Prediction Of Total Resistance Research Articles

Optimization and predictive modeling of membrane based produced water treatment using machine learning models

Membrane technology has become a crucial solution for treating produced water, playing a vital role in resource recovery and environmental preservation. The use of advanced Artificial Intelligence (AI) technique such as Machine Learning (ML) has introduced an innovative approach to define and model these treatment procedures such as trans-membrane pressure (TMP) and total membrane resistance. The research focuses specifically on the utilization of Artificial Neural Network (ANN) and Extreme Gradient Boosting (XGBoost) model to improve the precision and effectiveness of produced water treatment using membrane technology. Also, the study presented explores how the Artificial Neural Network (ANN) framework excels in predictive insights and process modeling, optimizing operational parameters in membrane-based treatment setups. Furthermore, integrating the Extreme Gradient Boosting (XGBoost) model highlights its ability to enhance system performance, providing accurate predictions and better control in complex treatment processes. It presents comprehensive research of combining Artificial Neural Network (ANN) and Extreme Gradient Boosting (XGBoost) model with membrane technology for produced water treatment, emphasizing their potential to improve optimization techniques and modeling approaches, ultimately enhancing water treatment efficiency and sustainability. Both the Artificial Neural Network (ANN) and Extreme Gradient Boosting (XGBoost) models achieved an impressive R-squared (R2) scores of 0.754, and 0.97 for Transmembrane Pressure (TMP), and 0.95 and 0.81 for Total Resistance prediction respectively.

Investigation of the optimum longitudinal single transverse step location for a high-speed craft

One of the crucial aspects of the conceptual design of a stepped planing hull is the prediction of its performance. To improve performance, the prediction of total resistance must become more accurate. In the field of research, both towing tank experiments and numerical analysis may be used to achieve this goal. In this study, experiments were conducted initially to investigate total resistance of a relatively high-speed craft without a transverse step. Later, numerical computations were carried out to validate the experimental results. After it was determined that the test results and CFD methods were in good agreement, the experimental method continued to investigate the resistance properties of the hull with four different configurations to evaluate the optimal longitudinal position of a single transverse step. The ideal longitudinal position of the single transverse step was evaluated based on a similar relatively high-speed hull with a velocity of up to beam Froude number (FrB) 2.56 in this study, focusing on the FrB range between 2.30 and 2.45.

Research on the Deep Learning Technology in the Hull Form Optimization Problem

A high−accuracy objective function evaluation method is crucial in ship hull form optimization. This study proposes a novel approximate ship hull form optimization framework using the deep learning technology, deep belief network algorithm. To illustrate the advantages of using the deep belief network algorithm in the prediction of total resistance, two traditional surrogate models (ELMAN and RBF neural networks) are also employed in this study to predict total resistance for different modified ship models. It can be seen from the results that the deep belief network algorithm is more suitable for forecasting total resistance of a DTMB5512 ship model than the traditional surrogate models. Following this, two design variables are selected to alter the bow geometry of the DTMB5512 ship model. The total resistance for different modified ship hulls is estimated using the deep belief network algorithm. Furthermore, an optimal solution with minimum total resistance in a two−dimensional space is obtained using the particle swarm optimization algorithm. The optimization results indicate that the optimization framework using the deep belief network algorithm can obtain an optimal solution with the smallest total resistance for different ship speeds.

An analysis of the effect of the bow entrance angle on ship resistance

Modifying the hull shape is one of the challenges in designing a ship. The angle of the ship's entrance is a significant determinant of the total resistance of the ship. This research aimed to analyze the total resistance of the ship due to changes in the shape of the ship's bow. This research used the Computational Fluid Dynamics (CFD) method with overset mesh technique to predict the ship's total resistance and trim angle. Parameters used in the five-speed numerical simulations. This research indicated that a change in the bow angle of the ship results in a 5% change in the ship's resistance for every change in the bow entrance angle. Therefore, the prediction of total resistance shows significant results in planning conditions. Compared to another bow entrance angle at low Fr, total resistance has no noticeable differences. Angle changes of the entrance of the ship's bow also significantly affected the trim conditions on the ship according to the speed. At Fr 1.03, the stern trim angle tended to decrease dramatically. As a result, the trim by stern under porpoising probably oscillates considerably.

