Accurate Deformation Research Articles

Investigations on the Load Capacity of Multilayer Foil Thrust Bearing Based on an Updated Complete Model

Abstract The multilayer gas foil thrust bearing (MLFTB) has four layers of foil structures with each having unique configuration and function. In this article, an updated complete model of the overlapped foil layers of MLFTB is established to calculate more accurate foil deformations and bearing performance by considering sufficient possibilities of separations between adjacent foil layers. The accuracy of the model is validated through the load capacity tests of MLFTB, which is presented for the first time in open publications. Based on the complete model, influences of individual and combined key foil structural factors on bearing load-carrying performance and top foil deformations are studied, revealing the function mechanism of each foil layer and obtaining the method of adjusting two-dimensional stiffness distributions. Furthermore, parametric optimizations toward improving the load capacity of this type of foil thrust bearing are conducted based on a developed algorithm. This study provides sufficient theoretical knowledge and designing methods for further investigations of MLFTB.

BDS/GPS deformation analysis of a long-span cable-stayed bridge based on colored noise filtering

Combining GPS and BDS technology to monitor the deformation of long-span railway bridges with stricter deformation control requirements is of significance to the safety and control of the bridge and the safety of railway traffic. Previous studies have ignored the influence of coloured noise in the deformation time series. This is not conducive to accurate deformation analysis of long-span railway bridges. Therefore, GPS, BDS and GPS/BDS monitoring data of Ganjiang Bridge located in Ganzhou city, Jiangxi Province, China are adopted in this paper to filter the coloured noise in the deformation time series by principal component analysis (PCA), and the influence of coloured noise on the deformation analysis results of railway bridge is analysed. The experimental results show that the diurnal temperature difference causes the mid-span and the tower of the railway cable-stayed bridge to deform with a period of about one day in the vertical and longitudinal directions, respectively. Ignoring colored noise will make the uncertainty of the deformation parameter estimation overly optimistic. PCA can significantly reduce the coloured noise, and thus reduce the uncertainty of deformation parameter estimation by about 73%. Moreover, the average difference between the daily periodic motion amplitudes of the monitoring points obtained by using GPS and BDS deformation time series is 1.65 mm. The use of GPS/BDS deformation time series is not only helpful to reduce the influence of coloured noise, but also can reduce the difference between amplitude analysis results obtained from GPS and BDS deformation time series.

Plastic hexahedral FEM for surgical simulation.

Soft-tissue manipulations, such as collecting, stretching or tearing tissue, are a common component of surgery. When too much force is applied, these manipulations result in a residual plastic deformation that surgeons should be aware of and that should be modeled by surgical simulation. Many tissues, vessels and organs can be modeled as offsets of curved simple shapes with primary directions, e.g., radial and axial for cylinders yield a rectangular mesh whose normal offset naturally yields a hexahedral mesh that can serve as a thick shell. Other organs are easy to embed into and deform following a hex mesh. We extend existing code for the volumetric finite element method (FEM) to model tissue plasticity as hexahedral thick shells or embedded organs. Specifically, the work extends the open source Simulation Open Framework Architecture and its newest hyperelastic deformation addition, Caribou, with focus on surgical simulation. The extension factors deformation gradients into (corotational or hyperelastic) elastic factors and plastic factors and enforces volume preservation. Limits on per-element twist, twist torque, material hardening and bounds on plasticity where elements invert avoid the need for re-meshing. Our hexahedral FEM avoids the biased outcomes of asymmetric coarsely-partitioned tetrahedral FEM. Caribou's hyperelastic FEM is extended to hex-FEM stretching plasticity. Our high-order accurate blended-vertex deformation enables coarse hex meshes to model large plastic rotational and stretch deformations without re-meshing. We compare a vertex-blended to a cell-centered piecewise constant approach; contrast plasticity based on corotational FEM and hyperelastic FEM; and test the computation under mesh refinement. The volume is preserved also for large deformations. On-the-fly generated hexahedral meshes can directly be used as finite element domains for plastic deformation based on corotational or hyperelastic elasticity. The outcome is suitable for surgical simulation.

Surface wave analysis of the skin for penetrating and non-penetrating projectile impact in porcine legs.

