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Title : Liquid Metal flow for magnetic fusion blanket
Speaker: Boniface Nkonga
Professor, Université Côte d’Azur,
INRIA, CNRS, LJAD, Nice, France.
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Understanding of the physics and control of thermonuclear fusion reactions has progressed in recent decades, with several fusion reactors being constructed and operated experimentally worldwide. Most explored configurations use a confinement system fueled by a Deuterium-Tricium (DT) plasma mixture. Magnetic confinement is the most advanced strategy for harnessing fusion energy for electrical power production. In this context, the DT plasma is confined by a strong magnetic field provided by superconducting magnet coils. Plasma activity is subject to instabilities (i.e., edge-localize modes and disruptions) that release significant flows of electrons, neutrons, alpha particles, and heat (thermal and radiative) outwards from the plasma confinement. A nuclear blanket protects the superconducting coils from the adverse effects of plasma activity and interfacing with several other components essential to the machine’s operation.

Liquid metal blanket face-to-plasma components offer an alternative to the most demanding protection challenges. They could withstand heat fluxes without permanent damage and open the door to entirely new magnetic fusion operating regimes. To realize this potential, innovative technologies must be developed. Liquid lithium surfaces are an innovation that could fulfill the promise of fusion power in electricity generation.

We are interested in the numerical modeling of liquid metal flowing as part of the blanket protection. This thin layer of metal flow is a promising alternative to protect against possible melting damages that Disruptions and MHD instabilities can cause in fusion devices. The liquid metal blanket will operate according to the principles of magnetohydrodynamics (MHD), which are the same principles that produce the Dynamo effect. Here, we will discuss some modeling and numerical challenges associated with the dynamic of a thin layer of metal flow under a strong magnetic field. This is one of the significant topics in the Inria project team CASTOR (https://team.inria.fr/castor/) and the Eurofusion project team JOREK (https://www.jorek.eu/).

- On the exploration of innovative concepts for fusion chamber technology. M.A. Abdou et al. Fusion Engineering and Design, 2001.
- Compact fusion blanket using plasma facing liquid Li-LiH walls and Pb pebbles, Victor Prosta, Sabine Ogier-Collin, Francesco A. Volpea, Journal of Nuclear Materials, 2024.

Boniface works on developing simulation tools for modeling plasma in tokamak-type devices. We are also looking at a new problem of trying to model flow of liquid metals under external magnetic fields and currents by shallow approximations and possibly even with full models where the free-surface is evolved using ALE-type methods. Below is a movie of a mesh movement achieved by solving an elasticity model, which is one ingredient in an ALE-type method.

During his visit, he will also give some research seminars which will be announced later.

]]>]]>Title: Bayesian inference using deep generative models

Date: 9 August 2024 at 2:00 PM

Venue: Auditorium and ZoomAbstract: Inverse problems arise in numerous science and engineering applications, such as medical imaging, weather forecasting and predicting the spread of wildfires. Bayesian inference provides a principled approach to solve inverse problems by considering a statistical framework, which is particularly useful when the measurement/output of the forward problem is corrupted by noise. However, Bayesian inference algorithms can be challenging to implement when the inferred field is high-dimensional, or when the known prior information is too complex.

In this talk, we will see how conditional Wasserstein generative adversarial networks (cWGANs) can be designed to learn and sample from conditional distributions in Bayesian inference problems. The proposed approach modifies earlier variants of the architecture proposed by Adler et al. (2018) and Ray et al. (2022) in two fundamental ways: i) the gradient penalty term in the GAN loss makes use of gradients with respect to all input variables of the critic, and ii) once trained, samples are generated from the posterior by considering an open ball around the measurement. These two modifications are motivated by a convergence proof that ensures the learned conditional distribution weakly approximates the true conditional distribution governing the data. Through simple examples we show that this leads to a more robust training process. We also demonstrate that this approach can be used to solve complex real-world inverse problems.

You can learn more about his work and his papers here

He is off to join a postdoc position with Hendrik Ranocha.

]]>He has a postdoc offer to work with Hendrik Ranocha.

