A Scalable Multi-GPU Accelerated CityFFD Solver for Whole City Urban Microclimate Simulations
Bibliographic record
Abstract
Achieving real-time, high-fidelity computational fluid dynamics (CFD) simulations for urban microclimate analysis remains a substantial challenge due to limitations in computational constraints, data acquisition, and over-simplification of the underlying physics. In recent years, the growing interest in smart city development, such as the integration of urban air mobility (UAM) systems and smart city planning strategies, has further amplified the demand for rapid, high-resolution urban microclimate analysis to support operational safety assessments and urban planning decisions. To illustrate the computational challenge, consider a typical urban domain spanning about 5 km × 5 km × 0.1 km (length × width × height) with a grid resolution of 1 m3, which would require approximately 2.5 billion computational cells, making both the computational cost and memory requirements prohibitively expensive. To mitigate these computational barriers, this work develops a novel computational framework that integrates the Message Passing Interface (MPI) and Computed Unified Device Architecture (CUDA) C++ with the CityFFD solver, enabling scalable parallel GPU-accelerated simulations for efficient large-scale computations while overcoming memory constraints. Specifically, the framework incorporates a parallel high-order Semi-Lagrangian (SL) solver for efficient advection equation solutions, a modified PaScal_TDMA 2.0 library for distributed tridiagonal matrix algorithm (TDMA) computations, and a parallel Jacobi algorithm with enhanced convergence properties for solving the Poisson equation, making it suitable for scalable parallel GPU-accelerated environments. The developed solver is validated through three benchmark cases: (1) a 2D isothermal lid-driven cavity problem, (2) a 3D isolated building, and (3) a 3D high-rise building surrounded by a group of low-rise buildings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".