Bluff-body flow-energy harvesters are commonly designed around translational vibration, whereas freely rotating polygonal sections provide an alternative route that harnesses rotational oscillations. In this study, Reynolds-number-dependent energy-harvesting responses of two reconfigurable sections, a variable-angle rhombic cylinder and a variable-side-length parallelogram cylinder, are investigated using an immersed boundary method. The body rotates freely about its centroid without a linear spring or external damping. Results show that at $Re=40$, decreasing the rhombus angle improves start-up and increases the bounded-oscillation amplitude, favoring oscillation-based harvesting, while the... More >

Achieving carbon neutrality in the aviation sector demands transformative technologies, and Artificial Intelligence (AI)-enabled aerial unmanned systems are emerging as a critical enabler of low-carbon flight operations, environmental monitoring, and sustainable airspace governance, yet a comprehensive synthesis that jointly addresses their technical architecture, climate-oriented applications, and regulatory pathways remains limited. This review provides a systematic analysis of AI-enabled aerial unmanned systems using a structured literature review framework aligned with PRISMA principles. We first summarize the evolution and classification of aerial unmanned platforms, then propose a laye... More >

With the expansion of artificial intelligence, data center energy consumption has surged, making its optimization critical for achieving carbon neutrality. Traditional fixed-rule controls rely too heavily on manual experience to explore the highest efficiency, while standard AI black-box models often lack interpretability and present evaluation challenges. This study introduces a Bayesian optimization-based methodology to explore the energy optimization potential of data center cooling systems. We enhanced the Resource Allocation and Power Simulator (RAPS) to generate dynamic workloads reflecting the authentic statistical characteristics of supercomputers for rigorous stress testing. This ge... More >

Accurate prediction of the internal temperature field in batteries is of critical importance for ensuring safe operation, extending battery lifespan, and ultimately accelerating the global transition to electric vehicles (EVs). Unsafe thermal conditions not only degrade battery performance but also hinder EV adoption. However, conventional 3D thermal models involve complex computations that struggle to meet the fast computational speed requirements of Battery Management Systems (BMS), and traditional neural network models are not accurate enough for temperature field prediction, especially for low-gradient fields. To achieve precise and rapid prediction of the internal battery temperature fi... More >