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Journal articles on the topic 'Structural optimization'

To see the other types of publications on this topic, follow the link: Structural optimization.

Author: Grafiati

Published: 4 June 2021

Last updated: 30 July 2024

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1

S, Nakkeeran. "Structural Optimization of Automotive Chassis." International Journal of Psychosocial Rehabilitation 23, no. 4 (July 20, 2019): 18–23. http://dx.doi.org/10.37200/ijpr/v23i4/pr190155.

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2

Yanev, Bojidar. "Structural optimization." Structure and Infrastructure Engineering 7, no. 6 (June 2011): 453–54. http://dx.doi.org/10.1080/15732479.2010.532634.

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3

SEGUCHI, Yasuyuki. "Structural Optimization." Journal of the Society of Mechanical Engineers 92, no. 847 (1989): 485–91. http://dx.doi.org/10.1299/jsmemag.92.847_485.

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4

A. Mota Soares, Carlos, Martin P. Bendsoe, Kyung K. Choi, and José Herskovits. "Structural optimization." Computers & Structures 86, no. 13-14 (July 2008): 1385. http://dx.doi.org/10.1016/j.compstruc.2007.05.016.

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5

Nowak, M. "Improved aeroelastic design through structural optimization." Bulletin of the Polish Academy of Sciences: Technical Sciences 60, no. 2 (October 1, 2012): 237–40. http://dx.doi.org/10.2478/v10175-012-0031-8.

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Abstract. The paper presents the idea of coupled multiphysics computations. It shows the concept and presents some preliminary results of static coupling of structural and fluid flow codes as well as biomimetic structural optimization. The model for the biomimetic optimization procedure was the biological phenomenon of trabecular bone functional adaptation. Thus, the presented structural bio-inspired optimization system is based on the principle of constant strain energy density on the surface of the structure. When the aeroelastic reactions are considered, such approach allows fulfilling the mechanical theorem for the stiffest design, comprising the optimizations of size, shape and topology of the internal structure of the wing.

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6

Enomoto, Hirohisa, and Shigeru Sakamoto. "Structural Optimization System." Journal of the Acoustical Society of America 129, no. 3 (2011): 1666. http://dx.doi.org/10.1121/1.3573317.

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7

Liang, Qing Quan. "Structural Design Optimization." Advances in Structural Engineering 10, no. 6 (December 2007): i—ii. http://dx.doi.org/10.1260/136943307783571463.

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8

Gutkowski, Witold, Jacek Bauer, and Zdzisław Iwanow. "Discrete structural optimization." Computer Methods in Applied Mechanics and Engineering 51, no. 1-3 (September 1985): 71–78. http://dx.doi.org/10.1016/0045-7825(85)90028-3.

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9

Frangopol, Dan M. "Probabilistic structural optimization." Progress in Structural Engineering and Materials 1, no. 2 (January 1998): 223–30. http://dx.doi.org/10.1002/pse.2260010216.

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10

Marti, Kurt. "Structural reliability and stochastic structural optimization." Mathematical Methods of Operations Research 46, no. 3 (October 1997): 285–86. http://dx.doi.org/10.1007/bf01194857.

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11

Han, Seog Young, Min Sue Kim, Sang Rak Kim, Won Goo Lee, Jin Shik Yu, and Jae Yong Park. "P-01 Topology Optimization of a PCB Substrate Based on Evolutionary Structural Optimization." Abstracts of ATEM : International Conference on Advanced Technology in Experimental Mechanics : Asian Conference on Experimental Mechanics 2007.6 (2007): _P—01–1_—_P—01–6_. http://dx.doi.org/10.1299/jsmeatem.2007.6._p-01-1_.

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12

Chen, Lu Yun. "Structural-Acoustic Topology Analysis Based on Evolutionary Structural Optimization." Applied Mechanics and Materials 575 (June 2014): 343–49. http://dx.doi.org/10.4028/www.scientific.net/amm.575.343.

