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Journal articles on the topic 'Structure interaction'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 January 2025

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1

Kolaki, Aravind I., and Basavaraj M. Gudadappanavar. "Performance Based Analysis of Framed Structure Considering Soil Structure Interaction." Bonfring International Journal of Man Machine Interface 4, Special Issue (July 30, 2016): 106–11. http://dx.doi.org/10.9756/bijmmi.8165.

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2

Toma, Milan, Rosalyn Chan-Akeley, Jonathan Arias, Gregory D. Kurgansky, and Wenbin Mao. "Fluid–Structure Interaction Analyses of Biological Systems Using Smoothed-Particle Hydrodynamics." Biology 10, no. 3 (March 2, 2021): 185. http://dx.doi.org/10.3390/biology10030185.

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Due to the inherent complexity of biological applications that more often than not include fluids and structures interacting together, the development of computational fluid–structure interaction models is necessary to achieve a quantitative understanding of their structure and function in both health and disease. The functions of biological structures usually include their interactions with the surrounding fluids. Hence, we contend that the use of fluid–structure interaction models in computational studies of biological systems is practical, if not necessary. The ultimate goal is to develop computational models to predict human biological processes. These models are meant to guide us through the multitude of possible diseases affecting our organs and lead to more effective methods for disease diagnosis, risk stratification, and therapy. This review paper summarizes computational models that use smoothed-particle hydrodynamics to simulate the fluid–structure interactions in complex biological systems.

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3

Patil, K. S., and Ajit K. Kakade. "Seismic Response of R.C. Structures With Different Steel Bracing Systems Considering Soil - Structure Interaction." Journal of Advances and Scholarly Researches in Allied Education 15, no. 2 (April 1, 2018): 411–13. http://dx.doi.org/10.29070/15/56856.

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4

Lee, Kyoungsoo, Ziaul Huque, Raghava Kommalapati, and Sang-Eul Han. "The Evaluation of Aerodynamic Interaction of Wind Blade Using Fluid Structure Interaction Method." Journal of Clean Energy Technologies 3, no. 4 (2015): 270–75. http://dx.doi.org/10.7763/jocet.2015.v3.207.

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5

Pattanashetti, Prateek, and M. S. Bhandiwad. "Seismic Performance of Regular and Irregular Flat Slab Structure with Soil Structure Interaction." Bonfring International Journal of Man Machine Interface 4, Special Issue (July 30, 2016): 215–19. http://dx.doi.org/10.9756/bijmmi.8186.

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6

Cacciola, Pierfrancesco, Maria Garcia Espinosa, and Alessandro Tombari. "Vibration control of piled-structures through structure-soil-structure-interaction." Soil Dynamics and Earthquake Engineering 77 (October 2015): 47–57. http://dx.doi.org/10.1016/j.soildyn.2015.04.006.

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7

Roy, Christine, Said Bolourchi, and Daniel Eggers. "Significance of structure–soil–structure interaction for closely spaced structures." Nuclear Engineering and Design 295 (December 2015): 680–87. http://dx.doi.org/10.1016/j.nucengdes.2015.07.067.

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8

Masia, Mark J., Peter W. Kleeman, and Robert E. Melchers. "Modeling Soil/Structure Interaction for Masonry Structures." Journal of Structural Engineering 130, no. 4 (April 2004): 641–49. http://dx.doi.org/10.1061/(asce)0733-9445(2004)130:4(641).

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9

Veletsos, A. S., A. M. Prasad, and G. Hahn. "Fluid-structure interaction effects for offshore structures." Earthquake Engineering & Structural Dynamics 16, no. 5 (July 1988): 631–52. http://dx.doi.org/10.1002/eqe.4290160502.

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10

Zhuo, Chen, Chengwei Zeng, Haoquan Liu, Huiwen Wang, Yunhui Peng, and Yunjie Zhao. "Advances and Mechanisms of RNA–Ligand Interaction Predictions." Life 15, no. 1 (January 15, 2025): 104. https://doi.org/10.3390/life15010104.