Secondary blast injuries may result from high-velocity projectile fragments which ultimately increase medical costs, reduce active work time, and decrease quality of life. The role of skin penetration requires more investigation in energy absorption and surface mechanics for implementation in computational ballistic models. High-speed ballistic penetration studies have not considered penetrating and non-penetrating biomechanical properties of the skin, including radial wave displacement, resultant surface wave speed, or projectile material influence. A helium-pressurized launcher was used to accelerate 3/8″ (9.525mm) diameter spherical projectiles toward seventeen whole porcine legs from seven pigs (39.53 ± 7.28kg) at projectile velocities below and above V50. Projectiles included a mix of materials: stainless steel (n = 26), Si3N4 (n = 24), and acetal plastic (n = 24). Tracker video analysis software was used to determine projectile velocity at impact from the perpendicular view and motion of the tissue displacement wave from the in-line view. Average radial wave displacement and surface wave speed were calculated for each projectile material and categorized by penetrating or non-penetrating impacts. Two-sample t-tests determined that non-penetrating projectiles resulted in significantly faster surface wave speeds in porcine skin for stainless steel (p = 0.002), plastic (p = 0.004), and Si3N4 ball bearings (p = 0.014), while ANOVA determined significant differences in radial wave displacement and surface wave speed between projectile materials. Surface wave speed was used to quantify mechanical properties of the skin including elastic modulus, shear modulus, and bulk modulus during ballistic impact, which may be implemented to simulate accurate deformation behavior in computational impact models.

Three-dimensional displacement field of the 2010 Mw 8.8 Maule earthquake from GPS and InSAR data with the improved ESISTEM-VCE method

The 2010 Mw 8.8 Maule earthquake occurred offshore central Chile and ruptured ∼500 km along the megathrust fault resulting from the oceanic Nazca plate subducting beneath the continental South American plate. The Maule earthquake produced remnant crustal displacements captured by a vast set of geodetic observations. However, given the nature of the observational techniques, it is challenging to extract its accurate three-dimensional coseismic deformation field with high spatial resolution. In this study, we modified the extended simultaneous and integrated strain tensor estimation from geodetic and satellite deformation measurements (ESISTEM) method with variance component estimation algorithm (ESISTEM-VCE) to retrieve the three-dimensional surface displacement field of this event by integrating the interferometric synthetic aperture radar (InSAR) and global positioning system (GPS) measurements. The ESISTEM-VCE method accounts for the spatial correlation of surface displacement among the adjacent points and determine the accurate weight ratios for different data sets, but also uses the uncertainties of GPS data and considers the different spatial scales from the different datasets. In the simulation experiments, the RMSEs of the ESISTEM-VCE method are smaller than those of the ESISTEM and ESISTEM-VCE (same d0) methods, and the improvements of 97.1%, 3.9%, and 84% are achieved in the east-west, north-south, and vertical components, respectively. Then, we apply the proposed methodology to the 2010 Mw 8.8 Maule earthquake, to obtain a three-dimensional displacement field that could provide fine deformation information. In the east-west component, the significant deformation is in the north of the epicenter, closed to the Constitución, with a maximum westward displacement of 495.5 cm. The displacement in the north-south component is relatively small compared to that in the east-west component. The maximum uplift reaches 211.8 cm, located at the southwest of the Concepción. Finally, the derived vertical displacements are also compared with field investigations, indicating that the ESISTEM-VCE method can obtain more accurate weight of different datasets and perform better than the ESISTEM method. The results highlight that the earthquake ruptured along the NE-SW direction, with a dominant thrust and a relatively small component of right-lateral strike-slip, coinciding with the characteristics of subduction and right-lateral shear. The experiments with the simulated and real data suggest that the improved ESISTEM-VCE method in this study is feasible and effective.

Digital image correlation based on convolutional neural networks

As an indispensable non-destructive testing technique, digital image correlation (DIC) has been increasingly applied to various engineering areas concerning deformation characterization. Inspired by artificial intelligence-related technologies, we here develop a new convolutional neural network-based theoretical framework for DIC analyses, hereafter called DIC-Net. A pyramidal structure is designed to ensure robustness and reliability of measurement results. Simultaneously, the second-order shape function is adopted to create training dataset, making the DIC-Net more suitable for solving complex deformation fields. Different from conventional DIC algorithms, the developed DIC-Net does not require specific correlation criterion, nor is it necessary to perform numerical iterative computations, which greatly enhances the efficiency of correlation calculations. The proposed DIC-Net not only provides an alternative approach to achieve accurate, precise and reliable deformation measurements, but also paves the way for developing high-efficiency DIC with real-time processing capabilities.

