Friday, 3 February 2017

Heliseismology - Introduction

I remember attending a lecture many years ago about the physics of stellar interiors with particular emphasis on the sun. It is amusing that the lecturer noted that one of the reasons for his interest was his freedom to construct models in the knowledge that it was very difficult to come up with the experimental evidence to verify the models! The field of helioseisomology and neutrino observations are changing this idea and we are gleening more information about the interior of our nearest star and other stars too.

Reka Jain gave an excellent presentation, her experience in this area is great and covers many levels of expertise including information inspiring young people [ref. 6]. Reka provided an overview of Helioseismology and  briefly discussed some recent successes of global and local Helioseismology.

My interest  in this area relates to the link between excitations in the solar atmosphere and the solar global oscillations see reference 13 and 14.

Helioseismology is the study of wave oscillations in the Sun. Observations of acoustic wave oscillations are used to make helioseismic studies of the interior of the Sun even though helioseismic techniques operate slightly differently on different length scales.The diagram below illustrates the variety of reflections and refractions occuring when acoustic waves propagate in the solar interior.

The helioseismic vibrations arise from convective and turbulent motions within the solar interior. We can identify two types of modes, pressure driven p-modes and gravity driven g-modes. Solving spherically symmetric equations of hydrodynamics (see our wobbling star) the modes can be understood in terms of spherical harmonics illustrated in the figure below. The predicted power spectrum gives rise to a series of distinct ridges 200000  modes have been detected of a possible million.



Reka's talk was particularly interesting because not only did she provide a clear overview  we heard about exciting advances including applications of helioseismology for studying
  • sunspot growth and evolution,
  • flux tubes convecting to the surface and the study of
  • neutrino observations to determine characteristics of g-mode oscillations.
  • neutrino propagation in stellar interiors to determine the properties of dark matter
Deubner [ref. 7] explained the p-mode oscillations using  the phase and group velocity of fluctuations of different solar spectral lines. Agreement with theoretical estimates was shown for acoustic waves trapped in the sun. With increasingly accurate models of the sun from helioseismology it is possible to use this higher quality data to study the propagation of neutrinos such probes can be used in studies of the difficult to find g-mode oscillations. One possibility is to use the sun as a probe for fundamental physics and cosmology. An important idea is the detection of dark matter and to understand the impact that this has onthe formation of stars [ref 8


ref. 8 Neutrion propagation in the interior
The Sun as a probe of Fundamental Physics and Cosmology The high quality data provided by helioseismology, solar neutrino flux measurements, spectral determination of solar abundances, nuclear reactions rates coefficients among other experimental data, leads to the highly accurate prediction of the internal structure of the present Sun - the standard solar model. In this talk, I have discussed how the standard solar model, the best representation of the real Sun, can be used to study the properties of dark matter, for which two complementary approaches have been developed: - to limit the number of theoretical candidates proposed as the dark matter particles, this analysis complements the experimental search of dark matter, and - as a template for the study of the impact of dark matter in the evolution of stars, which possibly occurs for stellar populations formed in regions of high density of dark matter, such as stars formed in the centre of galaxies and the first generations of stars.

 ref. 9 Predictions of solar cycle  changes in p-mode frequencies change with the solar cycle
The Sun's activity measured through many of its proxies varies in a periodic manner with an average duration of about 11 years. The empirical relations based on the periodicity are considered as the first generation methods to predict the maximum amplitude of the next solar cycle. These methods which are statistical in nature fall into two different categories: precursor methods and extrapolation methods and has been widely used in the later part of the 20th century. Recent advances include predictions based on non-linear methods and dynamo models, where the later predicts not only the maximum amplitude of the solar cycle but also the timing of the activity maximum. In this review, we focus on different prediction methods and compare their outcome for previous cycles with an emphasis on cycle 24. We further analyze and compare various predictions for solar cycle 25 and beyond.


