Estimating Hydrodynamic Sectional Loads for FPSOs Using Artificial Neural Networks

The desire to attain a deeper understanding of the fundamental aspects governing the mechanical properties of biofilms has become more prominent in recent years. This has largely been due to the realization that these sessile microbial communities often withstand environments where hydrodynamic turbulence and shearing forces are considerable. In the present study, Escherichia coli K-12 was used as a model system to develop a laboratory technique that can be used to quickly screen the mechanical integrity or stability of laboratory cultivated bacterial biofilms when exposed to such external, hydrodynamic shear forces. The screening method utilizes a custom-built, automated water jetting apparatus to generate and precisely apply a pressurized stream of water directly to biofilms cultured in multi-well plates. An optimized set of water jetting parameters was determined to resolve subtle to moderate differences in the mechanical stability of isogenic strains of E. coli K-12 as a function of percent biofilm removal. Mutations in both flagella biosynthesis (fliA) and acetate metabolism (ackA and ackA pta) were shown to impair the mechanical integrity of 24-h biofilms, while a "housekeeping" strain deficient in arginine metabolism (argD) exhibited a mechanical stability profile comparable to the parent strain.

ObjectivesSonic toothbrushes generate hydrodynamic shear forces for oral biofilm removal on tooth surfaces, but the effective thresholds for biofilm removal remain unexplored. This in vitro study aimed to investigate various threshold values for hydrodynamic biofilm removal in vitro.Materials and methodsA specialized test bench was designed with a known water flow field within a gap, ensuring that hydrodynamic shear forces on the wall were solely dependent on the volume flow, which was quantifiable using an integrated flow meter and proven by a computational fluid dynamics simulation. A young 20 h supragingival six-species biofilm was developed on hydroxyapatite disks (∅ 5 mm) and applied into the test bench, subjecting them to ascending force levels ranging from 0 to 135 Pa. The remaining biofilms were quantified using colony forming units (CFU) and subjected to statistical analysis through one-way ANOVA.ResultsVolume flow measures < 0.1 l/s: Error 1% of reading were established with the test bench. Untreated biofilms (0 Pa, no hydrodynamic shear forces) reached 7.7E7 CFU/harvest and differed significantly from all treated biofilm groups. CFU reductions of up to 2.3E6 were detected using 20 Pa, and reductions of two orders of magnitude were reached above wall shear forces of 45 Pa (6.9E5).ConclusionsCritical hydrodynamic force levels of at least 20 Pa appear to be necessary to have a discernible impact on initial biofilm removal.Clinical relevancePure hydrodynamic forces alone are insufficient for adequate biofilm removal. The addition of antiseptics is essential to penetrate and disrupt hydrodynamically loosened biofilm structures effectively.

Hydrodynamic shear force shaped the microbial community and function in the aerobic granular sequencing batch reactors for low carbon to nitrogen (C/N) municipal wastewater treatment

Introduction. Under the combined action of shear force and bending moment web-shear cracking takes place in the thin webs of reinforced concrete beams. The strength of the concrete struts between the cracks depends on the size of the web, concrete strength, parameters of the transverse and longitudinal reinforcement, and the shear span-to-depth ratio. Plane stress constitutive models for reinforced concrete are difficult to implement. For this reason, building codes employ empirical formulas for web-crushing strength which were obtained from the analysis of the existing experimental data. Using machine learning tools — artificial neural networks (ANN) — can serve as a solution that allows to take into account the impact of structural and loading parameters more accurately. The analyzed experimental base included the test results of 77 beams. The input layer of the ANN consisted of 7 independent variables, the output — of 1 dependent, and both linear and nonlinear functions were considered as activation functions.&#x0D; &#x0D; Materials and methods. The article studies I-shaped concrete beams with basalt fiber reinforced polymer and steel transverse reinforcement tested by authors. To ensure the statistical significance of independent variables in all indicators, the database of other authors was also considered. Neural networks were developed using STATISTICA software package. In the first stage, the input and output variables were normalized. The accuracy of the ANN model prediction was compared with the accuracy of regression models. In the last stage, the calculations were performed without normalizing the variables.&#x0D; &#x0D; Results. Artificial neural networks prediction has high accuracy. The relative error of prediction was 28.6 % for the regression method and 10.9 % for the ANN. For calculations without preliminary normalization relative error of prediction was 6.6 %.&#x0D; &#x0D; Conclusions. The results of research and other similar studies suggest ANN to be a promising tool for solving intractable problems of structural engineering.