]]>]]>A scalable asynchronous discontinuous Galerkin method for massively parallel flow simulations

Shubham Kumar Goswami

CDS, IISc, Bangalore

17 April 2024 at 2 PM

TIFR-CAM, Auditorium and on ZoomAccurate simulations of turbulent flows are crucial for comprehending complex phenomena in engineered systems and natural processes.These simulations are often computationally expensive and require the use of supercomputers, where scalability at extreme scales is significantly affected by communication overhead. To address this, an asynchronous computing approach for time-dependent partial differential equations (PDEs) that relaxes communication/synchronization at a mathematical level has been developed with finite difference schemes that are ideal for structured meshes. This work proposes an asynchronous discontinuous Galerkin (ADG) method, which has the potential to provide high-order accurate solutions for various flow problems on both structured and unstructured meshes, and demonstrates its scalability. We first propose a new method that combines asynchrony-tolerant and low-storage explicit RK schemes with reduced communication effort. The accuracy of this method is assessed both theoretically and numerically, and its scalability is demonstrated through simulations of the decaying turbulence. Subsequently, we introduce the asynchronous discontinuous Galerkin method, which combines the benefits of the DG method with asynchronous computing. The numerical properties of the proposed method are investigated, including local conservation,stability, and accuracy, where the method is shown to be, at most, first-order accurate. To recover accuracy, we developed new asynchrony-tolerant (AT) fluxes that utilize data from multiple time levels. To validate these theoretical findings, several numerical experiments are conducted based on both linear and nonlinear problems. Finally, we develop a parallel PDE solver based on the ADG method within an open-source finite element library deal.II using a communication-avoiding algorithm. Accuracy validation and scalability benchmarks of the solver are performed, demonstrating a speedup of up to 80% with the ADG method at an extreme scale with 9216 cores. The overall work highlights the potential benefits of the asynchronous approach for the development of accurate and scalable PDE solvers, paving the way for simulations of complex physical systems on massively parallel supercomputers.

General Purpose Alternative Finite Difference WENO (AFD-WENO) for Conservative Systems and Systems with Non-Conservative Products

By Dinshaw S. Balsara (Physics and ACMS, Univ. of Notre Dame)

2 PM, 9 January 2024, Auditorium, TIFR-CAM

In their landmark sequence of papers (Shu and Osher 1988, 1989) the authors presented two highly efficient finite difference WENO schemes. The latter finite difference WENO scheme (Shu and Osher 1989) has become wildly successful and garnered thousands of citations. We call that the FD-WENO scheme. However, in Shu and Osher (1988) they also presented an alternative finite difference WENO (AFD-WENO) scheme which was slower to catch on. We explain why that scheme was slower to catch on - it is because all ingredients that are needed to make a production code out of AFD-WENO were not available at that time. Besides, the scheme was not easy to understand at the time of its initial presentation. We demystify the AFD-WENO algorithm in this talk.

In this talk we explain why the AFD-WENO scheme, nevertheless, had several significant advantages, if it could be developed into an automated algorithm for production codes. This talk is devoted to developing AFD-WENO into a simple algorithm that is easily explained to others and also easily implemented in production codes. To reach that goal, we had to make several algorithmic innovations which we explain here.

The original FD-WENO schemes were also only viable for conservation laws. But the field has moved on and it is very normal for scientists and engineers to discover hyperbolic PDE systems that have non-conservative products, often with stiff source terms. To accommodate such PDE systems, we present the first of its kind AFD-WENO scheme that can retain strict conservation when the PDE is conservative, and yet, accommodate non-conservative products. This vastly expands the class of PDEs that can be treated with AFD-WENO schemes. Several examples are demonstrated in this talk.

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Metric-field based mesh adaptation for compressible flow simulations

Aravind Balan

Dept. of Aerospace Engg.

Indian Institute of Science

Bangalore14 November 2023 at 4:00 PM

TIFR-CAM, Bangalore and on ZoomAbstract: For resolving anisotropic flow features such as shocks, boundary layers, etc., in compressible flow simulations, anisotropic (stretched) meshes are much more efficient than isotropic meshes. Metric-field based mesh generation provides a suitable framework to incorporate anisotropic features of the solutions on the meshes. In this framework, the meshes are described by metric tensors that encode size, anisotropy and the orientation of the simplex mesh elements. For discontinuous Galerkin methods, discretization error control can be done using either h-adaptation, or hp-adaptation. Efficient h- and hp-adaptation methods based on metric fields are developed for discontinuous Galerkin methods for solving compressible flow simulations. The effectiveness of the adaptation methodology is demonstrated using both model and flow problems.

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