arXiv subjects

Stylianos Chatzidakis

Publications and source records attributed to Stylianos Chatzidakis.

At least 19 recordsLinked to original sources

arXiv2026

The PUR-1 Cyber-Physical Digital Twin

Digital twin technologies have the potential to improve operational flexibility and responsiveness capabilities of nuclear systems. To provide decision support, cyber event characterization, state estimation, predictive control, and real-time dynamic processing of operational data, however, an efficient digital twin needs to integrate multiple models (data-driven as well as physics-based) with explainability while at the same time maintain two-way synchronization with the physical facility at a time constant less than its operational cycle. In this work, we present the Purdue University Reactor One Digital Twin (PUR-1 DT), a cyber-physical digital twin with a complete high-fidelity physics-based and AI-driven virtual model stack (neutronics, thermal-hydraulics, point kinetics) which provides closed-loop explainable diagnostics, forecasting, predictive control, and action recommendation back to the reactor via two-way communications and a cyber-physical testbed. We demonstrate real-time synchronized state estimation and short-term forecasting over a full reactor operational cycle and conduct a series of benchmarking experiments to validate accuracy and latency. Our results show good agreement with experimental results and lay the groundwork for further development and experimental demonstration of DT-enabled functionalities in real-world facilities.

cs.CE↗

arXiv2026

Non-intrusive Monitoring of Sealed Microreactor Cores Using Physics-Informed Muon Scattering Tomography With Momentum Measurements

Next-generation microreactors enable remote deployment and semi-autonomous operation, but compact, sealed, heterogeneous cores limit conventional safeguard approaches that rely on access and bulk accountancy. Limited inspection access and complex internal geometry reduce sensitivity to localized anomalies such as missing fuel. Here we demonstrate missing-fuel detection in microreactor scale geometries using muon scattering tomography under realistic cosmic-ray conditions. We introduce $μ$TRec, a physics-informed framework that reconstructs event-level curved muon trajectories by combining a Gaussian multiple Coulomb scattering model with Bayesian updating, then maps scattering density through voxel wise M-values for core integrity verification. We evaluate a representative hexagonal core containing 61 fuel flakes with embedded control drums and shutdown rods, using both idealized 5 GeV muons and zenith-angle-dependent 0-60 GeV cosmic-ray spectra. A single missing fuel flake is detected with $3\times 10^{6}$ muons at 50 mm voxel resolution. Incorporating per-muon momentum further increases detectability by up to 149.85% for laser-driven sources and 105.11% for cosmic-ray sources relative to momentum-agnostic reconstruction. The approach remains robust under practical detector limits, with only an 8.88% reduction in detectability for 10 mm spatial resolution and 10% energy resolution. Compared with PoCA, $μ$TRec delivers 326.13% to 392.14% higher detectability at equal muon counts, enabling faster defect identification.

physics.app-ph↗

arXiv2025

An Unsupervised Deep Explainable AI Framework for Localization of Concurrent Replay Attacks in Nuclear Reactor Signals

Next generation advanced nuclear reactors are expected to be smaller both in size and power output, relying extensively on fully digital instrumentation and control systems. These reactors will generate a large flow of information in the form of multivariate time series data, conveying simultaneously various non linear cyber physical, process, control, sensor, and operational states. Ensuring data integrity against deception attacks is becoming increasingly important for networked communication and a requirement for safe and reliable operation. Current efforts to address replay attacks, almost universally focus on watermarking or supervised anomaly detection approaches without further identifying and characterizing the root cause of the anomaly. In addition, these approaches rely mostly on synthetic data with uncorrelated Gaussian process and measurement noise and full state feedback or are limited to univariate signals, signal stationarity, linear quadratic regulators, or other linear-time invariant state-space which may fail to capture any unmodeled system dynamics. In the realm of regulated nuclear cyber-physical systems, additional work is needed on characterization of replay attacks and explainability of predictions using real data. Here, we propose an unsupervised explainable AI framework based on a combination of autoencoder and customized windowSHAP algorithm to fully characterize real-time replay attacks, i.e., detection, source identification, timing and type, of increasing complexity during a dynamic time evolving reactor process. The proposed XAI framework was benchmarked on several real world datasets from Purdue's nuclear reactor PUR-1 with up to six signals concurrently being replayed. In all cases, the XAI framework was able to detect and identify the source and number of signals being replayed and the duration of the falsification with 95 percent or better accuracy.