A Numerical Study of Spray Strips Analysis on Fridsma Hull Form

Spray strips are deflectors added to the hull to reduce the Wetted Surface Area (WSA). The reduced WSA will decrease the total ship drag caused by the deflection of the spray strip installation. The research aimed to predict the function of the spray strip to improve ship performance using Computational Fluid Dynamics (CFD). The numerical approach in this study used the Finite Volume Method (FVM) with the RANS (Reynolds-averaged Navier–Stokes) equation to solve fluid dynamics problems. VOF (Volume of Fluid) was used to model the water and air phases. The results of this study indicated that the number of spray strips would have a significant effect compared to without using a spray strip. Spray strips with three strips could reduce the total resistance by 4.9% at Fr 1.78. Spray strips would increase the total resistance value by 2.1% at low speeds. Spray strips were effective for reducing total resistance at Fr > 1 or the planing mode conditions. The total resistance prediction used three suggestion profiles with the best performance to reduce total resistance by 6.0% at Fr 1.78.

The ghost fluid method for OpenFOAM: A comparative study in marine context

The open source CFD library OpenFOAM is utilized to implement the ghost fluid method (GFM). The implemented solver GFMFoam is compared against the standard volume-of-fluid (VOF) solver interFoam in OpenFOAM. For both VOF solvers, the scalar transport equation is solved similarly but the discretization of the momentum and continuity equations differ between GFMFoam and interFoam. The two methods are compared in four test cases with mild surface deformation. First, the GFM implementation is validated. Second, the results on a 2 d interface shear layer highlight the importance of the viscous term treatment in the GFM. Third, the model scale Wiggley hull results indicate that the GFMFoam is more sensitive to the grid aspect ratio and resolution than interFoam but the viscous resistance is better predicted. Also, GFMFoam is shown to be less sensitive to numerical ventilation than interFoam. Fourth, the results from the Hamburg Test Case indicate a total resistance prediction within 1% when evaluated against reference data at model scale and 10% at full scale. Based on the steady state test cases of the present study, GFMFoam has moderate benefits over interFoam but it does not clearly outperform interFoam.

Predicting ship frictional resistance due to biofouling using Reynolds-averaged Navier-Stokes simulations

Biofouling reduces the overall hydrodynamic performance of a ship and increases fuel consumption. Four different fouling resistant coatings were experimentally investigated to study their surface roughness properties, during two years of exposure at sea water. An open source RANS solver, OpenFOAM, with integrated rough wall function model, was verified and validated for frictional and total resistance prediction for smooth (no coating) surface conditions. Next, the solver was validated against available CFD and Empirical results for ship frictional resistance prediction with different levels of slime and fouling. Finally, frictional and total resistance was predicted for a ship hull surface with the experimentally studied coatings. The experimental outputs were used to model surface roughness properties of the ship hull, and predict the resistance performance of the ship after one and two years of exposure at sea, with the studied hull coatings. The study reveals that the determination of frictional resistance alone is not enough to understand the required change in propulsion power requirement due to fouling, total drag resistant prediction is also important. It also shows that coatings with initial low roughness may not retain the low resistance property over a long period of exposure to the sea, comparing to other coatings. Overall, the study concludes that CFD can prove to be a very effective tool to study the effect of biofouling on hull properties.

Research on total resistance of ice-going ship for different floe ice distributions based on virtual mass method

This paper presents the virtual mass method to implement the prediction of total resistance for ice-going ship in floe ice region based on the combined method of CFD and DEM. Two ways of floe ice distribution are adopted for the analysis and comparison. The synthetic ice model test has been conducted to determine the optimal virtual mass coefficients for the two different floe ice distributions. Moreover, the further verification and prediction are developed in different ice conditions. The results show that, the fixed and random distributions in numerical method can simulate the interaction of ship and ice vividly, the trend of total resistance varying with the speed and ice concentration obtained by the numerical simulation is consistent with the model test. The random distribution of floe ice has higher similarity and better accuracy than fixed distribution.

