A Quasi-Explicit Hydrodynamic Model for the Dynamic Analysis of a Moored FPSO Under Current Action

2001 ◽  
Vol 45 (04) ◽  
pp. 289-301 ◽  
Author(s):  
A. N. Simos ◽  
E. A. Tannuri ◽  
C. P. Pesce ◽  
J. A. P. Aranha
Keyword(s):  
Hydrodynamic Model ◽  
Experimental Validation ◽  
Single Point ◽  
Cross Flow ◽  
Dynamic Features ◽  
Moment Coefficient ◽  
Current Action ◽  
Proposed Model ◽  
Friction Curve ◽  
Yaw Moment

In an earlier work Leite et al (1998) developed a heuristic hydrodynamic model, based on the shrtwing theory, for the horizontal current forces on an FPSO system. The proposed model was quasi-explicit in the sense that it depends on the ship's main dimensions and on only three hydrodynamic coefficients, namely, the friction coefficient Cf for head on incidence, the drag coefficient CY for a cross-flow, and the related yaw moment coefficient lCY. As discussed in Leite et al (1998), these coefficients could even be estimated from the ITTC friction curve and from Hoerner's sectional results, which would then turn the hydrodynamic model explicit. The model has been tested against experimental results for the horizontal force coefficients, obtained both at IPT and at the Marin wave tank, and it has also been confronted with bifurcation experiments for a turret configuration realized at IPT. The agreement rendered good results in all cases tested. The heuristic approach has now been extended to incorporate the yaw velocity terms while preserving the quasi-explicit feature of the original model. The main purpose of the work herein is to present such a development together with some experimental validation. Using Froude scaling of different ships in distinct ballast conditions, the horizontal forces and moment in the yaw rotating tests were measured at IPT and at Marin and compared with those predicted by the heuristic model, the observed agreement again being fair enough. In an accompanying paper in this issue, the derived mathematical model is tested against experiments that emulate a single-point mooring of a tanker ship in order to disclose the model's ability to cope with the main dynamic features of the fishtailing instability problem.

Experimental Validation of a Quasi-Explicit Hydrodynamic Model: Fishtailing Instability of a Single-Point Moored Tanker in Rigid-Hawser Configuration

2001 ◽  
Vol 45 (04) ◽  
pp. 302-314
Author(s):  
E. A. Tannuri ◽  
A. N. Simos ◽  
A. J. P. Leite ◽  
J. A. P. Aranha
Keyword(s):  
Hydrodynamic Model ◽  
Close Agreement ◽  
Experimental Validation ◽  
Ad Hoc ◽  
Complex Dynamics ◽  
Single Point ◽  
Ocean Current ◽  
High Current ◽  
Dynamic Problems ◽  
High Current Velocity

The extended hydrodynamic model derived in Simos et al (2001), where the yaw velocity terms have been incorporated to the model proposed by Leite et al (1998) while preserving its quasi-explicit feature, is used here to study some typical dynamic problems of moored ships, specifically the fishtailing shtailing instability that may occur in a single-point mooring (SPM) system. Since the intention was to check the hydrodynamic model, the hawser was assumed rigid to avoid the complex dynamics that may occur when the actual hawser slackens and the obtained results were confronted with experimental ones, obtained at the IPT wave tank. The agreement is very good in the sense that not only the limit-cycle amplitudes are compatible but also the time series are very similar. For the VLCC model in ballasted condition (40%) the fishtailing shtailing instability occurs only for a relatively high current velocity and some Froude effect is then detectable. Using results from the static bifurcation experiment an ad hoc correction is proposed for such effect, showing a relatively close agreement between experiments and the theoretical model. This Froude effect correction is, however, not relevant for an actual SPM system subjected to a usual ocean current.

scholarly journals An Investigation into the Application of Deep Learning in the Detection and Mitigation of DDOS Attack on SDN Controllers

Technologies ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 14
Author(s):  
James Dzisi Gadze ◽  
Akua Acheampomaa Bamfo-Asante ◽  
Justice Owusu Agyemang ◽  
Henry Nunoo-Mensah ◽  
Kwasi Adu-Boahen Opare
Keyword(s):  
Deep Learning ◽  
Network Architecture ◽  
Learning Algorithm ◽  
Single Point ◽  
Denial Of Service ◽  
Specific Work ◽  
Learning Models ◽  
Ddos Attack ◽  
Deep Learning Algorithm ◽  
Proposed Model

