Building Simulation最新文献

The presence of water molecules in the air can impact how super cool broadband radiative coolers behave. Higher humidity in the lower atmosphere traps infrared radiation, reducing heat sent back to outer space. In this study, a mesoscale urban climate model is used to evaluate the newly developed super cool materials with broadband emissivity not selective in atmospheric window as an arsenal for urban heat management of tropical wet and dry cities like Kolkata. The results suggest that the energy balance over urban domain has substantially been altered by the city scale deployment of super cool broadband radiative cooling materials on the building rooftop. Bowen ratio and evaporative fraction values were found decreasing and increasing, respectively with a positive directional polynomial (R² = 0.968) relationship, after the implementation of super cool broadband radiative cooling materials and in comparison, to the unmitigated scenario. At high solar hour (14:00 LT), additional thermal variables of urban domain such as 2 m air temperature, surface skin temperature, urban canopy temperature, and roof surface temperature decrease by 2.3 °C, 5.4 °C, 0.8 °C, and 31.7 °C, respectively. Reflective super cool broadband materials achieve sub-ambient temperatures up to 11.7 °C during peak hours, reduce surface wind speed by 2.5 m s⁻¹, and lower the planetary boundary layer by 1475 m. The average daytime drop is approximately 7.3 °C, and at night, it is close to 2.4 °C. Deployment induces a “regional high” over urban areas, disrupting sea breeze onset and lowering the planetary boundary layer. Finally, an optimal cooling performance for super cool broadband radiative coolers can be achieved in lower humidity conditions, as their efficiency decreases with increased humidity. Though needing further investigation, these findings of nano-science-based super cool broadband materials offer valuable insights for policymakers and urban planners addressing thermal management in densely packed tropical urban environments.

The hot environment and the metabolic heat of commuting in summer caused individual overheating and intense thermal discomfort. Local cooling presents huge potential for optimizing thermal comfort. This study investigates the performance of a back cooling device, based on the semiconductor Peltier effect, in improving thermal comfort after summer commuting. We studied one case without cooling, and three cases with surface temperatures of the cooling device of 29, 27, and 25 °C using a simulated summer commute at a moderate activity level. The results showed that thermal sensation, perceived sweating rate, and skin temperature decreased markedly in the cooling cases compared to the non-cooling case, with the changes being most notable in the lower back, in contact with the cooling device. The decrease in overall thermal sensation and mean skin temperature was approximately 0.52 score and 0.31 °C on average, respectively, with a 1.71 score increase in overall thermal comfort. We contend that the surface temperature of local contact cooling devices should not be lower than 22 °C to minimize local overcooling. Back cooling devices present a huge potential for building energy-savings at ambient air temperature exceeding 30 °C. Moreover, the functional paradigms for individual comfort predict improved comfort performance in future applications. This study contributes to the understanding on the well-being and physiological recovery of individuals after a summer commuting.

Gas leakage accidents occur frequently in confined spaces, and heavy gases with a relative density greater than 1.15 among hazardous gases and greenhouse gases are commonly stored in confined spaces. However, atmospheric pollutant emission standards are becoming more stringent, and it is essential to remove heavy gas after accidents while reducing emissions to the atmosphere. This study proposes using a heavy gas collection tank (HGCT) to safeguard the internal environment and minimize emissions to the atmosphere. The capture efficiencies applicable to heavy-gas environments under different ventilation strategies are derived. This research analyzes the impact of the exhaust rate, leakage rate, density of heavy gas, and air supply modes on the indoor concentration distribution. The results demonstrate that the mass flow rate of heavy gas into the exhaust is positively correlated with the exhaust rate, but the gas from the exhaust system contains more air. The exhaust rate should be greater than four times the space volume per hour; otherwise, heavy gas above 1000 ppm accumulates to a height of 0.67 m at ground level. Finally, attachment ventilation as make-up air helps to reduce upstream heavy gas accumulation and reduces the extension of heavy gas along the room width. Combining an HGCT with floor slope and attachment ventilation achieves an efficiency of 96.28%. This study provides valuable insights and references for preventing hazardous heavy gas leakage.

