Global ETD Search

61	Comparative study of different methods for superstructure-foundation interactions Sharma, Prakriti 04 January 2022 (has links) Bridge failures in the past decade due to structural deficiencies demonstrated the clear need for a review of the current bridge analysis approaches. This study focuses on pile-supported bridges under predominantly static loading. A critical review of the current analysis approaches was performed. It was concluded that in the absence of an onerous iteration process, the current approaches often produce inaccurate and, in many cases, unsafe results since the interactions between superstructure and foundation are not fully considered. To address the inherent limitations of the current approaches, a computer program [Soil Spring Module (SSM) 2.0] was developed as a part of the study. SSM 2.0 can be used in conjunction with a frame analysis program to capture nonlinear load transfer from foundation elements to soil in different directions simultaneously. STAAD.Pro was selected for demonstration in this study. Using SSM 2.0 and STAAD.Pro, this study proposes a new analysis approach using the Integrated Analysis Process (IAP). The same methodology can be applied in other frame analysis programs. Kansas Bridge 45 was selected as a case study. Using the IAP approach, a series of integrated analyses including all superstructure elements (e.g., deck, girders and piers) and all foundation elements (e.g., pile caps and piles) were performed on Kansas Bridge 45 for different soil types and properties. Different from the conventional approaches, the full interactions between superstructure and foundation were considered simultaneously in a single analysis using the IAP approach. The analysis results from the IAP approach and the conventional approaches were examined. The advantages of the IAP approach were identified. Comparing to the conventional approaches in current practice, the proposed IAP approach does not involve crude assumptions or intensive iterations. Using the IAP approach, design engineers can complete structural and foundation analysis of pile-supported bridges with good accuracy in a timely manner. The same methodology can potentially be applied to other structure types. / Graduate / 2022-12-15 Pile foundation superstructure-foundation interaction bridge soil-structure interaction STAAD.Pro Integrated bridge analysis soft clay stiff clay soil- pile-structure interaction soil springs superstructure stiffness Davisson and Robinson superstructure response foundation response p-y t-z q-z
62	Konvergence řešení soustav algebraických rovnic / Algebraic Equations Solution Convergence Sehnalová, Pavla January 2007 (has links) The work describes techniques for solving systems of linear and differential equations. It explains the definition of conversion from system of linear to system of differential equations. The method of the elementary transmission and the transform algorithm are presented. Both of methods are demonstrated on simply examples and properties of conversion are shown. The work compares fast and accurate solutions of methods and algorithm. For computing examples and solving experiments following programs were used: TKSL and TKSL/C. The program TKSL/C was enriched with the graphic user interface which makes the conversion of systems and computing results easier.
63	PHYSICS-INFORMED NEURAL NETWORK SOLUTION OF POINT KINETICS EQUATIONS FOR PUR-1 DIGITAL TWIN Konstantinos Prantikos (14196773) 01 December 2022 (has links) <p> </p> <p>A <em>digital twin</em> (DT), which keeps track of nuclear reactor history to provide real-time predictions, has been recently proposed for nuclear reactor monitoring. A digital twin can be implemented using either a differential equations-based physics model, or a data-driven machine learning model<strong>. </strong>The principal challenge in physics model-based DT consists of achieving sufficient model fidelity to represent a complex experimental system, while the main challenge in data-driven DT appears in the extensive training requirements and potential lack of predictive ability. </p> <p>In this thesis, we investigate the performance of a hybrid approach, which is based on physics-informed neural networks (PINNs) that encode fundamental physical laws into the loss function of the neural network. In this way, PINNs establish theoretical constraints and biases to supplement measurement data and provide solution to several limitations of purely data-driven machine learning (ML) models. We develop a PINN model to solve the point kinetic equations (PKEs), which are time dependent stiff nonlinear ordinary differential equations that constitute a nuclear reactor reduced-order model under the approximation of ignoring the spatial dependence of the neutron flux. PKEs portray the kinetic behavior of the system, and this kind of approach is the basis for most analyses of reactor systems, except in cases where flux shapes are known to vary with time. This system describes the nuclear parameters such as neutron density concentration, the delayed neutron precursor density concentration and reactivity. Both neutron density and delayed neutron precursor density concentrations are the vital parameters for safety and the transient behavior of the reactor power. </p> <p>The PINN model solution of PKEs is developed to monitor a start-up transient of the Purdue University Reactor Number One (PUR-1) using experimental parameters for the reactivity feedback schedule and the neutron source. The facility under modeling, PUR-1, is a pool type small research reactor located in West Lafayette Indiana. It is an all-digital light water reactor (LWR) submerged into a deep-water pool and has a power output of 10kW. The results demonstrate strong agreement between the PINN solution and finite difference numerical solution of PKEs. We investigate PINNs performance in both data interpolation and extrapolation. </p> <p>The findings of this thesis research indicate that the PINN model achieved highest performance and lowest errors in data interpolation. In the case of extrapolation data, three different test cases were considered, the first where the extrapolation is performed in a five-seconds interval, the second where the extrapolation is performed in a 10-seconds interval, and the third where the extrapolation is performed in a 15-seconds interval. The extrapolation errors are comparable to those of interpolation predictions. Extrapolation accuracy decreases with increasing time interval.</p> Physics-informed neural networks point kinetics equations nuclear reactor stiff ordinary differential equations digital twin nuclear reactor monitoring