The figures above and below illustrate the variation of the meridional flow.


reference 11,12 helioseismic detection of supergranulation
We present measurements of the Sun’s sub-surface convective flows and provide evidence that the pattern of supergranulation is driven at the surface. The pattern subsequently descends slowly throughout the near-surface shear layer in a manner that is inconsistent with a 3D cellular structure. The flow measurements are obtained through the application of a new helioseismic technique based on traditional ring analysis. We measure the flow field over the course of eleven days and perform a correlation analysis between all possible pairs of depths and temporal separations. In congruence with previous studies, we find that the supergranulation pattern remains coherent at the surface for slightly less than two days and the instantaneous surface pattern is imprinted to a depth of 7 Mm. However, these correlation times and depths are deceptive. When we admit a potential time lag in the correlation, we find that peak correlation in the convective flows descends at a rate of 10-40 m s-1 (or equivalently 1-3 Mm per day). Furthermore, the correlation extends throughout all depths of the near-surface shear layer. This pattern-propagation rate is well matched by estimates of the speed of downflows obtained through the anelastic approximation. Direct integration of the measured speed indicates that the supergranulation pattern that first appears at the surface eventually reaches the bottom of the near-surface shear layer a month later. Thus, the downflows have a Rossby radius of deformation equal to the depth of the shear layer and we suggest that this equality may not be coincidental.

We present measurements of the Sun's sub-surface convective flows and provide evidence that the pattern of supergranulation is driven at the surface. The pattern subsequently descends slowly throughout the near-surface shear layer in a manner that is inconsistent with a 3-D cellular structure. The flow measurements are obtained through the application of a new helioseismic technique based on traditional ring analysis. We measure the flow field over the course of eleven days and perform a correlation analysis between all possible pairs of depths and temporal separations. In congruence with previous studies, we find that the supergranulation pattern remains coherent at the surface for slightly less than two days and the instantaneous surface pattern is imprinted to a depth of 7 Mm. However, these correlation times and depths are deceptive. When we admit a potential time lag in the correlation, we find that peak correlation in the convective flows descends at a rate of 10 - 30 m s-1 (or equivalently 1 - 3 Mm per day). Furthermore, the correlation extends throughout all depths of the near-surface shear layer. This pattern-propagation rate is well matched by estimates of the speed of down flows obtained through the anelastic approximation. Direct integration of the measured speed indicates that the supergranulation pattern that first appears at the surface eventually reaches the bottom of the near-surface shear layer a month later. Thus, the transit time is roughly equal to a solar rotation period and we suggest this equality may not be coincidental. 

References

  1. Helioseismology
  2. Lecture notes on stellar oscillations
  3. Introduction to helioseismology
  4. Leibacher, A New Description of the Solar Five-Minute Oscillation
  5. Ulrich, The Five-Minute Oscillations on the Solar Surface
  6. Junior introduction to Helioseismology 
  7. Deubner, F.-L., Acoustic waves and the geometric scale in the solar atmosphere  see also Some properties of velocity fields in the solar photosphere. V - Spatio-temporal analysis of high resolution spectra
  8. Lopes, The Sun as a probe of Fundamental Physics and Cosmology
  9. Tripathy, Predictions of solar cycle
  10. Jain K, Tripathy, Hill, Solar Activity in Cycle 24 - What do Acoustic Oscillations tell us? 
  11. Greer, Hindman and Toomre, Helioseismic Imaging of Supergranulation throughout the Sun’s Near-Surface Shear Layer 
  12. Hindman, Greer, Toomre, Helioseismic Imaging of Supergranulation throughout the Sun's Near-Surface Shear Layer 
  13. Solar wave theory blog: helioseismology 
  14. Solar wave theory blog: solar global oscillations 
  15. A Comparison Between Global Proxies of the Sun's Magnetic Activity Cycle: Inferences from Helioseismology 




















Monday, 16 January 2017

The Sun in the Clouds: Using Amazon EC2 for High Performance Computing

Over the last few years HPC on demand has become possible, there is a strong market lead from Microsoft with it's Azure service and by Amazon with EC2.

Traditional HPC systems are normally shared systems using scheduling software  to optimise utilisation. Although these systems satisfy the needs of specific communities with large computational demands (e.g. the computational solar physics community)  running such a system for a large diverse community  is a challenge. The main issues are the wait time for s system to come online, the necessity for scheduling jobs and the operating system and software stack is dictated by the user community.

On demand research computing infrastructures such as those delivered by Amazon-EC2 and Azure can reduce the time taken to get a system on line with the software stack required b y the researcher. Such an environment is ideal for researchers working interactively developing research applications, analysing and visualising data. Setting up such systems is much easier with open source deployment software such as Alces flight.

