Data-driven model for cross ventilation potential in high-density cities based on coupled CFD simulation and machine learning-Reference-Cited by-同舟云学术

Data-driven model for cross ventilation potential in high-density cities based on coupled CFD simulation and machine learning

Published:2019-11 Issue: Volume:165 Page:106394
ISSN:0360-1323
Container-title:Building and Environment
language:en
Short-container-title:Building and Environment

Author:

Ding Chao,Lam Khee Poh

Funder

Carnegie Mellon University

Publisher

Elsevier BV

Subject

Building and Construction,Geography, Planning and Development,Civil and Structural Engineering,Environmental Engineering

Reference64 articles.

1. Population Division. World Urbanization Prospects: the 2014 Revision (Highlight);United Nations Department of Economic and Social Affairs,2014

2. Transition to Sustainable Buildings: Strategies and Opportunities to 2050;Dulac,2013

3. 2018 Global Status Report: towards a Zero-Emission, Efficient and Resilient Buildings and Construction Sector;International Energy Agency and the United Nations Environment Programme,2018

4. Evaluating thermal comfort in mixedmode buildings: a field study in a subtropical climate;Luo;Build. Environ.,2015

5. Estimates of improved productivity and health from better indoor environments;Fisk;Indoor Air,1997

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