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Abstract Grid independence studies have emerged as essential methodological frameworks for comprehending the impact of domain resolution on simulating anisotropic turbulence at the river‐reach scale using large eddy simulation models. This study proposes a methodology to assess the loss of information in turbulent flow patterns when coarsening the computational domain, examined in a 1‐km transect of the Colorado River along Marble Canyon. Seven computational domain resolutions are explored to analyse the sensitivity of turbulent flow to spatial resolution changes, utilizing the turbulent kinetic energy (TKE) spectrum technique and spatiotemporal analysis of eddy structures via statistical metrics such as root mean square error (RMSE), Kullback‐Leibler (KL) divergence, Nash‐Sutcliffe model efficiency coefficient (NSE), wavelet power spectrum and grid convergence index (GCI). Based on physical principles and statistics, these metrics quantify information loss and assess domain resolutions. A computational fluid dynamic (CFD) model is developed by employing the detached eddy simulation (DES) technique, with boundary condition (BC) integrating the rough wall extension of the Spallart‐Allmaras model in cells near the bed. Evaluation of domain resolutions aims to identify grid cell sizes capturing flow behaviour and hydraulic characteristics, including primary and secondary flows, return currents, shear layers and primary and secondary eddies. The study observes an increase in data representation of the TKE spectrum with finer spatial domain resolution. Additionally, surface analysis, conducted via RMSE, KL and NSE metrics, identifies specific areas within the flow field showing high sensitivity to refining the grid cell sizes.
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Space syntax visibility graph analysis is not robust to changes in spatial and temporal resolution
Space syntax is an influential framework for quantifying the relationship between environmental geometry and human behavior. Although many studies report high syntactic–behavioral correlations, previous pedestrian data were collected at low spatiotemporal resolutions, and data transformations and sampling strategies vary widely; here, we systematically test the robustness of space syntax’s predictive strength by examining how these factors impact correlations. We used virtual reality and motion tracking to correlate 30 syntactic measures with high resolution walking trajectories downsampled at 10 grid resolutions and subjected to various log transformations. Overall, correlations declined with increasing grid resolution and were sensitive to data transformations. Moreover, simulations revealed spuriously high correlations (e.g. R 2 = 1) with sparsely sampled data (<23 locations). These results strongly suggest that syntactic–behavioral correlations are not robust to changes in spatiotemporal resolution, and that high correlations obtained in previous studies could be inflated due to transformations, data resolution, or sampling strategies.
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- Award ID(s):
- 0843940
- PAR ID:
- 10214044
- Date Published:
- Journal Name:
- Environment and Planning B: Urban Analytics and City Science
- ISSN:
- 2399-8083
- Page Range / eLocation ID:
- 239980831989762
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1177/2399808319897624
