Periodic Reporting for period 1 - Academics4Rail (Building a community of railway scientific researchers and academia for ERJU and enabling a network of PhDs (academia teaming with industry))
Periodo di rendicontazione: 2023-09-01 al 2025-02-28
The Academics4Rail (Ac4R) project, aligned with the Europe’s Rail Joint Undertaking (ERJU), aims to advance an integrated, sustainable European railway network. Key goals include fostering a scientific community to share knowledge on rail innovation, aligning with EU’s Green Deal by enhancing mobility, reducing environmental impact, and improving rail efficiency. With industry-academic collaboration, the project addresses critical issues such as aerodynamics, EMC, predictive maintenance, additive manufacturing, digital communications, and driving assistance. Social sciences contribute by considering the social impacts of technology, ensuring inclusivity and safety.
Scientific Community Building (WPs 1–3): Established a European scientific network, mapped key themes, and created a KPI framework to evaluate project impacts, aligned with environmental and social objectives.
PhD Research Programs (WPs 4–9):
PhD1: Developed aerodynamic models to reduce energy use and noise in freight transport.
PhD2: Created EMC models for rail electrification, ensuring interoperability.
PhD3: Innovated wheel re-profiling using additive manufacturing to extend lifespan and safety.
PhD4: Investigated wireless communication for virtual coupling, enhancing rail capacity.
PhD5: Developed predictive maintenance tools to optimize rail asset management.
PhD6: Explored AI-driven driving support for safe, semi-autonomous operations.
These activities yielded models, algorithms, and tools tackling rail industry challenges, contributing to asset longevity, safety, and environmental goals.
PhD Research Programs (WPs 4–9):
PhD1: Developed aerodynamic models to reduce energy use and noise in freight transport.
PhD2: Created EMC models for rail electrification, ensuring interoperability.
PhD3: Innovated wheel re-profiling using additive manufacturing to extend lifespan and safety.
PhD4: Investigated wireless communication for virtual coupling, enhancing rail capacity.
PhD5: Developed predictive maintenance tools to optimize rail asset management.
PhD6: Explored AI-driven driving support for safe, semi-autonomous operations.
These activities yielded models, algorithms, and tools tackling rail industry challenges, contributing to asset longevity, safety, and environmental goals.
Technological Innovations: Each PhD program developed unique tools that advance sustainability, interoperability, and efficiency in rail.
Environmental Impact: Reduced emissions and noise in freight rail, supporting EU climate goals.
Economic Impact: Predictive maintenance and wheel re-profiling offer significant cost savings, improving competitiveness.
Further Uptake Needs: Expanded research, pilot demonstrations, supportive regulations, and international collaboration are crucial for successful adoption of these innovations.
Environmental Impact: Reduced emissions and noise in freight rail, supporting EU climate goals.
Economic Impact: Predictive maintenance and wheel re-profiling offer significant cost savings, improving competitiveness.
Further Uptake Needs: Expanded research, pilot demonstrations, supportive regulations, and international collaboration are crucial for successful adoption of these innovations.