Numerical Prediction of Total Resistance Using Full Similarity Technique

Model tests are often conducted by researchers in a real or a numerical towing tank to calculate residuary resistance of a ship with the aid of Froude similarity. Common ITTC-1957 formula is usually employed to calculate frictional resistance. As computer technologies develop over time, CFD tools are used for calculating total resistance of a ship at full scale without establishing any dynamic similarities. In this paper, both Froude and Reynolds similarities are numerically implemented to four different model scales by using virtual fluids. The total resistance at different Fr numbers calculated by the numerical study is validated against the experimental data of DTMB 5512 (L=3.048 m) model hull. The results show that establishing Froude and Reynolds similarities together in numerical simulation is possible in principle. To determine whether it has advantages for prediction of full-scale ship total resistance by employing this method, it is also examined the model scale with the same number of elements and Reynolds number of the full-scale ship. Results show that numerical calculation of total resistance for a full-scale ship in a model scale by defining virtual fluids has only slight advantages on the prediction of residuary resistance. Additionally, no advantage in the calculation of frictional resistance is observed.

A Study on the Statistical Calibration of the Holtrop and Mennen Approximate Power Prediction Method for Full Hull Form, Low Froude Number Vessels

The article herein presents a statistical calibration study of the approximate power method of Holtrop and Mennen focused on adapting the method to vessels characterized as "full" hull forms and low design and operating speeds and, thus, low Froude numbers. The fitting of the method is done by adjusting the constants, coefficients, and components of the method's equations by a systematic variation process controlled by genetic algorithms. The database that the method is calibrated against is consisted by model test results from modern (built between 2010 and 2016) bulk carriers and tankers, the KVLCC2, and the method follows a multistage approach, calibrating first the model for the prediction of total resistance and applying the selfpropulsion equations afterward. The uncertainty of the new improved method is assessed and modeled with a nonlinear regression equation to enable the use of the calibrated method in the early ship design and optimization process. 1. Introduction One of the most important aspects of the design and study of vessels and ocean structures is the prediction of resistance and, thus, powering requirements for a range of operational speeds. Given the constant societal and legislative pressure for the reduction of the carbon footprint of shipping and maritime activities, the accurate and reliable powering prediction from an early stage and tightly integrated within the process itself as the respective optimization studies is imperative. Although towing tank model testing has been since early times the most reliable method for power predictions, because of its high cost and demand for a fixed and given geometry it can be used only in the basic design stage when the design parameters related to the vessel's hull geometry are fixed and serves as a final validation and benchmark to be used afterward in the conclusion of the shipbuilding process during sea trials. On the other hand, the last decade has seen the exponential growth of computational fluid dynamic solvers that solve Reynolds Averaged Navier Stokes equations over the hull form in finite volume approaches (Schneekluth & Bertram 1998; Specialist Committee on CFD 2014). Although originally the computational cost was penalizing its application in early design stages, the advances in computing hardware and software allowed the integration of Computational Fluid Dynamics (CFD) in the early ship hull form design and optimization (Specialist Committee on CFD 2014). Such methods, however, can be used in applications where variables such as the principal particulars have been fixed or have a small variance, with the focus being on the variation in local geometry and topological characteristics that have a significant effect on local flow phenomena (Specialist Committee on CFD 2014). However, when the application in question is a global optimization study, where there is a large variance in principal particulars (vessel's dimensions) and structural and cargo arrangements, there is an apparent need for a large number of optimization variants and, thus, evaluations (usually in the order of thousands). The application of CFD in such cases is impractical as the optimization algorithm will eventually require hundreds of thousands computational hours even in high-performance computers; therefore, empirical or statistical methods are better suited. The most prominent of these is the approximate power prediction method by Holtrop and Mennen (1982) together with its revision (Holtrop 1984). Although this methodology provides sufficient accuracy, the statistical sample of the hull forms on which it is based dates back to the 1970s and 1980s. Such hulls, although roughly similar, have some distinct deviations from modern commercial vessels. In the article presented herein, the authors attempt to make a calibration of the Holtrop and Mennen methodology via a systematic variation with the use of genetic algorithms. The calibration is based on a statistical sample of model test results of low Froude number (Fn) full hull forms (with Cb greater than 0.7) of modern commercial bulk carriers and tankers, and its focus is on the integration of the new coefficients in a holistic methodology for the optimization of large bulk carriers. The uncertainty of the new coefficients is also taken into account based on the sea trial results of an expanded statistical sample.

