SAFE MOORING OF LARGE CONTAINER SHIPS AT QUAY WALLS SUBJECT TO PASSING SHIP EFFECTS

Container traffic and individual ships’ sizes increased dramatically over the last decades, testing the existing harbour infrastructure to its limits. An important aspect regarding the safety of the berthed vessel is the quality of the mooring configuration. A case study is presented, where an 18000 TEU container vessel is moored at a quay. The motions of the moored vessel and the forces in its lines due to ship passages are simulated, using the potential software ROPES and the UGent in-house package Vlugmoor. Focus is on the mooring plan (operational parameter) and the characteristics of the individual lines (design parameter).

Alfvén eigenmode classification based on ECE diagnostics at DIII-D using deep recurrent neural networks

Modern tokamaks have achieved significant fusion production, but further progress towards steady-state operation has been stymied by a host of kinetic and MHD instabilities. Control and identification of these instabilities is often complicated, warranting the application of data-driven methods to complement and improve physical understanding. In particular, Alfvén eigenmodes are a class of ubiquitous mixed kinetic and MHD instabilities that are important to identify and control because they can lead to loss of confinement and potential damage to the walls of a plasma device. In the present work, we use reservoir computing networks (RCNs) to classify Alfvén eigenmodes in a large, expert-identified database of DIII-D discharges, covering a broad range of operational parameter space. Despite the large parameter space, we show excellent classification and prediction performance, with an average hit rate of 91% and false alarm ratio of 7%, indicating promise for future implementation with additional diagnostic data and consolidation into a real-time control strategy.

Anti-Melanoma Capability of Contactless Cold Atmospheric Plasma Treatment

In this study, we demonstrated that the widely used cold atmospheric plasma (CAP) jet could significantly inhibit the growth of melanoma cells using a contactless treatment method, The flow rate of helium gas was a key operational parameter to modulate electromagnetic (EM) effect on melanoma cells. Metal sheets with different sizes could be used as a strategy to control the strength of EM effect. More attractive, the EM effect from CAP could penetrate glass/polystyrene barriers as thick as 7 mm. All these discoveries presented the profound non-invasive nature of a physically based CAP treatment, which provided a solid foundation for CAP-based cutaneous/subcutaneous tumor therapy.

Response Surface Mesh with the Outer Input Method

Artificial intelligence in general and optimization tasks applied to the design of aerospace, space,and automotive structures, rely on response surfaces to forecast the output of functions, and are vital part of these methodologies. Yet they have important limitations, since greater precisions require greater data sets, thus, training or updating larger response surfaces become computationally expensive, sometimes unfeasible. This has been a bottle neck limitation to achieve more promising results, rendering many AI related task with a low efficiency.To solve this challenge, a new methodology created to segment response surfaces is hereby presented. Differently than other similar methodologies, the novel algorithm here presented named outer input method, has a very simple and robust operation. With only one operational parameter, maximum element size, it efficiently generates a near isopopulated mesh for any data set with any type of distribution, such as random, Cartesian, or clustered, for domains with any number of coordinates.Thus, it is possible to simplify the response surfaces by generating an ensemble of response surfaces, here denominated response surface mesh. This study demonstrates how a metamodel denominated Kriging, trained with a large data set, can be simplified with a response surface mesh, significantly reducing its often expensive computation costs> experiments here presented achieved an speed increase up to 180 times, while using a dual core parallel processingcomputer. This methodology can be applied to any metamodel, and metamodel elements can be easily parallelized and updated individually. Thus, its already faster training operation has its speed increased.

