Applications in engineering science最新文献

Under cyclic loading in ULCF and LCF regimes the strain plays a fundamental role in the analysis of the fatigue resistance and in the estimation of the fatigue life. The DIC method, which allows to acquire strain and deformation in large areas is applied in order to obtain the maximum reached strain, strain range, mean strain and SWT parameter during a fatigue test on a friction stir welded specimen. Since the distribution of these variables in the region of interest shows a geometry given by a symmetric bell, a Gaussian model is considered to model it. The evolution of these variables during the fatigue test up to failure is fitted by applying a third grade polynomial. The obtained results, show the plausibility of the proposed method to model the strain distribution and evolution under cyclic loading.

In a recent contribution to the fundamental understanding of polymer fluid dynamics, Khambhampati and Rajagopal (2021) established a connection between the natural configuration theory of Rajagopal and Srinivasa (2000) and the FENE-P model of Bird et al. (1987 [6]). In this paper we capitalize on the result in Khambhampati and Rajagopal (2021) and present a new class of FENE-P models using a more general Helmholtz potential within the conceptual framework of evolving natural configurations. To show its qualitative behavior, we exemplify with a classical Couette flow between infinite parallel plates. The model is capable of reproducing key features experimentally observed such as stress relaxation and the overshoot of the shear stress at the beginning of typical shear stress growth experiments. Comparison against the FENE-P type model obtained in Khambhampati and Rajagopal (2021) is used for comparison.

In the field of metal cutting, the cutting oil scatters in air as a microscale oil mist, which adversely affects the work environment. A baffle plate-type mist trap was manufactured as simple equipment for collecting oil mist floating in air. The oil mist collection rate and pressure loss were determined using experiments and numerical simulations while varying the number of baffle plates and inflow velocity of air. The experimental results showed that the pressure drop increased with the number of baffle plates, whereas the oil mist collection rate improved. It was also clarified that larger particles can be collected as the number of baffle plates increased. Numerical simulations showed that a high amount of oil mist was trapped upstream of the second baffle plate, and the baffle plate placed further downstream had minimal contribution to oil mist collection. In both the experiments and numerical simulations, the oil mist collection efficiency was the highest when six baffle plates were arranged. This is because the pressure drop increases depending on the number of baffle plates, whereas the mist collection rate is almost constant when many baffle plates are placed.

Ferroelectric ceramics experience polarization switching, which is macroscopically shown by nonlinear hysteretic responses when subjected to high compressive stresses and/or high amplitude of the electric field. We model the nonlinear hysteretic response of ferroelectric ceramics by considering evolutions of microstructural changes associated with changes in the dipole orientations due to both electrical and mechanical stimuli. We adopt the theory of multiple natural configurations, associated with multiple stress-free and electric-field-free states, in incorporating the effect of microstructural changes on describing the nonlinear electro-mechanical hysteretic response of ferroelectric ceramics. The first stress-free and electric-field-free states are associated with the original microstructure of the materials, in which the dipoles in the ferroelectric ceramics are randomly oriented. The new configurations are formed when the ferroelectric ceramics are subjected to relatively large stimuli, which align the dipole orientations. As each configuration is associated with a specific microstructure (a state of dipole orientations), mechanical and electrical properties characterized at different configurations will be different. To examine the model, experimental data on PZT (lead zirconate titanate) ceramics under electric and stress fields, available in the literature, are used. The model is capable of describing the hysteretic response in PZT under electro-mechanical stimuli.

The construction sector would greatly benefit from a strategy for optimizing high-performance concrete mixtures. However, traditional proportioning techniques are insufficient because of their high prices, usage restrictions, and inability to account for nonlinear interactions between components and concrete qualities. High-performance concrete (HPC) is a complicated composite material with highly nonlinear mechanical behaviour. When strength can be accurately predicted, design costs, design time, and material waste caused by several mixing trials can all be reduced. In this research, feed-forward neural network (FFNN), Elman neural network (ENN), support vector machine (SVM) and multilinear regression (MLR) were employed for predicting the compressive strength of HPC. The input variables include cement (C), cement strength (CeS), superplasticizer (S), fly ash (F), air entraining agent (A), coarse aggregate (CA), Sand (Sd) and water/binder (W/B) and 28 days’ compressive strength as the output variables. Finally, the results indicate that the proposed model has predictive robustness for predicting the compressive strength of HPC. The results showed that FFNN-M4, ENN-M4, SVM-M4, and MLR-M4 combination have the highest performance evaluation criteria of R²=0.9950, R²=0.9853, R²=0.9736, R²= 0.9678 in the testing phase respectively. The outcomes also show that the proposed model has high accuracy and effectiveness in predicting the compressive strength of HPC.