cs.LG↗

arXiv2025

Explainable Unsupervised Multi-Anomaly Detection and Temporal Localization in Nuclear Times Series Data with a Dual Attention-Based Autoencoder

The nuclear industry is advancing toward more new reactor designs, with next-generation reactors expected to be smaller in scale and power output. These systems have the potential to produce large volumes of information in the form of multivariate time-series data, which could be used for enhanced real-time monitoring and control. In this context, the development of remote autonomous or semi-autonomous control systems for reactor operation has gained significant interest. A critical first step toward such systems is an accurate diagnostics module capable of detecting and localizing anomalies within the reactor system. Recent studies have proposed various ML and DL approaches for anomaly detection in the nuclear domain. Despite promising results, key challenges remain, including limited to no explainability, lack of access to real-world data, and scarcity of abnormal events, which impedes benchmarking and characterization. Most existing studies treat these methods as black boxes, while recent work highlights the need for greater interpretability of ML/DL outputs in safety-critical domains. Here, we propose an unsupervised methodology based on an LSTM autoencoder with a dual attention mechanism for characterization of abnormal events in a real-world reactor radiation area monitoring system. The framework includes not only detection but also localization of the event and was evaluated using real-world datasets of increasing complexity from the PUR-1 research reactor. The attention mechanisms operate in both the feature and temporal dimensions, where the feature attention assigns weights to radiation sensors exhibiting abnormal patterns, while time attention highlights the specific timesteps where irregularities occur, thus enabling localization. By combining the results, the framework can identify both the affected sensors and the duration of each anomaly within a single unified network.

cs.LG↗

arXiv2025

Experimental Assessment of a Multi-Class AI/ML Architecture for Real-Time Characterization of Cyber Events in a Live Research Reactor

There is increased interest in applying Artificial Intelligence and Machine Learning (AI/ML) within the nuclear industry and nuclear engineering community. Effective implementation of AI/ML could offer benefits to the nuclear domain, including enhanced identification of anomalies, anticipation of system failures, and operational schedule optimization. However, limited work has been done to investigate the feasibility and applicability of AI/ML tools in a functioning nuclear reactor. Here, we go beyond the development of a single model and introduce a multi-layered AI/ML architecture that integrates both information technology and operational technology data streams to identify, characterize, and differentiate (i) among diverse cybersecurity events and (ii) between cyber events and other operational anomalies. Leveraging Purdue Universitys research reactor, PUR-1, we demonstrate this architecture through a representative use case that includes multiple concurrent false data injections and denial-of-service attacks of increasing complexity under realistic reactor conditions. The use case includes 14 system states (1 normal, 13 abnormal) and over 13.8 million multi-variate operational and information technology data points. The study demonstrated the capability of AI/ML to distinguish between normal, abnormal, and cybersecurity-related events, even under challenging conditions such as denial-of-service attacks. Combining operational and information technology data improved classification accuracy but posed challenges related to synchronization and collection during certain cyber events. While results indicate significant promise for AI/ML in nuclear cybersecurity, the findings also highlight the need for further refinement in handling complex event differentiation and multi-class architectures.

cs.LG↗

arXiv2025

A One-Class Explainable AI Framework for Identification of Non-Stationary Concurrent False Data Injections in Nuclear Reactor Signals

The transition of next generation advanced nuclear reactor systems from analog to fully digital instrumentation and control will necessitate robust mechanisms to safeguard against potential data integrity threats. One challenge is the real-time characterization of false data injections, which can mask sensor signals and potentially disrupt reactor control systems. While significant progress has been made in anomaly detection within reactor systems, potential false data injections have been shown to bypass conventional linear time-invariant state estimators and failure detectors based on statistical thresholds. The dynamic, nonlinear, multi-variate nature of sensor signals, combined with inherent noise and limited availability of real-world training data, makes the characterization of such threats and more importantly their differentiation from anticipated process anomalies particularly challenging. In this paper, we present an eXplainable AI (XAI) framework for identifying non-stationary concurrent replay attacks in nuclear reactor signals with minimal training data. The proposed framework leverages progress on recurrent neural networks and residual analysis coupled with a modified SHAP algorithm and rule-based correlations. The recurrent neural networks are trained only on normal operational data while for residual analysis we introduce an adaptive windowing technique to improve detection accuracy. We successfully benchmarked this framework on a real-world dataset from Purdue's nuclear reactor (PUR-1). We were able to detect false data injections with accuracy higher than 0.93 and less than 0.01 false positives, differentiate from expected process anomalies, and to identify the origin of the falsified signals.

