SEARCH · Search NASA
Results for “Structural models”
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.
Comparison of structurally diverse simulation models for prediction of epidemic outcomes caused by a long-distance dispersed pathogen
Long-distance dispersal (LDD) pathogens pose substantial challenges for epidemic control due to their ability to generate new infection foci at great distances. While various modeling approaches have been developed to understand and manage such outbreaks, little work has compared how models of different structures behave under shared conditions. Here, in this study, we compare four structurally distinct epidemiological models — EPIMUL, GEMF, PoPS, and Warwick — each adapted to simulate the spread of wheat stripe rust (WSR), a wind-dispersed LDD pathogen, under identical epidemiological parameters and dispersal kernel. Using data from a controlled field experiment, we evaluate the ability of each model to replicate disease prevalence under nine intervention scenarios that vary in timing and culling area. While the models differ substantially in design — ranging from spatial grid-based to network-based and raster-based frameworks — the shared dispersal kernel allowed for close alignment in their predictions. All models accurately captured general epidemic trends, particularly the strong effect of early intervention on disease suppression. We qualitatively compared their behavioral responses across scenarios and also evaluated an ensemble prediction by averaging across model outputs. Our findings highlight how integrating shared epidemiological components into distinct modeling frameworks can improve consistency and accuracy, while reinforcing the importance of early culling in managing LDD pathogen outbreaks.
Molecular Structure and Modeling of Water–Air and Ice–Air Interfaces Monitored by Sum-Frequency Generation
Not Available
The Climate Response to Emissions Reductions Due to COVID-19: Initial Results From CovidMIP
Many nations responded to the COVID-19 pandemic by restricting travel and other activities during 2020, resulting in temporarily reduced emissions of CO 2 , other greenhouse gases and ozone and aerosol precursors. We perform a coordinated Intercomparison, CovidMIP, of Earth System model simulations to assess the impact on climate of these emissions reductions. Eleven models performed multiple initial-condition ensembles to produce over 280 simulations spanning both initial condition and model structural uncertainty. We find model consensus on reduced aerosol amounts (particularly over East Asia) and associated increases in surface shortwave radiation levels. However, any impact on near-surface temperature or rainfall during 2020-2024 is extremely small and is not detectable in this initial analysis. Regional analyses on a finer scale, and closer attention to extremes (especially linked to changes in atmospheric composition and air quality) are required to test the impact of COVID-19-related emission reductions on near-term climate.
Quantum simulation of spin-boson models with structured bath
Not Available
CryoTEN: efficiently enhancing cryo-EM density maps using transformers
Abstract Motivation Cryogenic electron microscopy (cryo-EM) is a core experimental technique used to determine the structure of macromolecules such as proteins. However, the effectiveness of cryo-EM is often hindered by the noise and missing density values in cryo-EM density maps caused by experimental conditions such as low contrast and conformational heterogeneity. Although various global and local map-sharpening techniques are widely employed to improve cryo-EM density maps, it is still challenging to efficiently improve their quality for building better protein structures from them. Results In this study, we introduce CryoTEN—a 3D UNETR++ style transformer to improve cryo-EM maps effectively. CryoTEN is trained using a diverse set of 1295 cryo-EM maps as inputs and their corresponding simulated maps generated from known protein structures as targets. An independent test set containing 150 maps is used to evaluate CryoTEN, and the results demonstrate that it can robustly enhance the quality of cryo-EM density maps. In addition, automatic de novo protein structure modeling shows that protein structures built from the density maps processed by CryoTEN have substantially better quality than those built from the original maps. Compared to the existing state-of-the-art deep learning methods for enhancing cryo-EM density maps, CryoTEN ranks second in improving the quality of density maps, while running >10 times faster and requiring much less GPU memory than them. Availability and implementation The source code and data are freely available at https://github.com/jianlin-cheng/cryoten.
Structural dynamics modeling of spent nuclear fuel during hypothetical package drop events
The response of spent nuclear fuel (SNF) to hypothetical package drop events is of particular interest in the scope of spent fuel storage and transportation because of the mechanical shock encountered in such scenarios. Previous testing and modeling by the U.S. Department of Energy has demonstrated that the shock and vibration environment of normal shipping and handling conditions (excluding package drop events) is relatively benign and does not challenge the integrity of spent nuclear fuel. Cask drop events are worth considering because SNF packages are required to withstand free drops onto unyielding surfaces as part of their licensing basis. The acceleration experienced during drop events can be orders of magnitude higher, and thus more advanced models are needed to encompass potential nonlinear behavior of the fuel, such as spacer grid buckling and rod-to-rod impact. This work describes a number of finite element models developed to calculate the response of spent nuclear fuel to various hypothetical drop events that have been validated by package and fuel assembly drop tests conducted in the last decade. Sensitivity of the model response to factors such as package drop orientation, secondary impacts, and irradiated material properties as well as their potential impacts to fuel cladding integrity, was also investigated. Cask drops are not expected as a regular occurrence during SNF transportation, but this work helps raise the understanding of SNF mechanical loads to the point of consistency with the package design requirements.
