This article presents a complete guide from the linear static finite element theory to the software development of the educational finite element software for truss structure (EFESTS). First, linear static formulations of truss structure are summarized, including the element formulation, element assembly and solution. Object-oriented programming is introduced to design the software architecture. Furthermore, the relations between the classes and their instances are clearly described using the static class diagram and the dynamic sequence diagram, respectively. Finally, the linear static solver is intuitively verified by the deformable configuration and stress contour of a 50-bar truss structure. More importantly, a visualization of graphical user interfaces is developed to dynamically assemble the element matrix into the truss matrix, which used to be one of the most difficult skills for students to master. Finally, this software is free for educational research and can be downloaded from the website: http://www.zuowenjie.net/ efests.html. In the future, we will develop the geometric nonlinear static analysis solver for the truss structure.
A total of 15 bileaflet mechanical heart valves were studied in a pulse duplicator at the Helmholtz Institute (Aachen, Germany) under conditions approximating first, a physiological pressure curve and subsequently, a sinusoidal pressure curve. In this study Edwards-Duromedics valves of the modified specification were compared with the earlier version of the Edwards-Duromedics valve as well as with St. Jude Medical valves. Each valve was tested at a series of nine (9) conditions. At each condition, without altering the valve installation or the systemic conditions, each valve was filmed by two separate video systems: the Helmholtz Institute strobe light system and a high speed video recording system. All data, as recorded by each system, was then independently analyzed by both of the two contributing groups and subsequently compared. In this manner, it was possible to objectively verify not only the consistency of the data obtained, but to also determine the relative reliability of the methods for cavitation threshold detection.
To control the welding residual stress and deformation of metal inert gas (MIG) welding, the influence of welding process parameters and preheat parameters (welding speed, heat input, preheat temperature, and preheat area) is discussed, and a prediction model is established to select the optimal combination of process parameters. Thermomechanical numerical analysis was performed to obtain the residual welding deformation and stress according to a 100 × 150 × 50 × 4 mm aluminum alloy 6061-T6 T-joint. Owing to the complexity of the welding process, an optimal Latin hypercube sampling (OLHS) method was adopted for sampling with uniformity and stratification. Analysis of variance (ANOVA) was used to find the influence degree of welding speed (7.5–9 mm/s), heat input (1500–1700 W), preheat temperature (80–125 °C), and preheat area (12–36 mm). The range of research parameters are according to the material, welding method, thickness of the welding plate, and welding procedure specification. Artificial neural network (ANN) and multi-objective particle swarm optimization (MOPSO) was combined to find the effective parameters to minimize welding deformation and stress. The results showed that preheat temperature and welding speed had the greatest effect on the minimization of welding residual deformation and stress, followed by the preheat area, respectively. The Pareto front was obtained by using the MOPSO algorithm with ε-dominance. The welding residual deformation and stress are the minimum at the same time, when the welding parameters are selected as preheating temperature 85 °C and preheating area 12 mm, welding speed is 8.8 mm/s and heat input is 1535 W, respectively. The optimization results were validated by the finite element (FE) method. The error between the FE results and the Pareto optimal compromise solutions is less than 12.5%. The optimum solutions in the Pareto front can be chosen by designers according to actual demand.
Gradient and nongradient optimization algorithms are currently available for structural design in structural optimization course. Despite the successful application of gradient algorithm in structural optimization, nongradient algorithm is also extensively adopted to solve the structural optimization problem. However, the efficiency of nongradient algorithm has caused a heated debate recently. To clarify this issue for the graduate students, sequential linear programming and genetic algorithm are, respectively, chosen as the representatives of gradient and nongradient algorithms to solve truss size optimization problem. Firstly, the size optimization formulations of truss structure for sequential linear programming and genetic algorithm are summarized, respectively. Secondly, an educational finite element software for truss structure is developed by using the object-oriented programming to create the software framework. This study aims to provide an open-source, extensible, and benchmarking software, which do assist the students to understand the structural optimization process in engineering education. Finally, two benchmarking examples are introduced to compare the efficiency and accuracy of sequential linear programming and genetic algorithm.
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