Building and Environment最新文献

In the midst of the ongoing climate crisis, there is a growing need to study the effects of climate on humans, especially those of the microclimates that surround humans and their activities. Various models have been developed to simulate microclimates, but simulation models for densely populated areas experiencing heat waves have shown an issue with simulating temperatures that were too high compared to actual measurements. To overcome these limitations, this study aimed to develop and validate CFD model based on STAR-CCM+ in order to more accurately simulate the microclimate of dense residential areas. To improve the realistic simulation capability found in existing models, the model was designed to include solar radiation, humidity, ground radiation, longwave radiation exchange, heat storage and evaporation due to various urban surfaces, evapotranspiration from vegetation, convection, and heat transfer from buildings. For validation, comparisons were made between the simulation results and the actual measurements during this time. The results showed that the R² value of the model developed in this study increased from 0.86 to 0.91, indicating that the model was able to reproduce the measured results of the target site well. To avoid overfitting the model, the measurement results of the Han River AWS, another AWS operated by the Korea Meteorological Administration near the target site, were input. It was confirmed that the model reproduced the actual measurements of five locations in the target site well. The R² of temperature and humidity ranged from 0.96 to 0.98, and wind speed ranged from 0.64 to 0.81.

Feedback is critical to improve the sustainability of all buildings. Current post occupancy feedback is not useful for architects and designers and barriers to obtaining post-occupancy data have been well documented. In addition, there are delays in feedback of research conclusions appearing in Continuing Professional Development. Therefore, architects need timely feedback on their own building designs and methods they can use to obtain feedback for themselves. Previously, a literature review and survey of architects were conducted to identify gaps in feedback for school buildings compared to an Integral Sustainable Design (ISD) framework. A suite of ISD comprehensive on-line surveys were developed for various school user groups to target the identified gaps. This paper presents data from testing a novel survey of children in a case study and comparison of some questions to instrument measurement. The results show that the spatial questions with reasons yielded valuable insights. Some qualitative questions will require amendment to yield useful information. Univariate analysis shows that some thermal comfort questions would be suitable as a substitute for instrument measurement whereas lighting questions would not. Conversely, the question on vocal comprehension provided clear responses, supported by instrument measurement. Likert-style questions regarding sense of place, connection to outside, feelings of safety, etc. Were generally successful. Overall, the new ISD children's survey provides useful information for architects to address feedback gaps identified and will continue to improve with lessons from this case study.

Traditional methods for assessing urban thermal environments (UTEs) often rely on GIS and remote sensing data, suffering from data limitations in coverage, accuracy, and availability. To address these challenges, we established a novel framework developing visual intelligence simulated indices (VISIs) for improved UTE characterization. This framework combines GIS spatial data and pre-trained large-scale and local-trained GeoAI models to generate visual intelligence, extracting GIS-like visual elements from remote sensing imagery. These GIS-like VISIs comprehensively represent land surface temperature (LST) and urban fabric for cost-effective UTE analysis. We applied our framework to Zhengzhou, China, a city with diverse landscapes. Comparative experiments among Random Forests (RF), Neural Networks (NN), and Convolutional Neural Networks (CNN) demonstrated that RF models exhibited superior performance in LST representation (0.7 test R² and 0.9 training R²). Interestingly, CNNs performed better than RFs and NNs when less cloud cover (<6 %) was present, suggesting that data diversity is essential for CNN to learn generalizable features for LST representation. For urban fabric representation, feature importance analysis indicated that VISIs (37 %) derived from large-scale GeoAI models outperform spectral bands (20 %). Furthermore, both Pearson correlation and Shapley values showed that VISIs (e.g., building features) better characterize urban heat islands than traditional remote sensing ecological indices (RSEIs) such as NDBI, underscoring the cost-effectiveness of our visual intelligence approach. In summary, our visual intelligence method effectively consolidates UTE seasonality characterization into a predictable scenario even given the unpredictable availability of remote sensing and GIS data, revealing novel perspectives on sensing unseen details for UTEs.

For heating, ventilation, and air conditioning (HVAC) systems, the technology of optimal control can achieve significant energy savings by resetting the control setpoint in responding to the change of user load and weather condition. However, traditional method will fail in cold-start scenarios, in which the diversity of history data is limited. Under this case, significant prediction error will occur in extrapolation space, and the optimized control strategy cannot fully achieve energy saving potential or even increase energy consumption. To solve this problem, here we propose a novel awareness-guided incremental control optimization method. Its basic idea is to explicitly consider the prediction error of energy model during the control optimization process. The deep ensemble algorithm is firstly adopted to capture the prediction error. And the optimization process will be aware of such potential error, and make a tradeoff between conservative state and exploratory state. The control optimization process will start with the known optimal setpoint (conservative state) to achieve stable energy saving, and then gradually explores other unknown candidates (exploratory state) to achieve further energy saving. To validate our method, a virtual test bed of chilled water system is developed by Modelica language to compare fixed setpoint strategy, traditional method, and proposed method. The results demonstrate that the proposed method can avoid negative energy saving ratio during the early stage of control optimization. And it can also find optimal control strategy faster. Comparing with traditional method, the awareness-guided method can achieve further 2.9 % energy saving ratio, and the overall energy saving is between 6.1% and 13.9 %.

Sectional physics-based aerosol models imply a computational effort that hinders their use in building digital twins, real-time predictive control, and computer-based iterative optimization versus black-box approaches. The innovation of this paper lies in the proposal of a novel systematic methodology to optimize the number of size bins in sectional reduced-order models for particle concentration simulations. This allows its application in indoor air quality management and overcomes generalizability and data-dependency issues of black-box models. This method, based on k-means clustering, aims to ensure precision when tackling relatively fast fine and ultrafine particle simulations targeting the reduction of time and resource consumption from experimentally-determined aerosol size distribution time series. Consequently, three tests were carried out using combustion aerosols inside a custom-designed emission chamber to simulate emission hotspots in non-commercial and occupational settings. 2-, 3-, 4-, and 5-cluster classifications were evaluated for data coming from 13 particle size bins through silhouette analysis and the study of their temporal profiles. Results show that the 4-cluster classification summarizes the behavior of data in the 10–420 nm range, ensuing up to a 77 % improvement in the model's computational demand. Moreover, this method allows an accurate definition of the necessary size ranges to calculate nanoparticle concentrations inside the chamber and facilitates the interpretation of aerosol behavior and processes through the resulting clusters' temporal profiles.

The built environment significantly influences social interactions, which are crucial for residents, but little is known about how these interactions are affected by the community's sound environment. This study conducted sound intervention experiments in the community to investigate the impact of alterations in the sound environment on residents' social behaviours. The social interaction situations under five sound intervention conditions were recorded and evaluated from three dimensions: participation, occurrence, and depth. The results indicated that a more natural sound environment in the community leads to a higher proportion of socially interactive residents and an increased occurrence of social interactions among residents. Birdsong interventions increased paired social interactions by 16.3 % compared to traffic noise, while water sound interventions increased grouped social interactions by 16.6 % compared to the control. Compared to the frequency in the lowest group, individual prolonged pair social interactions increased by 0.26 occurrences with birdsong intervention, and prolonged group social interactions increased by 0.19 occurrences with water sounds intervention. The findings can inform community designers about the strategic use of sound to enhance the environment and promote social interactions among residents.