Finite Element Model for Wind Comfort Around a Tall Building: A Case Study of Tower of Qazaqstan

Narbayev, Bakdauren

doi:10.1007/978-3-031-10542-5_37

Computational Science and Its Applications – ICCSA 2022 Workshops

2022

DOI: 10.1007/978-3-031-10542-5_37

|View full text |Cite

Finite Element Model for Wind Comfort Around a Tall Building: A Case Study of Tower of Qazaqstan

Bakdauren Narbayev¹

Abstract: Pedestrian wind comfort plays an essential role in the urban environment. In our work, we consider a model obtained using Computational Fluid Dynamics (CFD) around a tall building. Our focus is the Tower of Qazaqstan or Abu Dhabi Plaza in Nur-Sultan city (Kazakhstan), which will be the tallest building in Central Asia with a height of 310.8 m. We investigated the effect of the wind velocity on pedestrians by solving the incompressible time-dependent Navier-Stokes equations in the deal.II library by the Finite … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Application of Physics-Informed Neural Networks to River Silting Simulation

Omarova,

Amirgaliyev,

Kozbakova

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

Water resource pollution, particularly in river channels, presents a grave environmental challenge that necessitates a comprehensive and systematic approach encompassing assessment, forecasting, and effective management. This article provides a comprehensive exploration of the methodology and modeling tools employed to scrutinize the process of river channel pollution due to silting, rooted in the fundamental principles of hydrodynamics and pollutant transport dynamics. The study’s methodology seamlessly integrates numerical simulations with state-of-the-art neural network techniques, with a specific focus on the physics-informed neural network (PINN) method. This innovative approach represents a groundbreaking fusion of artificial neural networks (ANNs) and physical equations, offering a more efficient and precise means of modeling a wide array of complex processes and phenomena. The proposed mathematical model, grounded in the Euler equation, has been meticulously implemented using the Ansys Fluent software package, ensuring accuracy and reliability in the computations. In a pivotal phase of the research, a thorough comparative analysis was conducted between the results derived using the PINN method and those obtained using conventional numerical approaches with the Ansys Fluent software package. The outcomes of this analysis revealed the superior performance of the PINN method, characterized by the generation of smoother pressure fluctuation profiles and a significantly reduced computation time, underscoring its potential as a transformative modeling tool. The calculated data originating from this study assume paramount significance in the ongoing battle against river sedimentation. Beyond this immediate application, these findings also serve as a valuable resource for creating predictive materials pertaining to river channel silting, thereby empowering decision-makers and environmental stakeholders with essential information. The utilization of modeling techniques to address pollution concerns in river channels holds the potential to revolutionize risk management and safeguard the integrity of our vital water resources. However, it is imperative to underscore that the effectiveness of such models hinges on ongoing monitoring and frequent data updates, ensuring that they remain aligned with real-world conditions. This research not only contributes to the enhanced understanding and proactive management of river channel pollution due to silting but also underscores the pivotal role of advanced modeling methodologies in the preservation of our invaluable water resources for present and future generations.

show abstract

Application of Physics-Informed Neural Networks to River Silting Simulation

Omarova,

Amirgaliyev,

Kozbakova

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Finite Element Model for Wind Comfort Around a Tall Building: A Case Study of Tower of Qazaqstan

Cited by 1 publication

References 17 publications

Application of Physics-Informed Neural Networks to River Silting Simulation

Application of Physics-Informed Neural Networks to River Silting Simulation

Contact Info

Product

Resources

About