Early estimation of the fatigue life of an oil well drill string reduces the risk associated with drill string fatigue failures. In this study, a low-order computationally efficient bond graph model of a vertical well drill string and a component-level higher-order finite element model of a drill pipe threaded connection are employed to predict the fatigue damage of a given drill pipe. The bond graph is a 3D lumped segment model developed using the Newton–Euler formulation and body fixed coordinates. It is parameterized using finite element modelling simulations. The stress history from the top-level model is applied to the component-level model that contains details such as threaded geometry. Then, a multi-axial, non-proportional, and variable amplitude (MNV) fatigue estimation is performed using an open-source finite element analysis code. The fatigue prognosis approach is then demonstrated in a drill string design case study that optimizes the placement of vibration stabilizers in the wellbore to avoid severe vibrations while minimizing fatigue damage. Optimal placement of stabilizers predicts a 200% increase in fatigue life of the most vulnerable component with reference to the worst-case scenario.

Flows of incompressible Navier–Stokes (Newtonian) fluids between adjacent surfaces are encountered in numerous practical applications, such as seal leakage and bearing lubrication. In seals, the flow is primarily pressure-driven, whereas, in bearings, the dominating driving force is due to shear. The governing Navier–Stokes system of equations can be significantly simplified due to the small distance between the surfaces compared to their size. From the simplified system, it is possible to derive a single lower-dimensional equation, known as the Reynolds equation, which describes the pressure field. Once the pressure field is computed, it can be used to determine the velocity field. This computational algorithm is much simpler to implement than a direct numerical solution of the Navier–Stokes equations and is therefore widely employed by engineers. The primary objective of this article is to investigate the possibility of deriving a type of Reynolds equation also for non-Newtonian fluids, using the balance of linear momentum. By considering power-law fluids we demonstrate that it is not possible for shear-driven flows, whereas it is feasible for pressure-driven flows. Additionally, we demonstrate that in the full $3D$ model, a normal stress boundary condition at the inlet/outlet implies a Dirichlet condition for the pressure in the Reynolds equation associated with pressure-driven flow. Furthermore, we establish that a Dirichlet condition for the velocity at the inlet/outlet in the $3D$ model results in a Neumann condition for the pressure in the Reynolds equation.

Innovalve is a transcatheter mitral valve replacement (TMVR) system with a unique fixation system that aims to grasp the chordae in a rotational maneuver.

Objectives

This study will examine the safety and efficacy of the transapical mitral implantation of Innovalve bioprosthesis in the porcine model.

Methods

Innovalve MV bioprosthesis was implanted in the porcine model (5 pigs) for a duration of 30 days.

Results

The Innovalve bioprosthesis was successfully implanted in all of the animals with accurate positioning, anchoring and good paravalvular sealing. At 30 days after implantation, all animals exhibited normal clinical status without evidence of cardiac impairment. The prosthetic valve seemed adequately adhered to the mitral annulus with fibrous tissue.

Conclusion

We have demonstrated that the implantation of the Innovalve in healthy porcine subjects is feasible and safe and that the valve function and interaction with normal biological tissue is favorable and predictable.

Metallurgical manufacturing processes commonly used in the industry (rolling, extrusion, shaping, machining, etc.) usually cause residual stress development which can remain after thermal heat treatments. These stresses can be detrimental for the in-service performance of structural components, which makes their study and understanding important. Residual stress variations are usually determined at a macroscopic scale (commonly, using diffraction methods). However, stress variations at the microscopic scale of the individual crystallites (grains), are also relevant. Contrary to the macroscopic residual stresses, microscopic residual stresses are difficult to quantify using conventional procedures. We propose to use machine learning to find equations that describe microscopic residual stresses. Concretely, we show that we are able to learn equations to reproduce the diffraction profiles from microstructural characteristics using genetic programming. We evaluate the learned equations using real neutron diffraction peaks as a reference, obtaining accurate results for the most frequent grain orientations with runtimes of a few minutes.