Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “ACCURACY”

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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Evaluation of the Reconstruction Accuracy of the Ultrasound Model–Based Image Reconstruction (U-MBIR) Method for Concrete to Optimize Damage Detection

Reinforced concrete is a critical structural material used to construct nuclear power plants (NPPs). As such, its safety and performance must be thoroughly examined throughout the life cycle of the NPP infrastructure system. Ultrasonic measurements have been an industry standard for both surface and subsurface inspections. To support the development of these techniques, Oak Ridge National Laboratory (ORNL) has researched and developed advanced image reconstruction algorithms to capture internal damage. The results and discussion presented herein summarize the current state of the ultrasonic model–based iterative reconstruction (U-MBIR) algorithm developed at ORNL. In the work documented in this report, the U-MBIR methodology was applied to four sets of ultrasonic data collected from concrete specimens. The results demonstrate that the U-MBIR algorithm can successfully detect defects within the four concrete samples. The reconstruction images help identify the specimen thickness, regions of delamination, and location of rebar embedded within the concrete. The reconstruction images allow engineers and technicians to characterize the internal defects within concrete specimens and structural members. Ultimately, this knowledge can guide engineers in making informed decisions regarding the performance, safety, and reliability of structural materials (i.e., reinforced concrete) throughout the life cycle of NPPs.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Refining NEAMS MARVEL Reactor Model Accuracy

This report outlines the progress of Idaho National Laboratory (INL) in improving the fidelity of the multiphysics model of the Microreactor Applications Research Validation and Evaluation (MARVEL) reactor developed in FY24. The multiphysics model firstly developed in FY24 leverages three single-physics models coupled via the MOOSE’s MultiApps and Transfer systems. The first single-physics model, that functions as the driver application, leverages Griffin to model the neutron transport in the core through the discontinuous finite element (DFEM) discrete ordinates solver (SN). The second single-physics model uses BISON to compute the solid temperature in the reactor. Finally, the System Analysis Module (SAM) was used to model the flow of the sodium-potassium eutectic in the primary loop. In this report we detail the following improvement and enhancements: • An updated mesh leveraging the latest feature in the reactor module (see Section 3.1.).

42 - ENGINEERING↗
DOE OSTI2023

Role of Simple Spatial Gradient in Reinforcing the Accuracy of Temperature Determination of HED Plasma via Spectral Line-Area Ratios

We report on the simulation of temperature gradients in tamped NaFMgO target-foil plasma, heated and backlit by z-pinch dynamic hohlraum radiation. Our approach compares the spectroscopic output of a collisional-radiative model (prismspect) with soft X-ray absorption spectra collected on Sandia National Laboratories’ (SNL) Z Pulsed Power Facility. The pattern of minimum χ2 is seen to agree with an efficient, three-parameter model. Results show that a negligible gradient in electron temperature Te is consistent with experimental data, justifying the assumptions of previous work. The predicted sensitivity of line spectra to the gradient-aligned profile of Te is documented for each spectral feature, so that the line-area ratio between a pair of spectral features may be assessed as a proxy for the existence and quantification of such gradients.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Understanding the Accuracy Limitations of Quantifying Methane Emissions Using Other Test Method 33A

Researchers have utilized Other Test Method (OTM) 33A to quantify methane emissions from natural gas infrastructure. Historically, errors have been reported based on a population of measurements compared to known controlled releases of methane. These errors have been reported as 2σ errors of ±70%. However, little research has been performed on the minimum attainable uncertainty of any one measurement. We present two methods of uncertainty estimation. The first was the measurement uncertainty of the state-of-the-art equipment, which was determined to be ±3.8% of the estimate. This was determined from bootstrapped measurements compared to controlled releases. The second approach of uncertainty estimation was a modified Hollinger and Richardson (H&R) method which was developed for quantifying the uncertainty of eddy covariance measurements. Using a modified version of this method applied to OTM 33A measurements, it was determined that uncertainty of any given measurement was ±17%. Combining measurement uncertainty with that of stochasticity produced a total minimum uncertainty of 17.4%. Due to the current nature of stationary single-sensor measurements and the stochasticity of atmospheric data, such uncertainties will always be present. This is critical in understanding the transport of methane emissions and indirect measurements obtained from the natural gas industry.

Heltzel, Robert↗
DOE OSTI2024

Enhancing Docking Accuracy with PECAN2, a 3D Atomic Neural Network Trained without Co-Complex Crystal Structures

Decades of drug development research have explored a vast chemical space for highly active compounds. The exponential growth of virtual libraries enables easy access to billions of synthesizable molecules. Computational modeling, particularly molecular docking, utilizes physics-based calculations to prioritize molecules for synthesis and testing. Nevertheless, the molecular docking process often yields docking poses with favorable scores that prove to be inaccurate with experimental testing. To address these issues, several approaches using machine learning (ML) have been proposed to filter incorrect poses based on the crystal structures. However, most of the methods are limited by the availability of structure data. Here, we propose a new pose classification approach, PECAN2 (Pose Classification with 3D Atomic Network 2), without the need for crystal structures, based on a 3D atomic neural network with Point Cloud Network (PCN). The new approach uses the correlation between docking scores and experimental data to assign labels, instead of relying on the crystal structures. We validate the proposed classifier on multiple datasets including human mu, delta, and kappa opioid receptors and SARS-CoV-2 Mpro. Our results demonstrate that leveraging the correlation between docking scores and experimental data alone enhances molecular docking performance by filtering out false positives and false negatives.

