Lakehead University Knowledge Commons

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Phase-field fracture modeling and energy-based stability analysis of cemented paste backfill
(2026) Nassir, Navid; Cui, Liang; Lu, Gongda; Bai, Hao; Gong, Yanglin; Deng, Jian
Cemented paste backfill is widely used in underground mining to provide ground support and enable the sustainable disposal of mine tailings. As mining operations encounter increasingly complex stope geometries, the fracture behaviour and stability of exposed CPB structures become critical concerns. Conventional stability analyses generally rely on strength-based elastoplastic models that cannot explicitly represent the initiation, propagation, and coalescence of cracks governing the quasi-brittle failure of CPB. They also rarely account for the continuous evolution of fracture properties caused by cement hydration. This dissertation therefore develops two hydration-dependent phase-field fracture models and an energy-based method for assessing the stability of CPB structures. First, an evolutive phase-field model is developed for tensile fracture by linking cement hydration and binder content to the elastic modulus and tensile fracture resistance of CPB. The model is validated against direct tensile, split-tensile, and semi-circular bending tests conducted at different curing ages and binder contents. The results demonstrate its ability to reproduce tensile crack initiation and propagation, load-displacement responses, and the transition from diffuse early-age damage to increasingly localized fracture with continued hydration. Second, a phase-field model is developed for mixed-mode fracture by decomposing the crack-driving energy into tensile and shear components and introducing distinct tensile and shear critical energy release rates. Experimental tests on single- and double-notched specimens, together with additional tensile and mixed-mode validation cases, demonstrate that the model captures crack initiation, propagation, interaction, and coalescence under complex loading conditions. Based on the proposed mixed-mode model, an Energy Limitation Method is developed for three-dimensional, field-scale stability assessment. The method progressively reduces the tensile and shear fracture resistance until unstable, through-going crack propagation occurs, thereby defining an energy-based stability index. Increasing cement content from 4.5% to 8% increased the stability index from 1.6 to 4.0, while increasing curing time from 28 to 120 days increased it from 1.75 to 2.65. In contrast, increasing backfill height from 30 to 60 m reduced the index from 5.35 to 1.25. Overall, the developed framework integrates hydration-dependent tensile and mixed-mode fracture modeling with energy-based stability assessment, providing a physically consistent approach for investigating crack-dominated failure and supporting the design of CPB structures.
Decision-aware battery energy management in smart grids
(2026) Ma, Yanzhang; Ameli, Amir; Yassine, Abdulsalam
Energy management in smart grids increasingly depends on integrating battery energy storage, whose charge and discharge decisions shift demand, shave peaks, and capture price differences. Because every such decision consumes battery life, and warranties cap how many cycles may be used per year, the operator’s problem is deciding when battery use is worth its cost. This thesis proposes artificial-intelligence mechanisms for managing battery energy storage under that constraint, one at each of two time scales. At the planning scale, the thesis proposes a decision-aware approach to long-horizon price-spread forecasting for warranty-constrained cycle allocation, judging forecasts by the realized value of the allocation decisions they induce rather than by prediction error. Ontario motivates the problem: Market Renewal replaced the legacy province-wide hourly price with a day-ahead zonal price in May 2025, leaving post-renewal history too short to carry method claims, so a public New York Independent System Operator (NYISO) archive spanning 2000–2026 and eleven load zones provides the benchmark. The results show that the most accurate model is never the most valuable one in any of the eleven zones, and that selecting on validation allocation value rather than validation error yields higher held-out value in ten of them. This matters because accuracy-first selection, the default in electricity price forecasting, leaves battery value unrealized. At the operational scale, the thesis proposes a preference-conditioned multi-agent reinforcement learning controller that balances district peak reduction against battery cycling within one trained policy. Its actor uses peak-oriented and cycling-oriented specialist heads over a shared backbone, with an operator preference interpolating their outputs rather than entering the actor input, alongside structural control that keeps the executed actions feasible. In CityLearn with seventeen buildings over 30 days and three random seeds, the most peak-oriented setting cuts the 95th percentile of peak-hour district grid demand from 40.48kW to 33.41 ± 2.33 kW, a 17.4% reduction. The core contribution is one controller whose operating preference can change without retraining. Two supporting studies are kept separate from this result: an oracle-style forecast diagnostic examines discharge timing, and a limited language-interface demonstration maps operator instructions to the preference of the frozen policy. Neither study establishes a deployable forecast pipeline or general language-interface reliability.
