Adrian Ilinca

This study numerically investigates buoyancy-driven melting of a Williamson non-Newtonian phase change material in a porous enclosure. The model incorporates an inclined magnetic field, thermal radiation, and viscous dissipation. The enthalpy–porosity method is employed to describe the melting process. The governing equations are solved using an implicit finite-difference scheme and successive over-relaxation. Grid and time-step refinement are performed to assess numerical sensitivity, while comparisons with limiting-case benchmarks are used to validate selected components of the numerical implementation. The effects of the governing parameters on fluid flow, heat transfer, and melting behavior are examined. Higher Grashof and Darcy numbers strengthen circulation and accelerate melting. In contrast, larger Williamson and magnetic parameters, together with greater mushy-zone resistance, suppress fluid motion and delay melting. Thermal radiation and viscous dissipation enhance heating and promote melting. Higher Stefan numbers also accelerate melting and increase the liquid fraction. Overall, the results demonstrate that buoyancy, permeability, thermal radiation, viscous heating, and Stefan number promote PCM melting, whereas magnetic damping, non-Newtonian effects, and mushy-zone resistance inhibit it, providing insight into the competing transport mechanisms governing PCM melting in porous enclosures.

Solar tracking systems can significantly increase photovoltaic (PV) energy production; however, conventional solutions often rely on sensors, feedback control systems, and continuous actuation, increasing cost, complexity, and maintenance requirements. This study presents a fully sensorless dual‐axis solar tracking system that combines astronomical ephemerides with ZIP‐code‐based geographic localization to determine the optimal orientation of PV panels without optical, irradiance, GPS, or positioning sensors. The proposed approach employs discrete actuation to minimize mechanical movement and reduce energy consumption while maintaining high tracking accuracy. Simulation results, supported by experimental validation of the tracking mechanism, indicate that the proposed tracker increases daily energy output by approximately 56.65% compared with an equivalent fixed PV panel, while the energy consumed by the tracking mechanism represents only 1.17% of the additional energy generated. The analysis further demonstrates that tracking intervals between 1 and 2.5 h provide an effective compromise between energy production, actuator energy consumption, and mechanical wear. In addition, the system remains highly effective with tracking errors up to 12°, highlighting its robustness and suitability for low‐cost implementations. The results show that a simple open‐loop control architecture can substantially improve PV energy production with minimal hardware. The proposed sensorless tracking strategy therefore offers a practical, reliable, and cost‐effective way to improve PV system performance, particularly in remote and resource‐constrained regions.

Artificial-cilia-driven transport is an important phenomenon in microfluidic systems, biomedical devices, porous transport media, and thermal-management applications, where controlled momentum, heat, and mass transfer are required. Accurate prediction of such coupled transport processes becomes challenging when magnetic effects, porous resistance, variable thermophysical properties, and chemical reactions are considered simultaneously. In the present study, we focus on the numerical and machine-learning prediction of artificial-cilia-driven ethylene glycol–silver (Ag–EG) nanofluid flow through a curved porous channel. The mathematical model incorporates Darcy–Brinkman–Forchheimer resistance, magnetic effects, viscous dissipation, heat generation, variable viscosity, thermal conductivity and diffusivity, and Arrhenius activation-energy effects. The governing nonlinear momentum, energy, and concentration equations are formulated in curvilinear coordinates under low-Reynolds-number conditions and solved using an implicit finite-difference method. A feed-forward artificial neural network is developed using 5000 numerical samples, which are divided into 70% training, 15% validation, and 15% testing subsets. The Levenberg–Marquardt algorithm is employed for network training, while MSE, RMSE, and (R²) are used to evaluate prediction accuracy. The results show that increasing the Darcy number enhances fluid velocity because of greater porous-medium permeability, whereas increasing the magnetic and Forchheimer parameters reduces the flow through Lorentz-force and nonlinear porous resistance, respectively. Increasing the thermal-conductivity parameter from 0.0 to 0.6 decreases the maximum temperature by about 15%, while increasing the activation-energy parameter from 0.1 to 1.0 increases the average species concentration by approximately 18–22%. The ANN predictions show close agreement with the numerical results. The developed numerical–machine-learning framework provides an efficient approach for predicting coupled transport behavior in cilia-driven porous-channel systems.

Diesel generators remain indispensable for supplying reliable electricity in isolated Arctic microgrids, where harsh environmental conditions, high fuel transportation costs, and stringent emission requirements motivate improvements in engine performance and environmental sustainability. This study proposes and validates a unified, surrogate-assisted tri-objective optimization framework to calibrate the engine operation of a pneumatically assisted CAT 3516 diesel generator under representative Arctic conditions at Raglan Mine, Canada. The optimization focuses on engine-level pneumatic assistance, while compressed air generation, storage, and distribution are outside the scope of the study. The framework integrates high-fidelity Diesel-RK simulations, adaptive Sobol sampling, Gaussian Process surrogate modeling with predictive uncertainty, global sensitivity analysis, deterministic multi-objective optimization, SIMUS-based Pareto ranking, and independent high-fidelity validation. We optimized three engine-control variables (start of injection, compressor pressure ratio, and fuel mass per cycle) simultaneously to maximize brake power while minimizing brake-specific fuel consumption and NO_x emissions. The surrogate models achieved coefficients of determination exceeding 0.97 for brake power, brake-specific fuel consumption, and peak cylinder pressure, while the NO_x surrogate reached an R² of 0.82. The selected operating point reduced NO_x emissions by 26.9%, increased brake power by 3.7%, and increased brake-specific fuel consumption by only 0.87% relative to the Arctic baseline. The proposed uncertainty-aware framework provides a computationally efficient and reproducible methodology for engine-level calibration of pneumatically assisted diesel generators operating in isolated Arctic hybrid energy systems.

