Building a community of railway scientific researchers and academia for ERJU and enabling a network of PhDs (academia teaming with industry)

Informations projet

Academics4Rail

N° de convention de subvention: 101121842

DOI

10.3030/101121842

Date de signature de la CE 30 Juin 2023

Date de début 1 Septembre 2023

Date de fin 28 Février 2027

Financé au titre de

• Climate, Energy and Mobility

Coût total

Aucune donnée

Contribution de l’UE

€ 1 807 237,50

Coordonné par

EURNEX e. V.
Germany

Periodic Reporting for period 2 - Academics4Rail (Building a community of railway scientific researchers and academia for ERJU and enabling a network of PhDs (academia teaming with industry))

Période du rapport: 2025-03-01 au 2026-02-28

Résumé du contexte et des objectifs généraux du projet

Context and Overall Objectives

Academics4Rail (Ac4R) addresses the need for a structured partnership between academia and Europe’s Rail Joint Undertaking (ERJU) in the context of the European Green Deal. The project aims to build a permanent scientific community that identifies research gaps, aligns academic priorities with ERJU’s strategic goals, and provides evidence-based feedback on how rail research contributes to policy objectives. It also nurtures the next generation of railway experts through collaborative PhD programmes co-directed by academia and industry.

Pathway to Impact

The project’s impact pathway is built on three elements: a scientific observatory that provides strategic intelligence on research gaps and PhD topics; a practical assessment framework that translates technical outputs into measurable societal, economic, and environmental impacts; and six PhD researchers who deliver targeted innovations:

PhD1: Aerodynamics of high-speed freight trains to reduce energy consumption and drag.

PhD2: Electromagnetic compatibility model for different electrification systems to improve reliability.

PhD3: Additive manufacturing for reprofiling worn railway wheels to reduce waste and maintenance costs.

PhD4: Digital communications for virtual coupling to increase network capacity.

PhD5: Prognostics and health management framework for predictive railway asset maintenance.

PhD6: AI-based driving assistance systems to optimize energy use and support drivers.

These PhDs work directly with industry partners such as Alstom, Mercitalia, and Trafikverket. Expected impacts include economic gains through reduced maintenance costs, environmental benefits from lower emissions and energy consumption, and a strengthened European rail research ecosystem that accelerates the transition to sustainable transport.

Travail effectué depuis le début du projet jusqu’à la fin de la période considérée dans le rapport et principaux résultats atteints jusqu’à présent

During this reporting period, Academics4Rail made significant technical and scientific progress across its work packages. WP2 completed its activities with the submission of D2.2 (position paper on research gaps) and D2.3 (catalogue of PhD topics), establishing the scientific observatory for ERJU. WP3 submitted D3.2 (Final Assessment Framework), delivering a pre-validated, elasticity-based model to evaluate ERJU’s contribution to Green Deal objectives.

All six PhD programmes reached their mid-term milestones with at least one peer-reviewed publication submitted per candidate. Specific achievements include:

PhD1: Published two journal papers, developed and validated CFD models against wind tunnel tests, and performed sensitivity analyses, achieving TRL 3-4.

PhD2: Developed a preliminary EMC model, strengthened industrial collaboration through data acquisition campaigns, achieving TRL 4.

PhD3: Advanced dynamic simulations and wear estimation, manufactured test specimens, and began twin-disc testing during a long-term stay at the University of Pardubice, achieving TRL 4.

PhD4: Developed and validated a dynamic Coloured Petri Net model for Train-to-Train communications incorporating 5G NR sidelink parameters, winning a Best Student Paper Award, achieving TRL 4.

PhD5: Advanced diagnostic and prognostic modelling using a full-scale test rig and Fiber Bragg sensing technology, submitting a journal paper on ballast deficiency detection, achieving TRL 5-6.

PhD6: Completed a state-of-the-art review, developed AI-based driving assistance models, and published a peer-reviewed paper on new Grades of Automation, achieving TRL 4.

All six PhDs provided documented evidence of industrial collaboration with ERJU members and railway stakeholders, ensuring alignment with EU-RAIL Flagship Areas and strengthening the link between academia and the rail sector.

Progrès au-delà de l’état des connaissances et impact potentiel prévu (y compris l’impact socio-économique et les conséquences sociétales plus larges du projet jusqu’à présent)

Academics4Rail has delivered several advancements that extend beyond the current state of the art. WP2 produced the first systematic, pan-European dynamic catalogue of 329 rail-related PhDs cross-referenced with ERJU Flagship Areas, establishing a new tool for strategic talent planning. WP3 developed a pre-validated, elasticity-based assessment framework that bridges the gap between technical KPIs and societal impacts, offering a novel methodology for evaluating rail research contributions to the European Green Deal.

The six PhDs have generated specific scientific breakthroughs:

PhD1: Created the first open-access database of freight wagon geometries and validated high-fidelity CFD models against wind tunnel tests, enabling more accurate aerodynamic simulations for freight trains.

PhD2: Developed a novel software tool for calculating electromagnetic compatibility effects across mixed AC/DC electrification systems, addressing a critical gap in infrastructure interoperability.

PhD3: Pioneered the application of metallic additive manufacturing for railway wheel reprofiling, with twin-disc testing providing first-of-its-kind data on mechanical behaviour of laser-deposited materials.

PhD4: Established a Coloured Petri Net modelling framework incorporating 5G NR sidelink parameters for dependability analysis of Train-to-Train communications, a novel contribution to virtual coupling safety assurance.

PhD5: Developed a prognostics and health management framework using Fiber Bragg sensing arrays for ballast deficiency detection, advancing predictive maintenance capabilities for switching and crossing systems.

PhD6: Proposed a new definition of Grades of Operational and Tactical Automation, contributing a refined framework for designing AI-based driving assistance systems.

Key Needs for Further Uptake: Further research is required to validate results at higher TRLs through real-world demonstration. Standardisation efforts will be critical, particularly for additive manufacturing and virtual coupling frameworks. Continued collaboration with ERJU, UIC, and ERRAC is essential to integrate the catalogue and assessment framework into future funding programmes and regulatory pathways.