ICALEPCS2023 - Table of Session: FR2A (Digital Twins & Simulation)

Paper	Title	Page
FR2AO01	How Accurate Laser Physics Modeling Is Enabling Nuclear Fusion Ignition Experiments	1620
	K.P. McCandless, R.H. Aden, A. Bhasker, R.T. Deveno, J.-M.G. Di Nicola, M. Erickson, T.E. Lanier, S.A. McLaren, G. Mennerat, F.X. Morrissey, J. Penner, T. Petersen, B.A. Raymond, S.E. Schrauth, M.F. Tam, K. Varadan, L. Waxer LLNL, Livermore, California, USA
	Funding: This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344 This last year we achieved an important milestone by reaching fusion ignition at Lawrence Livermore National Laboratory’s (LLNL) National Ignition Facility (NIF), a multi-decadal effort involving a large collaboration. The NIF facility contains a 192-beam 4.2 MJ neodymium glass laser (around 1053 nm) that is frequency converted to 351 nm light. To meet stringent laser performance required for ignition, laser modeling codes including the Virtual Beamline (VBL) and its predecessors are used as engines of the Laser Operations Performance Model (LPOM). VBL comprises an advanced nonlinear physics model that captures the response of all the NIF laser components (from IR to UV and nJ to MJ) and precisely computes the input beam power profile needed to deliver the desired UV output on target. NIF was built to access the extreme high energy density conditions needed to support the nation’s nuclear stockpile and to study Inertial Confinement Fusion (ICF). The design, operation and future enhancements to this laser system are guided by the VBL physics modeling code which uses best-in-class standards to enable high-resolution simulations on the Laboratory’s high-performance computing platforms. The future of repeated and optimized ignition experiments relies on the ability for the laser system to accurately model and produce desired power profiles at an expanded regime from the laser’s original design criteria. LLNL Release Number: LLNL-ABS-847846
	Slides FR2AO01 [3.580 MB]
DOI •	reference for this paper ※ doi:10.18429/JACoW-ICALEPCS2023-FR2AO01
About •	Received ※ 26 September 2023 — Revised ※ 12 October 2023 — Accepted ※ 05 December 2023 — Issued ※ 14 December 2023
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

FR2AO02	A Digital Twin for Neutron Instruments	1626
	S. Nourbakhsh, Y. Le Goc, P. Mutti ILL, Grenoble, France
	Data from virtual experiments are becoming an extremely valuable asset for research infrastructures in a multitude of aspects and different actors: for instrument scientists to develop and optimise current and future instruments; for training external users in the usage of the instrument control system; for scientists in studying, quantifying and reducing instrumental effects on acquired data. Furthermore large sets of simulated data are also a necessary ingredient for the development of surrogate models for faster and more accurate simulation, reduction and analysis of the data. The development of a digital twin of an instrument can answer such different needs with a single unified approach wrapping in a user-friendly envelop the knowledge about the instrument physical description, the specific of the simulation packages and their interaction, and the high performing computing setup. In this article we will present the general architecture of the digital twin prototype developed at the ILL in the framework of the PANOSC European project in close collaboration with other research facilities (ESS and EuXFel). The communication patterns (based on ZQM) and interaction between the control system (NOMAD), simulation software (McStas), instrument description and configuration, process management (CAMEO) will be detailed. The adoption of FAIR principles for data formats and policies in combination with open-source software make it a sustainable project both for development and maintenance in the mid and long-term.
	Slides FR2AO02 [1.245 MB]
DOI •	reference for this paper ※ doi:10.18429/JACoW-ICALEPCS2023-FR2AO02
About •	Received ※ 31 October 2023 — Revised ※ 02 November 2023 — Accepted ※ 05 December 2023 — Issued ※ 07 December 2023
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