Wave energy is a viable renewable resource for islands and isolated coastal areas looking to lessen their reliance on fossil fuels. However, the majority of wave energy converter (WEC) evaluations address site-specific limitations, including coastal erosion, sedimentary conditions, and cold-region processes, as secondary factors, with a primary focus on hydrodynamic performance and energy generation. This gap matters especially for low-lying island systems, where infrastructure resilience, coastline stability, and energy generation must be supported concurrently. Using the Magdalen Islands (Gulf of St. Lawrence, Canada) as a constraint-defining reference system, this work offers a site-constrained, erosion-aware critical analysis of WEC technology. The proposed framework uses an exclusion–prioritization strategy based on technical restrictions, such as seasonal ice processes, erosion-prone sandy beaches, and limited offshore infrastructure tolerance, rather than optimizing only for energy production. The analysis shows that several offshore WEC concepts raise significant technical and environmental concerns under these conditions. The most technically sound solutions are shore-based and nearshore-integrated technologies, especially oscillating water column (OWC) systems and overtopping converters, which feature limited seabed interaction, offer structural robustness, and allow integration with coastal protection infrastructure. The screening logic is expressed as an explicit two-stage procedure that combines a disqualifying exclusion rule based on physical survivability criteria with a weighted rating of the remaining candidates, and its outcome is shown to be robust to weighting within the resolution of the rating scheme. Compared to traditional energy-centric approaches, the framework supports more robust decision-making by offering a transferable methodology for early-stage technology screening in cold-region and erosion-sensitive coastal contexts.

Wind turbine blade composites represent a growing end-of-life (EoL) waste challenge because thermoset blades lack commercially mature recycling routes, whereas thermoplastic blades offer improved recyclability through dissolution. This study compares the net environmental impact of ten EoL strategies for six 45 m blade configurations comprising thermoset epoxy and thermoplastic Elium resin systems reinforced with glass fiber, carbon fiber, and hybrid laminates, using an eco-audit methodology based on cumulative primary energy demand. Solvolysis is the leading route for thermoset blades and dissolution for thermoplastic blades, reducing net impact to 74.9%, 31.7%, and 22.4% and to 53.6%, 23.4%, and 18.3% of the landfill benchmark for glass-fiber, hybrid, and carbon-fiber configurations, respectively. Thermoplastic blades therefore fall 296.0, 273.1, and 217.2 GJ below their thermoset counterparts. High-voltage fragmentation, conventional pyrolysis, and microwave-assisted pyrolysis are counterproductive for thermoset glass-fiber blades, exceeding landfill by 26.6%, 18.9%, and 8.1%, respectively, because of high process energy and the limited value of recovered glass fiber. Both material-system selection and recycling-route selection govern end-of-life performance, with the route dimension dominating for glass-fiber blades. A Monte Carlo analysis in which all parameters vary simultaneously confirms that solvolysis and dissolution remain the preferred routes for their respective material systems in every run. Thermoplastic blades fall below their thermoset counterparts in 100.0% and 98.8% of runs for glass-fiber and hybrid laminates, but in 80.3% for carbon fiber, where the advantage is indicated rather than established. These results highlight the importance of designing future wind turbine blades through material selection and recyclable composite systems.

The global energy sector is undergoing one of the most significant transformations in its history

The mining industry is among the most energy-intensive sectors and remains highly dependent on fossil fuels, particularly in remote, cold-climate regions where access to centralized electricity grids is limited. This dependence poses significant challenges in terms of operating costs, energy security, and greenhouse gas (GHG) emissions. This review provides a system-level analysis of energy consumption patterns, decarbonization pathways, and renewable energy integration strategies in the mining sector. The paper first examines the structure and drivers of energy demand in open-pit and underground mines, identifying transport systems, material handling, ventilation, and comminution processes as major energy consumers. It then analyzes technological and operational decarbonization strategies, including electrification, hybrid energy systems, renewable generation, and energy storage solutions. Particular attention is given to the technical constraints associated with site isolation, extreme climatic conditions, intermittency of renewable energy sources, and mine-life considerations. Case studies from the Canadian mining industry illustrate practical implementation challenges and achievable performance improvements. The analysis shows that while renewable energy technologies and storage systems are increasingly cost-competitive, deep decarbonization of mining operations requires integrated energy management, long-duration storage solutions, and site-specific hybrid system design. The review highlights engineering and strategic pathways that can progressively reduce fossil fuel dependence and support the transition toward low-carbon mining energy systems.