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The diversity and complexity of RNA include sequence, secondary structure, and tertiary structure characteristics. These elements are crucial for RNA’s specific recognition of other molecules. With advancements in biotechnology, RNA–ligand structures allow researchers to utilize experimental data to uncover the mechanisms of complex interactions. However, determining the structures of these complexes experimentally can be technically challenging and often results in low-resolution data. Many machine learning computational approaches have recently emerged to learn multiscale-level RNA features to predict the interactions. Predicting interactions remains an unexplored area. Therefore, studying RNA–ligand interactions is essential for understanding biological processes. In this review, we analyze the interaction characteristics of RNA–ligand complexes by examining RNA’s sequence, secondary structure, and tertiary structure. Our goal is to clarify how RNA specifically recognizes ligands. Additionally, we systematically discuss advancements in computational methods for predicting interactions and to guide future research directions. We aim to inspire the creation of more reliable RNA–ligand interaction prediction tools.

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11

Xing, Jing Tang. "Fluid-Structure Interaction." Strain 39, no. 4 (November 2003): 186–87. http://dx.doi.org/10.1046/j.0039-2103.2003.00067.x.

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12

Bazilevs, Yuri, Kenji Takizawa, and Tayfun E. Tezduyar. "Fluid–structure interaction." Computational Mechanics 55, no. 6 (May 10, 2015): 1057–58. http://dx.doi.org/10.1007/s00466-015-1162-1.

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13

Gershunov, Eugene M. "Structure-ridge interaction." Cold Regions Science and Technology 14, no. 1 (June 1987): 85–94. http://dx.doi.org/10.1016/0165-232x(87)90046-2.

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14

Henle, Thomas. "Structure – Function – Interaction." Nahrung/Food 47, no. 3 (June 1, 2003): 153. http://dx.doi.org/10.1002/food.200390036.

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15

Yau, Tak-Yu, William Sander, Christian Eidson, and Albert J. Courey. "SUMO Interacting Motifs: Structure and Function." Cells 10, no. 11 (October 21, 2021): 2825. http://dx.doi.org/10.3390/cells10112825.

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Small ubiquitin-related modifier (SUMO) is a member of the ubiquitin-related protein family. SUMO modulates protein function through covalent conjugation to lysine residues in a large number of proteins. Once covalently conjugated to a protein, SUMO often regulates that protein’s function by recruiting other cellular proteins. Recruitment frequently involves a non-covalent interaction between SUMO and a SUMO-interacting motif (SIM) in the interacting protein. SIMs generally consist of a four-residue-long hydrophobic stretch of amino acids with aliphatic non-polar side chains flanked on one side by negatively charged amino acid residues. The SIM assumes an extended β-strand-like conformation and binds to a conserved hydrophobic groove in SUMO. In addition to hydrophobic interactions between the SIM non-polar core and hydrophobic residues in the groove, the negatively charged residues in the SIM make favorable electrostatic contacts with positively charged residues in and around the groove. The SIM/SUMO interaction can be regulated by the phosphorylation of residues adjacent to the SIM hydrophobic core, which provide additional negative charges for favorable electrostatic interaction with SUMO. The SUMO interactome consists of hundreds or perhaps thousands of SIM-containing proteins, but we do not fully understand how each SUMOylated protein selects the set of SIM-containing proteins appropriate to its function. SIM/SUMO interactions have critical functions in a large number of essential cellular processes including the formation of membraneless organelles by liquid–liquid phase separation, epigenetic regulation of transcription through histone modification, DNA repair, and a variety of host–pathogen interactions.

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16

Zhang, Guanyu, Xiang Chen, and Decheng Wan. "MPS-FEM Coupled Method for Study of Wave-Structure Interaction." Journal of Marine Science and Application 18, no. 4 (October 15, 2019): 387–99. http://dx.doi.org/10.1007/s11804-019-00105-6.

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Abstract Nowadays, an increasing number of ships and marine structures are manufactured and inevitably operated in rough sea. As a result, some phenomena related to the violent fluid-elastic structure interactions (e.g., hydrodynamic slamming on marine vessels, tsunami impact on onshore structures, and sloshing in liquid containers) have aroused huge challenges to ocean engineering fields. In this paper, the moving particle semi-implicit (MPS) method and finite element method (FEM) coupled method is proposed for use in numerical investigations of the interaction between a regular wave and a horizontal suspended structure. The fluid domain calculated by the MPS method is dispersed into fluid particles, and the structure domain solved by the FEM method is dispersed into beam elements. The generation of the 2D regular wave is firstly conducted, and convergence verification is performed to determine appropriate particle spacing for the simulation. Next, the regular wave interacting with a rigid structure is initially performed and verified through the comparison with the laboratory experiments. By verification, the MPS-FEM coupled method can be applied to fluid-structure interaction (FSI) problems with waves. On this basis, taking the flexibility of structure into consideration, the elastic dynamic response of the structure subjected to the wave slamming is investigated, including the evolutions of the free surface, the variation of the wave impact pressures, the velocity distribution, and the structural deformation response. By comparison with the rigid case, the effects of the structural flexibility on wave-elastic structure interaction can be obtained.

