Journal Articles

Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/7915

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    A machine learning-guided semi-empirical model for predicting single-sided natural ventilation rates
    (Elsevier B V, 2025-10-01) Han JM; Wu W; Malkawi A
    Most of the state-of-the-art natural ventilation models were developed for either single-sided, or cross ventilation mode, or buoyancy-driven ventilation. Natural ventilation (NV) of a single zone may vary between different modes in different seasons depending on the design and the operation of other building systems. This paper tailors the machine learning embedded semi-empirical models to predict the natural ventilation rate in a single zone. The process of model development consists of two parts: 1) semi-empirical model development for single-sided ventilation with a local context 2) machine learning driven component to accurately predict a specific lab condition. By taking a case study, the series of steps were taken to validate model accuracy with an estimated flowrate in given window operable areas. Firstly, the contextual inputs and localized wind speed as well as window models were investigated. Finally, we developed a machine learning model to predict the localized lab environment by using pressure sensor's data on façade. The random forest model was trained and fine-tuned to predict localized pressure coefficients (Cp). Over 75 % of the predicted values fall within the model's ± 1 standard deviation credible interval, demonstrating not only high predictive reliability but also suitability for integration into empirical ventilation models. These results highlight the model's potential as a robust input generator for semi-empirical frameworks with locally collected weather data, particularly in applications involving window operation control and site-specific model calibration.
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    A Method toward Real-Time CFD Modeling for Natural Ventilation
    (MDPI (Basel, Switzerland), 2018-12-01) Wu W; Wang B; Malkawi A; Yoon N; Sehovic Z; Yan B
    Natural ventilation is often used as a passive technology to reduce building energy consumption. To leverage the rule-based natural ventilation control to more advanced control at multiple spatial scales, mathematical modeling is needed to calculate the real-time ventilation rate, indoor air temperatures, and velocities at high spatial resolution. This study aims to develop a real-time mathematical modeling framework based on computational fluid dynamics (CFD). The real-time concept is implemented by using real-time sensor data, e.g., wall surface temperatures as boundary conditions, while data assimilation is employed to implement real-time self-calibration. The proof of concept is demonstrated by a case study using synthetic data. The results show that the modeling framework can adequately predict real-time ventilation rates and indoor air temperatures. The data assimilation method can nudge the simulated air velocities toward the observed values to continuously calibrate the model. The real-time CFD modeling framework will be further tested by the real-time sensor data once building construction is fully completed.
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    Optimization of Window Positions for Wind-Driven Natural Ventilation Performance
    (MDPI (Basel, Switzerland), 2020-05-14) Yoon N; Piette MA; Han JM; Wu W; Malkawi A
    This paper optimizes opening positions on building facades to maximize the natural ventilation’s potential for ventilation and cooling purposes. The paper demonstrates how to apply computational fluid dynamics (CFD) simulation results to architectural design processes, and how the CFD-driven decisions impact ventilation and cooling: (1) background: A CFD helps predict the natural ventilation’s potential, the integration of CFD results into design decision-making has not been actively practiced; (2) methods: Pressure data on building facades were obtained from CFD simulations and mapped into the 3D modeling environment, which were then used to identify optimal positions of two openings of a zone. The effect of the selected opening positions was validated with building energy simulations; (3) results: The cross-comparison study of different window positions based on different geographical locations quantified the impact on natural ventilation effectiveness; and (4) conclusions: The optimized window position was shown to be effective, and some optimal solutions contradicted the typical cross-ventilation strategy.