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Radiative heat transfer analysis method for coupled simulation of convection and radiation in large-scale and complicated enclosures. Part 1-Accurate radiative heat transfer analysis based on Monte Carlo method
... The MARS difference scheme and SIMPLE pressure-velocity coupling was used. The solver combined the Monte Carlo method and the Zone method for radiative analysis [7]. Four turbulence models were evaluated: SST k-ω; realizable k-ε; low-Re k-ε; and v2-f models. ...
... The numerical setup included: turbulence model was the SST k-ω model as the baseline, and for comparison we also tested the low-Re k-ε, realizable k-ε, v2f, and high-Re k-ε; discretization scheme was MARS; pressure-velocity coupling was SIMPLE; and radiation analysis: CalcRad (Monte Carlo method + Zone method [7]). ...
... For turbulence, the SST k-ω turbulence model was used and, MARS is adopted for discretization scheme. For radiation analysis, CalcRad (Monte Carlo method + Zone method) was used [7]. ...
Recent indoor environmental design is requested to create comfortable and safety space in addition to the maximizing the energy conservation performance in buildings. In this point of view, it is important to enhance the prediction accuracy of indoor environmental quality in design stage. Commercial Computational Fluid Dynamics (CFD) software is practically applied in indoor environmental design recent years but the prediction accuracy of CFD simulation depends on the understanding for the fundamentals of fluid dynamics and the setting of appropriate boundary and numerical conditions as well. The series of this study aimed to provide with the practical information such as prediction accuracy and problematic areas related to CFD applications in indoor environment, air conditioning and ventilation, and then performed benchmark tests and reported the results. Especially in this Part 3, benchmark test results for numerical thermal manikins were introduced. SST k-ω model with fine mesh could provide sufficient accurate results and showed good agreement with experimental results.
... When examining the heat exchange between the human body and the surrounding environment, a human model that can express the details of the size and surface form of the body is necessary. Human models that express the details of the human form include those of Suzuki and Kakitsuba [58], Ozeki et al. [60], Omori et al. [61], Sørensen et al. [62], Torii et al. [63], Ito and Hotta [64], and Kurazumi et al. [65]. All of these models are of an adult. ...
... However, this method has not been verified. Omori et al. [61] made a human model that imitates the form of a thermal manikin with non-contacting body parts, but there is no explanation of the making process, and its validity has not been investigated. Sørensen et al. [62] used a non-contact 3D digitizer to make a humanoid model imitating the form of a thermal manikin in a seated position formed from small triangular elements. ...
... As with the optical analysis, we calculated the geometric factor via the Monte Carlo method, and analyzed the inter-reflection using the Gebhart absorption coefficient. The calculations were carried out using code developed by Omori et al. [14]. ...
... The number of cells is 52,272. The calculations were carried out using the commercial CFD code ''Star-CD,'' which has been widely used in recent convection designs, and using code developed by Omori et al. [14] for radiation, Table 2 shows the boundary conditions. The windows were opened for this calculation. ...
In single-objective optimization problems, with only one optimal design objective, the absolute optimal solution to maximize/minimize the objective function can be determined. However, in most real design problems, the optimization problems are multi-objective, where two or more independent design objectives must be optimized simultaneously, and no single absolute optimal solution necessarily exists. In these cases, it is helpful for designers to recognize the range of alternative solutions that exist in Pareto-optimal sets and choose an acceptable solution from among them. In this paper, the authors carried out multi-objective optimization using Multiple Objective Genetic Algorithms through a real case study involved in indoor environmental design – the design of outer windows. Then the authors analyzed structure of Pareto-optimal solution sets. Here we present the analysis process as well as the case study details, and show how the method proposed here is effective at finding an acceptable solution for multi-objective optimization problems.
... For each small surface, various factors were considered, including solar radiation, sky radiation, longwave radiation between the surface and other surfaces, convective heat transfer and latent heat transfer between the surface and ambient air, and conduction heat transfer through the surface. The shape factor was calculated by the Monte Carlo method [38,39] and the radiative heat transfer was calculated by Gebhart's absorption factor [40]. Due to the different absorptivity under shortwave and longwave radiation, the values of Gebhart's absorption factor were also different, so each surface was calculated separately. According to the former studies [34,41,42], and considering the above heat transfer, the urban surface temperature can be reproduced well. ...