FR2AO03	Machine Learning Based Virtual Beam Diagnostic Tool for Real-Time Operation at EuPRAXIA
	S. Pioli, R. Pompili LNF-INFN, Frascati, Italy E. Chiadroni Sapienza University of Rome, Rome, Italy A. Cianchi INFN-Roma II, Roma, Italy G. Latini, B. Serenellini INFN/LNF, Frascati, Italy V. Martinelli INFN/LNL, Legnaro (PD), Italy A. Mostacci SBAI, Roma, Italy
	The EuPRAXIA@SPARC_LAB facility will equip the Frascati National Laboratories (LNF) of the Italian Institute for Nuclear Physics (INFN) with an infrastructure, in the ESFRI roadmap, capable of an unique combination of a high brightness GeV-range electron beam generated in a state-of-the-art LINAC boosted by a plasma acceleration module designed as top-class quality, user-oriented and at the forefront of new acceleration technologies. In these context we present design of our approach and first results with different Machine Learning (ML) techniques on the SPARC_LAB facility for the Virtual Beam Diagnostic tool under development. The aim of such tool will be the real-time virtualization of beam dynamics images as they would be seen by destructive beam diagnostics flag. This digital-twin based methodology will assist and provide images of the electron beam spot along the EuPRAXIA LINAC without affecting beam uptime to users and improving beam dynamics modeling and optimization of this challenging future facility.
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

FR2AO04	A Physics-Based Simulator to Facilitate Reinforcement Learning in the RHIC Accelerator Complex	1630
	L.K. Nguyen, K.A. Brown, M.R. Costanzo, Y. Gao, M. Harvey, J.P. Jamilkowski, J. Morris, V. Schoefer BNL, Upton, New York, USA
	Funding: Work supported by Brookhaven Science Associates, LLC under Contract No. DE-SC0012704 with the U.S. Department of Energy. The successful use of machine learning (ML) in particle accelerators has greatly expanded in recent years; however, the realities of operations often mean very limited machine availability for ML development, impeding its progress in many cases. This paper presents a framework for exploiting physics-based simulations, coupled with real machine data structure, to facilitate the investigation and implementation of reinforcement learning (RL) algorithms, using the longitudinal bunch-merge process in the Booster and Alternating Gradient Synchrotron (AGS) at Brookhaven National Laboratory (BNL) as examples. Here, an initial fake wall current monitor (WCM) signal is fed through a noisy physics-based model simulating the behavior of bunches in the accelerator under given RF parameters and external perturbations between WCM samples; the resulting output becomes the input for the RL algorithm and subsequent pass through the simulated ring, whose RF parameters have been modified by the RL algorithm. This process continues until an optimal policy for the RF bunch merge gymnastics has been learned for injecting bunches with the required intensity and emittance into the Relativistic Heavy Ion Collider (RHIC), according to the physics model. Robustness of the RL algorithm can be evaluated by introducing other drifts and noisy scenarios before the algorithm is deployed and final optimization occurs in the field.
	Slides FR2AO04 [2.694 MB]
DOI •	reference for this paper ※ doi:10.18429/JACoW-ICALEPCS2023-FR2AO04
About •	Received ※ 04 October 2023 — Accepted ※ 05 December 2023 — Issued ※ 16 December 2023
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

FR2AO05	Python Library for Simulated Commissioning of Storage-Ring Accelerators	1637
	L. Malina, I.V. Agapov, J. Keil, E.S.H. Musa, B. Veglia DESY, Hamburg, Germany N. Carmignani, L.R. Carver, L. Hoummi, S.M. Liuzzo, T.P. Perron, S.M. White ESRF, Grenoble, France T. Hellert LBNL, Berkeley, California, USA
	Simulations of the commissioning procedure became vital to the storage-ring lattice design process. The achievable tolerances on lattice imperfections, such as equipment misalignments or magnet gradient errors, would, without correction, prohibit reaching the design parameters. We present a Python library which includes an extensive set of error sources in the accelerator lattice and provides a variety of correction algorithms to commission a storage ring. The underlying beam dynamics simulations are performed with pyAT. This project builds upon previous works and expands them in the direction of realistic control room experience and software maintainability. The performance is demonstrated using example commissioning studies, and further development plans are discussed.
DOI •	reference for this paper ※ doi:10.18429/JACoW-ICALEPCS2023-FR2AO05
About •	Received ※ 06 October 2023 — Revised ※ 27 October 2023 — Accepted ※ 05 December 2023 — Issued ※ 19 December 2023
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

