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In this paper, joint probability distribution for the size and mass of deep-sea manganese nodules is investigated and reliability-based design optimization (RBDO) of a deep-sea pilot mining robot is performed. As the size and mass of the manganese nodules are strongly correlated and their data are given as bivariate type I interval multiply censored data, a new statistical modeling method should be developed to deal with these issues. However, this is significantly difficult as the conventional methods cannot resolve these issues and there is no prior knowledge of the two physical properties. The proposed method, which employs the multinomial distribution to define the likelihood function and the Akaike information criterion to select the fittest marginal distribution and copula, provides a systematic approach to find the joint probability distribution using the type I interval multiply censored data. To demonstrate the accuracy and effectiveness of the proposed method, two numerical examples are tested. Then, the RBDO of the pilot mining robot is performed using the joint probability distribution resulted from the proposed method.

Design of a pick-up device using the Coanda effect in a deep-sea mining robot is vital to develop a reliable and sustainable deep-sea mining system. One of the crucial performance metrics of this device is the collection efficiency since it affects the mining efficiency of the entire system. However, the collection efficiency is significantly affected by the uncertainties of shape, size and mass of manganese nodules on the seabed. In this study, reliability-based design optimization (RBDO) was performed to improve the reliability of the collection efficiency of the pick-up device under these environmental uncertainties. First, a computational model based on the Coanda effect that predicts the collection efficiency of the pick-up device was developed. Next, RBDO based on the Akaike information criterion method was employed to design the pick-up device by using this model. The results demonstrated that the proposed design methodology significantly improved the design of the pick-up device for the pilot mining robot.

In the pilot miner system, the hydraulic type collector is sensitive to the traffic ability of moving on cohesive soil. Therefore, the traffic ability needs to be simultaneously considered with collector performances in the design process. This means that the system has coupled disciplines and constraints, which can reduce the size of the feasible region, rendering it difficult to use the current optimization techniques to search the optimum satisfying all of the constraints. In this study, a new technique which stochastically explores the optimum point with the highest probability, improving the objective and satisfying the constraints, is applied to the design of a deep-seabed pilot miner system.

In the context of reliability and maintainability, availability is either the probability that a system performs a required function at a specified time or within a required period or the ratio of the spent time against a period. Hence, availability is roughly similar to the operation ratio. The availability of a facility is directly related to costs when designing a large-scale facility, such as a nuclear power or manufacturing plant. A renewable energy system must also consider availability to produce a large quantity of electricity sustainably. This study designed a piezoelectric energy harvesting module that is integrated with a large quantity of PZT material and circuits in consideration of availability. Each component is prone to failure because the piezoelectric effect converts vibrated mechanical energy into electrical energy, and this can seriously degrade the performance of the entire module or cause total failure. Thus, availability needs to be considered at the conceptual design stage. In this study, we analyzed each component’s possible failure modes and calculated reliability based on the failure modes to evaluate the availability of the module. We examined the characteristics of availability, which could be changed by the different series and parallel combinations of the PZT material from which energy was extracted. We designed optimal combinations of series and parallel circuit configurations to secure the required availability and proposed the maintenance cycle of the piezoelectric energy harvesting module.

Piezoelectric energy harvesting—an eco-friendly technology—is gaining more interest from researchers owing to its high power density. Among the various piezoelectric types, many studies have focused on the PZT-based piezoelectric cantilever beam because it can easily achieve the resonance effect compared to other types. In real applications, however, it is not easy to match the loading frequency with the resonance frequency owing to environmental factors. This prevents the application of piezoelectric energy harvesting to real life products because the efficiency of energy extraction decreases considerably outside the resonance frequency domain. In this research, the Taguchi design, which is an effective methodology used to improve product quality without controlling any noise factors, ensures that the PZT-based piezoelectric cantilever beam can obtain robust and high output voltage over a wide frequency range. The PZT-based piezoelectric cantilever beam should be implemented using the multi-response signal-to-noise ratio of the Taguchi design method because it has the two important responses, the output voltage and its deviation. Experiments are performed to measure the output voltage of various harvesters at the resonance frequency and several other frequencies. Based on the experimental results, the Taguchi design method is implemented considering the characteristics of the responses, and an optimal design combination is selected. As a result, the selected optimal combination results in a 9.86% output voltage reduction and an 18.14% decrease in voltage deviation.

