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Surrogate models are employed in engineering analysis to replace detailed physics-based models to achieve computational efficiency in problems that require multiple evaluations of the model. The accuracy of the surrogate model depends on the quality and quantity of data collected from the expensive model. This paper investigates surrogate modeling options for problems with high-dimensionality in both the input and output spaces. Several methods for reducing the output dimension are investigated, namely, singular value decomposition (SVD), random projection, randomized SVD, and diffusion map; similarly, several methods for input dimension reduction are investigated, namely, variance-based sensitivity analysis and active subspace discovery. The most effective combination of options for input and output dimension reduction is identified in a systematic way, followed by the construction of Gaussian process surrogate models in the low-dimensional space. The prediction error in the original space includes both the reconstruction error and surrogate error; a systematic approach is developed to quantify and compare the relative contributions of the two types of errors. The proposed general, systematic approach of exploring available options is applied to an aircraft fuselage panel. The effectiveness of various dimension reduction techniques with surrogate model construction are investigated in terms of accuracy and computational effort.

This paper proposes a multi-level Bayesian calibration approach that fuses information from heterogeneous sources and accounts for uncertainties in modeling and measurements for time-dependent multi-component systems. The developed methodology has two elements: quantifying the uncertainty at component and system levels, by fusing all available information, and corrected model prediction. A multi-level Bayesian calibration approach is developed to estimate component-level and system-level parameters using measurement data that are obtained at different time instances for different system components. Such heterogeneous data are consumed in a sequential manner, and an iterative strategy is developed to calibrate the parameters at the two levels. This calibration strategy is implemented for two scenarios: offline and online. The offline calibration uses data that is collected over all the time-steps, whereas online calibration is performed in real-time as new measurements are obtained at each time-step. Analysis models and observation data for the thermo-mechanical behavior of gas turbine engine rotor blades are used to analyze the effectiveness of the proposed approach.

This paper summarizes a current outcome of DeepStar project titled, "21164 Standardization of Inspection to Enable Digital Twin (Phase 2)", which develops a digital twin framework for mooring chains with physics models, standardized inspection data template, and various analytical tools, including automated tools for data conversion, anomaly detection, etc., to enable real-time integrity monitoring and failure prediction. Four activities are included in this study: (1) literature study and industry workshop which collects industry feedback to prioritize the development of case studies for tools that aid digital twins; (2) development of automatic data converting tool to collect digitized data in standardized form from existing inspection reports; (3) development of automated anomaly/feature detection tool that detects anomaly, i.e., marine growth, and tracks link ID from existing ROV footage data; and (4) development of digital twin process which incorporates mooring analysis, failure analysis and integrity management. The failure analysis process incorporates standardized inspection data into the physics model while accounting uncertainty from each information source. Integrity management is achieved through a web-based condition visualization system, called digital twin dashboard.

The industries using floating facilities such as FPSO and offshore wind turbine are increasing. Since these vessels have been fixed and operated in the installed area for a long period of time, they cannot be regularly docked, inspected and repaired as opposed to normal ship case, and limited to the inspection of the hull outer plates from under the water and the inspection of inside the tanks are conducted once every five years. These inspections involving visual inspections and thickness measurements at representative points, only examine the current state, and don’t evaluate quantitatively the future potential (remaining life) over the subsequent long operation period. To predict residual life in order to maintain the integrity of these structures, digital twin technology is proposed to realize this demand. This paper shows the method to develop digital twin assessment which solve the insufficiency of conventional monitoring and simulation method in order to utilize for risk-based inspection (RBI) and condition-based maintenance (CBM) to the operators.

