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Recognizing human behaviors offers an effective approach to reduce building energy consumption while maintaining indoor thermal comfort. This study proposed Wi-PTAB, a novel framework designed to recognize personal thermal adaptive behavior (PTAB) using channel state information (CSI) derived from Wi-Fi signals. The key PTABs essential for the framework were identified through a literature review. Building on these key behaviors, the proposed framework comprises three key steps: CSI acquisition, preprocessing and signals of interest (SOI) extraction; CSI image generation; and feature extraction and classification. During the feature extraction and classification phase, two specialized models combining a 2D Convolutional Neural Network (2D CNN) with either Long Short-Term Memory (LSTM) or Gate Recurrent Unit (GRU) were developed. To evaluate the effectiveness of the proposed Wi-PTAB framework and models, CSI data for the key PTABs were collected from two rooms and processed into four structured datasets, using varying temporal feature partitioning parameters, including window size and step size. The evaluation process involved measuring performance indicators for the proposed models on the self-developed datasets and comparing their performance to that of other neural network models. Results revealed that both models outperformed alternatives across most datasets, and model 2D CNN–LSTM achieved the highest average accuracy of 86 % on dataset III with a temporal granularity of 18. Additionally, two guidelines were established for effectively identifying the defined PTAB: (1) data must be collected with actions executed at the center of transceivers and at a 45° angle to the transceiver. (2) signal segments should be processed with a window size of 0.5 s and a step size of 0.2 s. This study provides technical and theoretical support for non-intrusive and private personal thermal comfort prediction and smart building design.

The growth of computing power in data centers (DCs) leads to an increase in energy consumption and noise pollution of air cooling systems. Chip-level cooling with high-efficiency coolant is one of the promising methods to address the cooling challenge for high-power devices in DCs. Hybrid nanofluid (HNF) has the advantages of high thermal conductivity and good rheological properties. This study summarizes the numerical investigations of HNFs in mini/micro heat sinks, including the numerical methods, hydrothermal characteristics, and enhanced heat transfer technologies. The innovations of this paper include: (1) the characteristics, applicable conditions, and scenarios of each theoretical method and numerical method are clarified; (2) the molecular dynamics (MD) simulation can reveal the synergy effect, micro motion, and agglomeration morphology of different nanoparticles. Machine learning (ML) presents a feasible method for parameter prediction, which provides the opportunity for the intelligent regulation of the thermal performance of HNFs; (3) the HNFs flow boiling and the synergy of passive and active technologies may further improve the overall efficiency of liquid cooling systems in DCs. This review provides valuable insights and references for exploring the multi-phase flow and heat transport mechanisms of HNFs, and promoting the practical application of HNFs in chip-level liquid cooling in DCs.

As urban subway systems have become a primary mode of transportation, the issue of tunnel fires has garnered significant attention. The presence of piston winds generated by moving trains adds complexity to tunnel fire dynamics. To gain deeper insights into the fire characteristics, 3D dynamic simulations are conducted to explore the moving train fire in tunnel. The influence of fire source heat release rate (HRR), fan pressure, and train speed are studied. Comparative analysis explores smoke removal by vertical shaft and local semi-transverse ventilation. The results show that with the increase of fan pressure, the vertical shaft ventilation is enhanced, and the smoke exhaust layer is reduced from 210 to 125 m. Stopping the train downstream of the vertical shaft allows for better ventilation. Local semi-transverse ventilation is better than vertical shaft ventilation. The train stops below the local semi-transverse segment to achieve the best ventilation effect, but the location of the vent should be considered. This study lays a safety foundation for the development of long tunnels and the establishment of ventilation and smoke exhaust strategies during tunnel fires, providing a basis for the development of larger and longer railway tunnels.

As the demand for efficient travel increased, the train speed is greatly increased and the subway gradually appeared the express train and the slow train. Express trains pass through some stations without stopping. In this case, the pressure transient phenomenon in the tunnel will become severe. In this study, the dynamic mesh simulation is used to study the pressure variations of the tunnel, platform screen door (PSD), and airshaft caused by the piston wind which is generated by the high-speed train passing through the tunnel and station. The effects of train speed, modes of the train passing through the station, number of airshafts and station types on the aerodynamic pressure are analyzed. The results demonstrate that the increase in train speed brings new challenges to the loading capacity of tunnel structure. Airshafts are set at the entrance and exit of the station, which is conducive to the pressure relief of each part of the tunnel structure. Furthermore, a new type of station with two tracks (express line and slow line) is conducted. The peak pressure of PSD is reduced by 48% compared with the conventional station. This study will contribute to the improvement of subway construction and provide theoretical and data support for the operation of the high-speed train.

With the growing demand for efficient travel, tunnels are being lengthened and train speeds are being increased. High-speed subway brings new challenges to the flow environment of tunnel. Additionally, the emergence of express trains passing through stations without stopping has an enormous impact on the tunnel's and the station's flow field. This study focuses on a new type of station (NS) with overtaking and avoidance lines, allowing slow trains to stop for passengers to get on and off, while express trains pass through without stopping. The study analyzes pressure variation and wind speed when express trains pass through the NS without stopping. Different train speeds are taken into account. Based on this, the enhancement of station ventilation by utilizing the piston winds generated by express trains passing through NS is investigated. The air exchange effect and energy savings of the NS are explored. The results indicate that enhancing station air exchanges by fully utilizing piston wind saves about 190.68 kWh/day in mechanical ventilation energy consumption. This study contributes to the improvement of subway station construction and the advancement of subway train development. It can offer data assistance and theoretical direction for high-speed train operations.

