Irregular Geometry Research Articles

Electrodialysis with Irregular Membrane Spacers: Which is the Right Choice?

AbstractMembrane operations function well in turbulent conditions, requiring the use of a turbulence model. By increasing the velocity and generating eddies with membrane spacers, mass transport can be enhanced and permeability can be intensified while reducing concentration polarization. Spacers act as solid barriers for the bulk fluid and have been shown to facilitate mass transfer at channel thicknesses of 1 mm due to eddy promotion. Spacers are used in a variety of membrane processes including nanofiltration, reverse osmosis, electrodialysis, and membrane distillation. Membrane spacers can also be used for other applications such as membrane electrolysis, fuel cells, capacitive deionization, and electrodialysis with bipolar membranes. This work systematically reviews and compares irregular geometries of spacers, including Gyroid, tCLP, multilayered, crimped, and tortuous spacers. The review consistently concludes that the spacer geometrical properties are the primary determinants of the streamline effectiveness.

Modeling impedance boundary conditions and acoustic barriers using the immersed boundary method: The three-dimensional case.

One of the main challenges in time domain wave-based acoustics is the accurate simulation of both boundary conditions and barriers capable of reflecting and transmitting energy. Such scattering structures are generally of irregular geometry and characterised in terms of frequency-dependent reflectances and transittances. Conditions for numerical stability can be difficult to obtain in either case. Immersed boundary methods, which are heavily used in computational fluid dynamics applications, replace boundaries by discrete driving terms, avoiding volumetric meshing and staircasing approaches altogether. The main contribution of this article is a unified numerical treatment of both impedance boundary conditions and barriers capable of transmitting energy and suitable for use in the setting of wave-based acoustics. It is framed in terms of the immersed boundary method within a finite difference time domain scheme, using a dual set of matched discrete driving terms in both the conservation of mass and momentum equations that can be tuned against a desired reflectance or transmittance. Numerical results in three dimensions are presented, illustrating non-porous barriers and impedance boundary conditions, and highlight important features such as spurious leakage through an immersed boundary. A brief demonstration of conditions for numerical stability of the immersed boundary method in this context is provided in an appendix.

Learning Physically Realizable Skills for Online Packing of General 3D Shapes

We study the problem of learning online packing skills for irregular 3D shapes , which is arguably the most challenging setting of bin packing problems. The goal is to consecutively move a sequence of 3D objects with arbitrary shapes into a designated container with only partial observations of the object sequence. We take physical realizability into account, involving physics dynamics and constraints of a placement. The packing policy should understand the 3D geometry of the object to be packed and make effective decisions to accommodate it in the container in a physically realizable way. We propose a Reinforcement Learning (RL) pipeline to learn the policy. The complex irregular geometry and imperfect object placement together lead to huge solution space. Direct training in such space is prohibitively data intensive. We instead propose a theoretically provable method for candidate action generation to reduce the action space of RL and the learning burden. A parameterized policy is then learned to select the best placement from the candidates. Equipped with an efficient method of asynchronous RL acceleration and a data preparation process of simulation-ready training sequences, a mature packing policy can be trained in a physics-based environment within 48 hours. Through extensive evaluation on a variety of real-life shape datasets and comparisons with state-of-the-art baselines, we demonstrate that our method outperforms the best-performing baseline on all datasets by at least 12.8% in terms of packing utility. We also release our datasets and source code to support further research in this direction. 1

Investigation of the load sustaining micro mechanisms of cemented sand using the mesoscale FEM approach

While the mechanics of cemented granular material have already been approached by means of constitutive modeling, the capacity of introducing the precise internal structure of multiphase media in numerical simulations offers an alternative perspective of investigation. In specific, means of X-ray computed tomography are able to quantify the internal irregular geometry of the composite material in the form of three-dimensional images. Image analysis can be applied in order to distinguish the topology of each material phase and advanced meshing techniques are able to form a twin meshed domain. The current paper deals with the quantitative description of the morphology of small cemented sand samples, the application of developed image analysis algorithms to treat imaging artefacts (mainly due to beam hardening), the equivalent mesh generation via the image adapted meshing technique and the numerical testing using the Finite Element Method. Four samples are imaged, meshed and numerically tested in uniaxial compression; the mesoscale finite element simulations are able to display the intergranular stress transmission chains. A statistical analysis of the load carried by the granular skeleton provides evidence of the influence of the cementation degree and the compaction of the aggregates on the uniformity of the stress distribution.