A conceptual design of digital Intelligent Production Integrity Operating Windows (IP-IOW) system, which is an unique and transformational solution to the oil and gas offshore industry focusing on maximizing production while optimizing equipment operation, was developed through a Nippon Foundation (NF) - DeepStar® partnership project. This connects the fluids system with the equipment system using IP-IOW architecture and specifications of a digital platform. The developed digital IP-IOW architecture contains five major evaluation modules, which are Component risk analysis (CRA), Failure Mode and Effect Analysis (FMEA), Failure Evaluation (FE), Maintenance Evaluation (ME), and Reliability Availability, and Maintainability Analysis (RAMA) focusing on critical components, including subsea choke, flowline and riser, topside choke, topside equipment, and crude export line. Each module has a function of monitoring, risk evaluation or analysis of each component based on various existing databases and/or industrial standards. Component risk analysis (CRA) module is designed as a key module to evaluate individual risk of each component based on the evaluation results of the other modules and to provide IP-IOW dashboards through operation and maintenance analysis methods. CRA module analyzes operation and maintenance based on the likelihood of failure (probability of failure) as a function of operation and maintenance conditions and impact of the damage. Calculated safety operating windows (SOWs) and reliability operating windows (ROWs) would be indicated on the IP-IOW dashboard. In this project, detailed gap analysis was also conducted to gain an understanding of what relevant industry standards and practices have been published and how these publications have gaps with respect to IP-IOW. A completed search was made of technical indices and reference sources to identify codes and standards that may or can be used for developing Integrity Operating Windows (IOWs) for topside fixed equipment. Current API, DNV, and EI applicable recommended practices (RP) cover damaging effect, in-service inspection, risk ranking, repair items prioritization, and alteration of fixed equipment systems. However, the RPs do not cover how to integrate industry best practices into a real-time digital operating environment that is integral to the next generation O&M system. A critical recommendation is to connect the systems digitally allowing for data analytics using the Digital Twin of the asset. Several case studies on module development were conducted to demonstrate an example of the module development process and the workflow of module. RAM analysis on one of selected offshore production facilities identified top 30 high risk components from around 1200 components. A pilot physical model was also developed to enable the embodiment of a new industry recommended practice for offshore large scale of FPSO asset. This conceptual pilot has used the topside choke sand erosion connecting the fluid characterization (multi-phase flow) with the selected equipment to develop the correlation addressing major technical challenges of fixed equipment while maximizing production. An example of validation results of erosion module showed good agreement between actual inspection results and CFD calculation results. Overall, IP-IOW is designed based on rigorous CFD and field data to predict critical section of system and/or equipment likelihood of failure (LOF), such as Erosion with solids, flow induced vibration (FIV), acoustic induced vibration (AIV), vortex induced vibration (VIV), and corrosion in offshore environment. Mitsubishi Heavy Industries (MHI) has been sponsored by Nippon Foundation (NF) and DeepStar (DS) Joint Ocean Innovation R&D Program to perform a research study (Phase 1) of project titled "19143 Fixed Equipment Integrity Operating Windows based on Facility Operating Conditions" started from May 2019.

This paper presents a concept of a mooring digital twin frameworkand a standardized inspection datatemplate to enable digital twin. The mooring digital twin framework supports real-time and/or on-demand decision making in mooring integrity management, which minimizes the failure risk while reducing operation and maintenance cost by efficient inspection, monitoring, repair, and strengthening. An industry survey conducted through the DeepStar project 18403 identified a standard template for recording inspection data as a high priority item to enable application of the digital twins for integrity management. Further, mooring chain was selected as a critical mooring component for which a standard inspection template was needed. The characteristics of damage/performance prediction with the proposed mooring digital twin framework are (i) to utilize surrogates and/or reduced-order models trained by high-fidelity physics simulation models, (ii) to combine all available lifecycle data about the mooring system, (iii) to evaluate current and future asset conditions in a systematic way based on the concept of uncertainty quantification (UQ). The general and mooring-specific digital twin development workflows are described with the identified essential data, physics models, and several UQ methodologies such as surrogate modeling, local and global sensitivity analyses, Bayesian prediction etc. Also, the proposed digital twin system architecture is summarized to illustrate the dataflow in digital twin development andutilization. The prototype of mooring digital twin dashboard, web-based risk visualization and advisory system, is developed to demonstrate the capability to visualize the system health diagnosis and prognosis and suggest possible measures/solutions for the high-risk components as a digital twin's insight.

The effect of applied cyclic load, including the maximum plastic strain e max p and the plastic strain amplitude e a p on final misorientation level of post-mortem low alloy steel after low cycle fatigue (LCF) test, was analyzed by Electron Back-Scattered Diffraction (EBSD) in this research at first. Two misorientation parameters widely used in EBSD analysis, Kernel Average Misorientation (KAM) and Grain Reference Orientation Deviation (GROD), are compared in terms of accumulated plasticity characterization after LCF failure, where a bilinear function is found between final KAM and applied cyclic load e max p & e a p . Then the cyclic load effect on final misorientation distribution of post-mortem low alloy steel after fatigue crack propagation (FCP) test was further analyzed by EBSD based on the above bilinear function between final KAM and e max p & e a p , as well as the equivalent e ¯ max p & Δ e ¯ p distribution functions given by HRR field. −1/2 power law is established to describe final KAM distribution near crack path. The area SKAM between final KAM distribution curve and undeformed KAM0 base line within a certain range, rather than SGROD, is proposed as a better measurement of fatigue crack tip driving force ΔK in constant load ratio R condition. Meanwhile, the influence of load ratio R on final misorientation distribution is also discussed.

This study was conducted to elucidate the effects of annealing and hydrogenation on the tensile properties of an Fe–0.01 mass% C alloy processed by high-pressure torsion (HPT). By HPT processing, the tensile strength was increased to ∼1500 MPa through grain refinement. Low-temperature annealing further strengthened the HPT-processed specimen because of a simultaneous effect of carbide precipitation and grain refinement. Reduction in the dislocation density and the fraction of low-angle grain boundaries through warm-temperature annealing led to a decrease in hydrogen uptake when the specimens were exposed to high-pressure gaseous hydrogen, and they became less sensitive to hydrogen embrittlement (HE).