Computational fluid dynamics (CFD) methods are being increasingly used for predicting airflow fields around buildings, but personal computers can still take tens of hours to create a single design using traditional computing models. Considering both accuracy and efficiency, this study compared the performances of the conventional algorithm PIMPLE, fast fluid dynamics (FFD), semi-Lagrangian PISO (SLPISO), and implicit fast fluid dynamics (IFFD) in OpenFOAM for simulating wind flow around buildings. The effects of calculation parameters, including grid resolution, discrete-time step, and calculation time for these methods are analyzed. The results of the simulations are compared with wind tunnel tests. It is found that IFFD and FFD have the fastest calculation speeds, but also have the largest discrepancies with test data. The PIMPLE algorithm has the highest accuracy, but with the slowest calculation speed. The calculation speeds of the FFD, SLPISO, and IFFD models are 6.3, 3 and 13.3 times faster than the PIMPLE model, respectively. The calculation accuracy and speed of the SLPISO model are in between those of the IFFD, FFD and PIMPLE models. An appropriate algorithm for a project may be chosen based on the requirements of the project.

Ventilation system with thermal energy storage (TES) using phase change materials (PCMs) can be employed to save energy in buildings, which stores outdoor coldness in the PCMs at night and releases this energy to cool down the fresh ventilation air during the daytime. However, its performance depends on the design parameters. This paper presents a detailed parametric analysis to address the separate effect of each design parameter on the cooling energy supply and net electricity saving of the TES system against a conventional ventilation system for the climate of Beijing, by using a computational heat transfer model. A genetic algorithm (GA) is used to optimize the design parameters for maximizing the net electricity saving in four cities of China. The decision variables are related to the PCM melting temperature, PCM slab thickness and cold charging airflow rate. The results show that the saving-optimal solution is not unique and depends on the climate. GA optimization increases the net electricity saving by 10%-54%, with a mean value of 31%. Sensitivity analysis of net electricity saving to the above three variables is carried out. Likewise, the sensitivity of each variable is not unique and depends on the climate.

Changes in climate have significant impacts on built environment. Many of the potential effects of climate change on the building sector are not well known. Previous studies have used a small number of climate projection models and scenarios, with the majority only using one or two models with multiple scenarios. This study identified and analyzed twenty-three climate models with one or more scenario for each model for total of fifty-six model scenarios. Future hourly weather data between 2011 and 2099 was generated with the morphing algorithm for seven climate zones in the US. Using cooling degree day (CDD) and heating degree day (HDD) as energy impact indicators, the study revealed that different climate models (even within the same RCP scenario) yield largely different results for building energy implications. To simplify application, four reference climate models were selected to represent the full range of the fifty-six model outputs, whose accuracy was validated using historical data. The study explored the impacts of climate changes on energy use of five typical US building types in Ann Arbor, MI, as a demonstration, which presented a general trend of site energy decrease and source energy increase for this location. The research further examined the influences of humidity and found that dry bulb temperature dominates the changes in building energy consumption and relative humidity only has a relatively larger impact on extreme cases in cooling dominated climates.

Building heating, ventilation and air-conditioning (HVAC) system can be potential contaminant emission source. Released contaminants from the mechanical system are transported through the HVAC system and thus impact indoor air quality (IAQ). Effective control and improvement measures require accurate identification and prompt removal of contaminant sources from the HVAC system so as to eliminate the unfavourable influence on the IAQ. This paper studies the application of the adjoint probability method for identifying a dynamic (decaying) contaminant source in building HVAC system. A limited number of contaminant sensors are used to detect contaminant concentration variations at certain locations of the HVAC ductwork. Using the sensor inputs, the research is able to trace back and find the source location. A multi-zone airflow model, CONTAM, is employed to obtain a steady state airflow field for the studied building with detailed duct network, upon which the adjoint probability based inverse tracking method is applied. The study reveals that the adjoint probability method can effectively identify the decaying contaminant source location in building HVAC system with few properly located contaminant concentration sensors.

