Context: Digital Twins (DTs) provide a virtual representation of physical systems, enabling real-time interaction, monitoring, and decision-making. Despite their potential, building robust and interactive DTs remains challenging, especially when experimental data are scarce or fragmented across heterogeneous domains. Objective: This paper aims to propose a general, reproducible, and solver-independent framework for constructing interactive DTs. The goal is to integrate high-fidelity simulations, reduced-order modeling, and modular deployment into a methodology that can be consistently applied across sectors, ensuring replicability, flexibility, and knowledge transfer. Methods: The approach treats Computer-Aided Engineering (CAE) solvers as trusted black-box data generators. High-fidelity simulations are executed at scale on HPC infrastructures to generate synthetic datasets, which are then compressed using Proper Orthogonal Decomposition (POD) and Radial Basis Function (RBF)-based interpolation. The resulting reduced-order models (ROMs) are exported in interoperable formats such as Functional Mock-up Units (FMUs) and integrated into visualization environments including Unity, PyVista, and immersive VR dashboards. Results: The framework is validated through cross-domain case studies — automotive, medical, aeronautical, space, and sports engineering — following a unified template that highlights replicability, flexibility, and best practices for knowledge transfer in Digital Twins. Across these applications, the workflow demonstrates its ability to bridge high-fidelity simulations with interactive deployment, making advanced CAE knowledge accessible to a wider range of stakeholders. Conclusion: The results confirm that combining HPC-enabled data generation with reduced-order modeling and modular deployment provides an effective path toward interactive DTs. While POD-based ROMs remain robust and consolidated, emerging techniques such as graph neural operators promise to further enhance generalization.

The vacuum barrier (VB) is designed to separate the feeder into two distinct vacuum regions: the main cryostat vacuum and the feeder vacuum. This separation enhances thermal insulation and facilitates maintenance and access to feeder components. Beyond sustaining pressure under both normal operation and potential malfunctions, the VB also minimizes heat transfer from the environment to low-temperature systems. This paper details the optimization process of an initial VB design, utilizing a Radial Basis Functions-based mesh morphing approach. Shape variations were applied concurrently to structural and thermal simulations, enabling parameterization of the complex, coupled nonlinear system where the structural model withstands both pressure and temperature loads. The optimal VB configuration, meeting structural and thermal criteria, was ultimately identified through response surface optimisation.

A numerical simulation was carried out to study the transient snow drifting phenomena around buildings. The method employs the commercial CFD software Ansys Fluent with additional user-defined functions to model snow transport. Drifting snow analysis around buildings has been extensively studied, but there have been few validation examples in non-equilibrium flow fields around buildings using the saltation fetch distance to account for the snow transport rate. Therefore, in this study, we conducted snowdrift analysis in three types of non-equilibrium flow fields and compared the results with actual measurements. For cube-shaped buildings and two-level flat roofs, the simulation results captured the trends observed in the actual measurements. However, in the case of snow fence analysis, an underestimation of the accumulation amount was observed downstream of the snowdrifts.

Threaded connections are fundamental in engineering structures, yet their elastic–plastic behavior under load remains challenging to model analytically. The yield limit can be reached under relatively small external loads, and elastic–plastic behavior has predominantly been studied using finite element models. While these models are highly valuable, they are often restricted to specific cases. This paper presents a novel extension of Maduschka’s classical method, offering a fast and efficient analytical approach to evaluate the behavior of screw–nut–washer assemblies. The method tracks plastic strain progression from initial yielding to full yield conditions and is validated against high-fidelity axisymmetric and 3D finite element analyses (FEAs) across a range of thread dimensions (M16–M36). Results demonstrate strong agreement with FEA benchmarks while achieving significant computational speedups, making the method suitable for iterative and large-scale analyses. In addition, the comparison with results available in the literature further supports the reliability of the proposed method. Its robustness to variations in geometry, friction, and thread count positions it as a foundation for reduced-order models, ready for integration into complex finite element frameworks commonly used in structural health monitoring and digital twin technologies.

