A review on the turbulence modelling strategy for ship hydrodynamic simulations

Ship operations are accompanied by turbulent regimes that play a significant role in the hydrodynamic characteristics. With the ongoing development of computational technologies, it is now feasible to numerically simulate turbulent ship flows with a high degree of detail. Turbulent simulations, however, tend to be computationally expensive and require a trade off between computational costs and fidelity. Whilst a range of turbulence modelling strategies is available in Computational Fluid Dynamics, there is a lack of up-to-date recommendations on their suitability for different ship-flow simulation scenarios. Addressing this gap, the present work reviews the state-of-the-art of turbulence modelling for ship hydrodynamic applications. As a result, this paper introduces the most known turbulence modelling approaches used in the field, followed by a thorough discussion of their applicabilities and limitations. Furthermore, this paper provides recommendations for the selection of turbulence modelling strategies versus various ship simulation scenarios, such as resistance prediction, ship flow modelling, self-propulsion, and cavitation analyses. It is expected that the present paper will provide decision-making support by helping CFD users minimise the time spent on trial and error, as well as providing valuable insights to promote the advancement of turbulence modelling

Wave-GAN: A deep learning approach for the prediction of nonlinear regular wave loads and run-up on a fixed cylinder

Machine learning techniques have inspired reduced-order solutions in the fluid mechanics field that show benefits of unprecedented capability and efficiency. Targeting ocean-wave problems, this work has developed a novel data-driven computational approach, named Wave-GAN. This new tool is based upon the conditional Generative Adversarial Network (GAN) principle, and it provides the ability to predict three-dimensional nonlinear wave loads and run-up on a fixed structure. The paper presents the principle of Wave-GAN and an application example of regular waves interacting with a vertical fixed cylinder. Computational Fluid Dynamics (CFD) is used to provide training and testing datasets for the Wave-GAN deep learning network. Upon verification, Wave-GAN proved the ability to provide accurate results for predicting wave load and run-up for wave conditions that were not informed during training. Yet the CFD-comparative results were only obtained within seconds by the deep learning tool. The promising results demonstrate Wave-GAN's outstanding potential to act as a pioneering sample of applying machine learning techniques to wave-structural interaction problems. It is envisioned that the new approach could be extended to more complex shapes and wave conditions to facilitate the various design stages of marine and offshore engineering applications such as monopiles. As a result, enhanced reliability is expected to optimise structural performance and prevent environmental disasters

A Review on Applications of Machine Learning in Shipping Sustainability

The shipping industry faces a significant challenge as it needs to significantly lower the amounts of Green House Gas emissions at the same time as it is expected to meet the rising demand. Traditionally, optimising the fuel consumption for ships is done during the ship design stage and through operating it in a better way, for example, with more energy-efficient machinery, optimising the speed or route. During the last decade, the area of machine learning has evolved significantly, and these methods are applicable in many more fields than before. The field of ship efficiency improvement by using Machine Learning methods is significantly progressing due to the available volumes of data from online measuring, experiments and computations. This amount of data has made machine learning a powerful tool that has been successfully used to extract information and intricate patterns that can be translated into attractive ship energy savings. This article presents an overview of machine learning, current developments, and emerging opportunities for ship efficiency. This article covers the fundamentals of Machine Learning and discusses the methodologies available for ship efficiency optimisation. Besides, this article reveals the potentials of this promising technology and future challenges

Differential contribution of PB1-F2 to the virulence of highly pathogenic H5N1 influenza A virus in mammalian and avian species

Highly pathogenic avian influenza A viruses (HPAIV) of the H5N1 subtype occasionally transmit from birds to humans and can cause severe systemic infections in both hosts. PB1-F2 is an alternative translation product of the viral PB1 segment that was initially characterized as a pro-apoptotic mitochondrial viral pathogenicity factor. A full-length PB1-F2 has been present in all human influenza pandemic virus isolates of the 20(th) century, but appears to be lost evolutionarily over time as the new virus establishes itself and circulates in the human host. In contrast, the open reading frame (ORF) for PB1-F2 is exceptionally well-conserved in avian influenza virus isolates. Here we perform a comparative study to show for the first time that PB1-F2 is a pathogenicity determinant for HPAIV (A/Viet Nam/1203/2004, VN1203 (H5N1)) in both mammals and birds. In a mammalian host, the rare N66S polymorphism in PB1-F2 that was previously described to be associated with high lethality of the 1918 influenza A virus showed increased replication and virulence of a recombinant VN1203 H5N1 virus, while deletion of the entire PB1-F2 ORF had negligible effects. Interestingly, the N66S substituted virus efficiently invades the CNS and replicates in the brain of Mx+/+ mice. In ducks deletion of PB1-F2 clearly resulted in delayed onset of clinical symptoms and systemic spreading of virus, while variations at position 66 played only a minor role in pathogenesis. These data implicate PB1-F2 as an important pathogenicity factor in ducks independent of sequence variations at position 66. Our data could explain why PB1-F2 is conserved in avian influenza virus isolates and only impacts pathogenicity in mammals when containing certain amino acid motifs such as the rare N66S polymorphism

