arXiv subjects

Matthijs van Berkel

Publications and source records attributed to Matthijs van Berkel.

5 recordsLinked to original sources

arXiv2026

Novel methodology for obtaining design structure matrices using network identification

Design structure matrices (DSMs) are used to comprehensively represent complex systems. They visualize and describe the dependencies between various variables, processes, states, and events. As such they are used in several system engineering approaches, such as requirement and interface management, fault detection, and supervisory control. Currently, a DSM is typically built from knowledge of experts. This may lead to an incomplete or imbalanced DSMs. For instance, elements and links might be missing or superfluous. In this article, we propose a novel method to acquire the DSM using state-of-the-art network identification methods. This demonstrates a proof-of-principle of identifying DSMs from data as an additional tool to the standard heuristic approach. In the future, we plan to embed DSMs in system design and supervisory controllers. We apply this technique to identify the DSM of a fusion reactor modelled by a five-chamber plasma model describing the transport in a tokamak.

eess.SY↗

arXiv2026

Dynamic analysis and control design for the gas distribution and storage system of the tritium fuel cycle in EU-DEMO

This work is concerned with the development of control strategies for the Direct Internal Recycling Loop (DIRL) system, which is an essential part of the Tritium Fuel Cycle (TFC) for the fueling of fusion reactors. As a first step, a control-oriented model is developed that describes dynamic behavior of DIRL with interactions between the torus reactor, buffer and fuel units, and recirculation streams. This model is used to evaluate the controllability, stability of the DIRL, and interactions between input and output variables. Moreover, the direct recycling of isotopes from the exhaust gases is discussed from a control perspective. It is observed that Gas Distribution and Storage (GDS) within the DIRL is associated with significant control challenges due to input-output interactions and competing process objectives. Three control strategies are developed and evaluated for the GDS of the European Demonstration fusion power plant (EU-DEMO): a Multiple Input Multiple Output (MIMO) control scheme, a redesigned GDS configuration with extended input variables enabling decentralised Single Input Single Output (SISO) control, and an extended-input MIMO control scheme addressing protium dilution. All strategies are assessed against three control objectives: maintaining GDS pressure around a prescribed set-point to ensure process safety; regulating the tritium-deuterium fuelling ratio for optimal reactor operation; and managing protium concentration to prevent fuelling dilution.

eess.SY↗

arXiv2025

Estimating Reaction Rate Constants from Impedance Spectra: Simulating the Multistep Oxygen Evolution Reaction

The efficiency of water electrolysis in a photoelectrochemical cell is largely limited by the oxygen evolution reaction (OER) at its semiconductor photoanode. Reaction rate constants are key to investigating the slow kinetics of the multistep OER, as they indicate the rate-determining step. While these rate constants are usually calculated based on first-principles simulations, this research aims to estimate them from experimental electrochemical impedance spectroscopy (EIS) data. Starting from a microkinetic model for charge transfer at the semiconductor-electrolyte interface, an expression for the impedance as a function of the rate constants is derived. At lower potentials, the order of this impedance model is reduced, thus eliminating the rate constants corresponding to the last reaction steps. Moreover, it is shown that EIS data from at least two potentials needs to be combined in order to uniquely identify the rate constants of a particular reduced order model. Therefore, this work details a sample maximum likelihood estimator that integrates not only multiple frequencies, but also multiple potentials simultaneously. Measuring multiple periods of the current density and potential signals, allows this frequency domain estimator to take measurement uncertainty into account. In addition, due to the large numerical range of the rate constants, various scaling methods are implemented to achieve numerical stability. To find suitable initial values for the highly nonlinear optimization problem, different global estimation methods are compared. The complete estimation procedure of the rate constants is illustrated on simulated EIS data of a hematite photoanode.

cond-mat.mtrl-sci↗

arXiv2025

MPC strategies for density profile control with pellet fueling in nuclear fusion tokamaks under uncertainty

Control of the density profile based on pellet fueling for the ITER nuclear fusion tokamak involves a multi-rate nonlinear system with safety-critical constraints, input delays, and discrete actuators with parametric uncertainty. To address this challenging problem, we propose a multi-stage MPC (msMPC) approach to handle uncertainty in the presence of mixed-integer inputs. While the scenario tree of msMPC accounts for uncertainty, it also adds complexity to an already computationally intensive mixed-integer MPC (MI-MPC) problem. To achieve real-time density profile controller with discrete pellets and uncertainty handling, we systematically reduce the problem complexity by (1) reducing the identified prediction model size through dynamic mode decomposition with control, (2) applying principal component analysis to reduce the number of scenarios needed to capture the parametric uncertainty in msMPC, and (3) utilizing the penalty term homotopy for MPC (PTH-MPC) algorithm to reduce the computational burden caused by the presence of mixed-integer inputs. We compare the performance and safety of the msMPC strategy against a nominal MI-MPC in plant simulations, demonstrating the first predictive density control strategy with uncertainty handling, viable for real-time pellet fueling in ITER.

eess.SY↗

arXiv2023

Mixed-Integer MPC Strategies for Fueling and Density Control in Fusion Tokamaks

Model predictive control (MPC) is promising for fueling and core density feedback control in nuclear fusion tokamaks, where the primary actuators, frozen hydrogen fuel pellets fired into the plasma, are discrete. Previous density feedback control approaches have only approximated pellet injection as a continuous input due to the complexity that it introduces. In this letter, we model plasma density and pellet injection as a hybrid system and propose two MPC strategies for density control: mixed-integer (MI) MPC using a conventional mixed-integer programming (MIP) solver and MPC utilizing our novel modification of the penalty term homotopy (PTH) algorithm. By relaxing the integer requirements, the PTH algorithm transforms the MIP problem into a series of continuous optimization problems, reducing computational complexity. Our novel modification to the PTH algorithm ensures that it can handle path constraints, making it viable for constrained hybrid MPC in general. Both strategies perform well with regards to reference tracking without violating path constraints and satisfy the computation time limit for real-time control of the pellet injection system. However, the computation time of the PTH-based MPC strategy consistently outpaces the conventional MI-MPC strategy.

eess.SY↗