Optimization of numerical models through instrumentation data integration. Digital twin models for dams†

Abstract Mechatronics and control education is supported by laboratory intensive assignments that allow students acquire software and hardware skills to solve real world problems. However, COVID-19 force many schools to switch into remote learning complicating the instruction of practical assignments. This paper presents a novel proposal for interactive remote teaching of the laboratory component of the course ME-142: Mechatronics at the University of California, Merced using Digital Twins (DT) and the flipped classroom methodology. Each lab experience is composed by a set of on-demand supporting materials with the foundations of mechatronics simulation using MATLAB/Simulink to enhance and adapt the learning experience of the students. Once the students acquire advanced simulation skills, a set of Digital Twin models are provided to the students in order to begin their interaction with virtual representations of real systems for identification, analysis, controller design and validation, which are available online for remote access. By the end of the course, students were able not only to gain valuable experience with mechatronic systems but also interact and build advanced modelling techniques as Digital Twin, contributing to compensate the lack of remote hardware interaction.

Download Full-text

Digital Twin-Driven Condition-Based Maintenance

10.4018/978-1-7998-7943-5.ch005 ◽

2022 ◽

pp. 109-136

Author(s):

Adolfo Crespo del Castillo ◽

Marco Macchi ◽

Laura Cattaneo

Keyword(s):

Real Time ◽

Industry 4.0 ◽

Simulation Models ◽

Condition Based Maintenance ◽

Industrial Management ◽

Digital Twin ◽

Operation And Maintenance ◽

Twin Models ◽

Technical Professionals ◽

Case Development

The world is witnessing an all-level digitalization that guides the industry and business to a restructuration in order to adapt to the new requirements of the surrounding environment. That change also concerns the labour of the technical professionals and their formation. As a consequence of this deep consciousness-raising, this chapter will investigate and develop simulation models based on the current digitalization. The aim of this chapter is the exposition of a real case development of “digital twin” models framed as part of the condition-based maintenance paradigm to improve real-time assets operation and maintenance. This model contributes by providing real-time results that could turn into a basis for the industrial management decisions and place them in the Industry 4.0 paradigm environment.

Download Full-text

Digital Twin Models for Maintenance of Cable-Supported Bridges

International Conference on Smart Infrastructure and Construction 2019 (ICSIC) ◽

10.1680/icsic.64669.737 ◽

2019 ◽

Cited By ~ 1

Author(s):

CS Shim ◽

HR Kang ◽

NS Dang

Keyword(s):

Digital Twin ◽

Twin Models

Download Full-text

Innovative digital inspection methodology

The APPEA Journal ◽

10.1071/aj19109 ◽

2020 ◽

Vol 60 (1) ◽

pp. 77

Author(s):

Stephen A Anderson

Keyword(s):

Laser Scanning ◽

Design Parameters ◽

Destructive Testing ◽

Inspection Method ◽

Digital Twin ◽

3D Space ◽

Twin Models ◽

Quality Programs ◽

Non Destructive ◽

Digital Record

The paper describes an innovative digital inspection methodology that combines 3D laser scanning, metrology and advanced non-destructive testing data that is merged in 3D space to provide a digital record of the condition and mechanical integrity of critical assets. This advanced inspection method supports condition-based maintenance programs and digital twin models to determine future equipment condition, work scope and inspection schedules, while maintaining a digital record throughout the equipment lifecycle. Testing of the methodology includes 3D scanning of drill platforms, baseline scanning of blowout preventers and sheaves, for quality purposes, and the use of augmented reality for viewing scans. Phased array testing has been conducted on sub-components such as slew ring bolting. Data are combined into digital reports that show 3D images of the equipment with precise dimensional data and identified inspection areas. Such reports can be combined with digital twin models to confirm integrity of the equipment for certificate of conformance and baseline data for future integrity comparisons as equipment ages. This innovative inspection methodology will set a new standard for how equipment data are captured, stored and represented. The process provides a range of benefits for OEMs, drilling contractors and operators alike, including digital quality programs to baseline new equipment condition and compare with design parameters, delivering condition and integrity assessments of critical equipment items in-situ or on deck, providing a consistent methodology for inspection and dimensional control of operational equipment items, and providing precise equipment data that can complement digital twin and real time monitoring programs.