Outdoor wind and thermal environments in residential areas are greatly affected by the distance between buildings. A short distance is conducive to providing shade, and a long distance can enhance ventilation between buildings. In this study, four cities with different latitudes in China (Guangzhou, Wuhan, Beijing, and Harbin) were selected to research the relationship between the distance between buildings and thermal environments of residential areas. The results show that (1) when the distance between buildings is small, it is easier for wind paths to form. Wind paths can strengthen the wind velocity. When the distance between buildings exceeds 40–50 m, the building density is small, the building’s resistance to the wind becomes smaller and smaller, and the wind speed will gradually increase. (2) When the distance is in the range of 20–50 m, the MRT (mean radiant temperature) rise rate of each city is similar. For every 10 m increase in the distance between buildings, the MRT increases by about 1.25 °C. (3) D = 50 m (D/H = 1.19) is an inflection point. When D is less than 50 m, within the range of 20–50 m, the smaller the D is, the lower the SET* (standard effective temperature) is, while when D is more than 50 m, the opposite trend is observed.
... For each small surface, solar radiation, sky radiation, longwave radiation between it and other surfaces, convective heat transfer and latent heat transfer between it and the ambient air, and conduction heat transfer through it are considered. The shape factor is calculated using the Monte Carlo method [45,46] and the radiative heat transfer is calculated using Gebhart's absorption factor [47]. Given that the values of Gebhart's absorption factor are different because of the different absorptivities under shortwave and longwave radiations, they are calculated separately for each surface. ...
There has been an insufficient study of passive climate adaptability that considers both the summer and winter season for the outdoor thermal environment of hot-summer and cold-winter cities. In this study, we performed a quantitative simulation to research the passive climate adaptability of a residential area, considering piloti as the main method for climate adaptation in a hot-summer and cold-winter city in China. Numerical simulations were performed with a coupled simulation method of convection, radiation, and conduction. A cubic non-linear k–ε model proposed by Craft et al. was selected as the turbulence model and three-dimensional multi-reflections of shortwave and longwave radiations were considered in the radiation simulation. Through the simulation, we found that setting the piloti at the two ends of the building was the optimal piloti arrangement for climate adaptation. Then the relationship between the piloti ratio (0%, 20%, 40%, 60%, and 80%) and the outdoor thermal environment was studied. It could be concluded that with the increasing piloti ratio, the wind velocity increased, the mean radiant temperature (MRT) decreased slightly, and the average standard effective temperature (SET*) decreased to 3.6 °C in summer, while in winter, with the increasing piloti ratio, the wind velocity, MRT, and SET* changed slightly. The wind environment significantly affected the SET* value, and the piloti ratio should be between 12% and 38% to avoid wind-induced discomfort.
... [ Figure 1 ] Some typical form factor calculation algorithms are the Monte Carlo (sometimes called the Monte Carlo ray tracing) algorithm [3][4][5][6], double area integral algorithm [7], Mitalas-Stephenson's method [8], and the hemicube algorithm [9], which was developed for application in the field of CG. Konno et al. [7] have compared these four algorithms by simulating a typical model used in architectural engineering. ...
A fast and accurate algorithm is required for carrying out radiative heat transfer simulations designing thermally efficient devices. This paper describes the performance of the hemisphere algorithm, which was originally developed for fast form factor calculation for obtaining photorealistic three-dimensional computer graphics. We compare the performance of the hemisphere algorithm with that of two conventional algorithms that are widely used for radiative heat transfer simulations. The hemisphere algorithm is found to be significantly faster than the other algorithms, but it has an absolute error of 1.0×10−5. In addition, the result indicates that the hemisphere algorithm is suitable for simulating the trial and error process in the practical analysis of large-scale models due to its tolerable visualization of form factor distribution. © 2009 Wiley Periodicals, Inc. Heat Trans Asian Res; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/htj.20259
... In our analysis domain, arrangements and shapes of buildings and the ground are very complicated, making it very difficult to calculate view factors using traditional methods such as the integrated method (Murakami et al., 1995). The Monte Carlo method is used in this study to calculate the view factors (Omori et al., 2003), as it is easy to adapt to a complicated geometry. In the Monte Carlo method, if a radiation bunch of total number N itotal is emitted from solid face i, and the radiation bunch that reaches solid face j is assumed to be N ij , the view factor F ij is defined by Eq. (1): ...