In this paper, probability distribution for the size and mass of seafloor manganese nodules is investigated to improve the reliability of a pick-up device in a deep-sea manganese nodule miner. Because the size and mass of the manganese nodules are strongly correlated, statistical models for the size and mass cannot be estimated independently. In order to consider the correlation between the size and mass in a statistical model, the joint probability density function (PDF) is estimated by using copula. This method requires the estimation of the marginal distributions and copula for the two correlated environmental variables. However, this is significantly difficult when there is no prior knowledge of the two physical properties. The proposed method, which employs the Akaike information criterion to select the fittest marginal distributions and copula, provides a systematic procedure to determine a statistical model of correlated environmental variables without any prior knowledge of their distributions. To demonstrate the effectiveness of the proposed method, the joint PDF for the size and mass of manganese nodules is modeled by using the multivariate normal distribution and the proposed method. It was found that the proposed method provides more accurate and reliable estimation results for the two correlated environmental variables.

In this study, reliability evaluation in conceptual design stage is performed to develop the dehydration package of offshore plant equipment. Dehydration package is commonly used in the gas treatment system but has still being developed. Since the technology for the dehydration package relies on almost 100 % in overseas companies, it is necessary to acquire the design/engineering technology for dehydration package. The design must also consider reliability and safety with the special characteristic of ocean environment and the danger of gas process. Therefore, the concept of adsorption dehydration package and design philosophy for development of dehydration package is derived through reliability evaluation in conceptual design stage in this study. After defining the equipment for dehydration package by IDEF0, reliability evaluation of conceptual design for the dehydration package is performed using fault tree analysis (FTA), based on the result of hazard identification (HAZID) analysis. In the future, this result will be applied in detail design.

In rotating motors, an electromagnetic analysis using finite-element analysis is performed according to the time interval such as the steps of the rotation angle. If the uncertainty of time variable is necessary to be handled, the time interval should become smaller than uncertainty values. It causes an increase of the cost even if the surrogate model is applied. Thus, space-time kriging surrogate model is proposed to consider time variable as well as space variable in the design stage. The proposed model handles time variable and predicts responses between time intervals to obtain outputs for the tiny uncertainty of time variable. The performance of prediction is verified with simulation data and then the reliability estimated by the proposed model is compared with measured data from manufactured prototypes.

This paper concerns on the simulation-based proper design method for the efficient production of hydraulic cylinders using CAE techniques which are widely used in various industries. Simulation-based design techniques are actively being used to reduce time and cost for product design in the industries. However, the hydraulic cylinder cannot be designed with general simulation-based design method, because it is designed and manufactured by the standard regulations such as ISO and KS. To tackle this problem, we developed CAEbased proper design method that combines simulation-based design optimization with a production design method using commercial production database according to standard rules. This technology allows designers to efficiently select the best commercial products of the equipment and components required for the system. The developed technology was verified by applying it to the design study of the lifting pump mounting system in deep-seabed integrated mining system developed in 2015.

There is a large risk of damage, triggered by harsh ocean environments, associated with offshore structures, so structural health monitoring plays an important role in preventing the occurrence of critical and global structural failure from such damage. However, obstacles, such as applicability in the field and increasing calculation costs with increasing structural complexity, remain for real-time structure monitoring offshore. Therefore, this study proposes the comparison of cosine similarity with sensor data to overcome such challenges. As the comparison target, this method uses the rate of changes of natural frequencies before and after the occurrence of various damage scenarios, including not only single but multiple damages, which are organized by the experiment technique design. The comparison method alerts to the occurrence of damage using a normalized warning index, which enables workers to manage the risk of damage. By comparison, moreover, the case most similar with the current status is directly figured out without any additional analysis between monitoring and damage identification, which renders the damage identification process simpler. Plus, the averaged rate of errors in detection is suggested to evaluate the damage level more precisely, if needed. Therefore, this method contributes to the application of real-time structural health monitoring for offshore structures by providing an approach to improve the usability of the proposed technique.

