The Evolved Motions of a Marine Riser or Pipeline

2021 ◽  
Author(s):  
Robert F. Zueck
Keyword(s):  
Computational Models ◽  
Simple Structure ◽  
Structural Acoustics ◽  
Marine Riser ◽  
Dynamic Nature ◽  
Science And Engineering ◽  
Thin Structures ◽  
Engineering Models ◽  
Real Behavior ◽  
Geometric Changes

Abstract Analytical, experimental and computational models have historically been heavily simplified, linearized, and otherwise reduced. This paper shows how such model reductions eliminate the fundamental geometric changes that determine real behavior in cables, strings, moorings, guys, pipelines, riser, plates, skins, subsea hulls, and other such slender and thin structures. The paper details each physical quantity that we must add back into our overly reduced models to improve the basic nature, evolution, and accuracy of the resulting motions and vibrations. For example, even slight changes in local rotation anywhere along a cable can create large nonlinear changes in the dynamic nature of its behavior. The evolved complexity of the resulting global motions and vibrations in space and time often defy what we normally expect from such a simple structure. Although this paper focuses on the modeling of deep-water moorings and risers of an ocean platform, the same geometric effect is fundamental to most science and engineering models. Understanding how small changes in geometry can nonlinearly affect any structured behavior will help demystify much of the poorly-understood motions and vibrations in a large diversity of applications, including induced vibrations, sound, structural acoustics, aero-elasticity, sound, light and atomic radiation.

Fluid Drag With and Without Vortex-Induced Vibrations

2020 ◽  
Author(s):  
Robert F. Zueck
Keyword(s):  
Maximum Amplitude ◽  
Dynamic Nature ◽  
Physical Relationship ◽  
Flowing Fluid ◽  
Vortex Induced Vibrations ◽  
Sustained Action ◽  
Fluid Drag ◽  
Slender Structure ◽  
Conservation Principles ◽  
Geometric Changes

Abstract Fluid drag is an integrated force that depends on the velocity of the fluid flow relative to the motion of a structure. In previous OMAE papers, we used nonlinear physics-based time-domain simulations to show how fluid drag evolves geometric changes in slender (long and thin) structures. We then showed how these changes physically determine the specific dynamic nature of the vibrations that the fluid can induce in the structure. Induced vibrations are four-dimensional oscillations in a marine riser, suspended pipe or other slender structure, whereby the maximum amplitude of deflection is generally perpendicular to the sustained action. The sustained action is often fluid drag. In this paper, we study the physical relationship between fluid drag and induced vibrations. By focusing on the nonlinear interaction between fluid and structure, we revisit a longstanding belief that vortex-induced vibrations amplify fluid drag. Using nonlinear physics-based simulations of a slender structure interacting with flowing fluid, we show how amplification depends on the type of vibration (imposed or free). In other words, drag amplification can occur when we impose a vibration on the structure, but does not occur when we allow sufficient geometric freedom so that the fluid merely induces the structure to vibrate. Using simple visual experiments, we confirm that Vortex-Induced Vibrations (VIV) do not amplify fluid drag. This result is consistent with basic energy conservation principles.

scholarly journals Computational modelling for decision-making: where, why, what, who and how

2018 ◽  
Vol 5 (6) ◽  
pp. 172096 ◽  
Author(s):  
Muffy Calder ◽  
Claire Craig ◽  
Dave Culley ◽  
Richard de Cani ◽  
Christl A. Donnelly ◽  
...  
Keyword(s):  
Decision Making ◽  
Computational Models ◽  
Computational Modelling ◽  
Technical Skills ◽  
Complex World ◽  
Close Collaboration ◽  
Science And Engineering ◽  
Whole Process ◽  
Wide Range ◽  
Significant Factors

In order to deal with an increasingly complex world, we need ever more sophisticated computational models that can help us make decisions wisely and understand the potential consequences of choices. But creating a model requires far more than just raw data and technical skills: it requires a close collaboration between model commissioners, developers, users and reviewers. Good modelling requires its users and commissioners to understand more about the whole process, including the different kinds of purpose a model can have and the different technical bases. This paper offers a guide to the process of commissioning, developing and deploying models across a wide range of domains from public policy to science and engineering. It provides two checklists to help potential modellers, commissioners and users ensure they have considered the most significant factors that will determine success. We conclude there is a need to reinforce modelling as a discipline, so that misconstruction is less likely; to increase understanding of modelling in all domains, so that the misuse of models is reduced; and to bring commissioners closer to modelling, so that the results are more useful.

