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Cianciosa, Mark

Publications and source records attributed to Cianciosa, Mark.

13 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Observation of magnetic islands in tokamak plasmas during the suppression of edge-localized modes

In tokamaks, a leading platform for fusion energy, periodic filamentary plasma eruptions known as edge-localized modes occur in plasmas with high-energy confinement and steep pressure profiles at the plasma edge. These edge-localized modes could damage the tokamak wall but can be suppressed using small three-dimensional magnetic perturbations. Here we demonstrate that these magnetic perturbations can change the magnetic topology just inside the steep gradient region of the plasma edge. We identify signatures of a magnetic island, and their observation is linked to the suppression of edge-localized modes. We compare high-resolution measurements of perturbed magnetic surfaces with predictions from ideal magnetohydrodynamic theory where the magnetic topology is preserved. Although ideal magnetohydrodynamics adequately describes the measurements in plasmas exhibiting edge-localized modes, it proves insufficient for plasmas where these modes are suppressed. Nonlinear resistive magnetohydrodynamic modelling supports this observation. Our study experimentally confirms the predicted role of magnetic islands in inhibiting the occurrence of edge-localized modes. This will be beneficial for physics-based predictions in future fusion devices to control these modes.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

ASGarD: Adaptive Sparse Grid Discretization

Many areas of science exhibit physical processes that are described by high dimensional partial differential equations (PDEs), e.g., the 4D, 5D and 6D models describing magnetized fusion plasmas, models describing quantum chemistry, or derivatives pricing. Such problems are affected by the so-called “curse of dimensionality” where the number of degrees of freedom (or unknowns) required to be solved for scales as N D where N is the number of grid points in any given dimension D. A simple, albeit naive, 6D example is demonstrated in the left panel of Figure 1. With N = 1000 grid points in each dimension, the memory required just to store the solution vector, not to mention forming the matrix required to advance such a system in time, would exceed an exabyte - and also the available memory on the largest of supercomputers available today. The right panel of Figure 1 demonstrates potential savings for a range of problem dimensionalities and grid resolution. While there are methods to simulate such high-dimensional systems, they are mostly based on Monte-Carlo methods, which rely on a statistical sampling such that the resulting solutions include noise. Since the noise in such methods can only be reduced at a rate proportional to $\sqrt{N_p}$ where N p is the number of Monte-Carlo samples, there is a need for continuum, or grid/mesh-based methods for high-dimensional problems, which both do not suffer from noise and bypass the curse of dimensionality. We present a simulation framework that provides such a method using adaptive sparse grids.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Adaptive Generation of Training Data for ML Reduced Model Creation

Machine learning proxy models are often used to speed up or completely replace complex computational models. The greatly reduced and deterministic computational costs enable new use cases such as digital twin control systems and global optimization. The challenge of building these proxy models is generating the training data. A naive uniform sampling of the input space can result in a non-uniform sampling of the output space of a model. This can cause gaps in the training data coverage that can miss finer scale details resulting in poor accuracy. While larger and larger data sets could eventually fill in these gaps, the computational burden of full-scale simulation codes can make this prohibitive. In this paper, we present an adaptive data generation method that utilizes uncertainty estimation to identify regions where training data should be augmented. By targeting data generation to areas of need, representative data sets can be generated efficiently. The effectiveness of this method will be demonstrated on a simple one-dimensional function and a complex multidimensional physics model.

Cianciosa, Mark↗
DOE OSTI2022

Improving Predictions Under Uncertainty of Material Plasma Device Operations

Understanding the properties of materials when exposed to various plasma temperatures and fluxes is essential to the building and operating of fusion reactors. The Material Plasma Exposure eXperiment (MPEX) is an instrument currently being developed by the Department of Energy (DOE) for this purpose. MPEX is expected to come online in stages over the next five years. Proto-MPEX, the predecessor to MPEX, operated from 2014 to 2021, and was designed to understand the generation of plasma temperatures and fluxes at orders of magnitude below what will be obtained by MPEX. This work uses the recently developed stochastic neural network (SNN), a machine learning technique capable of operating under uncertainty to provide a surrogate model for the Proto-MPEX device. We demonstrate that SNN outperforms Bayesian neural network (BNN), a standard in the field of machine learning with uncertainty. The development of a robust surrogate of the Proto-MPEX will aid in the commissioning and operation of the MPEX device.

Archibald, Rick↗
DOE OSTI2021

Integrated model predictions on the impact of substrate damage on gas dynamics during ITER burning-plasma operations

Divertor design and choice of plasma-facing materials (PFM) will be essential to the success of next-generation fusion reactors as they operate under more powerful scenarios. Understanding and controlling interactions between the plasma and PFM is essential to making these choices. Within these plasma–material interactions and especially in tungsten (W), the interplay between the most abundant plasma species (hydrogen isotopes and helium, He) with the wall material alters fuel retention. However, this interplay is yet to be sufficiently understood to confidently project fuel retention levels to future fusion devices. The paper presents a series of integrated simulations of fusion plasmas and their interaction with tungsten. Specifically, this study assesses the impact of He plasma pre-exposure on hydrogenic species retention during 100 s of burning plasma operations (BPO) in ITER. Multiple pre-exposure scenarios are considered, including sub-surface damage resulting from exposures in the linear device PISCES and from early ITER He-operation. The predictions from these consecutive He-BPO exposures show that fuel content and spatial distribution in the material are largely determined by the He-induced damage, as manifest in: (i) changes in surface temperature expected during BPO have little effect on fuel retention in the presence of He-induced damage; (ii) gas content stabilizes quickly in substrates pre-exposed in PISCES, at levels set by the concentration of pre-existing vacancies, while it continues to increase in substrates initially pristine or pre-exposed to ITER He plasmas; (iii) the presence of He and He–V clusters in the near-surface region locally increases hydrogenic retention, but decreases its permeation; this results in hydrogenic species that remain closer to the surface in pre-damaged substrates, while the bulk content is higher for initially pristine cases. In summary, the interaction and binding of D and T with the pre-existing He–V clusters modifies retention and permeation of hydrogen species during ITER BPO.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Containers for Massive Ensemble of I/O Bound Hierarchical Coupled Simulations