Urban thermal situation is thought to have a great influence on the air quality in urban areas. In recent years, the urban thermal environment has become worse, such as the days on which the temperature goes above 30 °C, the sultry nights and heat stroke increase due to changes in terrestrial cover and increased anthropogenic heat emission accompanied by urbanization. Therefore, the urban thermal environment should be carefully investigated and accurately analyzed for a better study of the air quality. Here, in order to study the urban thermal environment in summer, (1) the actual status of an urban thermal environment in a complex urban area covering a large district heating and cooling (DHC) system in Tokyo is investigated using field measurements, and (2) a numerical simulation program which can be adapted to complex urban areas coupled with convection, radiation and conduction is developed and used to predict the urban thermal environment. Wind velocity, temperature and humidity are obtained from the simulation, which shows good agreement with results of the field measurement. The spatial distribution of the standard effective temperature (SET*), the comprehensive index of human thermal comfort, is also calculated using the above results, to estimate the thermal comfort at the pedestrian level. This urban thermal numerical simulation can be coupled with air pollution dispersion and chemical processes to provide a more precise air quality prediction in complex urban areas.
The present work investigated the outdoor thermal environment for different urban forms under the summer conditions of Sendai, Japan and Guangzhou, China. Sendai has a moderate humid subtropical climate, whereas Guangzhou has a humid subtropical climate. Numerical simulations were performed with a coupled simulation method of convection, radiation, and conduction. A cubic non-linear k–ε model proposed by Craft et al. was selected as the turbulence model and three-dimensional multireflections of shortwave and longwave radiations were considered in the radiation simulation. Seven urban forms (the ratios of building distance to building height were 0.24, 0.36, 0.48, 0.71, 0.95, 1.19, and 1.43.) were studied. The openness and compactness of the urban forms were compared by developing a new assessment system. The following results were obtained. (1) The distributions of wind velocity around the buildings became polarized as building distance decreased, and the proportion of low wind velocity grew large. These conditions mainly caused poor ventilation and thermal discomfort. (2) The cooling effects of building shade became increasingly significant as building distance decreased because of the low level of exposure to strong sunshine in compact forms. (3) Safe outdoor thermal conditions (standard effective temperature ≤37 °C) can be partially achieved in Sendai by decreasing building distance, whereas the same could not be achieved in Guangzhou. Further countermeasures are essential in Guangzhou.
A numerical model simulating surface and air temperatures in a parking automobile have been developed and numerical simulations were carried out to investigate thermal environment under summer solar radiation. In addition, comparisons with measurement results were conducted to verify and validate the simulation code. Numerical results in case of no ventilation showed that the maximum temperature was obtained and found to be over 90 degrees C. On the top of the front dashboard. On the other hand, air temperature near floorboard in the cabin was saturated to be at most 45 degrees C. They were consistent with the measurement results. Further, ventilating experiments were conducted. In these experiments, clean air freely came into the cabin from a front duct and hot air in the cabin was forced to be discharged at different flow rates from a rear duct located on the rear headrest in order to mitigate temperature rise. Then, temperature distributions around the driver's seat were investigated and compared with- and without-ventilation cases. Results show that temperature mitigation rate was almost proportional to the flow rate and the numerical model can estimate air and surface temperatures within ±5 K and ±10 K errors, respectively.
In this paper, the authors measure the convective heat transfer coefficient for the human body in the outdoor environment by means of a wind tunnel test and computational fluid dynamics (CFD) analysis. Sufficient accuracy for the CFD analysis is confirmed by comparing both results. The authors implement a sensitivity analysis based on CFD analysis for the mean convective heat transfer coefficient for the human body with changing values of velocity and turbulence intensity. From this, the authors develop a formula for the mean convective heat transfer coefficient for the human body in the outdoor environment which enables us to evaluate both the influences of velocity and turbulence intensity of the wind on the human body.
The integration of three-dimensional spatial distributions into building simulations is of significant interest, and computational fluid dynamics (CFD) analysis is widely employed in building design processes. For example, based on the experience of architects and engineers, CFD analyses are often conducted under steady boundary conditions to determine the degree of attainment of indoor environments. However, CFD analyses have large calculation costs and cannot be often used for simulations with unsteady boundary conditions such as energy simulations in the building design processes. Thus, we developed a method that calculates sensitivities from heat sources to an arbitrary point in an indoor environment and integrates them into simulations with unsteady boundary conditions. In the proposed method, CFD analysis is employed under steady boundary conditions to calculate the response factors, and the resulting sensitivities are integrated into simulations under unsteady boundary conditions. In the present study, the proposed method was applied to optimize the variables of an air conditioning control system. With our method, temperature changes at a sensor over time are calculated from the time series of air supply temperature. In total, 800 calculations were conducted, and the optimal variables that allow the temperature at the sensor to reach the target value quickly were obtained. Except for the time required to calculate the response factors, the optimization in the present study took only a few seconds. If only CFD analysis was used for the optimization, the calculations would take a year. Thus, calculating the sensitivities via CFD analysis and utilizing the results in simulations is a useful approach for solving optimization problems. Moreover, the proposed method is applicable to simulations that require three-dimensional spatial distributions to enhance the accuracy of the calculation such as energy simulations.
