Towards validated multiscale simulations for fusion

Harnessing energy produced by thermonuclear fusion reactions has the potential to provide a clean and inexpensive source of energy to Earth. However, throughout the past seven decades, physicists learned that creating our very own fusion energy source is very difficult to achieve. We constructed a component-based, multiscale fusion workflow to model fusion plasma inside the core of a tokamak device. To ensure the simulation results agree with experimental values, the model needs to undergo the process of verification, validation and uncertainty quantification (VVUQ). This paper will go over the VVUQ work carried out in the multiscale fusion workflow (MFW), with the help of the EasyVVUQ software library developed by the VECMA project. In particular, similarity of distributions from simulation and experiment is explored as a validation metric. Such initial validation measures provide insights into the accuracy of the simulation results. This article is part of the theme issue 'Reliability and reproducibility in computational science: implementing verification, validation and uncertainty quantification in silico'.

VECMAtk: a scalable verification, validation and uncertainty quantification toolkit for scientific simulations

We present the VECMA toolkit (VECMAtk), a flexible software environment for single and multiscale simulations that introduces directly applicable and reusable procedures for verification, validation (V&V), sensitivity analysis (SA) and uncertainty quantication (UQ). It enables users to verify key aspects of their applications, systematically compare and validate the simulation outputs against observational or benchmark data, and run simulations conveniently on any platform from the desktop to current multi-petascale computers. In this sequel to our paper on VECMAtk which we presented last year [1] we focus on a range of functional and performance improvements that we have introduced, cover newly introduced components, and applications examples from seven different domains such as conflict modelling and environmental sciences. We also present several implemented patterns for UQ/SA and V&V, and guide the reader through one example concerning COVID-19 modelling in detail. This article is part of the theme issue 'Reliability and reproducibility in computational science: implementing verification, validation and uncertainty quantification in silico'.

Application of the extreme scaling computing pattern on multiscale fusion plasma modelling

The extreme scaling pattern of the ComPat project is applied to a multi-scale workflow relevant to the magnetically confined fusion problem. This workflow combines transport, turbulence and equilibrium codes (together with additional auxiliaries such as initial conditions and numerical module), which aims at calculating the behaviour of a fusion plasma on long (transport) time scales based on information from much faster (turbulence) time scales. Initial findings of profile measurements are reported in this paper and indicate that, depending on the chosen performance metric for defining 'cost', such as time to completion, efficiency and total energy consumption of the mutliscale workflow, different choices on the number of cores would be made when determining the optimal execution configuration. A variant of the workflow which increases the inherent parallelism is presented, and shown to produce equivalent results at (typically) lower cost compared with the original workflow. This article is part of the theme issue 'Multiscale modelling, simulation and computing: from the desktop to the exascale'.

Performance of distributed multiscale simulations

Multiscale simulations model phenomena across natural scales using monolithic or component-based code, running on local or distributed resources. In this work, we investigate the performance of distributed multiscale computing of component-based models, guided by six multiscale applications with different characteristics and from several disciplines. Three modes of distributed multiscale computing are identified: supplementing local dependencies with large-scale resources, load distribution over multiple resources, and load balancing of small- and large-scale resources. We find that the first mode has the apparent benefit of increasing simulation speed, and the second mode can increase simulation speed if local resources are limited. Depending on resource reservation and model coupling topology, the third mode may result in a reduction of resource consumption.