Building air conditioning systems (ACs) can contribute to the stable operation of power grids by participating in peak load shaving programs, but the participants need a fast and accurate zone temperature prediction model, e.g., the detailed room thermal-resistance (RC) model, to improve peak shaving effect and avoid obvious thermal discomfort. However, when applying the detailed room RC model to multi-zone buildings, conventional studies mostly consider the heat transfer among neighboring rooms, which contributes little to the prediction accuracy improvement, but leads to complicated model structure and heavy computation. Thus, a distributed RC model is developed for multi-zone buildings in this study. Compared to conventional models, the proposed model considers the total heat transfer between the building and the air, and ignores the heat transfer among indoor air in neighboring rooms through internal walls with heavy thermal mass, thereby having comparable temperature prediction accuracy, simpler structure, and stronger robustness. Based on the model, the effectiveness of passive pre-cooling strategies in reducing the air conditioning loads during peak periods is investigated. Results indicate that the thermal insulation performance of opaque building envelope is quite important to the flexibility enhancement of air conditioning loads. With an uninsulated building envelope, passive pre-cooling is useless for the peak load shaving. In comparison, well insulated opaque building envelope enables the building thermal mass to be utilized through passive pre-cooling, which leads to the air conditioning loads during peak periods being further reduced by about 12%.

Indoor volatile organic compound (VOC) concentrations are often dynamic because the ventilation and emission rates of VOC usually change. Adsorption filters used for air purification may operate with a capacity that fluctuates with unsteady VOC concentrations in buildings. Modeling the dynamic interactions between adsorption filters and indoor air is crucial for predicting their performance under real-world conditions. This study presents a numerical model of partially reversible adsorption equilibrium coupled with a mass transfer model to create a predictive model for adsorption efficiency in environments with dynamic VOC concentrations. A honeycomb adsorption filter for benzene adsorption was simulated and tested, including the breakthrough and purging curve and the long-term efficiency in an experimental chamber with dynamic concentrations. The results reveal that the curve generated with the partially reversible adsorption equilibrium model closely aligns with the measured one. Furthermore, the model was coupled with a chamber model and the simulation results were compared with those calculated using the filter model with a single adsorption isotherm. When VOCs were emitted intermittently in the chamber and there was sufficient ventilation, the concentration peaks in the chamber derived from the models with different assumptions on adsorption reversibility were significantly different from each other. Moreover, it was observed that the reversible adsorption capacity of the filter was crucial for long-term operation in rooms with dynamic concentration. Despite the reversible adsorption capacity constituting only 6.7% of the total adsorption capacity of the tested filter, it contributes to a significant “peak shaving and valley filling” effect, even when the irreversible adsorption capacity is saturated. The adsorption reversibility should be taken as an important parameter for selecting adsorbents for dynamic concentration conditions.

Conventional automated machine learning (AutoML) technologies fall short in preprocessing low-quality raw data and adapting to varying indoor and outdoor environments, leading to accuracy reduction in forecasting short-term building energy loads. Moreover, their predictions are not transparent because of their black box nature. Hence, the building field currently lacks an AutoML framework capable of data quality enhancement, environment self-adaptation, and model interpretation. To address this research gap, an improved AutoML-based end-to-end data-driven modeling framework is proposed. Bayesian optimization is applied by this framework to find an optimal data preprocessing process for quality improvement of raw data. It bridges the gap where conventional AutoML technologies cannot automatically handle missing data and outliers. A sliding window-based model retraining strategy is utilized to achieve environment self-adaptation, contributing to the accuracy enhancement of AutoML technologies. Moreover, a local interpretable model-agnostic explanations-based approach is developed to interpret predictions made by the improved framework. It overcomes the poor interpretability of conventional AutoML technologies. The performance of the improved framework in forecasting one-hour ahead cooling loads is evaluated using two-year operational data from a real building. It is discovered that the accuracy of the improved framework increases by 4.24%–8.79% compared with four conventional frameworks for buildings with not only high-quality but also low-quality operational data. Furthermore, it is demonstrated that the developed model interpretation approach can effectively explain the predictions of the improved framework. The improved framework offers a novel perspective on creating accurate and reliable AutoML frameworks tailored to building energy load prediction tasks and other similar tasks.

Due to the fast-modeling speed and high accuracy, deep learning has attracted great interest in the field of fault diagnosis in building energy systems in recent years. However, the black-box nature makes deep learning models generally difficult to interpret. In order to compensate for the poor interpretability of deep learning models, this study proposed a fault diagnosis method based on interpretable graph neural network (GNN) suitable for building energy systems. The method is developed by following three main steps: (1) selecting NC-GNN as a fault diagnosis model for building energy systems and proposing a graph generation method applicable to the model, (2) developing an interpretation method based on InputXGradient for the NC-GNN, which is capable of outputting the importance of the node features and automatically locating the fault related features, (3) visualizing the results of model interpretation and validating by matching with expert knowledge and maintenance experience. Validation was performed using the public ASHRAE RP-1043 chiller fault data. The diagnosis results show that the proposed method has a diagnosis accuracy of over 96%. The interpretation results show that the method is capable of explaining the decision-making process of the model by identifying fault-discriminative features. For almost all seven faults, their fault-discriminative features were correctly identified.

