Loading…

An improved response function based stochastic meshless method for problems in elasto-statics

The current study proposes an improved response function (IRF) based stochastic element free Galerkin method (SEFGM) for the analysis of problems in elasto-statics, wherein Young’s modulus is modelled as a homogeneous random field with symmetric distribution characteristics. The proposed SEFGM appro...

Full description

Saved in:
Bibliographic Details
Published in:Computer methods in applied mechanics and engineering 2020-12, Vol.372, p.113419, Article 113419
Main Authors: Aswathy, M, Arun, C O
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c325t-6f547157b5a442b50568152e75ef2355bea64e54e52601cf5c26cb3eb95dd2a3
cites cdi_FETCH-LOGICAL-c325t-6f547157b5a442b50568152e75ef2355bea64e54e52601cf5c26cb3eb95dd2a3
container_end_page
container_issue
container_start_page 113419
container_title Computer methods in applied mechanics and engineering
container_volume 372
creator Aswathy, M
Arun, C O
description The current study proposes an improved response function (IRF) based stochastic element free Galerkin method (SEFGM) for the analysis of problems in elasto-statics, wherein Young’s modulus is modelled as a homogeneous random field with symmetric distribution characteristics. The proposed SEFGM approximates displacement as the sum of a deterministic part and a stochastic part. The stochastic part is modelled with the help of an IRF, which is a function of discretized set of random variables. Moving least square shape functions are employed to discretize the random field. Utilizing Taylor series expansions of stiffness matrix and force vector and IRF approximation of displacement, explicit expressions for system responses in terms of random variables are derived. Stochastic informations of system responses are evaluated by employing Monte Carlo Simulation (MCS) on the response function, which eliminates the need of construction and simulation of system matrices at each set of sample generation. 1D and 2D numerical examples in elasto-statics are solved using proposed method. Results are validated with those obtained by direct simulation of system of equations using MCS and also compared with other methods like second order perturbation and ad-hoc response function based SEFGM. Normalized computational times required for all the methods are also compared. It is found that the proposed method is computationally efficient and can produce accurate results even for higher coefficient of variation of input random fields. •A novel method is suggested for stochastic meshless analysis of problems in elasto-statics.•The method is based on an improved response function based stochastic element free Galerkin method.•Proposed method takes care of cross-dependency of random variables on response.•All probabilistic characteristics of structural responses can be captured.•The method is efficient and produces accurate results for higher coefficient of variation of input random fields.
doi_str_mv 10.1016/j.cma.2020.113419
format article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2477710292</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0045782520306046</els_id><sourcerecordid>2477710292</sourcerecordid><originalsourceid>FETCH-LOGICAL-c325t-6f547157b5a442b50568152e75ef2355bea64e54e52601cf5c26cb3eb95dd2a3</originalsourceid><addsrcrecordid>eNp9UE1LxDAQDaLguvoDvAU8d03SpmnxtCy6Cgte9iohTadsStusmeyC_94s9ewwMB-8N_N4hDxytuKMl8_9yo5mJZhIM88LXl-RBa9UnQmeV9dkwVghM1UJeUvuEHuWouJiQb7WE3XjMfgztDQAHv2EQLvTZKPzE20Mpj1Gbw8Go7N0BDwMgJiaePAt7Xygid0MMCJ1E4Uh4XyG0SQ03pObzgwID391SfZvr_vNe7b73H5s1rvM5kLGrOxkobhUjTRFIRrJZFlxKUBJ6EQuZQOmLECmFCXjtpNWlLbJoall2wqTL8nTfDYp-T4BRt37U5jSRy0KpRRnohYJxWeUDR4xQKePwY0m_GjO9MVE3etkor6YqGcTE-dl5kBSf3YQNFoHk4XWBbBRt979w_4FUWF6aA</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2477710292</pqid></control><display><type>article</type><title>An improved response function based stochastic meshless method for problems in elasto-statics</title><source>ScienceDirect Freedom Collection</source><creator>Aswathy, M ; Arun, C O</creator><creatorcontrib>Aswathy, M ; Arun, C O</creatorcontrib><description>The current study proposes an improved response function (IRF) based