Why is the fast-growing HVAC&R Building Service industry – with its direct ability to address the role that buildings play in the climate crisis – experiencing an inability to attract new talent? “Empowering Our Workforce: Building a Sustainable Future” delves into this question by highlighting the emergence of new technologies to the industry and explores the imperative to remain adaptive and forward-thinking in shaping the engineers and building scientists of the future.

The Gulf Organization for Research and Development (GORD) established in 2009, led by a team of highly skilled, and multi-disciplinary committed experts, is proud of its leadership in the sustainability drive of the built environment. GORD has pioneered a comprehensive framework for green building certification specific to the needs of the region, through development and implementation of Global Sustainability Assessment System (GSAS) for building and infrastructure from micro and macro level perspectives. The multi-disciplinary scientific research programs of GORD aim to foster innovation, advance knowledge, share experiences and build networks to enhance the sustainability of the built environment. In addition, the human and institutional capacity building needed to synergize the efforts towards sustainable development has led GORD to adopt multi-dimensional capacity building approaches for addressing the needs of public, governments and businesses. I believe that the holistic sustainability approach embarked upon by GORD will contribute to the global effort to meet the needs of the present without compromising the rights of future generations.

Urban design necessitates accurate predictions of wind and contaminant dispersion to foster sustainable cities. Although computational fluid dynamics (CFD) models like large-eddy simulation (LES) offer precise predictions, they are computationally intensive. Steady Reynolds-averaged Navier-Stokes (SRANS) models are somewhat faster but suffer from inaccuracies due to the approximations involved. This study introduces a two-stage CFD-based artificial intelligence (AI) model that significantly reduces computational costs while maintaining high accuracy. The AI model employs a graph neural network (GNN) framework, using a SRANS model with a coarse grid as the initial state, and then refines the CFD outputs to achieve high-fidelity results in a single inference step. The GNN model was trained using data on airflow and contaminant dispersions across various scenarios with differing building structures and densities, incorporating corrections from key parameters derived from LES. When applied to predict airflow and contaminant dispersion in a section of Tokyo with available experimental data, the CFD-based GNN model produced results comparable to those of LES, while reducing computational costs by three orders of magnitude at the same grid resolution.

Professor Hosni in this session will provide introductory remarks about ABET and its commissions and present the entire ABET Engineering Accreditation Process, before, during, and after the campus visit. This includes an accreditation timeline, self-study report, supplemental materials, criteria 1-8, common shortcomings including issues related to compliance with the Accreditation Policy and Procedure Manual (APPM) and recent changes in accreditation requirements including DEI