Methods of Determination of Frictional Resistance for Prediction of Total Resistance of Inland Waterway Vessels

Abstract In the present paper presented are the results of prediction of total resistance of inland waterway vessels based on model test data. In scaling the resistance from model to full scale the extrapolation with two-dimensional frictional resistance formulation (without form factor) was applied, combined with different methods of determination of frictional (viscous) resistance coefficient. There were used the equations that include the effect of water depth, with and without account for pressure gradient. It was shown that limited depth of water substantially affects the frictional resistance. The results of example calculations are compared to resistance prediction made using the ITTC 1957 model-ship correlation line. Example calculations take into account the limited depth of water. Depending on the applied method of determination of frictional resistance coefficient the resultant total resistance of inland waterway vessel is higher or lower than the resistance based on the ITTC 1957 correlation line. The effect of water depth depends on the ratio of water depth to ship draught (h/T), on ship speed, and on the composition of a convoy. The extrapolation of resistance was made without including the form factor. Computations are made based on model test data for an inland waterway cargo vessel, for a kombi-type convoy of an inland waterway cargo vessel and a dumb barge, and for a convoy of two dumb barges without a pushboat.

Performance prediction of hydrofoil- supported catamarans using experiment and ANNs

Using hydrofoils between two hulls of catamarans, hydrofoil supported catamaran (HYSUCAT), is one of the best way to improve the hydrodynamic characteristics of this vessels. In the present study, the hydrodynamic performance of three different hydrofoils of NACA 16, EPPLER 874 and Gottingen 11k are evaluated initially and experimentally via model tests. Afterward, the hydrodynamic performance of these hydrofoils is predicted by using appropriate artificial neural networks (ANNs). For this purpose, the total resistance, effective power, sinkage and trim of HYSUCAT is predicted under different Froude number (Fr) and hydrofoil type. According to the results achieved from the model tests, a significant decrease in total resistance and trim is observed using hydrofoils in the considered catamaran, where Gottingen 11k shows more effects on hydrodynamic performance of HYSUCAT compared to the other two hydrofoils. In addition, maximum mean square errors (MSE) of ANNs output in prediction of total resistance, effective power, sinkage and trim is achieved 0.000683, 0.000155, 0.000454 and 0.00688, respectively. Moreover, an equation is proposed to predict the hydrodynamic performance of the HYSUCAT using ANNs weights and bias.

Prediction of ship resistance in level ice based on empirical approach

A semi-empirical model to predict ship resistance in level ice based on Lindqvist's model is presented. This model assumes that contact between the ship and the ice is a case of symmetrical collision, and two contact cases are considered. Submersion force is calculated via Lindqvist's formula, and the crushing and breaking forces are determined by a concept of energy consideration during ship and ice impact. The effect of the contact coefficient is analyzed in the ice resistance prediction. To validate this model, the predicted results are compared with model test data of USCGC Healy and icebreaker Araon, and full-scale data of the icebreaker KV Svalbard. A relatively good agreement is achieved. As a result, the presented model is recommended for preliminary total resistance prediction in advance of the evaluation of the icebreaking performance of vessels.

Total resistance prediction of an intact and damaged tanker with flooded tanks in calm water

This paper presents the prediction of the total resistance of an intact and damaged ship model using the computational fluid dynamics (CFD) technique. The study is performed on the model of a tanker with a large hole in the bottom of the hull. The damage is based on statistical data on ship grounding accidents and the chosen hole size and location in the midship area represents its plausible size and location due to grounding. Reynolds-Averaged Navier-Stokes (RANS) equations with the volume of fluid (VOF) surface capturing technique are employed to solve the flow around the steadily advancing model of a damaged ship in calm water. The experiments, both on an intact and a damaged ship model that were carried out in the towing tank of the Brodarski Institute in Zagreb, Croatia, are used to evaluate the results. The numerical results are in a good agreement with the experimentally obtained results. The significant average increase of 27% in total resistance due to the altered flow around the hole and inside the flooded tanks can be observed for the analysed case. The study shows that the proposed CFD model and settings provide a good prediction of the total resistance together with the flow both around the damaged hull and inside the flooded tanks of the damaged tanker.

