查看更多>>摘要：Building-Integrated photovoltaics(BIPV)have emerged as a promising sustainable energy solution,relying on accurate energy production predictions and effective decarbonization strategies for efficient deployment.This paper presents a novel approach that combines photogrammetry and deep learning techniques to address the problem of BIPV decarbonization.The method is called BIM-AITIZATION referring to the integration of BIM data,AI techniques,and automation principles.It integrates photogrammetric data into practical BIM parameters.In addition,it enhances the precision and reliability of PV energy prediction by using artificial intelligence strategies.The primary aim of this approach is to offer advanced,data-driven energy forecasts and BIPV decarbonization while fully automating the underlying process.To achieve this,the first step is to capture point cloud data of the building through photogrammetric acquisition.This data undergoes preprocessing to identify and remove unwanted points,followed by plan segmentation to extract the plan facade.After that,a meteorological dataset is assembled,incorporating various attributes that influence energy production,including solar irradiance parameters as well as BIM parameters.Finally,machine and deep learning techniques are used for accurate photovoltaic energy predictions and the automation of the entire process.Extensive experiments are conducted,including multiple tests aimed at assessing the performance of diverse machine learning models.The objective is to identify the most suitable model for our specific application.Furthermore,a comparative analysis is undertaken,comparing the performance of the proposed model against that of various established BIPV software tools.The outcomes reveal that the proposed approach surpasses existing software solutions in both accuracy and precision.To extend its applicability,the approach is evaluated using a building case study,demonstrating its ability to generalize effectively to new building data.
查看更多>>摘要：Wind-driven rain(WDR)has a significant influence on the hygrothermal performance,durability,and energy consumption of building components.The calculation of WDR loads using semi-empirical models has been incorporated into the boundary conditions of coupled heat and moisture transfer models.However,prior research often relied on fixed WDR absorption ratio,which fail to accurately capture the water absorption characteristics of porous building materials under rainfall scenarios.Therefore,this study aims to investigate the coupled heat and moisture transfer of exterior walls under dynamic WDR boundary conditions,utilizing an empirically obtained WDR absorption ratio model based on field measurements.The developed coupled heat and moisture transfer model is validated against the HAMSTAD project.The findings reveal that the total WDR flux calculated with the dynamic WDR boundary is lower than that obtained with the fixed WDR boundary,with greater disparities observed in orientations experiencing higher WDR loads.The variations in moisture flow significantly impact the surface temperature and relative humidity of the walls,influencing the calculation of cooling and heating loads by different models.Compared to the transient heat transfer model,the coupled heat and moisture transfer model incorporating dynamic WDR boundary exhibits maximum increases of 17.6%and 16.2%in cooling and heating loads,respectively.The dynamic WDR boundary conditions provide more precise numerical values for surface moisture flux,offering valuable insights for the thermal design of building enclosures and load calculations for HVAC systems.
查看更多>>摘要：The energy consumption of heating,ventilation,and air conditioning(HVAC)systems holds a significant position in building energy usage,accounting for about 65%of the total energy consumption.Moreover,with the advancement of building automation,the energy consumption of ventilation systems continues to grow.This study focuses on improving the performance of spherical tuyeres in HVAC systems.It primarily utilizes neural networks and multi-island genetic algorithms(MIGA)for multi-parameter optimization.By employing methods such as structural parameterization,accurate and fast computational fluid dynamics(CFD)simulations,a minimized sample space,and a rational optimization strategy,the time cycle of the optimization process is shortened.Additionally,a new comprehensive evaluation index is proposed in this research to describe the performance of spherical tuyeres,which can be used to more accurately assess spherical tuyeres with different structures.The results show that by establishing a neural network prediction model and combining it with the multi-island genetic algorithm,a novel spherical tuyere design was successfully achieved.The optimized novel spherical tuyeres achieved a 27.05%reduction in the spherical tuyeres effective index(STEI)compared to the traditional spherical tuyeres.Moreover,the resistance decreased by 15.68%,and the jet length increased by 7.57%.The experimental results demonstrate that our proposed optimization method exhibits high accuracy,good generalization capability,and excellent agreement at different Reynolds numbers.
