Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “SMD”

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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Comparison of Ring Diagrams Based on the Doppler Shifts of Synthetic Data Obtained with Bisector Method and SDO/HMI Pipeline

Ring diagrams are cross-sections of three-dimensional spatiotemporal power spectra of solar oscillations. The rings reveal information about sub-surface flows and represent an important tool for helioseismology. Ring diagrams can be constructed using Doppler velocity or intensity maps of spectral lines. How the velocities are computed is an important factor for accuracy of information we can retrieve on subsurface flows. In our work, the ring diagrams are generated from Doppler shift data of synthesized Fe I 6173 Å line. We compare ring diagrams computed by two methods–HMI line-of-sight pipeline and the bisector of Fe line. Fe I line is synthesized for StellarBox 3D Radiative hydrodynamic simulations under LTE assumption. We aim to answer the following questions: 1.How do power spectra obtained from velocities computed with the HMI pipeline and bisector of the Fe I 6173Å compare? 2.How do the power spectral density retrieved with each method vary with heliocentric angle? 3.What is the effect of changing resolution on the power spectral density in ring diagrams obtained with the two methods?

SMD↗
NASA NTRS2024

PDC25 Epoch 1 Damage Risk Maps Content Key

These maps represent asteroid impact damage estimates modeled for the PDC25 Hypothetical Asteroid Impact Exercise, as part of the 9th AIAA Planetary Defense Conference (PDC2025). This exercise assesses a realistic but fictitious hypothetical scenario, not a real asteroid threat.

SMD↗
NASA NTRSDate not supplied

Towards Surrogate Modeling of Subgrid Turbulent Transport for 3D Radiative Hydrodynamic Simulations of the Quiet Sun

In this work, we investigate the use of deep learn-ing techniques as surrogate models, to enhance the estimationof effects of subgrid turbulent transport for 3D radiatuve hy-drodynamic simulations of the quiet Sun. We develop two dis-tinct 3D Convolutional Neural Networks (3DCNNs) to capturespatio-temporal dependencies in 3D velocity fields, leveragingdifferent activation functions and architectural designs. Thesemodels integrate both averaged velocity vector components andscalar features such as plasma density to enhance predictionaccuracy. Additionally, a Multilayer Perceptron (MLP) modelis employed to approximate complex nonlinear relationships,offering a comparison in performance between convolutionaland fully connected architectures. Logarithmic transformationis applied to the targets to handle heavily skewed data, im-proving model performance. All models are compared againsta physics-based Gradient Model. Results show that the 3DCNNmodels excel at approximating Reynolds stress tensors, makingthem a candidate for assisting in producing reduced resolutionsimulations, and thereby reducing computational overheadwhile maintaining higher accuracy than the baseline. Thesefindings demonstrate the potential of deep learning, particu-larly CNNs, to advance scalable and accurate simulations ofsolar dynamics, offering a promising alternative to traditionalturbulence models.

Heliophysics↗
NASA NTRSDate not supplied

Impact Of Rotation on the Sun’s Subsurface Structure

It is well known that the dynamical evolution of subsurface flows of the Sun and magnetic fields is strongly linked to rotation. Observational investigation of sub surface structuring and dynamics can be performed only with relatively low resolution using helioseismic inversion techniques. To confidently model the coupling of turbulent convection, magnetic fields, and rotation in the solar interior, we perform advanced radiative hydrodynamic and MHD modeling of local scales. To explore the coupling effects in the upper layers of the convection zone, we present results of analysis from a series of hydrodynamic simulations performed for a 25-Mm-deep computational domain at different latitudes obtained for 1) the Carrington rotation rate at all latitudes and 2) the realistic rotation rate from the global helioseismic inversions during the solar minimum. Using additional 3D radiative hydrodynamic models, we reveal how physical properties of the solar convection are affected by rotation.

Heliophysics↗
NASA NTRSDate not supplied

Exploring the Capabilities of a Machine Learning Algorithm to Detect Space Weather-Significant Emerging Active Regions

Active regions are a source of various phenomena responsible for Space Weather disturbances; therefore, developing a technology for early warning about upcoming magnetic activity is crucial to mitigate its impact. However, observational limitations and the high nonlinearity of processes associated with the accumulation of magnetic flux and its interaction with the surrounding plasma during the emergence through the convection zone make early activity detection a challenging problem. To address these challenges, we developed a physics-driven machine learning model that allows us to detect active regions (ARs) before they become visible on the solar surface by analyzing the power spectra of acoustic oscillations observed by the SDO/HMI instrument. This study is based on a time series of Doppler shift maps of 31x31-degree areas tracked with the Carrington rotation rate for four days before and after the emergence. The Doppler shift time series are processed into the oscillation power maps for four frequency ranges and accompanied by line-of-sight magnetograms and the continuum intensity maps from SDO/HMI. The resulting data are converted into a 1D time series representing the mean temporal variations of these quantities. The redacted time series are used as input to predict AR emergence using the Long Short Term Memory (LSTM) method. The training of the LSTM model is based on 40 ARs, which includes an independent analysis for each sub region that exhibits AR emergence or remains quiet. The emergence of magnetic flux (defined as a decrease of the continuum intensity) was detected with the developed LSTM algorithm from 5 to 48 hours before the reported time by NOAA. The developed model is capable of pointing to the time and location of active region formation. In this presentation, we discuss reasons that impact how early in advance the model can identify the upcoming activity and the possibility of improving the current predictive skills and steps to transition to the operational forecast.

Heliophysics↗