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 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.
Technology Innovation Institute (TII)'s Directed Energy Research Center (DERC) will participate in the Fluid Codes - Ansys User Conference on October 19, 2023, in Dubai. Dr. Chaouki Kasmi will join a panel on digital transformation in national R&D strategy, while Umar Hashmi and Gideon Appiah will present a paper on enhancing Marx generator reliability using ANSYS Maxwell. The conference will gather over 120 leaders to discuss innovation and R&D. Why it matters: TII's participation highlights the UAE's focus on advanced engineering and digital transformation in achieving its research and development goals.
KAUST's Clean Combustion Research Center (CCRC) is expanding its Cloudflame database, a platform providing computational tools and scientific data for combustion research. Cloudflame offers features like flame speed calculations, ignition delay simulation, and a Fuel Design Tool to formulate fuel mixtures. The platform allows researchers to compare findings, perform computations remotely, and receive results via email. Why it matters: Cloudflame fosters global collaborations and accelerates advancements in clean combustion technologies, crucial for energy saving and environmental conservation in the region and worldwide.
Ahmad Alabdulghani, a KAUST master's student in Energy Resources and Petroleum Engineering, is studying fluid flow mechanisms in heterogeneous media under the supervision of Professor Hussein Hoteit. Alabdulghani is a member of the Advanced Reservoir Modeling and Simulation (ARMS) research group at ANPERC. He previously worked at Saudi Aramco's EXPEC Advanced Research Center and aims to pursue a doctorate at KAUST. Why it matters: This highlights KAUST's role in developing Saudi talent for the energy sector and fostering collaboration between academia and industry.
KAUST Ph.D. student Raid AlRowais won the best paper award at the 11th International Meeting on Advances in ThermoFluids in Japan. The conference took place at Kyushu University. AlRowais received the award from Professor Takahiko Miyazaki. Why it matters: This award recognizes promising research and talent at KAUST in thermal and fluid sciences.
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.