Examining Wind Flow's Impact on Multi-Storey Buildings:A Quest for Quality Improvement

This scientific article delves into the intricacies of wind flow's impact on multi-storey buildings, presenting results from a series of experimental investigations. The research encompasses an examination of wind interactions with buildings of varying heights and geometric profiles. Furthermore, it unveils the effects of tall structures on the natural ventilation and smoke evacuation systems of shorter edifices, considering different wind flow directions. The study leverages specialized wind tunnel and measurement techniques for a comprehensive analysis of wind-induced loads on buildings. The acquired insights furnish crucial input for the design of single-story temporary modular constructions within densely populated urban areas, subject to wind-induced stresses. Additionally, they hold potential applicability in the advancement of energy-efficient technologies and strategies within the realm of construction. The acquired dataset underscores the criticality of scrutinizing wind flow's impact on structures of varied typologies and dimensions and will allow to significantly improve the quality and efficiency of modern buildings in the future

Approximating Computational Fluid Dynamics for Generative Design

Wind loads are a critical consideration in the early-stage design of tall buildings for mitigation of wind-induced forces through form modification. Existing research in computational fluid dynamics (CFD) development tends either towards fast-inaccurate or slow-accurate approaches; therefore offering either constrictive response time or inadequate accuracy. Novel approaches that combine both speed and accuracy are required to keep pace with developments in parametric design softwares, such as GenerativeComponents. These software tools, primarily used in early-stage generative design, allow for broad exploration and optimisation within the potential design space, which in turn requires commensurate fast-yet-accurate analysis tools. This thesis investigates the use of reduced-order models to approximate CFD simulations of wind pressure on tall buildings. It is hypothesised that: firstly, wind-induced surface pressure on tall buildings simulated by CFD can be locally approximated by geometric features; and secondly, reduced-order model predictions dominate CFD simulations in both time or accuracy and are therefore a novel non-dominated approach. Predictions are made of individual vertex pressure based on input features formed from local shape analysis. The vertex samples originate from a procedural model set which is evaluated with either steady-state Reynolds-averaged Navier-Stokes (RANS) or transient large eddy simulation (LES). An artificial neural network is used for model reduction with the training set of vertex samples; the basis methodology of which is tested on a range of study complexities. To prove the scalability of the approach, this culminates in the use of LES as the basis simulation, a test set of realistically complex building models, and an alternative approach to urban wind interference generalisation is also described, whereby a one-off large-scale context CFD simulation can be used as input to repeatable design model predictions. Furthermore, a prototype tool and an outline for its integration with an existing online analysis framework currently under development is presented. The quantitative and qualitative results of the studies show it is possible to approximate surface pressure from local shape features, thereby decoupling the prediction from the basis simulation. The reduced-order model can achieve fast-yet-accurate results, since prediction accuracy and time are invariant, or independent, of basis simulation accuracy and time; being instead solely a function of the reduced-order model performance and the geometric complexity or number of test mesh vertices. Evidence is demonstrated by the positioning of the results as a non-dominated solution in the time-accuracy objective space and the subsequent alteration of the existing Pareto frontier

Prognosis of Wind-tempted Mean Pressure Coefficients of Cross-shaped Tall Buildings Using Artificial Neural Network

The present paper focuses on the study of wind-induced responses of cross-plan shaped tall buildings. Initially, three parametric building models are studied for the purpose with a constant plan area 22500 mm2. The length and velocity scales are taken as 1:300 and 1:5, respectively. Wind angle of attack (WAA) is considered from 0° to 330° with an increment of 30°. At first, the external surface pressure coefficients (Cp) at different faces of the models are carried out for different wind occurrence angles employing Computational Fluid Dynamics method of simulated wind flow. Again, Fast Fourier Transform (FFT) fitted expressions as the sine and cosine function of WAA are proposed for attaining mean wind pressure coefficient on the building faces. The accuracy of the Fourier series expansions is justified by presenting histograms of sum square error (SSE), R2 value and root mean square error (RMSE). The results are also compared by training Artificial Neural Networks (ANN). Training is continued till Regression (R) values are more than 0.99 and Mean Squared Error (MSE) tends to 0, ensuring a close relationship among the outputs and targets. The face-wise value of (Cp) obtained using all three methods, are plotted. The error histograms of the ANN models show that the fitting data errors are spread within a reasonably good range. It is observed that the deviation in the result is not more than 5% in any case. Finally, the ANN predictions are presented for nine parametric models to cover a wide range of possible cross-shaped buildings

A multi-fidelity wind surface pressure assessment via machine learning: A high-rise building case

Computational fluid dynamics (CFD) represents an attractive tool for estimating wind pressures and wind loads on high-rise buildings. The CFD analyses can be conducted either by low-fidelity simulations (RANS) or by high-fidelity ones (LES). The low-fidelity model can efficiently estimate wind pressures over a large range of wind directions, but it generally lacks accuracy. On the other hand, the high-fidelity model generally exhibits satisfactory accuracy, yet, the high computational cost can limit the number of approaching wind angles that can be considered. In order to take advantage of the main benefits of these two CFD approaches, a multi-fidelity machine learning framework is investigated that aims to ensure the simulation accuracy while maintaining the computational efficiency. The study shows that the accurate prediction of distributions of mean and rms pressure over a high-rise building for the entire wind rose can be obtained by utilizing only 3 LES-related wind directions. The artificial neural network is shown to perform best among considered machine learning models. Moreover, hyperparameter optimization significantly improves the model predictions, increasing the ��2 value in the case of rms pressure by 60%. Dominant and ineffective features are determined that provide a route to solve a similar application more effectively

