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In this paper, a workflow for creating advanced aerodynamics design dashboards is proposed. A CAD modeler is directly linked to the CFD simulation results so that the designer can explore in real time, assisted by virtual reality (VR), how shape parameters affect the aerodynamics and choose the optimal combination to optimize performance. In this way, the time required for the conception of a new component can be drastically reduced because, even at the preliminary stage, the designer has all the necessary information to make more thoughtful choices. Thus, this work sets a highly ambitious and innovative goal: to create a smart design dashboard where every shape parameter is directly and in real-time linked to the results of the high-fidelity analyses. The OPAM (Open Parametric Aircraft Model), a simplified model of the Boeing 787, was considered as a case study. CAD parameterization and mesh morphing were combined to generate the design points (DPs), while Reduced Order Models (ROMs) were developed to link the results of the CFD analyses to the chosen parameterization. The ROMs were exported as FMUs (Functional Mockup Units) to be easily managed in any environment. Finally, a VR design dashboard was created in the Unity environment, enabling the interaction with the geometric model in order to observe in a fully immersive and intuitive environment how each shape parameter affects the physics involved. The MetaQuest 3 headset has been selected for these tests. Thus, the use of VR for a design platform represents another innovative aspect of this work. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper presents a CFD modeling approach for a 19-rod bundle fuel assembly with spiral semi-cylindrical ribs, which potentially can be used for liquid metal cooled fast reactor, as well as a numerical simulation of flow and thermal behavior under partially blocked conditions. The ribs on fuel rod surface are integrally formed with the cladding to enhance heat transfer while preventing detachment. However, the complex channel geometry presents challenges for grid generation, as using a large number of tetrahedral or polyhedral elements would consume significant computational resources. Therefore, this paper proposes a mesh morphing-based pre-processing method to establish a structured hexahedral mesh for such complex geometry. Using this approach, the 19-rods assembly is analyzed, and the result has been verified by conventional mesh scheme as well as experiment.

This paper presents a level-set topology optimization approach that uses conformal meshes for the analysis of the displacement field. The structure’s boundary is represented by the iso-contour of a level-set field discretized on a fixed background design mesh. The conformal mesh is updated for each design iteration via a PDE based mesh morphing process that identifies the set of facets in the background mesh that are homeomorphic to the boundary and relaxes the homeomorphic mesh to conform to the structure’s boundary and ensure high element quality. The conformal mesh allows for a more accurate computation of the response versus density and some level-set based methods which interpolate material properties using the volume fraction. Numerical examples illustrate the proposed approach by optimizing linear-elastic two- and three-dimensional structures, wherein insight into the performance of the mesh morphing process is provided. The examples also highlight the scalability of the approach. [ABSTRACT FROM AUTHOR]

In this paper, a workflow for creating advanced aerodynamics design dashboards is proposed. A CAD modeler is directly linked to the CFD simulation results so that the designer can explore in real time, assisted by virtual reality (VR), how shape parameters affect the aerodynamics and choose the optimal combination to optimize performance. In this way, the time required for the conception of a new component can be drastically reduced because, even at the preliminary stage, the designer has all the necessary information to make more thoughtful choices. Thus, this work sets a highly ambitious and innovative goal: to create a smart design dashboard where every shape parameter is directly and in real-time linked to the results of the high-fidelity analyses. The OPAM (Open Parametric Aircraft Model), a simplified model of the Boeing 787, was considered as a case study. CAD parameterization and mesh morphing were combined to generate the design points (DPs), while Reduced Order Models (ROMs) were developed to link the results of the CFD analyses to the chosen parameterization. The ROMs were exported as FMUs (Functional Mockup Units) to be easily managed in any environment. Finally, a VR design dashboard was created in the Unity environment, enabling the interaction with the geometric model in order to observe in a fully immersive and intuitive environment how each shape parameter affects the physics involved. The MetaQuest 3 headset has been selected for these tests. Thus, the use of VR for a design platform represents another innovative aspect of this work.

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.

