Application of Digital Twins and Metaverse in the Field of Fluid Machinery Pumps and Fans: A Review.

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.

Contactless sleep posture measurements for demand-controlled sleep thermal comfort: A pilot study.

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.

Adaptive Wall-Based Attachment Ventilation: A Comparative Study on Its Effectiveness in Airborne Infection Isolation Rooms with Negative Pressure.

The transmission of coronavirus disease 2019 (COVID-19) has presented challenges for the control of the indoor environment of isolation wards. Scientific air distribution design and operation management are crucial to ensure the environmental safety of medical staff. This paper proposes the application of adaptive wall-based attachment ventilation and evaluates this air supply mode based on contaminants dispersion, removal efficiency, thermal comfort, and operating expense. Adaptive wall-based attachment ventilation provides a direct supply of fresh air to the occupied zone. In comparison with a ceiling air supply or upper sidewall air supply, adaptive wall-based attachment ventilation results in a 15%-47% lower average concentration of contaminants, for a continual release of contaminants at the same air changes per hour (ACH; 10 h-1). The contaminant removal efficiency of complete mixing ventilation cannot exceed 1. For adaptive wall-based attachment ventilation, the contaminant removal efficiency is an exponential function of the ACH. Compared with the ceiling air supply mode or upper sidewall air supply mode, adaptive wall-based attachment ventilation achieves a similar thermal comfort level (predicted mean vote (PMV) of -0.1-0.4; draught rate of 2.5%-6.7%) and a similar performance in removing contaminants, but has a lower ACH and uses less energy.

Application of Internet of Things in academic buildings for space use efficiency using occupancy and booking data.

Environmental sustainability in academic buildings can be improved with management interventions such as improving space use efficiency supported by large data from the Internet of Things (IoT). Due to the potentials, the interest in the use of IoT tools for facility management is high among universities. However, empirical studies on this topic are scarce. To address the knowledge gap in this area, this study proposes and examines a process model with steps to measure space use and to improve space use efficiency by IoT tools in academic buildings. The applicability of the model is investigated in 8 lecture halls in a university building by using occupancy and booking data from IoT tools. Four space use indicators are developed to visualize the data and quantify space use, and based on them, the strategies and interventions for space use efficiency are proposed and discussed.

Determining the environmental impact of material hauling with wheel loaders during earthmoving operations.

A method has been developed to estimate the environmental impact of wheel loaders used in earthmoving operations. The impact is evaluated in terms of energy use and emissions of air pollutants (CO2, CO, NOx, CH4, VOC, and PM) based on the fuel consumption per cubic meter of hauled material. In addition, the effects of selected operational factors on emissions during earthmoving activities were investigated to provide better guidance for practitioners during the early planning stage of construction projects. The relationships between six independent parameters relating to wheel loaders and jobsite conditions (namely loader utilization rates, loading time, bucket payload, horsepower, load factor, and server capacity) were analyzed using artificial neural networks, machine performance data from manufacturer's handbooks, and discrete event simulations of selected earthmoving scenarios. A sensitivity analysis showed that the load factor is the largest contributor to air pollutant emissions, and that the best way to minimize environmental impact is to maximize the wheel loaders' effective utilization rates. The new method will enable planners and contractors to accurately assess the environmental impact of wheel loaders and/or hauling activities during earthmoving operations in the early stages of construction projects. Implications: There is an urgent need for effective ways of benchmarking and mitigating emissions due to construction operations, and particularly those due to construction equipment, during the pre-construction phase of construction projects. Artificial Neural Networks (ANN) are shown to be powerful tools for analyzing the complex relationships that determine the environmental impact of construction operations and for developing simple models that can be used in the early stages of project planning to select machine configurations and work plans that minimize emissions and energy consumption. Using such a model, it is shown that the fuel consumption and emissions of wheel loaders are primarily determined by their engine load, utilization rate, and bucket payload. Moreover, project planners can minimize the environmental impact of wheel loader operations by selecting work plans and equipment configurations that minimize wheel loaders' idle time and avoid bucket payloads that exceed the upper limits specified by the equipment manufacturer.

A Total Bounded Variation Approach to Low Visibility Estimation on Expressways

Low visibility on expressways caused by heavy fog and haze is a main reason for traffic accidents. Real-time estimation of atmospheric visibility is an effective way to reduce traffic accident rates. With the development of computer technology, estimating atmospheric visibility via computer vision becomes a research focus. However, the estimation accuracy should be enhanced since fog and haze are complex and time-varying. In this paper, a total bounded variation (TBV) approach to estimate low visibility (less than 300 m) is introduced. Surveillance images of fog and haze are processed as blurred images (pseudo-blurred images), while the surveillance images at selected road points on sunny days are handled as clear images, when considering fog and haze as noise superimposed on the clear images. By combining image spectrum and TBV, the features of foggy and hazy images can be extracted. The extraction results are compared with features of images on sunny days. Firstly, the low visibility surveillance images can be filtered out according to spectrum features of foggy and hazy images. For foggy and hazy images with visibility less than 300 m, the high-frequency coefficient ratio of Fourier (discrete cosine) transform is less than 20%, while the low-frequency coefficient ratio is between 100% and 120%. Secondly, the relationship between TBV and real visibility is established based on machine learning and piecewise stationary time series analysis. The established piecewise function can be used for visibility estimation. Finally, the visibility estimation approach proposed is validated based on real surveillance video data. The validation results are compared with the results of image contrast model. Besides, the big video data are collected from the Tongqi expressway, Jiangsu, China. A total of 1,782,000 frames were used and the relative errors of the approach proposed are less than 10%.