High-accuracy prediction and compensation of industrial robot stiffness deformation

Industrial robots (IRs) are promising options for machining large complex structural parts due to the higher flexibility, larger operating space, and lower cost compared with multi-axis machine tools. However, the relatively low posture-dependent stiffness and large stiffness deformation of IRs significantly deteriorate the contour accuracy of milling in which the cutting force is large generally. It is very complex to achieve a precise stiffness model and predict stiffness deformation of IRs because of the joint clearance, drift of zero-position, and other nonlinear factors. The conventional stiffness model of IRs only takes each joint as a constant linear torsion spring into consideration and ignores other difficult-to-model factors, which leads to low-accuracy identified results and thereafter induces deformation prediction errors. The data-driven approach can be used to obtain an accurate stiffness and deformation model, but a large amount of experimental data is required and it will cost enormous time and effort. In order to circumvent the experimental data deficiency and difficult-to-model issue, a simulation-driven transfer learning method named Adaptive Domain Adversarial Neural Network with Dual-Regressions (ADANN-2R) is designed for robot deformation prediction. Amounts of coarse deformation data, which are generated by the conventional stiffness model, are regarded as source data. And few real deformation data, which are obtained by deformation experiments, are regarded as target data. The Dual-Regressions are designed after the feature extractor, and the weighting parameters are adjusted adaptively to tackle the different magnitude of the regression loss and domain discrimination loss. The ADANN-2R aligns the simulated source data and real target data to perform adversarial training, and an accurate target deformation predictor is achieved. Experimental results indicate that the proposed ADANN-2R can obtain high-accuracy prediction with few real data compared with the conventional stiffness model. Compared with the path without deformation compensation and the pre-compensated path using the conventional stiffness model, the maximum position error of the pre-compensated path using the proposed ADANN-2R is reduced by 78.12% and 32.45%, respectively.

Experimental Study on the Seismic Behavior of Squat SRC Shear Walls with High Axial Load Ratio

This paper aims to study the seismic behavior of squat steel-reinforced concrete (SRC) shear walls with a high axial load ratio. Nine squat SRC shear walls with varying axial load ratios, steel ratios, and horizontal distributed reinforcement ratios were tested under lateral cyclic reversed loading and an axial load. The failure process, load-deformation hysteretic response, shear strength, ductility, and the strain of the specimens are reported. The results show that all the specimens failed in shear with the crushing of the web concrete. No axial failure occurred after the web concrete was crushed since the boundary elements encased with structural steel sections maintained the axial load. Larger steel ratios reduced the buckling degree of the structural steel. A larger horizontal distributed reinforcement ratio was clearly beneficial for the ductility and energy dissipation capacity of the specimen, while it had a negligible effect on the shear strength. The Chinese code provided an extremely conservative prediction of the shear strength of the tested squat SRC shear walls with a mean calculated-experimental strength ratio of 0.42. An improved formula was established mainly by the modification of the shear resistance contributed by the concrete and the structural steel, leading to a mean calculated-experimental strength ratio of 0.74. More experimental data are still needed to establish more accurate deformation acceptance criteria for SRC shear walls and to promote the performance-based seismic evaluation of SRC structures.

Accurate Tissue Deformation Modeling Using a Kalman Filter and ADMM-Based Projective Dynamics

In low-dose-rate permanent-seed (LDR-PS) implant brachytherapy, it is crucial to predict the movement of internal target points (planned radioactive seed locations) under the effect of external forces. Accurate prediction of the target locations is critical for precise seed implantation, as inaccurate seed implantation diminishes the effectiveness of radiotherapy. Therefore, developing a model to simulate tissue dynamics is necessary. All physics-based tissue models have model-reality mismatches due to unmodeled dynamics, a problem which should be addressed. In this work, we propose the KF-ADMM method as a solution, which compensates for a portion of unmodeled dynamic terms existing in the alternating direction method of multipliers (ADMM)-based projective dynamics (PD) tissue simulator through Kalman filtering. This method provides accurate predictions of the location of inner tissue points with an error of around 0.8 mm. Experiments on a breast tissue phantom are performed to evaluate the efficacy of the proposed approach. According to the results, the accuracy of tissue deformation is enhanced by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$52 \%$</tex-math></inline-formula> on average, and the convergence rate is accelerated compared to an ADMM-based PD tissue simulator.

