Journal Articles
Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/7915
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Item Data-driven virtual sensor systems for dynamic temperature monitoring along food supply chains(Elsevier Ltd, 2026-01-01) Duan F; Meng X; Wu W; Zou Y; Zeng XContinuous monitoring of perishable food temperatures along supply chains is crucial for quality assurance and reducing food loss and waste. However, cost and installation constraints restrict sensor deployment, compromising the reliability of temperature monitoring. This study proposes a data-driven virtual sensor system that leverages deep learning to integrate multi-source data, enabling temperature estimation at sensor-inaccessible locations and thus reducing dependence on extensive physical sensor deployment. The system was evaluated across postharvest processing, storage, and transport. Results indicate that, with a fixed number of physical sensors, increasing the virtual-to-physical sensor ratio from 16 to 32 maintains the root mean square error below 0.3 °C. Further analysis shows that sensor placement within pallets has minimal impact on performance, whereas the choice of data sources and model architecture exerts a significant influence. Notably, a configuration of one sensor per pallet with a BiLSTM + attention model outperforms shallow networks, demonstrating the potential of data-driven virtual sensor system to enhance temperature monitoring and efficiency along food supply chains.Item Indoor Particulate Matter Transfer in CNC Machining Workshop and The Influence of Ventilation Strategies—A Case Study(MDPI (Basel, Switzerland), 2023-04-04) Yao H; Qiu S; Lv Y; Wei S; Li A; Long Z; Wu W; Shen X; Daneshazarian RParticulate matter in Computer Numerical Control (CNC) machining workshop is harmful to workers’ health. This paper studies particulate matter transfer and the performance of various ventilation strategies in a CNC machining workshop. To obtain the boundary condition of the particle field, instruments were installed to obtain the particle size attenuation characteristics and source strength, respectively. The results show that the 99% cumulative mass concentration of particles is distributed within 1.5 μm, and the release rate of particles from the full enclosure. Next, the indoor flow field and particle field were simulated by numerical simulation with the measured boundary conditions. The working area’s age of air, particle concentration, and ventilation efficiency were compared between four displacement ventilation methods and one mixed ventilation method. The results show that the working area’s mean particle concentration and ventilation efficiency under longitudinal displacement ventilation is better than other methods. At the same time, the mean age of air is slightly worse. In addition, mixed ventilation can obtain lower mean age of air, but the particle concentration is higher in the working area. The bilateral longitudinal ventilation can be improved by placing axial circulation fans with vertical upward outlets in the center of the workshop.Item Human-Machine Function Allocation Method for Submersible Fault Detection Tasks(MDPI (Basel, Switzerland), 2024-11-19) Yang C; Pang L; Wu W; Cao X; Lu B; Piao MThe operation and support (OS) officer is responsible for buoyancy regulation and fault detection of onboard equipment in the civil submersible. The OS officer carries out the above tasks through the human-machine interface (HMI) of a submersible buoyancy regulation and support (SBRS) system. However, the OS officer often faces uneven task frequency produced by fault tasks, which leads to an unbalanced mental workload and individual failures. To address this issue, we proposed a human-machine function allocation method based on level of automation (LOA) taxonomy and submersible task complexity (STC), aimed at improving human-machine cooperation in submersible fault detection tasks. Based on this method, we identified the LOA2 as the optimal human-computer function allocation scheme. In this study, three measurement techniques (subjective scale, work performance, and physiological status) were used to test 15 subjects to validate the effectiveness of the proposed optimal human-machine function allocation scheme. The GAMM test results also indicate that the proposed optimal human-machine function allocation scheme (LOA2) can improve the work performance of the operating system officials under low or high workloads and reduce the subjective workload.Item The Impact of Building Level of Detail Modelling Strategies: Insights into Building and Urban Energy Modelling(MDPI (Basel, Switzerland), 2024-09-11) Bishop D; Mohkam M; Williams BLM; Wu W; Bellamy L; Torgal FPLevel of detail (LoD) is an important factor in urban building energy modelling (UBEM), affecting functionality and accuracy. This work assesses the impacts of the LoD of the roof, window, and zoning on a comprehensive range of outcomes (annual heating load, peak heating demand, overheating, and time-series heating error) in a representative New Zealand house. Lower-LoD roof scenarios produce mean absolute error results ranging from 1.5% for peak heating power to 99% for overheating. Windows and shading both affect solar gains, so lower-LoD windows and/or shading elements can considerably reduce model accuracy. The LoD of internal zoning has the greatest effect on time-series accuracy, producing mean absolute heating error of up to 66 W. These results indicate that low-LoD “shoebox” models, common in UBEM, can produce significant errors which aggregate at scale. Accurate internal zoning models and accurate window size and placement have the greatest potential for error reduction, but