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arxiv: 2606.07625 · v2 · pith:EBENIS6Lnew · submitted 2026-05-30 · ⚛️ physics.ao-ph · physics.flu-dyn

Building drag and shielding in a realistic urban environment

Jingzi Huang , Omduth Coceal , Marco Placidi , Zheng-Tong Xie , Maarten van Reeuwijk This is my paper

Pith reviewed 2026-06-30 11:27 UTC · model grok-4.3

classification ⚛️ physics.ao-ph physics.flu-dyn
keywords urban dragbuilding shieldinglarge-eddy simulationwind directionurban morphologydrag coefficientatmospheric boundary layersheltering effects
0
0 comments X

The pith

Upstream shielding primarily controls drag on individual buildings in realistic urban layouts.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper examines drag and shielding using large-eddy simulations of 110 buildings on the University of Bristol campus across 24 wind directions. Total campus drag shows only moderate variation with direction, yet drag on single buildings changes substantially, with roughly 20 percent of buildings producing 80 percent of the total drag. Two simple dimensionless parameters are defined to measure shielding: the upstream fetch ratio L_s/H_s and the relative height ratio H_s/H. These distinguish near-wake from far-wake conditions and sheltered from exposed buildings. The work shows shielding as the main driver of building drag and calls for drag coefficients that incorporate shielding effects.

Core claim

In the Bristol campus morphology, shielding by upstream buildings is the dominant control on the drag force experienced by individual structures. The upstream fetch ratio L_s/H_s and relative height ratio H_s/H provide a straightforward way to classify shielding conditions for any building and wind direction. Application of these ratios shows that a few large, exposed buildings account for most of the campus drag, while many smaller or sheltered buildings contribute little.

What carries the argument

The upstream fetch ratio L_s/H_s and relative height ratio H_s/H, two dimensionless parameters that quantify the distance and height of the nearest upstream obstacle relative to a target building and thereby classify its shielding regime.

If this is right

  • Shielding-aware definitions of effective frontal area and drag coefficient are required for accurate urban drag estimates.
  • The two ratios allow rapid classification of sheltered versus exposed buildings without full flow simulation.
  • A small subset of large buildings dominates total urban drag in this morphology.
  • The framework can be applied to other complex city layouts to map drag distribution.
  • Individual building drag varies far more with wind direction than does the campus-wide total drag.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • City-scale wind-load models could incorporate the two ratios to adjust drag coefficients for local sheltering.
  • Urban design guidelines might use these ratios to identify buildings likely to experience high drag and plan accordingly.
  • The same parameters could help estimate how changes in building height or spacing would alter overall city drag.

Load-bearing premise

The large-eddy simulations of the Bristol campus layout correctly reproduce the actual drag forces and shielding patterns that would occur in the real environment.

What would settle it

Field measurements of wind forces on several Bristol campus buildings under known wind directions, compared directly against the simulated drag values for the same conditions.

Figures

Figures reproduced from arXiv: 2606.07625 by Jingzi Huang, Maarten van Reeuwijk, Marco Placidi, Omduth Coceal, Zheng-Tong Xie.

