Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “base effect”

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 1,567 records · Page 87
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A Neural Differential Equation Formulation for Modeling Atmospheric Effects in Hyperspectral Images

Atmospheric correction is the process for removing atmospheric effects from spectral data; a necessary step for recovering salient spectral properties. The complex interactions between the atmosphere and light are dominated by absorbance and scattering physics. Existing methods for modeling atmospheric interactions typically rely on deep knowledge of relevant environmental conditions and high-fidelity numerical simulations of the governing physics in order to obtain accurate estimates of these effects. Additionally, existing approaches often require a subject matter expert for pre/post-processing of the data. Model-based approaches for removing atmospheric effects struggle in situations where such domain expertise is not available, and require significant human effort and computational power even when that expertise is available. In contrast, we propose a data-driven approach the uses Neural Differential Equations (NDEs) to accurately learn the interactions between electromagnetic radiation and the atmospheric without access to location specific environmental information. Once trained, the NDE can be applied bi-directionally; to apply or remove atmospheric effects. We demonstrate the effectiveness and utility of these techniques on an example multi-spectral scene.

Koch, James V.↗
NASA NTRS1975

Machine processing of S-192 and supporting aircraft data: Studies of atmospheric effects, agricultural classifications, and land resource mapping

Two tasks of machine processing of S-192 multispectral scanner data are reviewed. In the first task, the effects of changing atmospheric and base altitude on the ability to machine-classify agricultural crops were investigated. A classifier and atmospheric effects simulation model was devised and its accuracy verified by comparison of its predicted results with S-192 processed results. In the second task, land resource maps of a mountainous area near Cripple Creek, Colorado were prepared from S-192 data collected on 4 August 1973.

Thomson, F.↗
NASA NTRS2003

Simulations of Ground and Space-Based Oxygen Atom Experiments

A low-earth orbit (LEO) materials erosion scenario and the ground-based experiment designed to simulate it are compared using the direct-simulation Monte Carlo (DSMC) method. The DSMC model provides a detailed description of the interactions between the hyperthermal gas flow and a normally oriented flat plate for each case. We find that while the general characteristics of the LEO exposure are represented in the ground-based experiment, multi-collision effects can potentially alter the impact energy and directionality of the impinging molecules in the ground-based experiment. Multi-collision phenomena also affect downstream flux measurements.

Finchum, A.↗
DOE OSTI2021

Surfactants and cloud droplet activation: A systematic extension of Köhler theory based on analysis of droplet stability

The activation of aerosol particles to form cloud droplets, a necessary first step in cloud formation, controls much of the impact that aerosols have on clouds and climate. Recently, there has been a surge of interest in extending the Köhler theory of cloud droplet activation to include surface active (typically organic) as well as water-soluble (typically inorganic) aerosol components, but a systematic framework for doing this has yet to be developed. Here, we apply a droplet stability analysis to this end. Ideal and Szyszkowski–Langmuir surfactant models are analyzed to demonstrate the new approach, but the underlying theoretical framework is fundamental and model free. A key finding is that superficial densities at the cloud activation threshold (Köhler maximum) are significantly sub-monolayer, with fractional coverage ranging from 69% to 85% for the organic compounds and mixtures studied. The result, significant for model inventories of cloud condensation nuclei, is a weakening of the surfactant effect relative to expectations based on bulk sample measurements. Analytical results are obtained for the loci of Köhler maxima and applied to aerosol mixtures containing an arbitrary number of water-soluble and surfactant components.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

First observation of increased DT yield over prediction due to addition of hydrogen