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

GenAI-Based Digital Twins Aided Data Augmentation Increases Accuracy in Real-Time Cokurtosis-Based Anomaly Detection of Wearable Data

Early detection of potential infectious disease outbreaks is crucial for developing effective interventions. In this study, we introduce advanced anomaly detection methods tailored for health datasets collected from wearables, offering insights at both individual and population levels. Leveraging real-world physiological data from wearables, including heart rate and activity, we developed a framework for the early detection of infection in individuals. Despite the availability of data from recent pandemics, substantial gaps remain in data collection, hindering method development. To bridge this gap, we utilized Wasserstein Generative Adversarial Networks (WGANs) to generate realistic synthetic wearable data, augmenting our dataset for training. Subsequently, we use these augmented datasets to implement a cokurtosis-based technique for anomaly detection in multivariate time-series data. Our approach includes a comprehensive assessment of uncertainties in synthetic data compared to the actual data upon which it was modeled, as well as the uncertainty associated with fine-tuning anomaly detection thresholds in physiological measurements. Through our work, we present an enhanced method for early anomaly detection in multivariate datasets, with promising applications in healthcare and beyond. This framework could revolutionize early detection strategies and significantly impact public health response efforts in future pandemics.

Data-Driven Digital Twins↗
DOE OSTI2021

SDSS-IV MaNGA: Modeling the Spectral Line-spread Function to Subpercent Accuracy

The Sloan Digital Sky Survey IV Mapping Nearby Galaxies at APO (MaNGA) program has been operating from 2014 to 2020, and has now observed a sample of 9269 galaxies in the low redshift universe (z ∼ 0.05) with integral-field spectroscopy. With rest-optical (λλ0.36–1.0 μm) spectral resolution R ∼ 2000 the instrumental spectral line-spread function (LSF) typically has 1σ width of about 70 km s{sup −1}, which poses a challenge for the study of the typically 20–30 km s{sup −1} velocity dispersion of the ionized gas in present-day disk galaxies. In this contribution, we present a major revision of the MaNGA data pipeline architecture, focusing particularly on a variety of factors impacting the effective LSF (e.g., under-sampling, spectral rectification, and data cube construction). Through comparison with external assessments of the MaNGA data provided by substantially higher-resolution R ∼ 10,000 instruments, we demonstrate that the revised MPL-10 pipeline measures the instrumental LSF sufficiently accurately (≤0.6% systematic, 2% random around the wavelength of Hα) that it enables reliable measurements of astrophysical velocity dispersions σ {sub Hα} ∼ 20 km s{sup −1} for spaxels with emission lines detected at signal-to-noise ratio > 50. Velocity dispersions derived from [O II], Hβ, [O III], [N II], and [S II] are consistent with those derived from Hα to within about 2% at σ {sub Hα} > 30 km s{sup −1}. Although the impact of these changes to the estimated LSF will be minimal at velocity dispersions greater than about 100 km s{sup −1}, scientific results from previous data releases that are based on dispersions far below the instrumental resolution should be reevaluated.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

DESI Mg II Absorbers: Extinction Characteristics and Quasar Redshift Accuracy

In this paper, we study how absorption-line systems affect the spectra and redshifts of quasi-stellar objects (QSOs), using catalogs of Mg II absorbers from the early data release and first data release of the Dark Energy Spectroscopic Instrument. We determine the reddening effect of an absorption system by fitting an unreddened template spectrum to a sample of 50,674 QSO spectra that contain Mg II absorbers. We find that reddening caused by intervening absorbers (v off > 3500 km s −1 ) has an average color excess of $\overline{E(B-V)}$ = 0.04 mag. We find that the E(B − V) tends to be greater for absorbers at low redshifts, or those having Mg II absorption lines with higher equivalent widths, but shows no clear trend with v off for intervening systems. However, the $\overline{E(B-V)}$ of associated absorbers, those at v off < 3500 km s −1 , shows a strong trend with v off , increasing rapidly with decreasing v off and peaking (∼0.15 mag) around v off = 0 km s −1 . We demonstrate that Mg II absorbers impact redshift estimation for QSOs by investigating the distributions of v off for associated absorbers. We find that at z > 1.5, these distributions broaden and bifurcate in a nonphysical manner. In an effort to mitigate this effect, we mask pixels associated with the Mg II absorption lines and recalculate the QSO redshifts. We find that we can recover voff populations in better agreement with those for z < 1.5 absorbers and in doing so typically shift background QSO redshifts by Δz ≈ ± 0.005.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Gamma ray Imaging Based Methods to Improve the Accuracy of Uranium Holdup Quantification

Deposits in commercial uranium processing facilities of interest to nuclear safeguards are predominantly low enriched uranium (LEU). The shapes and sizes of holdup deposits can vary significantly, thus making it difficult to model the deposits accurately for calibration purposes. This presents a challenge to currently employed methods such as the Generalized Geometry Holdup (GGH) that rely on quantifying 235U mass by simplifying deposit shapes as a point, a line, or an area. In this work, gamma ray imaging using high energy resolution Germanium Gamma Imagers (GeGI™) is employed to determine the distribution of uranium inside the source containment. Two types of imaging methods are employed; coded-aperture imaging and Compton imaging. Gamma-ray emissions seen in different regions of the image are quantified using an inverse gamma-ray transport solver being developed at ORNL. Using measured data, the inverse method will be used to solve for unknown source parameters such as source matrix thickness, density, and attenuation due to container wall and shielding. The intrinsic efficiency of the detector is determined based on a library of built-in response functions and key dimensions of the detector (e.g., thickness and radius of the detector crystal). Measurements are performed in both imaging modalities using uranium sources of well-known masses and enrichments configured inside mocked-up holdup fixtures. Results from the two imaging modalities are intercompared. The paper presents preliminary results of the imaging measurements and the progress made in the computational methods to quantify uranium mass.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