Richardson extrapolation based integration scheme using FSDT over Quad elements: Application to thick FGM plates

In this work, our new quadrature schemes based on the Richardson extrapolation (RE) and centroid or midpoint rule are further applied to the quadrilateral Mindlin plate element for static and free vibration analysis. In the process of generating element matrices, to apply RE and midpoint quadrature rule, there are two approximations are particularly considered. As the first approximation, element matrices are calculated at centroid of whole plate element. For stabilizing function or second approximation, each quadrilateral element is divided into four sub-quadrilaterals, either centroid of the each sub-quadrilateral plate for Element midpoint method (EM-plate method) or midpoint of the element edges are used for Element edge method (EE-plate method) to compute the all the element matrices. Then, both the approximations are added with the RE based weighting functions. Generally, midpoint based quadrature avoids the shear-locking phenomenon in the plate elements and the RE enhances accuracy and rate of convergence of the final solution without hourglass or zero-energy issues. The numerical examples validated that the RE based EM-plate method and EE-plate method are free of shear-locking, ability to pass the patch test, better rate of convergence with less number of sampling points. Also, the new schemes confirms the three important properties such as (i) can produce high accurate solutions with better rate of convergence for problems for irregular geometries in the static analysis; (ii) can produce high accurate solutions without hourglass issues in free vibration analysis; and (iii) can generate the accurate values of high frequencies of plate elements over irregular meshes.

Enhancing prestack seismic data by sparse radon transform and dynamic waveform matching

Abstract In the field of complex underground geological structures and irregular topography, prestack seismic data often have a low signal-to-noise ratio (SNR), in which weakly reflected signals are buried beneath strong incoherent, and scattered noise. Stacking, such as beamforming along the moveout surfaces of coherent local events, can significantly improve seismic data quality. Accurate and efficient estimation of the moveout for an irregular acquisition geometry and uneven illumination is important in a complex environment. In this paper, a new optimal stacking approach for enhancing weak prestack reflection signals is presented. The proposed method mainly includes regional division and moveout estimation. Optimal stacking should be implemented within local time and space domains. Based on beam-ray theory, we designed a reasonable regional division of the common-shot (CS), common-receiver (CR) and common-middle-point (CMP) domains. Then, we proposed using the sparse radon transform and dynamic waveform matching method to estimate the moveout surfaces of local reflection events. The sparse radon transform was applied to obtain the linear moveout to ensure the correctness of the reflection wave direction. The residual nonlinear disturbance was estimated using the dynamic waveform matching method. Tests on synthetic and field data demonstrated the effectiveness of the proposed method, which can effectively improve the SNR of prestack seismic data and attenuate incoherent noise.

Sparse‐node acquisition for data fitting velocity model building

AbstractThe salt interpretation can be quite time‐intensive and challenging. Full‐waveform inversion, as a data‐driven optimization algorithm with full wavefield modelling, has become one of the essential tools for earth model building. However, full‐waveform inversion application in the complex salt geology, especially with streamer data collection, is limited, whereas the ocean‐bottom node with ultra‐long offsets and low frequencies unleashes the power of full‐waveform inversion. Sparse ocean‐bottom node acquisition has become a standard approach to improve the earth model building by the addition of ultra‐long offsets and possibly low frequencies. The new survey designs with ocean‐bottom node coupled with simultaneous shooting can be deployed on a regional basis covering thousands of square kilometres in a cost‐effective manner. In complex geological settings, including irregular salt geometry, salt interpretation has a direct impact on subsalt imaging. The recent development of robust objective functions allows full‐waveform inversion work with sparse ocean‐bottom node surveys in the deep‐water environment as the Gulf of Mexico to build and refine the salt geometries and correct the background velocity error in subsalt, uncovering the structural configuration of the basin that has not been seen before. Although full‐waveform inversion mostly employs the acoustic wave equation, we know that the high velocity boundaries in the earth model may require elastic wavefield propagation. Increased physics in full‐waveform inversion allows us to interrogate the power of elastic full‐waveform inversion versus acoustic full‐waveform inversion, and here, we demonstrate that elastic full‐waveform inversion has an advantage over acoustic full‐waveform inversion despite the extra cost associated with implementing the more complete physics.

