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Numerical optimization of the ramp-down phase with the RAPTOR code

Publications associées (37)

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We discuss how the combination of experimental observations and rapid modeling has enabled to improve understanding of the tokamak ramp-down phase in ASDEX Upgrade. A series of dedicated experiments has been performed, to disentangle the effect of individu ...

Bristol2024

Predicting in Uncertain Environments: Methods for Robust Machine Learning

Paul Thierry Yves Rolland

One of the main goal of Artificial Intelligence is to develop models capable of providing valuable predictions in real-world environments. In particular, Machine Learning (ML) seeks to design such models by learning from examples coming from this same envi ...

EPFL2022

Model-based optimization of magnetic control in the TCV tokamak: design and experiments

Federico Pesamosca

Thermonuclear controlled fusion is a promising answer to the current energy and climate issues, providing a safe carbon-free source of energy which is virtually inexhaustible. In magnetic confinement thermonuclear fusion based on tokamak reactors, hydrogen ...

EPFL2021

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We propose Kernel Predictive Control (KPC), a learning-based predictive control strategy that enjoys deterministic guarantees of safety. Noise-corrupted samples of the unknown system dynamics are used to learn several models through the formalism of non-pa ...

2021

Maximum-likelihood estimation of kinetic parameters via the extent-based incremental approach

Dominique Bonvin, Julien Léo Billeter, Diogo Filipe Mateus Rodrigues

For lumped homogeneous reaction systems, this paper presents a kinetic model identification scheme that provides maximum-likelihood parameter estimates and guarantees convergence to global optimality. The use of the extent-based incremental approach allows ...

PERGAMON-ELSEVIER SCIENCE LTD2019

Analysis of output modifier adaptation for real-time optimization

Dominique Bonvin, Alejandro Gabriel Marchetti

In the context of real-time optimization, modifier-adaptation schemes update the model-based optimization problem by adding first-order correction terms to the cost and constraint functions of the optimization problem. This guarantees meeting the plant fir ...

PERGAMON-ELSEVIER SCIENCE LTD2019

Realistic multi-machine tokamak profile simulations and numerical ramp-down optimization using the RAPTOR code

Anna Teplukhina

Predictive modelling of plasma profiles is an essential part of ongoing research in tokamak plasmas, required for a successful realization of future fusion reactors. This thesis focuses on upgrading the RAPTOR code to extend the area of its applicability f ...

EPFL2018

Simulation of profile evolution from ramp-up to ramp-down and optimization of tokamak plasma termination with the RAPTOR code

Olivier Sauter, Federico Alberto Alfredo Felici, Antoine Pierre Emmanuel Alexis Merle, Doohyun Kim, Anna Teplukhina, Donghyun Kim

The present work demonstrates the capabilities of the transport code RAPTOR as a fast and reliable simulator of plasma profiles for the entire plasma discharge, i.e. from ramp-up to ramp-down. This code focuses, at this stage, on the simulation of electron ...

2017

Model predictive control schemes for power electronic applications are characterized by a great variety of problem formulations. In this paper, we consider a three phase voltage source converter with an arbitrary number of voltage levels and derive a model ...

2017

Simplex Queues for Hot-Data Download

Elie Najm, Emina Soljanin

In cloud storage systems, hot data is usually replicated over multiple disks/servers in order to accommodate simultaneous access by multiple users as well as increase the fault tolerance of the system. Recent cloud storage research has proposed using avail ...

2017