How Accurate Laser Physics Modeling Is Enabling Nuclear Fusion Ignition Experiments

1620

K.P. McCandless, R.H. Aden, A. Bhasker, R.T. Deveno, J.-M.G. Di Nicola, M. Erickson, T.E. Lanier, S.A. McLaren, G. Mennerat, F.X. Morrissey, J. Penner, T. Petersen, B.A. Raymond, S.E. Schrauth, M.F. Tam, K. Varadan, L. Waxer
LLNL, Livermore, California, USA

Funding: This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344
This last year we achieved an important milestone by reaching fusion ignition at Lawrence Livermore National Laboratory’s (LLNL) National Ignition Facility (NIF), a multi-decadal effort involving a large collaboration. The NIF facility contains a 192-beam 4.2 MJ neodymium glass laser (around 1053 nm) that is frequency converted to 351 nm light. To meet stringent laser performance required for ignition, laser modeling codes including the Virtual Beamline (VBL) and its predecessors are used as engines of the Laser Operations Performance Model (LPOM). VBL comprises an advanced nonlinear physics model that captures the response of all the NIF laser components (from IR to UV and nJ to MJ) and precisely computes the input beam power profile needed to deliver the desired UV output on target. NIF was built to access the extreme high energy density conditions needed to support the nation’s nuclear stockpile and to study Inertial Confinement Fusion (ICF). The design, operation and future enhancements to this laser system are guided by the VBL physics modeling code which uses best-in-class standards to enable high-resolution simulations on the Laboratory’s high-performance computing platforms. The future of repeated and optimized ignition experiments relies on the ability for the laser system to accurately model and produce desired power profiles at an expanded regime from the laser’s original design criteria.
LLNL Release Number: LLNL-ABS-847846

Slides FR2AO01 [3.580 MB]

DOI •

reference for this paper ※ doi:10.18429/JACoW-ICALEPCS2023-FR2AO01

About •

Received ※ 26 September 2023 — Revised ※ 12 October 2023 — Accepted ※ 05 December 2023 — Issued ※ 14 December 2023

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

FR2AO02

A Digital Twin for Neutron Instruments

1626

S. Nourbakhsh, Y. Le Goc, P. Mutti
ILL, Grenoble, France

Data from virtual experiments are becoming an extremely valuable asset for research infrastructures in a multitude of aspects and different actors: for instrument scientists to develop and optimise current and future instruments; for training external users in the usage of the instrument control system; for scientists in studying, quantifying and reducing instrumental effects on acquired data. Furthermore large sets of simulated data are also a necessary ingredient for the development of surrogate models for faster and more accurate simulation, reduction and analysis of the data. The development of a digital twin of an instrument can answer such different needs with a single unified approach wrapping in a user-friendly envelop the knowledge about the instrument physical description, the specific of the simulation packages and their interaction, and the high performing computing setup. In this article we will present the general architecture of the digital twin prototype developed at the ILL in the framework of the PANOSC European project in close collaboration with other research facilities (ESS and EuXFel). The communication patterns (based on ZQM) and interaction between the control system (NOMAD), simulation software (McStas), instrument description and configuration, process management (CAMEO) will be detailed. The adoption of FAIR principles for data formats and policies in combination with open-source software make it a sustainable project both for development and maintenance in the mid and long-term.

Slides FR2AO02 [1.245 MB]

DOI •

reference for this paper ※ doi:10.18429/JACoW-ICALEPCS2023-FR2AO02

About •

Received ※ 31 October 2023 — Revised ※ 02 November 2023 — Accepted ※ 05 December 2023 — Issued ※ 07 December 2023

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

FR2AO03

Machine Learning Based Virtual Beam Diagnostic Tool for Real-Time Operation at EuPRAXIA

S. Pioli, R. Pompili
LNF-INFN, Frascati, Italy
E. Chiadroni
Sapienza University of Rome, Rome, Italy
A. Cianchi
INFN-Roma II, Roma, Italy
G. Latini, B. Serenellini
INFN/LNF, Frascati, Italy
V. Martinelli
INFN/LNL, Legnaro (PD), Italy
A. Mostacci
SBAI, Roma, Italy