Figure 1
Figure 1. Figure 1: Definition sketch (a) of solid-fluid interface 𝜕Ω𝑓 and 3-D normal vectors 𝑵 of interface. The solid domain is in grey, while the white indicates the fluid domain Ω𝑓 . The surface of an individual building 𝑞 is outlined in red; (b) of 𝑵 decomposition, the vector 𝑵 is unit and points into the fluid domain. but also allows the evaluation of the drag acting on an individual building by specifying the integrati… view at source ↗
Figure 1
Figure 1. Figure 1: Schematic definition of (a) the solid-fluid interface 𝜕Ω𝑓 and the 3-D normal vectors 𝑵 of the interface, where the solid domain is shown in grey and the fluid domain Ω𝑓 in white. The surface of an individual building 𝑞 is outlined in red; (b) the decomposition of 𝑵, where 𝑵 is a unit vector pointing into the fluid domain. 2. Methodology To analyse building drag and upstream shielding, this section primaril… view at source ↗
Figure 2
Figure 2. Figure 2: (a) A satellite plane view of the campus of the University of Bristol, overlaid with a footprint of the simulation morphology. From Google Maps. (b) A 3-D view of the simulated campus morphology (Bi et al., 2025), with the colour indicating the building height level. A global Cartesian coordinate system (𝑋, 𝑌 , 𝑍) is defined with the origin at the campus centre of the plane view, where positive 𝑋, 𝑌 are al… view at source ↗
Figure 3
Figure 3. Figure 3: The vertical profiles of plane-averaged velocity (a, e), total kinematic stress (b, f) and disperisive stress (c, g) in streamwise direction (upper row) and spanwise (bottom row) direction, respectively. The streamwise distributed drag 𝑓𝐷 (d). The solid line represents the overall mean value across the wind directions, while the shaded band denotes the range between the minimum and maximum values. The dash… view at source ↗
Figure 4
Figure 4. Figure 4: (a) The frontal area index 𝜆𝑓 and the drag coefficient 𝐶𝑑 of the entire morphology varying with the wind direction 𝜃, where the solid and dashed vertical lines label the principal direction and secondary direction of the morphology, respectively. (b) A plane view of the morphology with the principal direction (thick solid) and secondary direction (thick dashed) from the centre and marked by solid line and … view at source ↗
Figure 4
Figure 4. Figure 4: (a) The frontal area index 𝜆𝑓 and the drag coefficient 𝑐𝐷 of the entire campus varying with the wind direction 𝜃, where the solid and dashed vertical lines label the principal direction and secondary direction of the morphology, respectively. (b) A plane view of the morphology with the principal direction (thick solid) and secondary direction (thick dashed) from the centre and marked by a solid line and a … view at source ↗
Figure 5
Figure 5. Figure 5: (a) Cumulative distribution function (CDF) of direction-averaged building drag, where buildings are ranked in descending order of drag. (b) Highlight of the top 20 buildings with the highest drag, where colour indicates their proportion in the total drag of all buildings. The top 3 buildings are labelled with names. 4.2. Direction-averaged drag on individual buildings To investigate the drag acting on indi… view at source ↗
Figure 5
Figure 5. Figure 5: Building drag under wind direction 𝜃 = 180◦ (wind direction indicated by the light blue arrow at the bottom-left corner): (a) Drag of each building represented by colour, normalised by the total campus drag. The three buildings with the largest drag are numbered. Black arrows indicate the resultant force direction of each building, with lengths proportional to the logarithm of the resultant force magnitude… view at source ↗
Figure 6
Figure 6. Figure 6: (a) Sketch of the open space (the purple patch) and shielding height in front of a target T-shaped building 42 under the wind direction 𝜃 = 180° shown as the purple arrow. The double-head arrow marks the maximum width of the fetch perpendicular to the wind direction. Variation of the shielding effect parameters (b) 𝐿𝑠∕𝐻𝑠 and (c) 𝐻𝑠∕𝐻 of the target building with the wind direction. The (d) cumulative integr… view at source ↗
Figure 6
Figure 6. Figure 6: (a) Sketch of the open space (the purple patch) and shielding height in front of a target T-shaped building 31 under the wind direction 𝜃 = 180° shown as the purple arrow. The horizontal double-headed arrow marks the maximum width of the fetch 𝑊 perpendicular to the wind direction, and the vertical double-headed arrow indicates the fetch length 𝐿𝑠 starting from the most windward point of the target buildin… view at source ↗
Figure 7
Figure 7. Figure 7: (a) Dependence of the averaged drag coefficient of an individual building on the shielding parameters 𝐿𝑠∕𝐻𝑠 and 𝐻𝑠∕𝐻. (b) The probability of the shielding parameters 𝐿𝑠∕𝐻𝑠 and 𝐻𝑠∕𝐻 occurring on the campus. The red dashed lines classify the domain into four regimes (S1 -S4) according to the wake location and shielding situation. Near wake (NW) Far wake (FW) Percentage Average 𝐶𝑑 Percentage Average 𝐶𝑑 No Shi… view at source ↗
Figure 8
Figure 8. Figure 8: (a) Example of the modified drag procedure at 𝜃 = 180° (wind direction indicated by the arrow), showing buildings excluded from the calculation. All excluded buildings lie within the near-wake shielding regime (S1) and are greyed out in this case. A highlight of the modified frontal area in regime S3 is presented in the right corner. (b-d) Comparison of the modified drag coefficient, total drag stress, and… view at source ↗
Figure 9
Figure 9. Figure 9: (a) Facet mesh of an example building, with a representative facet highlighted together with its associated nearest Cartesian grid cells. (b) The facet pressure of building 42 on the Bristol campus. (c) The drag density field 𝜌𝐷 converted from the facet data of building 42. straightforward to show that ∑ 𝑖,𝑗,𝑘 𝜌𝜙;𝑖𝑗𝑘Δ𝑥Δ𝑦Δ𝑧𝑘 = ∑ 𝑚 𝜙𝑚𝐴𝑚, (23) The left-hand side is a discrete volume integral, which we can rew… view at source ↗
Figure 9
Figure 9. Figure 9: All 110 campus buildings, numbered from 1 to 110 in order from southwest to northeast, overlaid with four representative point stations (A-D) used for local velocity validation. The buildings are colored according to their height. time- and plane-averaged velocity profiles, as well as vertical profiles at four local stations, are collected and compared. The four local stations (A-D) are marked in [PITH_FU… view at source ↗
Figure 10
Figure 10. Figure 10: The height-masking footprint ℎ of building 42, overlaid with its principal (𝒗1 in black) and secondary (𝒗2 in grey) directions, originating at the building centroid (indicated by a circle). The dashed arrow indicates the wind direction 𝜃, while 𝛼 denotes the angle between the wind direction and 𝒗1 . represents the contribution of facet 𝑚 to the frontal area. Denoting the associated volumetric frontal area… view at source ↗
Figure 10
Figure 10. Figure 10: Grid-sensitivity analysis and cross-code verification. Results from PALM simulations at the current resolution and uDALES simulations at a doubled resolution (grid spacing halved) are compared against the current uDALES results. (a-d) Horizontally plane-averaged streamwise velocity profiles for four wind directions, 𝜃 = 0◦ , 90◦ , 180◦ , 270◦ , respectively; refined uDALES results are available for 𝜃 = 0◦… view at source ↗
Figure 11
Figure 11. Figure 11: (a) Facet mesh of an example building, with a representative facet highlighted together with its associated nearest Cartesian grid cells. (b) The facet pressure of building 31 on the Bristol campus. (c) The drag density field 𝜌𝐷 converted from the facet data of building 31. This is an important result, since it links the superficial average of the volumetric density directly to the surface integral. Indee… view at source ↗
Figure 12
Figure 12. Figure 12: The height-masking footprint ℎ of building 31, overlaid with its principal (𝒗1 in black) and secondary (𝒗2 in grey) directions, originating at the building centroid (indicated by a circle). The dashed arrow indicates the wind direction 𝜃, while 𝛼 denotes the angle between the wind direction and 𝒗1 . (Athanatopoulou and Doudoumis, 2008;Jolliffe and Cadima, 2016). The novelty of the approach described here … view at source ↗
Figure 13
Figure 13. Figure 13: (a) The colour plot of the separation parameter Γ against different threshold pairs 𝐿𝑠∕𝐻𝑠 , 𝐻𝑠∕𝐻 . The larger the Γ, the better the thresholds satisfy the separation requirements. The star marks the optimal thresholds, and the black contour marks Γ = 0.25. (b) Box plots of the building drag coefficient for the four regimes identified by the optimal threshold. S1: shielding near-wake, S2: shielding far-wak… view at source ↗
read the original abstract