eess.SY↗

arXiv2025

Demonstration of Quantum-Secure Communications in a Nuclear Reactor

Quantum key distribution (QKD), one of the latest cryptographic techniques, founded on the laws of quantum mechanics rather than mathematical complexity, promises for the first time unconditional secure remote communications. Integrating this technology into the next generation nuclear systems - designed for universal data collection and real-time sharing as well as cutting-edge instrumentation and increased dependency on digital technologies - could provide significant benefits enabling secure, unattended, and autonomous operation in remote areas, e.g., microreactors and fission batteries. However, any practical implementation on a critical reactor system must meet strict requirements on latency, control system compatibility, stability, and performance under operational transients. Here, we report the complete end-to-end demonstration of a phase-encoding decoy-state BB84 protocol QKD system under prototypic conditions on Purdue's fully digital nuclear reactor, PUR-1. The system was installed in PUR-1 successfully executing real-time encryption and decryption of 2,000 signals over optic fiber distances up to 82 km using OTP-based encryption and up to 140 km with AES-based encryption. For a core of 68 signals, OTP-secure communication was achieved for up to 135 km. The QKD system maintained a stable secret key rate of 320 kbps and a quantum bit error of 3.8% at 54 km. Our results demonstrate that OTP-based encryption introduces minimal latency while the more key-efficient AES and ASCON encryption schemes can significantly increase the number of signals encrypted without latency penalties. Additionally, implementation of a dynamic key pool ensures several hours of secure key availability during potential system downtimes. This work shows the potential of quantum-based secure remote communications for future digitally driven nuclear reactor technologies.

quant-ph↗

arXiv2025

$μ$TRec: A Muon Trajectory Reconstruction Algorithm for Enhanced Scattering Tomography

Cosmic ray muons enable non-invasive imaging of dense structures through multipleCoulomb scattering (MCS), with scattering angles dependent on atomic number (Z). Traditional algorithms like Point of Closest Approach (PoCA) assume single scattering, limiting accuracy. This work presents the $μ$TRec algorithm, which models muon paths using a Bayesian approach with Gaussian approximations, accounting for MCS and energy loss. $μ$TRec is applied to simulate muon imaging of dry storage casks (DSCs) used for spent nuclear fuel, with four loading configurations: fully loaded, one row missing, one assembly missing, and half assembly missing. The results demonstrate improved accuracy and resolution in identifying missing assemblies compared to conventional methods. It is observed that the $μ$TRec algorithm exhibits markedly superior performance over the classical PoCA method achieving respective improvements of 122% in signal-to-noise ratio (SNR), 35% in contrast-to-noise ratio (CNR), and 201% in detection power (DP) for the case of one missing fuel assembly with a muon flux of $10^6$ and a voxel size of 5 cm. Furthermore, μTRec supports high-resolution reconstruction with voxel sizes as small as 1 cm which enables the accurate localization of structural components such as the 2.5 cm thick steel canister. Notably, $μ$TRec is also capable of reliably detecting a single missing fuel assembly at a muon flux as low as $10^5$, a task that remains infeasible using PoCA under the same conditions.

physics.app-ph↗

arXiv2025

Methodology For the Evaluation of Critical Components of the Scatterable Radiation Monitor Under Radiation Fields

One of the persisting challenges associated with the peaceful use of the atom is monitoring the subsequent radiation. For example, nuclear power plants need environmental monitoring to ensure public trust. While this may be conducted by the frequent monitoring of low-cost thermoluminescent dosimeters placed in concentric rings around the plant, there have been several solutions for a modernized real-time digital alternative to the TLD. The Scatterable Radiation Monitoring device is an STM32 microprocessor-based design aiming at the wireless transmission of environmental radiation data.