Uncertainty-based multidisciplinary design optimization (UMDO) has been widely acknowledged as an advanced methodology to address competing objectives and reliable constraints of complex systems by coupling relationship of disciplines involved in the system. UMDO process consists of three parts. Two parts are to define the system with uncertainty and to formulate the design optimization problem. The third part is to quantitatively analyze the uncertainty of the system output considering the uncertainty propagation in the multidiscipline analysis. One of the major issues in the UMDO research is that the uncertainty propagation makes uncertainty analysis difficult in the complex system. The conventional methods are based on the parametric approach could possibly cause the error when the parametric approach has ill-estimated distribution because data is often insufficient or limited. Therefore, it is required to develop a nonparametric approach to directly use data. In this work, the nonparametric approach for uncertainty-based multidisciplinary design optimization considering limited data is proposed. To handle limited data, three processes are also adopted. To verify the performance of the proposed method, mathematical and engineering examples are illustrated.

This paper introduces the concept of an advanced simulation-based design, “Feedback Loop Design”, and its realization method. The conventional simulation-based design was used for verification of the proposed design because the proposed design has a single process consisting of a design and the design verification. The feedback loop design involves a circular process consisting of the design, design verification, and design modification using an optimization design algorithm and multidisciplinary analysis tools. Optimized solutions can thereby be obtained through a circular design process. A software integration framework, which can combine various design algorithms and analysis tools as well as their interface methods is needed to realize the feedback loop design system. In this study, the software integration framework used is the Remote Component Environment (RCE) (DLR, Germany), and the analysis tool used is the commercial software, DAFUL & ANSYS, for verification of the developed concept.

In the past decades, many reliability analyses have been developed and applied to engineering fields considering uncertainties of input and output random variables as normal distributions. However, when input uncertainty is taken into the system as extreme events such as weather, temperature, environmental conditions etc., output distribution cannot be described by normal distribution. On the other hand, one of distributions to analyze reliability of a system under extreme events is generalized Pareto distribution. Generalized Pareto distribution has been developed and applied for modelling extreme events. However, conventional methods estimate only the shape and scale parameters by assuming that the location parameter is chosen by experiences focused only on the tail distribution. However, since the tail distribution affected by the body distribution and vice versa, both the body and tail distributions should be considered when the parameters of distribution are estimated. In this study, therefore, a new parameter estimation method is proposed to determine shape, scale and location parameters simultaneously by combining likelihood functions of body and tail distributions using Akaike information criterion and generalized Pareto distribution, respectively. Finally, the parameters of body and tail distributions are estimated by maximum likelihood estimation. The proposed method is verified by using mathematical examples with and without inclusion of extreme events. Results show that the proposed method can estimate parameters and distributions for body and tail distributions as well as the more accurate reliability of system under extreme events.

대체모델을 이용한 신뢰성기반 최적설계에서 최적해와 신뢰도의 정확성은 제한조건경계의 대체모델의 정확도에 영향을 받는다. 기존 제안된 제한조건경계 샘플링 기법은 제한조건경계에 실험점을 생성하여 이러한 정확성을 높일 수 있었다. 하지만, 제한조건경계 샘플링 기법은 최적해와 먼 부근의 제한조건경계에도 불필요한 실험점을 생성하여 과도한 계산비용이 발생한다. 본 논문에서는 크리깅 대체모델의 통계적 정보를 이용하여 최적해 근처의 제한조건경계에 실험점을 생성하는 효율적인 제한조건경계 샘플링 기법을 제안한다. 제안한 기법의 효율성과 정확성은 수학예제를 통하여 확인한다.

*** 한국해양과학기술원 부설 선박해양플랜트연구소 해양플랜트산업기술센터 ABSTRACT: This paper reports a simulation-based design method for the optimized arrangement design of buoyancy modules in a marine riser system. A buoyancy module is used for the safe operation and structural stability of the riser. Engineers design buoyancy modules based on experience and experimental data. However, they are difficult to design because of the difficulty of conducting real sea experiments and quantifying the data. Therefore, a simulation-based design method is needed to tackle this problem. In this study, we developed a simulation-based design algorithm using a multi-body dynamic simulation and genetic algorithm to perform optimization arrangement design of a buoyancy module. The design results are discussed in this paper.