3D topology optimization using a density approach with penalization usually produces truss-like, open-walled structures. The coarser the mesh, the smaller the volume fraction, and the faster the penalization is increased, the more pronounced this effect tends to be. However, closed-walled designs are often more efficient and have other beneficial properties. For instance, closed walls can contribute to achieving self-supporting designs for additive manufacturing that potentially require fewer sacrificial support structures than truss-like designs. This paper presents a two-step optimization procedure for generating closed-walled designs using coarse meshes. The first step takes the usual Eulerian approach of performing a SIMP-based topology optimization on a fixed mesh. To keep thin geometrical features, like walls with a thickness below element size, penalization is switched off deliberately where the formation of such features is detected. Adopting a Lagrangian description, intermediate densities still present in the optimized design are subsequently eliminated in a second step by shrinking each element according to its density. By analogy with volumetric thermal contraction, this is accomplished by solving a fictitious thermo-elastic problem where the temperature has been replaced by a density expression. The outcome is a morphed mesh with a somewhat smoothed surface and a volume close to the specified material volume limit. This body-fitted representation of the design considerably simplifies the final conversion into a manufacturable CAD-type geometry. The two-step optimization procedure is applied to a cantilever, a torsion rod, and a disk reinforcement benchmark problem. Optimized designs are closed-walled and show very good agreement to those found for much finer meshes. Problem-specific stiffness improvements over truss-like designs between 6 % and almost 30 % were achieved and confirmed the findings previously reported by other authors. [ABSTRACT FROM AUTHOR]

Particulate deposition effects on flow and heat transfer in an internal swirl tube subjected to fly ash ingestion were investigated by constructing an unsteady simulation framework, in which a particle-wall interaction model and a mesh morphing technique were implemented. Swirling flows in the swirl tube were induced by two tangential jet nozzles. Particles having a mean diameter of 6.5 µm were released from the nozzle inlets to model an exposure duration of 4500 h for engine operation in real fly ash environment using scale factors in the unsteady simulations. Particle deposition and its dynamic process were examined, and the effects of deposition on the swirling flow were quantified by comparing time-averaged velocity profiles, vorticity, pressure loss, and heat transfer with those from a clean tube without deposition. Results reveal that the most upstream section of the swirl tube captures the majority of the particles and the deposition distributions show a spiral pattern over the tube wall. The total mass of the deposits within the tube linearly increases, while local deposition thickness has a nonlinear relationship with the exposure time due to the interaction of the particles with the swirling flow. The deposition can generate a maximum of 15% reduction in cross-sectional area of the tube within the exposure duration, resulting in a reduced swirl number, because of the accelerated axial velocity and the decreased circumferential velocity, and further lower heat transfer in the downstream section of the tube relative to the clean tube case. However, as the heat transfer in the upstream deposition section is enhanced by the roughness due to the deposition, area-averaged heat transfer throughout the entire swirl tube is slightly improved by 4.0% but simultaneously a 179% higher pressure loss is observed, leading to an overall thermal performance value of 0.79 (relative to 1.0 for a clean tube), indicating substantial degradation of cooling performance in the fouled swirl tube. [ABSTRACT FROM AUTHOR]

The generation of subject-specific finite element models of the spine is generally a time-consuming process based on computed tomography (CT) images, where scanning exposes subjects to harmful radiation. In this study, a method is presented for the automatic generation of spine finite element models using images from a single magnetic resonance (MR) sequence. The thoracic and lumbar spine of eight adult volunteers was imaged using a 3D multi-echo-gradient-echo sagittal MR sequence. A deep-learning method was used to generate synthetic CT images from the MR images. A pre-trained deep-learning network was used for the automatic segmentation of vertebrae from the synthetic CT images. Another deep-learning network was trained for the automatic segmentation of intervertebral discs from the MR images. The automatic segmentations were validated against manual segmentations for two subjects, one with scoliosis, and another with a spine implant. A template mesh of the spine was registered to the segmentations in three steps using a Bayesian coherent point drift algorithm. First, rigid registration was applied on the complete spine. Second, non-rigid registration was used for the individual discs and vertebrae. Third, the complete spine was non-rigidly registered to the individually registered discs and vertebrae. Comparison of the automatic and manual segmentations led to dice-scores of 0.93–0.96 for all vertebrae and discs. The lowest dice-score was in the disc at the height of the implant where artifacts led to under-segmentation. The mean distance between the morphed meshes and the segmentations was below 1 mm. In conclusion, the presented method can be used to automatically generate accurate subject-specific spine models.

