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Springs are fundamental components in mechanical systems, crucial for ensuring the safety and functionality of mechanisms. Timoshenko's stiffness formula accounts for both bending and torsional energy effects, providing accurate results for small deformations. However, when the deformation becomes large, the spring stiffness becomes a nonlinear problem due to the changing inclination angle and radius during deformation. In this study, we derive a formula for the cylindrical spring stiffness under nonlinear large deformation by considering two assumptions: the invariability of the polar angle at any point and spring wire length during deformation. This formula incorporates the effects of inclination and radius changes on the spring wire. We analyze the stiffness of the cylindrical spring with different initial inclinations using the finite element method (FEM). FEM results were compared with those obtained from Timoshenko's formula, Hiroyuki's formula, and the derived formula. For small deformations, the FEM results matched well with all three formulas. However, for nonlinear large deformations, the calculated results from Timoshenko's formula showed a discrepancy of up to 32.58% compared to the FEM results. The modified Hiroyuki formula also exhibited slightly poorer agreement with the FEM results than the formula proposed in this paper. On the other hand, our derived formula demonstrated excellent agreement with the FEM results for nonlinear large deformations. Therefore, our stiffness formula for cylindrical springs is recommended for mechanical engineering spring design applications involving nonlinear large deformations. [ABSTRACT FROM AUTHOR]

A practical scale mechanical decladder that can slit spent nuclear fuel rod-cuts (hulls + pellets) of several tens of kg HM/batch is being developed to supply UO2 pellets to a voloxidation process. The mechanical decladder is an apparatus for separating and recovering fuel material and cladding tubes by horizontally slitting the cladding tube of a fuel rod and a defective irradiated fuel rod. In this study, we address the engineering design of the mechanical decladder for the pretesting of rod-cut slitting. To obtain the requirements of the mechanical decladder, we first manufactured a slitter for testing based on the decladding and shearing conditions of hulls and pellets. The performance test of the testing device for decladding was carried out using a 2-CUT blade module and a 3-CUT blade module. We evaluated the decladding methods for the mechanical decladder and selected the 3-CUT blade module based on the results. A buckling measurement instrument was used to perform a buckling verification test according to the length of a rod-cut and to determine decladder dimensions. The optimum decladding rod-cut length for buckling prevention was calculated. Furthermore, we analyzed the decladding mechanism for various slitting methods. Design/fabrication and preliminary tests of the practical scale mechanical decladder were also performed. For this purpose, we constructed the main mechanism by utilizing the SolidWorks modeling and analysis program and fabricated a new mechanical decladder. Based on the derived requirements, a mechanical decladder with three main modules was designed and fabricated for testing. Simulated rod-cuts of zircaloy were also manufactured to test the basic performance of the decladder, and a data acquisition system was constructed using RSC 232 to measure decladding force and velocity. In the basic test, the rod-cut was completely sectioned into three evenly spaced locations by the new mechanical decladder. [ABSTRACT FROM AUTHOR]

The rack columns have so distinctive characteristics in their design, which have regular perforations to facilitate installation of the rack system that it is more difficult to be analyzed with traditional cold-formed steel structures design theory or standards. The emergence of industrial “big-data” has created better innovative thinking for those working in various fields including science, engineering, and business. The main contribution of this paper lies in that, with engineering data from finite element simulation and physical test, a novel data-driven model (DDM) using artificial neural network technology is proposed for optimization design of thin-walled steel specific perforated members. The data-driven model based on machine learning is able to provide a more effective help for decision-making of innovative design in steel members. The results of the case study indicate that compared with the traditional finite element simulation and physical test, the DDM for the solving the hard problem of complicated steel perforated column design seems to be very promising. [ABSTRACT FROM AUTHOR]