In case of offshore support vessels, it is quite important for their behaviours in low speed manoeuvrability. It is not easy to operate offshore support vessels and it raises an importance to train crew by a ship handling simulator. Therefore it is quite hot issue for ship handling simulator vendors to provide mathematical model of support vessels, but from the users’ point-of-view the model used for the simulator is not clear. Up to now some mathematical models are proposed in the literature, but the validation is not yet done carefully. For example, cross flow model is the main part for hydrodynamic force in low speed ship manoeuvring. From model ship experiments of various types of ships and from some comparison study, it is found that further discussion is necessary. In this paper, sway force and yaw moment as well as surge force will be treated in low speed condition with certain sway and yaw motions. There are many experiment results for such hydrodynamic forces and moment, especially for sway force and yaw moment, for sway motion, but those for yaw motion is quite limited. In this paper, such hydrodynamic forces and moment for both sway and yaw motions are calculated and validated with experiment results. Some manoeuvring simulation will also demonstrate which terms and/or expressions in the mathematical model are affecting the motion.

Validation of 4DOF maneuvering coefficients optimization using hydrodynamic force and moment estimated from free-running model test results

When bottom mounted structures of large displacement are immersed in the sea, earthquake induced hydrodynamic loads may become important design factors. Specifically, as the earth oscillates, a bottom mounted structure is forced to describe time dependent motion in an otherwise still fluid. As a result, hydrodynamic loads in addition to the inertial loads of the structure itself are induced. In this paper, a theoretical approach to the calculation of these hydrodynamic loads is outlined and numerical results are presented for several submerged configurations. Practical geometries considered include a submerged oil storage tank configuration and a conical configuration as has been proposed for offshore drilling rig designs for deployment in the Arctic. Also, computations were carried out for a sphere and vertical circular cylinder and various comparisons with classical results are made. Numerical results for these submerged structures are presented in the form of a dimensionless hydrodynamic load parameter or added mass coefficient. Results corresponding to a number of different water depths are presented to show the rather sizable effect of the relative water depth on the hydrodynamic force. It is shown that for typical earthquake frequencies, the effect of the free water surface is to reduce the hydrodynamic loads in comparison to the corresponding infinite depth values. Experimental results obtained by vibration testing are presented for a submerged sphere and a vertical circular cylinder. These results show excellent agreement with the theoretical results. Introduction Principal attention in earthquake engineering has been given to the generation of tsunami waves, the shoreline run-up and damage caused by these waves, as well as the damage to dry land structures caused by strong ground motion. With the increased deployment of large submerged structures, an additional facet of this important problem has come to light. Namely, if a large bottom mounted structure submerged in the ocean is excited by oscillatory ground motion, hydrodynamic loads in addition to its own inertia forces come into play. As the structure is caused to move through the water, hydrodynamic forces which are dependent upon the size and shape of the structure as well as the water depth and frequency of oscillation arise. On account of the large density of water, these forces are often quite large and have considerable influence on the structural design. When an object is accelerated through a fluid there are, in general, two types of forces that are recognized, one being a drag component and a second, the inertial component. The nature of the flow produced by the time dependent motion of the immersed rigid object and the relative contribution of these two components of force is generally considered to be strongly dependent on the amplitude of the relative fluid motion in comparison to the characteristic lineal dimension of the object. For example, Keulegan and Carpenter [1] found that, for harmonic motion of the fluid past a fixed circular cylinder, major flow separation did not occur and the forces were well represented by potential flow values provided the amplitude of the motion was less than about a half diameter.