Here, in a number of reported instances, implosions utilizing fuel mixtures have resulted in anomalously low fusion yields below those predicted by radiation-hydrodynamics simulations. Inter-species ion diffusion has been suggested as a possible cause of the observed yield degradation in fuel mixture implosions. An experimental platform utilizing hydro-equivalent deuterium–tritium (DT), deuterium–tritium–hydrogen (DTH), and deuterium-tritium-helium3 (DT 3 He) capsule implosions was developed to determine whether the inter-species ion diffusion theory may describe the resulting fuel mixture implosion behavior. The implosion experiments were performed at the Omega laser facility. X-ray images and shell areal density diagnostics results show that the hydro-equivalent three capsules (DT, DTH, and DT 3 He) have similar compression behavior. However, nuclear yield deviation was observed from the scaling determined using a fusion yield formula. In the DT 3 He mixture, a reduced yield of a factor of 0.65 ± 0.13 was observed, which is similar to a yield reduction observed in D 3 He mixture by Rygg et al. (i.e., Rygg effect). In contrast, in the DTH mixture, a factor of 1.17 ±0.15 yield increase was observed, which we named the inverse Rygg effect. The yield increase observed in the DTH mixture is consistent with the inter-species ion diffusion theory where lighter H diffuses away from the core and concentrated DT in the core produces higher yield. An inter-species ion diffusion model, the Zimmerman–Paquette–Kagan–Zhdanov model, implemented in a Lagrangian radiation-hydrodynamics fluid code, was also used to analyze the present data, without the need to assume hydrodynamic equivalence of the capsules, but it does not completely explain the DTH or DT 3 He capsules although its effects are in the correct direction. Simulation-based Bayesian inference was used in the latter analysis to quantify the uncertainty in the numerical simulations. The simulation-based analysis resulted in an inferred Rygg-effect yield decrease factor of 0.91 ± 0.02 for the DT 3 He mixture, and an inferred inverse-Rygg yield increase factor of 1.21 ± 0.04 for the DTH mixture, based on simulations ignoring ion diffusion.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1998

Galileo Probe Measurements of Thermal and Solar Radiation Fluxes in the Jovian Atmosphere

The Galileo probe net flux radiometer (NFR) measured radiation fluxes in Jupiter's atmosphere from about 0.44 to 14 bars, using five spectral channels to separate solar and thermal components. Onboard calibration results confirm that the NFR responded to radiation approximately as expected. NFR channels also responded to a superimposed thermal perturbation, which can be approximately removed using blind channel measurements and physical constraints. Evidence for the expected NH3 cloud was seen in the spectral character of spin-induced modulations of the direct solar beam signals. These results are consistent with an overlying cloud of small NH3 ice particles (0.5-0.75 microns in radius) of optical depth 1.5-2 at 0.5 microns. Such a cloud would have so little effect on thermal fluxes that NFR thermal channels provide no additional constraints on its properties. However, evidence for heating near 0.45 bar in the NFR thermal channels would seem to require either an additional opacity source beyond this small-particle cloud, implying a heterogeneous cloud structure to avoid conflicts with solar modulation results, or a change in temperature lapse rate just above the probe measurements. The large thermal flux levels imply water vapor mixing ratios that are only 6% of solar at 10 bars, but possibly increasing with depth, and significantly subsaturated ammonia at pressures less than 3 bars. If deep NH3 mixing ratios at the probe entry site are 3-4 times ground-based inferences, as suggested by probe radio signal attenuation, then only half as much water is needed to match NFR observations. No evidence of a water cloud was seen near the 5-bar level. The 5-microns thermal channel detected the presumed NH4SH cloud base near 1.35 bars. Effects of this cloud were also seen in the solar channel upflux measurements but not in the solar net fluxes, implying that the cloud is a conservative scatterer of sunlight. The minor thermal signature of this cloud is compatible with particle radii near 3 gm, but it cannot rule out smaller particles. Deeper than about 3 bars, solar channels indicate unexpectedly large absorption of sunlight at wavelengths longer than 0.6 microns, which might be due to unaccounted-for absorption by NH3 between 0.65 and 1.5 microns.

Sromovsky, L. A.↗
NASA NTRS1998

Galileo Probe Measurements of Thermal and Solar Radiation Fluxes in the Jovian Atmosphere

The Galileo probe net flux radiometer (NFR) measured radiation fluxes in Jupiter's atmosphere from about 0.44 to 14 bars, using five spectral channels to separate solar and thermal components. Onboard calibration results confirm that the NFR responded to radiation approximately as expected. NFR channels also responded to a superimposed thermal perturbation, which can be approximately removed using blind channel measurements and physical constraints. Evidence for the expected NH3 cloud was seen in the spectral character of spin-induced modulations of the direct solar beam signals. These results are consistent with an overlying cloud of small NH3 ice particles (0.5-0.75 microns in radius) of optical depth 1.5-2 at 0.5 microns. Such a cloud would have so little effect on thermal fluxes that NFR thermal channels provide no additional constraints on its properties. However, evidence for heating near 0.45 bar in the NFR thermal channels would seem to require either an additional opacity source beyond this small-particle cloud, implying a heterogeneous-cloud structure to avoid conflicts with solar modulation results, or a change in temperature lapse rate just above the probe measurements. The large thermal flux levels imply water vapor mixing ratios that are only 6% of solar at 10 bars, but possibly increasing with depth, and significantly subsaturated ammonia at pressures less than 3 bars. If deep NH3 mixing ratios at the probe entry site are 3-4 times ground-based inferences, as suggested by probe radio signal attenuation, then only half as much water is needed to match NFR observations. No evidence of a water cloud was seen near the 5-bar level. The 5 microns thermal channel detected the presumed NH4SH cloud base near 1.35 bars. Effects of this cloud were also seen in the solar channel upflux measurements but not in the solar net fluxes, implying that the cloud is a conservative scatterer of sunlight. The minor thermal signature of this cloud is compatible with particle radii near 3 microns, but it cannot rule out smaller particles. Deeper than about 3 bars, solar channels indicate unexpectedly large absorption of sunlight at wavelengths longer than 0.6 microns, which might be due to unaccounted-for absorption by NH3 between 0.65 and 1.5 microns.