Shielding by upstream buildings is a fundamental control on urban drag, yet its influence remains poorly quantified in realistic urban environments. Here, we investigate shielding effects using building-resolved large-eddy simulations of the University of Bristol campus, comprising 110 buildings of varying height, shape and orientation. Twenty-four wind directions are considered, allowing each building to experience a wide range of upstream shielding conditions. While the total drag of the campus exhibits only moderate directional variability, the drag acting on individual buildings varies substantially. In the present case, approximately $20\%$ of buildings account for $80\%$ of the total drag, which is primarily attributed to a small number of large buildings that contribute disproportionately high drag forces. To quantify shielding, we introduce two dimensionless parameters: the upstream fetch ratio, $L_s/H_s$, and the relative height ratio, $H_s/H$, where $L_s$ is the distance to the nearest upstream obstacle, $H_s$ is the height of the upstream obstacle, and $H$ is the height of the target building. These parameters distinguish between near- and far-wake conditions and between sheltered and exposed buildings, providing a simple method to characterise shielding effects in realistic urban environments. The study provides valuable quantitative insight into drag and shielding in the Bristol campus morphology; more importantly, it establishes a general framework for analysing drag and shielding that can be applied in other complex urban environments. The results identify shielding as a primary control on building drag and motivate shielding-aware measures of effective frontal area and drag coefficient

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper uses building-resolved large-eddy simulations of the University of Bristol campus (110 buildings) across 24 wind directions to quantify shielding effects on drag. It reports that ~20% of buildings account for 80% of total drag (driven by a few large buildings), introduces the upstream fetch ratio L_s/H_s and relative height ratio H_s/H to distinguish near/far-wake and sheltered/exposed regimes, and concludes that shielding is a primary control on building drag while motivating shielding-aware effective frontal area and drag coefficient measures. The work also claims to establish a general framework applicable to other urban morphologies.