Wild Wood Gridshells: Mixed-Reality Construction of Nonstandard Wood

Irregular wood is often downcycled despite having significant embedded strength. Reintegrating this wood into structural assemblies can improve material efficiency in the built environment. This work implemented material logic in a design-to-fabrication workflow for building structures using bifurcated tree branches to leverage this potential (Figure 1). This process is demonstrated through the design and construction of a prototype. A user-oriented computational interface is proposed that manages irregular geometries, matching and optimization algorithms, and structural simulation for design iteration. The demonstrated workflow, which concludes with augmented reality (AR) assisted fabrication, facilitates designing with varying materials, enabling upcycling a wide range of nonstandard building elements. At scale, this methodology can significantly reduce the environmental impact of construction.

Numerical simulation of a dry joint masonry arch in the Volubilis site (Morocco)

The archaeological site of Volubilis is one of the world heritages recognized by UNESCO. It is located in Morocco. It contains several historical monuments. Most of these monuments are composed of masonry arches that give them a particular aesthetic. This makes their conservation essential to ensure their durability. These masonry arches are composed of dry-jointed stone blocks with an irregular geometry that makes understanding their mechanical behavior a complex task. With the aim of reinforcing the masonry structures in the Volubilis site, composed of masonry arches, a mechanical behavior study of these structures is necessary to detect their areas of weakness, modes of failure, distribution of stresses as well as their bearing capacity. In this regard, it is necessary to develop a numerical tool to analyze the mechanical behavior of these arches in the face of the stresses that affect their structures. To do this, a masonry arch is numerically simulated using the finite element software ANSYS and integrating two models implemented in this software. A “ Concrete ” model based on the William-Warnke criterion is used to simulate the failure modes by cracking in the three spatial directions. The interfaces between the stone blocks were modeled using the Cohesive Zone Model to predict the failure and damage modes at these interfaces and, more precisely, their separations in the normal and tangential directions. The physical and mechanical properties of the stone block were determined from the experimental tests, while the other properties were estimated from a literature review. The results obtained in this study show the stress distribution and the mode of failure of this arch. The failure is presented as three hinges that develop as the load is applied. These results obtained numerically in this study show good agreement with those obtained experimentally, which are presented in the literature.

Radial basis function-differential quadrature-based physics-informed neural network for steady incompressible flows

In this work, a radial basis function differential quadrature-based physics-informed neural network (RBFDQ-PINN) is proposed to simulate steady incompressible flows. The conventional physics-informed neural network (PINN) makes use of the physical equation as a constraint to ensure that the solution satisfies the physical law and the automatic differentiation (AD) method to calculate derivatives at collocation points. Although the AD-PINN is expedient in evaluating derivatives at arbitrary points, it is time-consuming with higher-order derivatives and may lead to nonphysical solutions with sparse samples. Alternatively, the finite difference (FD) method can facilitate the calculation of derivatives, but the FD-PINN will increase the computational cost when handling random point distributions, especially with higher-order discretization schemes. To address these issues, the radial basis function differential quadrature (RBFDQ) method is incorporated into the PINN to replace the AD method for the calculation of derivatives. The RBFDQ method equips with high efficiency in the calculation of high-order derivatives as compared with the AD method and great flexibility in the distribution of mesh points as compared with the FD method. As a result, the proposed RBFDQ-PINN is not only more efficient and accurate but also applicable to irregular geometries. To demonstrate its effectiveness, the RBFDQ-PINN is tested in sample problems such as the lid-driven cavity flow, the channel flow over a backward-facing step, and the flow around a circular cylinder. Numerical results reveal that the RBFDQ-PINN achieves satisfactory accuracy without any labeled collocation points, whereas the AD-PINN struggles to solve some cases, especially for high Reynolds number flows.