Hot spot stress and fatigue strength assessment of marine 5059 aluminum alloy reinforced structure considering welding effect

AbstractWith the increasing demand for lightweight ships, marine alloy materials such as aluminum alloy and titanium alloy are widely used; 5059 aluminum alloy is considered as an ideal steel for hull structure because of its good mechanical properties and corrosion resistance. The influence of welding residual stress and welding deformation must be considered in fatigue evaluation of thin‐plate welded structures. However, due to the more complex welding deformation and residual stress distribution of thin‐plate welded structures, the fatigue evaluation of thin‐plate welded structures is challenged. In this paper, a series of fatigue tests of actual reinforced T‐shaped welded joints are carried out, and the accurate welding deformation and residual stress are obtained by numerical simulation. Finally, taking welding deformation and residual stress as initial conditions, the hot spot stress S‐N curves of typical joints are modified by hot spot stress method.

Mechanical buckling analysis of functionally graded plates using an accurate shear deformation theory

In this present study, we are interested in the use of a precise theory of shear deformation for the buckling analysis of plates with functional gradation simply supported such as the refined theory of plates with four variables. Several parameters of comparison have been used, dimensional and nondimensional. The displacement field is compatible with this study, the nonuse of shear correction factors is satisfied, and the choice of material is very precise in such a way are variable according to the thickness of the plate and on the other hand to make comparison with other researcher and confirms that this study gives precise results and converges. The transverse shear stresses vary through the thickness, the results found is also studied and discussed.

A Highly Accurate Method for Deformation Reconstruction of Smart Deformable Structures Based on Flexible Strain Sensors.

Smart deformable structures that integrate designing, sensing, and controlling technology have been widely applied in the fields of aerospace, robotics, and biomedical engineering due to their multi-functional requirements. The deformation reconstruction method essential for security monitoring and shape controlling, especially for the large deflection deformation, remains a challenge on accuracy and efficiency. This paper takes a wind tunnel’s fixed-flexible nozzle (FFN) plate as the research object to develop a highly accurate deformation reconstruction method based on sensing information from flexible strain sensors. The mechanical behaviors of the FFN plate with large deflection deformation, which is modeled as a cantilever beam, are studied to analyze the relationship of the strain and moment. Furthermore, the large deflection factor and shell bending theory are creatively utilized to derive and modify the strain–moment based reconstruction method (SMRM), where the contour of the FFN plate is solved by particular elliptic integrals. As a result, structural simulation based on ABAQUS further demonstrates that the reconstruction error of SMRM is 21.13% less than that of the classic Ko-based reconstruction method (KORM). An FFN prototype accompanied by customized flexible sensors is developed to evaluate the accuracy and efficiency of the SMRM, resulting in a maximum relative error of 3.97% that is acceptable for practical applications in smart deformable structures, not limited to the FFN plate.

Covid-19 Sürecinde Diş Hekimliği Eğitimi: Etkisi, Gelişen Teknolojiler ve Eğilimler

Dental education requires students to acquire a certain skill set in addition to academic-based theoretical education. Changes in the education method, during the Covid-19 pandemic, have had different effects on dentistry students. The e-learning method offers advantages such as removing physical limits and supporting self-learning and creativity. However, clinical learning of dental education and the psychological state of the students affected negatively all around the world. Digital Simulation Technologies (DSTs) including augmented reality (AR), virtual reality (VR), and haptic simulation, have been a valuable resource coping with adverse situations in dental education due to the impact of the COVID-19 pandemic. DSTs need to be developed in the future on finger support, tactile sensation, force feedback, high screen resolution, depth perception in stereoscopic images, accurate deformation simulation, different training difficulty levels, big data technology in dental skills training.

Ultrafast laser stress figuring for accurate deformation of thin mirrors.

Fabricating freeform mirrors relies on accurate optical figuring processes capable of arbitrarily modifying low-spatial frequency height without creating higher-spatial frequency errors. We present a scalable process to accurately figure thin mirrors using stress generated by a focused ultrafast laser. We applied ultrafast laser stress figuring (ULSF) to four thin fused silica mirrors to correct them to 10-20 nm RMS over 28 Zernike terms, in 2-3 iterations, without significantly affecting higher-frequency errors. We measured the mirrors over a month and found that dielectric-coated mirrors were stable but stability of aluminum-coated mirrors was inconclusive. The accuracy and throughput for ULSF is on par with existing deterministic figuring processes, yet ULSF doesn't significantly affect mid-spatial frequency errors, can be applied after mirror coating, and can scale to higher throughput using mature laser processing technologies. ULSF offers new potential to rapidly and accurately shape freeform mirrors.

