查看更多>>摘要：Urban mobility is a critical contributor to greenhouse gas emissions,accounting for over 30％of urban carbon emissions in the United States in 2021.Addressing this challenge requires a comprehensive and data-driven approach to transform transportation systems into sustainable networks.This paper presents an integrated framework that leverages artificial intelligence(AI),machine learning(ML),and life cycle assessment(LCA)to analyze,model,and optimize urban mobility.The framework consists of four key components:AI-powered analysis and models,synthetic urban mobility data generation,LCA for environmental footprint analysis,and data-driven policy interventions.By combining these elements,the framework not only deciphers complex mobility patterns but also quantifies their environmental impacts,providing actionable insights for policy decisions aimed at reducing carbon emissions and promoting sustainable urban transportation.The implications of this approach extend beyond individual cities,offering a blueprint for global sustainable urban mobility.
查看更多>>摘要：Selective visual attention determines what pedestrians notice and ignore in urban environment.If consistency exists between different individuals'visual attention,designers can modify design by underlining mechanisms to better meet user needs.However,the mechanism of pedestrians'visual attention remains poorly understood,and it is challenging to forecast which position will attract pedestrians more in urban environment.To address this gap,we employed 360° video and immersive virtual reality to simulate walking scenarios and record eye movement in 138 participants.Our findings reveal a remarkable consistency in fixation distribution across individuals,exceeding both chance and orientation bias.One driver of this consistency emerges as a strategy of information maximization,with participants tending to fixate areas of higher local entropy.Additionally,we built the first eye movement dataset for panorama videos of diverse urban walking scenes,and developed a predictive model to forecast pedestrians'visual attention by supervised deep learning.The predictive model aids designers in better understanding how pedestrians will visually interact with the urban environment during the design phase.The dataset and code of predictive model are available at https://github.com/LiamXie/UrbanVisualAttention.
查看更多>>摘要：Occupant-centric control(OCC)is intelligent control of building systems based on the real comfort needs of occupants.This paper provides a comprehensive review of how real-world data on energy-related occupant behavior(OB)can be integrated and applied in OCC systems.The aim is to accurately portray the real occupant needs and improve energy efficiency without sacrificing occupant comfort.This paper first introduces two types of OB:detailed occupancy states and energy-interaction behaviors,including methods to monitor,establish,and predict these OB.Then,OCC is divided into real-time control and model-based predictive control,and each of these four scenarios is discussed.It extensively reviews OCC methods for different equipment in four cases,covering control strategies,control scales,comfort enhancement scenarios,and energy-saving potential for each category.It is summarized that despite extensive research on OB,there are still significant challenges in integrating this research into OCC.A major issue is the lack of a bridge connecting monitoring acquired information and controls.In addition,the article reviews the current state of OCC platform development.The future direction should be combined with advanced Internet of Things(IoT)technologies,WiFi,and other communication technologies to obtain information about people's behavior and real needs in order to create truly energy efficient and comfortable smart environments.The article also discusses how enhancing the real-time feedback capability of the OCC system can help improve the overall control system capability and the importance of testing through experimentation.
查看更多>>摘要：The increasing demand for cooling energy in data centers has become a global concern.Existing studies lack a comprehensive analysis of the energy performance of widely used multi-chiller cooling systems in air-cooled data centers throughout their lifecycle,especially concerning progressive loading.To bridge this gap,this study conducts a thorough assessment of the energy performance of multi-chiller cooling systems throughout the entire lifecycle.Additionally,the impact of climate conditions on the energy efficiency of the cooling systems is analyzed,considering design variations for typical climates.Multi-chiller cooling system models are developed using the test data of cooling equipment and typical control algorithms.The energy performance of the cooling system is thoroughly analyzed under full-range cooling loads and climate conditions.Results show that free cooling time could differ up to 1442 hours at different part load ratios in the same location.Furthermore,the cooling system's coefficient of performance(COP)varies significantly,by up to 6,at different part load ratios,corresponding to a difference in power usage effectiveness(PUE)up to 0.14.Notably,the average cooling system COP throughout the lifecycle loading is found to be only 11.7,2.9 lower than the design system COP.
