J
Jens Forssén
Researcher at Chalmers University of Technology
Publications - 122
Citations - 1218
Jens Forssén is an academic researcher from Chalmers University of Technology. The author has contributed to research in topics: Noise & Traffic noise. The author has an hindex of 16, co-authored 118 publications receiving 1042 citations. Previous affiliations of Jens Forssén include École centrale de Lyon.
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The potential of building envelope greening to achieve quietness
TL;DR: In this article, the potential of wall vegetation systems, green roofs, vegetated low screens at roof edges, and also combinations of such treatments, have been studied by means of combining 2D and 3D full-wave numerical methodologies.
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Using natural means to reduce surface transport noise during propagation outdoors
T. Van Renterghem,Jens Forssén,Keith Attenborough,Philippe Jean,Jérôme Defrance,Maarten Hornikx,Jian Kang +6 more
TL;DR: In this paper, the authors present a review of ways of reducing surface transport noise by natural means, which can be easily (visually) incorporated in the landscape or help with greening the (sub)urban environment.
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The extended Fourier pseudospectral time-domain method for atmospheric sound propagation
TL;DR: An extended Fourier pseudospectral time-domain (PSTD) method is presented to model atmospheric sound propagation by solving the linearized Euler equations and is found to be well suited for Atmospheric sound propagation simulations where effects of complex meteorology and straight rigid boundary surfaces are to be investigated.
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Noise abatement schemes for shielded canyons
Mcj Maarten Hornikx,Jens Forssén +1 more
TL;DR: In this article, access to quiet areas in cities is important to avoid adverse health effects due to road traffic noise, and most urban areas which are or can become quiet (L A,eq
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Modelling the interior sound field of a railway vehicle using statistical energy analysis
TL;DR: In this article, the authors modeled the sound field in train compartments, treated as a series of connected air cavities, using statistical energy analysis, and used an adjusted SEA model to predict the rate of spatial decay within a cavity.