The emergence of building condenser water systems with all-variable speed pumps and tower fans allows for increased efficiency and flexibility of chiller plants in partial load operation but also increases the control complexity of condenser water systems. This study aims to develop an integrated modeling technique for evaluating and optimizing the energy performance of such a condenser water system. The proposed system model is based on the semi-physical semi-empirical chiller, pump, and cooling tower models, with capabilities of fully considering the hydraulic and thermal interactions in the condenser water loop, being solved analytically and much faster than iterative solvers and supporting the explicit optimization of the pump and tower fan frequency. A mathematical approach, based on the system model and constrained optimization technique, is subsequently established to evaluate the energy performance of a typical dual setpoint-based variable speed strategy and find its energy-saving potential and most efficient operation by jointly optimizing pumps and tower fans. An all-variable speed chiller plant from Wuhan, China, is used for a case study to validate the system model’s accuracy and explore its applicability. The results showed that the system model can accurately simulate the condenser water system’s performance under various operating conditions. By optimizing the frequencies of pumps and tower fans, the total system energy consumption can be reduced by 12%–13% compared to the fixed dual setpoint-based strategy with range and approach setpoints of 4 °C and 2 °C. In contrast, the energy-saving potential of optimizing the cooling tower sequencing is insignificant. A simple joint speed control method for optimizing the pumps and tower fans emerged, i.e., the optimal pump and fan frequency are linearly correlated (if both are non-extremes) and depend on the chiller part load ratio only, irrespective of the ambient wet-bulb temperature and chilled water supply temperature. It was also found that the oversizing issue has further limited the energy-saving space of the studied system and results in the range and approach setpoints being inaccessible. The study’s findings can serve as references to the operation optimization of all-variable speed condenser water systems in the future.

In many chiller plants, high coefficient of performance (COP) is only achieved at a few favorable part load ratios (PLRs), while the COP is low at many other non-favorable PLRs. To address this issue, this study proposes a generic load regulation strategy that aims to maintain chiller plants operating at high COP, particularly under non-favorable PLRs. This is achieved by incorporating thermal energy storage (TES) units and timely optimizing the charging and discharging power of the integrated TES units. The optimal charging and discharging power is determined by solving a dynamic optimization problem, taking into account the performance constraints of the TES units and the chiller plants. To provide an overview of the energy-saving potential of the proposed strategy, a comprehensive analysis was conducted, considering factors such as building load profiles, COP/PLR curves of chillers, and attributes of the TES units. The analysis revealed that the proposed load regulation strategy has the potential to achieve energy savings ranging from 5.7% to 10.8% for chiller plants with poor COPs under unfavorable PLRs, particularly in buildings with significant load variations.

In hot climates, the large amount of cooling load in electric vehicle (EV) results in a lot of battery energy consumption, leading the decrease of driving range. With the widespread application of windows in EV, the electrochromic glass (EC) shows great prospect in lowering the cooling load. However, researches on the application of EC in EV lack the consideration of both passive cooling measures and passenger comfort, which limits the further application of EC. In this paper, we proposed an idea combining the novel techniques of both electrochromism and radiative cooling. Computational fluid dynamics (CFD) is modeled to simulate the application of electrochromic and radiative cooling coupled smart windows in hot parking conditions, exploring the improvement effect of the window on the thermal environment, comfort and energy saving of the EV. The results indicate that, under the intense sunlight with an outdoor temperature of 33 °C, activating the air conditioning to maintain an average interior temperature of 26 °C, the coupled windows reduced the cooling capacity of the air conditioning by 762 W compared to regular windows, which can further increase the range of EV. Meanwhile, compared to simple electrochromic fully colored glass, the integration of radiative cooling technology can lower the window surface temperature by up to 10.7 °C. Moreover, compared to regular windows, the coupled windows lowered the standard effective temperature (SET*) for passengers by approximately 7 °C, significantly improving comfort. These research findings are expected to provide guidance for optimizing window design and enhancing the performance of EV.