Automated image-based generation of finite element models for masonry buildings

To predict the response of masonry buildings to various types of loads, engineers use finite element models, specifically solid-element and macro-element models. For predicting masonry responses to seismic events in particular, equivalent frame models—a subcategory of macro-element models—are a common choice because of their low computational cost. However, an existing bottleneck in modeling pipelines is generating the geometry of the model, which is currently a slow and laborious process that is done manually using computer-aided design tools. In this paper, we address this by automating the modelling process using recent advancements in computer vision and machine learning. We present an image-based end-to-end pipeline that automatically generates finite element meshes for solid-element and equivalent-frame models of the outer walls of free-standing historical masonry buildings. As the input, our framework requires RGB images of the buildings that are processed using structure-from-motion algorithms, which create 3D geometries, and convolutional neural networks, which segment the openings and their corners. These layers are then combined to generate level of detail models. We tested our pipeline on structures with irregular surface geometries and opening layouts. While generating the solid element mesh from the level of detail model is straightforward, generating equivalent frame models required algorithms for segmenting the façade and the meshing. Experts in the field analyzed the generated equivalent frame models and determined them to be useful for numerical modeling. These finite element geometries will be invaluable for future predictions of the seismic response of damaged and undamaged buildings. The codes and dataset are publicly available for future studies and benchmarking (https://github.com/eesd-epfl/FEM_buildings and https://doi.org/10.5281/zenodo.8094306).

Elucidating the Morphology of the Endoplasmic Reticulum: Puzzles and Perspectives.

Artificial or synthetic organelles are a key challenge for bottom-up synthetic biology. So far, synthetic organelles have typically been based on spherical membrane compartments, used to spatially confine selected chemical reactions. In vivo, these compartments are often far from being spherical and can exhibit rather complex architectures. A particularly fascinating example is provided by the endoplasmic reticulum (ER), which extends throughout the whole cell by forming a continuous network of membrane nanotubes connected by three-way junctions. The nanotubes have a typical diameter of between 50 and 100 nm. In spite of much experimental progress, several fundamental aspects of the ER morphology remain elusive. A long-standing puzzle is the straight appearance of the tubules in the light microscope, which form irregular polygons with contact angles close to 120°. Another puzzling aspect is the nanoscopic shapes of the tubules and junctions, for which very different images have been obtained by electron microcopy and structured illumination microscopy. Furthermore, both the formation and maintenance of the reticular networks require GTP and GTP-hydrolyzing membrane proteins. In fact, the networks are destroyed by the fragmentation of nanotubes when the supply of GTP is interrupted. Here, it is argued that all of these puzzling observations are intimately related to each other and to the dimerization of two membrane proteins anchored to the same membrane. So far, the functional significance of this dimerization process remained elusive and, thus, seemed to waste a lot of GTP. However, this process can generate an effective membrane tension that stabilizes the irregular polygonal geometry of the reticular networks and prevents the fragmentation of their tubules, thereby maintaining the integrity of the ER. By incorporating the GTP-hydrolyzing membrane proteins into giant unilamellar vesicles, the effective membrane tension will become accessible to systematic experimental studies.

Path-conservative positivity-preserving well-balanced finite volume WENO method for porous shallow water equations

The present study constructed a path-conservative high-order positivity-preserving well-balanced finite volume Riemann solver for the one-dimensional porous shallow water equations with discontinuous porosity and bottom topography. First, the finite difference equations were formulated using the path-conservative approach to treat non-conservative products in source terms. As the solution depends on the paths, the family of paths was formulated for the solutions to satisfy the mass and energy conservations over the smooth paths connecting the discontinuous geometry (porosity and/or bottom topography). Such a property was achieved using the stationary wave, a stationary weak solution of the porous shallow water equations. Second, to increase computational accuracy, the weighted essentially non-oscillatory (WENO) and the Runge-Kutta methods were implemented for the spatiotemporal discretization of the porous shallow water equations, respectively. Third, a positivity-preserving limiter was employed to provide robustness and efficiency in handling wet and dry problems. Finally, all procedures in the present model were formulated to ensure the well-balanced property (i.e., the exact conservation of initial still water conditions over the irregular geometry). A set of numerical experiments was performed to verify that the present model exhibits positivity-preserving and well-balanced properties along with high-order accuracy and the shock-capturing ability for all types of Riemann problems, including eight wet cases and five dry cases. The numerical results of all cases agree with the analytical solutions.