Download Full-text

Development and Assessment of a Nearly Autonomous Management and Control System During a Single Loss of Flow Accident

Volume 1: Beyond Design Basis; Codes and Standards; Computational Fluid Dynamics (CFD); Decontamination and Decommissioning; Nuclear Fuel and Engineering; Nuclear Plant Engineering ◽

10.1115/icone2020-16908 ◽

2020 ◽

Author(s):

Linyu Lin ◽

Paridhi Athe ◽

Pascal Rouxelin ◽

Nam Dinh ◽

Jeffrey Lane

Keyword(s):

Knowledge Base ◽

Confusion Matrix ◽

Data Generation ◽

Digital Twin ◽

Control Functions ◽

Digital Twins ◽

Twin Models ◽

Autonomous Management ◽

And Performance ◽

And Control

Abstract In this work, a Nearly Autonomous Management and Control (NAMAC) system is designed to diagnose the reactor state and provide recommendations to the operator for maintaining the safety and performance of the reactor. A three layer-hierarchical workflow is suggested to guide the design and development of the NAMAC system. The three layers in this workflow corresponds to knowledge base, digital twin developmental layer (for different NAMAC functions), and NAMAC operational layer. Digital twin in NAMAC is described as knowledge acquisition system to support different autonomous control functions. Therefore, based on the knowledge base, a set of digital twin models is trained to determine the plant state, predict behavior of physical components or systems, and rank available control options. The trained digital twin models are assembled according to NAMAC operational workflow to support decision-making process in selecting the optimal control actions during an accident scenario. To demonstrate the capability of the NAMAC system, a case study is designed, where a baseline NAMAC is implemented for operating a simulator of the Experimental Breeder Reactor II (EBR-II) during a single loss of flow accident. Training database for development of digital twin models is obtained by sampling the control parameters in the GOTHIC data generation engine. After the training and testing, the digital twins are assembled into a NAMAC system according to the operational workflow. This NAMAC system is coupled with the GOTHIC plant simulator, and a confusion matrix is generated to illustrate the accuracy and robustness of implemented NAMAC system. It is found that within the training databases, NAMAC can make reasonable recommendations with zero confusion rate. However, when the scenario is beyond the training cases, the confusion rate increases, especially when the scenarios are more severe. Therefore, a discrepancy checker is added to detect unexpected reactor states and alert operators for safety-minded actions.

Download Full-text

RECLAIM: Toward a New Era of Refurbishment and Remanufacturing of Industrial Equipment

Frontiers in Artificial Intelligence ◽

10.3389/frai.2020.570562 ◽

2021 ◽

Vol 3 ◽

Author(s):

Angeliki Zacharaki ◽

Thanasis Vafeiadis ◽

Nikolaos Kolokas ◽

Aikaterini Vaxevani ◽

Yuchun Xu ◽

...

Keyword(s):

New Technologies ◽

Predictive Analytics ◽

Big Data Analytics ◽

Industrial Equipment ◽

New Era ◽

Digital Twin ◽

Simulation Engine ◽

Twin Models ◽

Funded Project ◽

Available Information

Refurbishment and remanufacturing are the industrial processes whereby used products or parts that constitute the product are restored. Remanufacturing is the process of restoring the functionality of the product or a part of it to “as-new” quality, whereas refurbishment is the process of restoring the product itself or part of it to “like-new” quality, without being as thorough as remanufacturing. Within this context, the EU-funded project RECLAIM presents a new idea on refurbishment and remanufacturing based on big data analytics, machine learning, predictive analytics, and optimization models using deep learning techniques and digital twin models with the aim of enabling the stakeholders to make informed decisions about whether to remanufacture, upgrade, or repair heavy machinery that is toward its end-of-life. The RECLAIM project additionally provides novel strategies and technologies that enable the reuse of industrial equipment in old, renewed, and new factories, with the goal of saving valuable resources by recycling equipment and using them in a different application, instead of discarding them after use. For instance, RECLAIM provides a simulation engine using digital twin in order to predict maintenance needs and potential faults of large industrial equipment. This simulation engine keeps the virtual twins available to store all available information during the lifetime of a machine, such as maintenance operations, and this information can be used to perform an economic estimation of the machine's refurbishment costs. The RECLAIM project envisages developing new technologies and strategies aligned with the circular economy and in support of a new model for the management of large industrial equipment that approaches the end of its design life. This model aims to reduce substantially the opportunity cost of retaining strategies (both moneywise and resourcewise) by allowing relatively old equipment that faces the prospect of decommissioning to reclaim its functionalities and role in the overall production system.

Download Full-text