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17

ZHU, ZHENGWEI, ANDREY TOVCHIGRECHKO, TATIANA BARONOVA, YING GAO, DOMINIQUE DOUGUET, NICHOLAS O'TOOLE, and ILYA A. VAKSER. "LARGE-SCALE STRUCTURAL MODELING OF PROTEIN COMPLEXES AT LOW RESOLUTION." Journal of Bioinformatics and Computational Biology 06, no. 04 (August 2008): 789–810. http://dx.doi.org/10.1142/s0219720008003679.

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Structural aspects of protein–protein interactions provided by large-scale, genome-wide studies are essential for the description of life processes at the molecular level. A methodology is developed that applies the protein docking approach (GRAMM), based on the knowledge of experimentally determined protein–protein structures (DOCKGROUND resource) and properties of intermolecular energy landscapes, to genome-wide systems of protein interactions. The full sequence-to-structure-of-complex modeling pipeline is implemented in the Genome Wide Docking Database (GWIDD) resource. Protein interaction data are imported to GWIDD from external datasets of experimentally determined interaction networks. Essential information is extracted and unified to form the GWIDD database. Structures of individual interacting proteins in the database are retrieved (if available) or modeled, and protein complex structures are predicted by the docking program. All protein sequence, structure, and docking information is conveniently accessible through a Web interface.

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18

Stondus, Jigmat, and Rajni Kant. "CAMBRIDGE STRUCTURE DATABASE ANALYSIS OF MOLECULAR INTERACTION ENERGIES IN BROMINESUBSTITUTED COUMARIN STRUCTURES." RASAYAN Journal of Chemistry 15, no. 02 (2022): 991–1008. http://dx.doi.org/10.31788/rjc.2022.1526853.

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Although the non-covalent interactions such as hydrogen bonds and Van der Waals bonds are considered as weak but have a significant impact on the characteristics of the molecule in solution and the crystalline phase. The nature and strength of such intermolecular interactions result in various physicochemical and biological properties in crystal structures. In the present study, a quantitative analysis of intermolecular interaction in the crystal packing of some bromine substituted coumarin derivatives has been undertaken for lattice energy and intermolecular interaction energies analyses using a computational approach. The analysis shows that the energy contribution of halogen bonds such as C-Br…O and C-Br…π is quite significant in the crystal structures of bromine substituted coumarins. Besides, the C-H…O, C-H…Br and π…π interactions are also found to have a profound effect on the molecular packing of these structures. Molecular interactions with reference to the packing mechanism in each molecule are studied in detail. It is expected that empirical analysis of molecular energy interactions will help in understanding the role of various structural motifs in crystal packing

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19

Souza Amado de Carvalho, Rodolpho, Md Shamiul Islam Rasel, Nitesh K. Khandelwal, and Thomas M. Tomasiak. "Cryo-EM reveals a phosphorylated R-domain envelops the NBD1 catalytic domain in an ABC transporter." Life Science Alliance 7, no. 11 (August 29, 2024): e202402779. http://dx.doi.org/10.26508/lsa.202402779.

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Many ATP-binding cassette transporters are regulated by phosphorylation on long and disordered loops which presents a challenge to visualize with structural methods. We have trapped an activated state of the regulatory domain (R-domain) of yeast cadmium factor 1 (Ycf1) by enzymatically enriching the phosphorylated state. A 3.23 Å cryo-EM structure reveals an R-domain structure with four phosphorylated residues and the position for the entire R-domain. The structure reveals key R-domain interactions including a bridging interaction between NBD1 and NBD2 and an interaction with the R-insertion, another regulatory region. We scanned these interactions by systematically replacing segments along the entire R-domain with scrambled combinations of alanine, glycine, and glutamine and probing function under cellular conditions that require the Ycf1 function. We find a close match with these interactions and interacting regions on our R-domain structure that points to the importance of most well-structured segments for function. We propose a model where the R-domain stabilizes a transport-competent state upon phosphorylation by enveloping NBD1 entirely.

