Aan Yudianto

Profile Description:

Aan Yudianto, is a dedicated and driven individual currently pursuing a PhD in Computational Automotive Aerodynamics with a focus on data-driven design optimisation for freight transport vehicles. He holds a Master of Science degree in Automotive Engineering from Politecnico di Torino, Italy, where he gained valuable expertise and insights into advanced concepts and practices within the field.

Prior to his master's studies, he completed his undergraduate education at Yogyakarta State University, Indonesia, earning a Bachelor's degree in Automotive Engineering Education. This provided him with a solid foundation in automotive engineering principles and methodologies.

His commitment to continuous learning and international exposure is evident through his participation in the Automotive Engineering and Management Short Course at Technische Hochschule Ingolstadt, Germany. This experience allowed him to broaden his perspective on automotive engineering practices and management approaches, further enriching his knowledge and skills.

His research interests primarily revolve around the application of Computational Fluid Dynamics (CFD) and data-driven methodologies in the field of computational automotive aerodynamics. With a specific focus on freight transport vehicles, he aims to optimise their aerodynamic design through a comprehensive understanding of fluid dynamics. By leveraging data-driven approaches and employing advanced optimisation techniques such as adjoint optimisation, He aims to develop innovative solutions that enhance the efficiency and performance of these vehicles.

Through his academic journey and diverse educational experiences, he has acquired profound knowledge in CFD, data-driven techniques, and large vehicle aerodynamics. His multidisciplinary background and passion for computational automotive aerodynamics equip him with the skills necessary to push the boundaries of knowledge in the field. His ultimate goal is to contribute to the development of practical and sustainable solutions that optimise the aerodynamic design of freight transport vehicles, leading to improved fuel efficiency, reduced emissions, and a more sustainable transportation industry.

• Road-vehicle aerodynamics
• Computational Fluid Dynamics (CFD)
• Data-driven approaches in CFD
• Vehicle aerodynamic optimisation
• Adjoint optimisation