Correlated Damage Probabilities of Bridges In Seismic Risk Assessment of Transportation Networks

This paper aims to identify and use a logistic regression approach to model the spatial correlation of damage probabilities in expanded transportation networks. This paper uses Bayesian theory and the multinomial logistic model to analyze the different damage states and damage probabilities of bridges by considering the damage correlation. The correlation of the damage probabilities is considered both in different bridges of a network and in the different damage states of each bridge. The correlation model of the damage probabilities is considered in the seismic assessment of a part of the Tehran transportation network with 26 bridges. Moreover, the extra daily traffic time (EDTT) is selected as an operational parameter of the transportation network, and the shortest path algorithm is considered to select the path between two nodes. The results show that including the correlation of the damage probabilities decreases the travel time of the selected network. The average decreasing in the correlated case compared to the uncorrelated case, using two selected EDTT models are 53% and 71%, respectively.

Response Surface Mesh with the Outer Input Method

Artificial intelligence in general and optimization tasks applied to the design of aerospace, space,and automotive structures, rely on response surfaces to forecast the output of functions, and are vital part of these methodologies. Yet they have important limitations, since greater precisions require greater data sets, thus, training or updating larger response surfaces become computationally expensive, sometimes unfeasible. This has been a bottle neck limitation to achieve more promising results, rendering many AI related task with a low efficiency.To solve this challenge, a new methodology created to segment response surfaces is hereby presented. Differently than other similar methodologies, the novel algorithm here presented named outer input method, has a very simple and robust operation. With only one operational parameter, maximum element size, it efficiently generates a near isopopulated mesh for any data set with any type of distribution, such as random, Cartesian, or clustered, for domains with any number of coordinates.Thus, it is possible to simplify the response surfaces by generating an ensemble of response surfaces, here denominated response surface mesh. This study demonstrates how a metamodel denominated Kriging, trained with a large data set, can be simplified with a response surface mesh, significantly reducing its often expensive computation costs> experiments here presented achieved an speed increase up to 180 times, while using a dual core parallel processingcomputer. This methodology can be applied to any metamodel, and metamodel elements can be easily parallelized and updated individually. Thus, its already faster training operation has its speed increased.

Spectral characteristics of initial and irradiated GaAsP LEDs

The optical characteristics of the GaAs1-хPх output LEDs and LEDs irradiated with electrons with Е = 2 MeV, Ф = 1015 ÷ 1016 cm-2 were studied. The width of the band gap of the GaAs1-хPх (х = 0.45) solid solution was estimated. Its growth is caused by the heating of carriers by the field of the p-n junction. The damage coefficients of the lifetime of minority charge carriers for irradiated GaAsP LEDs have been calculated and the consequences of exposure to radiation on the operational parameter Т1, which determines the thermal stability of the diodes, have been analyzed.

Anaerobic digestion of dried/shredded food waste in a periodic anaerobic baffled reactor

The objective of the current work is to study the impact of the operational parameters' variation (HRT, OLR and T) on biomethane productivity in a Periodic Anaerobic Baffled Reactor (PABR). The feedstock used was a biomass product named FORBI (Food Residue Biomass), which is dried and shredded source-separated household food waste. The Periodic Anaerobic Baffled Reactor (PABR) is an innovative, high-rate bioreactor. Apart from the Hydraulic Retention Time (HRT) and the Organic Loading Rate (OLR), an important operational parameter is the Switching Period (T) of the feeding compartment: when T is high, the bioreactor operation is similar to an Anaerobic baffled reactor (ABR), while when it is low, the operation approaches that of an Upflow Anaerobic Sludge Blanket Reactor (UASBR). Nine distinct experimental phases were conducted, during which the operational parameters of the PABR were consecutively modified: the HRT varied from 9 to 2.5 days, T between 2 days and 1 and finally the OLR from 1.24 gCOD/Lbioreactor*d to 8.08 gCOD/Lbioreactor*d. The maximum biomethane yield was 384 LCH4/kgFORBI corresponding to the operation at HRT = 5 d, OLR = 2.14 gCOD/Lbioreactor*d and T = 2 days. Similar efficiency (333 LCH4/kg­FORBI) was achieved at higher OLR (4.53 gCOD/Lbioreactor*d).