methods able to sample elements from probability density functions (PDF) with known shapes, several approximations need to be made when the distribution is unknown. In this paper the sampling method, as well as the inference of the underlying distribution, are both handled with a data-driven method known as generative adversarial networks (GAN), which trains two competing neural networks to produce a network that can effectively generate samples from the training set distribution. In practice, it is often necessary to draw samples from conditional distributions. When the conditional variables are continuous, only one (if any) data point corresponding to a particular value of a conditioning variable may be available, which is not sufficient to estimate the conditional distribution. This work handles this problem using an a priori estimation of the conditional moments of a PDF. Herein, two approaches, stochastic estimation, and an external neural network are compared for computing these moments; however, any preferred method can be used. The algorithm is demonstrated in the case of the deconvolution of a filtered turbulent flow field. It is shown that all the versions of the proposed algorithm effectively sample the target conditional distribution with minimal impact on the quality of the samples compared to state-of-the-art methods. Additionally, the procedure can be used as a metric for the diversity of samples generated by a conditional GAN (cGAN) conditioned with continuous variables.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Conditional Point Sampling: A Monte Carlo Method for Radiation Transport in Stochastic Media.

Current methods for stochastic media transport are either computationally expensive or, by nature, approximate. Moreover, none of the well-developed, benchmarked approximate methods can compute the variance caused by the stochastic mixing, a quantity especially important to safety calculations. Therefore, we derive and apply a new conditional probability function (CPF) for use in the recently developed stochastic media transport algorithm Conditional Point Sampling (CoPS), which 1) leverages the full intra-particle memory of CoPS to yield errorless computation of stochastic media outputs in 1D, binary, Markovian-mixed media, and 2) leverages the full inter-particle memory of CoPS and the recently developed Embedded Variance Deconvolution method to yield computation of the variance in transport outputs caused by stochastic material mixing. Numerical results demonstrate errorless stochastic media transport as compared to reference benchmark solutions with the new CPF for this class of stochastic mixing as well as the ability to compute the variance caused by the stochastic mixing via CoPS. Using previously derived, non-errorless CPFs, CoPS is further found to be more accurate than the atomic mix approximation, Chord Length Sampling (CLS), and most of memory-enhanced versions of CLS surveyed. In addition, we study the compounding behavior of CPF error as a function of cohort size (where a cohort is a group of histories that share intra-particle memory) and recommend that small cohorts be used when computing the variance in transport outputs caused by stochastic mixing.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2024

DEIMoS GUI: An Open-Source User Interface for a High-Dimensional Mass Spectrometry Data Processing Tool

In this paper, we report the creation of a graphical user interface (GUI) for the Data Extraction for Integrated Multidimensional Spectrometry (DEIMoS) tool. DEIMoS is a Python package to process data from high-dimensional mass spectrometry measurements. It is divided into several modules, each representing a data processing step, such as peak detection, alignment, and tandem mass spectra extraction and deconvolution. The inputs for and outputs from DEIMoS can include millions of N-dimensional data points, which can be challenging to visualize in a way that is interactive, informative, and responsive. Here, we used the HoloViz Python data stack, including DataShader and Param, to create an interactive visualization of mass spectrometry data. We believe the GUI will increase the accessibility of DEIMoS, and the visualization methods could be useful for other open-source mass spectrometry tools.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Structural analysis of molten materials by a remote fiber optic Raman sensor

This study presents a novel in situ high-temperature fiber optic Raman probe that enables the study of the physical properties and structure of molten samples at temperatures up to 1400 °C. To demonstrate the functionality of the high-temperature fiber optic Raman probe, different composition mold fluxes were evaluated in this report. The Raman spectra at flux molten temperature were successfully collected and analyzed. A deconvolution algorithm was employed to identify peaks in the spectra associated with the molecular structure of the components in each sample. Here, the experimental results demonstrate that the composition-dependent Raman signal shift can be detected at high temperatures, indicating that molten materials analysis using a high-temperature Raman system shows significant promise. This flexible and reliable high-temperature Raman measurement method has great potential for various applications, such as materials development, composition and structure monitoring during high-temperature processing, chemical identification, and process monitoring in industrial production.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Inference of Induced Fracture Geometries Using Fiber-Optic Distributed Strain Sensing in Hydraulic Fracture Test Site 2

Fiber-optic Distributed Strain Sensor observations in a vertical well allow rapid inference of the time evolution of nearby hydraulic fracture heights and their vertical aperture distributions during stimulation and depressurization. We describe a computationally efficient deconvolutional model based on an elastostatic kernel for fracture opening within an isotropic, homogeneous, linear elastic medium. Furthermore, an application of this model to observations from the Hydraulic Fracture Test Site 2 reveals that ultimate fracture heights are typically greater than those indicated by microseismic monitoring, and some hydraulic fractures continue their upward growth for several hours after high-pressure injections ceased.

