2024 PDG seminars
Global MHD simulations of solar wind streams in the inner heliosphere using sunRunner3D
Understanding the large-scale three-dimensional structure of the inner heliosphere, while important in its own right, is crucial for space weather applications, such as forecasting the time of arrival and propagation of coronal mass ejections (CMEs). This study uses sunRunner3D (SR3D), a 3-D MHD model, to simulate solar wind (SW) streams and generate background states. SR3D employs the boundary conditions generated by CORHEL and the PLUTO code to compute the plasma properties of the SW with the magnetohydrodynamic (MHD) approximation up to 1.1 AU in the inner heliosphere. We demonstrate that SR3D reproduces global features of Corotating Interaction Regions (CIRs) observed by Earth-based spacecraft (OMNI) and STEREO-A for a set of Carrington rotations that cover a period that lays in the late declining phase of solar cycle 24. Additionally, we demonstrate that the model solutions are valid in the corotating and inertial frames of references. Moreover, a comparison between SR3D simulations and in-situ measurements shows reasonable agreement with the observations, and our results are comparable to those achieved by Predictive Science Inc.'s MAS code and SWASTi-SW framework.
Institute for Astronomy, Astrophysics,
Space Applications and Remote Sensing (IAASARS),
National Observatory of Athens (NOA),
Greece
14 March 2024
LT6, Hicks Building and online
Swirling motions in the lower solar atmosphere: detection, statistics and profile analysis from multi-wavelength observations
Ubiquitous vortical motions in the solar atmosphere have been recently revealed by high resolution observations from both space-borne and ground-based observatories in quiet, as well as, in active regions. In chromospheric observations obtained in spectral lines, such as the Halpha and Ca II IR, they manifest themselves as swirling dark spiral- and circular-shaped patches known as “chromospheric swirls”. Their suggested contribution to the channeling of energy, mass and momentum from the sub-photospheric to the higher layers of the solar atmosphere places them amongst potential candidates for atmospheric heating. In this context, their detection and statistical information concerning their population and several significant physical parameters and properties are of vital importance. To overcome the drawbacks of the detection methods based on visual inspection or on the Local Correlation Tracking (LCT) techniques, we have developed a novel automated detection method, which is purely based on their morphological characteristics. We will be presenting a brief description of the algorithm and the results from its application on high-resolution observations obtained with the CRisp Imaging SpectroPolarimeter (CRISP) of the Swedish 1-m Solar Telescope (SST) in three chromospheric spectral lines, namely, the Halpha, Ca II IR and Ca II K lines. The results include several statistical parameters such as their number, spatial distribution and temporal evolution, as well as significant physical parameters, such as radii and lifetimes. Specifically, for the estimation of the mean lifetime, apart from the usual approaches, the statistical method of survival analysis was implemented. This approach, which estimates more accurately the mean lifetime of a population, although common in several other scientific fields, is scarcely used in solar physics/astrophysics. We will finally be focusing on co-spatially detected swirling structures in all three chromospheric lines and the profile analysis performed to derive significant physical parameters, such as line-of-sight velocities, FWHM and equivalent widths.
ID_2024.mp4Institute for Solar-Terrestrial Physics, Germany
Anchor University, Lagos, Nigeria
22 February 2024
Probing space weather effects: from the D-region to the thermosphere
The response of the lower ionosphere (D-region) to geomagnetic storms is subtle and not well developed. To understand and/or substantiate storm-induced ionospheric response in the D-region, this work explore a multi-parametric approach that combines the diagnostics of the D, E and F regions parameter, to probe the associated dynamics. By simultaneously combining observed variations of very low frequency (VLF) radio waves amplitude (in the receiver-transmitter great circle path, TRGCP) with ionosonde and GNSS data we show that geomagnetic storms can have strong and far-reaching impact on the lower ionosphere. In the thermosphere, space weather event can significantly perturb the temperature and density profile of the region, leading to enhanced atmospheric drag on satellites operating in low Earth orbit (LEO). Using a new drag model, we simulate and show aerodynamic drag effect on LEO satellites during intervals of elevated solar activity. Finally, we describe ongoing effort to advance this area of research in our region through viable collaborations and scientific cooperation.
VN_2024_1.mp4VN_2024_2.mp4
Bridging the gap between numerical simulations and experiments through computer vision and deep learning
Several physics and engineering problems require knowledge of physical properties to be completely characterized. However, such properties cannot always be easily obtained experimentally, which means that numerical simulations are often necessary to study the problem in question. In the present work, we propose to leverage data from numerical simulations to extract relevant information from experimental visualizations of the flow field using deep learning. We uniquely treat the image semantic segmentation as an image-to-image translation task that infers semantic labels of structures from the input images in a supervised way. The present methodology exploits the semantic proximity between images from the numerical and experimental domains to translate any properties of interest between them. Example applications are shown for a moving airfoil as well as for predicting forces on a sand dune. Extrapolation and interpolation for different flow regimes never seen by the neural network are discussed.
RM_2024.mp4
Identifying and Tracking Coronal Flux Ropes
To understand solar eruptions and the destabilization mechanism of the corresponding flux ropes, modelling the magnetic field in the solar corona in a time-dependent manner is commonly employed. However, identifying the field lines of solar flux ropes in simulation data is not trivial. We therefore developed a method for detecting and tracking flux ropes from modelling data. The extraction procedure uses a combination of some proxy map as input (for example the field line twist) and mathematical morphology algorithms, such as the morphological opening or the morphological gradient. The method is validated by applying it to time-dependent magnetofrictional model (TMFM) simulations of active regions AR11176 and AR12473. With full access to the flux rope field lines, we investigate the eruptivity and propagation of the flux ropes through the modelling domain. Finally, the methodology is also wrapped into a graphical user interface (GUI) to further simplify its application.
AW_2024.mp4