Events

The Zienkiewicz Institute holds various events throughout the year, including a prestigious annual Zienkiewicz Lecture.

Morphing and moving matter: mimicking nature

Speaker: Mingchao Liu, Department of Mechanical Engineering, University of Birmingham, Birmingham, UK

Time: 14:00-15:30, 31^st October 2024

Location/Room: Room 010 (ground Floor), Engineering North, Bay Campus How to find us

Nature's ingenuity serves as a profound source of inspiration for developing advanced materials and robotic systems. In this presentation, we explore how biological phenomena inform innovative engineering solutions, focusing on morphing structures and moving mechanisms, both grounded in our understanding of the underlaying mechanics principles. We highlight morphing structure designs inspired by the segmentation architectures found in biological organisms and the dehydration-induced corrugated folding observed in Rhapis excelsa leaves. These designs emphasize adaptability and efficient shape transformation, showcasing the potential for creating functional, morphable systems. Additionally, we examine moving mechanisms, featuring a snap-through enabled insect-scale jumping robot modeled after click beetles and a magnetic robot inspired by the coordinated movements of cilia. These systems prioritize effective modeling to achieve rapid, efficient motion and agile navigation in complex environments. By integrating principles from biology and mechanics, this presentation illustrates how natural strategies can lead to cutting-edge technological advancements, offering new perspectives on the design and modeling of intelligent systems.

For more info contact: Mokarram Hossain 

Dr. Mingchao Liu is currently an Assistant Professor at the University of Birmingham in the UK. Before moving to Birmingham, he was a Presidential Postdoctoral Fellow at Nanyang Technological University (NTU) in Singapore from 2022 to 2023, and a Newton International Fellow at the Mathematical Institute, University of Oxford, sponsored by the Royal Society from 2018 to 2021. He received his Ph.D. in Solid Mechanics from Tsinghua University in 2018, and his B.Eng. in Engineering Mechanics from Shandong University in 2013. He was also an Endeavor Research Fellow at the University of Sydney in 2017 and was awarded the Extreme Mechanics Letters (EML) Young Investigator Award in 2023. His current research is mainly focused on the mechanics of slender structures, particularly dynamic instabilities, and their applications in modeling and designing robotic metamaterials with innovative functions. These include programmable robotic behaviors such as shape-morphing, locomotion, mechanical sensing, actuation, and memory, as well as tunable mechanical properties.

Registration open for event with Hartree Centre

“Extreme Scaling Computing and Emerging Computational Approaches in Science and Engineering” 

The main purpose of the workshop is to scope future collaborations between Swansea and the Hartree Centre. Subjects covered include Artificial Intelligence, Automotive, Transport and Logistics, Computational Chemistry, Computational Fluid Dynamics, Digital Manufacturing, Fusion, High-Performance Computing, Materials, Quantum Computing and Quantum Technologies. The event will have brief thematic talks delivered by both Swansea University Academics and Hartree Centre researchers. The workshop will also have plenty of time for networking and discussions, to which everyone who is interested is invited to participate. 

Contact: Biagio Lucini

Dominik K. Klein

Neural networks meet hyperelasticity: One single constitutive modelling approach applicable to all materials

Date: 18th July 2024
Time: 14:00-15:30
Venue: Room B001, Engineering Central, Bay Campus

Bio: Dominik K. Klein is a research assistant at the Cyber-Physical Simulation Group of Technical University of Darmstadt (Germany), which he joined in 2021 after finishing his master's studies in mechanics. There, he conducts research on constitutive modeling with physics-augmented neural networks. In close collaborations with universities all over Germany, Spain, and the UK, he largely contributed to establishing hyperelastic neural network-based constitutive models focusing on polyconvexity.

Abstract: In the last decades, a vast amount of highly specialized metamaterials has been developed, and with advancing requirements in engineering applications, the trend is growing. Often comprised of complex multiphysical and parametrized microstructures, these materials can be tailored for each specific application. At the same time, this sets a challenge for the mechanical description of such materials, as they behave highly nonlinear. Thus, we envision the use of physics-augmented neural networks (PANNs), circumventing the current limitations of analytically formulated material models associated with their lack of flexibility. We proposed several hyperelastic PANN constitutive models which fulfill important constitutive conditions such as objectivity and ellipticity by construction. Thereby, the focus lies on the formulation of polyconvex constitutive models. Using strain invariants as inputs for convex neural networks, a polyconvex potential is constructed, which is complemented by additional growth and normalization terms. Furthermore, we demonstrated the straightforward extension of the proposed constitutive modeling framework to electro-elastic and parametrized material behavior. Overall, the proposed PANN models combine the extraordinary flexibility of neural networks with a sound mechanical basis, resulting in constitutive models which are applicable to a large variety of material behavior. We demonstrated the applicability of the PANN constitutive modeling framework for complex microstructured materials and experimental data of rubber-like materials with highly nonlinear and parametrized behavior. In all cases studied, the PANN models yield excellent prediction qualities. Furthermore, we demonstrated the applicability of the PANN constitutive model for complex finite element analysis. The PANN constitutive models showed excellent prediction quality and numerical stability in highly challenging simulation scenarios, including large deformations and instability phenomena.