Finite element human body models (HBMs) are becoming increasingly important numerical tools for traffic safety. Developing a validated and reliable HBM from the start requires integrated efforts and continues to be a challenging task. Mesh morphing is an efficient technique to generate personalized HBMs accounting for individual anatomy once a baseline model has been developed. This study presents a new image registration–based mesh morphing method to generate personalized HBMs. The method is demonstrated by morphing four baseline HBMs (SAFER, THUMS, and VIVA+ in both seated and standing postures) into ten subjects with varying heights, body mass indices (BMIs), and sex. The resulting personalized HBMs show comparable element quality to the baseline models. This method enables the comparison of HBMs by morphing them into the same subject, eliminating geometric differences. The method also shows superior geometry correction capabilities, which facilitates converting a seated HBM to a standing one, combined with additional positioning tools. Furthermore, this method can be extended to personalize other models, and the feasibility of morphing vehicle models has been illustrated. In conclusion, this new image registration–based mesh morphing method allows rapid and robust personalization of HBMs, facilitating personalized simulations.

The forecasted growth in dynamic global air fleet size in the coming decades, together with the need to introduce disruptive technologies supporting net-zero emission air transport, demands more efficient design and optimization workflows. This research focuses on developing an aerodynamic optimization framework suited for multi-objective studies of small aircraft engine air-intake ducts in multiple flight conditions. In addition to the refinement of the duct's performance criteria, the work aims to improve the economic efficiency of the process. The optimization scheme combines the advantages of Kriging-based Efficient Global Optimization (EGO) with the Radial Basis Functions (RBF)-based mesh morphing technique and the Chebyshev-type Achievement Scalarizing Function (ASF) for handling multiple objectives and design points. The proposed framework is applied to an aerodynamic optimization study of an I-31T aircraft turboprop engine intake system. The workflow successfully reduces the air-duct pressure losses and mitigates the flow distortion at the engine compressor's front face in three considered flight phases. The results prove the framework's potential for solving complex multi-point air-intake duct problems and the capacity of the ASF-based formulation to guide optimization toward the designer's preferred objective targets. [ABSTRACT FROM AUTHOR]

Front wings are a key element in the aerodynamic performance of Formula 1 race cars. Thus, their optimisation makes an important contribution to the performance of cars in races. However, their design is constrained by regulation, which makes it more difficult to find good designs. The present work develops a hybrid shape optimisation approach to obtain an optimal five-element airfoil front wing under the FIA regulations and 17 design parameters. A first baseline design is obtained by parametric optimisation, on which the adjoint method is applied for shape optimisation via Mesh Morphing with Radial Basis Functions. The optimal front wing candidate obtained outperforms the parametric baseline up to a 25% at certain local positions. This shows that the proposed and tested hybrid approach can be a very efficient alternative. Although a direct 3D optimisation approach could be developed, the computational costs would be dramatically increased (possibly unaffordable for such a complex five-element front wing realistic shape with 17 design parameters and regulatory constraints). Thus, the present approach is of strong interest if the computational budget is low and/or a fast new front wing design is desired, which is a frequent scenario in Formula 1 race car design. [ABSTRACT FROM AUTHOR]

Childbirth is a challenging event that can lead to long-term consequences such as prolapse or incontinence. While computational models are widely used to mimic vaginal delivery, their integration into clinical practice is hindered by time constraints. The primary goal of this study is to introduce an artificial intelligence pipeline that leverages patient-specific surrogate modeling to predict pelvic floor injuries during vaginal delivery. A finite element-based machine learning approach was implemented to generate a dataset with information from finite element simulations. Thousands of childbirth simulations were conducted, varying the dimensions of the pelvic floor muscles and the mechanical properties used for their characterization. Additionally, a mesh morphing algorithm was developed to obtain patient-specific models. Machine learning models, specifically tree-based algorithms such as Random Forest (RF) and Extreme Gradient Boosting, as well as Artificial Neural Networks, were trained to predict the nodal coordinates of nodes within the pelvic floor, aiming to predict the muscle stretch during a critical interval. The results indicate that the RF model performs best, with a mean absolute error (MAE) of 0.086 mm and a mean absolute percentage error of 0.38%. Overall, more than 80% of the nodes have an error smaller than 0.1 mm. The MAE for the calculated stretch is equal to 0.0011. The implemented pipeline allows loading the trained model and making predictions in less than 11 s. This work demonstrates the feasibility of implementing a machine learning framework in clinical practice to predict potential maternal injuries and assist in medical-decision making. [Display omitted] • Finite element-based machine learning approach to predict childbirth injuries. • Mesh-morphing algorithm to obtain patient-specific models of the pelvic floor. • Random forest model yields lowest mean absolute error for stretch prediction. • Real-time biomechanical analysis achievable and suitable for clinical applications. • Use of artificial intelligence tools contributes to reducing maternal morbidity. [ABSTRACT FROM AUTHOR]