We present our experience using containers to scale up a massive ensemble of coupled I/O bound workloads on the NERSC Cori supercomputer. We describe the design of a hierarchical simulation structure using the Integrated Plasma Simulator (IPS) that enables the flexible execution of coupled simulations at the system, node, and core level using the same coupling abstraction and API. The hierarchical design allows for the node-level execution to be efficiently executed using containers while not impacting the structure of the simulation at the system level. We demonstrate the viability of the approach by presenting experimental results from applications in coupled fusion plasma simulations that illustrate the performance impact of using containers to deploy the node-level workloads, in conjunction with the user mountable XFS file systems to ameliorate the load on the Lustre parallel file system. We also present results from production runs showing the ability of the ensemble simulations to scale to hundreds of Cori Haswell nodes, with little or no overhead.

Elwasif, Wael↗
DOE OSTI2020

Edge ICRF simulations in 3D geometry: From MHD equilibrium to coupling determination

We present in this work a consistent numerical scheme that allows the computation of 3D magnetic fields a nd 3D density profiles and their usage in ion cyclotron range of frequencies (ICRF) coupling simulations. We first utilize the PARVMEC code to compute the 3D free-boundary plasma equilibrium in the ideal magnetohydrodynamic (MHD) approximation. Since the PARVMEC solution is only defined within the last closed flux surface (LCFS), the magnetic field domain is extended to the scrape-off layer (SOL) via the BMW code, which computes a divergence-free magnetic field solution arising from the external conductors’ vacuum field a nd t he PARVMEC flux surface currents. This magnetic reconstruction is then used in the EMC3-EIRENE transport code in order to compute 3D density profiles. In the last step, the RAPLICASOL code is utilized to compute the ICRF antenna S-matrices resulting from the 3D density profiles. We exemplify this scheme for the ASDEX Upgrade tokamak. A new implementation of a curved model for the ASDEX Upgrade ICRF 2-strap antenna in RAPLICASOL allows simulations in realistic geometry, without any coordinate transformations.

Lopez, Graham↗
DOE OSTI2020

FY2018 Fusion Theory and Simulation Milestone on Plasma Materials Interaction (PMI) (Final Report)

The interaction of the boundary plasma with the material surfaces in magnetically confined plasmas is among the most critical problems in fusion energy science. In FY2018, perform high performance computing simulations with coupled boundary plasma physics and materials surface models to predict the fuel recycling and tritium retention of the ITER divertor for D-T burning plasma conditions, accounting for erosion, re-deposition and impurity transport in the plasma boundary, and an initial evaluation of the influence of material deposition on the recycling and retention.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

Nested Workflows for Loosely Coupled HPC Simulations

The increasing complexity of modern scientific simulations has given rise to the notion of re-usability as a means to reduce development time and effort. One form of re-usability involves hierarchical modelling, where the code and artifacts that model a physical phenomenon are used as the building blocks for more complex coupled physical systems. This reusability mode allows for improvements in constituents sub-models to impact the overall fidelity of the entire system with minimal effort. This benefit, however, hinges upon the ability to provide a fairly loose coupling across model boundaries, where changes in any sub-model do not result in wholesale changes to other sub-models, or in the structure and details of the code for the entire physical system. In this paper, we present the design, implementation, and case studies for incorporating sub-workflows as building blocks in a framework for loosely coupled high performance simulations. We outline the issues involved in providing lightweight customization points for full workflows that allows their use unchanged in a nested-workflow setting, while providing each sub-workflow with a separate execution context that ensures non-interference with other parts of the simulation. We present several use cases that demonstrate the successful use of the proposed design and its flexibility in accommodating different customization and scaling requirements.

Elwasif, Wael↗
DOE OSTI2019

Machine learning for analysis of atomic spectral data

Physics based forward models are the basis on which many experimental diagnostics are interpreted. For some diagnostics, models can be computationally expensive which precludes their use in real time analysis. Reduced models have the potential to capture sufficient physics thereby enabling the desired real time analysis. Using statistical inference and machine learning techniques the application of reduced models for inversion of atomic spectral data used to diagnose magnetic fields in a plasma will be examined. Two approaches are considered, (a) a reduction of the forward model where traditional inversion can be performed on the proxy model, and (b) a reduction of the direct inverse where parameters are a function of measured signal. The resulting inversion is sufficiently fast to be utilized in an online context for digital twinning, and ultimately real-time prediction, design, and control of plasma systems, such as tokamaks. Furthermore, these methods will be demonstrated on both simulated and experimentally measured data.

74 ATOMIC AND MOLECULAR PHYSICS↗