Steel Lazy Wave Riser (SLWR), which is a kind of ultra deepwater riser system, is a slender structure with length of over 2,000m. The shape design of the SLWR is very important for operational stability of the SLWR, because the SLWR has strong structural characteristics and are affected by various ocean environmental loads. In this research, the simulation-based design process is studied to find the optimal shape of SLWR at the installation. First, available parameters and responses are derived and the dynamic characteristics of them were analyzed to select the effective parameters on the responses using multibody dynamic simulation. Second, the simulation-based design framework for shape design of the SLWR was developed using opensource-based PIDO(Process Integration and Design Optimization) software, and developed dynamic simulation model. Finally, the shape design of the SLWR at the installation was performed using the developed design framework.

This paper presents an effective damage detection method using a meta model. A meta model is an approximation model that uses the relations between the design and response variables. It eliminates the need for repetitive analyses of computationally expensive models during the optimization process. In this study, a response surface model was employed as the meta model. The surface model was estimated using the correlation of the stiffness and natural frequencies of the structures. The locations and values of the damages were identified using a meta model-based damage detection method. Two numerical examples (a cantilever beam and jacket structure) were considered to verify the performance of the proposed method. As a result, the damages to the structures were accurately detected.

효율적인 최적설계를 위해 공학분야에 도입된 대체모델의 정확성은 표본점에 큰 영향을 받는다. 대체모델의 정확성을 높이는 방법으로 기 추출한 응답을 이용하는 순차실험계획이 제안되었다. 크리깅 대체모델의 상관계수를 가중치로 적용하여 대체모델의 정확성을 향상시킨 연구가 있었으나, 주어진 정보가 부족하거나 상관계수가 잘못 추정된 경우 표본점이 잘못 추출되어 대체모델의 강건성이 저하된다. 본 논문에서는 기존 순차실험계획의 여러 문제점을 제시하고, 이를 해결하기 위한 가중함수 기반 순차 최소거리최대화계획을 제안한다. 제안하는 순차실험계획의 효용성을 수학 함수에 적용하여 기존 순차실험계획들과 비교하여 정확성과 강건성이 향상됨을 예시한다.

Sequential surrogate model-based global optimization algorithms, such as super-EGO, have been developed to increase the efficiency of commonly used global optimization technique as well as to ensure the accuracy of optimization. However, earlier studies have drawbacks because there are three phases in the optimization loop and empirical parameters. We propose a united sampling criterion to simplify the algorithm and to achieve the global optimum of problems with constraints without any empirical parameters. It is able to select the points located in a feasible region with high model uncertainty as well as the points along the boundary of constraint at the lowest objective value. The mean squared error determines which criterion is more dominant among the infill sampling criterion and boundary sampling criterion. Also, the method guarantees the accuracy of the surrogate model because the sample points are not located within extremely small regions like super-EGO. The performance of the proposed method, such as the solvability of a problem, convergence properties, and efficiency, are validated through nonlinear numerical examples with disconnected feasible regions.

Reliability-based robust design optimization (RBRDO) is a notable method to secure the quality and feasibility of performances from uncertainties. In this paper, geometric uncertainties of a stator sheet that can occur during a stamping process are considered as the uncertainty of controllable variables. Then, as the uncertainty of uncontrollable variables, skew angle due to inexact lamination is considered. No researches on the effect of this uncertainty have been performed. To analyze the effects of the uncertainties, the kernel density estimation (KDE) was employed to estimate the distribution because of its convenience, flexibility, and robustness. Then, RBRDO with the KDE is performed and the optimum results are compared with real data measured from manufactured motors.

공학분야의 신뢰성 해석은 점점 더 높은 신뢰도 영역에 대한 확률밀도함수의 예측을 요구한다. 따라서 높은 신뢰도를 정확하게 해석하기 위해 분포의 꼬리부분을 정확하게 표현해야 한다. 최근 들어 꼬리부분에 대한 표본만을 이용해 꼬리 모형을 생성하여 신뢰도를 추정할 수 있는 방법인 일반화파레토 분포에 대한 연구가 활발히 진행되고 있다. 하지만 기존의 연구에서는 부정확한 임계점 추정으로 꼬리 부분에서 신뢰도의 정확도가 떨어진다. 따라서 본 논문에서는 아카이케 정보척도를 이용하여 임계점을 정확하고 강건하게 추정하고 이를 통해 꼬리 모형의 정확도를 향상시키는 아카이케 정보척도 기반 일반화파레토 분포 기법을 제안한다. 또한 제안하는 기법을 이용한 신뢰성 해석을 수행하여 정확도가 향상된 신뢰성 해석 결과를 도출하였다.

