Skip to content
DraconiticResearch
Search Sources API
197,768 studies31 sourcesUpdated 29 min ago
PreprintCC0arXiv

FDTD Model of Noise Pollution from an AI Data Center

Abstract

The sudden growth in AI data centers is raising numerous environmental concerns. Low-frequency noise pollution is a concern for local residents, that may not just be an annoyance but also carry significant health impacts. An FDTD model of sound propagation over the topography near an AI data center is presented. The topography plays a large role in the level of noise pollution, with areas of sudden elevation on either side of the river near the AI data center more susceptible to high noise levels, while areas further afield largely protected from noise pollution by the topography. Understanding the role that topography plays in noise pollution, and capturing the noise pollution in specific topographies, can help identify areas where noise mitigation strategies might be best employed.

Related

  1. Temporary gas turbines powering a US AI data center created a cross-state air pollution hotspot
    Will W. Qiang, Bo Huang, Xingjian Liu +1
    arXivPreprintarXiv licenceFull textAtmospheric science & meteorologyStatistical & nonlinear physicsarXiv
  2. TT-FDTD: Tensor Train Accelerated Three-Dimensional FDTD With Logarithmic Cost of Spatial Operators
    Chris Nguyen, Vladimir Okhmatovski
    arXivPreprintCC BY 4.0Full textComputational physicsarXiv
  3. Modeling coherent nonlinear microscopy of axially layered anisotropic materials using FDTD
    Mohammad Reza Farhadinia, Nicolas Olivier
    Optics ExpressPeer-reviewedCC BY 4.0Full textOptics & photonicsHAL
  4. Three-Dimensional FDTD Simulation of Superconducting Terahertz Emitters Using an Effective Medium Approximation
    Ryota Kobayashi, Itsuhiro Kakeya
    arXivPreprintCC BY 4.0Full textCondensed matterarXiv
  5. A Highly Scalable Quantized Tensor-Train FDTD Framework for the Simulation of Three-Dimensional Electromagnetic Scattering Problems
    Daan Vanhaecke, Emile Vanderstraeten, Dries Vande Ginste
    arXivPreprintarXiv licenceFull textNumerical analysis & scientific computingComputational physicsarXiv
BibTeX
@misc{buxton2026fdtd,
  title = {{FDTD Model of Noise Pollution from an AI Data Center}},
  author = {Gavin Buxton},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2610.14899},
  eprint = {2610.14899},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2610.14899},
  note = {CC0},
}
RIS
TY  - UNPB
TI  - FDTD Model of Noise Pollution from an AI Data Center
AU  - Gavin Buxton
PY  - 2026
DA  - 2026/10/10
PB  - arXiv
DO  - 10.48550/arxiv.2610.14899
UR  - https://arxiv.org/abs/2610.14899
AB  - The sudden growth in AI data centers is raising numerous environmental concerns. Low-frequency noise pollution is a concern for local residents, that may not just be an annoyance but also carry significant health impacts. An FDTD model of sound propagation over the topography near an AI data center is presented. The topography plays a large role in the level of noise pollution, with areas of sudden elevation on either side of the river near the AI data center more susceptible to high noise levels, while areas further afield largely protected from noise pollution by the topography. Understanding the role that topography plays in noise pollution, and capturing the noise pollution in specific topographies, can help identify areas where noise mitigation strategies might be best employed.
ER  -