Adhesion moment model for estimating particle detachment from a surface

Hydrodynamics vs. membrane drag determines the mobility of bioengineered proteins during flow.

SummaryThis paper presents an alternative approach for predicting the dynamic wind response of tall buildings using artificial neural network (ANN). The ANN model was developed, trained, and validated based on the data generated in the context of Indian Wind Code (IWC), IS 875 (Part 3):2015. According to the IWC, dynamic wind responses can be calculated for a specific configuration of buildings. The dynamic wind loads and their corresponding responses of structures other than the specified configurations in IWC have to be estimated by wind tunnel tests or computational techniques, which are expensive and time intensive. Alternatively, ANN is an efficient and economical computational analysis tool that can be implemented to estimate the dynamic wind response of a building. In this paper, ANN models were developed to predict base shear and base bending moment of a tall building in along‐ and across‐wind direction by giving the input as the configuration of the building, wind velocity, and terrain category. Multilayer perceptron ANN models with back‐propagation training algorithm was adopted. On comparison of results, it was found that the predicted values obtained from the ANN models and the calculated responses acquired using IWC standards are almost similar. Using the best fit model of ANN, an extensive parametric study was performed to predict the dynamic wind response of tall buildings for the configurations on which IWC is silent. Based on the results obtained from this study, design charts are developed for the prediction of dynamic wind response of tall buildings.

Longitudinal strength of high block coefficient merchant ships in irregular waves

This study investigates the influence of cutting parameters on drilling performance for Ti6Al4V alloy using TiN-coated carbide drill bits in a dry drilling environment. The surface roughness, feed force, cutting moment, cutting temperature, tool performance, hole diameter, circularity, and cylindricity values of the hole were studied. The optimal drilling parameters were determined using ANOVA to achieve the desired hole sizes, and prediction models were developed using artificial neural networks (ANN). The experimental results revealed that the feed rate was the most influential parameter for feed forces (92%), whereas the cutting speed had the greatest effect on tool performance (98%). It was determined as a result of the analyses that the optimal feed rate for tool performance, cutting moment, surface roughness, cutting temperature, and feed forces was 0.05 mm/rev. The diameter of the hole was positively affected at high cutting speeds and feed rates. The circularity and cylindricity of the hole were adversely affected by these cutting conditions. Adhesive wear was seen on the drill bits in all cutting conditions, and surface roughness was negatively affected by this adhesive wear. Artificial neural networks (ANN) were used to predict operating cost outputs (feed force, tool performance) and hole quality outputs (surface roughness, deviation of hole diameter). The compatibility of the experimental results and the ANN model was examined in the control experiment. ANN was able to predict the surface roughness and hole diameter with deviation values of 3% and 7%, respectively. Similarly, the deviation values for feed force and tool performance were determined at 3% and 10%, respectively.

Predicting the seismic behavior of concrete gravity dams is a critical challenge in earthquake engineering. This Study investigates the potential of Artificial Neural Networks (ANNs) in predicting the natural frequency of concrete gravity dams based on their geometric and mechanical properties. A dataset of 320 numerical simulations was developed to train and evaluate different artificial neural network architectures. The results indicate that simple neural networks with one or two hidden layers provide strong predictive capabilities for predicting the fundamental frequency, depending on the number of neurons in each layer. However, the proposed approach does not yet incorporate all the factors that may influence the seismic response of a dam, such as hydrodynamic forces and realistic seismic input. Future research could integrate nonlinear modeling and realistic earthquake excitations to validate and enhance the trends identified in this Study. These findings underscore the potential of data-driven modeling, such as neural networks, to evaluate seismic vulnerability, especially for massive concrete structures.

Flexural Characterization of Foam Cored E-Glass Reinforced Smart Materials Using Artificial Neural Network

Effects of pitch angle on a near free surface underwater vehicle