Nour Atassi Atassi

Mrs. Nour Atassi Atassi


Research Focus & Metrics

Academic Career Length

3Years (2024-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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15 March 2024 in Entropy
Verified
Journal Article
Open access
PDF file
ABAmira K. F. Val Baker
Amira K. F. Val Baker
Mate CsanadMate Csanad
Mate Csanad
NFNicolas Fellas
Nicolas Fellas
NANour Atassi Atassi
Nour Atassi Atassi
IMIa Mgvdliashvili
Ia Mgvdliashvili
Paul OomenPaul Oomen
Paul Oomen
In general, sound waves propagate radially outwards from a point source. These waves will continue in the same direction, decreasing in intensity, unless a boundary condition is met. To arrive at a universal understanding of the relation between frequency and wave propagation within spatial boundaries, we explore the maximum entropy states that are realized as resonant modes. For both circular and polygonal Chladni plates, a model is presented that successfully recreates the nodal line patterns to a first approximation. We discuss the benefits of such a model and the future work necessary to develop the model to its full predictive ability.

31 January 2024 in Preprints.org
Verified
Preprint
Open access
PDF file
ABAmira K. F. Val Baker
Amira K. F. Val Baker
Mate CsanadMate Csanad
Mate Csanad
NFNicolas Fellas
Nicolas Fellas
NANour Atassi Atassi
Nour Atassi Atassi
IMIa Mgvdliashvili
Ia Mgvdliashvili
Paul OomenPaul Oomen
Paul Oomen
In general sound waves propagate radially outwards from a point source. These waves will continue in the same direction, decreasing in intensity, unless a boundary condition is met. To arrive at a universal understanding of the relation between frequency and wave propagation within spatial boundaries, an analysis and comparison is made between the results of Chladni plate experiments and Chladni plate simulations. We consider a finite range of frequencies and plate geometries and present a model that has been shown to successfully recreate the nodal line patterns to a first approximation. We discuss the benefits of such a model and the future work necessary to develop the model to its full predictive capacity.