Significance. If the simulated drag forces are accurate, the identification of a strong 20/80 drag distribution and the simple dimensionless parameters for classifying shielding provide a practical, morphology-aware approach that could improve urban canopy drag parameterizations and wind-load estimates beyond current uniform or isolated-building assumptions.

major comments (2)
  1. [Abstract] Abstract: the central claim that shielding is the primary control on drag (and the proposed utility of L_s/H_s and H_s/H) is extracted directly from the LES-derived per-building force budgets, yet the manuscript reports no validation of these drag values against field measurements, wind-tunnel data, or any sensitivity tests to grid resolution, domain truncation, wall modeling, or SGS closure. Any systematic bias in the force budgets would propagate directly into the 20/80 attribution and the wake-regime distinctions.
  2. The quantitative results (20/80 distribution, directional variability of individual-building drag) treat the LES outputs as faithful representations of real aerodynamic loading, but no error estimation, grid-convergence checks, or post-processing details for force integration are described, leaving the load-bearing numerical foundation unverified.
minor comments (1)
  1. [Abstract] The abstract states that the framework 'can be applied in other complex urban environments' but provides no concrete example or test on a second morphology to support this generality claim.

Simulated Author's Rebuttal

2 responses · 1 unresolved

We thank the referee for the constructive comments on validation and numerical verification. These points identify a genuine limitation in the current manuscript. We address each comment below and indicate where revisions will be made. The study focuses on relative drag partitioning and a shielding classification framework rather than absolute force values, but we agree that explicit discussion of numerical foundations is warranted.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claim that shielding is the primary control on drag (and the proposed utility of L_s/H_s and H_s/H) is extracted directly from the LES-derived per-building force budgets, yet the manuscript reports no validation of these drag values against field measurements, wind-tunnel data, or any sensitivity tests to grid resolution, domain truncation, wall modeling, or SGS closure. Any systematic bias in the force budgets would propagate directly into the 20/80 attribution and the wake-regime distinctions.

    Authors: We agree that direct validation of the per-building drag forces against measurements is absent. No field or wind-tunnel data exist for the Bristol campus at building-resolved scale across 24 directions. The LES setup follows standard practices used in prior urban canopy studies (e.g., references to validated codes and closures will be added). The 20/80 distribution and regime distinctions are relative within the simulated ensemble; absolute magnitudes are not claimed to be predictive. In revision we will (i) add an explicit limitations paragraph on the lack of site-specific validation, (ii) reference existing LES validation benchmarks for similar urban morphologies, and (iii) include a short discussion of how systematic bias would affect the reported ratios. No new simulations are planned. revision: partial

  2. Referee: [—] The quantitative results (20/80 distribution, directional variability of individual-building drag) treat the LES outputs as faithful representations of real aerodynamic loading, but no error estimation, grid-convergence checks, or post-processing details for force integration are described, leaving the load-bearing numerical foundation unverified.

    Authors: We accept that error bars, grid-convergence data, and force-integration details are not reported. The manuscript will be revised to include: (a) the exact surface-integration procedure used to obtain per-building forces, (b) any available domain-size and resolution sensitivity tests performed during setup, and (c) a brief statement on the absence of a full grid-convergence study for the 110-building domain (computational cost precludes repeating all 24 directions at multiple resolutions). These additions will be placed in a new Methods subsection. We do not claim the forces are error-free; the framework is presented as morphology-aware rather than quantitatively predictive. revision: partial

standing simulated objections not resolved
  • Direct validation of per-building drag forces against field or wind-tunnel measurements for the Bristol campus

Circularity Check

0 steps flagged

No circularity; results from direct LES force budgets and new geometric definitions

full rationale

The paper computes total and per-building drag directly from building-resolved large-eddy simulations of the Bristol campus morphology across 24 wind directions. The parameters L_s/H_s and H_s/H are introduced explicitly as new dimensionless ratios based on upstream geometry (distance and height of nearest obstacle relative to target building height), then used to bin the simulation outputs into wake regimes. No equations define drag in terms of these ratios or vice versa; the attribution of drag variation to shielding follows from the raw force budgets rather than any fitted parameter or self-referential relation. No load-bearing self-citations or uniqueness theorems are invoked in the provided text. The derivation chain is therefore self-contained against the simulation data.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the accuracy of LES for urban flows and the representativeness of the Bristol campus. No explicit free parameters or invented physical entities are introduced; the two ratios are geometric constructs derived from building positions and heights.

axioms (1)
  • domain assumption Large-eddy simulation resolves the dominant turbulent structures and surface drag forces sufficiently for the campus morphology
    Invoked implicitly by relying on building-resolved LES results without reported validation against measurements.

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