This paper presents an efficient optimisation method to improve the main hull of a trimaran ship, whilst proposing a computational fluid dynamics-based automated approach to reduce total resistance. A mesh-based method is introduced to modify a wave-piercing bow trimaran hull at two cruise and sprint speeds. Therefore, the problem pertains to a hydrodynamic multi-objective optimisation problem. Radial basis function-based mesh-morphing tool is implemented to alter the geometry at the mesh level. Mesh-morphing tool leads to elimination of geometry and mesh regeneration steps that consequently provides a shortcut for the designer's extrication from optimisation time and complexity of geometry modification. Ten global parameters accomplish expansion and contraction of the 10 sections, which are known as Magnification Factor. Optimisation results and design comparison illustrate the applicability and efficiency of the proposed technique. The results demonstrate 6.77% reduction in total resistance at cruise speed and 1.55% at sprint speed. [ABSTRACT FROM AUTHOR]

This article presents a fully differentiated CAD‐free shape parameterization coupled with a grid displacement method for performing adjoint‐based aerodynamic shape optimization efficiently. Both tools are integrated into an adjoint‐based aerodynamic shape optimization process, where the shape changes and the corresponding grid adaptation take place simultaneously, in a single step. The role of the proposed shape parameterization technique is to control the shape changes of the aerodynamic body under study during shape optimization, and to modify it according to the spatial distribution of the gradient. The latter frequently contains numerical noise, due to the limited resolution of spatial discretization schemes, which can result in irregular surfaces, if the raw gradient is used directly. The proposed parameterization undertakes the elimination of this noise, thus ensuring smooth surfaces during the shape optimization. More specifically, a subset of the nodes belonging to the design surface is selected as the design vector (handles) and is responsible for controlling the surface displacements. The analytical differentiation of the parameterization, considering the adjoint morphing technique for the computation of the grid sensitivities, allows for its integration within a gradient‐based optimization process, where the adjoint method is used to compute the gradient of the objective w.r.t. all nodal positions. The propagation of this gradient information to the handles is efficiently and accurately achieved through the inclusion of the differentiated parameterization expression. The developed CAD‐free process chain is successfully demonstrated in an automotive S‐section cooling duct and a serpentine‐like one, used for internal turbomachinery blade cooling. [ABSTRACT FROM AUTHOR]

Aviation industry is constantly striving for more efficient design processes in respect to optimal time, human and computational resources utilization. This implies a need for application of an approximation techniques enabling for fast responses generation with maintained level of results quality. This study focuses on an advancement of aerodynamic shape optimization process of a small aircraft engine intake system by introduction of a surrogate modelling step into the design loop. The multi-objective metamodel assisted optimization is carried out in order to reduce pressure losses along the engine intake duct and increase flow homogeneity at the engine compressor intake plane. Latin Hypercube Design method is utilized in order to sample the design space. A set of initial objective function evaluations is generated with application of Reynolds-averaged Navier–Stokes solver. The ensemble of samples is further used to train a Kriging-based surrogate model. The Efficient Global Optimization algorithm basing on the Expected Improvement function is employed to gradually increase the metamodel prediction quality by usage of sequential sampling technique. Finally, the optimal point predicted by the Kriging surrogate is validated against the high-fidelity model with usage of the Computational Fluid Dynamics code. The paper presents an application of the abovementioned methodology to the design process of the I-31T aircraft turboprop engine intake system. Proposed Kriging-based optimization workflow is utilized in order to reduce pressure losses and improve flow homogeneity in the engine air-intake duct. [ABSTRACT FROM AUTHOR]