Deep-seabed test miner operated by a self-propelled mining system moving on soft soil is an essential device to secure floating and towing performances. The performances of the tracked vehicle are seriously influenced by noise factors such as the shear strength of the seafloor, bottom current, seafloor slope, speed of tracked vehicle, reaction forces of flexible hose, steering ratio, etc. Due to uncertainties related to noise factors, the design of a deep-sea manganese nodules test miner that satisfies target reliabilities is difficult. Therefore, reliability-based design optimization (RBDO) is required to guarantee system reliability under circumstances where uncertainties related to noise factors prevail. Among noise factors, the bottom current, a bimodal distribution, is censored due to the observation limit of measurement devices. Therefore, estimated distribution of the bottom current is inaccurate without considering these characteristics and the result of RBDO cannot be guaranteed. In this paper, we define censored data as unknown values over the limit of observation. If this data is estimated by using Akaike information criterion (AIC) that cannot consider the characteristics of censored data, the distribution of estimated data cannot guarantee accurate reliability. Therefore, censored AIC that can consider the characteristics of data is used to estimate accurate distribution of the bottom current. Finally, RBDO, under circumstances where uncertainties related to noise factors combined censored data are present, is performed on the mobility of a deep-sea manganese nodules test miner.

Principle of tolerance design is receiving increased research focus to determine the optimal tradeoff between manufacturing cost and quality. However, current tolerance designs are not suitable for products such as electromagnetic devices that require high reliability, i.e., a very low failure rate. In this paper, a new tolerance design named as reliability-based optimum tolerance design is formulated and performed to guarantee the high reliability of the products while maximizing manufacturing tolerances. The proposed method quantifies the reliability using reliability analysis, which reflects the tolerance in the tolerance design. To validate the proposed method, tolerance design is applied to two examples: 1) a magnetic circuit ( C -core) and 2) a mass-produced interior permanent magnet motor that contains manufacturing tolerances of permanent magnet.

The manufacturing tolerance of the permanent magnets (PMs) is inherent in the produce of the machine. These tolerances influence back electromotive force (EMF), which is an important response of the motor. In order to enhance the quality of the product, the authors need to reduce these tolerances, but it is very difficult and involves a number of costs. Therefore, this study presents a robust design of the EMF characteristic analysis considering the manufacturing tolerances of PM in the interior PM synchronous motor (IPMSM). Among the robust optimisation method, Taguchi robust design based on orthogonal array was applied. Finally, the validity of the analysis result is verified by comparing the optimum design model with present design model (mass production model) ones.

The main objective of this paper is to develop a new damage detection method. The newly developed damage detection method uses natural frequencies change ratios between non damaged structures and damaged structures. Two parameters, cosine similarity and magnitude index, are considered to estimate location and severity of the damage in the structure. A numerical example, a jacket structure, was considered to verify the performance of the proposed method. As a result, the damages in the structures is accurately detected.

A deep-sea pressure vessel needs to protect the internal electrical equipment from the high external pressure. Thus, the pressure vessel should be designed to be watertight and structurally safe. In this study, a cylindrical-type pressure vessel comprising a hollow cylinder and cover plates at both ends is investigated. For communication between the internal electronic equipment and the external device, holes are bored on the cover plate to install underwater connectors. Considering the type of internal equipment and underwater connector specifications, multiple holes may be required. These holes can affect the structural safety of the pressure vessel cover plate. In this study, the optimum design of the hole arrangement in consideration of the structural safety of the cover plate was performed.