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Application of the evolutionary structural optimization (ESO) approach for structural acoustic optimization is investigated. Combining the sizing optimization, a more appropriate rejection criterion for ESO was put forward. There is a trial of applying modified evolutionary structural optimization (MESO) method in dynamic response problem. Finally, the topology optimization of plate structure under harmonic loading as example, the MESO approach is conducted. The numerical results show that the MESO is feasible in topology optimization analysis; and it expands the application of traditional ESO theory.

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13

Rojas-Labanda, Susana, and Mathias Stolpe. "Benchmarking optimization solvers for structural topology optimization." Structural and Multidisciplinary Optimization 52, no. 3 (May 17, 2015): 527–47. http://dx.doi.org/10.1007/s00158-015-1250-z.

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14

AMIR, Hossain M., and Takashi HASEGAWA. "Nonlinear discrete structural optimization." Doboku Gakkai Ronbunshu, no. 392 (1988): 61–71. http://dx.doi.org/10.2208/jscej.1988.392_61.

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15

Ohtsubo, Hideomi, and Masaru Fukumura. "Reliability-Based Structural Optimization." Journal of the Society of Naval Architects of Japan 1991, no. 170 (1991): 493–501. http://dx.doi.org/10.2534/jjasnaoe1968.1991.170_493.

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16

Akin, J. E., and Javier Arjona‐Baez. "Enhancing structural topology optimization." Engineering Computations 18, no. 3/4 (May 2001): 663–75. http://dx.doi.org/10.1108/02644400110387640.

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17

Cohn, M. Z., and A. S. Dinovitzer. "Application of Structural Optimization." Journal of Structural Engineering 120, no. 2 (February 1994): 617–50. http://dx.doi.org/10.1061/(asce)0733-9445(1994)120:2(617).

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18

HOIT, MARC, ALFREDO SOEIRO, and FERNANDO FAGUNDO. "INTEGRATED STRUCTURAL SIZING OPTIMIZATION." Engineering Optimization 12, no. 3 (October 1987): 207–18. http://dx.doi.org/10.1080/03052158708941095.

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19

Petiau, C. "Structural optimization of aircraft." Thin-Walled Structures 11, no. 1-2 (January 1991): 43–64. http://dx.doi.org/10.1016/0263-8231(91)90010-g.

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20

Liu, Xia, Wei-Jian Yi, Q. S. Li, and Pu-Sheng Shen. "Genetic evolutionary structural optimization." Journal of Constructional Steel Research 64, no. 3 (March 2008): 305–11. http://dx.doi.org/10.1016/j.jcsr.2007.08.002.

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21

Hernández, Santiago. "Advances in structural optimization." Advances in Engineering Software 41, no. 7-8 (July 2010): 909. http://dx.doi.org/10.1016/j.advengsoft.2010.07.005.

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22

Milman, M., M. Salama, R. E. Scheid, R. Bruno, and J. S. Gibson. "Combined control-structural optimization." Computational Mechanics 8, no. 1 (1991): 1–18. http://dx.doi.org/10.1007/bf00370544.

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23

Levy, Robert. "Review of Structural Optimization." IES Journal Part A: Civil & Structural Engineering 4, no. 1 (February 2011): 53–54. http://dx.doi.org/10.1080/19373260.2011.539073.

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24

Wall, Wolfgang A., Moritz A. Frenzel, and Christian Cyron. "Isogeometric structural shape optimization." Computer Methods in Applied Mechanics and Engineering 197, no. 33-40 (June 2008): 2976–88. http://dx.doi.org/10.1016/j.cma.2008.01.025.

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25

Patnaik, S. N., J. D. Guptill, and L. Berke. "Singularity in structural optimization." International Journal for Numerical Methods in Engineering 36, no. 6 (March 30, 1993): 931–44. http://dx.doi.org/10.1002/nme.1620360604.

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26

Murphy, Ryan, Chikwesiri Imediegwu, Robert Hewson, and Matthew Santer. "Multiscale structural optimization with concurrent coupling between scales." Structural and Multidisciplinary Optimization 63, no. 4 (January 8, 2021): 1721–41. http://dx.doi.org/10.1007/s00158-020-02773-3.

