Influence of the design parameters on the stress state of saddle-supported pipelines: an artificial neural network approach |
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| Affiliation: | 1. Center for Proton Therapy, Paul Scherrer Institute, Villigen, Switzerland;2. Department of Physics, ETH-Hönggerberg, Zurich, Switzerland;3. Department of Biomedical Sciences of Cells and Systems, Section Molecular Cell Biology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands;4. Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands;6. Radiation Oncology Department, University Hospital of Geneva, Geneva, Switzerland;5. Radiation Oncology Department, Inselspital Universitätsspital Bern, Bern, Switzerland;1. Department of Electrical Engineering, School of Technology, Pandit Deendayal Energy University, Gandhinagar, 382426, India;2. Department of Solar Energy, School of Technology, Pandit Deendayal Energy University, Gandhinagar, 382426, India;3. Department of Chemical Engineering, School of Technology, Pandit Deendayal Energy University, Gandhinagar, 382426, India;4. Department of Mechanical Engineering, School of Technology, Pandit Deendayal Energy University, Gandhinagar, 382426, India;5. Department of Electrical Engineering, National Institute of Technology Manipur, Imphal, 795004, India;6. Department of Chemistry, Faculty of Science, King Khalid University, P.O. Box 9004, Abha, 61413, Saudi Arabia;7. Department of Physics, School of Technology, Pandit Deendayal Energy University, Gandhinagar, 382426, India;1. ETEC Department & MOBI Research Group, Vrije Universiteit Brussel (VUB), Pleinlaan 2, 1050 Brussel, Belgium;2. Flanders Make, 3001 Heverlee, Belgium;3. Global Energy Interconnection Research Institute Europe GmbH, 10623 Berlin, Germany;1. Research Group MOBI – Mobility, Logistics, and Automotive Technology Research Centre, Vrije Universiteit Brussel, Pleinlaan 2, Brussels 1050, Belgium;2. Flanders Make, Heverlee 3001, Belgium;3. School of Computer Science, Faculty of Engineering and Information Technology, University of Sydney, Camperdown, NSW 2006, Australia;4. Department of Energy Technology, KTH Royal Institute of Technology, SE-10044 Stockholm, Sweden |
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| Abstract: | A neural network methodology is herein applied to numerically treat the sensitivity analysis problem of above-ground pipelines under static loading by taking into account the possibility of development of uplifting phenomena at the pipe–saddle interfaces. Assuming classical frictionless unilateral contact to mathematically describe the pipeline support conditions coupled by an appropriate finite element scheme, the discrete problem is put in the form of an inequality constrained quadratic optimization problem with respect to either displacements or stresses. In order to investigate the structural response and the stress states of the above-ground pipeline at hand with respect to the variation of critical design parameters which are the pipe thickness and the support conditions, the sensitivity analysis problem is formulated as a quadratic programming problem with the design parameters appearing in the quadratic term. The feasibility of using appropriately designed neural networks to model the complicated nonlinear relationship between the several input parameters associated with above-ground pipelines and their support conditions is thus demonstrated. |
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