Sromovsky, L. A.↗
DOE OSTI2021

Cloud drop number concentrations over the western North Atlantic Ocean: seasonal cycle, aerosol interrelationships, and other influential factors

Cloud drop number concentrations (N d ) over the western North Atlantic Ocean (WNAO) are generally highest during the winter (DJF) and lowest in summer (JJA), in contrast to aerosol proxy variables (aerosoloptical depth, aerosol index, surface aerosol mass concentrations, surface cloud condensation nuclei (CCN) concentrations) that generally peak inspring (MAM) and JJA with minima in DJF. Using aircraft, satellite remote sensing, ground-based in situ measurement data, and reanalysis data, we characterize factors explaining the divergent seasonal cycles and furthermore probe into factors influencing N d on seasonal timescales. The results can be summarized well by features most pronounced in DJF, including features associated with cold-air outbreak (CAO) conditions such as enhanced values of CAO index, planetary boundary layer height (PBLH),low-level liquid cloud fraction, and cloud-top height, in addition to winds aligned with continental outflow. Data sorted into high- and low-N d days in each season, especially in DJF, revealed that all of these conditions were enhanced on the high-N d days, including reduced sea level pressure and stronger wind speeds. Although aerosols may be more abundant in MAM and JJA, the conditions needed to activate those particles into cloud droplets are weaker than in colder months, which is demonstrated by calculations of the strongest (weakest) aerosol indirect effects in DJF (JJA) based on comparing N d to perturbations in four different aerosol proxy variables (total and sulfate aerosol optical depth, aerosol index, surface mass concentration of sulfate). We used three machine learning models and up to 14 input variables to infer about most influential factors related to N d for DJF and JJA, with the best performance obtained with gradient-boosted regression tree (GBRT) analysis. The model results indicated that cloud fraction was the most important input variable, followed by some combination (depending on season) of CAO index and surface mass concentrations of sulfate and organic carbon. Future work is recommended to further understand aspects uncovered here such as impacts of free tropospheric aerosol entrainment on clouds, degree of boundary layer coupling, wet scavenging, and giant CCN effects on aerosol–N d relationships, updraft velocity, and vertical structure of cloud properties such as adiabaticity that impact the satellite estimation of N d .

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Journey Over Destination: Differentiable Sensor Placement Enhances Generalization [Poster]

The challenge of reconstructing spatial fields that change over time from limited sensor data has been a focal point for many research studies. Various machine learning methods have been used in attempts to address this complex issue, including convolutional neural networks. All of the proposed methods share a common requirement that the user needs to manually determine the sensor positions. This requirement remains a limiting factor in the ongoing quest for efficient learning and accurate field reconstruction. This study aims to present a method that enables a model to optimize sensor positions via backpropagation, thereby facilitating the model’s exploration of the spatial domain and enhancing sensor positioning effectively. Indexing naturally incorporates discrete decisions. This operation is nondifferentiable which is a requirement for the application of gradient-based optimization methods. We showcased its effectiveness by training an attention-based neural network, which achieved top-tier performance on two separate datasets. To our knowledge, this represents the first fully end-to-end differentiable workflow for enhancing sensor placement within a neural network model.

