diff --git a/_bibliography/papers.bib b/_bibliography/papers.bib index cffdb43a08d8b..ef5e1c30f7c5a 100644 --- a/_bibliography/papers.bib +++ b/_bibliography/papers.bib @@ -11,6 +11,7 @@ @inproceedings{rothe2025influence url = {https://publications.ibpsa.org/conference/paper/?id=bs2025_1754}, preview = {bs-2025-vegetation.png}, bibtex_show = {true}, + code = {https://github.com/SustainableUrbanSystemsLab/CS-ChinmayRothe}, selected = {false}, google_scholar_id = {TQgYirikUcIC} } @@ -353,7 +354,7 @@ @inproceedings{kastner2020solving url = {https://www.researchgate.net/publication/346039320_Solving_Thermal_Bridging_Problems_for_Architectural_Applications_with_OpenFOAM}, abstract = {Although recent advancements in computational architecture show promising capabilities, it remains difficult for architects to conduct advanced simulations due to the limited software interoperability. For thermal bridging analyses, the architectural community traditionally relies on specific software tools that are not integrated into a CAD environment. To integrate such analyses into the ongoing design process, we implement a software tool to run heat transfer simulations with OpenFOAM from Grasshopper and Rhinoceros. This paper presents an implementation for box-shaped geometries and compares its results to a thermal bridge analysis from a validated simulation engine. We show that OpenFOAM's chtMultiregionFoam solver is capable of accurately predicting temperature distributions in a geometry setup with 13 different regions and 8 different materials. In conclusion, we show that heat transfer studies can be highly automated and integrated into an iterative design process.}, bibtex_show = {true}, - code = {https://github.com/kastnerp/HeatFlux}, + code = {https://github.com/SustainableUrbanSystemsLab/CP-SimAUD-HeatFlux}, google_scholar_id = {qjMakFHDy7sC}, preview = {sim-aud-2020-02.jpg} } @@ -438,4 +439,18 @@ @misc{vangelova2024gni bibtex_show = {true}, preview = {gni-2024-01.png}, code = {https://github.com/SustainableUrbanSystemsLab/CP-GNI2024-Symposium-Enriching-geospatial-building-datasets} -} \ No newline at end of file +} +@article{KARADAG2026107614, +title = {Machine learning for urban wind simulation: A comprehensive review}, +journal = {Sustainable Cities and Society}, +pages = {107614}, +year = {2026}, +issn = {2210-6707}, +doi = {https://doi.org/10.1016/j.scs.2026.107614}, +url = {https://www.sciencedirect.com/science/article/pii/S221067072600497X}, +author = {Ilker Karadag and Danny Smyl and Patrick Kastner}, +keywords = {Urban wind simulation, Machine learning, Computational fluid dynamics, Pedestrian-level wind, Surrogate modeling}, +abstract = {Urban wind simulation plays an important role in pedestrian comfort assessment, wind safety evaluation, air quality analysis, and broader urban environmental planning. Conventional computational fluid dynamics (CFD) approaches, although physically grounded, remain computationally demanding and highly reliant on mesh quality, which makes them impractical for large-scale or iterative urban design workflows. This study reviews recent developments in data-driven, physics-informed, and hybrid machine learning methods for urban wind prediction, with attention to their modeling assumptions, validation strategies, and generalization behavior. The literature is organized along the urban wind simulation pipeline to provide a structured overview of current surrogate modeling approaches and their practical limitations. The review highlights open challenges related to robustness, benchmarking, and uncertainty quantification, and discusses directions for improving the reliability and scalability of ML-based urban wind modeling.}, +preview = {karadag_abstract.png}, +bibtex_show = {true} +} diff --git a/_data/citations.yml b/_data/citations.yml index d62280601a028..3bfbfede363c9 100644 --- a/_data/citations.yml +++ b/_data/citations.yml @@ -1,12 +1,12 @@ metadata: - last_updated: '2026-05-22' + last_updated: '2026-06-22' papers: XNaNgkwAAAAJ:-f6ydRqryjwC: citations: 5 title: 'OpenPyStruct: Open-source toolkit for machine learning-driven structural optimization' year: '2025' XNaNgkwAAAAJ:2osOgNQ5qMEC: - citations: 16 + citations: 17 title: Predicting Space Usage by Multi-Objective Assessment of Outdoor Thermal Comfort around a University Campus year: '2020' XNaNgkwAAAAJ:4DMP91E08xMC: @@ -33,10 +33,6 @@ papers: citations: 14 title: Towards High-Resolution Annual Outdoor Thermal Comfort Mapping In Urban Design year: '2019' - XNaNgkwAAAAJ:HDshCWvjkbEC: - citations: 0 - title: 'Establishing a Fire Risk Map Based on Planned Spatial Layouts and Environment in Rural Planning: A Case Study of a Stereotype Village in China' - year: '2025' XNaNgkwAAAAJ:IWHjjKOFINEC: citations: 0 title: How Much Computational Complexity is Necessary to Model Relevant Aspects in Microclimate Urban Physics? @@ -54,7 +50,7 @@ papers: title: 'Eddy3D: A toolkit for decoupled outdoor thermal comfort simulations in urban areas' year: '2022' XNaNgkwAAAAJ:TQgYirikUcIC: - citations: 0 + citations: 1 title: 'The influence of vegetation structure on urban microclimate: A CFD analysis of urban block and vegetation densities' year: '2025' XNaNgkwAAAAJ:Tyk-4Ss8FVUC: @@ -62,7 +58,7 @@ papers: title: Fighting Hunger in the Digital Era year: '2018' XNaNgkwAAAAJ:UeHWp8X0CEIC: - citations: 79 + citations: 81 title: 'From energy performative to livable Mediterranean cities: An annual outdoor thermal comfort and energy balance cross-climatic typological study' year: '2020' XNaNgkwAAAAJ:WF5omc3nYNoC: @@ -86,11 +82,11 @@ papers: title: 'Comparative Modeling of Urban Microclimate and Outdoor Thermal Comfort: A Case Study of Georgia Tech Campus' year: '2026' XNaNgkwAAAAJ:_kc_bZDykSQC: - citations: 111 + citations: 113 title: A GAN-based surrogate model for instantaneous urban wind flow prediction year: '2023' XNaNgkwAAAAJ:d1gkVwhDpl0C: - citations: 59 + citations: 62 title: A cylindrical meshing methodology for annual urban computational fluid dynamics simulations year: '2019' XNaNgkwAAAAJ:dhFuZR0502QC: @@ -134,7 +130,7 @@ papers: title: Solving Thermal Bridging Problems for Architectural Applications with OpenFOAM year: '2020' XNaNgkwAAAAJ:roLk4NBRz8UC: - citations: 43 + citations: 44 title: Modeling outdoor thermal comfort along cycling routes at varying levels of physical accuracy to predict bike ridership in Cambridge, MA year: '2022' XNaNgkwAAAAJ:u5HHmVD_uO8C: diff --git a/_layouts/about.liquid b/_layouts/about.liquid index 02d496b7d4a40..dd3626c3c91e3 100644 --- a/_layouts/about.liquid +++ b/_layouts/about.liquid @@ -50,9 +50,6 @@ layout: default {% if page.latest_posts and page.latest_posts.enabled %} -

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