physics.ins-det↗

arXiv2024

Open-Source Optimization of Hybrid Monte-Carlo Methods for Fast Response Modeling of NaI(Tl) and HPGe Gamma Detectors

Modeling the response of gamma detectors has long been a challenge within the nuclear community. Significant research has been conducted to digitally replicate instruments that can cost over $100,000 and are difficult to operate outside a laboratory setting. Subsequently, there have been multiple attempts to create codes that replicate the response of sodium-iodide and high purity germanium detectors for the purpose of deriving data related to gamma ray interaction with matter. While robust programs do exist, they are often subject to export controls and/or they are not intuitive to use. Through the use of the Hybrid Monte-Carlo methods, MATLAB can be used to produce a fast first-order response of various gamma ray detectors. The combination of a graphics user interface with a numerical based script allows for an open-source and intuitive code. When benchmarked with experimental data from Co-60, Cs-137, and Na-22 the code can numerically calculate a response comparable to experimental and industry standard response codes. Through this code, it is shown that a savings in computational requirements and the inclusion of an intuitive user experience does not heavily compromise data when compared to other standard codes or experimental results. When the application is installed on a computer with 16 cores, the average time to simulate the benchmarked isotopes is 0.26 seconds and 1.63 seconds on a four-core machine. The results indicate that simple gamma detectors can be modeled in an open-source format. The anticipation for the MATLAB application is to be a tool that can be easily accessible and provide datasets for use in an academic setting requiring the gamma ray detectors. Ultimately, providing evidence that Hybrid Monte-Carlo codes in an open-source format can benefit the nuclear community.

physics.ins-det↗

arXiv2023

NuQKD: A Modular Quantum Key Distribution Simulation Framework for Engineering Applications

An experimental Quantum Key Distribution (QKD) implementation requires advanced costly hardware, unavailable in most research environments, making protocol testing and performance evaluation complicated. Historically, this has been a major motivation for the development of QKD simulation frameworks, to allow researchers to obtain insight before proceeding into practical implementations. Several simulators have been introduced over the recent years. However, only four are publicly available, only one of which models equipment imperfections. Currently, no open-source simulator includes all following capabilities: channel attenuation modelling, equipment imperfections and effect on key rates, estimation of elapsed time during quantum channel processes, use of truly random binary sequences for qubits and measurement bases, shared-bit fraction customization. In this paper, we present NuQKD, an open-source modular, intuitive simulator, featuring all the above capabilities. NuQKD establishes communication between two computer terminals, accepts custom inputs (iterations, raw key size, interception rate etc.) and evaluates the sifted key length, Quantum Bit Error Rate (QBER), elapsed communication time and more). NuQKD capabilities include optical fiber and free-space simulation, modeling of equipment/channel imperfections, bitstrings from True Random Number Generator, modular design and automated evaluation of performance metrics. We expect NuQKD to enable convenient and accurate representation of actual experimental conditions.

quant-ph↗

arXiv2023

Using Cosmic Ray Muons to Assess Geological Characteristics in the Subsurface

Cosmic rays are energetic nuclei and elementary particles that originate from stars and intergalactic events. The interaction of these particles with the upper atmosphere produces a range of secondary particles that reach the surface of the earth, of which muons are the most prominent. With enough energy, muons can travel up to a few kilometers beneath the surface of the earth before being stopped completely. The terrestrial muon flux profile and associated zenith angle can be utilized to determine geological characteristics of a location without having to use conventional methods. This work intends to use a low-power plastic scintillator-based muon detection system for this non-destructive geological assay methodology. 4 custom designed plastic scintillation panels are used to realize two orthogonal detection planes. Simultaneous triggers between detectors from two planes indicate a coincidence event which is recorded using a data acquisition system from FNAL. In order to quantify the systematic uncertainties associated with the detector, such as energy depositions and angular resolution of the detector design, a Monte Carlo simulation using Geant4 is being developed. Simulated and experimental data will drive the development and validation of a reconstruction algorithm that, upon completion, is expected to predict average overburden and rock density. Extended detector exposure to muons can be used as a means to understand changes in the surrounding environment like rock porosity. On the experimental front, the measured flux data will be used to benchmark independent and established models. Successful proof-of-concept demonstration of this technology can open doors for long term non-invasive geological monitoring. The detector design, and experimental methodology are detailed in this work.

