Upscaling of a dual-permeability Monte Carlo simulation model for contaminant transport in fractured networks by genetic algorithm parameter identification

International audienceThe transport of radionuclides in fractured media plays a fundamental role in determining the level of risk offered by a radioactive waste repository in terms of expected doses. Discrete Fracture Networks (DFN) methods can provide detailed solutions to the problem of modeling the contaminant transport in fractured media. However, within the framework of the performance assessment (PA) of radioactive waste repositories, the computational efforts required are not compatible with the repeated calculations that need to be performed for the probabilistic uncertainty and sensitivity analyses of PA. In this paper, we present a novel upscaling approach, which consists in computing the detailed numerical fractured flow and transport solutions on a small scale and use the results to derive the equivalent continuum parameters of a lean, one-dimensional Dual-Permeability, Monte Carlo Simulation (DPMCS) model by means of a Genetic Algorithm search. The proposed upscaling procedure is illustrated with reference to a realistic case study of migration taken from literature

Multiple local particle filter for high-dimensional system identification

Nonlinearity and high dimensionality emerge as two primary challenges in the realm of system identification within the context of structural health monitoring (SHM) applications. Particle filter (PF) has been demonstrated efficient for nonlinear identification, but it suffers from the curse of dimensionality and behaves poorly in high-dimensional problems. The idea of state and measurement partitioning has been used in many PF algorithms to simplify high-dimensional identification problems into the identification of several lower-dimensional subgroups, but with very few applications to SHM problems. In this context, by combining multiple particle filters (MPF) with the decay of correlations property, this paper develops a novel multiple local particle filter (MLPF) for high-dimensional identification problems. A whole state vector is partitioned into several state subgroups, each consisting of fewer state components and then estimated by one PF through a novel likelihood including the local state and measurement vectors. The feasibility and efficiency of the proposed method are tested through a benchmark toy example, a case study of a twenty-story Bouc-Wen frame structure under ground motion, and an experimental study of fatigue delamination growth in composites

Vulnerability Analysis of Power Transmission Grids Subject to Cascading Failures

Cascading failures are a major threat to interconnected systems, such as electrical power transmission networks. Typically, approaches proposed for devising optimized control strategies are demonstrated with reference to a few test systems of reference (IEEE systems). However, this limits the robustness of the proposed strategies with respect to different power grid structures. Recently, this issue has been addressed by considering synthetic networks randomly generated for mimicking power transmission grids’ characteristics. These networks can be used for investigating the vulnerability of power networks to cascading failures. In this work, we propose to apply a recent algorithm for sampling random power grid topologies with realistic electrical parameters and further extend it to the random allocation of generation and load. Integration with a realistic cascade simulation tool, then, allows us to perform thorough statistical analyses of power grids with respect to their cascading failure behavior, thus offering a powerful tool for identifying the strengths and weaknesses of different grid classes. New metrics for ranking the control and mitigation effort requirements of individual cascade scenarios and/or of grid configurations are defined and computed. Finally, genetic algorithms are used to identify strategies to improve the robustness of existing power networks

Model selection and parameter estimation in structural dynamics using approximate Bayesian computation

This paper will introduce the use of the approximate Bayesian computation (ABC) algorithm for model selection and parameter estimation in structural dynamics. ABC is a likelihood-free method typically used when the likelihood function is either intractable or cannot be approached in a closed form. To circumvent the evaluation of the likelihood function, simulation from a forward model is at the core of the ABC algorithm. The algorithm offers the possibility to use different metrics and summary statistics representative of the data to carry out Bayesian inference. The efficacy of the algorithm in structural dynamics is demonstrated through three different illustrative examples of nonlinear system identification: cubic and cubic-quintic models, the Bouc-Wen model and the Duffing oscillator. The obtained results suggest that ABC is a promising alternative to deal with model selection and parameter estimation issues, specifically for systems with complex behaviours