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20

Kang, Xiaofang, Zongqin Wu, Jian Wu, Qiwen Huang, Boyang Ou, and Shancheng Lei. "Design and Parameter Optimization of the Soil-Structure Interaction on Structures with Electromagnetic Damper." Buildings 13, no. 7 (June 28, 2023): 1655. http://dx.doi.org/10.3390/buildings13071655.

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Currently, the application of electromagnetic dampers in structural vibration control and energy harvesting has become increasingly widespread. The optimization research of electromagnetic dampers in building design has also received more attention. Previous studies on vibration control of building structures with electromagnetic dampers have been conducted under fixed foundations, neglecting the effect of soil-structure interaction on building structures with electromagnetic dampers. The main contribution of this paper is to fill the research gap in the study of building structural vibration control with electromagnetic dampers considering soil-structure interaction. An effective design and parameter optimization method for building structures with both soil-structure interaction and electromagnetic energy harvesting is explored. The soil-structure interaction is taken into account, and the building model with electromagnetic dampers is improved to form a coupled vibration reduction system with both structural vibration control and energy harvesting functions. The dynamic equations of the system with both structural vibration control and energy harvesting are derived and then optimized using the H2 norm criterion and Monte Carlo-mode search method. A single-layer building structure is used as an example to study the influence of soil-structure interaction on building structures equipped with electromagnetic dampers under strong earthquake action. The dynamic response and energy harvesting of building structures under earthquake action considering soil-structure interaction are analyzed and evaluated. The results show that the influence of soil-structure interaction on building structures equipped with electromagnetic dampers needs to be considered. As the soil density decreases, the dynamic response of the building structure under earthquake action becomes larger using the electromagnetic damper system. Compared to the use of fixed foundations, the energy harvesting effect of building structures with electromagnetic dampers is weakened when considering soil-structure interactions.

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21

Lou, Menglin, Huaifeng Wang, Xi Chen, and Yongmei Zhai. "Structure–soil–structure interaction: Literature review." Soil Dynamics and Earthquake Engineering 31, no. 12 (December 2011): 1724–31. http://dx.doi.org/10.1016/j.soildyn.2011.07.008.

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22

Knappett, J. A., P. Madden, and K. Caucis. "Seismic structure–soil–structure interaction between pairs of adjacent building structures." Géotechnique 65, no. 5 (May 2015): 429–41. http://dx.doi.org/10.1680/geot.sip.14.p.059.

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23

Ollerton, Jeff, Duncan McCollin, Daphne G. Fautin, and Gerald R. Allen. "Finding NEMO: nestedness engendered by mutualistic organization in anemonefish and their hosts." Proceedings of the Royal Society B: Biological Sciences 274, no. 1609 (November 29, 2006): 591–98. http://dx.doi.org/10.1098/rspb.2006.3758.

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The interaction structure of mutualistic relationships, in terms of relative specialization of the partners, is important to understanding their ecology and evolution. Analyses of the mutualistic interaction between anemonefish and their host sea anemones show that the relationship is highly nested in structure, generalist species interacting with one another and specialist species interacting mainly with generalists. This supports the hypothesis that the configuration of mutualistic interactions will tend towards nestedness. In this case, the structure of the interaction is at a much larger scale than previously hypothesized, across more than 180° of longitude and some 60° of latitude, probably owing to the pelagic dispersal capabilities of these species in a marine environment. Additionally, we found weak support for the hypothesis that geographically widespread species should be more generalized in their interactions than species with small ranges. This study extends understanding of the structure of mutualistic relationships into previously unexplored taxonomic and physical realms, and suggests how nestedness analysis can be applied to the conservation of obligate species interactions.

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24

Griffith, Boyce E., and Neelesh A. Patankar. "Immersed Methods for Fluid–Structure Interaction." Annual Review of Fluid Mechanics 52, no. 1 (January 5, 2020): 421–48. http://dx.doi.org/10.1146/annurev-fluid-010719-060228.