A SCAN-TO-BIM WORKFLOW PROPOSAL FOR CULTURAL HERITAGE. AUTOMATIC POINT CLOUD SEGMENTATION AND PARAMETRIC-ADAPTIVE MODELLING OF VAULTED SYSTEMS

Abstract. Cultural Heritage has been significantly impacted by advancements in the Information and Communications Technology domains, which have inspired a strong multidisciplinary interest and enabled the development of innovative strategies for the preservation, management, and enhancement of the heritage itself. Notably, the digitisation process, which entails the acquisition of 3D data obtained through cutting-edge LiDAR and photogrammetric scanning techniques, is set up as an advantageous tool for producing an accurate representation of the historical buildings. In addition, point clouds and reliable HBIM models have caught the minds of the architectural community, and are now receiving huge backing from Artificial Intelligence. Such support is provided by procedures that link semantic features to structural and decorative elements. In this scenario, the following research is presented: the aim is to test an automated iterative process within a scan-to-BIM methodology, starting from automatic point cloud segmentation operations with open-source, model-fitting algorithms. This method will prove to be a solid support for the final phase of the 3D parametric/adaptive reconstruction that’s also compatible with BIM Authoring. The study focuses on various masonry vaulted systems. These types of structures are first examined using ideal models, which were perfectly discretised and set up by the user, and then employed as a starting point for validating the parameters of the RANSAC algorithm on point clouds acquired by laser scanners. These latter ones nevertheless have irregular geometries, making comprehension, analysis, and management far more challenging.

Extraction of a floor plan from a points cloud: some metrological considerations

The design of evacuation plans for safety purposes is based on the graphical synthesis and visualization of data derived from high-performance measurement systems. The data post-processing requires some suitable procedures including many steps, to obtain the information needed. These are related to the global use of the measurement system, to the real measurement operating conditions, to the complexity of the building, to the simplifying hypotheses, to the data management. As a preliminary step, this work outlines a procedure for extracting a floor plan from a points cloud acquired by means of a laser scanner. The test case is a baroque cathedral, being a large structure and presenting some elements of irregular geometry. Some critical passages have been examined, presenting similar widths but different characteristics i.e., flat and regular geometries of the elements on the walls, in some cases, statues, columns and adornments, in some others. The data management plays a key-role in the correct interpretation of the outcomes, having a great impact on the results reliability. The obtained estimated variability of the passage width is in the order of few tenth centimeters. This is strictly related to the specific operating procedure. How this affects simulation software for route optimization of emergency paths will be the object of further investigations.

A comparison of RANS models used for CFD prediction of turbulent flow and heat transfer in rough and smooth channels

