Engineering topics

Kosovichev, Alexander

Publications and source records attributed to Kosovichev, Alexander.

7 records

NASA NTRS2019

Characterization of Subsurface Flow Dynamics for Forecasting of Solar Activity

Evolution of large-scale magnetic field structures in the solar photosphere and corona is controlled by motions beneath the visible surface of the Sun. Subsurface plasma flows play a critical role in formation and evolution of active regions and their activity. We analyze subsurface flow maps provided by the local helioseismology pipeline from the Helioseismic and Magnetic Imager (HMI) data on board the Solar Dynamics Observatory, and investigate links between flow characteristics and magnetic activity. The primary goal is to determine flow descriptors, which can improve solar activity forecasts. In particular, by employing machine learning classifiers, we test how the flow helicity and velocity shear descriptors can improve the prediction of initiation of flares and CME eruptions.

Kosovichev, Alexander↗

NASA NTRS2019

Cluster Analysis of IRIS Spectroscopic Line Profiles and SDO/AIA EUV Emission in Observations and RMHD Simulations of the Solar Atmosphere

Spatially-resolved observations from the IRIS and SDO/AIA satellites, especially when coupled with realistic 3D RMHD simulations, are a powerful tool for analysis of processes in the solar chromosphere, transition region, and corona. However, the complexity of the data makes understanding the observations and modeling results difficult. In this work, we apply unsupervised clustering algorithms for analysis of observational and synthetic chromospheric Mg II h&k 2796Å&2803Å and transition region C II 1334Å&1335Å line profiles observed by IRIS, and extreme ultraviolet (EUV) emission observed by SDO/AIA, for various types of problems. The synthetic line profiles are computed for simulations of the quiescent solar atmosphere (using the StellarBox and RH1.5 codes). The K-Means clustering algorithm is applied, and the selection of an optimal number of clusters is supported by the average silhouette width technique. We discuss applications of the line profile clustering method to 1) visualization of computational and observational spectroscopic imaging data; 2) understanding of evolutionary trends and behavior patterns of quiet Sun emission and during solar flares; and 3) recognition of heating events and shock waves.

Sadykov, Viacheslav↗

NASA NTRS2019

Cluster Analysis of Spectroscopic Line Profiles and EUV Emission in RMHD Simulations and Observations of the Solar Atmosphere

Spatially-resolved observations from the IRIS, SDO/AIA, and other space mission and ground-based telescopes, coupled with realistic 3D RMHD simulations, are a powerful tool for analysis of processes in the solar atmosphere. To better understand the dynamical and thermodynamic properties in the simulation data and their connection to observations, it is essential to determine similarities in the behaviors of the synthesized and observed emission. However, the complexity of observational data and physical processes makes comparison of observations and modeling results difficult. In this work, we show the initial results of application of K-Means clustering (unsupervised machine learning) algorithm to two different problems: 1) recognition of the typical spectroscopic line profiles observed by IRIS during solar flares and their typical dynamic behavior; 2) recognition of shocks and heating events in synthetic AIA emission data obtained from StellarBox quiet-Sun simulations. The average silhouette width technique for the KMeans algorithm is utilized in different ways to obtain optimal numbers of clusters. We discuss application of the emission clustering to visualizations of the computational volume, understanding its evolutionary trends and behavior patterns, and inversion (reconstruction) of physical properties of the solar atmosphere from synthesizes emission data.

Sadykov, Viacheslav↗

NASA NTRS2018

Using Machine-Learning Methods and Expert Prediction Probabilities to Forecast Solar Flares