Physics-Informed Neural Networks for the Condition Monitoring of Rotating Shafts

Condition monitoring of rotating shafts is essential for ensuring the reliability and optimal performance of machinery in diverse industries. In this context, as industrial systems become increasingly complex, the need for efficient data processing techniques is paramount. Deep learning has emerged as a dominant approach due to its capacity to capture intricate data patterns and relationships. However, a prevalent challenge lies in the black-box nature of many deep learning algorithms, which often operate without adhering to the underlying physical characteristics intrinsic to the studied phenomena. To address this limitation and enhance the fusion of data-driven methodologies with the fundamental physics of the system under study, this paper leverages physics-informed neural networks (PINNs). Specifically, a simple but realistic numerical case study of an extended Jeffcott rotor model, encompassing damping effects and anisotropic supports for a more comprehensive modelling, is considered. PINNs are used for the estimation of five parameters that characterize the health state of the system. These parameters encompass the radial and angular position of the static unbalance due to the disk installed on the shaft, the stiffness along the principal axes of elasticity, and the non-rotating damping coefficient. The estimation is conducted solely by exploiting the displacement signals from the centre of the disk and, to showcase the efficacy and precision provided by this novel methodology, various scenarios involving different constant rotational speeds are examined. Additionally, the impact of noisy input data is also taken into account within the analysis and the performance is compared to that of traditional optimization algorithms used for parameters estimation

Real-Time Detection of Internal Short Circuits in Lithium-Ion Batteries using an Extend Kalman Filter: A Novel Approach Combining Electrical and Thermal Measurements

Concerns over fuel scarcity and environmental degradation largely drive the increasing popularity of electric vehicles (EVs). Lithium-ion batteries (LIBs), known for their high energy and power densities, are the favored power source for EVs. Over the past few decades, research has been concentrated on ensuring these batteries operate efficiently, safely, and reliably. A key issue impacting the safety of Li-ion batteries is thermal runaway (TR), which can lead to hazardous battery fires. Internal short circuits (ISCs) are often the primary cause of these TR incidents, making the early detection of spontaneous ISC formation a pivotal diagnostic task. This research introduces an innovative ISC detection technique for cylindrical Li-ion battery cells. This technique is based on the augmentation of the model state vector in an extended Kalman filter (EKF), combining both classical voltage measurements to surface temperature observations. This framework enables real-time estimation of the internal ISC state while maintaining computational efficiency. The proposed method is tested numerically considering a high-fidelity numerical plant cycled using charge-depleting tests that mimic a practical battery cell working cycle at various C rates and at different ambient temperatures to account for both load and environmental uncertainties. The results demonstrate the robustness and effectiveness of the method. In addition, the method has been proven to be computationally efficient, demonstrating the feasibility of its real-time implementation

Online Core Temperature Estimation for Lithium-Ion Batteries via an Aging-Integrated ECM-1D Coupled Model-Based Algorithm

Thermal management is pivotal for ensuring the safe and efficient operation of LIBs under dynamic conditions. Accurate core temperature monitoring remains a key BTMS challenge for predicting thermal distributions and mitigating TR risks. This study proposes a real-time core temperature estimation framework integrating joint EKF with an electro-thermal-aging model (ECM-1D). Using only surface temperature and voltage measurements, it simultaneously estimates core temperature, SOC, and capacity with bidirectional electro-thermal coupling. The hybrid approach pre-calibrates temperature/SOC/SOH-dependent parameters offline while updating capacity online. Validation under extreme conditions (high-rate cycling, aging, and ISCs) demonstrates 60% lower core temperature RMSE during high-rate cycling, a maximum estimation error below 0.9 K, and 58.9% reduction in SOC estimation error under aging conditions versus existing methods. The framework reliably tracks core temperature trends despite parasitic heat and signal noise, enabling earlier critical temperature warnings. This provides a foundation for TR prevention, advancing battery safety for EV and grid storage applications. Future extensions could integrate physical aging mechanisms and enhance fault detection capabilities

Convolutional autoencoder-based framework for damage localization under variable temperature