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Fluid–structure interaction is ubiquitous in nature and occurs at all biological scales. Immersed methods provide mathematical and computational frameworks for modeling fluid–structure systems. These methods, which typically use an Eulerian description of the fluid and a Lagrangian description of the structure, can treat thin immersed boundaries and volumetric bodies, and they can model structures that are flexible or rigid or that move with prescribed deformational kinematics. Immersed formulations do not require body-fitted discretizations and thereby avoid the frequent grid regeneration that can otherwise be required for models involving large deformations and displacements. This article reviews immersed methods for both elastic structures and structures with prescribed kinematics. It considers formulations using integral operators to connect the Eulerian and Lagrangian frames and methods that directly apply jump conditions along fluid–structure interfaces. Benchmark problems demonstrate the effectiveness of these methods, and selected applications at Reynolds numbers up to approximately 20,000 highlight their impact in biological and biomedical modeling and simulation.

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25

Sauer, Nils Christian, and Simone Kauffeld. "The Structure of Interaction at Meetings: A Social Network Analysis." Zeitschrift für Arbeits- und Organisationspsychologie A&O 60, no. 1 (January 2016): 33–49. http://dx.doi.org/10.1026/0932-4089/a000201.

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Abstract. Which factors contribute to effective meetings? The interaction among participants plays a key role. Interaction is a relational, interdependent process that constitutes social structure. Applying a network perspective to meeting interactions allows us to take account of the social structure. The aim of this study was to use social network analysis to distinguish functional and dysfunctional interaction structures and gain insight into the facilitation of meetings by analyzing antecedents and consequences of functional interaction structures. Data were based on a field study in which 51 regular meetings were videotaped and coded with act4teams. Analyses revealed that compared with dysfunctional networks, functional interaction is less centralized and has a positive effect on team performance. Social similarity has a crucial effect on functional interaction because participants significantly interact with others who are similar in personal initiative and self-efficacy. Our results provide important information about how to assist the interaction process and promote team success.

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26

Taneda, Akito, and Kengo Sato. "A Web Server for Designing Molecular Switches Composed of Two Interacting RNAs." International Journal of Molecular Sciences 22, no. 5 (March 8, 2021): 2720. http://dx.doi.org/10.3390/ijms22052720.

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The programmability of RNA–RNA interactions through intermolecular base-pairing has been successfully exploited to design a variety of RNA devices that artificially regulate gene expression. An in silico design for interacting structured RNA sequences that satisfies multiple design criteria becomes a complex multi-objective problem. Although multi-objective optimization is a powerful technique that explores a vast solution space without empirical weights between design objectives, to date, no web service for multi-objective design of RNA switches that utilizes RNA–RNA interaction has been proposed. We developed a web server, which is based on a multi-objective design algorithm called MODENA, to design two interacting RNAs that form a complex in silico. By predicting the secondary structures with RactIP during the design process, we can design RNAs that form a joint secondary structure with an external pseudoknot. The energy barrier upon the complex formation is modeled by an interaction seed that is optimized in the design algorithm. We benchmarked the RNA switch design approaches (MODENA+RactIP and MODENA+RNAcofold) for the target structures based on natural RNA-RNA interactions. As a result, MODENA+RactIP showed high design performance for the benchmark datasets.

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27

El Naggar, Hany. "Special Issue — Soil–structure interaction of buried structures." Canadian Journal of Civil Engineering 48, no. 2 (February 2021): v—vi. http://dx.doi.org/10.1139/cjce-2020-0825.

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28

KONDO, Hirofumi, Setsuo YAMAMOTO, and Yukio SASAKI. "Fluid-structure interaction analysis program for axisymmetric structures." JSME international journal. Ser. 3, Vibration, control engineering, engineering for industry 33, no. 3 (1990): 315–22. http://dx.doi.org/10.1299/jsmec1988.33.315.

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29

Sadek, Marwan, Fadi Hage Chehade, Bassem Ali, and Ahmed Arab. "Seismic Soil Structure interaction for Shear wall structures." MATEC Web of Conferences 281 (2019): 02006. http://dx.doi.org/10.1051/matecconf/201928102006.

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For soft soil subjected to earthquake loading, the soil non linearity could significantly amplify the ground motion. This paper presents a 3D numerical study on the influence of soil non linearity on the seismic soil structure interaction for shear wall structures. Numerical simulations are conducted for both elastic and elastoplastic behaviour for the soil. Real ground motions records are used in the study. The analysis is focused on the seismic induced response of the soil and the structure in terms of displacement and velocity. The results show that considering elastic model for the soil behaviour is not sufficient and could significantly affect the seismic induced response of the system.