查看更多>>摘要：Deep learning(DL),especially convolutional neural networks(CNNs),has been widely applied in air handling unit(AHU)fault diagnosis(FD).However,its application faces two major challenges.Firstly,the accessibility of operational state variables for AHU systems is limited in practical,and the effectiveness and applicability of existing DL methods for diagnosis require further validation.Secondly,the interpretability performance of DL models under various information scenarios needs further exploration.To address these challenges,this study utilized publicly available ASHRAE RP-1312 AHU fault data and employed CNNs to construct three FD models under three various information scenarios.Furthermore,the layer-wise relevance propagation(LRP)method was used to interpret and explain the effects of these three various information scenarios on the CNN models.An R-threshold was proposed to systematically differentiate diagnostic criteria,which further elucidates the intrinsic reasons behind correct and incorrect decisions made by the models.The results showed that the CNN-based diagnostic models demonstrated good applicability under the three various information scenarios,with an average diagnostic accuracy of 98.55％.The LRP method provided good interpretation and explanation for understanding the decision mechanism of CNN models for the unlimited information scenarios.For the very limited information scenario,since the variables are restricted,although LRP can reveal key variables in the model's decision-making process,these key variables have certain limitations in terms of data and physical explanations for further improving the model's interpretation.Finally,an in-depth analysis of model parameters—such as the number of convolutional layers,learning rate,β parameters,and training set size—was conducted to examine their impact on the interpretative results.This study contributes to clarifying the effects of various information scenarios on the diagnostic performance and interpretability of LRP-based CNN models for AHU FD,which helps provide improved reliability of DL models in practical applications.
查看更多>>摘要：Respiratory protection is critical to minimize airborne infection risk for healthcare workers.The main factor affecting infection risk in the medical scenario is leakage between the respirator and the healthcare workers'face.However,it is difficult to quantify the effectiveness of respirators due to the numerous influencing factors.The medical activities were simulated by the fitting test,and the inverse of the results were used to get the total inward leakage of respirators.114 subjects were divided according to gender,profession,age,risk area,and BMIs,participated in fitting test of four N95 respirators.The result showed that the value range of total inward leakage for tested N95 respirators was between 0.50％and 2.39％.Flat-folded respirators were more prone to leakage than cup-shaped ones,so it was essential to account for the impact of respirator shape.Similarly,this study found that gender and profession had no significant correlation with total inward leakage.Some medical procedures include bending,and turning head from side to side,or up and down,which had a significant influence on the protective efficacy of the N95 respirator.Facial dimensions and BMI exerted a more pronounced influence on the protective efficacy of the N95 respirator,and they were in the correlations.Healthcare personnels wore a well-fitting N95 respirator properly for excellent protection,reducing occurrence of nosocomial infections and occupational exposures,and optimizing respiratory protection strategies.
查看更多>>摘要：About 1/3 of human life is spent sleeping.The hypoxic and cold environment in high-altitude areas leads to sleep disorders that are more prominently harmful to the human body.To improve the quality of human sleep in high-altitude areas,this study explored the thermal and oxygen environment regulation for plateau sleep.In this study,the influencing factors of the diffusion of oxygenic-thermal coupled airflow were determined through the theoretical analysis of a thermal fluid mechanic jet.This study used computational fluid dynamics(CFD)to investigate the diffusion characteristics of the oxygenic-thermal coupled airflow with a sleeping experiment conducted on the plateau.The results showed that the influence of the thermal plume at 0.1 m near the human face was larger,and the oxygenic-thermal coupled airflow diffusion process was mainly divided into three phases over time.The size and time to stabilize the oxygen volume fraction in the inhalation zone varied between conditions and were strongly influenced by the temperature difference of the supply air.The effects of the thermal and oxygen environment were analyzed using indicators such as facial-area speed ratio,draft risk,and personal oxygen inhalation efficiency.The optimal design strategies were recommended with an outlet air velocity of 1.5 m/s,a temperature difference of 8 K between the outlet airflow and the indoor background air,and an outlet oxygen volume fraction of 30％.The results can provide implications for regulating the thermal and oxygen environment to improve human sleep quality in high-altitude areas.