their implementation is limited at scale due to data availability and automation barriers. Conversely, modest error reductions can be obtained via simple model improvements, such as the inclusion of eaves and window border shading. Overall, modellers should select LoD elements according to specific accuracy requirements.Item 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 AMost 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.Item Thermal energy storage–coupled heat pump systems: Review of configurations and modelling approaches(Elsevier, 2026-01-01) Zhou J; Wu W; Bellamy L; Bishop DHeat pump systems (HP) are effective technologies for reducing energy consumption and carbon emissions for space heating and cooling of buildings. However, with large-scale deployment, increased electrical demands can place significant stress on power networks. Integrating Thermal Energy Storage (TES) with HP systems offers a viable strategy to mitigate peak power demands and enhance overall energy efficiency by decoupling heat generation and use, hence power intensive heat-generation can be shifted to off-peak and more efficient times. Due to these benefits, the combination of HP and TES systems have gained increasing attention. A number of reviews have examined specific HP-TES configurations and applications, however a comprehensive analysis of HP-TES coupled systems and particularly their modelling approaches remains limited. This paper classifies HP and TES technologies, highlighting their respective benefits and limitations. It further examines various HP-TES system configurations and applications, with a particular focus on modelling approaches. By providing a structured and comparative overview of available modelling methods, this review supports researchers and engineers in selecting the most suitable modelling approach based on system complexity, computational constraints, and specific objectives, facilitating the optimization of HP-TES systems for enhanced energy efficiency and sustainability.Item 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 BNatural 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.Item 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 AThis 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.Item Cooling demand reduction with nighttime natural ventilation to cool internal thermal mass under harmonic design-day weather conditions(Elsevier Ltd, 2025-02-01) Li M; Shen X; Wu W; Cetin K; Mcintyre F; Wang L; Ding L; Bishop D; Bellamy L; Liu MCooling demand is steadily increasing across different climate zones due to global warming. A potential solution for cooling demand reduction is applying nighttime natural ventilation to cool internal thermal mass. However, a simplified and accurate modelling framework to assess the technique is still missing. The goal of the study is to build that framework integrated with a validated internal thermal mass model and apply the framework to quantify the cooling demand reduction potential in a space with different thermal mass and envelope configurations and in different climate zones. Results show that using Granite as internal thermal mass is three times more effective than concrete to reduce peak cooling load. Adding too much internal thermal mass can create adverse effects on cooling load reduction. The optimum thickness of internal thermal mass is between 28 and 45 mm. Envelope construction also has an influence on the performance of nighttime cooling. Applying the technique in buildings with lightweight structures reduces peak cooling load by 35.9% more than heavyweight structures. As heavyweight structures delay the release of the daily absorbed heat and cause higher indoor air temperatures at night. The two belts between the Tropic of Cancer and 60 degrees north latitude, and between the Tropic of Capricorn and 45 degrees south latitude are suitable for nighttime natural ventilation of internal thermal mass, achieving the annual cooling demand reduction above 1.25 kWh m−2. In Dessert climate zones, the technique exhibits an extraordinary potential to reduce cooling demand, up to 6.67 kWh m−2 per year.Item UAV Assisted Livestock Distribution Monitoring and Quantification: A Low-Cost and High-Precision Solution(MDPI AG, 29/09/2023) Ji W; Luo Y; Liao Y; Wu W; Wei X; Yang Y; Shen Y; Ma Q; He X; Yi S; Sun YGrazing management is one of the most widely practiced land uses globally. Quantifying the spatiotemporal distribution of livestock is critical for effective management of livestock-grassland grazing ecosystem. However, to date, there are few convincing solutions for livestock dynamic monitor and key parameters quantification under actual grazing situations. In this study, we proposed a pragmatic method for quantifying the grazing density (GD) and herding proximities (HP) based on unmanned aerial vehicles (UAVs). We further tested its feasibility at three typical household pastures on the Qinghai-Tibetan Plateau, China. We found that: (1) yak herds grazing followed a rotational grazing pattern spontaneously within the pastures, (2) Dispersion Index of yak herds varied as an M-shaped curve within one day, and it was the lowest in July and August, and (3) the average distance between the yak herd and the campsites in the cold season was significantly shorter than that in the warm season. In this study, we developed a method to characterize the dynamic GD and HP of yak herds precisely and effectively. This method is ideal for studying animal behavior and determining the correlation between the distribution of pastoral livestock and resource usability, delivering critical information for the development of grassland ecosystem and the implementation of sustainable grassland management.