58 GEOSCIENCES↗
NASA NTRS1996

Formation of Ordered Arrays of Proteins on Surfaces

Van der Waals (dispersion) forces contribute to interactions of proteins with other molecules or with surfaces, but because of the structural complexity of protein molecules, the magnitude of these effects is usually estimated based on idealized models of the molecular geometry, e.g., spheres or spheroids. The calculations reported here seek to account for both the geometric irregularity of protein molecules and the material properties of the interacting media. While the latter are found to fall in the generally accepted range, the molecular shape is shown to cause the magnitudes of the interactions to differ significantly from those calculated using idealized models, with important consequences. First, the roughness of the molecular surface leads to much lower average interaction energies for both protein-protein and protein-surface cases relative to calculations in which the protein molecule is approximated as a sphere. These results indicate that a form of steric stabilization may be an important effect in protein solutions. Underlying this behavior is appreciable orientational dependence, one reflection of which is that molecules of complementary shape are found to exhibit very strong attractive dispersion interactions. Although this has been widely discussed previously in the context of molecular recognition processes, the broader implications of these phenomena may also be important at larger molecular separations, e.g., in the dynamics of aggregation, precipitation and crystal growth.

Lenhoff, A. M.↗
NASA NTRS1996

Van der Waals Interactions Involving Proteins

Van der Waals (dispersion) forces contribute to interactions of proteins with other molecules or with surfaces, but because of the structural complexity of protein molecules, the magnitude of these effects is usually estimated based on idealized models of the molecular geometry, e.g., spheres or spheroids. The calculations reported here seek to account for both the geometric irregularity of protein molecules and the material properties of the interacting media. Whereas the latter are found to fall in the generally accepted range, the molecular shape is shown to cause the magnitudes of the interactions to differ significantly from those calculated using idealized models. with important consequences. First, the roughness of the molecular surface leads to much lower average interaction energies for both protein-protein and protein-surface cases relative to calculations in which the protein molecule is approximated as a sphere. These results indicate that a form of steric stabilization may be an important effect in protein solutions. Underlying this behavior is appreciable orientational dependence, one reflection of which is that molecules of complementary shape are found to exhibit very strong attractive dispersion interactions. Although this has been widely discussed previously in the context of molecular recognition processes, the broader implications of these phenomena may also be important at larger molecular separations, e.g., in the dynamics of aggregation, precipitation, and crystal growth.

Roth, Charles M.↗
DOE OSTI2020

Helium effects on the surface and subsurface evolutions in single-crystalline tungsten

Tungsten (W) has been perceived as one of the most promising plasma facing materials (PFMs) for future fusion reactors. In the past decade, its behavior under irradiation and helium (He) plasma interaction has been extensively studied. However, some key knowledge gaps still exist, such as the influence of crystallographic orientation on the surface and subsurface evolutions. In this work, we focus on the He ion-beam irradiation damage effects in mirror-polished single-crystalline W samples with three different surface planes of {100}, {110} and {111}. Irradiation was performed at room temperature using 40 keV He+ to a fluence of 1 × 10 16 /cm 2 , followed by thermal desorption spectroscopy (TDS) up to ~1920 K. The microstructures of He-irradiated W before and after TDS heat treatment were characterized by scanning and transmission electron microscopy. Subsurface He bubbles were imaged in all irradiated samples, but newly formed <111>-oriented surface grains and surface blisters were only observed in W {100} and {110} starting orientations. These results reveal that radiation damage, He thermal desorption, and surface/subsurface evolution are all strongly dependent on crystallographic orientation. Underlying physical mechanisms are discussed based on ion channeling effects, He-vacancy interactions, and surface diffusion. In conclusion, these findings provide new insights into He effects in W.

36 MATERIALS SCIENCE↗
NASA NTRS1975

Effects of high doping levels on silicon solar cell performance

Open-circuit voltages measured in silicon solar cells made from 0.01 ohm-cm material are 150 mV lower than voltages calculated from simple diffusion theory and cannot be explained by poor diffusion lengths or surface leakage currents. An analytical study was made to determine whether high doping effects, which increase the intrinsic carrier concentration, could account for the low observed voltages and to determine the limits on voltage and efficiency imposed by high doping effects. The results indicate that the observed variation of voltage with base resistivity is predicted by these effects. A maximum efficiency of 19% (AMO) and a voltage of 0.7 volts were calculated for 0.1 ohm-cm cells assuming an optimum diffused layer impurity profile.

Godlewski, M. P.↗
NASA NTRS1976

A turbulence model for nonequilibrium adverse pressure gradient flows

This paper presents a method for calculating compressible adverse pressure gradient boundary layers by using an algebraic eddy viscosity turbulence model that has been modified for variable pressure gradient and turbulence memory effects. The pressure gradient corrections are based on previous incompressible data correlations. Several methods for including the effects of turbulence memory are evaluated. A new lag model, which gives good agreement with available experimental data, is developed. Finally, a correlation is developed for the lag length parameter employed in the model as a function of the known experimental flow variables.

