Optimizing urban morphology: Evolutionary design and multi-objective optimization of thermal comfort and energy performance-based city forms for microclimate adaptation DOI
N.M. Castrejon-Esparza, M.E. González-Trevizo, K.E. Martínez-Torres

et al.

Energy and Buildings, Journal Year: 2025, Volume and Issue: unknown, P. 115750 - 115750

Published: April 1, 2025

Language: Английский

Carbon-friendly design method of tunnel lining segments based on Pareto optimal analysis DOI
Tao Liu,

Hehua Zhu,

Yi Shen

et al.

Tunnelling and Underground Space Technology, Journal Year: 2025, Volume and Issue: 161, P. 106602 - 106602

Published: April 4, 2025

Language: Английский

Citations

0

Response Surface Methodology using desirability functions for multiobjective optimization to minimize indoor overheating hours and maximize useful daylight illuminance DOI Creative Commons
Juan Gamero-Salinas, Jesús López–Fidalgo

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 9, 2025

Improving thermal comfort often impacts daylight, creating trade-offs that remain underexplored, particularly in tropical dwellings. Overheating metrics-essential for assessing conditions warm regions-are entirely absent from daylight performance analysis. Response Surface Methodology (RSM) and desirability functions were employed to optimize the of a typical low-rise housing typology. Specifically, this approach simultaneously optimized Indoor Hours (IOH) Useful Daylight Illuminance (UDI) metrics through an Overall Desirability (D). Each response required only 138 simulation runs (~ 30 h: 276 runs) determine optimal values passive strategies: window-to-wall ratio (WWR) roof overhang depth across four orientations (eight factors). Initial screening based on [Formula: see text] fractional factorial design, identified key factors using stepwise Lasso regression, narrowed down three: south west, WWR south. Then, RSM optimization yielded solution (west/south overhang: 3.78 m, west WWR: 3.76%, 29.3%) with D 0.625 (IOH: 8.33%, UDI: 79.67%). Finally, robustness analysis 1,000 bootstrap replications provided 95% confidence intervals values. This study balances few experiments computationally-efficient multiobjective approach.

Language: Английский

Citations

0

Designing multilayer graphene membranes with well seawater desalination performance using machine learning combined with multi-objective optimization DOI
Qiang Xie, Yu Qiao,

Huaxi Guo

et al.

Journal of environmental chemical engineering, Journal Year: 2025, Volume and Issue: unknown, P. 116552 - 116552

Published: April 1, 2025

Language: Английский

Citations

0

A two-layer intelligent decision-making model for solar photovoltaic panel retrofit at the regional scale DOI

Dingyuan Ma,

Si‐Yu Yue,

Shu Su

et al.

Solar Energy, Journal Year: 2025, Volume and Issue: 294, P. 113496 - 113496

Published: April 14, 2025

Language: Английский

Citations

0

Optimizing urban morphology: Evolutionary design and multi-objective optimization of thermal comfort and energy performance-based city forms for microclimate adaptation DOI
N.M. Castrejon-Esparza, M.E. González-Trevizo, K.E. Martínez-Torres

et al.

Energy and Buildings, Journal Year: 2025, Volume and Issue: unknown, P. 115750 - 115750

Published: April 1, 2025

Language: Английский

Citations

0