MAG-FIL: Electromagnetic flow-on-demand devices for pushing fluids into concrete cracks
Source abstract
Abstract
• Magnetic flow-on-demand technology enables more effective crack filling. • Electromagnetic pushing devices offer versatility for on-site crack repair. • Experimental and numerical studies confirm feasibility of pushing fluids. • The magnetic device is scalable and tunable for concrete crack filling operations. An economical and sustainable approach to extend the service life of deteriorating concrete infrastructure involves filling and healing cracks. However, effectively infiltrating crack-filling solutions through the depths of concrete cracks of varying widths remains a significant challenge. To tackle this, our study explores an innovative concept: designing scalable and tunable electromagnetic devices called MAG-FIL. These devices utilize magnetic flow-on-demand technology to enhance crack-filling operations. Unlike conventional magnetic systems where fluids flow towards a magnet, MAG-FIL is designed to push fluids away from the magnet. This pushing mechanism is crucial when both the crack-filling materials and magnetic tools must operate on the same surface, due to limitations imposed by large element size, restricted access to the opposite side and steel reinforcement. Apart from existing solutions, MAG-FIL’s design is intentionally simple yet highly tunable, enabling it to be easily adapted to meet the specific requirements of concrete crack filling. Through a series of experimental and numerical studies, we have demonstrated the potential of employing magnetic technology to improve infiltration and have identified key factors influencing performance. By carefully considering the interactions between these factors, we present strategies to optimize the benefits derived from utilizing MAG-FIL.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Microfluidic and Bio-sensing Technologies · Electrowetting and Microfluidic Technologies · Modular Robots and Swarm Intelligence
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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