In this post  we use Alces flight and Amazon EC2 to set up a compute node with solar physics software developed at The University of Sheffield e.g. SMAUG, SAC and pysac. We use amazon ec2 nodes with GPUs to run smaug and compare the costs  and performance for running codes on local HPC infrastructure and on Amazon EC2. We describe how to set up the software stack on Amazon EC2 using Alces flight.

To run the benchmarks the following steps were followed
  1. Set up and configure account with Amazon EC2
  2. Set up machine using Alces flight
  3. Configure flight and install the required packages
  4. Configure smaug and run the benchmarks
  5. Performance results

Set up and configure account with Amazon EC2

Setting Up and Configuring Account with EC2 is straightforward registration by visiting the AWS page at

After logging in with you account you are presented with the console which has a large range of services. We will use the EC2 service to get started we need to set up a keypair which will be used to allow secure access to the machine. To configure this from the EC2 console select key pairs under the network and security group. This shows a list of active keypairs that you own, there is also a button to enable creation and deletion of the key pairs. We also import the keypairs from here to our local/client machine so that we can access our machine.

The amazon documentation is very good here is a link to the information on setting up a keypair.
to access my amazon instances it was necessary to import the Amazon key (pem file) and copy into the .ssh folder under my home directory, note that permissions for this key must be set correctly e.g. using the command
chmod 400 my-key-pair.pem
For the putty and winscp clients from windows it is necessary to convert the pem key to a ppk file using puttygen. Details are given on the Amazon docs

Set up machine using Alces flight

Using alces flight has been made much easier with some excellent articles and documentation. The Amazon news article provides a good overview. 
New in AWS Marketplace: Alces Flight – Effortless HPC on Demand
However, to get started the online documentation is once again very good.
Flight Appliance Documentation

When we are configuring the machine it's important to use the Amazon spot market and initially to use instances of type t2.micro this reduces set up costs to a minimum. As soon as the machine has been configured we set the instance type to either g2.2xlarge or p2.8xlarge for our GPU tests

Setting up the instance with Alces flight


  1. Login to you amazon account select the EC2 dashboard click on instances to see all your machines    https://aws.amazon.com/ec2/
  2. Visit the alces flight documentation at http://docs.alces-flight.com select the launching aws instance option e.g. step by step instructions http://docs.alces-flight.com/en/stable/launch-aws/launching_on_aws.html
  3. Goto amzon aws market place https://aws.amazon.com/marketplace in the search box type “alces flight” and select community edition select region for pricing e.g. EU (Ireland), Select delivery type e.g. AMI or HPC instance
  4. From the next dialog check versio, region and EC2 instance type also check the key pair used (this should correspond to the name of one of the keys in your AWS managment session check under network&security -> keypairs, there is an option to create a key pair here
  5. Clicking one click launch will fire up the following messsage - check your AWS console under instances - get coffee while it initialises
  6. Login using ssh -Y -i “yourkey.pem” alces@XX.XXX.XX.X (the pem downloaded from console and should be visible from where you login)

Usage instructions and connection information for the instance  can be obtained by ticking the activated instance in the EC2 control panel and clicking the usage instructions tab or by right clicking the activated instance in control panel and selecting Connect from the drop down menu.

Configure flight and install the required packages

 When you have logged into your instance, the node should start with the prompt unconfigured and the warning message that it is not yet operational. Use the command alces configure node. For running single GPU tests with smaug we just require a master node without any slave nodes.

The following commands return the configuration information

alces about node
alces about identity
To start a gnome session use the command:
alces session start gnome
The session will respond with connection information which can be used with client applications such as tiger VNC.
Typical commands used from Alces flight are:
Commands to use from alces flight

'module avail'            - show available application environments
'module add <modulename>' - add a module to your current environment

'alces gridware'          - manage software for your environment
'alces howto'             - guides on how to use your research environment
'alces session'           - start and manage interactive sessions
'alces storage'           - configure and address storage facilities
'alces template'          - tailored job script templates

'qstat'                   - show summary of running jobs
'qsub'                    - submit a job script
'qdesktop'                - submit an interactive session request

