Perm National Research Polytechnic University

Transcatheter aortic valve implantation (TAVI) is an innovative minimally invasive procedure designed to replace a damaged or narrowed aortic valve. One of important factor is a device selection, which means variety of stent frame types. Bending stiffness of TAVI stent frame provides an ability to withstand external deformation, when it is implanted in the aortic annulus. Patient-specific manufacturing can solve a problem of patient-device sizing and compatibility. For customized manufacturing it is necessary to find a relation between TAVI implant design and strength to avoid post-operative complications. Nevertheless, studies of relations between structure of TAVI stents and their mechanical performance are rare. Thus, eight stent geometries (a–h) including commercial ones (a, b) and own-developed (c–h) were manufactured by filament fused fabrication. Basing on mechanical tests, features of TAVI stent frames, numerical simulations of stent deployment and bending were carried out. Three-point bending was also performed on manufactured stents. The least average value of bending stiffness was shown by models a, e, f, g (0.92–1.09 kN⋅mm2), while the greatest average value was obtained for c model (5.05 kN⋅mm2). Additionally, correlation analysis was performed. To reach bending stiffness of 6 kN⸱mm2, waist position should be 22.4 mm, parameters vertical and horizontal should be 3.4 and 8.7, respectively. The proposed study can help to choose proper TAVI stent frame design basing on demanding stiffness of aorta to provide accurate short- and long-term performance of the prothesis.

Accurately predicting Flowing Bottom-Hole Pressure (FBHP) is critical for optimizing oil and gas production. Existing predictive methods often rely on oversimplified or complex, yet computationally expensive, models that fail to capture the intrinsic nonlinearities of well dynamics, leading to inaccurate predictions and potential economic losses. This paper introduces a three-layer heterogeneous stacking ensemble model to address the latter challenge. In particular, the key novelty of the developed work is a hierarchical architecture that integrates five distinct Machine Learning (ML) base learners, two meta-learners, and a final super-learner, i.e., an additional meta-model that combines the outputs of the meta-learners to capture complex, non-linear relationships in the data. When evaluated on a field dataset (total dataset samples N=795; test set samples N=199), the proposed Super Learner Stacking model (ST-S) demonstrated superior predictive performance on the independent test set, achieving R-squared (R2) = 0.857±0.006 and Root Mean Squared Error (RMSE) = 146.382±2.806. In addition, the ST-S model outperformed all individual models and simpler stacking ensembles reported in the article. As a result, the developed ST-S model provides a robust, data-driven tool for FBHP prediction, achieving high predictive accuracy without resorting to computationally expensive methods, thereby supporting improved well management and production optimization.

[Objective]The Middle East is a key region for oil and gas within the Belt and Road Initiative,but it faces challenges because most oil fields lack optimized fracturing designs.To achieve the desired stimulation results,high-displacement fracturing fluid operations are often used,which can lead to significant groundwater contamination from broken gel fluid.Visual plate experiments are commonly employed to optimize fracturing parameters;however,they are time-consuming and require substantial material,which hinders the development of green and intelligent oilfields.Therefore,this study aimed to develop an efficient machine learning model to predict the equilibrium proppant bank height within primary fractures and to use the output to inversely optimize the fracturing fluid displacement rate.[Methods]After a comprehensive review of existing literature,a dataset was established,and data standardization was carried out.By evaluating predictive performance using five-fold cross-validation and a test set,four machine learning algorithms were compared,and the best predictive model was selected.A method leveraging this optimal model to improve both the plate experiment process and the fracturing fluid displacement rate was then proposed.[Results]The random forest model showed the best predictive performance,achieving a coefficient of determination of 0.947,a root mean square error of 3.62,and a mean absolute error of 2.27 on the test set.For the validation set with 21 samples,the absolute error was within 1.50 cm,with a fitted curve slope of 1.07 and an intercept of-0.81.The inversely designed,optimized fracturing fluid displacement rate was significantly lower than the empirical rate,saving at least 106.2 m3 of fracturing fluid per hour while still ensuring effective fracture filling.[Conclusions]This study not only addresses current limitations of plate experiments but also offers guidance for designing fracturing fluid displacement rates in field operations within the Middle East.

The viscoelastic properties of ultraviolet radiation-curable polymer coatings of optical fibers were studied experimentally and numerically. The test setup was completed, and a series of natural experiments were conducted for an extended temperature range from -110 °C to +120 °C using a dynamic mechanical analyzer (DMA). Discrete dependencies of the complex modulus on temperature and frequency of kinematic loading were obtained. The problem of multiparametric optimization was solved. Defining relations were obtained for protective coating polymers, making it possible to describe the thermomechanical behavior of the glass-forming materials under consideration in a wide temperature range, including relaxation transition. The optimal solution was found for 18 series terms at the selected reference temperature Tr = -70 °C, C1 = 20.036, and C2 = 32.666 for the DeSolite 3471-1-152A material. The optimal solution was found for 60 series terms at the selected reference temperature Tr = 0 °C, C1 = 40,242.2827, and C2 = 267,448.888 for the DeSolite DS-2015 material. The models were verified according to the data of creep experiments. The capabilities of the viscoelastic model were demonstrated by the example of a numerical experiment on free thermal heating/cooling of a Panda-type optical fiber.

The study focuses on examining the impact of internal manufacturing defects (voids and wrinkles) on the mechanical behavior of structural carbon fiber reinforced polymers (CFRP) under shear loading. Specimens of VKU/VSE1212 material with a [0/90]10 layup were artificially embedded with defects: circular voids (Ø 20 mm) and square voids (20×20 mm) at a concentration (area fraction) of 5.3%, as well as wrinkles increasing thickness by 10%. In-plane shear tests were conducted according to ASTM D7078 using a Vic-3D system for digital image correlation (DIC) to record strain fields. Results revealed that voids at 5.3% concentration (area fraction) reduced shear strength by 2.2% (from 72.1 MPa to 70.5–70.6 MPa, regardless of geometry, indicating that the defect area, rather than its shape, governs the failure response. The shear modulus of void-containing specimens (~4.48 GPa) did not differ from defect-free specimens (~4.49 GPa). Wrinkles increased the shear modulus to 5.39 GPa due to added layers but reduced strength by ~3% (to 69.9 MPa). DIC analysis confirmed the method’s efficacy for tracking strain localization in defect zones. Failure modes across all specimens involved vertical crack formation between V-notches. These results help systematize knowledge on of manufacturing defects on CFRP shear properties and hold practical significance for refining design tolerances in critical components, developing non-destructive testing methods, optimizing manufacturing processes.