In the dynamic field of wind engineering, the relationship between research and consulting plays a pivotal role in driving progress. Recent developments in Computational Fluid Dynamics (CFD), Artificial Intelligence (AI), and Machine Learning (ML) have ushered in a new era of possibilities, transforming how wind engineers approach their work.
Scientific research and applied research often differ in their environments and objectives. While scientific research typically occurs in controlled settings, applied research, especially in professional consulting, operates in a faster-paced, diverse, and demand-driven landscape. Wind engineering consultants tackle real-world challenges that may lack sufficient scientific research, creating a cycle where practitioners turn to researchers for insights. This alliance ensures that researchers stay attuned to practical problems, fostering progress in the field.
The significance of CFD research and development
Balancing quality, cost, and turnaround time is a perpetual challenge in consulting. To achieve this equilibrium, consultants rely on proven tools and processes that meet industry standards and reduce risks. However, CFD, a computational approach to wind engineering, has historically required extensive research to attain industry standards, hindering its widespread use.
In recent years, wind engineering consultants’ application of CFD, AI, and ML has provided researchers with crucial data and industry validation. This advancement allows CFD to be applied more effectively, meeting the consulting industry’s quality, cost-effectiveness, and speed demands.
Computational wind engineering’s evolution
Computational Wind Engineering (CWE) has evolved significantly in recent decades, with a steep growth curve over the past 20 years. A decade ago, CWE was primarily used for qualitative, early-design assessments with limited impact on critical decisions. Today, technological advancements enable broader applications and high-fidelity simulations, making CFD a valid alternative to wind tunnel testing for many scenarios.
Advancements in CFD and AI have revolutionized the consulting landscape, empowering wind engineering specialists to provide more efficient solutions. These tools allow consultants to support communities in reducing the environmental footprint of building projects and planning for livable cities.
San Francisco’s environmental impact bylaws mandate wind tunnel testing for building-induced wind effects on pedestrian experiences. A developer proposed a towering building in a case study that could disrupt natural wind flows, leading to problematic pedestrian wind conditions. Traditional wind tunnel testing required numerous iterations and substantial time and cost. When a new design team took over, they opted for CFD and ML in the initial design phase, significantly improving project efficiency and cost-effectiveness.
Holistic assessment and solution development
Traditionally, wind engineering solutions focused on individual problems, often neglecting interconnected issues. However, using the right tools allows for simultaneous assessments of multiple disciplines, considering various aspects of the human experience. This multidisciplinary approach aligns with the growing emphasis on urban livability, addressing residents’ physical and psychological needs.
Consultants can now utilize low-fidelity tools like ML for preliminary assessments, reducing costs and speeding up decision-making processes. This approach quickly identifies critical issues, providing valuable insights for the final design phase.
Engineering better outcomes
The collaborative efforts of wind engineering researchers and practitioners have led to a remarkable transformation in the field. Recent advancements in CFD, AI, and ML have expanded the toolkit available to consultants, increasing accessibility to wind and environmental studies. The growing confidence in CFD’s application is evident as many cities incorporate it into their bylaws.
