PBNV2
01/05/2017
With transportation noise being the second most deadly environmental pollutant in Europe, engineering for future mobility must be inspired by ecology, economy and health to enable green and silent vehicles. Legislation defines maximum noise emission limits that have to be complied with during standardized pass-by noise test procedures. Given novel, often electrified, vehicle powertrain concepts, new pass-by noise evaluation approaches are required.
The proposed PBNv2 project (Next generation pass-by noise approaches for new powertrain vehicles) brings together early stage researchers and experienced specialists from key players in academia and industry across Europe covering different scientific disciplines and industrial stakeholders from a broad range of backgrounds to optimally tackle the challenges ahead. The fellows will be trained in innovative PhD topics as well as receiving specific theoretical and practical education in the field of pass-by noise engineering, tackling as well the pass-by noise aspects of the ‘source’, the ‘transfer path’ and the ‘receiver’.
PBNv2 is formed by 10 beneficiaries combining leading education institutes, top research institutions and leading companies as well as 5 partner organisations established in European automotive R&D, to assist in the dissemination and public engagement or PBNv2 results, and in providing dedicated training to enhance the entrepreneurial of the ESRs. The fellows will profit from top scientific research guidance in combination with highly relevant industrial supervision. Together these participants address the triple-I dimension of research training, being International, Interdisciplinary and Intersectoral.
Furthermore, the industry will gain from the specific training of the young researchers.
The general goal of PBNv2 is to support next generation pass-by noise approaches allowing quiet and safe vehicles. The aim is to provide specific and timely education of young researchers in this inter-/multidisciplinary field.
This project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement number 721615.