physics.ins-det↗

arXiv2023

A New Momentum-Integrated Muon Tomography Imaging Algorithm

For decades, the application of muon tomography to spent nuclear fuel (SNF) cask imaging has been theoretically evaluated and experimentally verified by many research groups around the world, including Los Alamos National Laboratory in the United States, Canadian Nuclear Laboratory in Canada, the National Institute for Nuclear Physics in Italy, and Toshiba in Japan. Although monitoring of SNF using cosmic ray muons has attracted significant attention as a promising nontraditional nondestructive radiographic technique, the wide application of muon tomography is often limited because of the natural low cosmic ray muon flux at sea level: 100 m-2min-1sr-1. Recent studies suggest measuring muon momentum in muon scattering tomography (MST) applications to address this challenge. Some techniques have been discussed; however, an imaging algorithm for momentum-coupled MST had not been developed. This paper presents a new imaging algorithm for MST which integrates muon scattering angle and momentum in a single M-value. To develop a relationship between muon momentum and scattering angle distribution, various material samples (Al, Fe, Pb, and U) were thoroughly investigated using a Monte Carlo particle transport code GEANT4 simulation. Reconstructed images of an SNF cask using the new algorithm are presented herein to demonstrate the benefit of measuring muon momentum in MST. In this analysis a missing fuel assembly (FA) was located in the dry storage cask.

physics.ins-det↗

arXiv2022

Monitoring Spent Nuclear Fuel in a Dry Cask Using Momentum Integrated Muon Scattering Tomography

Nuclear materials accountability and nonproliferation are among the critical tasks to be addressed for the advancement of nuclear energy in the United States. Monitoring spent nuclear fuel is important to continue reliable stewardship of SNF storage. Cosmic ray muons have been acknowledged a promising radiographic tool for monitoring SNF due to their highly penetrative nature and high energy. Cosmic ray muons are more suitable and have been used for imaging large and dense objects. Despite their potential in various applications, the wide application of cosmic ray muons is limited by the naturally low intensity at sea level. To efficiently utilize cosmic ray muons in engineering applications, trajectory and momentum must be measured. Although various studies demonstrate that there is significant potential for measuring momentum in muon applications, it is still difficult to measure both muon scattering angle and momentum in the field. To fill this critical gap, a muon spectrometer using multilayer pressurized gas Cherenkov radiators was proposed. However, existing muon tomographic algorithms were developed assuming monoenergetic muon scattering and are not optimized for a measured polyenergetic momentum spectrum. In this work, we develop and evaluate a momentum integrated muon scattering tomography algorithm. We evaluate the algorithm on its capability to identify a missing fuel assembly from a SNF dry cask. Our results demonstrate that image resolution using MMST is significantly improved when measuring muon momentum and it can reduce monitoring time by a factor of 10 when compared to that of a conventional muon imaging technique in terms of systematically finding a missing FA.

physics.ins-det↗

arXiv2022

Development of Compact Muon Spectrometer Using Multiple Pressurized Gas Cherenkov Radiators

In both particle physics and muon applications, a high-resolution muon momentum measurement capability plays a significant role not only in providing valuable information on the properties of subatomic particles but also in improving the utilizability of cosmic ray muons. Typically, muon momentum is measured by reconstructing a curved muon path using a strong magnetic field and muon trackers. Alternatively, a time-of-flight and Cherenkov ring imager are less frequently applied, especially when there is a need to avoid a magnetic field. However, measurement resolution is much less than that of magnetic spectrometers, approximately 20% whereas it is nearly 4% or less when using magnets and trackers. Here, we propose a different paradigm to estimate muon momentum that utilizes multiple pressurized gas Cherenkov radiators. Using the fact that the refractive index of gas medium varies depending on its pressure and temperature, we can optimize the muon Cherenkov threshold momentum levels for which a muon signal will be detected. In this work, we demonstrate that muon momentum can be estimated with mean resolution of {sigma_p}/p < 20% and mean classification rate of 90.08% in the momentum range of 0.1 to 10.0 GeV/c by analyzing optical photon signals from each Cherenkov radiator. We anticipate our new spectrometer will significantly improve quality of imaging and reduce scanning time in cosmic ray muon applications by being incorporated with existing instruments.