'aws help'                - show help for AWS CLI

's3cmd --help'            - show help for S3cmd
's3cmd ls [<bucket>]'     - list objects or buckets
's3cmd put <file> <s3>'   - put file into bucket
's3cmd get <s3> <file>'   - get file from bucket

To install the required packages we use the alces gridware install command. Alces gridware is a powerful and extremely useful utility. To see the diverse range of packages which may be installed see the repository at
The following lines can be placed in a script file which may be run and can be used for testing and experimenting with the applications that we will use 

#!/bin/bash

#set up packages using alces gridware
alces gridware install compilers/gcc
alces gridware install ffmpeg
alces gridware install imagemagick
alces gridware install graphicsmagick
alces gridware install grace
alces gridware install paraview
alces gridware install nvidia-cuda
alces gridware install anaconda3

Further information for working with the ALCES cluster is at
To install the required sac and smaug applications for running our tests the following commands were run from a script file

#!/bin/bash

#script to upload an install projects in an alces flight installation

cd ~
mkdir proj


svn checkout --username mikeg64 https://ccpforge.cse.rl.ac.uk/svn/sac/branches/sac_working
svn checkout --username mikeg64 https://ccpforge.cse.rl.ac.uk/svn/sac/dev/smaug


git clone https://github.com/mikeg64/athena_pmode

Configure smaug and run the benchmarks

To install the version of smaug on github (version 788e630) used for the performance tests the following git commands were used

cd smaug
git checkout 788e6303aa2ac670f8490f6e193587cbedf7383a

Or
cd smaug
git reset --hard 788e630
We will be using the Orszag-Tang model test to set this change to the src folder of the smaug distribution and type
make ot

For compiling using the version of CUDA on the amazon instance load the cuda compiler module installed with alces gridware. To find the path to the nvcc compiler type which nvcc. The path should be used in the make_inputs file it will be necessary to change the CUDA and CUDACCFLAGS as illustrated below.

Compilation using
CUDA = /opt/gridware/depots/77cbfdae/el7/pkg/libs/nvidia-cuda/7.5.18/bin/toolkit
CUDACCFLAGS = --ptxas-options=-v -arch sm_20  -maxrregcount=32 -DUSE_SAC

It is necessary to set the architecture flag correctly guidelines for setting the architecture flag for GPUs are documented by NVIDIA, the GPU feature list is described at
http://docs.nvidia.com/cuda/cuda-compiler-driver-nvcc/index.html#gpu-feature-list

In the CUDA naming scheme, GPUs are named sm_xy, where x denotes the GPU generation number, and y the version in that generation. Additionally, to facilitate comparing GPU capabilities, CUDA attempts to choose its GPU names such that if x1y1 <= x2y2 then all non-ISA related capabilities of sm_x1y1 are included in those of sm_x2y2. From this it indeed follows that sm_30 is the base Kepler model, and it also explains why higher entries in the tables are always functional extensions to the lower entries. This is denoted by the plus sign in the table. Moreover, if we abstract from the instruction encoding, it implies that sm_30's functionality will continue to be included in all later GPU generations. As we will see next, this property will be the foundation for application compatibility support by nvcc.

sm_20 (deprecated)
Basic features
+ Fermi support
sm_30 and sm_32
+ Kepler support
+ Unified memory programming
sm_35
+ Dynamic parallelism support
sm_50, sm_52, and sm_53
+ Maxwell support


After configuring the machine we stop the instance state and change the instance type to g2.2xlarge ready to run the compiled version of smaug on the GPU.

The GPU infor for our Amazon image is as follows
Device Grid K520 on Amazon g2.2xlarge
4132864 GDR memory
CUDA ver 3.0
8 multiprocessors
0.797GHz clock

The configuration files for the Orszag-tang test are contained in a public compressed archive at the following link
The bit.ly shortened link is
http://bit.ly/2hXW6ZL 

wget http://bit.ly/2hXW6ZL

To use these we change the model size and config filename in the iosmaugparams.h file in the include directory.

Performance results

The table below shows the timings obtained for a range of different NVIDIA GPUs these are the timings in seconds for 100 iterations

The speed up factor compared to a single core of the Intel Xeon X5650 Westmere CPU is shown in the plot below

Although our results demonstrate the K520  is slower than our results for the M2070  and the K20 it still demonstrates the usefulness of the K520 for running smaller models for MHD.  We will report on the results for the larger Amazon GPU instances as tests are continued. The ease with which we have been able to access and set up GPUs is highly encouraging.