Atomically precise, custom-design origami graphene nanostructures
The construction of atomically precise carbon nanostructures holds promise for developing materials for scientific study and nanotechnology applications. Here, we show that graphene origami is an efficient way to convert graphene into atomically precise, complex nanostructures. By scanning tunneling microscope manipulation at low temperature, we repeatedly fold and unfold graphene nanoislands (GNIs) along an arbitrarily chosen direction. A bilayer graphene stack featuring a tunable twist angle and a tubular edge connection between the layers is formed. Folding single-crystal GNIs creates tubular edges with specified chirality and one-dimensional electronic features similar to those of carbon nanotubes, whereas folding bicrystal GNIs creates well-defined intramolecular junctions. Both origami structural models and electronic band structures are computed to complement analysis of the experimental results. The present atomically precise graphene origami provides a platform for constructing carbon nanostructures with engineered quantum properties and, ultimately, quantum machines.
Kronecker-structured covariance models for multiway data
Many applications produce multiway data of exceedingly high dimension. Modeling such multi-way data is important in multichannel signal and video processing where sensors produce multi-indexed data, e.g. over spatial, frequency, and temporal dimensions. We will address the challenges of covariance representation of multiway data and review some of the progress in statistical modeling of multiway covariance over the past two decades, focusing on tensor-valued covariance models and their inference. We will illustrate through a space weather application: predicting the evolution of solar active regions over time.
Analysis of the NorthStar Inconel 718 Window
NorthStar Medical Radioisotopes, LLC, is planning to produce the important medical radioisotope molybdenum-99 (Mo-99) through photonuclear reactions in molybdenum-100 (Mo-100). In this approach, a target comprising multiple thin disks of enriched molybdenum metal will be bombarded with a 40-MeV electron beam. Because enriched Mo-100 is expensive, there is a desire to utilize as much beam power as possible to achieve maximum production yield and minimize the size of the target. This requirement leads to very high beam power density (heat deposition in the target), which sets challenging requirements for cooling of the target. In the latest concept developed by NorthStar, a stack of target (sintered molybdenum) disks is irradiated from two sides by a 40-MeV electron beam, with a total power of 240 kW (120 kW from each side). As shown in Figure 1, the target disks are cooled by pressurized helium gas flowing through channels between the disks. A housing encloses the target disks and helium coolant. Thinned regions on opposite sides of the housing act as windows, forming the pressure boundary between the helium within the housing and the evacuated beam tube. A pulsed (50-Hz) 40-MeV electron beam is used, with 120 kW total power and a 10% duty cycle. The pulsing beam creates a corresponding cyclic thermal load within the window material. To examine the effects of the cyclic thermal load on the window, a combination of analyses was performed, including computational fluid dynamics (CFD) and finite element analysis (FEA). The analyses presented in this report were primarily performed using the finite element method. However, because of the curved surface of the window, CFD analysis was used to calculate the convective cooling boundary included in the FEA model. Transient thermal and structural models were used to explore the window geometry, including thickness, radius of curvature, and diameter, while staying within the temperature and stress limits of the material as well as maintaining mechanical stability (i.e., avoiding buckling). Additionally, variations in the size and location of the electron beam were evaluated. Multiple parameters for the window and the electron beam were examined throughout the analysis. Upper and lower bounds were established for the window geometry parameters, and limitations were established for the size and position of the electron beam.
Structural analyses of macromolecules by solution scattering (CRADA Final Report)
New innovative Small Angle X-ray Scattering (SAXS) methods to visualized macromolecules in solution at low resolution has led the SIBYLS group at Lawrence Berkeley Lab in being a world leader in biological SAXS. This research is particularly important now as LBNL establishes high-priority research topics including molecular to mesoscale analysis. This new expanding area of research relies on and ties in with our efforts towards developing a solution structure modeling tool for RNA structure prediction. Due to the pandemic our efforts focused on the SARS-CoV-2 proteins and RNA interactions. Specifically, we characterized SARS-CoC-2 proteins interaction with RNA (Wilanowski et al. 2021 -Hammel group) and Nucleoprotein interaction with RNA (Schneidman group).
Neural Network Ensemble With Embedded Hamiltonian Constraints for Modeling Nonlinear Structural Dynamics.
Abstract not provided.
Hamiltonian-Constrained Neural Networks for Modeling Nonlinear Structural Systems.
Abstract not provided.
Ensemble of Numerics-Informed Neural Networks with Embedded Hamiltonian Constraints for Modeling Nonlinear Structural Dynamics.
Abstract not provided.
Incorporating Machine Learning Models Within Structural Systems With Localized Nonlinearities.
Abstract not provided.
A trade off study of metal and composite turbines using fluid-structure interaction modeling.
Abstract not provided.