Shape-memory polymers (SMPs) are a widely-used class of smart materials capable of recovering a pre-defined permanent shape from a deformed temporary configuration when exposed to external stimuli. A crucial step in this behavior is shape-programming, which enables the fixation of the temporary shape. Simulating this step through numerical models can be both computationally expensive and prone to inaccuracies. This is primarily due to the difficulty in identifying the appropriate set of boundary conditions needed to deform the structure into the desired temporary shape, often requiring multiple trial-and-error iterations. This paper proposes a computational approach that overcomes such a difficulty and enables an accurate simulation of the shape-memory cycle. The core innovation lies in the use of mesh morphing techniques to directly impose the temporary shape, thereby eliminating the need to determine complex mechanical loading conditions during the programming step. This method is integrated within a finite element framework and applied to a representative 4D printed structure. Numerical results confirm the robustness and accuracy of the approach, which replicates the recovery behavior of the SMP while significantly reducing computational effort in finding and applying the right set of boundary conditions. This work provides a valuable tool for the design of SMP systems.

Aerodynamics is a key factor in time-trial cycling. Over the years, various aspects have been investigated, including positioning, clothing, bicycle design, and helmet shape. The present study focuses on the development of a methodology for the aerodynamic optimization of a time-trial helmet through the implementation of a reduced-order model, alongside advanced simulation techniques, such as computational fluid dynamics, radial basis functions, mesh morphing, and response surface methodology. The implementation of a reduced-order model enhances the understanding of aerodynamic interactions compared to traditional optimization workflows reported in sports-related research, facilitating the identification of an optimal helmet shape during the design phase. The study offers practical insights for refining helmet design. Starting with a baseline teardrop profile, several morphing configurations are systematically tested, resulting in a 10% reduction in the drag force acting on the helmet. The reduced-order model also facilitates the analysis of turbulent flow patterns on the cyclist’s body, providing a detailed understanding of aerodynamic interactions. By leveraging reduced-order models and advanced simulation techniques, this study contributes to ongoing efforts to reduce the aerodynamic resistance of time-trial helmets, ultimately supporting the goal of improved athlete performance.

Introduction The pre-operative planning and intra-operative navigation of the endovascular aneurysm repair (EVAR) procedure are currently challenged by the aortic deformations that occur due to the insertion of a stiff guidewire. Hence, a fast and accurate predictive tool may help clinicians in the decision-making process and during surgical navigation, potentially reducing the radiations and contrast dose. To this aim, we generated a reduced order model (ROM) trained on parametric finite element simulations of the aortic wall-guidewire interaction. Method A Design of Experiments (DOE) consisting of 300 scenarios was created spanning over seven parameters. Radial basis functions were used to achieve a morphological parametrization of the aortic geometry. The ROM was built using 200 scenarios for training and the remaining 100 for validation. Results The developed ROM estimated the displacement of aortic nodes with a relative error below 5.5% for all the considered validation cases. From a preliminary analysis, the aortic elasticity, the stiffness of the guidewire and the tortuosity of the cannulated iliac artery proved to be the most influential parameters. Conclusions Once built, the ROM provided almost real-time and accurate estimations of the guidewire-induced aortic displacement field, thus potentially being a promising pre- and intra-operative tool for clinicians.

In this modern space exploration era, the lunar site offers new opportunities such as the deployment of an X-ray observatory capable of observing half of the sky simultaneously, but also may allowing access to the entire solid angle by exploiting the rotation of the moon around its own axis. The Lunar Electromagnetic Monitor in X-rays (LEM-X) is an All Sky Monitor for the X-ray band (2-50 keV) based on the concept of coded aperture camera. The basic element is the camera of the Wide Field Monitor and the concept is to realize a "dome" composed of N identical modules that observe different directions, thus covering an overall simultaneous field of view of 2 pi in the sky. In this paper, we describe the preliminary study of a support structure for that camera, the design solutions adopted for the most important thermo-mechanical drivers, which have been elaborated and used for the demonstration of compliance to the system requirements with the environment. In particular, we reported some different thermal and mechanical scenario of this preliminary structure and the optimization of critical components with innovative and accurate fem analysis.

The CUbesat Solar Polarimeter (CUSP) project aims to develop a constellation of two CubeSats orbiting the Earth to measure the linear polarisation of solar flares in the hard X-ray band by means of a Compton scattering polarimeter on board of each satellite. CUSP will allow to study the magnetic reconnection and particle acceleration in the flaring magnetic structures. CUSP is a project approved for a Phase A study by the Italian Space Agency in the framework of the Alcor program aimed to develop CubeSat technologies and missions.