Further details about SMAUG and benchtesting are given in the publication below.

Useful References

Guide for researchers by Amazon
AWS Global Data Egress Waiver removing the worry of estimating network traffic charges and set up invoice billing.

New P2 Instance Type for Amazon EC2 – Up to 16 GPUs

AWS Marketplace - alces flight instances

A Fast MHD Code for Gravitationally Stratified Media using Graphical Processing Units: SMAUG

Magnetohydrodynamic code for gravitationally-stratified media

 

 

 

Thursday, 8 September 2016

Simulations of the Dynamics Generated by Solar Global OscillatingEigenmodes Generated in the Solar Atmosphere

This post continues a series of posts on global solar oscillation phenomena, we present results from Magneto Hydrodynamics simulations of solar oscillation phenomena in a gravitationally stratified atmosphere based on the VALIIIc model of the solar atmosphere.

The solar atmosphere exhibits a diverse range of wave phenomena, one of the earliest to be discovered was the five minute oscillation, the p-mode. The solar p modes are generated by global resonant oscillations and turbulent motions just beneath the photosphere. The aim of the work described here has been to investigate the dynamics in the solar atmosphere which are generated by solar global eigenmodes of oscillation. In addition we want to understand the mechanisms of leakage of these global oscillations into the atmosphere. It is also important for solar physicists to understand the conditions under which chromospheric dynamics evolve as a result of the 5 minute global oscillations - (spicules, waves).

There is increasing observational evidence of ubiquitous intensity oscillations and the detection of large scale solar oscillations in the solar corona. The Leakage of energy through the solar atmosphere (reference 1-5)i can be understood through theoretical studies of wave propagation through stratified atmospheres and by understanding the influence of the magnetic field on these motions. Great insight has been provided using studies of the solutions of the Klein-Gordon equation and by understanding the effect of the so called atmospheric cut-off and how this varies with atmospheric stratfication, more details are given in references 6-7.

For this work we have performed a range of numerical simulations of a model of the quiet solar atmosphere based on the VALIIc, the gravitationally stratified model has been excited by a periodic driver located at a position corresponding to the temperature minimum in the solar atmosphere. Full details of the procedure and the models performed are detailed at

http://solarwavetheory.blogspot.co.uk/2015/07/characterisation-of-modes-for-solar.html

Taking this work forward we have:
  1. Undertaken simulations for a greater range of wave modes
  2. Computed the energy flux through the solar atmosphere for drivers with different periods and for different wave modes.
Earlier simulations had been performed for the (0,0),(0,1),(0,2) and (0,3) modes as detailed in the earlier postings simulations were performed for normal modes and for driver period values of 180s, 300s and 30s. The latter periods correspond to the Chromospheric resonance, the five minute mode and a period below the cut off frequency.

For driver period 300s

Mode Amplitude (m/s) Label
(1,1) 175 spic5b1_1
(1,2) 137.28 spic5b1_2
(1,3) 110.7 spic5b1_3
(2,3) 99.0 spic5b2_3
(2,2) 116.7 spic5b2_2
(3,3) 87.5 spic5b3_3

For driver period 180s

Mode Amplitude (m/s) Label
(1,1) 175.3 spic6b1_1
(1,2) 137.5 spic6b1_2
(1,3) 110.9 spic6b1_3
(2,3) 99.2 spic6b2_3
(2,2) 116.9 spic6b2_2
(3,3) 87.7 spic6b3_3

Since we are investigating the leakage of energy into the solar atmosphere, for consistency it is necessary to ensure that for the different modes the driver amplitude is set to a value which provides the same total amount of energy over the model cross section and per unit time. The amplitudes for the (n,m) mode given in the above table are determined using the following relation.

 Where
Tm maximum period used for the simulations, T00 is the period used for the (0,0) mode with amplitude A00 here we used A00=500m/s.

For many of the earlier models we presented distance time-plots for the vertical component of the plasma velocity. Below we present the vertical component of the plasma velocity for the (2,3) mode 180s driver. At a height of  2.3Mm, a wave propagating across the transition zone can be observed.



