KAUST researchers have found that dust clouds in the Arabian Peninsula are three times larger than previously estimated by current models. The study, published in the Journal of Geophysical Research: Atmospheres, uses refined mathematical models and data collected since 2012 to analyze the impact of coarse dust particles. The updated model indicates that larger particles contribute to over 80% of dust mass on land, leading to significant efficiency loss for solar technology, estimated at 15-45% depending on location. Why it matters: Accurate dust modeling is crucial for the strategic deployment and maintenance of solar technology, supporting Saudi Arabia's sustainable economy goals.
KAUST and McLaren Racing have announced a five-year research partnership focused on R&D and extreme performance technology for Formula 1 cars. The collaboration will leverage KAUST's expertise in areas like sensors, electronics, numerical simulations, and fuel/engine combustion research. KAUST researchers will develop new experimental methods, mathematical models, and train students to understand complex systems. Why it matters: This partnership allows KAUST to apply its research to a real-world laboratory (Formula 1), fostering innovation in fuel technology, combustion, sensors, and algorithms with potential spillover effects for the broader automotive and engineering sectors in the region.
KAUST Associate Professor Taous-Meriem Laleg-Kirati leads the Estimation, Modeling and ANalysis (EMAN) research group, focusing on control theory, system modeling, and signal applications. Her group develops mathematical models and algorithms to control processes relying on real-time feedback, especially for systems where experimental data is limited. The EMAN group recently developed a real-time control algorithm for a solar membrane distillation system, increasing water production by over 50% in simulations. Why it matters: Laleg-Kirati's work advances both engineering and healthcare by combining model-based research with AI, offering opportunities for personalized medicine and efficient resource management in the region.