Total Resistance Prediction of Self Propelled Coal Barge (Spcb) Using Computational Fluid Dynamic

In the present paper the computational fluid dynamic (CFD) technique is applied to predict the total resistance (Rt) of Self Propelled Coal Barge (SPCB). The simulation process was executed using ANSYS®software based on fluid flow (CFX) solver. The selected CFD method is volume of fluid (VOF). The solid modeling of SPCB is developed using Maxsurf®. Boundary conditions are set on each domain area covering bottom , free surface , inlet , outlet , ship and wall . The variation of computaitional grid ( meshing grid ) which is used in computation are SST ( Shear Stress Transport )67,000, SST 73,000, SST 103,000, SST 117,000, SST 127,000, SST 147,000, SST 157,000, and SST 200,500. Total resistance (Rt) resulted from CFX computation is validated with total resistance (Rt) resulted from Holtrop. The larger of grid meshing size, the better of validation result. The CFD technique demonstrated good agreement with Holtrop formulae in predicting the total resistance (Rt) of SPCB.

Surface Ship Total Resistance Prediction Based on a Nonlinear Free Surface Potential Flow Solver and a Reynolds-Averaged Navier-Stokes Viscous Correction

A fast turnaround and an accurate computational fluid dynamics (CFD) approach for ship total resistance prediction is developed. The approach consists of a nonlinear free surface potential flow solver (PShip code) with a wet-or-dry transom stern model, and a Reynolds-averaged Navier-Stokes (RANS) equation solver that solves viscous free surface flow with a prescribed free surface given from the PShip. The prescribed free surface RANS predicts a viscous correction to the pressure resistance (viscous form) and viscous flow field around the hull. The viscous free surface flow solved this way avoids the time-consuming RANS iterations to resolve the free surface profile. The method, however, requires employing a flow characteristic-based nonreflecting boundary condition at the free surface. The approach can predict the components of ship resistance, the associated wave profile around the hull, and the sinkage and trim of the ship. Validation of the approach is presented with Wigley, Series 60 (CB = 0.6), and NSWCCD Model 5415 hulls. An overall accuracy of ±2% for ship total resistance prediction is achieved. The approach is applied to evaluating the effects of a stern flap on a DD 968 model on ship performance. An empirical viscous form resistance formula is also devised for a quick ship total resistance estimate.

Application of two‐phase fluid approach for free‐surface ship flow simulation

Computational fluid dynamics methodology is applied to simulate the three‐dimensional free‐surface ship flow. Different from the widely used height function approach, a two‐phase fluid approach is adopted to capture the free surface variation generated by a ship with forward speed. The free surface is determined by the fluid concentration in a volume‐of‐fluid method. In the present study, the calculated results qualitatively match the measurements not only in the free‐surface wave system, but also in the total resistance prediction and the axial velocity distribution at the propeller plane.

Economic aspects of commercially produced solar water heaters

Using hydrofoils between two hulls of catamarans, hydrofoil supported catamaran (HYSUCAT), is one of the best way to improve the hydrodynamic characteristics of this vessels. In the present study, the hydrodynamic performance of three different hydrofoils of NACA 16, EPPLER 874 and Gottingen 11k are evaluated initially and experimentally via model tests. Afterward, the hydrodynamic performance of these hydrofoils is predicted by using appropriate artificial neural networks (ANNs). For this purpose, the total resistance, effective power, sinkage and trim of HYSUCAT is predicted under different Froude number (Fr) and hydrofoil type. According to the results achieved from the model tests, a significant decrease in total resistance and trim is observed using hydrofoils in the considered catamaran, where Gottingen 11k shows more effects on hydrodynamic performance of HYSUCAT compared to the other two hydrofoils. In addition, maximum mean square errors (MSE) of ANNs output in prediction of total resistance, effective power, sinkage and trim is achieved 0.000683, 0.000155, 0.000454 and 0.00688, respectively. Moreover, an equation is proposed to predict the hydrodynamic performance of the HYSUCAT using ANNs weights and bias.