Confounding factors such as variable temperature have an impact on Lamb wave behaviour, affecting the accuracy of damage localization methods based on such waves. In this study, an innovative approach to Lamb wave prediction based on convolutional autoencoders (CAEs) is proposed and applied to an experimental dataset consisting of Lamb wave acquisitions on a Carbon Fiber Reinforced Polymer (CFRP) plate under varying temperatures. Leveraging an experimental dataset of Lamb wave signals acquired from a CFRP plate at different temperatures, this research focuses on utilizing CAEs to enhance the accuracy and reliability of damage localization. This algorithm extracts critical features from Lamb wave data, effectively recognizing subtle wave properties variations, thus significantly improving the precision of damage localization. Two different architectures of CAEs were evaluated. One which uses the temperature value as a direct input into the latent space of the autoencoder, and another that does not process the temperature value. This analysis was performed to demonstrate the actual impact of the temperature information on prediction accuracy and, furthermore, the accuracy of the CAEs at predicting Lamb wave signals for temperatures outside of their training dataset. The results obtained demonstrate that the inclusion of the temperature information into the autoencoder architecture not only increased its accuracy for temperatures within its training dataset but also increased its robustness with regards to temperature variations, displaying better performance at predicting Lamb wave signals for temperatures outside of its training dataset. The algorithm proposed here presents a way forward for increasing the robustness and reliability of damage localization methods based on Lamb waves

Temperature enhanced early detection of internal short circuits in lithium-ion batteries using an extended Kalman filter

Fuel shortages and environmental concerns primarily drive the recent growth in electrification. Lithium-ion batteries are the preferred power source for electric vehicles and other applications due to their high energy and power densities. Among potential issues that can affect Li-ion batteries, thermal runaway is a significant concern, believed to be primarily caused by internal short circuits. Therefore, early internal short circuit detection has become a critical task for any Li-ion battery-powered engineering system prioritizing safety. This paper presents a novel online internal short circuit detection method based on the state vector augmentation of an extended Kalman filter with: (i) voltage and surface temperature observations, (ii) a hysteresis state, and (iii) a state related to the internal short circuit. The proposed method is assessed numerically, mimicking an electrical vehicle battery working cycle. The framework allows for an online estimation of the internal short circuit state while remaining computationally lean, thus potentially allowing for implementation into commercial BMSs, and it is proven to capture the internal short circuit occurrence within safety limits. Additionally, the advantages of including both voltage and surface temperature observations have been highlighted. Future work envisaged towards field implementation of the technique in BMS is eventually and briefly discussed

Explainability of convolutional neural networks for damage diagnosis using transmissibility functions

Structural health monitoring (SHM) is crucial for ensuring the safety and efficiency of engineering systems. Vibration-based signals have been widely employed in numerous studies for damage detection, localization and quantification, thanks to their high sensitivity to structural damages and versatile applicability across various structures. Among these, transmissibility functions (TFs) have emerged as particularly promising because they can be derived from output-only measurements, making them practical for real-world applications. Vibration-based features, including those captured by TFs, provide a wealth of damage-related information, but extracting these features can be challenging with traditional methods and often requires a priori knowledge. To address this challenge, deep learning has gained significant attention for its capacity to handle intricate patterns and extract hidden features. However, deep learning algorithms are often perceived as black-box models due to their complex and heterogeneous layers, which hinder transparency and raise concerns, especially in critical domains. Although numerous studies have highlighted the diagnostic potential of deep learning in SHM with vibration-based data, there has been limited exploration of the rationale behind using deep neural networks (DNNs) for processing TFs. This paper addresses this gap by introducing explainable artificial intelligence (XAI) methods in two distinct TF-based damage diagnostic case studies: a numerical aluminium structural beam and a large-scale experimental steel frame. In the former, damage is simulated as local stiffness reductions, while in the latter, damage is introduced by loosening joint bolts. In both cases, a convolutional neural network (CNN) is used to process raw TF data for damage detection, localization, and quantification in the beam, and for detection and localization in the frame. Subsequently, an XAI method based on the layer-wise relevance propagation (LRP) algorithm is employed to identify the most relevant input features. A pattern analysis is presented to explain recurrent patterns, and a comparison with other XAI methods is proposed for further validation. The results demonstrate that the CNN provides accurate predictions for both case studies, with its focus on damage-sensitive features aligning closely with findings in the existing literature