Dynamics of Axial Rods With Frictional Joints

2016 ◽  
Author(s):  
Allen Mathis ◽  
D. Dane Quinn
Keyword(s):  
Numerical Analysis ◽  
Modal Analysis ◽  
Recent Work ◽  
Nonlinear Effects ◽  
High Fidelity ◽  
Reduced Order Models ◽  
Reduced Order ◽  
Modern Engineering ◽  
Computationally Expensive ◽  
Frictional Joints

Almost every modern engineering structure incorporates some form of mechanical interface, a connection between two otherwise separate mechanical structures. Complex machines and structures such as automobiles, bridges, aircraft, rockets, etc. rely heavily on these interfaces; however, high-fidelity numerical analysis of such connected structures is currently extremely difficult and computationally expensive due to the disparate length and time scales of the interface as compared to those characterizing the overall structure. This paper utilizes recent work in modal analysis of joints using reduced-order models to study the nonlinear effects of these systems while remaining computationally tractable.

Effect of Local Model Dynamics on Flexible Riser Tensile Armor Wire Stress Predictions

2019 ◽  
Author(s):  
Gabriel Rombado ◽  
Krassimir Doynov ◽  
Nathan Cooke ◽  
Arya Majed
Keyword(s):  
Local Model ◽  
Local Analysis ◽  
High Fidelity ◽  
Reduced Order Models ◽  
Flexible Riser ◽  
3D Fem ◽  
Reduced Order ◽  
Irregular Wave ◽  
The Impact ◽  
Stress Hysteresis

Abstract Accurate time-consistent computation of tensile armor wire stresses remains a major challenge in flexible riser fatigue life predictions and integrity management. Accuracy requires capturing the kinematics of the flexible’s helically contra-wound tensile armor layers and their interaction with the other metallic and thermo-plastic layers in a dynamic simulation. It is generally accepted that high fidelity 3D Finite Element Models (FEMs) can best capture the complex kinematics and produce accurate stresses. The local model is typically constructed of few “pitch lengths” of the 3D FEM. Local analysis involves enforcing tension and nodal rotation time-histories on the local model and extracting wire stresses at critical fatigue locations along risers. While local analysis involving a few bending cycles can be executed on modern multi-core computers, static simulations typically require computation times of 24–48 hours for a single cycle and do not capture the effect of dynamics of the local model. With this computational constraint, 1-hr long irregular wave fatigue simulations with 3D FEM local model become computationally infeasible. The nonlinear dynamic substructuring (NDS) approach has been utilized in the past to overcome this computation challenge. Reduced order models are numerical methods for efficiently solving high fidelity FEM. NDS utilizes reduced-order models and numerical algorithms to significantly decrease the computation time associated with the irregular wave fatigue simulations of the high fidelity flexible FEM. Because NDS is a simulation-based approach, effects such as local model tension stiffening and inertial resistance to the global rotation inputs are fully captured and the impact on wire stresses can be discerned. A 14” inner diameter (ID) flexible riser with a four-tensile armor layer configuration is modeled and simulated using the NDS approach. The 5m long local model is first driven at different “speeds” of harmonic (regular wave) rotation inputs to illustrate inertial effects. For the faster input, the impact of local model inertia on wire stresses is immediately apparent by the increase in wire stresses and change in the shape of the wire stress hysteresis curve. Next, the local model is simulated to irregular wave inputs. It is again shown that the inclusion of local model inertia increases wire stresses and modifies the shape of the wire stress hysteresis.

scholarly journals A machine learning framework for rapid forecasting and history matching in unconventional reservoirs

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shriram Srinivasan ◽  
Daniel O’Malley ◽  
Maruti K. Mudunuru ◽  
Matthew R. Sweeney ◽  
Jeffrey D. Hyman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Transfer Learning ◽  
History Matching ◽  
Time History ◽  
Unconventional Reservoirs ◽  
Production Data ◽  
High Fidelity ◽  
Reduced Order Models ◽  
Reduced Order

AbstractWe present a novel workflow for forecasting production in unconventional reservoirs using reduced-order models and machine-learning. Our physics-informed machine-learning workflow addresses the challenges to real-time reservoir management in unconventionals, namely the lack of data (i.e., the time-frame for which the wells have been producing), and the significant computational expense of high-fidelity modeling. We do this by applying the machine-learning paradigm of transfer learning, where we combine fast, but less accurate reduced-order models with slow, but accurate high-fidelity models. We use the Patzek model (Proc Natl Acad Sci 11:19731–19736, 10.1073/pnas.1313380110, 2013) as the reduced-order model to generate synthetic production data and supplement this data with synthetic production data obtained from high-fidelity discrete fracture network simulations of the site of interest. Our results demonstrate that training with low-fidelity models is not sufficient for accurate forecasting, but transfer learning is able to augment the knowledge and perform well once trained with the small set of results from the high-fidelity model. Such a physics-informed machine-learning (PIML) workflow, grounded in physics, is a viable candidate for real-time history matching and production forecasting in a fractured shale gas reservoir.

Finite element analysis of mechanical behavior of electrical wire harnesses: High fidelity and reduced-order models

2020 ◽  
Vol 165 ◽  
pp. 105188
Author(s):  
Ehsan Taghipour ◽  
Sai Siddhartha Vemula ◽  
Kushal Gargesh ◽  
Leon M. Headings ◽  
Marcelo J. Dapino ◽  
...  
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Mechanical Behavior ◽  
High Fidelity ◽  
Reduced Order Models ◽  
Element Analysis ◽  
Electrical Wire ◽  
Reduced Order

scholarly journals Efficient Therapy Planning via Model Reduction for Laser-Induced Interstitial Thermotherapy

2018 ◽  
Author(s):  
Kevin Tolle ◽  
Nicole Marheineke
Keyword(s):  
Optimal Control ◽  
Optimal Control Problem ◽  
Reduced Order Models ◽  
Therapy Planning ◽  
Constrained Optimal Control ◽  
Reduced Order ◽  
Computationally Expensive ◽  
Simplified Modeling ◽  
Interstitial Thermotherapy ◽  
Constrained Optimal Control Problem

We investigate the planning of minimally invasive tumor treatments via laser-induced thermotherapy. The goal is to control the laser in order to obtain an optimal treatment, e.g. eradicating the tumor, while leaving as much healthy tissue unharmed as possible. To this end, we define a PDE-constrained optimal control problem. As these problems are usually computationally expensive, we propose a simplified modeling approach using reduced-order models. Numerical results illustrate the viability of our approach.

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