Anisotropic-cyclicgraphene: A new two-dimensional semiconducting carbon allotrope

Potentially new, single-atom thick semiconducting 2D-graphene-like material, called Anisotropic-cyclicgraphene, have been generated by the two stage searching strategy linking molecular and ab initio approach. The candidate derived from the evolutionary based algorithm and molecular simulations was then profoundly analysed using first-principles density functional theory from the structural, mechanical, phonon, and electronic properties point of view. The proposed polymorph of graphene (rP16-P1m1) is mechanically, dynamically, and thermally stable and can be semiconducting with a direct band gap of 0.829 eV.Comment: 15 pages, 14 figure

Comparison of numerical and experimental strain distributions in composite panel for aerospace applications

[EN] In structural applications of aerospace industry, weight efficiency, understood as minimal weight and maximal stiffness, is of great importance. This criterion can be achieved by composite lightweight structures. Typical structures for aforementioned applications are sandwich panels (e.g., with honeycomb core) and stiffened panels (e.g., with blade ribs, T-bar ribs, or hat ribs). In this paper, a hat-stiffened panel, made of carbon/epoxy woven composite, is considered. Results of experiments, consisting of loading the panel and measuring exciting forces and strains (using strain gages), are presented. The results are compared to strains distribution obtained from finite element model of the panel.The research was partially funded from financial resources from the statutory subsidy of the Faculty of Mechanical Engineering, Silesian University of Technology, in 2021. W.M. acknowledges the National Agency for Academic Exchange of Poland (under the Academic International Partnerships program, grant agreement PPI/APM/2018/1/00004) for supporting training in the University of Minho, which enabled execution of the study.Mucha, W.; Kuś, W.; Viana, J.; Nunes, J. (2022). Comparison of numerical and experimental strain distributions in composite panel for aerospace applications. En Proceedings of the YIC 2021 - VI ECCOMAS Young Investigators Conference. Editorial Universitat Politècnica de València. 403-411. https://doi.org/10.4995/YIC2021.2021.12572OCS40341

Operational load monitoring of a composite panel using artificial neural networks

Operational Load Monitoring consists of the real-time reading and recording of the number and level of strains and stresses during load cycles withstood by a structure in its normal operating environment, in order to make more reliable predictions about its remaining lifetime in service. This is particularly important in aeronautical and aerospace industries, where it is very relevant to extend the components useful life without compromising flight safety. Sensors, like strain gauges, should be mounted on points of the structure where highest strains or stresses are expected. However, if the structure in its normal operating environment is subjected to variable exciting forces acting in different points over time, the number of places where data will have be acquired largely increases. The main idea presented in this paper is that instead of mounting a high number of sensors, an artificial neural network can be trained on the base of finite element simulations in order to estimate the state of the structure in its most stressed points based on data acquired just by a few sensors. The model should also be validated using experimental data to confirm proper predictions of the artificial neural network. An example with an omega-stiffened composite structural panel (a typical part used in aerospace applications) is provided. Artificial neural network was trained using a high-accuracy finite element model of the structure to process data from six strain gauges and return information about the state of the panel during different load cases. The trained neural network was tested in an experimental stand and the measurements confirmed the usefulness of presented approach.The project and publication of this article were financed by the Polish National Agency for Academic Exchange (project number: PPI/APM/2018/1/00004) in the framework of Academic International Partnerships program

Parallel and distributed computations in evolutionary and immune optimization of laminates

Abstract The paper deals with the application of the parallel and distributed calculations for global optimization of composite structures. Evolutionary Algorithm and Artificial Immune System are employed as global optimization methods. The aim of the optimization is to find the best stacking sequence of laminates for given criteria. To reduce the computational time parallel versions of global optimization algorithms are used. Computational grid is used to perform distributed computations. A boundary-value problem for laminates is solved by means of Finite Element Method commercial software. Numerical examples presenting efficiency of proposed attitude are attached

Identification of brains tissue hyperelastic parameters

The goal of the paper is to correlate real brain deflection with its numerical model as the 3D model of a fragment of the brain and suction pipe. The model is analyzed with the Finite Element Method with use of Ansys software. The brain tissue can undergo large strains, which is why it is described by a hyperelastic material. The Mooney-Rivlin material model is used for numerical analyzes. The inverse problem is solved with use of optimization Non-Linear Programming by Quadratic Lagrangian (NLPQL)

Operational Load Monitoring of a Composite Panel Using Artificial Neural Networks

Operational Load Monitoring consists of the real-time reading and recording of the number and level of strains and stresses during load cycles withstood by a structure in its normal operating environment, in order to make more reliable predictions about its remaining lifetime in service. This is particularly important in aeronautical and aerospace industries, where it is very relevant to extend the components useful life without compromising flight safety. Sensors, like strain gauges, should be mounted on points of the structure where highest strains or stresses are expected. However, if the structure in its normal operating environment is subjected to variable exciting forces acting in different points over time, the number of places where data will have be acquired largely increases. The main idea presented in this paper is that instead of mounting a high number of sensors, an artificial neural network can be trained on the base of finite element simulations in order to estimate the state of the structure in its most stressed points based on data acquired just by a few sensors. The model should also be validated using experimental data to confirm proper predictions of the artificial neural network. An example with an omega-stiffened composite structural panel (a typical part used in aerospace applications) is provided. Artificial neural network was trained using a high-accuracy finite element model of the structure to process data from six strain gauges and return information about the state of the panel during different load cases. The trained neural network was tested in an experimental stand and the measurements confirmed the usefulness of presented approach

Two-Step Geometry Design Method, Numerical Simulations and Experimental Studies of Bioresorbable Stents

The stent-implantation process during angioplasty procedures usually involves clamping the stent onto a catheter to a size that allows delivery to the place inside the artery. Finding the right geometrical form of the stent to ensure good functionality in the open form and to enable the clamping process is one of the key elements in the stent-design process. In the first part of the work, an original two-step procedure for stent-geometry design was proposed. This was due to the necessary selection of a geometry that would provide adequate support to the blood-vessel wall without causing damage to the vessel. Numerical simulations of the crimping and deployment processes were performed to verify the method. At the end of this stage, the optimal stent was selected for further testing. In addition, numerical simulations of selected experimental tests (catheter-crimping process, compression process) were used to verify the obtained geometrical forms. The results of experimental tests on stents produced by the microinjection method are presented. The digital image correlation (DIC) method was used to compare the results of numerical simulation and experimental tests. The two-step modeling approach was found to help select the appropriate geometry of the expanded stent, which is an extremely important step in the design of the crimping process. In the part of the paper where the results obtained by numerical simulation were compared with those gained by experiment and using the DIC method, a good compatibility of the displacement results can be observed. For both longitudinal and transverse (pinch) stent compression, the results practically coincide. The paper presents also the application of the DIC method which significantly expands the research possibilities, allowing for a detailed inspection of the deformation state and, above all, verification of local dangerous areas. This approach significantly increases the possibility of assessing the quality of the stents