查看更多>>摘要：Installing photovoltaic(PV)systems is an essential step for low-carbon development.The economics of PV systems are strongly impacted by the electricity price and the shadowing effect from neighboring buildings.This study evaluates the PV generation potential and economics of 20 cities in China under three shadowing conditions.First,the building geometry models under three shadowing conditions for the 20 cities were constructed using QGIS.Then,60 building models with PV systems and shadows from surrounding buildings were generated by City Buildings,Energy,and Sustainability(CityBES),an open platform,to simulate the PV power generation.Finally,the study presented one economic analysis model to evaluate the profitability by combining the market cost of rooftop PV systems and electricity prices in China.The economic model included four indicators:payback period(static and dynamic),net present value(NPV),and internal rate of return(IRR).The results show that the reduction of PV power generation ranges from 8.29%to 16.01%under medium shadowing,and experiences a maximum decrease of up to 39.71%under high shadowing.Further economic analysis shows that almost all the regions show reliable potential,obtaining an IRR higher than the reference value(5%).Nenjiang has the highest economic profit,with the highest NPV(86,181.15 RMB)and IRR(30.14%)under no shadowing among 20 cities.It also should be mentioned that the alignment between electricity price distribution and the solar power generation curve will directly impact the economic potential of PV systems.
查看更多>>摘要：The cabin air pressure remains lower than the horizontal atmospheric pressure when the airplane is in flight.Air pressure is one of the parameters that must be taken into consideration while studying the thermal environment of an airplane cabin.There are still no reference values for aircraft cabins despite the fact that numerous studies on low pressure heat transfer have demonstrated the connection between convective heat transfer coefficient(CHTC)and air pressure.In this paper,a correction method for CHTC under low pressure conditions was established by using the dummy heat dissipation in the low-pressure cabin experiment.On this basis,a thermal environment simulation model was developed,then was applied to the simulation of a seven-row aircraft cabin containing 42 passengers,and the CHTC and heat loss of dummy surface in the cabin were obtained.Finally,the results of PMV calculated by using heat dissipation and air parameters at sampling points were compared.The results show that the modified CHTC can accurately reflect the cabin thermal environment under low pressure conditions,and the correction of CHTC can be realized by adjusting the turbulent Prandtl number,which is nonlinear correlated with the pressure.The simulation results of the thermal environment in the seven-row cabin show that the CHTC changes by about 42%before and after modification.The air pressure decreases during take-off,which reduces the average CHTC of the crew surface from 5.09 W/(m2·K)to 4.56 W/(m2·K),but the air temperature rises by about 0.2 ℃ as a whole.The deviation of PMV results calculated by using simulated heat loss data and using air parameters of measuring points in space is up to 0.5,but the latter is representative for calculating the thermal comfort level of the whole cabin.
查看更多>>摘要：Pumping ventilation(PV),a special single-sided ventilation(SSV),has been certified as an effective strategy to improve the air exchange rate of SSV.However,most studies targeted on the single space,and few studies have been focused on the effect of internal partitioning on PV.This paper aims to evaluate the ventilation performance of PV influenced by different configurations of internal partitioning.Computational fluid dynamics(CFD)simulation was used to predict the flow fields and ventilation rates.The width(w/H),height(h/H)and location(d/H)are the three main internal partition parameters considered in this study.The simulation results showed that the total,mean and fluctuating ventilation rates all decrease with wider internal partitions.The normalized total ventilation rate decreases by 7.6%when w/H is increased from 50%to 75%.However,the reduction rate is only 0.23%between w/H = 0 and 25%,and only 0.61%between w/H = 25%and 50%.The ventilation rate is hardly reduced by increasing the partition width when w/H<50%,whereas greatly reduced by wider partition for w/H>50%.Increasing the partition height will reduce the mean ventilation rate but promote the fluctuating and total ventilation rate in some cases.An increase of total ventilation rate by 1.4%is observed from h/H = 50%to 75%.The ventilation rate is larger when the internal partition is attached to the leeward or windward wall.The total,mean and fluctuating ventilation rates for d/H = 50%are relatively higher than d/H = 0 by 1.5%,3.1%and 0.8%,respectively.Hence the internal partition should be mounted attached to the windward wall so as to obtain the greatest pumping ventilation rate.The periodicity of pumping flow oscillation and pumping frequency are independent of the partition configurations.The peak power of pumping flow is the lowest for the widest internal partition and is negatively affected by the partition height,but it generally has a positive correlation with the distance between the partition and leeward wall.Present research will help to understand pumping ventilation mechanism in real buildings with internal partitioning and provide theoretical basis for developing unsteady natural ventilation technology in low-carbon buildings.
