KAUST recently hosted the Research Conference: Predictive Complex Computational Fluid Dynamics (PCCFD) from May 22 to 24. The conference brought together local and international CFD scientists from academia and industry to discuss the latest work and findings in CFD. Topics included variable-order algorithms, adaptive mesh refinement, fluid-structure interaction, and uncertainty quantification. Why it matters: The conference highlights KAUST's commitment to advancing CFD research and its applications in various fields, including aerospace, oil industry, and environmental science.
KAUST researchers used the Shaheen XC40 supercomputer to simulate airflow around a McLaren 17D Formula One front wing endplate. They then 3D printed the wing with colored flow patterns to visualize key aerodynamic features. The team combined expertise from the Extreme Computing Research Center (ECRC), the Advanced Algorithm and Numerical Simulations Lab (AANSLab), and the Prototyping and Product Development Core Lab (PCL). Why it matters: This project showcases KAUST's supercomputing and 3D printing capabilities for advanced engineering applications, potentially impacting fields beyond Formula One aerodynamics.
KAUST scientists are developing models to predict extreme weather events like the 2009 Jeddah flood, which caused significant damage. Prof. Ibrahim Hoteit's team is using data from satellites, international sources, and local entities like PME and the Jeddah Municipality to build high-resolution models. The aim is to improve predictions of extreme rain events by one or two days and issue timely warnings. Why it matters: Improving extreme weather prediction is crucial for mitigating the impact of climate change in vulnerable regions like the GCC.
A DeepMind researcher presented work on incorporating symmetries into machine learning models, with applications to lattice-QCD and molecular dynamics. The work includes permutation and translation-invariant normalizing flows for free-energy estimation in molecular dynamics. They also presented U(N) and SU(N) Gauge-equivariant normalizing flows for pure Gauge simulations and its extensions to incorporate fermions in lattice-QCD. Why it matters: Applying symmetry principles to generative models could improve AI's ability to model complex physical systems relevant to materials science and other fields in the region.
KAUST researchers have developed a detailed 3D dynamic model using data from the February 2023 Turkiye earthquake to improve earthquake simulations. The model incorporates 3D fault geometry and Earth structure for realistic simulations of ground shaking. It explains complex ground shaking patterns and the impact of supershear ruptures, which can amplify damage far from the epicenter. Why it matters: This research provides a more accurate understanding of earthquake rupture processes, crucial for seismic hazard assessment and infrastructure planning in seismically active regions like the Middle East.
KAUST hosted the Advances in Uncertainty Quantification Methods, Algorithms and Applications conference (UQAW2016) in January 2016. The event featured 75 presentations and 20 invited speakers from various countries. Professor Raul Tempone presented research on computational approaches to fouling accumulation and wear degradation using stochastic differential equations. Why it matters: This work provides a new computational approach based on stochastic differential equations to predict fouling patterns of heat exchangers which can optimize maintenance operations and reduce engine shut-down periods.
Nobuyuki Umetani from the University of Tokyo presented a talk on using AI to accelerate simulations and optimization for 3D shape designs. The talk covered interactive approaches integrating physical simulation into geometric modeling. Specific applications discussed included musical instruments, garment design, aerodynamic design, and floor plan design. Why it matters: This highlights growing interest in AI techniques at MBZUAI and across the GCC for streamlining engineering design and simulation processes.
KAUST researchers studied the meteorological origins of sea-level extremes in the Red Sea using computer simulations and the ADCIRC storm surge model. They validated their datasets with hourly sea-level observations from six tidal gauges along the Saudi coast. The study found that wind variations over the southern part of the sea are the main drivers of basin-wide sea-level extremes. Why it matters: This research provides critical insights for managing and developing the Red Sea coastline, including megacity projects and tourism, while mitigating their impact on the marine environment.