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AbstractA robust three-dimensional multiscale structural optimization framework with concurrent coupling between scales is presented. Concurrent coupling ensures that only the microscale data required to evaluate the macroscale model during each iteration of optimization is collected and results in considerable computational savings. This represents the principal novelty of this framework and permits a previously intractable number of design variables to be used in the parametrization of the microscale geometry, which in turn enables accessibility to a greater range of extremal point properties during optimization. Additionally, the microscale data collected during optimization is stored in a reusable database, further reducing the computational expense of optimization. Application of this methodology enables structures with precise functionally graded mechanical properties over two scales to be derived, which satisfy one or multiple functional objectives. Two classical compliance minimization problems are solved within this paper and benchmarked against a Solid Isotropic Material with Penalization (SIMP)–based topology optimization. Only a small fraction of the microstructure database is required to derive the optimized multiscale solutions, which demonstrates a significant reduction in the computational expense of optimization in comparison to contemporary sequential frameworks. In addition, both cases demonstrate a significant reduction in the compliance functional in comparison to the equivalent SIMP-based optimizations.

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27

Babich, S. V., and V. О. Davydov. "Objective function for municipal heat supply systems’ structural optimization." Odes’kyi Politechnichnyi Universytet. Pratsi, no. 1 (March 31, 2015): 134–40. http://dx.doi.org/10.15276/opu.1.45.2015.22.

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28

Poitras, Gérard Jacques, Gabriel Cormier, and Armel Stanislas Nabolle. "Peloton Dynamics Optimization: Algorithm for Discrete Structural Optimization." Journal of Structural Engineering 147, no. 10 (October 2021): 04021164. http://dx.doi.org/10.1061/(asce)st.1943-541x.0003113.

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29

Hu, Xing Guo, and He Ming Cheng. "Truss Optimization Based on the Evolutionary Structural Optimization." Advanced Materials Research 915-916 (April 2014): 281–84. http://dx.doi.org/10.4028/www.scientific.net/amr.915-916.281.

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The Evolutionary Structural Optimization (ESO) as an important structural topology optimization method has been widely used in many fields of engineering optimization. However, due to some technical constraints, the use of ESO for the truss optimization is relatively less. A method for truss optimization that combines the ESO method and the Stress Ratio method is proposed in this paper. This method solves the problems of ESO for truss optimization that the sectional area of bars cannot be changed and the speed of optimization cannot be easily controlled. It can be widely used in truss optimization and can get the same good result as other methods (such as GA and SA, etc.). Furthermore, the method proposed in this paper has the advantage that it can be easily programmed in the commercial software (such as Ansys and Abaqus, etc.) owing to its relatively simple optimization principle.

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30

Luh, Guan-Chun, and Chun-Yi Lin. "Structural topology optimization using ant colony optimization algorithm." Applied Soft Computing 9, no. 4 (September 2009): 1343–53. http://dx.doi.org/10.1016/j.asoc.2009.06.001.

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31

Dede, Tayfun, and Yusuf Ayvaz. "Structural optimization with teaching-learning-based optimization algorithm." Structural Engineering and Mechanics 47, no. 4 (August 25, 2013): 495–511. http://dx.doi.org/10.12989/sem.2013.47.4.495.

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32

Huang, X., Y. M. Xie, and M. C. Burry. "Advantages of Bi-Directional Evolutionary Structural Optimization (BESO) over Evolutionary Structural Optimization (ESO)." Advances in Structural Engineering 10, no. 6 (December 2007): 727–37. http://dx.doi.org/10.1260/136943307783571436.

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33

Xie, Hua Long, Hui Min Guo, Qing Bao Wang, and Yong Xian Liu. "The Spindle Structural Optimization Design of HTC3250µn NC Machine Tool Based on ANSYS." Advanced Materials Research 457-458 (January 2012): 60–64. http://dx.doi.org/10.4028/www.scientific.net/amr.457-458.60.