The next movie shows the wave propagation at a height of 4.7Mm. At this height although the intensity of the plasma motion is reduced it is observed that there is a significant energy flux at this height. There appear to be two motions, one corresponding directly to the driver and the other motion which may be induced by reflections from the simulation box.




We compute the energy flux at different heights through the solar atmosphere using the following energy flux relation (see Bogdan ref. 8, quantities in the equations below subscripted with a b  are background variables.
The kinetic pressure is given by
The following plots display the energy flux at specific heights for the different modes and the driver periods

Energy Flux at 4Mm
Energy Flux at 5.5Mm

We noted that all the simulations were set with an amplitude resulting in a driver delivering the same quantity of energy over a specified amount of time. For some of the models we repeated the simulations but kept the amplitude for all drivers (i.e. all modes and driver frequencies) fixed at A=350m/s.

The next plots show the ratio of the energy flux for each of the driver periods and modes. In each case we have plotted the ratio of the flux for the fixed energy case to the flux for the fixed amplitude case.
Energy Flux Ratio at 4Mm
Energy Flux Ratio at 5.5Mm

The following bar charts show bar charts of the log10(energyFlux) for different modes. The cases for different driver periods are shown on different plots. We plot the energy flux at 4Mm and 5.5Mm i.e. the flux in the solar corona.

Energy Flux at 4Mm for 180s p-Mode Driver

Energy Flux at 5.5Mm for 180s p-Mode Driver

Energy Flux at 4Mm for 300s p-Mode Driver

Energy Flux at 5.5Mm for 300s p-Mode Driver

The energy  flux bar diagrams indicate that the fundamental mode delivers the maximum amount of energy. It is apparent that modes with odd numbers have a smaller energy flux  than for those cases with even mode numbers. The 180s driver delivers significantly more energy.

The energy flux ratio plots are interesting because they suggest that there is little variation in the flux ratio for drivers with different periods. Thus with a range of different drivers periods and modes we have a finite contribution to energy delivered to the solar atmosphere. The results of the simulations corroborate the ubiquity of the observed coronal intensity oscillations and naturally support  some of that characteristics alluded by theoretical modelling using the Klein-Gordon equation.

In further work we are currently undertaking simulations in which the oscillations are driven by configurations with magnetic fields, one such configuration is a thin 1kG vertical flux tube.
  1. Didkovsky, L.; Kosovichev, A.; Judge, D.; Wieman, S.; Woods, T., Variability of Solar Five-Minute Oscillations in the Corona as Observed by the Extreme Ultraviolet Spectrophotometer (ESP) on the Solar Dynamics Observatory/Extreme Ultraviolet Variability Experiment (SDO/EVE), Solar Physics, Volume 287, Issue 1-2, pp. 171-184 
  2. R. Erdelyi, R. Zheng, G. Verth, & P. H. Keys, Ubiquitous concurrent intensity oscillations in the solar atmosphere detected by SDO/AIA, 2016 submitted to ApJ 
  3. Marsh, M. S.; Walsh, R. W. p-Mode Propagation through the Transition Region into the Solar Corona. I. Observations, The Astrophysical Journal, Volume 643, Issue 1, pp. 540-548.
  4.  Freij, N et al, The Detection of Upwardly Propagating Waves Channeling Energy from the Chromosphere to the Low Corona, The Astrophysical Journal, Volume 791, Issue 1, article id. 61, 7 pp. (2014).
  5. Ireland, J.; McAteer, R. T. J.; Inglis, A. R., Coronal Fourier Power Spectra: Implications for Coronal Seismology and Coronal Heating, The Astrophysical Journal, Volume 798, Issue 1, article id. 1, 12 pp. (2015). 
  6. Taroyan, Y.; Erdélyi, R.; Malins, C. Propagation of p-modes into the solar atmosphere, Proceedings of SOHO 18/GONG 2006/HELAS I, Beyond the spherical Sun (ESA SP-624). 7-11 August 2006, Sheffield, UK. Editor: Karen Fletcher. Scientific Editor: Michael Thompson. 
  7. Malins, C.; Erdélyi, R. Direct Propagation of Photospheric Acoustic p Modes into Nonmagnetic Solar Atmosphere, Solar Physics, Volume 246, Issue 1, pp.41-52 
  8. Bogdan, T. J. et al, 2003 ApJ 599 626-660