查看更多>>摘要：The evapotranspiration(ET)from urban vegetation is recognized as an effective way to improve the urban thermal environment.However,the traditional ET measurement approaches,such as the sap flow method,are limited in providing urban ET for specific areas.Consequently,establishing quantitative relationship between cooling effects and ET becomes challenging,which is crucial for understanding the role of urban greenery in mitigating urban heat.To overcome this challenge,an estimation model based on the three-temperature(3T)model was developed for accurately estimating urban ET by employing a reference tree with accurate ET data.In contrast to the 3T model,this study proposed a new method(modified 3T model)that considers the influence of the three-dimensional crown structure on the energy balance.Additionally,it retains the advantage over traditional ET models by not requiring the calculation of resistance parameters.This reduces the impact of uncertainty in the estimated resistance parameters on the ET results.The modified 3T model was validated based on the sap flow method and Bowen ratio system,where the results indicated good agreement between the modified 3T model and measured ET data,with root mean square errors of 22.2 W·m-2 and 26.4 W·m-2,respectively.Furthermore,the absolute relative error of ET estimation was affected by energy-related factors,such as solar radiation,air temperature,and relative humidity.The modified 3T model exhibited low sensitivity to surface temperatures,where the average sensitivity coefficients were below±0.15 from 0600 local time(LT)to 1700 LT.Considering the low accuracy of surface temperatures measured by infrared cameras(±2 ℃),it would be beneficial to combine the modified 3T model with unmanned aerial vehicles and infrared remote sensing to measure urban ET.
查看更多>>摘要：In dental clinics with an open floor plan,the risk of patient-to-patient transmission of respiratory disease is a concern.During dental procedures large amounts of bioaerosol are produced and patients cannot wear personal protective equipment.This paper examines how to effectively deploy air cleaner to reduce the infection risk in dental clinics with an open floor plan.Various locations of air cleaners at various clean air delivery rates(CADRs)were investigated.The dispersion of bioaerosol was studied through numerical simulations,and risk assessment was performed by a dose-response method.The findings indicated that dental patients downstream of the background ventilation have a higher infection risk than those to the left and right of an infected patient(i.e.,the source).The lowest infection risks for the adjacent patients were found when the air cleaner was place opposite to the dentists,i.e.,on the floor at low CADR levels of 2.2 m3/min or on the bench at CADR levels of 4.4 m3/min or greater.The results of this study indicated that air cleaner can mitigate the risk of patient-to-patient transmission of SARS-CoV-2 in dental clinics with an open floor plan.Background CADR levels determine the optimal placement of air cleaners.
查看更多>>摘要：Urban greenery is widely recognised as a strategy to mitigate urban overheating,and its shading capacity is crucial for improving microclimate and thermal comfort.Nevertheless,research on modelling the vegetation canopy radiation transfer(VCRT)process for the microscale is lacking,and most existing VCRT models are taken out from mesoscale models.In this study,we used canopy morphology and structure as an entry point to construct a VCRT model for the microscale,from the mesoscale model.Firstly,100 m and 1000 m were defined as the critical scales by scaling,and the effect of leaf distribution on VCRT was analysed.The VCRT model was made applicable to the microscale by introducing a scattered radiation source term and 6 leaf inclination distribution(LID)functions,and the results showed that the proposed model could improve the accuracy of canopy radiation absorptivity by about 11％.In addition,both leaf area index(LAI)and LID had significant effects on VCRT,and vegetation with LAI above 2 and spreading leaves had better shading effects.It is worth noting that urban greenery has an exciting potential for thermal comfort improvement,with the potential to regulate even extremely hot weather from near"very hot"(148 W/m2)to almost"comfortable"(69 W/m2).This study is a catalyst for improving the predictability of the VCRT process for microclimate and thermal comfort,providing theoretical support and implications for mitigating urban overheating and enhancing urban thermal resilience.
NETL
NSTL
万方数据
维普