stochastic element free Galerkin method (SEFGM) for the analysis of problems in elasto-statics, wherein Young’s modulus is modelled as a homogeneous random field with symmetric distribution characteristics. The proposed SEFGM approximates displacement as the sum of a deterministic part and a stochastic part. The stochastic part is modelled with the help of an IRF, which is a function of discretized set of random variables. Moving least square shape functions are employed to discretize the random field. Utilizing Taylor series expansions of stiffness matrix and force vector and IRF approximation of displacement, explicit expressions for system responses in terms of random variables are derived. Stochastic informations of system responses are evaluated by employing Monte Carlo Simulation (MCS) on the response function, which eliminates the need of construction and simulation of system matrices at each set of sample generation. 1D and 2D numerical examples in elasto-statics are solved using proposed method. Results are validated with those obtained by direct simulation of system of equations using MCS and also compared with other methods like second order perturbation and ad-hoc response function based SEFGM. Normalized computational times required for all the methods are also compared. It is found that the proposed method is computationally efficient and can produce accurate results even for higher coefficient of variation of input random fields. •A novel method is suggested for stochastic meshless analysis of problems in elasto-statics.•The method is based on an improved response function based stochastic element free Galerkin method.•Proposed method takes care of cross-dependency of random variables on response.•All probabilistic characteristics of structural responses can be captured.•The method is efficient and produces accurate results for higher coefficient of variation of input random fields.</description><identifier>ISSN: 0045-7825</identifier><identifier>EISSN: 1879-2138</identifier><identifier>DOI: 10.1016/j.cma.2020.113419</identifier><language>eng</language><publisher>Amsterdam: Elsevier B.V</publisher><subject>Ad-hoc response function ; Coefficient of variation ; Fields (mathematics) ; Finite element method ; Galerkin method ; Improved response function ; Matrix algebra ; Matrix methods ; Meshless methods ; Modulus of elasticity ; Monte Carlo simulation ; Perturbation ; Random field ; Random variables ; Response functions ; Second order perturbation ; Series expansion ; Shape functions ; Simulation ; Stiffness matrix ; Stochastic element free Galerkin method ; Stochastic processes ; Taylor series</subject><ispartof>Computer methods in applied mechanics and engineering, 2020-12, Vol.372, p.113419, Article 113419</ispartof><rights>2020 Elsevier B.V.</rights><rights>Copyright Elsevier BV Dec 1, 2020</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c325t-6f547157b5a442b50568152e75ef2355bea64e54e52601cf5c26cb3eb95dd2a3</citedby><cites>FETCH-LOGICAL-c325t-6f547157b5a442b50568152e75ef2355bea64e54e52601cf5c26cb3eb95dd2a3</cites><orcidid>0000-0003-4828-6362</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Aswathy, M</creatorcontrib><creatorcontrib>Arun, C O</creatorcontrib><title>An improved response function based stochastic meshless method for problems in elasto-statics</title><title>Computer methods in applied mechanics and engineering</title><description>The current study proposes an improved response function (IRF) based stochastic element free Galerkin method (SEFGM) for the analysis of problems in elasto-statics, wherein Young’s modulus is modelled as a homogeneous random field with symmetric distribution characteristics. The proposed SEFGM approximates displacement as the sum of a deterministic part and a stochastic part. The stochastic part is modelled with the help of an IRF, which is a function of discretized set of random variables. Moving least square shape functions are employed to discretize the random field. Utilizing Taylor series expansions of stiffness matrix and force vector and IRF approximation of displacement, explicit expressions for system responses in terms of random variables are derived. Stochastic informations of system responses are evaluated by employing Monte Carlo Simulation (MCS) on the response function, which eliminates the need of construction and simulation of system matrices at each set of sample generation. 