Saturday, 23 April 2016

Dynamic Sun Conference: MHD waves in the Solar Atmosphere

In March I attended the dynamic sun conference, the theme of the conference was the study of MHD waves in the Solar Atmosphere. The purpose of the visit to this conference was fourfold
  1. Draw attention to work undertaken by IT services to support research needs of the solar physics community
  2. Publicise work on work investigating the  dynamics generated by the solar global oscillations
  3. Improve understanding of solar physics
  4. Network with the solar physics community and understand some of the research computing needs
Covering observational, theoretical and numerical studies of solar wave dynamics, the programme was excellent. The conference included  a visit to the sacred and iconic river Ganges. With so many good presentations, poster presentations and discussions it's challenging to cover everything in this relatively brief blog article.

The conference opened with  a discussion of wave coupling across the range of scale heights in the solar atmosphere and discussed the influence of magnetic field structures in opening windows channelling energy through the solar atmosphere. We hear how coupling was guided by 4 principles.
  1. The ramp effect - magnetoacoustic portal effect from inclined magnetic field
  2. Fast/slow mode wave conversion (Alfven speed matches acoustic wave speed)
  3. Fast wave reflection (horizontal wave speed same as acoustic wave speed)
  4. Fast/alfven mode conversion
The conversion of p-mode energy to fast modes and subsequent hypothesis of sunspot halos arising from the reflection of the fast modes higher in the atmosphere. This was followed by a discussion of methods for observing p-mode MHD wave generation in sunspots through helioseismology and vector polarimetry. Such observations are essential for explaining the uncertainties in the coupling and conversion mechanisms discussed above.

These topics are discussed in the following references
We heard about the preponderence of vortical motions in the solar atmosphere and how these drive the generation of Alfven waves thereby propagating energy efficiently to the upper layers of the chromosphere and the corona. A feature which clearly came across was the vortex motions generated by bumping and shear motions of the solar granular structures in the photsphere
We heard a presentation on Chromospheric heating. Mats Carlsson talked about models of the solar atmosphere. Because our models a good representation of the ever changing solar atmosphere, this was particularly interesting. Currently models are limited to the time averaged VAL3C model.

A discussion with Mats revealed a preference for the xeon-phi over omnipath for running large models using codes such as Bifrost. I also heard about the Bifrost implementation for handling boundaries using the method of characteristics for open end solutions.
Continuing the numerical simulation theme  there was fascinating discussion by a team from The University of Tokyo presented radiative MHD simulations of chromospheric jets. Visualisations of simulated  spicules were presented they claimed a dependence of jet length on coronal density. The simulation tool made use modelling thermal processes e.g. using radiative cooling and some ionization phenomena. Although the visualisations were compelling it was commented that it had overcomplicated the dominant physical processes responsible for spicule generation.  The MHD tool made use of the weighted essentilly non-oscillating  (WENO-Z) scheme for advancing the solutions. These are discussed in the following papers.
The conference excursions were excellent after a full day we enjoyed a Ganges boat trip to see the Agni Pujua (the worhsip to fire at ) Dashashwamedh Ghat. 
We also enjoyed a morning boat trip to observe the sunrise, what a fitting way to start another full day of solar physics.

Robertus provided an overview of MHD waves in localised magnetic structures. He started with an overview of models of the solar atmosphere and described how they have evolved as our knowledge has increased with improving observational, theoretical and computational understanding.



 The diagram above illustrates the sensitivity of different spectral regions for different parts of the atmosphere.
Studies have revealed the complex structures within the Chromosphere, such studies are dependent on developments in ground and space based instrumentation. We heard about a range of observatories in India and the forthcoming DKIST telescope based in Hawaii. With a primary mirror of 4.24m the DKIST solar telescope will be the largest in the world using adaptive optics to provide detailed imagery of the sun. We heard that it would have a 20km spatial resolution or 0.03" resolution at 500nm or 0.08" at 1.6micron.  Operations are expected to start in December 2019.

Bhola Dwivedi from Banares Hindu University closed the conference
Bhola talked about many things, he mentioned the Man-Mahal observatory which I visited on my final day in Varanasi.