Increasing risks of energy security and greenhouse gas emission due to the growing urbanization trend have prompted the need for urban energy demand prediction and management, in which the building energy consumption is the main cause. This paper reviews the recent advances and state-of-the-art in modeling building stock energy consumption, including both the top-down and bottom-up approaches. The study compares and summarizes the strengths and weaknesses of each primary method. Specific focus has been paid to the bottom-up stochastic engineering modeling methods, which hold sound quantitative theory bases as well as considering uncertain reality conditions. Stochastic building stock energy models account for the uncertainties that are the main limitation in existing building stock models. Discussions are provided regarding the process in the current stochastic building stock energy model. Challenges and possible future directions are examined for the improvement of stochastic building stock energy model.

Air pollution is becoming more and more severe in large cities. Accurate and rapid identification of outdoor pollutant sources can facilitate proper and effective air quality management in urban environments. Traditional "trial–error" process is time consuming and is incapacity in distinguishing multiple potential sources, which is common in urban pollution. Inverse prediction methods such as probability based adjoint modelling method have shown viability for locating indoor contaminant sources. This paper advances the adjoint probability method to track outdoor pollutant sources of constant release. The study develops an inverse modelling algorithm that can promptly locate multiple outdoor pollutant sources with limited pollution information detected by a movable sensor. Two numerical field experiments are conducted to illustrate and verify the predictions: one in an open space and the other in an urban environment. The developed algorithm promptly and accurately identifies the source locations in both cases. The requirement of an accurate urban building model is the primary prerequisite of the developed algorithm for urban application.

Building integrated photovoltaics (BIPV) receives growing attentions due to both architectural and engineering favorability. Large commercial building envelopes present a great potential of utilizing solar radiation, especially in climate zones with rich solar resources. Most current studies have been focused on predicting and optimizing power generation of BIPV on designed envelope systems, which leaves limited room for performance improvement of BIPV. This study introduces a framework of an optimization method that formulates the best building envelope shapes and the most matching BIPV systems. A set of criteria are established to determine the best alternatives of envelope variations, upon which the power generation and economic impact of different BIPV systems are evaluated and compared. The proposed optimization process was demonstrated using a general commercial building design application in Egypt. The developed tool can help designers in achieving an optimized building envelope that is most suitable for PV integration.

Conventional designers typically count on thermal equilibrium and require ventilation rates of a space to design ventilation systems for the space. This design, however, may not provide a conformable and healthy micro-environment for each occupant due to the non-uniformity in airflow, temperature and ventilation effectiveness as well as potential conflicts in thermal comfort, indoor air quality (IAQ) and energy consumption. This study proposes two new design methods: the constraint method and the optimization method, by using advanced simulation techniques— computational fluid dynamics (CFD) based multi-objective genetic algorithm (MOGA). Using predicted mean vote (PMV), percentage dissatisfied of draft (PD) and age of air around occupants as the design goals, the simulations predict the performance curves for the three indices that can thus determine the optimal solutions. A simple 2D office and a 3D aircraft cabin were evaluated, as demonstrations, which reveal both methods have superior performance in system design. The optimization method provides more accurate results while the constraint method needs less computation efforts.

Computational fluid dynamics (CFD) is a useful tool in building indoor environment study. However, the notorious computational effort of CFD is a significant drawback that restricts its applications in many areas and stages. Factors such as grid resolution and turbulence modeling are the main reasons that lead to large computing cost of this method. This study investigates the feasibility of utilizing inherent numerical viscosity induced by coarse CFD grid, coupled with simplest turbulence model, to greatly reduce the computational cost while maintaining reasonable modeling accuracy of CFD. Numerical viscosity introduced from space discretization in a carefully specified coarse grid resolution may have similar magnitude as turbulence viscosity for typical indoor airflows. This presents potentials of substituting sophisticated turbulence models with inherent numerical viscosity models from coarse grid CFD that are often used in fast CFD analysis. Case studies were conducted to validate the analytical findings, by comparing the coarse grid CFD predictions with the grid-independent CFD solutions as well as experimental data obtained from literature. The study shows that a uniform coarse grid can be applied, along with a constant turbulence viscosity model, to reasonably predict general airflow patterns in typical indoor environments. Although such predictions may not be as precise as fine-grid CFDs with well validated complex turbulence models, the accuracy is acceptable for indoor environment study, especially at an early stage of a project. The computing speed is about 100 times faster than a fine-grid CFD, which makes it possible to simulate a complicated 3-dimensional building in real-time (or near real-time) with personal computer.