A deformation model and draping behavior analysis of plain weave fabric with low-twist yarn on continuous surface

Fiber-reinforced composites have been widely applied in aerospace, transportation and other industrial applications. An effective method shaping the complex performs is draping plain fabrics on the mandrel surface. However, it is a challenge to realize and predict accurately the deformation of fabric. To establish an accurate deformation mode, plain weave fabrics with low-twist yarn were classified into a stable structure that are not easy to deform and an unstable structures with large deformability based on microstructure characteristics. A variable microstructure unit-cell model has been established to analyze the variation of fabric geometry and performance during deformation. To analyze the draping behavior of fabric with large deformation, a four-node unit was used to mesh the fabric, and then each node on the fabric was mapped to the mandrel surface. The deformation of fabric after draping was simulated by the continuous change of the unit mesh. Analysis results showed that the thickness of preform decreases with the increase of the major-axis of the yarn cross-section, and the formability of fabric increases with the increase of fabric pitch. The accuracy and effectiveness of the geometric mapping method are verified by the fabric drape experiment.

Accuracy of virtual surgical planning and custom three-dimensionally printed osteotomy and reduction guides for acute uni- and biapical correction of antebrachial deformities in dogs.

To report clinical experience using virtual surgical planning (VSP) and surgical application of 3D printed custom surgical guides to facilitate uni- and biapical correction of antebrachial deformities in dogs. 11 dogs (13 antebrachial deformity corrections). Using CT-based bone models, VSP was performed, and surgical guides were designed and 3D printed. The guides were used to execute osteotomies and align bone segments. Postoperative CTs were obtained to compare limb alignment with the VSP. Long-term assessment of lameness and cosmesis were compared with preoperative status. Guides were successfully utilized and postoperative analysis was available for 10 of 13 deformities. Guides were abandoned in 2 deformities due to soft tissue tension. Evaluation of postoperative frontal, sagittal, axial, and translational limb alignment revealed that over 90% of parameters were within the acceptable range of ≤ 5° angulation and rotation or ≤ 5 mm of translation from the VSP. Lameness scores were improved in 7/8 deformities with associated preoperative lameness, and posture was improved in 10/10 deformities in which guides were deployed. Complications included reduced range of carpal motion (n = 2), implant sensitivity (n = 2), fracture (n = 1), and tendon laceration (n = 1). VSP and customized surgical guide application facilitated accurate antebrachial limb deformity correction in the majority of deformities in this case series. The use of VSP and 3D printed guides would appear to be a viable and accurate approach for correction of both uni- and biapical antebrachial deformities in dogs.

Real-time dynamic simulation for highly accurate spatiotemporal brain deformation from impact

Real-time dynamic simulation remains a significant challenge for spatiotemporal data of high dimension and resolution. In this study, we establish a transformer neural network (TNN) originally developed for natural language processing and a separate convolutional neural network (CNN) to estimate five-dimensional (5D) spatiotemporal brain–skull relative displacement resulting from impact (isotropic spatial resolution of 4 mm with temporal resolution of 1 ms). Sequential training is applied to train (N = 5184 samples) the two neural networks for estimating the complete 5D displacement across a temporal duration of 60 ms. We find that TNN slightly but consistently outperforms CNN in accuracy for both displacement and the resulting voxel-wise four-dimensional (4D) maximum principal strain (e.g., root mean squared error (RMSE) of ∼1.0% vs. ∼1.6%, with coefficient of determination, R2 >0.99 vs. >0.98, respectively, and normalized RMSE (NRMSE) at peak displacement of 2%–3%, based on an independent testing dataset; N = 314). Their accuracies are similar for a range of real-world impacts drawn from various published sources (dummy, helmet, football, soccer, and car crash; average RMSE/NRMSE of ∼0.3 mm/∼4%–5% and average R2 of ∼0.98 at peak displacement). Accuracy in strain rate is also illustrated in one case (NRMSE of 7.8% and of 0.91). Sequential training is effective for allowing instantaneous estimation of 5D displacement with high accuracy, although TNN poses a heavier computational burden in training. This work enables efficient characterization of the intrinsically dynamic brain strain in impact critical for downstream multiscale axonal injury model simulation. This is also the first application of TNN in biomechanics and biomedical engineering, which offers important insight into how real-time dynamic simulations can be achieved across diverse engineering fields.