Numerical optimization procedures are one of the most powerful approaches with which to support design processes. Their implementation, nevertheless, involves several conceptual and practical complexities. One of the key points relates to the geometric parameterization technique to be adopted and its coupling with the numerical solver. This paper describes the setup of a procedure in which the shape parameterization, based on mesh morphing, is integrated into the analysis tool, accessing the grid nodes directly within the solver environment. Such a coupling offers several advantages in terms of robustness and computational time. Furthermore, the ability to morph the mesh "on the fly" during the computation, without heavy Input/Output operations, extends the solver's capability to evaluate multidisciplinary phenomena. The procedure was preliminary tested on a simple typical shape optimization problem and then applied to a complex setup of an industrial case: the identification of the shape of a Volvo side-view mirror that minimizes the accumulation of water on the lens of a camera mounted beneath. [ABSTRACT FROM AUTHOR]

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. 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. 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. 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. • A methodology for a reduced order model (ROM) to estimate the guidewire-induced displacement of abdominal aorta during EVAR is obtained. • The workflow to obtain the ROM from CT images can be executed within 3 hours and 15 minutes, given access to HPC resources. • The obtained ROM allows to explore with sufficient accuracy different scenarios, varying seven parameters of interest. [ABSTRACT FROM AUTHOR]

Hydrofoils are utilized as instruments to improve the hydrodynamic performance of marine equipment. In this paper, the motion of a 2D NACA0012 hydrofoil advancing in water near the free surface was simulated, and a mesh morphing-adjoint based optimizer was used to maximize its lift-to-drag ratio. Ansys-Fluent was used as a CFD solver, and a mesh-morphing tool was used as a geometry reconstruction tool. Furthermore, the Adjoint solver was applied to evaluate the sensitivities of the objective function to all solution variables. Defined control points around the geometry are design variables that move in an appropriate direction through shape sensitivity. The computational results were validated against available experimental data and published numerical findings. Subsequently, different hydrodynamic characteristics of the optimized hydrofoil were compared to those of the original model at different angles of attack of 3, 3.5, 4, 4.5, 5, 5.5, 6, and 6.5°, and optimized shapes were determined. It was observed that the shape of the optimized hydrofoil was totally dependent on the angle of attack, which produced different lift-to-drag ratios. It is also seen that among higher angles of attack at which improvement in the L/D ratio became steady, the drag coefficient was the lowest at 5°. Therefore, it can be concluded that the appropriate angle of attack for a hydrofoil installation on the ship hull is 5°. Further investigation was conducted concerning the evolution of shape optimization, sensitivity analysis, free surface elevation, flow characteristics, and hydrodynamic performance of the hydrofoil at a 5° angle of attack. [ABSTRACT FROM AUTHOR]

A CFD-based adjoint solver tool is presented to modify the hullform of a trimaran ship. Cost function of optimization process is considered total for resistance of trimaran at cruise speed. Mesh sensitivity gives sensitivity of the cost function associated with mesh deformation. Mesh morphing tool is implemented to alter the geometry without the need to revisit the CAD environment. The position of mesh morphing's control points are the design variables used in the optimization process. The aim of this optimization is to demonstrate the limitations and capabilities of the mesh morphing adjoint-based method for hydrodynamic complex geometries. Based on the numerical findings, a 6.67% reduction in total resistance of the trimaran is observed in a limited number of iterations. Meanwhile, major modification is observed on the side hull. Development of a CFD-based optimization platform with less computational time and effort (five optimization loops) is the result of the present study. [ABSTRACT FROM AUTHOR]

Front wings are a key element in the aerodynamic performance of Formula 1 race cars. Thus, their optimisation makes an important contribution to the performance of cars in races. However, their design is constrained by regulation, which makes it more difficult to find good designs. The present work develops a hybrid shape optimisation approach to obtain an optimal five-element airfoil front wing under the FIA regulations and 17 design parameters. A first baseline design is obtained by parametric optimisation, on which the adjoint method is applied for shape optimisation via Mesh Morphing with Radial Basis Functions. The optimal front wing candidate obtained outperforms the parametric baseline up to a 25% at certain local positions. This shows that the proposed and tested hybrid approach can be a very efficient alternative. Although a direct 3D optimisation approach could be developed, the computational costs would be dramatically increased (possibly unaffordable for such a complex five-element front wing realistic shape with 17 design parameters and regulatory constraints). Thus, the present approach is of strong interest if the computational budget is low and/or a fast new front wing design is desired, which is a frequent scenario in Formula 1 race car design.