physics.ins-det↗

arXiv2022

Non-linear Cherenkov Muon Spectrometer Using Multi-Layer Pressurized C3F8 Gas Radiators

A new Cherenkov muon spectrometer using diversely sized C3F8 gas radiators and nonlinear threshold muon momentum levels was studied using Geant4 simulations. Despite its compact size (<1m3), the performance of the spectrometer was significantly improved over the earlier design (linear and uniform) in two aspects, (i) a stable momentum measurement resolution and (ii) a balanced expected Cherenkov photon yields in all radiators. In addition, CO2 gas radiators are replaced by heavier gas radiators, C3F8, and the sizes of radiators for high momentum levels (low gas pressure) are enlarged to increase the Cherenkov photon yields. Specifically, the absolute momentum resolution, σp, for all levels is replaced by the relative momentum resolution, σp/p. To demonstrate the functionality of our proposed Cherenkov muon spectrometer, we presented the results of classification rates as a function of muon momentum with various discriminator levels and reconstructed cosmic ray muon spectrum. When the combination of two or more discriminators are used, the average classification rate is 90.08%. In addition, our proposed Cherenkov muon spectrometer successfully reconstructed the cosmic ray muon spectrum and the results show a good agreement with the actual spectrum within 1σ in most momentum levels.

physics.ins-det↗

arXiv2022

The Effect of Cosmic Ray Muon Momentum Measurement for Monitoring Shielded Special Nuclear Materials

Recently, cosmic ray muons have been considered as a potential high energy radiation probe for monitoring and interrogation of dense, well-shielded special nuclear materials (SNM). Due to their high-penetrative nature, cosmic ray muons can easily penetrate shielded nuclear materials with minimal absorption and with leaving the target objects intact. However, despite the potential benefits from using cosmic ray muons for SNM monitoring, their widespread application has been limited for various reasons, including relatively low cosmic ray muon flux at sea level and the difficulty of measuring muon momentum in the field which can increase resolution and reduce measurement time. In this work, we explore in detail the effect of cosmic ray muon momentum measurement, focusing specifically on SNM monitoring applications. Three different types of SNMs (HEU, LEU, and Pu) surrounded by lead shielding with five different thicknesses (0, 5, 10, 20, and 30 cm), are analyzed using Monte Carlo simulation for three momentum measurement resolution levels (perfect, limited, and absent). 1000 muons were generated in the simulation which translates to 4 minutes of measurement time for a standard cargo container. We found that it is possible to identify and separate HEU, LEU, and Pu with high accuracy (> 3 s.d.) when using muon momentum measurement (perfect and limited) even when 30 cm-thick lead shielding was used. Currently, it is not possible to identify or separate the SNMs with 30 cm thick lead shielding without muon momentum knowledge. Our results show that the effect of measuring muon momentum can be significant and can result in reduced measurement times by a factor of 3 to 4 and/or improved monitoring and imaging resolution.

physics.ins-det↗

arXiv2022

A New Semi-Empirical Model for Cosmic Ray Muon Flux Estimation

Cosmic ray muons have emerged as a non-conventional high-energy radiation probe to monitor dense and large objects. Muons are the most abundant cosmic radiation on Earth, however, their flux at sea level is approximately 10,000 min^-1m^-2 much less than that of induced radiation. In addition, cosmic ray muon flux depends on not only various natural conditions (e.g., zenith angle, altitude, or solar activities) but also the geometric characteristic of detectors. Since the low muon flux typically results in long measurement times, an accurate estimation of measurable muon counts is important for muon applications. Here we propose a simple and versatile semi-empirical model to improve the accuracy in muon flux estimation at all zenith angles by incorporating the geometric parameters of detectors, and we name this the Effective Solid Angle model. To demonstrate the functionality, our model is compared with i) the cosine-squared, ii) PARMA model, and iii) Monte-Carlo simulations, and iv) measurements. Our results show that the muon count rate estimation capability is significantly improved resulting in increasing a mean C/E from 0.7 to 0.95. By selecting an appropriate intensity correlation, the model can be easily extended to estimate muon flux at various altitude and underground level.

astro-ph.IM↗