Refined InSAR tropospheric delay correction for wide-area landslide identification and monitoring

SAR Interferometry (InSAR) proves to be effective for investigating landslides. However, its measurement accuracy is largely limited by the complex atmospheric delay distortion in alpine valley regions, resulting in poor performance of landslides detection and monitoring. Particularly, the spatial atmospheric heterogeneity over wide areas cannot be accurately reflected by conventional empirical phase-elevation models or external data-based methods. In this study, we proposed a multi-temporal moving-window linear model (MMLM) to correct the tropospheric delay for wide-area landslides investigation. This is a linear regression model based on the elevation-phase relationship for modeling multi-temporal phases within a sliding local window. It mitigates the influence of local turbulent phase, local landslide deformation, and phase unwrapping error on parameter estimation, providing precise heterogeneous atmospheric corrections for wide-area InSAR landslide identification and monitoring. A simulation experiment was conducted to analyze the sensitivity of model parameters settings and evaluate the effectiveness of the MMLM model. Furthermore, we demonstrated the performance of the MMLM model through a comparison with the ERA5, GACOS, spatial-temporal filtering, and traditional linear model using descending and ascending Sentinel-1 data over the reservoir area of the Lianghekou hydropower station. Among the above-mentioned methods, the standard deviation of original unwrapped phases achieved the largest decrease of more than 35% and 50% after correction by the MMLM model for the descending and ascending Sentinel-1 tracks, respectively. In addition, the accurate deformation corrected by the MMLM model improved the landslides investigation, not only can help for delineating landslide boundaries in space but also retrieving movement evolution in time.

Deep learning-based digital volume correlation

With the rapid development of 3D volume imaging technology, digital volume correlation (DVC) has already made great progresses and become an indispensable tool in various engineering kingdoms. Inspired by artificial intelligence-related technologies, we develop a novel deep learning-based DVC methodology for 3D deformation analyses with the aid of a set of well-trained neural networks, hereafter referred to as DVC-Net, to implement accurate, precise and robust 3D deformation characterization in an efficient manner in this work. This proposed DVC-Net comprises three sub-networks, i.e., DVC-NetI, DVC-NetS and DVC-NetD, which are used in turn for integer-voxel searching, sub-voxel registration and displacement field denoising. Unlike traditional DVC methods, e.g., subvolume-based local DVC or finite-element method-based global DVC, the proposed DVC-Net neither explicitly relies on correlation criteria nor requires numerical iterative calculations, which greatly reduces computational complexity of the DVC analyses and therefore improves computational efficiency by at least 2∼3 orders of magnitude. Besides, it is suitable for dealing with various practical problems arising in the DVC-related studies such as volumetric images that experience changes in lighting conditions or noisy images. It is anticipated that the proposed DVC-Net can provide an alternative method for high-performance volumetric deformation characterization.

Embedded Computational Heart Model for External Ventricular Assist Device Investigations

PurposeExternal cardiac assist devices are based on a promising and simple concept for treating heart failure, but they are surprisingly difficult to design. Thus, a structured approach combining experiments with computer-based optimization is essential. The latter provides the motivation for the work presented in this paper.MethodsWe present a computational modeling framework for realistic representation of the heart’s tissue structure, electrophysiology and actuation. The passive heart tissue is described by a nonlinear anisotropic material law, considering fiber and sheetlet directions. For muscle contraction, an orthotropic active-strain model is employed, initiated by a periodically propagating electrical potential. The model allows for boundary conditions at the epicardium accounting for external assist devices, and it is coupled to a circulation network providing appropriate pressure boundary conditions inside the ventricles.ResultsSimulated results from an unsupported healthy and a pathological heart model are presented and reproduce accurate deformations compared to phenomenological measurements. Moreover, cardiac output and ventricular pressure signals are in good agreement too. By investigating the impact of applying an exemplary external actuation to the pathological heart model, it shows that cardiac patches can restore a healthy blood flow.ConclusionWe demonstrate that the devised computational modeling framework is capable of predicting characteristic trends (e.g. apex shortening, wall thickening and apex twisting) of a healthy heart, and that it can be used to study pathological hearts and external activation thereof.