The EuPRAXIA@SPARC_LAB facility will equip the Frascati National Laboratories (LNF) of the Italian Institute for Nuclear Physics (INFN) with an infrastructure, in the ESFRI roadmap, capable of an unique combination of a high brightness GeV-range electron beam generated in a state-of-the-art LINAC boosted by a plasma acceleration module designed as top-class quality, user-oriented and at the forefront of new acceleration technologies. In these context we present design of our approach and first results with different Machine Learning (ML) techniques on the SPARC_LAB facility for the Virtual Beam Diagnostic tool under development. The aim of such tool will be the real-time virtualization of beam dynamics images as they would be seen by destructive beam diagnostics flag. This digital-twin based methodology will assist and provide images of the electron beam spot along the EuPRAXIA LINAC without affecting beam uptime to users and improving beam dynamics modeling and optimization of this challenging future facility.

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

FR2AO04

A Physics-Based Simulator to Facilitate Reinforcement Learning in the RHIC Accelerator Complex

1630

L.K. Nguyen, K.A. Brown, M.R. Costanzo, Y. Gao, M. Harvey, J.P. Jamilkowski, J. Morris, V. Schoefer
BNL, Upton, New York, USA

Funding: Work supported by Brookhaven Science Associates, LLC under Contract No. DE-SC0012704 with the U.S. Department of Energy.
The successful use of machine learning (ML) in particle accelerators has greatly expanded in recent years; however, the realities of operations often mean very limited machine availability for ML development, impeding its progress in many cases. This paper presents a framework for exploiting physics-based simulations, coupled with real machine data structure, to facilitate the investigation and implementation of reinforcement learning (RL) algorithms, using the longitudinal bunch-merge process in the Booster and Alternating Gradient Synchrotron (AGS) at Brookhaven National Laboratory (BNL) as examples. Here, an initial fake wall current monitor (WCM) signal is fed through a noisy physics-based model simulating the behavior of bunches in the accelerator under given RF parameters and external perturbations between WCM samples; the resulting output becomes the input for the RL algorithm and subsequent pass through the simulated ring, whose RF parameters have been modified by the RL algorithm. This process continues until an optimal policy for the RF bunch merge gymnastics has been learned for injecting bunches with the required intensity and emittance into the Relativistic Heavy Ion Collider (RHIC), according to the physics model. Robustness of the RL algorithm can be evaluated by introducing other drifts and noisy scenarios before the algorithm is deployed and final optimization occurs in the field.

Slides FR2AO04 [2.694 MB]

DOI •

reference for this paper ※ doi:10.18429/JACoW-ICALEPCS2023-FR2AO04

About •

Received ※ 04 October 2023 — Accepted ※ 05 December 2023 — Issued ※ 16 December 2023

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

FR2AO05

Python Library for Simulated Commissioning of Storage-Ring Accelerators

1637

L. Malina, I.V. Agapov, J. Keil, E.S.H. Musa, B. Veglia
DESY, Hamburg, Germany
N. Carmignani, L.R. Carver, L. Hoummi, S.M. Liuzzo, T.P. Perron, S.M. White
ESRF, Grenoble, France
T. Hellert
LBNL, Berkeley, California, USA

Simulations of the commissioning procedure became vital to the storage-ring lattice design process. The achievable tolerances on lattice imperfections, such as equipment misalignments or magnet gradient errors, would, without correction, prohibit reaching the design parameters. We present a Python library which includes an extensive set of error sources in the accelerator lattice and provides a variety of correction algorithms to commission a storage ring. The underlying beam dynamics simulations are performed with pyAT. This project builds upon previous works and expands them in the direction of realistic control room experience and software maintainability. The performance is demonstrated using example commissioning studies, and further development plans are discussed.

DOI •

reference for this paper ※ doi:10.18429/JACoW-ICALEPCS2023-FR2AO05

About •

Received ※ 06 October 2023 — Revised ※ 27 October 2023 — Accepted ※ 05 December 2023 — Issued ※ 19 December 2023

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)