Structural optimization plays a pivotal role in the design and development of engine heads, as it directly affects the performance, efficiency, and durability of internal combustion engines. In this paper, we propose a novel approach for the thermo-structural optimization of the internal surfaces of the engine heads, using the Biological Growth Method (BGM). The BGM is a bio-inspired technique that mimics the growth patterns observed in some biological organisms, able to achieve superior structural efficiency thus generating optimal designs. The effectiveness of the BGM methodology, coupled with mesh morphing techniques based on Radial Basis Functions (RBF), is in this work demonstrated for several districts, effectively reducing material usage and enhancing structural performance.

The treatment for asthma and chronic obstructive pulmonary disease relies on forced inhalation of drug particles. Their distribution is essential for maximizing the outcomes. Patient-specific computational fluid dynamics (CFD) simulations can be used to optimize these therapies. In this regard, this study focuses on creating a parametric model of the human respiratory tract from which synthetic anatomies for particle deposition analysis through CFD simulation could be derived. A baseline geometry up to the fourth generation of bronchioles was extracted from a CT dataset. Radial basis function (RBF) mesh morphing acting on a dedicated tree structure was used to modify this baseline mesh, extracting 1000 synthetic anatomies. A total of 26 geometrical parameters affecting branch lengths, angles, and diameters were controlled. Morphed models underwent CFD simulations to analyze airflow and particle dynamics. Mesh morphing was crucial in generating high-quality computational grids, with 96% of the synthetic database being immediately suitable for accurate CFD simulations. Variations in wall shear stress, particle accretion rate, and turbulent kinetic energy across different anatomies highlighted the impact of the anatomical shape on drug delivery and deposition. The study successfully demonstrates the potential of tree-structure-based RBF mesh morphing in generating parametric airways for drug delivery studies.

This work explores the feasibility of parametrizing the Total Human Model for Safety (THUMS) based on statistical anthropometric percentile using mesh morphing of THUMS AM50 driven by radial basis functions (RBF). The study first establishes the implementation criteria for mesh morphing, recognizing the modulation of shape differences between THUMS AM50 and AM95 as indicative of anthropometric variability between the generic percentile and the 50th percentile. The RBF problem is defined by selecting source points on well-distributed edges of the FE HBM and calculating displacement fields assigned to them. The mesh morphing process from the 50th to the generic percentile is fully automated and takes approximately 10 s. Shifting focus from modeling to verification, the study evaluates the effectiveness of the adopted mesh morphing strategy by comparing the shape of THUMS AM95 with that of its parametric counterpart AM50m95 (to indicate the 50th percentile transformed into the 95th) in a frontal sled test. Geometric verification confirmed close correspondence between THUMS AM95 and AM50m95, with a global average deviation of just 3.6 mm. Kinematic verification comparing the average displacements of specific landmark points is good at some locations but some detected deviations require further investigation. The parametric model is then used to span in the range 35–95 comparing AM50m35, AM50, AM50m75, and AM50m95 showing how percentile variation turns into an almost linear change of average displacement registered during the sled test.

In this work, the feasibility of using reduced models (ROMs) for optimizing a scoop air intake for aeronautical applications was evaluated. Since the air intake is exposed to aerodynamic loads, a two-way fluid-structure interaction workflow was used to characterize the component. The goal is to create an optimization dashboard that allows both scalar quantities (the parameters that are intended to be monitored during the design and optimization of the air intake) and field quantities to be evaluated in real time. In this way, the designer can have a full understanding of the physics of the problem and make more informed design choices. In addition, in this way it is possible to visualize results from different physics in a single dashboard, linking different components, interacting with models in real time. A mesh morphing technique based on Radial Basis Functions (RBFs) was used. The result was very interesting both from a structural point of view (mass reduction over 90% and maximum strain reduction of 36%) and from a fluid dynamic point of view (outlet pressure 86% higher and drag 32% lower) and the generated ROMs proved to be a very accurate (ROM relative error in the order of 7%) and flexible tool.