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Jarernprasert, Sittipong, Enrique Bazan-Zurita, and Jacobo Bielak. "Seismic soil-structure interaction response of inelastic structures." Soil Dynamics and Earthquake Engineering 47 (April 2013): 132–43. http://dx.doi.org/10.1016/j.soildyn.2012.08.008.

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31

Glück, M., M. Breuer, F. Durst, A. Halfmann, and E. Rank. "Computation of fluid–structure interaction on lightweight structures." Journal of Wind Engineering and Industrial Aerodynamics 89, no. 14-15 (December 2001): 1351–68. http://dx.doi.org/10.1016/s0167-6105(01)00150-7.

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32

Wu, Yue, Xiaoying Sun, and Shizhao Shen. "Computation of wind–structure interaction on tension structures." Journal of Wind Engineering and Industrial Aerodynamics 96, no. 10-11 (October 2008): 2019–32. http://dx.doi.org/10.1016/j.jweia.2008.02.043.

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33

Gomez, Daniel, Shirley J. Dyke, and Shirley Rietdyk. "Structured uncertainty for a pedestrian-structure interaction model." Journal of Sound and Vibration 474 (May 2020): 115237. http://dx.doi.org/10.1016/j.jsv.2020.115237.

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34

Zhang, X. Z., F. Y. Cheng, and M. L. Lou. "Intelligent hybrid controlled structures with soil-structure interaction." Structural Engineering and Mechanics 17, no. 3_4 (March 25, 2004): 573–91. http://dx.doi.org/10.12989/sem.2004.17.3_4.573.

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35

Wang, Xiaolin, Ken Kamrin, and Chris H. Rycroft. "An incompressible Eulerian method for fluid–structure interaction with mixed soft and rigid solids." Physics of Fluids 34, no. 3 (March 2022): 033604. http://dx.doi.org/10.1063/5.0082233.

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We present a general simulation approach for incompressible fluid–structure interactions in a fully Eulerian framework using the reference map technique. The approach is suitable for modeling one or more rigid or finitely deformable objects or soft objects with rigid components interacting with the fluid and with each other. It is also extended to control the kinematics of structures in fluids. The model is based on our previous Eulerian fluid–soft solver [Rycroft et al., “Reference map technique for incompressible fluid–structure interaction,” J. Fluid Mech. 898, A9 (2020)] and generalized to rigid structures by constraining the deformation-rate tensor in a projection framework. Several numerical examples are presented to illustrate the capability of the method.

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36

April-LeQuéré, Philippe, Ioan Nistor, and Abdolmajid Mohammadian. "SCOUR AMPLIFICATION CAUSED BY STRUCTURE PROXIMITY IN EXTREME FLOWS." Coastal Engineering Proceedings, no. 37 (September 1, 2023): 11. http://dx.doi.org/10.9753/icce.v37.structures.11.

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Forensic engineering field surveys of recent tsunamis (Saatcioglu et al. 2005, Chock et al. 2013) highlighted the importance of scour-related damage to structures located in coastal communities. To date, only a limited number of studies have investigated the interaction of extreme hydrodynamic flows and groups of structures, and none have studied the scour around multiple structures interacting with each other. One field example discussed by Yeh et al. (2013) documented flow concentration in between two tsunami-resistant buildings, leading to a deep scour hole between them and infrastructure failures onshore of the gap between the two buildings. This field example shows that multiple buildings, often crammed, lead to complex flow-structure interactions, leading to flow and scour either amplification or reduction depending on the relative position of the buildings. Nouri et al. (2010) and Thomas et al. (2015) investigated the flow velocity amplification caused by structures proximity, which concentrated the flow onto a downstream monitored structure. Their results informed the ASCE7 Ch.6 “Tsunami Loads and Effect” standard on flow velocity amplification caused by nearby structures. However, in this standard, there is currently no link between flow velocity amplification factors and their effects on scour around structures.

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37

Zhou, Xiang Yang, and Qi Lin Zhang. "Numerical Simulation of Fluid-Structure Interaction for Tension Membrane Structures." Advanced Materials Research 457-458 (January 2012): 1062–65. http://dx.doi.org/10.4028/www.scientific.net/amr.457-458.1062.