1D and 2D numerical examples in elasto-statics are solved using proposed method. Results are validated with those obtained by direct simulation of system of equations using MCS and also compared with other methods like second order perturbation and ad-hoc response function based SEFGM. Normalized computational times required for all the methods are also compared. It is found that the proposed method is computationally efficient and can produce accurate results even for higher coefficient of variation of input random fields. •A novel method is suggested for stochastic meshless analysis of problems in elasto-statics.•The method is based on an improved response function based stochastic element free Galerkin method.•Proposed method takes care of cross-dependency of random variables on response.•All probabilistic characteristics of structural responses can be captured.•The method is efficient and produces accurate results for higher coefficient of variation of input random fields.</description><subject>Ad-hoc response function</subject><subject>Coefficient of variation</subject><subject>Fields (mathematics)</subject><subject>Finite element method</subject><subject>Galerkin method</subject><subject>Improved response function</subject><subject>Matrix algebra</subject><subject>Matrix methods</subject><subject>Meshless methods</subject><subject>Modulus of elasticity</subject><subject>Monte Carlo simulation</subject><subject>Perturbation</subject><subject>Random field</subject><subject>Random variables</subject><subject>Response functions</subject><subject>Second order perturbation</subject><subject>Series expansion</subject><subject>Shape functions</subject><subject>Simulation</subject><subject>Stiffness matrix</subject><subject>Stochastic element free Galerkin method</subject><subject>Stochastic processes</subject><subject>Taylor series</subject><issn>0045-7825</issn><issn>1879-2138</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp9UE1LxDAQDaLguvoDvAU8d03SpmnxtCy6Cgte9iohTadsStusmeyC_94s9ewwMB-8N_N4hDxytuKMl8_9yo5mJZhIM88LXl-RBa9UnQmeV9dkwVghM1UJeUvuEHuWouJiQb7WE3XjMfgztDQAHv2EQLvTZKPzE20Mpj1Gbw8Go7N0BDwMgJiaePAt7Xygid0MMCJ1E4Uh4XyG0SQ03pObzgwID391SfZvr_vNe7b73H5s1rvM5kLGrOxkobhUjTRFIRrJZFlxKUBJ6EQuZQOmLECmFCXjtpNWlLbJoall2wqTL8nTfDYp-T4BRt37U5jSRy0KpRRnohYJxWeUDR4xQKePwY0m_GjO9MVE3etkor6YqGcTE-dl5kBSf3YQNFoHk4XWBbBRt979w_4FUWF6aA</recordid><startdate>20201201</startdate><enddate>20201201</enddate><creator>Aswathy, M</creator><creator>Arun, C O</creator><general>Elsevier B.V</general><general>Elsevier BV</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0003-4828-6362</orcidid></search><sort><creationdate>20201201</creationdate><title>An improved response function based stochastic meshless method for problems in elasto-statics</title><author>Aswathy, M ; Arun, C O</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c325t-6f547157b5a442b50568152e75ef2355bea64e54e52601cf5c26cb3eb95dd2a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Ad-hoc response function</topic><topic>Coefficient of variation</topic><topic>Fields (mathematics)</topic><topic>Finite element method</topic><topic>Galerkin method</topic><topic>Improved response function</topic><topic>Matrix algebra</topic><topic>Matrix methods</topic><topic>Meshless methods</topic><topic>Modulus of elasticity</topic><topic>Monte Carlo simulation</topic><topic>Perturbation</topic><topic>Random field</topic><topic>Random variables</topic><topic>Response functions</topic><topic>Second order perturbation</topic><topic>Series expansion</topic><topic>Shape functions</topic><topic>Simulation</topic><topic>Stiffness matrix</topic><topic>Stochastic element free Galerkin method</topic><topic>Stochastic processes</topic><topic>Taylor series</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Aswathy, M</creatorcontrib><creatorcontrib>Arun, C O</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Mechanical &amp; Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Computer methods in applied mechanics and engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Aswathy, M</au><au>Arun, C O</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An improved response function based stochastic meshless method for problems in elasto-statics</atitle><jtitle>Computer methods in applied mechanics and engineering</jtitle><date>2020-12-01</date><risdate>2020</risdate><volume>372</volume><spage>113419</spage><pages>113419-</pages><artnum>113419</artnum><issn>0045-7825</issn><eissn>1879-2138</eissn><abstract>The current study proposes an improved response function (IRF) based stochastic element free Galerkin method (SEFGM) for the analysis of problems in elasto-statics, wherein Young’s modulus is modelled as a homogeneous random field with symmetric distribution characteristics. The proposed SEFGM approximates displacement as the sum of a deterministic part and a stochastic part. The stochastic part is modelled with the help of an IRF, which is a function of discretized set of random variables. Moving least square shape functions are employed to discretize the random field. Utilizing Taylor series expansions of stiffness matrix and force vector and IRF approximation of displacement, explicit expressions for system responses in terms of random variables are derived. Stochastic informations of system responses are evaluated by employing Monte Carlo Simulation (MCS) on the response function, which eliminates the need of construction and simulation of system matrices at each set of sample generation. 1D and 2D numerical examples in elasto-statics are solved using proposed method. Results are validated with those obtained by direct simulation of system of equations using MCS and also compared with other methods like second order perturbation and ad-hoc response function based SEFGM. Normalized computational times required for all the methods are also compared. It is found that the proposed method is computationally efficient and can produce accurate results even for higher coefficient of variation of input random fields. •A novel method is suggested for stochastic meshless analysis of problems in elasto-statics.•The method is based on an improved response function based stochastic element free Galerkin method.•Proposed method takes care of cross-dependency of random variables on response.•All probabilistic characteristics of structural responses can be captured.•The method is efficient and produces accurate results for higher coefficient of variation of input random fields.</abstract><cop>Amsterdam</cop><pub>Elsevier B.V</pub><doi>10.1016/j.cma.2020.113419</doi><orcidid>https://orcid.org/0000-0003-4828-6362</orcidid></addata></record>
fulltext fulltext
identifier ISSN: 0045-7825
ispartof Computer methods in applied mechanics and engineering, 2020-12, Vol.372, p.113419, Article 113419
issn 0045-7825
1879-2138
language eng
recordid cdi_proquest_journals_2477710292
source ScienceDirect Freedom Collection
subjects Ad-hoc response function
Coefficient of variation
Fields (mathematics)
Finite element method
Galerkin method
Improved response function
Matrix algebra
Matrix methods
Meshless methods
Modulus of elasticity
Monte Carlo simulation
Perturbation
Random field
Random variables
Response functions
Second order perturbation
Series expansion
Shape functions
Simulation
Stiffness matrix
Stochastic element free Galerkin method
Stochastic processes
Taylor series
title An improved response function based stochastic meshless method for problems in elasto-statics
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-03T04%3A05%3A45IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=An%20improved%20response%20function%20based%20stochastic%20meshless%20method%20for%20problems%20in%20elasto-statics&rft.jtitle=Computer%20methods%20in%20applied%20mechanics%20and%20engineering&rft.au=Aswathy,%20M&rft.date=2020-12-01&rft.volume=372&rft.spage=113419&rft.pages=113419-&rft.artnum=113419&rft.issn=0045-7825&rft.eissn=1879-2138&rft_id=info:doi/10.1016/j.cma.2020.113419&rft_dat=%3Cproquest_cross%3E2477710292%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c325t-6f547157b5a442b50568152e75ef2355bea64e54e52601cf5c26cb3eb95dd2a3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2477710292&rft_id=info:pmid/&rfr_iscdi=true