Satadru Dey

Yukta Pareek, Khadija Omar Said, Satadru Dey et al.

Underground mining operations are actively exploring the use of large-format lithium-ion batteries (LIBs) to power their equipment. LIBs have high energy density, long cycle life, and favorable safety record. They also have low noise, heat, and emission footprints. This fosters a conducive workplace environment for underground mining personnel. However, many occurrences of LIB failure have resulted in dangerous situations in underground mines. The combustion products, including toxic emissions, can rapidly travel throughout the mine using the ventilation network. Therefore, it is critical to monitor the temperature and smoke concentration underground at all times to ensure the safety of the miners. High-fidelity models can be developed for specific scenarios of LIB failure, but are computationally prohibitive for large underground mine volumes, complex geometries, and long duration combustion events. To mitigate computation-related issues associated with high-fidelity models, we developed cyber-physical systems (CPS) models to examine temperature and smoke dynamics. The mine supervisory control center, acting as the cyber framework, operates in conjunction with the physical underground mine. The CPS models, trained on high-fidelity computational fluid dynamics (CFD) model data sets, present an exceptional estimate of the evolution of temperature and smoke concentration in the underground mine tunnel. Once implemented, the research results can help mine operators make informed decisions during emergencies.

Mandana Mohammadi Looey, Marissa Loraine Scalise, Amrita Basak et al.

The trade-off between model fidelity and computational cost remains a central challenge in the computational modeling of extrusion-based 3D printing, particularly for real time optimization and control. Although high fidelity simulations have advanced considerably for offline analysis, dynamical modeling tailored for online, control-oriented applications is still significantly underdeveloped. In this study, we propose a reduced order dynamical flow model that captures the transient behavior of extrusion-based 3D printing. The model is grounded in physics-based principles derived from the Navier Stokes equations and further simplified through spatial averaging and input dependent parameterization. To assess its performance, the model is identified via a nonlinear least squares approach using Computational Fluid Dynamics (CFD) simulation data spanning a range of printing conditions and subsequently validated across multiple combinations of training and testing scenarios. The results demonstrate strong agreement with the CFD data within the nozzle, the nozzle substrate gap, and the deposited layer regions. Overall, the proposed reduced order model successfully captures the dominant flow dynamics of the process while maintaining a level of simplicity compatible with real time control and optimization.

Mandana Mohammadi Looey, Amrita Basak, Satadru Dey

Extrusion-based 3D printing of cementitious materials enables fabrication of complex structures, however it is highly sensitive to disturbances, material property variations, and process uncertainties that decrease flow stability and dimensional fidelity. To address these challenges, this study proposes a robust linear quadratic optimal control framework for regulating material extrusion in cementitious direct ink writing systems. The printer is modeled using two coupled subsystems: an actuation system representing nozzle flow dynamics and a printing system describing the printed strand flow on the build plate. A hybrid control architecture combining sliding mode control for disturbance rejection with linear quadratic optimal feedback for energy-efficient tracking is developed to ensure robustness and optimality. In simulation case studies, the control architecture guarantees acceptable convergence of nozzle and strand flow tracking errors under bounded disturbances.

Mandana Mohammadi Looey, Amrita Basak, Satadru Dey

Direct Ink Writing (DIW) has gained attention for its potential to reduce printing time and material waste. However, maintaining precise geometry and consistent print quality remains challenging under dynamically varying operating conditions. This paper presents a control-focused approach using a model reference adaptive control (MRAC) strategy based on a reduced-order model (ROM) of extrusion-based 3D printing for a candidate cementitious material system. The proposed controller actively compensates for uncertainties and disturbances by adjusting process parameters in real time, with the objective of minimizing reference-tracking errors. Stability and convergence are rigorously verified via Lyapunov analysis, demonstrating that tracking errors asymptotically approach zero. Performance evaluation under realistic simulation scenarios confirms the effectiveness of the adaptive control framework in maintaining accurate and robust extrusion behavior.