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The optimization of spindle has important significance. The optimization method based on ANSYS is introduced and spindle mathematical mode of HTC3250µn NC machine tool is given. By scanning of design variables, the main optimized design variables are determined. The single objective and multi-objective optimizations are done. In the end, the main size comparison of spindle before and after optimization is given.

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34

Lewiński, T., S. Czarnecki, G. Dzierżanowski, and T. Sokół. "Topology optimization in structural mechanics." Bulletin of the Polish Academy of Sciences: Technical Sciences 61, no. 1 (March 1, 2013): 23–37. http://dx.doi.org/10.2478/bpasts-2013-0002.

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Abstract Optimization of structural topology, called briefly: topology optimization, is a relatively new branch of structural optimization. Its aim is to create optimal structures, instead of correcting the dimensions or changing the shapes of initial designs. For being able to create the structure, one should have a possibility to handle the members of zero stiffness or admit the material of singular constitutive properties, i.e. void. In the present paper, four fundamental problems of topology optimization are discussed: Michell’s structures, two-material layout problem in light of the relaxation by homogenization theory, optimal shape design and the free material design. Their features are disclosed by presenting results for selected problems concerning the same feasible domain, boundary conditions and applied loading. This discussion provides a short introduction into current topics of topology optimization

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35

Sysoeva, V. V., and V. V. Chedrik. "ALGORITHMS FOR STRUCTURAL TOPOLOGY OPTIMIZATION." TsAGI Science Journal 42, no. 2 (2011): 259–74. http://dx.doi.org/10.1615/tsagiscij.v42.i2.90.

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36

Yushkova, Ekaterina, Vladimir Lebedev, Pavel Yakovlev, and Maria Akmanova. "Exergy pinch analysis structural optimization." Energy Safety and Energy Economy 5 (November 2020): 37–41. http://dx.doi.org/10.18635/2071-2219-2020-5-37-41.

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Structural optimization is an important tool in the power system design process. This paper contains general principles of structural optimization in thermal power and an algorithm of developing interconnection between heat exchangers. As an example, pinch and exergy analysis for energy efficient design of a crude oil refinery facility was performed. The pinch and exergy analysis counts qualitative and quantitative parameters of thermal processes. This method showed a lack of energy efficiency in a given example and losses of exergy which can be potentially utilized in a manufacturing process.

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37

Wang, Yi Yang, and Chen Chen Wang. "Structural Optimization of Elevated Building." Applied Mechanics and Materials 351-352 (August 2013): 1460–68. http://dx.doi.org/10.4028/www.scientific.net/amm.351-352.1460.

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This paper finds that the elevated building normally located in Chongqings mountainous areas can be optimized in both integral structure and individual elements. Through the calculation of typical structure, the weaknesses of elevated building are the unevenness of the internal force caused by external loads and the over-length of supporting bars. Meanwhile, through the way of numerical modeling which uses the features of two-dimensional member bar system, specific solutions are pointed out. The conclusion can be drawn through the analysis of results and data that the more direct the method of transferring force is, the more stable the structure is. In addition, within the experience of practical engineering programs, strengths and weaknesses are also put forward through comparing measures within themselves, which can enhance the ability to resist the impulsive or extreme external force.

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38

Sobieszczanski-Sobieski, Jaroslaw, Benjamin B. James, and Augustine R. Dovi. "Structural optimization by multilevel decomposition." AIAA Journal 23, no. 11 (November 1985): 1775–82. http://dx.doi.org/10.2514/3.9165.

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39

Yoshida, Noriaki, and Garret N. Vanderplaats. "Structural optimization using beam elements." AIAA Journal 26, no. 4 (April 1988): 454–62. http://dx.doi.org/10.2514/3.9915.

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40

Grandhi, R. V., and V. B. Venkayya. "Structural optimization with frequency constraints." AIAA Journal 26, no. 7 (July 1988): 858–66. http://dx.doi.org/10.2514/3.9979.

