Numerical simulations of the cardiovascular system are growing in popularity due to the increasing availability of computational power, and their proven contribution to the understanding of pathodynamics and validation of medical devices with in-silico trials as a potential future breakthrough. Such simulations are performed on volumetric meshes reconstructed from patient-specific imaging data. These meshes are most often unstructured, and result in a brutally large amount of elements, significantly increasing the computational complexity of the simulations, whilst potentially adversely affecting their accuracy. To reduce such complexity, we introduce a new approach for fully automatic generation of higher-order, structured hexahedral meshes of tubular structures, with a focus on healthy blood vessels. The structures are modeled as skeleton-based convolution surfaces. From the same skeleton, the topology is captured by a block-structure, and the geometry by a higher-order surface mesh. Grading may be induced to obtain tailored refinement, thus resolving, e.g., boundary layers. The volumetric meshing is then performed via transfinite mappings. The resulting meshes are of arbitrary order, their elements are of good quality, while the spatial resolution may be as coarse as needed, greatly reducing computing time. Their suitability for practical applications is showcased by a simulation of physiological blood flow modelled by a generalised Newtonian fluid in the human aorta.

In this paper, the effect of using a piezoelectric material has been shown on postponing the flutter phenomenon on a regular blade. System response of a smart blade with only flapwise and edgewise plunge DOF shows that the oscillations of the smart blade can be effectively decayed in a very short time by using efficient piezopatches in the flapwise and edgewise plunge DOF. Furthermore, in a smart blade with four DOF, it has been indicated having piezopatches in flapwise and edgewise plunge DOF can defer the flutter speed 81.41% which is a noticeable increase in the flutter speed. Finally, by adding a piezopatch to the pitch DOF to a smart blade, it is possible to postpone the flutter speed 155% which is a very considerable increase.

Combining performance and numerical stability is a key issue in co-simulation. The Transmission Line Modeling method uses physically motivated communication delays to ensure numerical stability for stiff connections. However, using a fixed communication delay may limit performance for some models. This paper proposes Steady-State Identification for enabling variable communication delays. Three algorithms for online Steady-State Identification are evaluated in three different co-simulation models. All algorithms are able to identify steady state and can thereby determine when communication delays can be allowed to increase without compromising accuracy and stability. The results show a reduction in number of the solver derivative evaluations by roughly 40–60% depending on the model. The proposed method additionally supports connections with asymmetric communication delays, which allows each sub-model to independently control the delay of its input variables. Models supporting delay-size control can thereby be connected to those that do not, so that the step length of each individual sub-model is maximized. Controlling the delay-size in sub-models also makes the method independent of the master co-simulation algorithm.

EWCO accumulated a long list of online first articles over the years. To accommodate these backlogs, EWCO published six regular issues and five supplementary issues in Volume 38 during 2022, with about 50 articles in each issue. According to the Web of Science Journal Citation Reports, the impact factor of EWCO has increased to 8.083 ( 2021) and the number of downloads reached 222,369 (2021). Two special issues have been published in Volume 38, including Image-Based Methods in Computational Medicine by Adrian Buganza Tepole, Rafael Grytz, Maria Holland, and Johannes Weickenmeier (Issue 5); and Numerical Simulation for Additive Manufacturing Processes and Products by Alessandro Reali, Ferdinando Auricchio, Michele Chiumenti, and Ernst Rank (Issue 6). In addition, the special issue of Computational Modeling Based on Nonlocal Theory by Timon Rabczuk, Erkan Oterkus, and Xiaoying Zhuang is ready and will be published in 2023. The special issue of UKACM 2022: Advances in Computational Mechanics by Jelena Ninic, Kristoffer van der Zee, Matteo Icardi, and Fangying Wang is on-going and will be completed in 2023. We approved two new special issue proposals which will