FIELD MEASUREMENT AND NUMERICAL STUDY OF EXTERNAL WIND PRESSURE OF RIBBED COOLING TOWER

The hyperbolic thin-shell cooling tower is a typical wind-sensitive structure. The full-size measurement is the most direct and important way to study the distribution of wind pressure on the surface of the cooling tower. Due to the limitations of engineering conditions and meteorological conditions, the field measured data are relatively lacking, and the field test data of ribbed cooling towers are less. In order to analyze the wind pressure distribution on the surface of the cooling tower, we chose a ribbed cooling tower in Toksun County, Xinjiang, China, where there are strong winds all year round, and field measurements were carried out to understand the wind load characteristics of the tower under the perennial dominant wind direction and the maximum wind direction. It was found that the absolute value of the negative pressure on the leeward side was larger than that in the code and the fluctuating wind pressure coefficient fluctuates greatly when the field measured wind speed was greater than 10m/s (15 meters above the ground). For circular section cooling tower, the Reynolds number (Re) has great influence on wind pressure. With the increase of Re, the absolute value of the average negative pressure of the tail wind pressure coefficient increases, which should be paid attention to in design. The regression curves of the average wind pressure coefficients measured on site under several typical working conditions are given by using the least square method, and its form is consistent with the standard (but the coefficients are different). In addition, Fluent software was used to calculate the external wind pressure of the cooling tower, and the field measured results were compared with the Chinese code, German code and numerical calculation, and the results were consistent.

Impact of Turbulence Models of Wind Pressure on two Buildings with Atypical Cross-Sections

The article deals with the numerical analysis of the wind pressure distribution on a group of two high-rise buildings of different shape for different wind directions. The first building has the shape of a circular cylinder and the second was created by a combination of semicircles and a longitudinal member. The floor plan of the second building was similar to the letter S. The simulations were realized as 3D steady RANS. CFD results were compared with experimental measurements in the wind tunnel of the Slovak University of Technology in Bratislava. The results were processed using statistical methods such as correlation coefficient, fractional bias and fraction of data within a factor of 1.3, which determined the most suitable CFD model. The purpose of the present article was to verify the distribution of the external pressure coefficient on scale models at a scale of 1:350, which are located in the Atmospheric Surface Layer (ASL). In numerical modeling, the most important thing was to ensure similarity with the flow in the experimental Atmospheric Boundary Layer (ABL) and with the flow around the models. SST k–ω was evaluated as the most suitable turbulent model for the given type of problem. Turbulent models had a decisive influence on the overall distribution of external wind pressures on objects. The results showed that the most suitable orientation of the objects in terms of the external wind pressure coefficient is 0°, when the cylinder produced a shielding effect, with min mean cpe = −0.786. The most unfavorable wind effects were shown by the wind direction of 90° and 135° with the value min mean cpe = −1.361.

The role of complex airflow simulation tools for overheatingassessment of passive houses

Passive Houses are characterized mainly by construction concepts that greatly reduce energy usage during the winter, but that can lead to significant overheating during the hotter summer days. Since in the Passive House concept thermal comfort during the summer mainly relies on natural ventilation to provide indoor cooling, the importance of airflow modeling tools for overheating prediction needs to be investigated. This research analyzes the effect of simplifications commonly made in airflow modeling techniques on the overheating assessment of Passive Houses by collecting measured data and calibrating a thermal model with a Passive House case study. Utilizing the calibrated model, a standalone Building Energy Model (BEM), BEM coupled with an Airflow Network Model (AFN), and BEM coupled with an AFN supported by the wind pressure coefficient values obtained from Computational Fluid Dynamics (CFD) simulation were created. The outcome of each modeling approach was then compared against each other. Results showed that the default infiltration and natural ventilation input values commonly utilized in literature, when compared to those obtained from either the AFN or AFN+CFD, are significantly overestimating the natural ventilation potential of Passive House buildings, resulting in a lower number of overheating hours (39.9% decrease) and inaccurate overheating evaluation outcomes. Therefore, the paper concludes that the use of at least an AFN is necessary when estimating the overheating hours of Passive Houses.