58 GEOSCIENCES↗
DOE OSTI2021

Molecular Origin of Strain-Induced Chain Alignment in PDPP-Based Semiconducting Polymeric Thin Films

Donor–acceptor (D–A) type semiconducting polymers have shown great potential for the application of deformable and stretchable electronics in recent decades. However, due to their heterogeneous structure with rigid backbones and long solubilizing side chains, the fundamental understanding of their molecular picture upon mechanical deformation still lacks investigation. Here, the molecular orientation of diketopyrrolopyrrole (DPP)-based D–A polymer thin films is probed under tensile deformation via both experimental measurements and molecular modeling. The detailed morphological analysis demonstrates highly aligned polymer crystallites upon deformation, while the degree of backbone alignment is limited within the crystalline domain. Besides, the aromatic ring on polymer backbones rotates parallel to the strain direction despite the relatively low overall chain anisotropy. Additionally, the effect of side-chain length on the DPP chain alignment is observed to be less noticeable. These observations are distinct from traditional linear-chain semicrystalline polymers like polyethylene due to distinct characteristics of backbone/side-chain combination and the crystallographic characteristics in DPP polymers. Furthermore, a stable and isotropic charge carrier mobility is obtained from fabricated organic field-effect transistors. This study deconvolutes the alignment of different components within the thin-film microstructure and highlights that crystallite rotation and chain slippage are the primary deformation mechanisms for semiconducting polymers.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Formation and Detriments of Residual Alkaline Compounds on High-Nickel Layered Oxide Cathodes

High-nickel layered oxides LiNi x M 1-x O 2 (x ≥ 0.9) have emerged as promising cathode materials for automotive batteries due to their high energy density and lower cost. However, the formation and accumulation of surface alkaline compounds during storage hinder their mass production and commercialization. Here, in this study, a validated chemical method is employed to deconvolute and quantify the evolution of each residual lithium compound in four representative cathodes during ambient-air storage, viz., LiNiO 2 (LNO), LiNi 0.95 Co 0.05 O 2 (NC), LiNi 0.95 Mn 0.05 O 2 (NM), and LiNi 0.95 Al 0.05 O 2 (NA). Furthermore, the activation energy of the reaction between water and the cathode is determined by measuring the leached LiOH concentration at various temperatures. While residual lithium and time-of-flight secondary-ion mass spectrometry measurements collectively reveal that the air stability overall follows the trend of NM > NA ≈ NC > LNO, the aged NM exhibits the highest charge-transfer resistance and the worst electrochemical performance among the cathodes. In situ, X-ray diffraction and scanning transmission electron microscopy unveil that the aged NM is plagued by a large area of resistive spinel-like M 3–x Li x O 4 phases, leading to aggravated particle reaction heterogeneity. Finally, a one-step recalcination method is demonstrated effective in fully restoring the degraded cathodes. This work provides insights into overcoming air sensitivity issues of high-Ni cathodes.

(S)TEM↗
DOE OSTI2025

Kinetically Dormant Ni‐Rich Layered Cathode During High‐Voltage Operation

Abstract The degradation of Ni‐rich cathodes during long‐term operation at high voltage has garnered significant attention from both academia and industry. Despite many post‐mortem qualitative structural analyses, precise quantification of their individual and coupling contributions to the overall capacity degradation remains challenging. Here, by leveraging multiscale synchrotron X‐ray probes, electron microscopy, and post‐galvanostatic intermittent titration technique, the thermodynamically irreversible and kinetically reversible capacity loss is successfully deconvoluted in a polycrystalline LiNi 0.83 Mn 0.1 Co 0.07 O 2 cathode during long‐term charge/discharge cycling in full cell configuration. Contradicting the dramatic capacity loss, the layered structure remains highly alive even after 1000 cycles at 4.6 V while undergoing a three‐order of magnitude reduction in the mass transfer kinetics, leading to almost fully recoverable capacity under kinetic‐free conditions. Such kinetic dormant behavior after cycling is not simply ascribed to poor chemical diffusion by reconstructed cathode surface but highly synchronizes with the lattice strain evolution stemming from the structural heterogeneity between deeply delithiated layered and degraded rock‐salt phases at high voltage. These findings deepen the degradation mechanism of high‐voltage cathodes to achieve long‐cycling and fast‐charging performance.

36 MATERIALS SCIENCE↗