Antibiotics in foods: Determination and quantification by HPLC with fluorescence detection

A monitorizaçao de resíduos de antibióticos em alimentos, é uma área de preocupaçao crescente e muito importante devido ao seu potencial impacto na saúde humana. Os antibióticos sao admnistrados nos animais produtores de alimento, nao só no tratamento de doenças mas também subterapêutícamente para manter a saúde e promover o crescimento. O uso de antibióticos nao autorizados, ou o nao cumprimento das orientaçoes de admnistraçao dos antibióticos autorizados, pode resultar na presença de níveis elevados de residuos de antibióticos nos produtos alimentares. Por este motivo, a monitorizaçao de residuos de antibióticos em alimentos deve constituir um programa de vigilância. A cromatografia líquida de alta resoluçao (HPLC) constitui o método de eleiçao mais extensamente utilizado na identificaçao e quantificaçao de resíduos de antibióticos em alimentos. A detecçao fluorimétrica constitui um poderoso modo de detecçao de residuos de antibióticos, principalmente quando há a necessidade de reduzir ou mesmo eliminar as interferencias alimentares, devido á sua maior sensibilidade e especificidade.The monitoring of food material s for antibiotic residues is an area of increasing concern and importance due to the potential impact on human health. Antibiotics are used in food-producing animals not only for treatment of disease, but also subtherapeutically to mantain health and promove growth. The use of unauthorized antibiotics or the failure to follow label directions for approved antibiotics, could result in unsafe antibiotic residues in food products. Therefore, monitoring antibiotic residues in food forms part of a general policy to prevent unnapproved uses of antibiotics. Liquid chromatography has become the analytical method of choice for the identification and quantification of antibiotic residues in food. Fluorescence detection has been proved to be a valuable tool for antibiotic residue analysis, where interferences from food. components must be reduced or eliminated, due of the higher specifity and sensitivity

Derivation of the Planck Spectrum for Relativistic Classical Scalar Radiation from Thermal Equilibrium in an Accelerating Frame

The Planck spectrum of thermal scalar radiation is derived suggestively within classical physics by the use of an accelerating coordinate frame. The derivation has an analogue in Boltzmann's derivation of the Maxwell velocity distribution for thermal particle velocities by considering the thermal equilibrium of noninteracting particles in a uniform gravitational field. For the case of radiation, the gravitational field is provided by the acceleration of a Rindler frame through Minkowski spacetime. Classical zero-point radiation and relativistic physics enter in an essential way in the derivation which is based upon the behavior of free radiation fields and the assumption that the field correlation functions contain but a single correlation time in thermal equilibrium. The work has connections with the thermal effects of acceleration found in relativistic quantum field theory.Comment: 23 page

Brownian Entanglement

We show that for two classical brownian particles there exists an analog of continuous-variable quantum entanglement: The common probability distribution of the two coordinates and the corresponding coarse-grained velocities cannot be prepared via mixing of any factorized distributions referring to the two particles in separate. This is possible for particles which interacted in the past, but do not interact in the present. Three factors are crucial for the effect: 1) separation of time-scales of coordinate and momentum which motivates the definition of coarse-grained velocities; 2) the resulting uncertainty relations between the coordinate of the brownian particle and the change of its coarse-grained velocity; 3) the fact that the coarse-grained velocity, though pertaining to a single brownian particle, is defined on a common context of two particles. The brownian entanglement is a consequence of a coarse-grained description and disappears for a finer resolution of the brownian motion. We discuss possibilities of its experimental realizations in examples of macroscopic brownian motion.Comment: 18 pages, no figure

Selfoscillations of Suspended Carbon Nanotubes with a Deflection Sensitive Resistance under Voltage Bias

We theoretically investigate the electro-mechanics of a Suspended Carbon Nanotube with a Deflection Sensitive Resistance subjected to a homogeneous Magnetic Field and a constant Voltage Bias. We show that, (with the exception of a singular case), for a sufficiently high magnetic field the time-independent state of charge transport through the nanotube becomes unstable to selfexcitations of the mechanical vibration accompanied by oscialltions in the voltage drop and current across the nanotube.Comment: 4 pages, 1 figur