Working Paper
J. M. Han, H. W. Samuelson, and Other researchers. Working Paper. “Architectural Design Considerations for Healthy Sleep Environments in Low-Energy Buildings .” Building and Environment.
J. M. Han and A. Malkawi. Working Paper. “Computationally efficient CFD prediction using 3D convolutional neural networks for the building design.” Applied Energy.
J. M. Han, W. Wu, and A. Malkawi. Working Paper. “Machine learning guided semi-empirical model for natural ventilation assessment.” Renewable and Sustainable Energy Review.
J. M. Han, S. Lim, and A. Malkawi. Submitted. “New metric for naturally ventilated building design and operation of windows at home in U.S. during COVID-19.” Building and Environment.
J. M.* Han, E. S. Choi, and A. Malkawi. 8/23/2021. “CoolVox: Advanced 3D convolutional neural network models for predicting solar radiation on building façades.” Building Simulation .
J. M.* Han, Y.Q. Ang, A. Malkawi, and H. W. Samuelson. 1/11/2021. “Using recurrent neural networks for localized weather prediction with combined use of public airport data and on-site measurements.” Building and Environment, 192, 107601.Abstract
Weather data is a crucial input for myriad applications in the built environment, including building energy
modeling and daylight analysis. Building science practitioners and researchers have been able to select from a
variety of weather files, such as Weather Year for Energy Calculation 2 (WYEC2) and the Typical Meteorological
Year (TMY). However, commonly used weather files are typically synthesized to represent trends over a relatively
longer periods of time, and are often unable to accurately depict climatic conditions that result from local
contexts, such as the heat island effect, wind flow, even local temperature and relative humidity. This results in
discrepancies in building performance simulations.
This study proposes a methodology using recurrent neural networks to generate synthetic localized weather
data that are significantly more accurate and representative of local conditions than standard weather files. The
predictions were validated against actual on-site measurements, and achieved a low mean square error of 2.96
and over 185% improvement in validation accuracy. Overall, the performance of selected models has shown over
100% improvements in test accuracy compared with standard weather files and weather station data at the
nearest airport. The proposed methodology can be used to morph generic weather files to accurately represent
localized conditions, or generate localized data for a longer time span with only a subset of data available/
collected. This is useful for downstream built environment applications, especially building energy modeling,
since representative weather data capturing trends of temperature and other variables will result in enhanced
accuracies of the building energy models. The method can also be used in urban analysis pipelines to enhance
resilience against climate change.
J. M. Han, C. K. Chang, and A. Malkawi. 9/1/2020. “ARINet: Using 3D convolutional neural networks to estimate annual radiation intensities on building facades.” In The 2020 Building Performance Analysis Conference and SimBuild co-organized by ASHRAE and IBPSA-USA, Pp. 252-259. arinet_using_3d_convolutional_neural_networks_to_estimate_annual_radiation_intensities_on_building_facades.pdf
W. Wu, J. M.* Han, and A. Malkawi. 6/15/2020. “Simplified direct forcing approach for dynamic modeling of building natural ventilation.” Building and Environment, 188, 107509.Abstract
Natural ventilation is a promising approach to provide passive cooling in highly energy efficient buildings. A
widely applied method to evaluate the performance of natural ventilation is computational fluid dynamics (CFD).
However, dynamic modeling of natural ventilation from 1 h to the next is very challenging because state-of-theart
CFD simulations treat windows as fixed wall boundary surfaces. The objective of this study is to propose a
direct forcing approach to implement dynamic window operations in CFD simulations. The direct forcing
approach marks a band of computational cells according to window positions, and adds an ad-hoc body force to
the momentum equations and turbulence production term to the kinetic energy equation. The direct forcing
approach shows a high level of performance when predicting volume flow rates through window apertures. The
relative deviation was found to vary between 2.2% and 14%, depending on the reference wind speeds. Direct
forcing also showed good performance when predicting the height of the neutral plane when the wind incident
angle was less than 135◦. The direct forcing approach can be applied to study the dynamic daily or weekly CO2
variations in naturally ventilated buildings with predefined control algorithms. Future work will consider the
influence of wall shear stresses and zero normal velocity to improve the accuracy of the direct forcing approach
as applied to wind incident angles larger than 135◦.
Piette M. A. Han J. M. Wu W. Malkawi A. & Yoon, N.*. 5/14/2020. “Optimization of window positions for wind-driven natural ventilation performance.” Energies, 13, 2464. energies-13-02464-v3.pdf
N. Yoon, J. M. Han, and A. Malkawi. 9/2019. “Finding the optimum window locations of a single zone: To maximize the wind-driven natural ventilation potential.” In The 16th International Building Performance Simulation Association Conference, Pp. 578-584. Rome, Italy.
J. M. Han, A. Malkawi, and K. Z. Gajos. 9/2019. “Eabbit 1.0: New environmental analysis software for solar energy representation.” In The 16th International Building Performance Simulation Association Conference, Pp. 2599-2605. Rome, Italy.
J. M. Han and D. Park. 9/2019. “Seasonal optimization of a dynamic thermo-optical ETFE façade system.” The 16th International Building Performance Simulation Association Conference, Pp. 4887-4893.
Jung Min Han. 5/8/2016. “Green Design Decision-Making Toolbox (GDDT) Vol 1. Photovoltaics and Green roofs.” Architecture (Building performance and diagnostics).
Jihyun Park, Yue Lei, Ye Song, Jung Min Han, June Young Park, Jie Zhao, Azizan Aziz, Vivian Loftness, and Ruben Moron Rojas. 4/21/2015. “Comparison of the results obtained with simplified IEQ toolkit and robust instrument in POE field studies.” Engineering Sustainability 4/21/2015. poe_jpark.pdf