查看更多>>摘要：The global concern over indoor air pollution in public vehicles has grown significantly.With a focus on enhancing passengers'comfort and health,this study endeavors to investigate the distribution characteristics of formaldehyde within a high-speed train cabin by employing a computational fluid dynamics(CFD)model which is experimentally validated in a real cabin scenario.The research focuses on analyzing the impact of air supply modes,temperature,relative humidity,and fresh air change rate on the distribution and concentration of formaldehyde.The results demonstrate that the difference in average formaldehyde concentration between the two air supply modes is below 1.3%,but the top air supply mode leads to a higher accumulation of formaldehyde near the sidewalls,while the bottom air supply mode promotes a more uniform distribution of formaldehyde.Furthermore,the temperature,relative humidity,and fresh air change rate are the primary factors affecting formaldehyde concentration levels,but they have modest effects on formaldehyde's distribution pattern within the cabin.As the temperature and relative humidity increase,the changes in formaldehyde concentrations in response to variations in these factors become more evident.Importantly,the formaldehyde concentration may surpass the standard limit of 0.10 mg/m3 if the fresh air change rate falls below 212 m3/h.This research provides a systematic approach and referenceable results for exploring formaldehyde pollution in high-speed train cabins.
查看更多>>摘要：Building occupant presence during varying periods is crucial to the performance studies of buildings and city regions.However,the understanding of the building occupancies on the university campus remains limited.To address this gap,our study employs field measurements,payment records,course arrangements,and building access systems to depict the occupancy patterns of the canteen,dormitory,library,and teaching and lab buildings during weekdays and weekends.We found that the occupancy rates across different buildings are somehow interrelated,given that the total number of occupants on campus is generally constant.Notably,dormitory occupancy rates tend to be low during the morning and afternoon course hours,which inversely correlates with the high occupancy rates in the teaching and lab buildings during these periods.Similarly,canteens experience surges in occupancy during meal times,which coincide with a decrease in library usage.Moreover,we established appliance operation schedules for dormitories through surveys and on-site investigations.Water dispensers and electronic devices were identified as the primary energy consumers for both male and female occupants,with desk-top fans and hairdryers being significant energy users for male and female occupants,respectively.These findings are essential for energy studies within a campus setting,underlining the importance of considering occupant behaviors on a regional scale.
查看更多>>摘要：Pedestrian wind flow is a critical factor in designing livable residential environments under growing complex urban conditions.Predicting pedestrian wind flow during the early design stages is essential but currently suffers from inefficiencies in numerical simulations.Deep learning,particularly generative adversarial networks(GAN),has been increasingly adopted as an alternative method to provide efficient prediction of pedestrian wind flow.However,existing GAN-based wind flow prediction schemes have limitations due to the lack of considering the spatial and frequency characteristics of wind flow images.This study proposes a novel approach termed SFGAN,which embeds spatial and frequency characteristics to enhance pedestrian wind flow prediction.In the spatial domain,Gaussian blur is employed to decompose wind flow into components containing wind speed and distinguished flow edges,which are used as the embedded spatial characteristics.Detailed information of wind flow is obtained through discrete wavelet transformation and used as the embedded frequency characteristics.These spatial and frequency characteristics of wind flow are jointly utilized to enforce consistency between the predicted wind flow and ground truth during the training phase,thereby leading to enhanced predictions.Experimental results demonstrate that SFGAN clearly improves wind flow prediction,reducing Wind_MAE,Wind_RMSE and the Fréchet Inception Distance(FID)score by 5.35%,6.52%and 12.30%,compared to the previous best method,respectively.We also analyze the effectiveness of incorporating the spatial and frequency characteristics of wind flow in predicting pedestrian wind flow.SFGAN reduces errors in predicting wind flow at large error intervals and performs well in wake regions and regions surrounding buildings.The enhanced predictions provide a better understanding of performance variability,bringing insights at the early design stage to improve pedestrian wind comfort.The proposed spatial-frequency loss term is general and can be flexibly integrated with other generative models to enhance performance with only a slight computational cost.
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