CFD of convective heat transfer remains a challenge, especially in channels with irregular rough walls due to the complex fluid dynamics over and between these roughness elements. This study investigates the performance of four commonly utilised RANS CFD models, namely Spalart-Allmaras (SA), Realisable k-ε, SST k-ω, and Reynolds Stress Model (RSM) in estimating the velocity and temperature profile as well as the skin friction coefficient and Nusselt number in smooth and rough channels. The CFD results were compared with DNS and experimental data in the literature. The roughness-resolving approach was used for all models to resolve the realistic geometry of irregular roughness in channel flows. The smooth-channel flow study was conducted at Reτ = 500, 1000, 2000, and 5000 and Pr = 0.71. Over this range, compared to smooth-wall DNS data from literature, the RSM showed consistent and satisfactory performance in estimating the velocity and temperature profiles within 2.54% and 4.21%, respectively; leading to the skin friction coefficient and Nusselt number in the smooth channels being predicted very well within 1.94% and 2.03%, respectively. The rough-channel flow study was performed at Reτ = 240, 360, 540, 720, 1000 and Pr = 1, with computational cells 7.8 times that of the smooth channel to resolve the irregular roughness geometry. Over this range, the Realisable k-ε showed the best performance in estimating the roughness function within 6.85%, while the results of the other models were wider at uncertainty levels of up to 36.9%. Similarly, the Realisable k-ε demonstrated the best performance in estimating the skin friction and Nusselt number in rough channels within 2.76% and 9.50%, respectively, while the performance of the other models was to a wider latitude ranging between 15.62% to 26.34%. In the present study, the Realisable k-ε with enhanced wall treatment showed the best capabilities in capturing the mean flow characteristics of both smooth and rough channels; however, its usage should be approached with caution in flows at higher Reynolds numbers. RANS models are also discussed for forced convection heat transfer at high Reynolds numbers. The results of this study should be useful in CFD studies on the effects of roughness in complex geometries without the need for LES and DNS.

Investigation of heat transfer processes in multi-sized solar-sintered regolith for lunar ISRU program

Effective Thermal Conductivity (ETC) prediction of sintered lunar regolith plays a crucial role in developing extraterrestrial thermal processing and construction techniques for In-situ Resource Utilization (ISRU) projects on the Moon. Most existing literature has been focused on the packed lunar regolith without considering the sintering mechanism. Herein, we presented a mechanistic model to estimate the ETC of sintered lunar regolith. The Monte-Carlo method was applied to simulate the thermal neck resistance between multi-scaled particles, while the irregular particle geometry was characterized by shape factors. The presented model was experimentally validated by the measured ETC and specific surface area of fabricated lunar simulant samples LMS-1 with various granularities sintered under air, argon gas and low vacuum (10−3 Pa). Furthermore, the heat transfer process within solar sintered LMS-1 bed was simulated coupled with the ETC model. Transient behaviors of microstructure, thermal conductivity and temperature profile during solar heating were simulated iteratively. The simulated temperature profiles at the steady state concur well with measured data obtained from the solar sinter testing with an average Root Mean Squared Error (RMSE) of 5.5%, which performs better than the un-sintered ETC model (RMSE=12.7%). The measured sintered depth was improved by 45% with an optimized particle size distribution (PSD) arrangement.

Heat transfer enhancement in a corrugated chamber filled with hybrid nanofluid under an influence of internal heated plate

PurposeThe purpose of this research is to scrutinize numerically the effect of internally equipped nonuniformly heated plate within wavy cavity on heat transfer enhancement in the case of hybrid nanofluid flow.Design/methodology/approachThe two-dimensional, steady, laminar, Newtonian and incompressible thermo-fluid flow phenomenon has been investigated numerically using Galerkin method. The considered parameters including number of waves (3–7), nondimensional length of heated plate (0.4–0.8), plate inclination angle (0º–90º), Rayleigh number (103–106) and concentration of nanoparticles (0.0–2.0) have been investigated in combination with involving hybrid nanofluid as a working fluid to augment thermal properties effectively. Two vertical wavy boundaries have low temperature whilst the other horizontal surfaces are adiabatic.FindingsThe Rayleigh number has a moderate impact on the values of Nusselt number, and skin friction parameter varied from 103 to 105 while it strongly affects them for Ra = 106, where Nu is roughly doubled (approximately 200%) in comparison with its value at Ra = 105 for all cases. Stream function is changed by the orientation of heated plate and Ra values, where its maximum value was 12.9 in horizontal position and 13.6 at vertical one. Results indicate a separation from the wavy walls at low Ra which tends to keep stagnation region at the deep parts of corrugated walls contrary the case at high Ra. The behavior of the isotherm contours tends to be distributed more evenly at lower values of Ra and angle of inclination lower than 45º. The resulting properties from mixing two materials for hybrid nanofluid into one base fluid show a good compromise between thermal capacity and heat conductivity, which is improved by 16% that leads to enhanced convective energy transport in the wavy chamber.Originality/valueThe originality of this work is the considered physical phenomenon where an influence of internal nonuniformly heated plate has been studied for the irregular geometry filled with a hybrid nanofluid. Such analysis allows defining the possible heat transfer enhancement for such an irregular cavity and inner heated plate.