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Comprehensive studies on effect of fluid-structure interaction and dynamic response for tension structure were conducted by the numerical simulation. An iterative coupling approach for time-dependent fluid-structure interactions is applied to tension membranous structures with large displacements. The coupling method connects a flow-condition-based interpolation element for incompressible fluids with a finite element for geometrically nonlinear problems. A membranous roof with saddle shape exposed to fluctuating wind field at atmosphere boundary layer was investigated for the coupling algorithm. The dynamic response and the fluctuating pressure on member structure were calculated according to the coupling configuration.

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38

Wang, Huai-feng, Meng-lin Lou, Xi Chen, and Yong-mei Zhai. "Structure–soil–structure interaction between underground structure and ground structure." Soil Dynamics and Earthquake Engineering 54 (November 2013): 31–38. http://dx.doi.org/10.1016/j.soildyn.2013.07.015.

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39

Hou, Gene, Jin Wang, and Anita Layton. "Numerical Methods for Fluid-Structure Interaction — A Review." Communications in Computational Physics 12, no. 2 (August 2012): 337–77. http://dx.doi.org/10.4208/cicp.291210.290411s.

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AbstractThe interactions between incompressible fluid flows and immersed structures are nonlinear multi-physics phenomena that have applications to a wide range of scientific and engineering disciplines. In this article, we review representative numerical methods based on conforming and non-conforming meshes that are currently available for computing fluid-structure interaction problems, with an emphasis on some of the recent developments in the field. A goal is to categorize the selected methods and assess their accuracy and efficiency. We discuss challenges faced by researchers in this field, and we emphasize the importance of interdisciplinary effort for advancing the study in fluid-structure interactions.

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40

Ortiz, Jose L., and Alan A. Barhorst. "Modeling Fluid-Structure Interaction." Journal of Guidance, Control, and Dynamics 20, no. 6 (November 1997): 1221–28. http://dx.doi.org/10.2514/2.4180.

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41

Ko, Sung H. "Structure–fluid interaction problems." Journal of the Acoustical Society of America 88, no. 1 (July 1990): 367. http://dx.doi.org/10.1121/1.399912.

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42

de Nooy, Wouter. "Structure from interaction events." Big Data & Society 2, no. 2 (December 2015): 205395171560373. http://dx.doi.org/10.1177/2053951715603732.

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43

Huerta, A., and W. K. Liu. "Viscous Flow Structure Interaction." Journal of Pressure Vessel Technology 110, no. 1 (February 1, 1988): 15–21. http://dx.doi.org/10.1115/1.3265561.

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Considerable research activities in vibration and seismic analysis for various fluid-structure systems have been carried out in the past two decades. Most of the approaches are formulated within the framework of finite elements, and the majority of work deals with inviscid fluids. However, there has been little work done in the area of fluid-structure interaction problems accounting for flow separation and nonlinear phenomenon of steady streaming. In this paper, the Arbitrary Lagrangian Eulerian (ALE) finite element method is extended to address the flow separation and nonlinear phenomenon of steady streaming for arbitrarily shaped bodies undergoing large periodic motion in a viscous fluid. The results are designed to evaluate the fluid force acting on the body; thus, the coupled rigid body-viscous flow problem can be simplified to a standard structural problem using the concept of added mass and added damping. Formulas for these two constants are given for the particular case of a cylinder immersed in an infinite viscous fluid. The finite element modeling is based on a pressure-velocity mixed formulation and a streamline upwind Petrov/Galerkin technique. All computations are performed using a personal computer.

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Pécol, Philippe, Stefano Dal Pont, Silvano Erlicher, and Pierre Argoul. "Modelling crowd-structure interaction." Mécanique & Industries 11, no. 6 (November 2010): 495–504. http://dx.doi.org/10.1051/meca/2010057.

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Gershunov, Eugene M. "Structure-rubble field interaction." Cold Regions Science and Technology 14, no. 1 (June 1987): 95–103. http://dx.doi.org/10.1016/0165-232x(87)90047-4.

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Hara, Kodai, Masayuki Uchida, Risa Tagata, Hideshi Yokoyama, Yoshinobu Ishikawa, Asami Hishiki, and Hiroshi Hashimoto. "Structure of proliferating cell nuclear antigen (PCNA) bound to an APIM peptide reveals the universality of PCNA interaction." Acta Crystallographica Section F Structural Biology Communications 74, no. 4 (March 22, 2018): 214–21. http://dx.doi.org/10.1107/s2053230x18003242.