5 European & African Conference on Wind Engineering

The 5th European-African Conference of Wind Engineering is hosted in Florence, Tuscany, the city and the region where, in the early 15th century, pioneers moved the first steps, laying down the foundation stones of Mechanics and Applied Sciences (including fluid mechanics). These origins are well reflected by the astonishing visionary and revolutionary studies of Leonardo Da Vinci, whose kaleidoscopic genius intended the human being to become able to fly even 500 years ago… This is why the Organising Committee has decided to pay tribute to such a Genius by choosing Leonardo's "flying sphere" as the brand of 5th EACWE

Aerodynamic Optimization and Wind Load Evaluation Framework for Tall Buildings

Wind is the governing load case for majority of tall buildings, thus requiring a wind responsive design approach to control and assess wind-induced loads and responses. The building shape is one of the main parameters that affects the aerodynamics that creates a unique opportunity to control the wind load and consequently building cost without affecting the structural elements. Therefore, aerodynamic mitigation has triggered many researchers to investigate various building shapes that can be categorized into local (e.g. corners) and global mitigations (e.g. twisting). Majority of the previous studies compare different types of mitigations based on a single set of dimensions for each mitigation types. However, each mitigation can produce a wide range of aerodynamic performances by changing the dimensions. Thus, the first millstone of this thesis is developing an aerodynamic optimization procedure (AOP) to reduce the wind load by coupling Genetic Algorithm, Computational Fluid Dynamics (CFD) and an Artificial Neural Network surrogate model. The proposed procedure is adopted to optimize building corners (i.e. local) using three-dimensional CFD simulations of a two-dimensional turbulent flow. The AOP is then extended to examine global mitigations (i.e. twisting and opening) by conducting CFD simulations of three dimensional turbulent wind flow. The procedure is examined in single- and multi-objective optimization problems by comparing the aerodynamic performance of optimal shapes to less optimal ones. The second milestone is to develop accurate numerical wind load evaluation model to validate the performance of the optimized shapes. This is primary achieved through the development of a robust inflow generation technique, called the Consistent Discrete Random Flow Generation (CDRFG). The technique is capable of generating a flow field that matches the target velocity and turbulence profiles in addition to, maintaining the coherency and the continuity of the flow. The technique is validated for a standalone building and for a building located at a city center by comparing the wind pressure distributions and building responses with experimental results (wind tunnel tests). In general, the research accomplished in this thesis provides an advancement in numerical climate responsive design techniques, which enhances the resiliency and sustainability of the urban built environment

Doctor of Philosophy

dissertationThe present work focuses on developing a holistic understanding of flow and dispersion in urban environments. Toward this end, ideas are drawn from the fields of physical modeling, inverse modeling, and optimization in urban fluid dynamics. The physical modeling part of the dissertation investigates flow in the vicinity of tall buildings using wind tunnel two-dimensional particle image velocimetry (PIV) measurements. The data obtained have been used to evaluate and improve urban wind and dispersion models. In the inverse modeling part of the dissertation, an event reconstruction tool is developed to quickly and accurately characterize the source parameters of chemical / biological / radiological (CBR) agents released into the atmosphere in an urban domain. Event reconstruction is performed using concentration measurements obtained from a distributed sensor network in the city, where the spatial coordinates of the sensors are known a priori. Source characterization comprises retrieving several source parameters including the spatial coordinates of the source, the source strength, the wind speed, and wind direction at the source, etc. The Gaussian plume model is adopted as the forward model, and derivative-based optimization is chosen to take advantage of its simple analytical nature. The solution technique developed is independent of the forward model used and is comprised of stochastic search with regularized gradient optimization. The final part of the dissertation is comprised of urban form optimization. The problem of identification of urban forms that result in the best environmental conditions is referred to as the urban form optimization problem (UFOP). The decision variables optimized include the spatial locations and the physical dimensions of the buildings and the wind speed and wind direction over the domain of interest. For the UFOP, the quick urban and industrial complex (QUIC) dispersion model is used as the forward model. The UFOP is cast as a single optimization problem, and simulated annealing and genetic algorithms are used in the solution procedure

New Advances in Fluid Structure Interaction

Fluid–structure interactions (FSIs) play a crucial role in the design, construction, service and maintenance of many engineering applications, e.g., aircraft, towers, pipes, offshore platforms and long-span bridges. The old Tacoma Narrows Bridge (1940) is probably one of the most infamous examples of serious accidents due to the action of FSIs. Aircraft wings and wind-turbine blades can be broken because of FSI-induced oscillations. To alleviate or eliminate these unfavorable effects, FSIs must be dealt with in ocean, coastal, offshore and marine engineering to design safe and sustainable engineering structures. In addition, the wind effects on plants and the resultant wind-induced motions are examples of FSIs in nature. To meet the objectives of progress and innovation in FSIs in various scenarios of engineering applications and control schemes, this book includes 15 research studies and collects the most recent and cutting-edge developments on these relevant issues. The topics cover different areas associated with FSIs, including wind loads, flow control, energy harvesting, buffeting and flutter, complex flow characteristics, train–bridge interactions and the application of neural networks in related fields. In summary, these complementary contributions in this publication provide a volume of recent knowledge in the growing field of FSIs