Non-line analysis of stiffness in compression conditions

• Autorzy: Kahsin M. , Stecki D.

Identyfikatory

DOI

10.2478/pomr-2018-0060

• Języki publikacji: EN

Abstrakty

EN

The analyzes were aimed at demonstrating the influence of parameters describing the deformation of the structure on the uncertainty of critical force, and the impact of technological imperfections on stress uncertainty in compression conditions. In a linear buckling analysis, the problem is considered only for the initial, permanent state of the stiffness matrix. In the case of demonstrating the influence of initial deformations on the behavior of the structure under load, it is necessary to visualize changes in stiffness over time. To this end, a non-linear MES analysis was carried out, which will take into account local changes in the stiffness of the model through a gradual increase in the load. Thus, the difference in stiffness is taken into account, which in the linear problem is infinite. The analysis was used to examine the local and global sensitivity of the parameters describing: plating thickness as well as deformation caused by the technological process on the stress value reduced by Huber hypothesis, and the value of normal stress. To take into account the influence of non-specified values of the magnitude of geometric deviations, and their simultaneous influence on the range of obtained results, the Experimental Planning Method and the Surface Method of Answers were used.

Słowa kluczowe

EN

numerical simulations; uncertainty of model parameters; reliability of results; statistical approach; limit load

• Wydawca: Gdańsk University of Technology, Faculty of Ocean Engineering & Ship Technology
• Czasopismo: Polish Maritime Research
• Rocznik: 2018
• Tom: nr 2
• Strony: 100--107
• Opis fizyczny: Bibliogr. 16 poz., rys., tab.

Twórcy

autor

Kahsin M.

• Gdańsk University of Technology Faculty of Ocean Engineering and Ship Technology 11/12 Narutowicza St. 80-233 Gdańsk Poland

autor

Stecki D.

• Gdańsk Shiprepair Yard „Remontowa” S.A. Na Ostrowiu 1 80-958 Gdańsk Poland

Bibliografia

• 1. Broekea G. Voorna G., Ligtenbergb A.: Which Sensitivity Analysis Method Should I Use for My Agent-Based Model?. Journal of Artificial Societies and Social Simulation 19, 2016.
• 2. Chen, Z., Ajit Shenoi, R.: Influence of welding sequence on welding deformation and residual stress of a stiffened plate structure. Ocean Engineering, Volume 106, 15 September 2015, Pages 271–280.
• 3. Czitrom V.: One-Factor-at-a-Time Versus Designed Experiments, American Statistician. Vol. 53, 1999.
• 4. Det Norske Veritas, Recomended Practice: Determination of Structural Capacity by Non-linear FE analysis Methods, June 2013
• 5. Fisher R.A.: The Design of Experiments. Eighth Edition. Oliver and Boyd, Edinburgh 1966.
• 6. International Association of Classification Societies: Technical Background Report for CSR, 2016.
• 7. Kahsin M., Luczak M.: Numerical Model Quality Assessment of Offshore Wind Turbine Supporting Structure Based on Experimental Data. Structural health monitoring 2015: system reliability for verification and implementation, vols. 1 and 2, pp. 2817-2824 Published: 2015.
• 8. Kyung K. Choi, Nam H. Kim: Structural Sensitivity Analysis and Optimization 1. Mechanical engineering series, Berlin, Germany, 2013
• 9. Marino S., Hogue I.B., et al.: A Methodology For Performing Global Uncertainty And Sensitivity Analysis In Systems Biology, 2008
• 10. Mathews D.G.: Design and Analysis of Experiments, Eighth edition. Arizona State University, 2013.
• 11. Mathews D.G.: Design of Experiments with MINITAB. American Society for Quality, 2005.
• 12. Montgomery, D.C.: Design and Analysis of Experiments, Eighth edition.Arizona State University, 2013.
• 13. Polski Rejestr Statków, Standardy budowy i napraw statków. Publikacja informacyjna nr 16/I, Gdańsk 2011.
• 14. Saltelli A., Ratto M., Andres, et al.: Global Sensitivity Analysis: The Primer. John Wiley & Sons. (2008).
• 15. Stocki R.: Analiza niezawodności i optymalizacja odpornościowa. Instytut Podstawowych Problemów Techniki Polskiej Akademii Nauk, Warszawa 2010
• 16. Viana, F.A.C., Haftka R.T., Steffen V.: Multiple Surrogates: How Cross-Validation Errors Can Help Us to Obtain the Best Predictor. Structural and Multidisciplinary Optimization, Vol. 39, No. 4, 2009, pp. 439-457.

Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).