Detection and mitigation of cyber attacks in microgrid secondary control systems
(2026) Zhang, Rubin; Ameli, Amir; Nasir Uddin, Mohammad; Wei, Qiang
Communication-assisted secondary control is essential for restoring the voltage and frequency deviations that remain after primary control in renewable-integrated microgrids. While primary control provides rapid local stabilization and power sharing, secondary control coordinates distributed energy resources (DERs) to restore system-level voltage and frequency toward their nominal values. However, its dependence on communication networks exposes both the measurement-feedback and command-actuation pathways to false-data injection (FDI) attacks. Compromised measurements can mislead supervisory control decisions, while manipulated commands can drive DERs toward unsafe operating conditions. Effective cybersecurity mechanisms must therefore detect compromised information and prevent it from propagating through the closed-loop secondary-control system. This thesis develops a lightweight, model-free, dual-pathway Koopman-based framework that integrates real-time attack detection and mitigation into communication-assisted secondary control. The proposed framework deploys independent local Koopman predictors on the measurement and command sides. Identified from attack-free operational data, these predictors capture relationships between communicated signals and locally available cyber–physical information without requiring an explicit analytical model of the complete microgrid. During online operation, each received sample is compared with its locally predicted value. The resulting prediction residual is processed through a recursive compensation mechanism that estimates and removes the anomalous signal component on a sample-by-sample basis. Consequently, measurements and control commands are continuously safeguarded before being delivered to the supervisory controller or executed by the DERs. In parallel, consecutive residual evaluations establish robust attack and recovery states. This confirmation mechanism prevents brief threshold exceedances caused by measurement noise, prediction uncertainty, or normal system transients from producing false attack-state transitions, without delaying the sample-by-sample signal safeguarding process. The proposed framework is evaluated on a protocol-aware Power Systems Computer Aided Design (PSCAD)–Graphical Network Simulator-3 (GNS3) cyber–physical cosimulation platform that captures the closed-loop interactions among electrical dynamics, supervisory control, IEC 60870-5-104 communication, cyber-attack propagation, and cybersecurity actions. Simulation results demonstrate the potential of local Koopman-based cyber–physical consistency validation and recursive signal mitigation as a modular cybersecurity framework for communication-assisted microgrid secondary control.
A leakage-aware and reliability-focused evaluation framework for multiclass Alzheimer’s Disease staging using MRI slices
(2026) Boyle, Tanner; Bajwa, Garima; Akilan, Thangarajah; Alkhateeb, Abedalrhman; Cava, Dominique
Two-dimensional magnetic resonance imaging (MRI) studies of Alzheimer’s disease (AD) can overestimate performance when slices from the same subject appear in both training and assessment sets. This thesis evaluated a leakage-aware framework for multiclass AD staging based on subject-independent partitioning, subject-level prediction, calibration, and external validation. Three model families were compared on 347 OASIS subjects using subject-wise crossvalidation: a convolutional neural network (CNN), a Vision Transformer (ViT), and a Hybrid CNN–ViT architecture. A matched slice-random ablation quantified leakageassociated inflation. Supporting analyses examined imbalance-aware objectives and subject-level reliability. Generalizability was tested on 502 ADNI subjects after OASIS model-selection decisions were fixed. Slice-random evaluation contaminated 98.3% of assessment subjects and inflated macro- F1 by 0.252–0.288. Under subject-wise evaluation, the Hybrid achieved the highest OASIS macro-F1 (0.711). On ADNI, the ViT achieved the highest external macro-F1 (0.718), reversing the internal ranking. These findings show that evaluation protocol and independent cohort testing can materially change conclusions about model quality. Keywords: Alzheimer’s disease, ADNI, data leakage, MRI, subject-wise cross-
Three-phase transformerless series-connected current source inverter with capacitor voltage balancing control method
(2026) Wu, Kechen; Wei, Qiang; Zhou, Yushi; Dekka, Apparao
In medium-voltage and high-power applications, the series-connected current source converter (SC-CSC) is a good candidate. However, most existing series-connected CSCs require transformers. Recently, a single-phase transformerless series-connected current source converter has been proposed. It eliminates the bulky and costly transformer, achieving lower cost and smaller system size. In this work, the three-phase version of this inverter is proposed and investigated. Since the original single-phase topology cannot be directly extended to the three-phase system, the original topology is modified to eliminate circulating currents. In practical implementations, mismatches in capacitors can destroy the inherent voltage balance of the inverter. To address this issue, a voltage balancing control strategy is introduced to regulate the capacitor voltages under parameter mismatch conditions. The proposed control method restores the capacitor voltage balance without modifying the topology or affecting its current balancing characteristics. The operation principles of the proposed three-phase inverter and the effectiveness of its capacitor voltage balancing strategy are validated through MATLAB/Simulink simulation results.