Fluid structure interaction (FSI) is a complex phenomenon that in several applications cannot be neglected. Given its complexity and multi-disciplinarity the solution of FSI problems is difficult and time consuming, requiring not only the solution of the structural and fluid domains, but also the use of expensive numerical methods to couple the two physics and to properly update the numerical grid. Advanced mesh morphing can be used to embed into the fluid grid the vector fields resulting from structural calculations. The main advantage is that such embedding and the related computational costs occur only at initialization of the computation. A proper combination of embedded vector fields can be used to tackle steady and transient FSI problems by structural modes superposition, for the case of linear structures, or to impose a full non-linear displacement time history. Radial basis functions interpolation, a powerful and precise meshless tool, is used in this work to combine the vector fields and propagate their effect to the full fluid domain of interest. A review of industrial high fidelity FSI problems tackled by means of the proposed method and RBF is given for steady, transient, and non-linear transient FSI problems. [ABSTRACT FROM AUTHOR]

Numerical optimization procedures are one of the most powerful approaches with which to support design processes. Their implementation, nevertheless, involves several conceptual and practical complexities. One of the key points relates to the geometric parameterization technique to be adopted and its coupling with the numerical solver. This paper describes the setup of a procedure in which the shape parameterization, based on mesh morphing, is integrated into the analysis tool, accessing the grid nodes directly within the solver environment. Such a coupling offers several advantages in terms of robustness and computational time. Furthermore, the ability to morph the mesh “on the fly” during the computation, without heavy Input/Output operations, extends the solver’s capability to evaluate multidisciplinary phenomena. The procedure was preliminary tested on a simple typical shape optimization problem and then applied to a complex setup of an industrial case: the identification of the shape of a Volvo side-view mirror that minimizes the accumulation of water on the lens of a camera mounted beneath.

Featured Application: Patient-specific, 3D CAD model of the mandible and of the proximal femur can be obtained from a limited set of measurements; these models can be used for custom-made designs or for pre-operative planning. Principal components analysis is a powerful technique which can be used to reduce data dimensionality. With reference to three-dimensional bone shape models, it can be used to generate an unlimited number of models, defined by thousands of nodes, from a limited (less than twenty) number of scalars. The full procedure has been here described in detail and tested. Two databases were used as input data: the first database comprised 40 mandibles, while the second one comprised 98 proximal femurs. The "average shape" and principal components that were required to cover at least 90% of the whole variance were identified for both bones, as well as the statistical distributions of the respective principal components weights. Fifteen principal components sufficed to describe the mandibular shape, while nine components sufficed to describe the proximal femur morphology. A routine has been set up to generate any number of mandible or proximal femur geometries, according to the actual statistical shape distributions. The set-up procedure can be generalized to any bone shape given a sufficiently large database of the respective 3D shapes. [ABSTRACT FROM AUTHOR]

• Numerical simulations have a crucial role in the investigation of aorta. • Changes in wall geometries during cardiac cycle influence aortic fluid dynamic. • RBF mesh morphing technique is able to generate transient shape deformations. • A transient prescribed wall motion simulation can overcome FSI required assumptions. [Display omitted] Numerical simulations to evaluate thoracic aortic hemodynamics include a computational fluid dynamic (CFD) approach or fluid-structure interaction (FSI) approach. While CFD neglects the arterial deformation along the cardiac cycle by applying a rigid wall simplification, on the other side the FSI simulation requires a lot of assumptions for the material properties definition and high computational costs. The aim of this study is to investigate the feasibility of a new strategy, based on Radial Basis Functions (RBF) mesh morphing technique and transient simulations, able to introduce the patient-specific changes in aortic geometry during the cardiac cycle. Starting from medical images, aorta models at different phases of cardiac cycle were reconstructed and a transient shape deformation was obtained by proper activating incremental RBF solutions during the simulation process. The results, in terms of main hemodynamic parameters, were compared with two performed CFD simulations for the aortic model at minimum and maximum volume. Our implemented strategy copes the actual arterial variation during cardiac cycle with high accuracy, capturing the impact of geometrical variations on fluid dynamics, overcoming the complexity of a standard FSI approach. [ABSTRACT FROM AUTHOR]