Software-Defined Networking (SDN) is a new paradigm that revolutionizes the idea of a software-driven network through the separation of control and data planes. It addresses the problems of traditional network architecture. Nevertheless, this brilliant architecture is exposed to several security threats, e.g., the distributed denial of service (DDoS) attack, which is hard to contain in such software-based networks. The concept of a centralized controller in SDN makes it a single point of attack as well as a single point of failure. In this paper, deep learning-based models, long-short term memory (LSTM) and convolutional neural network (CNN), are investigated. It illustrates their possibility and efficiency in being used in detecting and mitigating DDoS attack. The paper focuses on TCP, UDP, and ICMP flood attacks that target the controller. The performance of the models was evaluated based on the accuracy, recall, and true negative rate. We compared the performance of the deep learning models with classical machine learning models. We further provide details on the time taken to detect and mitigate the attack. Our results show that RNN LSTM is a viable deep learning algorithm that can be applied in the detection and mitigation of DDoS in the SDN controller. Our proposed model produced an accuracy of 89.63%, which outperformed linear-based models such as SVM (86.85%) and Naive Bayes (82.61%). Although KNN, which is a linear-based model, outperformed our proposed model (achieving an accuracy of 99.4%), our proposed model provides a good trade-off between precision and recall, which makes it suitable for DDoS classification. In addition, it was realized that the split ratio of the training and testing datasets can give different results in the performance of a deep learning algorithm used in a specific work. The model achieved the best performance when a split of 70/30 was used in comparison to 80/20 and 60/40 split ratios.

The Finnish Climate Change Act: Architecture, Functions, and Challenges

Climate Law ◽  
2014 ◽  
Vol 4 (3-4) ◽  
pp. 301-326 ◽  
Author(s):  
Ismo Pölönen
Keyword(s):  
Climate Change ◽  
Climate Policy ◽  
Policy Making ◽  
Dynamic Features ◽  
Policy Choices ◽  
Legal Implications ◽  
Proposed Model ◽  
Term Energy ◽  
Public Scrutiny ◽  
The Cost

The article examines the key features and functions of the proposed Finnish Climate Change Act (fcca). It also analyses the legal implications of the Act and the qualities and factors which may limit its effectiveness. The paper argues that, despite its weak legal implications, the fcca would provide the regulatory preconditions for higher-quality climate policy-making in Finland, and it has the capacity to play an important role in national climate policy. The fcca would deliver regulatory foundations for systematic and integrated climate policy-making, also enabling wide public scrutiny. The proposed model leaves room for manifold climate-policy choices in varying societal and economical contexts. The cost of dynamic features is the relalow predictability in terms of sectorial paths on emission reductions. Another relevant challenge relates to the intended preparation of overlapping mid-term energy and climate plans with instruments of the fcca.

scholarly journals Computational simulation of round thermal jets in an ambient cross flow using a large-scale hydrodynamic model

2019 ◽  
Vol 58 (6) ◽  
pp. 920-937
Author(s):  
Daniela Malcangio ◽  
Alan Cuthbertson ◽  
Mouldi Ben Meftah ◽  
Michele Mossa
Keyword(s):  
Hydrodynamic Model ◽  
Large Scale ◽  
Computational Simulation ◽  
Cross Flow

The Shadow Effect on the Dynamics of a Shuttle Tanker Connected in Tandem With a FPSO

2003 ◽  
Author(s):  
Carlos H. Fucatu ◽  
Kazuo Nishimoto
Keyword(s):  
Dynamic Behaviour ◽  
Dynamic Instability ◽  
Single Point ◽  
Great Influence ◽  
Oil Industry ◽  
Common Procedure ◽  
Shadow Effect ◽  
Proposed Model ◽  
Offshore Oil Industry ◽  
Offshore Oil

The ship based Floating Production Storage and Offloading system (FPSO) has been largely used in the recent offshore oil exploration. In most of the cases the oil stored in FPSO is offloaded to a shuttle ship that is connected by a hawser in tandem configuration. The problem of dynamic instability that arises in several ship mooring systems, like SPM and SMS subjected to the environmental forces, may also be present in the tandem system. Although the tandem mooring is a common procedure in the offshore oil industry, there are few publications related to the theme. Among these, there are none concerned with the environmental forces interference caused by FPSO on shuttle ship, here called as shadow effect. It is well known that the dynamic behaviour of a moored ship, in particular SPM system, is hardly affected by the environmental forces. Therefore, it is expected that shadow effect on the environmental forces acting on the shuttle ship will cause great influence in its dynamic behaviour, and consequently in the dynamics of whole FPSO-shuttle system. These phenomena could be observed in experiments with single point moored shuttle ships with and without the FPSO in upstream position. Therefore, the shadow effect should be considered in analysis of dynamic behaviour of two ships connected in tandem. Among the commercial simulators that analyse tandem systems there are none that consider shadow effect, making their analysis different from the real world. This paper presents an empirical model of the current shadow effect. The model was implemented in a numerical simulator, named DYNASIM. The comparison between numerical results and experimental one showed that the proposed model is effective.