It has long been known that studying connection between solar flares and properties of magnetic field in active regions is very important for understanding the flare physics and developing space weather forecasts. The Helioseismic and Magnetic Imager onboard the Solar Dynamics Observatory (SDO/HMI) obtains tremendous amounts of magnetic field data products. However the operational NOAA Space Weather Prediction Center (SWPC) forecasts of solar flares still represent prediction probabilities issued by the experts. In this research we investigate the possibilities to enhance the daily operational flare forecasts performed at the SWPC by developing a synergy of the expert predictions and physics-based criteria, and by employing machine-learning methods. Among the physics-based criteria we consider the descriptors of the Polarity Inversion Line (PIL) and Space weather HMI Active Region Patches (SHARP), and derive from them daily characteristics of the entire Sun. We also consider the daily descriptors of the GOES Soft X-Ray (SXR) 1-8 Angstroms flux such as the flare history of the previous days and averaged X-Ray flux. We estimate the effectiveness in separation of flaring and non-flaring cases for each characteristic, as well as for the expert prediction probabilities, and find that some PIL, SHARP and SXR descriptors are as effective as the expert prediction probabilities and should be considered to issue the flare forecast. Finally, we train and test several Machine-Learning classification algorithms (Support Vector Classifiers with various kernel functions, k-Nearest Neighbor Classifier, Random Forest Classifier, and Neural Networks) using the most effective descriptors and expert prediction probabilities, and compare the obtained predictions with the current SWPC forecasts.

Machine-Learning↗

NASA NTRS2014

Realistic Modeling of Multi-Scale MHD Dynamics of the Solar Atmosphere

Realistic 3D radiative MHD simulations open new perspectives for understanding the turbulent dynamics of the solar surface, its coupling to the atmosphere, and the physical mechanisms of generation and transport of non-thermal energy. Traditionally, plasma eruptions and wave phenomena in the solar atmosphere are modeled by prescribing artificial driving mechanisms using magnetic or gas pressure forces that might arise from magnetic field emergence or reconnection instabilities. In contrast, our 'ab initio' simulations provide a realistic description of solar dynamics naturally driven by solar energy flow. By simulating the upper convection zone and the solar atmosphere, we can investigate in detail the physical processes of turbulent magnetoconvection, generation and amplification of magnetic fields, excitation of MHD waves, and plasma eruptions. We present recent simulation results of the multi-scale dynamics of quiet-Sun regions, and energetic effects in the atmosphere and compare with observations. For the comparisons we calculate synthetic spectro-polarimetric data to model observational data of SDO, Hinode, and New Solar Telescope.

Realistic Modeling↗

NASA NTRS2012

Systematic Center-To-Limb Variation in Measured Helioseismic Travel Times and Its Effect on Inferences of Solar Interior Meridional Flows

We report on a systematic center-to-limb variation in measured helioseismic travel times, which must be taken into account for an accurate determination of solar interior meridional flows. The systematic variation, found in time-distance helioseismology analysis using SDO/HMI and SDO/AIA observations, is different in both travel-time magnitude and variation trend for different observables. It is not clear what causes this systematic effect. Subtracting the longitude-dependent east-west travel times, obtained along the equatorial area, from the latitude-dependent north-south travel times, obtained along the central meridian area, gives remarkably similar results for different observables. We suggest this as an effective procedure for removing the systematic center-to-limb variation. The subsurface meridional flows obtained from inversion of the corrected travel times are approximately 10 m s−1 slower than those obtained without removing the systematic effect. The detected center-to-limb variation may have important implications in the derivation of meridional flows in the deep interior and needs to be better understood.

Sun: oscillations↗

NASA NTRS2004

High-Order Shock-Capturing Methods for Modeling Dynamics of the Solar Atmosphere

We use one-dimensional high-order central shock capturing numerical methods to study the response of various model solar atmospheres to forcing at the solar surface. The dynamics of the atmosphere is modeled with the Euler equations in a variable-sized flux tube in the presence of gravity. We study dynamics of the atmosphere suggestive of spicule formation and coronal oscillations. These studies are performed on observationally-derived model atmospheres above the quiet sun and above sunspots. To perform these simulations, we provide a new extension of existing second- and third- order shock-capturing methods to irregular grids. We also solve the problem of numerically maintaining initial hydrostatic balance via the introduction of new variables in the model equations and a careful initialization mechanism. We find several striking results: all model atmospheres respond to a single impulsive perturbation with several strong shock waves consistent with the rebound-shock model. These shock waves lift material and the transition region well into the initial corona, and the sensitivity of this lift to the initial impulse depends non-linearly on the details of the atmosphere model. We also reproduce an observed 3-minute coronal oscillation above sunspots compared to 5-minute oscillations above the quiet sun.

Bryson, Steve↗