arXiv subjects

Emre Yildirim

Publications and source records attributed to Emre Yildirim.

4 recordsLinked to original sources

arXiv2026

Multiscale Assessment of Tritium Behavior in Preliminary Fusion Pilot Plant Design Using Surrogate Models in TMAP8

The complexity and significance of multiscale phenomena in fusion energy systems make advanced modeling necessary for designing, optimizing, and safely deploying fusion plants. Tritium accountancy is one of those challenges for deuterium-tritium fusion systems. Its availability is constrained by its short half-life (12.33 years) and limited natural abundance, which require fusion plants to breed tritium onsite. Therefore, accurate tritium accountancy is essential for effective resource management, safety, and economics in fusion plants. Through the U.S. Department of Energy milestone program, Tokamak Energy Ltd. is developing a fusion pilot plant design and evaluating tritium retention and loss in key components and their effect on the fuel cycle. To rapidly explore design trade-offs and quantify design decisions on tritium management, this study presents a multiscale analysis to investigate tritium diffusion, trapping, and recovery in key plasma-facing components. To enhance computational efficiency, we integrate surrogate models at the component-level within a fuel cycle model at the system-level, enabling rapid evaluation of tritium recycling dynamics and inventory under various operational scenarios. The goal of this study is twofold: (1) demonstrate the feasibility of utilizing surrogate models to increase the accuracy of fuel cycle modeling, and (2) rapidly evaluate the performance of fusion technologies to accelerate design iterations. This multiscale model provides the tritium transport and retention behavior and supports the plasma-facing components design optimization in normal and bake-out operations. The work is implemented using the Tritium Migration Analysis Program, Version 8 (TMAP8), an open-source application for tritium transport analysis in fusion systems.

physics.comp-ph↗

arXiv2025

Orchestrated Couplings: A Time-Varying Edge Weight Framework for Efficient Event-Triggered Multiagent Networks

In this paper, we focus on reducing node-to-node information exchange in distributed control of multiagent networks while improving the overall network performance. Specifically, we consider a multiagent network that is composed of leader and follower nodes over a time-varying, connected, and undirected graph. In contrast to existing works on the event-triggered distributed control literature, we propose a time-varying edge weight event-triggered control framework. In this framework, each node dynamically adjusts its edge weights by increasing them during the transient (active) phase and decreasing them during the steady-state (idle) phase of the multiagent network. This not only reduces the number of events in the network but also improves the performance (i.e., convergence speed and control effort) of the overall multiagent network. System-theoretically, we first prove the closed-loop stability of the proposed event-triggered distributed control framework, where we then show that this framework does not exhibit a Zeno behavior. Finally, illustrative numerical examples are provided to demonstrate the efficacy of this framework.

eess.SY↗

arXiv2024

Regulating Stability Margins in Symbiotic Control: A Low-Pass Filter Approach

Symbiotic control synergistically integrates fixed-gain control and adaptive learning architectures to mitigate system uncertainties more predictably than adaptive learning alone and without requiring prior knowledge of uncertainty bounds as compared to fixed-gain control alone. Specifically, increasing the fixed-gain control parameter achieves a desired level of closed-loop system performance while the adaptive law simultaneously learns and suppresses the system uncertainties. However, stability margins can be reduced when this parameter is large and this paper aims to address this practical challenge. To this end, we propose a new fixed-gain control architecture predicated on a low-pass filter approach to regulate stability margins in the symbiotic control framework. In addition to the presented system-theoretical results focusing on the stability of the closed-loop system, we provide two illustrative numerical examples to demonstrate how the low-pass filter parameters are chosen for the stability margin regulation problem without significantly compromising the closed-loop system performance.

eess.SY↗

arXiv2024

Symbiotic Control of Uncertain Dynamical Systems: Harnessing Synergy Between Fixed-Gain Control and Adaptive Learning Architectures

Both fixed-gain control and adaptive learning architectures aim to mitigate the effects of uncertainties. In particular, fixed-gain control offers more predictable closed-loop system behavior but requires the knowledge of uncertainty bounds. In contrast, while adaptive learning does not necessarily require such knowledge, it often results in less predictable closed-loop system behavior compared to fixed-gain control. To this end, this paper presents a novel symbiotic control framework that offers the strengths of fixed-gain control and adaptive learning architectures. Specifically, this framework synergistically integrates these architectures to mitigate the effects of uncertainties in a more predictable manner as compared to adaptive learning alone and it does not require any knowledge on such uncertainties. Both parametric and nonparametric uncertainties are considered, where we utilize neural networks to approximate the unknown uncertainty basis for the latter case. Counterintuitively, the proposed framework has the ability to achieve a desired level of closed-loop system behavior even with an insufficient number of neurons (e.g., when the neural network approximation error is large) or in the face of injudiciously selected adaptive learning parameters (e.g., high leakage term parameters).

eess.SY↗