The electric motor has been one of the most important inventions of the last two centuries and has become increasingly important in the last 10 years thanks to its efficiency and its ability to reduce pollution. Given this increasing importance of electrical motors, an interest in design and optimization approaches for motor components is arising. Given the different physics involved in the transformation from electrical to mechanical energy, a valuable and reliable approach has to be applied in the optimization process. In past years, Mesh Morphing based on Radial Basis Functions (RBF) has largely proved its validity in generating shape modification for Finite Element Method (FEM) models. In this work, authors will demonstrate the advantages in applying two opposite approaches for shape optimization using RBF Mesh Morphing applied to electrical motors rotors. These components need to be accurately designed in order to guarantee an adequate life duration by reducing stresses in the rotating components. The two approaches that will be described are the parameter based one, in which a set of design parameters will be varied to generate shape modification used to feed a meta-model that will be used to identify an optimal configuration, and a parameter-less approach, in which the shape modification will be driven by FEM analysis results, with the aim to reduce stress hot-spots.

BACKGROUND: To say data is revolutionising the medical sector would be a vast understatement. The amount of medical data available today is unprecedented and has the potential to enable to date unseen forms of healthcare. To process this huge amount of data, an equally huge amount of computing power is required, which cannot be provided by regular desktop computers. These areas can be (and already are) supported by High-Performance-Computing (HPC), High-Performance Data Analytics (HPDA), and AI (together “HPC+”). OBJECTIVE: This overview article aims to show state-of-the-art examples of studies supported by the National Competence Centres (NCCs) in HPC+ within the EuroCC project, employing HPC, HPDA and AI for medical applications. METHOD: The included studies on different applications of HPC in the medical sector were sourced from the National Competence Centres in HPC and compiled into an overview article. Methods include the application of HPC+ for medical image processing, high-performance medical and pharmaceutical data analytics, an application for pediatric dosimetry, and a cloud-based HPC platform to support systemic pulmonary shunting procedures. RESULTS: This article showcases state-of-the-art applications and large-scale data analytics in the medical sector employing HPC+ within surgery, medical image processing in diagnostics, nutritional support of patients in hospitals, treating congenital heart diseases in children, and within basic research. CONCLUSION: HPC+ support scientific fields from research to industrial applications in the medical area, enabling researchers to run faster and more complex calculations, simulations and data analyses for the direct benefit of patients, doctors, clinicians and as an accelerator for medical research.

The DEMO tokamak exhibits extraordinary complexity due to the constraints and requirements pertaining to different fields of physics and engineering. The multidisciplinary nature of the DEMO system makes its design phase extremely challenging since different and often opposite requirements need to be accounted for. Toroidal field (TF) coils generate the toroidal magnetic field required to magnetically confine the plasma particles and support at the same time the poloidal field coils. They must bear tremendous loads deriving from electromagnetic interactions between the coil currents and the generated magnetic field. An efficient tokamak design aims at minimizing the energy stored in its magnetic field and hence at reducing the toroidal volume within the TF coils whose shape would hence ideally mimic co-centrically the shape of the plasma. In order to bear the enormous forces a D-shape is most suitable for the TF coils as it allows them to resist the very large compression on the inner side and to carry the electro-magnetic (EM) pressure mainly by membrane stresses preventing large bending to occur on the outer side. At the same time the divertor structures must fit within the TF coils and this requires adaptations of the TF coil shape in the case of so-called advanced divertor configurations (ADCs), which require larger divertor structures. This article shows the TF coils adapted to ADCs using a structural optimisation procedure applied to the reference shape. The introduced strategy takes as structural optimum the iso-stress profile associated to each coil. A continuous transformation, based on radial basis functions mesh morphing, turns the baseline finite element (FE) model into its iso-stress counterpart, with a series of intermediate configurations available for electromagnetic and structural investigations as output. The adopted strategy allowed to determine, for each of the ADC cases, a candidate shape. Static membrane stress levels during magnetization could be reduced significantly from more than 700 MPa to below 450 MPa.