64	SYNTHESIS AND VISCOELASTIC PROPERTIES OF GELS OBTAINED FROM LINEAR AND BRANCHED POLYMERS Debnath, Dibyendu, Debnath 24 May 2018 (has links) No description available. Polymer Chemistry Polymers Chemistry Materials Science
65	SYNTHESIS AND VISCOELASTIC PROPERTIES OF GELS OBTAINED FROM LINEAR AND BRANCHED POLYMERS Debnath, Dibyendu 24 May 2018 (has links) No description available. Chemistry Materials Science Polymer Chemistry Polymers
66	High order numerical methods for a unified theory of fluid and solid mechanics Chiocchetti, Simone 10 June 2022 (has links) This dissertation is a contribution to the development of a unified model of continuum mechanics, describing both fluids and elastic solids as a general continua, with a simple material parameter choice being the distinction between inviscid or viscous fluid, or elastic solids or visco-elasto-plastic media. Additional physical effects such as surface tension, rate-dependent material failure and fatigue can be, and have been, included in the same formalism. The model extends a hyperelastic formulation of solid mechanics in Eulerian coordinates to fluid flows by means of stiff algebraic relaxation source terms. The governing equations are then solved by means of high order ADER Discontinuous Galerkin and Finite Volume schemes on fixed Cartesian meshes and on moving unstructured polygonal meshes with adaptive connectivity, the latter constructed and moved by means of a in- house Fortran library for the generation of high quality Delaunay and Voronoi meshes. Further, the thesis introduces a new family of exponential-type and semi- analytical time-integration methods for the stiff source terms governing friction and pressure relaxation in Baer-Nunziato compressible multiphase flows, as well as for relaxation in the unified model of continuum mechanics, associated with viscosity and plasticity, and heat conduction effects. Theoretical consideration about the model are also given, from the solution of weak hyperbolicity issues affecting some special cases of the governing equations, to the computation of accurate eigenvalue estimates, to the discussion of the geometrical structure of the equations and involution constraints of curl type, then enforced both via a GLM curl cleaning method, and by means of special involution-preserving discrete differential operators, implemented in a semi-implicit framework. Concerning applications to real-world problems, this thesis includes simulation ranging from low-Mach viscous two-phase flow, to shockwaves in compressible viscous flow on unstructured moving grids, to diffuse interface crack formation in solids. High order ADER Discontinuous Galerkin DG Finite Volume FV Finite Element FE FEM DGFEM CFD Voronoi Delaunay Unstructured meshe Fortran Fluid mechanic Solid Mechanic Continuum Mechanic Baer Nunziato Surface tension Multiphase flow Compressible flow Navier Stoke Semi-implicit GLM cleaning Partial differential equation Numerical method Stiff source Finite rate stiff relaxation Hyperbolic equation Metodi d'alto ordine ADER Discontinous Galerkin Volumi Finiti Elementi Finiti Meccanica dei fluidi Meccanica dei solidi Meccanica dei continui Fluidi multifase Fluidi comprimibili Metodi semi-impliciti Equazioni alle derivate parziali Metodi numerici Equazioni iperboliche Griglie non strutturate
67	Study of Higher Order Split-Step Methods for Stiff Stochastic Differential Equations Singh, Samar B January 2013 (has links) (PDF) Stochastic differential equations(SDEs) play an important role in many branches of engineering and science including economics, finance, chemistry, biology, mechanics etc. SDEs (with m-dimensional Wiener process) arising in many applications do not have explicit solutions, which implies the development of effective numerical methods for such systems. For SDEs, one can classify the numerical methods into three classes: fully implicit methods, semi-implicit methods and explicit methods. In order to solve SDEs, the computation of Newton iteration is necessary for the implicit and semi-implicit methods whereas for the explicit methods we do not need such computation. In this thesis the common theme is to construct explicit numerical methods with strong order 1.0 and 1.5 for solving Itˆo SDEs. The five-stage Milstein(FSM)methods, split-step forward Milstein(SSFM)methods and M-stage split-step strong Taylor(M-SSST) methods are constructed for solving SDEs. The FSM, SSFM and M-SSST methods are fully explicit methods. It is proved that the FSM and SSFM methods are convergent with strong order 1.0, and M-SSST methods are convergent with strong order 1.5.Stiﬀness is a very important issue for the numerical treatment of SDEs, similar to the case of deterministic ordinary differential equations. Stochastic stiffness can lead someone to use smaller step-size for the numerical simulation of the SDEs. However, such issues can be handled using numerical methods with better stability properties. The analysis of stability (with multidimensional Wiener process) shows that the mean-square stable regions of the FSM methods are unbounded. The analysis of stability shows that the mean-square stable regions of the FSM and SSFM methods are larger than the Milstein and three-stage Milstein methods. The M-SSST methods possess large mean square stability region as compared to the order 1.5 strong Itˆo-Taylor method. SDE systems simulated with the FSM, SSFM and M-SSST methods show the computational efficiency of the methods. In this work, we also consider the problem of computing numerical solutions for stochastic delay differential equations(SDDEs) of Itˆo form with a constant lag in the argument. The fully explicit methods, the predictor-corrector Euler(PCE)methods, are constructed for solving SDDEs. It is proved that the PCE methods are convergent with strong order γ = ½ in the mean-square sense. The conditions under which the PCE methods are MS-stable and GMS-stable are less restrictive as compared to the conditions for the Euler method. Stochastic Differential Equations Mean Square Convergence Explicit Numerical Methods Weiner Process Higher Order Split-Step Methods Mean Square Stability Split-Step Methods Five Stage Milstein (FSM) Methods M-Stage Split-Step Strong Taylor Method Numerical Methods Predictor-corrector type Methods M-SSST Methods Stiff Stochastic Differential Equations Mathematics

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