Further Analysis of Non-Gaussian Wind Pressure Peak Factor Calculation

The wind pressure time history of high-rise building cladding is mostly non-Gaussian distribution, and there is a one-to-one correspondence between a specified guarantee rate and its corresponding peak factor. A stepwise search method for calculating the peak factor of non-Gaussian wind pressure and a gradual independent segmentation method for extracting independent peak values have been proposed to determine the relationship accurately in the previous study. Based on the given experiment and calculation results in the existing research results, more analysis can be given to enrich the study on this topic. In this paper, some characteristics of wind pressure coefficient time series in time and frequency domain are analysed. Based on the basic theory of fractal, the R/S analysis of wind pressure time series is made, and the fractal characteristics of wind pressure coefficient time series are explained. Based on the statistical theory, the relationship characteristics between high-order statistics and peak factors are studied. The correlation between the guarantee rate and the corresponding peak factor is analysed, and the guarantee rates calculated by the Davenport peak factor method are evaluated. The power spectrum characteristics of fluctuating wind pressure are analysed and the relationship between turbulence characteristic frequency and optimal observation time interval is discussed.

Experimental Study on the Effects of Aspect Ratio on the Wind Pressure Coefficient of Piloti Buildings

Owing to strong winds during the typhoon season, damage to pilotis in the form of dropout of the exterior materials occurs frequently. Pilotis placed at the end exhibit a large peak wind pressure coefficient of the ceiling. In this study, the experimental wind direction angle of wind pressure tests was conducted in seven directions, with wind test angles varying from 0° to 90° at intervals of 15°, centered on the piloti position, which was accomplished using the wind tunnel experimental system. Regardless of the height of the building, the maximum peak wind pressure coefficient was observed at the center of the piloti, whereas the minimum peak wind pressure coefficient was noted at the corners, which corresponds with the wind direction inside the piloti. The distribution of the peak wind pressure coefficient was similar for both suburban and urban environments. However, in urban areas, the maximum peak wind pressure coefficient was approximately 1.4–1.7 times greater than that in suburban areas. The maximum peak wind pressure coefficient of the piloti ceiling was observed at the inside corner, whereas the minimum peak wind pressure coefficient was noted at the outer edge of the ceiling. As the height of the building increased, the maximum peak wind pressure coefficient decreased. Suburban and urban areas exhibited similar peak wind pressure distributions; however, the maximum peak wind pressure coefficient in urban areas was approximately 1.2–1.5 times larger than that in suburban areas.

Effects of Interference on Local Peak Pressures Between Two Buildings with an Elliptical Cross-Section

The interference effects on the distribution of external wind pressure coefficient between two high-rise buildings with an elliptical cross section were studied experimentally at the Boundary Layer Wind Tunnel (BLWT) at the Faculty of Civil Engineering STU in Bratislava, Slovakia. Various arrangements of models, which were derived from the breadth ratio, were investigated. The peak value of the external wind pressure coefficient for a stand-alone model was measured and compared with the peak value in the case of interference. The measurements showed that the wind loads on buildings in a close vicinity are considerably different from those on a stand-alone building. The interference effects significantly affect negative pressure zones. The optimal and critical arrangements of buildings were evaluated. The elimination of peak negative external wind pressure coefficients can be reduced by half. On the other hand, the interference effects had a strong impact on increasing the peak value of the negative external wind pressure coefficient, which can be more than roughly double compared to an isolated building.

Simulation and Analysis of Wind Pressure Coefficient of Landslide-Type Long-Span Roof Structure

This article carries out a numerical simulation of a landslide-type long-span roof structure, Harbin Wanda Cultural Industry Complex. The maximum span of the landslide-type roof is 150 m and the minimum span is 90 m, with a minimum height of 40 m and a maximum height of 120 m, and the roof area is divided into three different parts. The large eddy simulation (LES) method is used to simulate and record the wind pressure coefficient of the roof. The distribution law and cause of the mean wind pressure coefficient of the roof are firstly analyzed, and the comparison with the existing wind tunnel test data proves the validity of the numerical simulation. Secondly, a qualitative analysis is made on the distribution of root mean square (RMS) fluctuating coefficients. Subsequently, the non-Gaussian characteristics of the roof are briefly discussed, and the peak factor distribution is calculated. Finally, based on the total wind pressure coefficient, a simple evaluation method for judging favorable and unfavorable wind direction angles is proposed, and only the shape of the roof and wind angle need to be known.