Abdominal aortic aneurysm patients are regularly monitored to assess aneurysm development and risk of rupture. A preventive surgical procedure is recommended when the maximum aortic antero-posterior diameter, periodically assessed on two-dimensional abdominal ultrasound scans, reaches 5.5 mm. Although the maximum diameter criterion has limited ability to predict aneurysm rupture, no clinically relevant tool that could complement the current guidelines has emerged so far. In vivo cyclic strains in the aneurysm wall are related to the wall response to blood pressure pulse, and therefore, they can be linked to wall mechanical properties, which in turn contribute to determining the risk of rupture. This work aimed to enable biomechanical estimations in the aneurysm wall by providing a fast and semi-automatic method to post-process dynamic clinical ultrasound sequences and by mapping the cross-sectional strains on the B-mode image. Specifically, the Sparse Demons algorithm was employed to track the wall motion throughout multiple cardiac cycles. Then, the cyclic strains were mapped by means of radial basis function interpolation and differentiation. We applied our method to two-dimensional sequences from eight patients. The automatic part of the analysis took under 1.5 min per cardiac cycle. The tracking method was validated against simulated ultrasound sequences, and a maximum root mean square error of 0.22 mm was found. The strain was calculated both with our method and with the established finite-element method, and a very good agreement was found, with mean differences of one order of magnitude smaller than the image spatial resolution. Most patients exhibited a strain pattern that suggests interaction with the spine. To conclude, our method is a promising tool for investigating abdominal aortic aneurysm wall biomechanics as it can provide a fast and accurate measurement of the cyclic wall strains from clinical ultrasound sequences.

Endoluminal reconstruction using flow diverters represents a novel paradigm for the minimally invasive treatment of intracranial aneurysms. The configuration assumed by these very dense braided stents once deployed within the parent vessel is not easily predictable and medical volumetric images alone may be insufficient to plan the treatment satisfactorily. Therefore, here we propose a fast and accurate machine learning and reduced order modelling framework, based on finite element simulations, to assist practitioners in the planning and interventional stages. It consists of a first classification step to determine a priori whether a simulation will be successful (good conformity between stent and vessel) or not from a clinical perspective, followed by a regression step that provides an approximated solution of the deployed stent configuration. The latter is achieved using a non-intrusive reduced order modelling scheme that combines the proper orthogonal decomposition algorithm and Gaussian process regression. The workflow was validated on an idealized intracranial artery with a saccular aneurysm and the effect of six geometrical and surgical parameters on the outcome of stent deployment was studied. We trained six machine learning models on a dataset of varying size and obtained classifiers with up to 95% accuracy in predicting the deployment outcome. The support vector machine model outperformed the others when considering a small dataset of 50 training cases, with an accuracy of 93% and a specificity of 97%. On the other hand, real-time predictions of the stent deployed configuration were achieved with an average validation error between predicted and high-fidelity results never greater than the spatial resolution of 3D rotational angiography, the imaging technique with the best spatial resolution (0.15 mm). Such accurate predictions can be reached even with a small database of 47 simulations: by increasing the training simulations to 147, the average prediction error is reduced to 0.07 mm. These results are promising as they demonstrate the ability of these techniques to achieve simulations within a few milliseconds while retaining the mechanical realism and predictability of the stent deployed configuration.

The current guidelines for the ascending aortic aneurysm (AsAA) treatment recommend surgery mainly according to the maximum diameter assessment. This criterion has already proven to be often inefficient in identifying patients at high risk of aneurysm growth and rupture. In this study, we propose a method to compute a set of local shape features that, in addition to the maximum diameter D , are intended to improve the classification performances for the ascending aortic aneurysm growth risk assessment. Apart from D , these are the ratio DCR between D and the length of the ascending aorta centerline, the ratio EILR between the length of the external and the internal lines and the tortuosity T . 50 patients with two 3D acquisitions at least 6 months apart were segmented and the growth rate (GR) with the shape features related to the first exam computed. The correlation between them has been investigated. After, the dataset was divided into two classes according to the growth rate value. We used six different classifiers with input data exclusively from the first exam to predict the class to which each patient belonged. A first classification was performed using only D and a second with all the shape features together. The performances have been evaluated by computing accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUROC) and positive (negative) likelihood ratio LHR+ (LHR−). A positive correlation was observed between growth rate and DCR ( r = 0.511, p = 1.3e-4) and between GR and EILR ( r = 0.472, p = 2.7e-4). Overall, the classifiers based on the four metrics outperformed the same ones based only on D . Among the diameter-based classifiers, k-nearest neighbours (KNN) reported the best accuracy (86%), sensitivity (55.6%), AUROC (0.74), LHR+ (7.62) and LHR− (0.48). Concerning the classifiers based on the four shape features, we obtained the best accuracy (94%), sensitivity (66.7%), specificity (100%), AUROC (0.94), LHR+ (+ ∞ ) and LHR− (0.33) with support vector machine (SVM). This demonstrates how automatic shape features detection combined with risk classification criteria could be crucial in planning the follow-up of patients with ascending aortic aneurysm and in predicting the possible dangerous progression of the disease.