Outdoor thermal environment represented by the urban heat island phenomenon has become markedly worse in recent years due to the change in land covering and the increase in the artificial heat release that accompanies urbanization. The heat released by air conditioning is thought to be one of the factors in the deterioration of the outdoor thermal environment. A large number of District Heating and Cooling Systems (DHC) have been introduced into large cities, such as Tokyo, in Japan. As the heat loads of the buildings supplied by the system are released together from the DHC system's cooling towers in the summer, it is necessary to ensure that the method used to discharge this heat has a minimum influence on the outdoor thermal environment around these cooling towers. In this study, in order to clarify the influence of the DHC system's cooling towers on the outdoor thermal environment around the DHC system in the summer: 1) the actual situation of the outdoor environment around the Shinjuku DHC center (next to Shinjuku Park Tower, a 52-storey building), which has the largest cooling capacity in the world (cooling capacity: 207,680kw; supplied floor space: 2,200,000 m 2 ; eight cooling towers) is investigated from field measurement; 2) a simulation program for the thermal environment adapted to an unstructured computational grid and which is suitable for complex urban areas coupled with convection, radiation and conduction is developed based on a thermal environment evaluation method (Harayama, 2002; Yoshida, 2000a; Yoshida, 2000b). The spatial distributions of wind velocity, air temperature, and humidity in the area where the field measurement were made are analyzed by the simulation program. The numerical results are compared to the field measurement in order to confirm the accuracy of the simulation program. The influence of the cooling towers on the outdoor environment is analyzed. Furthermore, the spatial distributions of SET* (Standard Effective Temperature) is also calculated using the above results to estimate thermal comfort at pedestrian level.
Indoor climate has a three-dimensional spatial distribution caused by three-dimensional airflow. To obtain the accurate knowledge of building performance, it is demanded to integrate the spatial distribution into building simulations. Thus, CFD analysis is necessary in design process. However, usually only a few case of CFD could be executable in real design process, because of the large computational load. The main subject of this study is the development of a method; how to extract heat transport phenomena in rooms from such limited CFD analyses, and how to integrate the data into a nodal analysis. Using the method, we can calculate indoor environment, including the spatial distribution of temperature with a very light computational load and with almost the same degree of precision as CFD simulation.
In this study, we measure the convective heat transfer coefficient for the human body in the outdoor environment by means of a wind tunnel test and computational fluid dynamics (CFD) analysis. A comparison of the results shows that the CFD analysis is sufficiently accurate. We conduct a sensitivity analysis based on CFD analysis for the mean convective heat transfer coefficient for the human body with changing values of velocity and turbulence intensity. We then develop a formula for the mean convective heat transfer coefficient for the human body in the outdoor environment that can evaluate both the influences of velocity and turbulence intensity of the wind on the human body. This formula is more effective for evaluating thermal comfort in the outdoor environment where there is large turbulence, because conventional formulae can evaluate only the influences of velocity. While the conventional formula produces an unrealistic increase in sensible temperature, the new formula causes the temperature to decrease, which is more reasonable, based on an analysis of thermal comfort in a tree-planted city block.
The heat generated from an air-conditioning equipment or other thermal loads is distributed throughout a room by a three-dimensional
airflow. This three-dimensional airflow creates a three-dimensional heat distribution in a room. To better understand building
performance, we must integrate this spatial distribution into building simulations. Thus, three-dimensional computational
fluid dynamics (CFD) analysis is necessary in design process because most conventional building energy simulations still employ
a temperature that is averaged across the space of a room. However, usually only a few cases of CFD analyses are executable
in real design process because of the large computational load they require. This paper presents a new, simplified method
to calculate heat transport phenomena in rooms, based on a few cases of CFD analysis, and to integrate data into a nodal analysis.
This method can be used to calculate an indoor environment, including the spatial distribution of temperature, with a computational
load that is much lighter than it is in a simulation using CFD alone. Furthermore, in terms of precision, it is a far more
reliable method than the conventional simulation, which assumes the perfect mixing of heat in a room. In the paper, we apply
this method to simulate the control of air conditioning. Ordinarily, the reproduction of the phenomena shown in the calculation
examples requires substantial manpower and costly computing resources for experimentation or CFD analysis. With our calculation
method, it is possible to reproduce the same calculation results in a very short time with a PC. And we checked the potential
to the practical use through a verification calculation with CFD analysis.
Keywordsresponse factor-CFD-temperature distribution-air-conditioning control-thermal environment
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