Simulation of Launching and Self-Upending of a Spar Hull

The goal of this research is to develop an effective and efficient plan for the installation of a spar using the barge launching method. Relative to the conventional method of installing spars, the barge launching method enables the elimination of some operations and therefore has the potential to reduce installation costs and schedule. Through numerical simulations based on fundamental equations of motion, the trajectory analysis of the spar and barge during all stages of the launching and spar upending process is performed to verify that the spar, as designed, can be safely installed using the barge launching method. The derivation of the equations motions based on conservation of momentum and use of free body diagrams is provided. The coupled equations of motion are integrated in time and the results are sufficiently reasonable to understand the global behavior of the dynamics of the spar and the barge on the sea. The numerical time integration of the matrix system of equations is performed using Matlab ODE solver based on fourth and fifth order Runge-Kutta formulas. A detailed flow chart for the simulation procedure is provided. Two basic launching scenarios are considered: launching from the top of the spar and from the bottom of the spar. For each of these launch scenarios, three cases involving different trim angles and kinetic friction coefficients are investigated. Based on detailed analysis of the simulation results it is concluded that although both launch scenarios may be feasible, the bottom launch scenario occurs at slower speed and is therefore preferable

Simulation of Launching and Self-Upending of a Spar Hull

The goal of this research is to develop an effective and efficient plan for the installation of a spar using the barge launching method. Relative to the conventional method of installing spars, the barge launching method enables the elimination of some operations and therefore has the potential to reduce installation costs and schedule. Through numerical simulations based on fundamental equations of motion, the trajectory analysis of the spar and barge during all stages of the launching and spar upending process is performed to verify that the spar, as designed, can be safely installed using the barge launching method. The derivation of the equations motions based on conservation of momentum and use of free body diagrams is provided. The coupled equations of motion are integrated in time and the results are sufficiently reasonable to understand the global behavior of the dynamics of the spar and the barge on the sea. The numerical time integration of the matrix system of equations is performed using Matlab ODE solver based on fourth and fifth order Runge-Kutta formulas. A detailed flow chart for the simulation procedure is provided. Two basic launching scenarios are considered: launching from the top of the spar and from the bottom of the spar. For each of these launch scenarios, three cases involving different trim angles and kinetic friction coefficients are investigated. Based on detailed analysis of the simulation results it is concluded that although both launch scenarios may be feasible, the bottom launch scenario occurs at slower speed and is therefore preferable

Simultaneous Energy Storage and Seawater Desalination using Rechargeable Seawater Battery: Feasibility and Future Directions

Rechargeable seawater battery (SWB) is a unique energy storage system that can directly transform seawater into renewable energy. Placing a desalination compartment between SWB anode and cathode (denoted as seawater battery desalination; SWB-D) enables seawater desalination while charging SWB. Since seawater desalination is a mature technology, primarily occupied by membrane-based processes such as reverse osmosis (RO), the energy cost has to be considered for alternative desalination technologies. So far, the feasibility of the SWB-D system based on the unit cost per desalinated water ($m(-3)) has been insufficiently discussed. Therefore, this perspective aims to provide this information and offer future research directions based on the detailed cost analysis. Based on the calculations, the current SWB-D system is expected to have an equipment cost of approximate to 1.02$ m(-3) (lower than 0.60-1.20 $ m(-3) of RO), when 96% of the energy is recovered and stable performance for 1000 cycles is achieved. The anion exchange membrane (AEM) and separator contributes greatly to the material cost occupying 50% and 41% of the total cost, respectively. Therefore, future studies focusing on creating low cost AEMs and separators will pave the way for the large-scale application of SWB-D

Automation of membrane capacitive deionization process using reinforcement learning

Capacitive deionization (CDI) is an alternative desalination technology that uses electrochemical ion separation. Although several attempts have been made to maximize the energy efficiency and productivity of CDI with conventional control methods, it is difficult to optimize the CDI processes because of the complex correlation between the operational conditions and the composition of feed water. To address these challenges, we applied deep reinforcement learning (DRL) to automatically control the membrane capacitive deionization (MCDI) process, which is one of the representative CDI processes, to accomplish high energy efficiency while desalinating water. In the DRL model, the numerical model is combined as the environment that provides states according to the actions. The feed water conditions, that is, the input state of the DRL, were assumed to have a random salt concentration and constant foulant concentration. The model was constructed to minimize energy consumption and maximize desalted water volume per cycle. After training of 1,000 episodes, the DRL model achieved a 22.07% reduction in specific energy consumption (from 0.054 to 0.042 kWh m???3) and 11.60% increase in water desalted water volume per cycle (from 1.96??10???5 to 2.19??10???5 m3), achieving the desired degree of desalination, compared to the first episode. This improved performance was because the trained model selected the optimized operating conditions of current, voltage, and the number and intensity of flushing. Furthermore, it was possible to train the model depending on demand by modifying the reward function of the DRL model. The fundamental principle described in this study for applying the DRL model in MCDI operations can be the cornerstone of a fully automated water desalination process

