arXiv subjects

D. M. Orlov

Publications and source records attributed to D. M. Orlov.

7 recordsLinked to original sources

arXiv2026

Effect of Controlled Magnetic Island Bifurcation on Electron Diffusion

Magnetic islands strongly influence cross-field electron transport in magnetized plasmas. In particular, bifurcations of the island topology modify the number and location of O-points, X-points, and separatrix boundaries, thereby altering diffusion pathways. In recent DIII-D experiments, external magnetic perturbations were used to rotate and periodically bifurcate the island on the q = 2 surface, causing a switchback between a q = 2/1-dominated structure and a narrower q = 4/2-dominated structure. To investigate how this topological change affects electron transport, we employ the field line tracing code TRIP3D with an implemented collisional operator. Thermal, tracer electrons launched from O-points, X-points, and outside separatrix boundaries reveal distinct diffusion regimes, including classical, subdiffusive, and superdiffusive behavior, depending on both the dominant island mode and launch location. These results suggest that island bifurcation can alter electron diffusion across rational surfaces, with direct implications for particle confinement. While the present work emphasizes diffusion as a general framework, the findings provide insight into the conditions under which electron trapping into an island or stochastization of the island's separatrix can enable additional mechanisms, such as the generation of energetic electrons.

physics.plasm-ph↗

arXiv2025

Reconstruction-free magnetic control of DIII-D plasma with deep reinforcement learning

Precise control of plasma shape and position is essential for stable tokamak operation and achieving commercial fusion energy. Traditional control methods rely on equilibrium reconstruction and linearized models, limiting adaptability and real-time performance. Here,the first application of deep reinforcement learning (RL) for magnetic plasma control on the mid-size DIII-D tokamak is presented, demonstrating a nonlinear approach that improves robustness and flexibility across plasma scenarios. Using the Soft Actor-Critic algorithm, this method eliminates the need for equilibrium reconstruction, enabling high-speed control execution and scalability on larger fusion devices. NSFsim, a 2D Grad-Shafranov equilibration solver with a circuit equation and a 1D transport solver, is used to train the agent. Its capability of reproducing the kinetic parameter evolution alongside magnetic equilibria evolution appears to be an essential factor significantly affecting control quality. RL-based controllers demonstrated robust magnetic control in experimental application at DIII-D, preserving control performance in transient events during plasma discharges, and reaching target parameters from the first discharge without additional tuning or modifications. The approach itself has significant generalization potential across devices and targets. This work represents a step toward AI-driven, real-time plasma control, advancing the feasibility of next-generation fusion reactors.

physics.plasm-ph↗

arXiv2025

Reconstructing the Plasma Boundary with a Reduced Set of Diagnostics

This study investigates the feasibility of reconstructing the last closed flux surface (LCFS) in the DIII-D tokamak using neural network models trained on reduced input feature sets, addressing an ill-posed task. Two models are compared: one trained solely on coil currents and another incorporating coil currents, plasma current, and loop voltage. The model trained exclusively on coil currents achieved a mean point displacement of 0.04 m on a held-out test set, while the inclusion of plasma current and loop voltage reduced the error to 0.03 m. This comparison highlights the trade-offs between input feature complexity and reconstruction accuracy, demonstrating the potential of machine learning algorithms to perform effectively in data-limited environments, such as those expected in Fusion Power Plants (FPP) due to diagnostic constraints imposed by the presence of blankets and shielding.

physics.plasm-ph↗

arXiv2024

Meteoroid Ablation within the Jovian Atmosphere: Implications on the Oxygen Delivery to the Gas Giant's Atmosphere

The detection of water molecules within the atmosphere of Jupiter, first by the Galileo Atmospheric Probe, and later by the Juno spacecraft, has given rise to the question of whether those molecules are sourced endogenously or exogenously. One hypothesis is that the ablation of meteoroids deposited the necessary oxygen into the atmosphere, which was subsequently used in chemical processes to form water. This paper aims to evaluate this hypothesis by simulating the ablation of carbonaceous objects entering the planet's atmosphere to determine the possible rates of oxygen delivery and the most likely altitude for such delivery within the Jovian atmosphere. We estimate that carbonaceous meteoroids have the potential to deliver $\sim 1.4 \times 10^7 \, \text{kg/m}^2$ of oxygen over a billion years. We further estimate that most of the ablation is expected to occur in the stratosphere, or 450-400 km above the region of 1 bar of atmospheric pressure. In comparison, Interplanetary Dust Particles (IDPs) are estimated to deliver roughly $\sim 10^2 \, \text{kg/m}^2$ of oxygen over the same period.

astro-ph.EP↗

arXiv2022

Core Box Image Recognition and its Improvement with a New Augmentation Technique

Most methods for automated full-bore rock core image analysis (description, colour, properties distribution, etc.) are based on separate core column analyses. The core is usually imaged in a box because of the significant amount of time taken to get an image for each core column. The work presents an innovative method and algorithm for core columns extraction from core boxes. The conditions for core boxes imaging may differ tremendously. Such differences are disastrous for machine learning algorithms which need a large dataset describing all possible data variations. Still, such images have some standard features - a box and core. Thus, we can emulate different environments with a unique augmentation described in this work. It is called template-like augmentation (TLA). The method is described and tested on various environments, and results are compared on an algorithm trained on both 'traditional' data and a mix of traditional and TLA data. The algorithm trained with TLA data provides better metrics and can detect core on most new images, unlike the algorithm trained on data without TLA. The algorithm for core column extraction implemented in an automated core description system speeds up the core box processing by a factor of 20.

cs.CV↗

arXiv2020

Wide operational windows of edge-localized mode suppression by resonant magnetic perturbations in the DIII-D tokamak

Edge-Localized-Mode (ELM) suppression by resonant magnetic perturbations (RMPs) generally occurs over very narrow ranges of the plasma current (or magnetic safety factor q95) in the DIII-D tokamak. However, wide q95 ranges of ELM suppression are needed for the safety and operational flexibility of ITER and future reactors. In DIII-D ITER Similar Shape (ISS) plasmas with n=3 RMPs, the range of q95 for ELM suppression is found to increase with decreasing electron density. Nonlinear two-fluid MHD simulations reproduce the observed q95 windows of ELM suppression and the dependence on plasma density, based on the conditions for resonant field penetration at the top of the pedestal. When the RMP amplitude is close to the threshold for resonant field penetration, only narrow isolated magnetic islands form near the top of the pedestal, leading to narrow q95 windows of ELM suppression. However, as the threshold for field penetration decreases with decreasing density, resonant field penetration can take place over a wider range of q95. For sufficiently low density (penetration threshold) multiple magnetic islands form near the top of the pedestal giving rise to continuous q95 windows of ELM suppression. The model predicts that wide q95 windows of ELM suppression can be achieved at substantially higher pedestal pressure in DIII-D by shifting to higher toroidal mode number (n=4) RMPs.

physics.plasm-ph↗

arXiv2019

Deep Convolutions for In-Depth Automated Rock Typing

The description of rocks is one of the most time-consuming tasks in the everyday work of a geologist, especially when very accurate description is required. We here present a method that reduces the time needed for accurate description of rocks, enabling the geologist to work more efficiently. We describe the application of methods based on color distribution analysis and feature extraction. Then we focus on a new approach, used by us, which is based on convolutional neural networks. We used several well-known neural network architectures (AlexNet, VGG, GoogLeNet, ResNet) and made a comparison of their performance. The precision of the algorithms is up to 95% on the validation set with GoogLeNet architecture. The best of the proposed algorithms can describe 50 m of full-size core in one minute.

cs.CV↗