scholarly journals Deep Graph Learning for Circuit Deobfuscation

2021 ◽  
Vol 4 ◽  
Author(s):  
Zhiqian Chen ◽  
Lei Zhang ◽  
Gaurav Kolhe ◽  
Hadi Mardani Kamali ◽  
Setareh Rafatirad ◽  
...  
Keyword(s):  
Survival Analysis ◽  
Integrated Circuits ◽  
Defense Mechanism ◽  
Computational Cost ◽  
Conjunctive Normal Form ◽  
Dynamic Features ◽  
Digital Integrated Circuits ◽  
Learning Techniques ◽  
Proposed Model ◽  
Effectiveness And Efficiency

Circuit obfuscation is a recently proposed defense mechanism to protect the intellectual property (IP) of digital integrated circuits (ICs) from reverse engineering. There have been effective schemes, such as satisfiability (SAT)-checking based attacks that can potentially decrypt obfuscated circuits, which is called deobfuscation. Deobfuscation runtime could be days or years, depending on the layouts of the obfuscated ICs. Hence, accurately pre-estimating the deobfuscation runtime within a reasonable amount of time is crucial for IC designers to optimize their defense. However, it is challenging due to (1) the complexity of graph-structured circuit; (2) the varying-size topology of obfuscated circuits; (3) requirement on efficiency for deobfuscation method. This study proposes a framework that predicts the deobfuscation runtime based on graph deep learning techniques to address the challenges mentioned above. A conjunctive normal form (CNF) bipartite graph is utilized to characterize the complexity of this SAT problem by analyzing the SAT attack method. Multi-order information of the graph matrix is designed to identify the essential features and reduce the computational cost. To overcome the difficulty in capturing the dynamic size of the CNF graph, an energy-based kernel is proposed to aggregate dynamic features into an identical vector space. Then, we designed a framework, Deep Survival Analysis with Graph (DSAG), which integrates energy-based layers and predicts runtime inspired by censored regression in survival analysis. Integrating uncensored data with censored data, the proposed model improves the standard regression significantly. DSAG is an end-to-end framework that can automatically extract the determinant features for deobfuscation runtime. Extensive experiments on benchmarks demonstrate its effectiveness and efficiency.

The Effects of Nylon Polymer Threads on Strain-Loading Hysteresis Behavior of Supercoiled Polymer (SCP) Artificial Muscles

2019 ◽  
Author(s):  
Revanth Konda ◽  
Jun Zhang
Keyword(s):  
Thermal Activation ◽  
Experimental Validation ◽  
Linear Models ◽  
Artificial Muscle ◽  
Power Input ◽  
Artificial Muscles ◽  
Helical Spring ◽  
Spring Model ◽  
Actuation Mechanism ◽  
Proposed Model

Abstract Supercoiled polymers (SCP) actuator, as a recently discovered artificial muscle, has attracted a lot of attention as a compliant and compact actuation mechanism. SCP actuators can be fabricated from nylon polymer threads, and generates up to 20% strain under thermal activation. A common challenge, however, is to accurately and efficiently estimate the performance of SCP actuators considering their significant hysteresis among loading, strain, and power input. Previous studies adopted either linear models that failed to capture the hysteresis or phenomenological models that required tedious procedures for identification and implementation. In this paper, a physics-inspired model is presented to efficiently capture and estimate SCP actuators’ strain – loading hysteresis by analyzing the properties of nylon threads from which they are fabricated. The strains of SCP actuators are found to be linear to that of the nylon threads under the same loading conditions. An efficient approach is proposed to characterize and estimate the strain – loading hysteresis of SCP actuators fabricated with different numbers of nylon threads. A helical spring model is adopted to obtain the stiffness of SCP actuators with different configurations. Experimental validation involving two-ply, four-ply, and six-ply nylon threads and SCP actuators are provided to confirm the effectiveness of the proposed model.

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