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Proliferating cell nuclear antigen (PCNA) provides a molecular platform for numerous protein–protein interactions in DNA metabolism. A large number of proteins associated with PCNA have a well characterized sequence termed the PCNA-interacting protein box motif (PIPM). Another PCNA-interacting sequence termed the AlkB homologue 2 PCNA-interacting motif (APIM), comprising the five consensus residues (K/R)-(F/Y/W)-(L/I/V/A)-(L/I/V/A)-(K/R), has also been identified in various proteins. In contrast to that with PIPM, the PCNA–APIM interaction is less well understood. Here, the crystal structure of PCNA bound to a peptide carrying an APIM consensus sequence, RFLVK, was determined and structure-based interaction analysis was performed. The APIM peptide binds to the PIPM-binding pocket on PCNA in a similar way to PIPM. The phenylalanine and leucine residues within the APIM consensus sequence and a hydrophobic residue that precedes the APIM consensus sequence are crucially involved in interactions with the hydrophobic pocket of PCNA. This interaction is essential for overall binding. These results provide a structural basis for regulation of the PCNA interaction and might aid in the development of specific inhibitors of this interaction.

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Su, Qi, Aming Li, Long Wang, and H. Eugene Stanley. "Spatial reciprocity in the evolution of cooperation." Proceedings of the Royal Society B: Biological Sciences 286, no. 1900 (April 3, 2019): 20190041. http://dx.doi.org/10.1098/rspb.2019.0041.

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Cooperation is key to the survival of all biological systems. The spatial structure of a system constrains who interacts with whom (interaction partner) and who acquires new traits from whom (role model). Understanding when and to what degree a spatial structure affects the evolution of cooperation is an important and challenging topic. Here, we provide an analytical formula to predict when natural selection favours cooperation where the effects of a spatial structure are described by a single parameter. We find that a spatial structure promotes cooperation (spatial reciprocity) when interaction partners overlap role models. When they do not, spatial structure inhibits cooperation even without cooperation dilemmas. Furthermore, a spatial structure in which individuals interact with their role models more often shows stronger reciprocity. Thus, imitating individuals with frequent interactions facilitates cooperation. Our findings are applicable to both pairwise and group interactions and show that strong social ties might hinder, while asymmetric spatial structures for interaction and trait dispersal could promote cooperation.

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Mészáros, Bálint, László Dobson, Erzsébet Fichó, and István Simon. "Sequence and Structure Properties Uncover the Natural Classification of Protein Complexes Formed by Intrinsically Disordered Proteins via Mutual Synergistic Folding." International Journal of Molecular Sciences 20, no. 21 (November 1, 2019): 5460. http://dx.doi.org/10.3390/ijms20215460.

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Intrinsically disordered proteins mediate crucial biological functions through their interactions with other proteins. Mutual synergistic folding (MSF) occurs when all interacting proteins are disordered, folding into a stable structure in the course of the complex formation. In these cases, the folding and binding processes occur in parallel, lending the resulting structures uniquely heterogeneous features. Currently there are no dedicated classification approaches that take into account the particular biological and biophysical properties of MSF complexes. Here, we present a scalable clustering-based classification scheme, built on redundancy-filtered features that describe the sequence and structure properties of the complexes and the role of the interaction, which is directly responsible for structure formation. Using this approach, we define six major types of MSF complexes, corresponding to biologically meaningful groups. Hence, the presented method also shows that differences in binding strength, subcellular localization, and regulation are encoded in the sequence and structural properties of proteins. While current protein structure classification methods can also handle complex structures, we show that the developed scheme is fundamentally different, and since it takes into account defining features of MSF complexes, it serves as a better representation of structures arising through this specific interaction mode.

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Madani, B., F. Behnamfar, and H. Tajmir Riahi. "Dynamic response of structures subjected to pounding and structure–soil–structure interaction." Soil Dynamics and Earthquake Engineering 78 (November 2015): 46–60. http://dx.doi.org/10.1016/j.soildyn.2015.07.002.

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Jabary, R. N., and S. P. G. Madabhushi. "Structure-soil-structure interaction effects on structures retrofitted with tuned mass dampers." Soil Dynamics and Earthquake Engineering 100 (September 2017): 301–15. http://dx.doi.org/10.1016/j.soildyn.2017.05.017.

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