The performance of a system can be affected by the variance of noise factors, which arise owing to uncertainties of the material properties and environmental factors acting on the system. For robust design optimization of the system performance, it is necessary to minimize the effect of the variance of the noise factors that are impossible to control. However, present robust design techniques consider the variation of design factors, and not the noise factors, as being important. Furthermore, it is necessary to assume a normal distribution; however, a normal distribution is often not suitable to estimate the variations. In this study, a robust design technique is proposed to consider the variation of noise factors that are estimated as non-normal distributions in a real experiment. As an example of an engineering problem, a deep-sea manganese nodule miner tracked vehicle is used to demonstrate the feasibility of the proposed method.

The performance of a system can be affected by various variables such as manufacturing tolerances, uncertainties of material properties, and environmental factors acting on the system. Robust design optimization has attracted much attention in the design of products because it can find the best design solution that minimizes the variance of the response while considering the distribution of the variables. However, the computational cost and accuracy of optimization have thus far been a challenging problem. In this study, robust design optimization using the multiplicative decomposition method is proposed in order to solve these problems. Because the proposed method calculates the mean and variance of the system directly from the kriging metamodel using the multiplicative decomposition method, it can be used to search for a robust optimum design accurately and efficiently. Several mathematical and engineering examples are used to demonstrate the feasibility of the proposed method.

A deep-sea manganese nodule miner consists of 4 parts: the pickup device, crusher, disposal device, and tracked vehicle. The tracked vehicle is an essential component to keep the self-propelled miner moving across deep-sea soil. The performances of the tracked vehicle are influenced by noise factors: the shear strength of the seafloor, bottom current, seafloor slope, track speed, reaction forces of flexible hose, etc. It is necessary to adopt a robust design method that improves the performances and minimizes the variation caused by noise factors. Taguchi`s method, the most widely known robust design method, searches for the robust optimum using an orthogonal array composed of the product of the inner array and outer array. In this paper, we propose a new screening technique to reduce the number of input factors and apply the MRSN (Multi-Response Signal to Noise) ratio to convert multiple performances into single one in order to overcome the difficulties and limitations of using Taguchi`s method in a case with many input factors and multiple performances. A test miner was already designed and tested. It has about 1/10 the capacity of a commercial one and was successfully operated at an in-shore area. Taguchi`s robust design was applied to the tracked vehicle of the test miner, and design improvements were implemented for the vehicle.

This paper presents the robust design of back electromotive force (EMF) characteristic analysis considering the manufacturing tolerances of the permanent magnets (PM) in the interior permanent magnet synchronous motor (IPMSM). We choose the design variables that make an impact on the EMF, and then perform the robust design of EMF using the Taguchi method in order to reduce the effect of variation of the noise variables caused by the manufacturing tolerances. Taguchi method, the most widely known robust design method, is searched for the robust optimum using orthogonal array composed of the product of inner array and outer array. Finally, the robust optimum combination of design variables is selected basis on the result about the manufacturing tolerances of PM. The optimization is verified by comparison of average and variance between robust model and initial model according to the variation of noise factor.

TEAM workshop problem 22 for SEMS is multi-objective optimization problem with many design variables and nonlinear constraints. In order to guarantee reliable performances, the reliability-based design optimization along with accurate prediction of the effects of uncertainties has to be executed. In this paper, multiplicative decomposition method is applied to perform reliability analysis for reliability based design optimization.

This paper presents the Multi-response Taguchi robust design of back electromotive force (EMF) and cogging torque considering the manufacturing tolerances for electric machine. Control factors which affect the back EMF and cogging torque are chosen. The design using the Multi-response Taguchi method is performed in order to reduce the effect of variation of the noise factors caused by the manufacturing tolerances. In the Taguchi robust design, multi-response can not be considered. General Taguchi robust design is hard to employ. Therefore, Multi-response signal to noise (MRSN) ratio based on Taguchi robust design considering multiple responses is employed. Finally, the robust optimum combination of control factors is selected on the basis of the result used by Taguchi robust design based on MRSN.

A deep-sea manganese nodules test miner has not only coupled relationship between system components but also various design requirements of each system to meet the specified multitasks. To accomplish the multiobjectives of complex systems, multidisciplinary design optimization (MDO) is performed. Metamodels such as the kriging model and the response surface model are employed to reduce computational costs for MDO and to integrate component systems in a design framework. After verifying the accuracy of each metamodel, metamodel-based MDO for a deep-ocean test miner is formulated and performed. Finally, results and advantages of the proposed design methodology are discussed.