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41

Bennett, J. A., and R. V. Lust. "Conservative methods for structural optimization." AIAA Journal 28, no. 8 (August 1990): 1491–96. http://dx.doi.org/10.2514/3.25243.

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42

Ibabe, Julen, Antero Jokinen, Jari Larkiola, and Gurutze Arruabarrena. "Structural Optimization and Additive Manufacturing." Key Engineering Materials 611-612 (May 2014): 811–17. http://dx.doi.org/10.4028/www.scientific.net/kem.611-612.811.

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Additive Manufacturing technology offers almost unlimited capacity when manufacturing parts with complex geometries which could be impossible to get with conventional manufacturing processes. This paper is based on the study of a particular real part which has been redesigned and manufactured using an AM process. The challenge consists of redesigning the geometry of an originally aluminium made part, in order to get a new stainless steel made model with same mechanical properties but with less weight. The new design is the result of a structural optimization process based on Finite Element simulations which is carried out bearing in mind the facilities that an AM process offers.

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43

Webb, David, Wissam Alobaidi, and Eric Sandgren. "Structural Design via Genetic Optimization." Modern Mechanical Engineering 07, no. 03 (2017): 73–90. http://dx.doi.org/10.4236/mme.2017.73006.

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44

Fonseca, Ijar M., and Peter M. Bainum. "Integrated Structural and Control Optimization." Journal of Vibration and Control 10, no. 10 (October 2004): 1377–91. http://dx.doi.org/10.1177/1077546304042043.

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This paper focuses on the integrated structural/control optimization of a large space structure with a robot arm subject to the gravity-gradient torque through a semi-analytical approach. It is well known that the computer effort to compute numerically derivatives of the constraints with respect to design variables makes the process expensive and time-consuming. In this sense, a semi-analytical approach may represent a good alternative when optimizing systems that require sensitivity calculations with respect to design parameters. In this study, constraints from the structure and control disciplines are imposed on the optimization process with the aim of obtaining the structure’s minimum weight and the optimum control performance. In the process optimization, the sensitivity of the constraints is computed by a semi-analytical approach. This approach combines the use of analytical derivatives of the mass and stiffness matrices with the numerical solution of the eigenvalue problem to obtain the eigenvalue derivative with respect to the design variables. The analytical derivatives are easy to obtain since our space structure is a long one-dimensional beam-like spacecraft.

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45

Tahk, Min-Jea, Youdan Kim, and Changho Nam. "Coevolutionary Approaches to Structural Optimization." AIAA Journal 37, no. 8 (August 1999): 1019–21. http://dx.doi.org/10.2514/2.7568.

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46

Raveh, Daniella E., Yuval Levy, and Moti Karpel. "Structural Optimization Using Computational Aerodynamics." AIAA Journal 38, no. 10 (October 2000): 1974–82. http://dx.doi.org/10.2514/2.853.

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47

KAMIMURA, Masato, Yoshiyuki HATTA, Shuta ITO, Yoshinori KOGA, Toshiaki SAKURAI, and Kunihiro TAKAHASHI. "Structural Optimization and Load Paths." Transactions of the Japan Society of Mechanical Engineers Series A 74, no. 737 (2008): 6–12. http://dx.doi.org/10.1299/kikaia.74.6.

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48

Frangopol, Dan M. "Structural Optimization Using Reliability Concepts." Journal of Structural Engineering 111, no. 11 (November 1985): 2288–301. http://dx.doi.org/10.1061/(asce)0733-9445(1985)111:11(2288).

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49

Levy, Robert, and Ovadia E. Lev. "Recent Developments in Structural Optimization." Journal of Structural Engineering 113, no. 9 (September 1987): 1939–62. http://dx.doi.org/10.1061/(asce)0733-9445(1987)113:9(1939).

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50

Stewart, Mark G., and Robert E. Melchers. "Optimization of Structural Design Checking." Journal of Structural Engineering 115, no. 10 (October 1989): 2448–60. http://dx.doi.org/10.1061/(asce)0733-9445(1989)115:10(2448).

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