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ENVI-met is a widely applied computational platform for simulating urban microclimates, with its BIO-met module enabling the calculation of thermal comfort indices such as PMV, PET, UTCI, and SET*. These indices provide valuable guidance for assessing outdoor thermal conditions, yet their accuracy depends on calibration against real-world data. This chapter evaluates the precision of ENVI-met simulations by combining field experiments with model-based outputs in a transitional space on a university campus in Guangzhou. Walking trials under controlled metabolic conditions were paired with simultaneous environmental monitoring and three-dimensional ENVI-met modeling of the test site. Results revealed systematic deviations, with simulated air temperatures exceeding measured values by up to 2 °C and wind speed variations being insufficiently captured. To reconcile these differences, experimental data were integrated with BIO-met predictions to generate refined β-model indices, which were then compared with previously developed α-models. The overlapping trends between α- and β-models, together with the formulation of an enhanced calibration algorithm, demonstrate the potential to improve BIO-met’s predictive reliability. These advancements support more accurate evaluation of outdoor thermal comfort and provide a practical basis for integrating simulation tools with field data in sustainable urban design.

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Improved Thermal Index Models for Outdoor Thermal Comfort Evaluation—Calibration of Simulation Results Using Experimental Data

  • 章节

  • 发表日期：

    2026年

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  • Zhaosong Fang,
  • Sheng Zhang,
  • Zhang Lin,
  • Xiwen Feng,
  • Yuchun Zhang

摘要

ENVI-met is a widely applied computational platform for simulating urban microclimates, with its BIO-met module enabling the calculation of thermal comfort indices such as PMV, PET, UTCI, and SET*. These indices provide valuable guidance for assessing outdoor thermal conditions, yet their accuracy depends on calibration against real-world data. This chapter evaluates the precision of ENVI-met simulations by combining field experiments with model-based outputs in a transitional space on a university campus in Guangzhou. Walking trials under controlled metabolic conditions were paired with simultaneous environmental monitoring and three-dimensional ENVI-met modeling of the test site. Results revealed systematic deviations, with simulated air temperatures exceeding measured values by up to 2 °C and wind speed variations being insufficiently captured. To reconcile these differences, experimental data were integrated with BIO-met predictions to generate refined β-model indices, which were then compared with previously developed α-models. The overlapping trends between α- and β-models, together with the formulation of an enhanced calibration algorithm, demonstrate the potential to improve BIO-met’s predictive reliability. These advancements support more accurate evaluation of outdoor thermal comfort and provide a practical basis for integrating simulation tools with field data in sustainable urban design.