Seismic Vulnerability Assessment and Mapping of Gyeongju, South Korea Using Frequency Ratio, Decision Tree, and Random Forest

The main purpose of this study was to compare the prediction accuracies of various seismic vulnerability assessment and mapping methods. We applied the frequency ratio (FR), decision tree (DT), and random forest (RF) methods to seismic data for Gyeongju, South Korea. A magnitude 5.8 earthquake occurred in Gyeongju on 12 September 2016. Buildings damaged during the earthquake were used as dependent variables, and 18 sub-indicators related to seismic vulnerability were used as independent variables. Seismic data were used to construct a model for each method, and the models’ results and prediction accuracies were validated using receiver operating characteristic (ROC) curves. The success rates of the FR, DT, and RF models were 0.661, 0.899, and 1.000, and their prediction rates were 0.655, 0.851, and 0.949, respectively. The importance of each indicator was determined, and the peak ground acceleration (PGA) and distance to epicenter were found to have the greatest impact on seismic vulnerability in the DT and RF models. The constructed models were applied to all buildings in Gyeongju to derive prediction values, which were then normalized to between 0 and 1, and then divided into five classes at equal intervals to create seismic vulnerability maps. An analysis of the class distribution of building damage in each of the 23 administrative districts showed that district 15 (Wolseong) was the most vulnerable area and districts 2 (Gangdong), 18 (Yangbuk), and 23 (Yangnam) were the safest areas

Influence of organic matter on seawater battery desalination performance

A rechargeable seawater battery desalination (SWB-D) system stores energy in a battery cell while removing salts from saline water via a sodium superionic conductor membrane and an anion exchange membrane. However, the electrochemical performance often degrades owing to the organic fouling generated on the ion exchange membranes. In this study, we investigated the fouling behavior of the SWB-D system by individually dissolving three different types of organic matter-humic acid, sodium alginate, and bovine-serum-albumin. In terms of the salt-removal performance of the SWB-D system, gradual degradation was observed over three charging cycles using hydrophobic humic acid (-13 %) and bovine-serum-albumin (-18 %), whereas no degradation was caused by hydrophilic sodium alginate. Continuous water flow mitigated the fouling behavior, and a large volume of saline water enabled longer charging. The increase in the electrical resistance of the SWB-D system was measured in the presence of organic matter using electrochemical impedance spectroscopy and the four-electrode method. Additionally, the presence of fouling layer was identified using field-emission scanningelectron microscopy, energy-dispersive X-ray spectroscopy, and Fourier-transform infrared spectrometry. In conclusion, the results demonstrated that the hydrophobic organic matter in the feed water could be unfavorable when operating the SWB-D system

An open-source deep learning model for predicting effluent concentration in capacitive deionization

To effectively evaluate the performance of capacitive deionization (CDI), an electrochemical ion separation technol-ogy, it is necessary to accurately estimate the number of ions removed (effluent concentration) according to energy consumption. Herein, we propose and evaluate a deep learning model for predicting the effluent concentration of a CDI process. The developed deep learning model exhibited excellent prediction accuracy for both constant current and constant voltage modes (R2 >= 0.968), and the accuracy increased with the data size. This model was based on the open-source language, Python, and the code has since been distributed with proper instructions for general use. Owing to the nature of the data-oriented deep learning model, the findings of this study are not only applicable to conventional CDI but also to various types of CDI (membrane CDI, flow CDI, faradaic CDI, etc.). Therefore, by referring to the examples shown in this study, we hope that this open-source deep learning code will be widely used in CDI research

Explainable deep learning model for membrane capacitive deionization operated under fouling conditions

To avoid fouling problems during operation, membrane capacitive deionization (MCDI) requires proper cleaning processes. In this study, we assessed seven different conditions to investigate the effects of flushing conditions and foulant concentration on the recovery rate of the MCDI salt adsorption capacity. Two representative deep learning models, namely the long short-term memory (LSTM) and temporal fusion transformer (TFT) models, were developed to simulate effluent salt concentrations under fouling conditions. The prediction results obtained using the two models indicated that the TFT model (R2, 0.945-0.993; RMSE, 0.051-0.151) was superior to the LSTM model (R2, 0.631-0.993; RMSE, 0.051-0.740) in terms of performance and applicability. Analyses of the permutation importance and attention weights were performed to evaluate the importance of input variables and the model-training process. The interpretation of the models based on attention scores revealed that the TFT model used the applied voltage and implementation of flushing as important inputs, which contributed to higher prediction accuracy. Thus, the proposed model could be utilized as an interpretable artificial intelligence model in practical applications to improve the efficiency of MCDI operations involving flushing processes