Finite element head (FE) models are important numerical tools to study head injuries and develop protection systems. The generation of anatomically accurate and subject-specific head models with conforming hexahedral meshes remains a significant challenge. The focus of this study is to present two developmental works: first, an anatomically detailed FE head model with conforming hexahedral meshes that has smooth interfaces between the brain and the cerebrospinal fluid, embedded with white matter (WM) fiber tracts; second, a morphing approach for subject-specific head model generation via a new hierarchical image registration pipeline integrating Demons and Dramms deformable registration algorithms. The performance of the head model is evaluated by comparing model predictions with experimental data of brain–skull relative motion, brain strain, and intracranial pressure. To demonstrate the applicability of the head model and the pipeline, six subject-specific head models of largely varying intracranial volume and shape are generated, incorporated with subject-specific WM fiber tracts. DICE similarity coefficients for cranial, brain mask, local brain regions, and lateral ventricles are calculated to evaluate personalization accuracy, demonstrating the efficiency of the pipeline in generating detailed subject-specific head models achieving satisfactory element quality without further mesh repairing. The six head models are then subjected to the same concussive loading to study the sensitivity of brain strain to inter-subject variability of the brain and WM fiber morphology. The simulation results show significant differences in maximum principal strain and axonal strain in local brain regions (one-way ANOVA test, p < 0.001), as well as their locations also vary among the subjects, demonstrating the need to further investigate the significance of subject-specific models. The techniques developed in this study may contribute to better evaluation of individual brain injury and the development of individualized head protection systems in the future. This study also contains general aspects the research community may find useful: on the use of experimental brain strain close to or at injury level for head model validation; the hierarchical image registration pipeline can be used to morph other head models, such as smoothed-voxel models. [ABSTRACT FROM AUTHOR]

Here, a distinct procedure was adopted to optimize the shape of the NACA 0012 airfoil profile for Reynolds number, Mach number, and angle of attack equal to 2e6, 0.15, and 10.24 deg, respectively. The airfoil shape was appropriately parameterized, and mesh morphing tools were used to bypass the remeshing process. Computational fluid dynamic (CFD) simulations were carried out for all combinations of selected parameters. Response surfaces were constructed for the drag and lift coefficients of the airfoil based on several statistical criteria. Subsequently, the Pareto front was used to solve the multi-objective optimization problem. Eventually, three single-objective types of optimization problems were studied. A 10 % reduction in drag coefficient was estimated for the drag minimization problem, a 22 % improvement in lift coefficient was found in case of lift maximization problem, and a 6 % reduction in drag coefficient was determined in the lift constrained drag minimization problem. [ABSTRACT FROM AUTHOR]

Background: Sagittal alignment of the cervical spine might influence the development of radiological adjacent segment pathology (RASP) after central corpectomy (CC). Range of motion (ROM) of the adjacent segments is closely linked to the development of RASP. Methods: To investigate the ROM of the adjacent segments after CC, we developed a C2-T1 finite element (FE) model. The model with a lordotic sagittal alignment served as the baseline model. Models with straight and kyphotic alignment were generated using mesh morphing methods. Single-level corpectomy at C5 was done on these models. Segmental ROMs of intact and corpectomized spines were compared for physiologic flexion-extension loads. Results: The flexion ROM decreased by an average of 13% with the change in sagittal alignment from lordosis to kyphosis; however, a consistent decrease was not observed in extension. After CC, the ROM increased by an average of 95% and 31% in the superior and inferior adjacent segments. With kyphotic change in the sagittal alignment, the postoperative increase in flexion ROM exhibited a decreasing trend, while this was not seen in extension. Conclusions: Kyphotic changes of the intact spine resulted in segmental stiffening, and after corpectomy, it resulted in inconsistent variations of segmental extension ROMs. [ABSTRACT FROM AUTHOR]