Purpose: Simulation-based optimisation (SO) is a popular optimisation approach for building and civil engineering construction planning. However, in the framework of SO, the simulation is continuously invoked during the optimisation trajectory, which increases the computational loads to levels unrealistic for timely construction decisions. Modification on the optimisation settings such as reducing searching ability is a popular method to address this challenge, but the quality measurement of the obtained optimal decisions, also termed as optimisation quality, is also reduced by this setting. Therefore, this study aims to develop an optimisation approach for construction planning that reduces the high computational loads of SO and provides reliable optimisation quality simultaneously.

Design/methodology/approach: This study proposes the optimisation approach by modifying the SO framework through establishing an embedded connection between simulation and optimisation technologies. This approach reduces the computational loads and ensures the optimisation quality associated with the conventional SO approach by accurately learning the knowledge from construction simulations using embedded ensemble learning algorithms, which automatically provides efficient and reliable fitness evaluations for optimisation iterations.

Findings: A large-scale project application shows that the proposed approach was able to reduce computational loads of SO by approximately 90%. Meanwhile, the proposed approach outperformed SO in terms of optimisation quality when the optimisation has limited searching ability.

Originality/value: The core contribution of this research is to provide an innovative method that improves efficiency and ensures effectiveness, simultaneously, of the well-known SO approach in construction applications. The proposed method is an alternative approach to SO that can run on standard computing platforms and support nearly real-time construction on-site decision-making.

Limited by the narrow space of the tunnel, fire has become one of the important factors threatening the safe operation of subway especially the fire on a moving vehicle. Researches on moving fire through the large length-width ratio of tunnels are extremely lacking at present. This study investigates the characteristics of moving fire and the influence of mechanical ventilation under different conditions. First, a three-dimensional model of the tunnel with the moving train is established, and the dynamic mesh simulation is conducted and validated by the experiments. Then, the variation of smoke temperature distribution under different fire source intensities, train speeds, and blocking ratios are studied. Based on the smoke flow characteristics, the mechanical ventilation with different layouts of draught fans is investigated. It is found that the maximum growth rate of smoke spread is 70.90% with an increase of two fans. Finally, the influence of the tunnel shaft on the smoke characteristics under different fire source locations is discussed. When the fire source is under the shaft, the smoke discharge efficiency reaches the highest, almost 5.89% and 21.91% higher than other conditions. This study provides the basis and reference for the research and control of the tunnel moving fire.

Digital twins technology (DTT) is an application framework with breakthrough rules. With the deep integration of the virtual information world and physical space, it becomes the basis for realizing intelligent machining production lines, which is of great significance to intelligent processing in industrial manufacturing. This review aims to study the application of DTT and the Metaverse in fluid machinery in the past 5 years by summarizing the application status of pumps and fans in fluid machinery from the perspective of DTT and the Metaverse through the collection, classification, and summary of relevant literature in the past 5 years. The research found that in addition to relatively mature applications in intelligent manufacturing, DTT and Metaverse technologies play a critical role in the development of new pump products and technologies and are widely used in numerical simulation and fault detection in fluid machinery for various pumps and other fields. Among fan-type fluid machinery, twin fans can comprehensively use technologies, such as perception, calculation, modeling, and deep learning, to provide efficient smart solutions for fan operation detection, power generation visualization, production monitoring, and operation monitoring. Still, there are some limitations. For example, real-time and accuracy cannot fully meet the requirements in the mechanical environment with high-precision requirements. However, there are also some solutions that have achieved good results. For instance, it is possible to achieve significant noise reduction and better aerodynamic performance of the axial fan by improving the sawtooth parameters of the fan and rearranging the sawtooth area. However, there are few application cases of the Metaverse in fluid machinery. The cases are limited to operating real equipment from a virtual environment and require the combination of virtual reality and DTT. The application effect still needs further verification.

Thermal comfort during sleep is essential for both sleep quality and human health while sleeping. There are currently few effective contactless methods for detecting the sleep thermal comfort at any time of day or night. In this paper, a vision-based detection approach for human thermal comfort while sleeping was proposed, which is intended to avoid overcooling/overheating supply, meet the thermal comfort needs of human sleep, and improve human sleep quality and health. Based on 438 valid questionnaire surveys, 10 types of thermal comfort sleep postures were summarized. By using a large number of data captured, a fundamental framework of detection algorithm was constructed to detect human sleeping postures, and corresponding weighting model was established. A total of 2.65 million frames of posture data in natural sleep status were collected, and thermal comfort-related sleep postures dataset was created. Finally, the robustness and effectiveness of the proposed algorithm were validated. The validation results show that the sleeping posture and human skeleton keypoints can be used for estimating sleeping thermal comfort, and the the quilt coverage area can be fused to improve the detection accuracy.

Space heating can constitute 60–80% of the total energy use of buildings in cold climates. Efficient heating techniques in buildings still rely on operating strategies. In this paper, a church with radiant floor heating in a cold climate is taken as a case of a large single-zone building to analyze the energy use for heating. Field measurements and numerical analysis are both used in the study. Different operating modes of heating, including intermittent heating and constant set-point heating, are compared for energy saving, reliability on indoor climate, and thermal comfort. The intermittent heating by an all-air system with supplied air temperature control results in the highest energy use. The constant set-point air temperature radiant floor heating aided by a warm air system (return air temperature control) is least affected by outdoor temperature with the best reliability and met the thermal comfort requirements throughout the heating season. The main novelty is that an operating mode of cyclic set-point air temperature is proposed. It is found that the small thermal inertia of heating systems should be preferred when the operating mode of cyclic set-point temperature is used to reduce the warm-up period. The results suggest how to operate and reduce the energy use of radiant floor heating systems in a large single-zone building.