Horstman, C. C.↗
DOE OSTI2022

Magnetoimpedance Biosensors and Real-Time Healthcare Monitors: Progress, Opportunities, and Challenges

A small DC magnetic field can induce an enormous response in the impedance of a soft magnetic conductor in various forms of wire, ribbon, and thin film. Also known as the giant magnetoimpedance (GMI) effect, this phenomenon forms the basis for the development of high-performance magnetic biosensors with magnetic field sensitivity down to the picoTesla regime at room temperature. Over the past decade, some state-of-the-art prototypes have become available for trial tests due to continuous efforts to improve the sensitivity of GMI biosensors for the ultrasensitive detection of biological entities and biomagnetic field detection of human activities through the use of magnetic nanoparticles as biomarkers. In this review, we highlight recent advances in the development of GMI biosensors and review medical devices for applications in biomedical diagnostics and healthcare monitoring, including real-time monitoring of respiratory motion in COVID-19 patients at various stages. We also discuss exciting research opportunities and existing challenges that will stimulate further study into ultrasensitive magnetic biosensors and healthcare monitors based on the GMI effect.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Wide-Scale Clinical Implementation of Knowledge-Based Planning: An Investigation of Workforce Efficiency, Need for Post-automation Refinement, and Data-Driven Model Maintenance

Our purpose was to investigate the effect of automated knowledge-based planning (KBP) on real-world clinical workflow efficiency, assess whether manual refinement of KBP plans improves plan quality across multiple disease sites, and develop a data-driven method to periodically improve KBP automated planning routines.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

Time-frequency based cyber security defense of wide-area control system for fast frequency reserve

Global power systems are transiting from conventional fossil fuel energy to renewable energies due to their environmental benefits. The increasing penetration of renewable energies presents challenges for power system operation. The efficiency and sufficiency of responsive reserves have become increasingly important for power systems with a high proportion of renewable energies. The Fast Frequency Reserve (FFR), especially the Wide-area Monitoring System (WAMS)-based FFR, is a promising and effective solution to secure and enhance the stability of power systems. However, cyber security has become a new challenge for the WAMS-based FFR system. Cyber attacks on the FFR control system may threaten the safety of power system operation due to the rapid power controllability requirement of FFR. Therefore, to address this problem, a time-frequency based cyber security defense framework is proposed to detect the cyber spoofing of synchrophasor data in WAMS-based FFR control systems. This paper first introduces the Continuous Wavelet Transforms (CWTs) to decompose spoofing signals. Then, the Dual-frequency Scale Convolutional Neural Networks (DSCNN) is proposed to identify the time-frequency domains matrix from two frequency scales. Integrating CWTs and DSCNN, an identification framework called CWTs-DSCNN is further proposed to detect the spoofing attacks in the WAMS-based FFR system. Multiple experiments using the actual data from FNET/GridEye are performed to verify the effectiveness of the framework in securing WAMS-based FFR systems.

25 ENERGY STORAGE↗
DOE OSTI2024

Towards verifiable cancer digital twins: tissue level modeling protocol for precision medicine

Cancer exhibits substantial heterogeneity, manifesting as distinct morphological and molecular variations across tumors, which frequently undermines the efficacy of conventional oncological treatments. Developments in multiomics and sequencing technologies have paved the way for unraveling this heterogeneity. Nevertheless, the complexity of the data gathered from these methods cannot be fully interpreted through multimodal data analysis alone. Mathematical modeling plays a crucial role in delineating the underlying mechanisms to explain sources of heterogeneity using patient-specific data. Intra-tumoral diversity necessitates the development of precision oncology therapies utilizing multiphysics, multiscale mathematical models for cancer. This review discusses recent advancements in computational methodologies for precision oncology, highlighting the potential of cancer digital twins to enhance patient-specific decision-making in clinical settings. We review computational efforts in building patient-informed cellular and tissue-level models for cancer and propose a computational framework that utilizes agent-based modeling as an effective conduit to integrate cancer systems models that encode signaling at the cellular scale with digital twin models that predict tissue-level response in a tumor microenvironment customized to patient information. Furthermore, we discuss machine learning approaches to building surrogates for these complex mathematical models. These surrogates can potentially be used to conduct sensitivity analysis, verification, validation, and uncertainty quantification, which is especially important for tumor studies due to their dynamic nature.

60 APPLIED LIFE SCIENCES↗