Flexible Coated Conductive Textiles as Ohmic Heaters in Car Seats

Pleasant interior conditions within cars, as well as a comfortable feeling, are primary needs of car drivers to perform vital body functions during driving. In a cooler outer environment, a warming system within seats can quite feasibly maintain a realistic thermal balance in the body. An inbuilt heating system in car seats can not only provide adequate heat to the driver but is also relevant to minimizing the energy consumption within the vehicle interior. In order to evenly distribute the heat over the body contact area of the automotive seats, conductive textiles are proposed. In fact, these textiles behave as a semiconductor (as an electrical conductor and also creating resistance, which in turn creates heat). Flexible textile ohmic heaters present great advantages due to their ability to bend, stretch, and stitch. These properties make them valuable to ensure uniform heaters for irregular geometries. The present review highlights the use of different textile-based ohmic heaters, their fabrication methods, range of heating, and durability. Moreover, this review also focuses on the comparative mechanical performance and comfort properties of the presented fabrics (used in car seats). This study is beneficial for future trends of minimizing energy consumption and providing an effective way to reduce the carbon emissions and air pollution produced by vehicles.

CARD11: Polymeric Endoluminal Paving for Prevention of Paravalvular Leak in Transcatheter Aortic Valve Replacement

Background: Calcific aortic valve disease and associated aortic stenosis is increasingly common, with prevalence across the age spectrum. Transcatheter aortic valve replacement (TAVR) has emerged as a life-saving, non-surgical alternative for replacement of diseased aortic valves, restoring non-obstructive blood flow from the heart. While effective, current TAVR technology remains limited by adaptability to patient-specific anatomy, leading to incomplete deployment and non-conformal fit resulting in paravalvular leak. Polymeric endoluminal paving was previously developed as a methodology allowing deployment of polymeric materials on tissue surfaces, serving as structural materials and barriers. Here we adapt the paving methodology to fill gaps and defects resulting from incomplete TAVR valve deployment to limit paravalvular leak. Specifically, we assess the efficacy pf polymeric paving in vitro as a means of gap filling and leak reduction in valves deployed in 3-D physical models of severely stenosed aortic valves. We hypothesize that applied polymeric biomaterials will fill and conform to irregular aortic valve geometries acting as a sealant to reduce paravalvular leak. Methods: Polymeric hydrogels were prepared from ABA block copolymer macromers composed of diacrylate terminated by PLA-b-PEG-b-PLA. Hydrogel networks were prepared by swelling these macromers, following the addition of free radical initiators. Hollow tubular constructs were formed via melt-processing in PDMS molds to fit as a “sleeve” around deployed PolyV® nitinol stent (Fig 1A). The sealing efficacy of polymers was examined using a silicone replica of CAVD aortic root with deployed stent + polymer construct (Fig 1B). A rubber stopper was used to fully occlude aortic lumen and restrict flow to paravalvular areas for quantitative analysis. To test sealing against hydrostatic pressure, valve construct was connected to Tygon tubing and water was added at equivalent 10 mmHg increments at 5-minute intervals. Volume from paravalvular flow was measured at each step. Experiments were performed with N = 4 polymeric sleeves. Results: We found significant reduction in paravalvular leakage with polymer sleeves compared to no polymer at all pressures tested. Figure 1C shows the mean ± standard deviation volume of water leaked over 5 minutes of corresponding hydrostatic pressure. No leakage was detected up to 60mmHg for all polymer sleeves tested. At approximately 85-90 mmHg, the rubber stopper failed, however the polymer sleeve remained intact. Conclusions: Polymeric paving utilized with TAVR is a promising combinative therapy for preventing paravalvular leakage. Our results indicate that potential polymeric biomaterials are capable of conforming to non-physiologic aortic valve morphologies, filling and sealing paravalvular gaps, while withstanding physiologic trans-aortic pressures. With further translation, polymer paving can be useful in improving TAVR outcomes.