University of Mons

About: University of Mons is a education organization based out in Mons, Belgium. It is known for research contribution in the topics: Large Hadron Collider & Standard Model. The organization has 3073 authors who have published 9465 publications receiving 294776 citations.

Flame retardant polymer materials : An update and the future for 3D printing developments

Abstract: Fire safety has become a major concern due to the ubiquitous use of polymers. The development of flame retardant polymer materials has consequently experienced a huge growth in market size. New strategies and legislation have also been proposed to save lives and property. The science and economics of flame retardancy, fire regulations, and new technologies are under permanent evolution. This review paper focuses on revisiting and classifying recent developments in the knowledge and technology of flame retardant polymer materials and demonstrating the qualitative and quantitative analyses carried out on their flame retardant properties. In particular, it comprehensively addresses the progress made and the future prospects for designing precise structures via innovative technologies, particularly 3D printing - as the state-of-the-art manufacturing methodology providing innovative features in this realm of research - and their flame retardancy performances. Indeed, the strategies driving the technologies of innovative flame retardant polymer materials and 3D printing technology are approaching a practical juncture in the near future.

Modelling, simulation and evaluation of ground vibration caused by rail vehicles

Abstract: There is a great need to develop rail networks over long distances and within cities as more sustainable transport options. However, noise and vibration are seen as a negative environmental consequ...

Identification and filtering of uncharacteristic noise in the CMS hadron calorimeter

Abstract: Commissioning studies of the CMS hadron calorimeter have identified sporadic uncharacteristic noise and a small number of malfunctioning calorimeter channels. Algorithms have been developed to identify and address these problems in the data. The methods have been tested on cosmic ray muon data, calorimeter noise data, and single beam data collected with CMS in 2008. The noise rejection algorithms can be applied to LHC collision data at the trigger level or in the offline analysis. The application of the algorithms at the trigger level is shown to remove 90% of noise events with fake missing transverse energy above 100 GeV, which is sufficient for the CMS physics trigger operation.