Mechano-chemo-biological computational models for arteries in health, disease and healing: from tissue remodelling to drug-eluting devices

2020 ◽  
Vol 26 ◽  
Author(s):  
Michele Marino ◽  
Giuseppe Vairo ◽  
Peter Wriggers
Keyword(s):  
In Silico ◽  
Computational Models ◽  
Computational Modelling ◽  
Tissue Remodelling ◽  
Medical Problems ◽  
Computational Biomechanics ◽  
Biological Phenomena ◽  
Engineering Models ◽  
Drug Eluting ◽  
Health And Disease

: This review aims to highlight urgent priorities for the computational biomechanics community in the framework of mechano-chemo-biological models. Recent approaches, promising directions and open challenges on the computational modelling of arterial tissues in health and disease are introduced and investigated, together with in silico approaches for the analysis of drug-eluting stents that promote a pharmacological-induced healing. The paper addresses a number of chemo-biological phenomena that are generally neglected in biomechanical engineering models but are most likely instrumental for the onset and the progression of arterial diseases. An interdisciplinary effort is thus encouraged for providing the tools for an effective in silico insight into medical problems. An integrated mechano-chemo-biological perspective is believed to be a fundamental missing piece for crossing the bridge between computational engineering and life sciences, and for bringing computational biomechanics into medical research and clinical practice.

Validating Dynamic Engineering Models Under Uncertainty

2016 ◽  
Vol 138 (11) ◽  
Author(s):  
Zequn Wang ◽  
Yan Fu ◽  
Ren-Jye Yang ◽  
Saeed Barbat ◽  
Wei Chen
Keyword(s):  
Dynamic Model ◽  
Dynamic Systems ◽  
Computational Models ◽  
Time Series Data ◽  
Integral Transform ◽  
Random Coefficients ◽  
Series Data ◽  
Input Space ◽  
Practical Applications ◽  
Engineering Models

Validating dynamic engineering models is critically important in practical applications by assessing the agreement between simulation results and experimental observations. Though significant progresses have been made, the existing metrics lack the capability of managing uncertainty in both simulations and experiments. In addition, it is challenging to validate a dynamic model aggregately over both the time domain and a model input space with data at multiple validation sites. To overcome these difficulties, this paper presents an area-based metric to systemically handle uncertainty and validate computational models for dynamic systems over an input space by simultaneously integrating the information from multiple validation sites. To manage the complexity associated with a high-dimensional data space, eigenanalysis is performed for the time series data from simulations at each validation site to extract the important features. A truncated Karhunen–Loève (KL) expansion is then constructed to represent the responses of dynamic systems, resulting in a set of uncorrelated random coefficients with unit variance. With the development of a hierarchical data-fusion strategy, probability integral transform (PIT) is then employed to pool all the resulting random coefficients from multiple validation sites across the input space into a single aggregated metric. The dynamic model is thus validated by calculating the cumulative area difference of the cumulative density functions. The proposed model validation metric for dynamic systems is illustrated with a mathematical example, a supported beam problem with stochastic loads, and real data from the vehicle occupant-restraint system.

Sequential Difficulty Effects During Strategy Execution

2012 ◽  
Vol 59 (5) ◽  
pp. 295-301 ◽  
Author(s):  
Kim Uittenhove ◽  
Patrick Lemaire
Keyword(s):  
Computational Models ◽  
Preceding Trial ◽  
Human Cognition ◽  
Strategy Execution ◽  
Strategy Performance ◽  
Empirical Implications

In two experiments, we tested the hypothesis that strategy performance on a given trial is influenced by the difficulty of the strategy executed on the immediately preceding trial, an effect that we call strategy sequential difficulty effect. Participants’ task was to provide approximate sums to two-digit addition problems by using cued rounding strategies. Results showed that performance was poorer after a difficult strategy than after an easy strategy. Our results have important theoretical and empirical implications for computational models of strategy choices and for furthering our understanding of strategic variations in arithmetic as well as in human cognition in general.

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