scholarly journals Digital World Spawns Identical Twins

2017 ◽  
Vol 139 (10) ◽  
pp. 32-37
Author(s):  
Jean Thilmany
Keyword(s):  
Virtual World ◽  
Digital Twin ◽  
Digital Product ◽  
Digital World ◽  
Digital Twins ◽  
Product Models ◽  
Twin Models ◽  
Product And Process ◽  
Operation Smart ◽  
Smart Factories

This article explores the concept of digital twins and reasons why manufacturers prefer digital replicas of products, machines, processes, or even entire factories. A digital twin models the robotic line with such high fidelity that the engineer can do all this in the virtual world. Digital twins are the foundation of tomorrow’s smarter workplace. A factory’s digital twin must be robust enough to capture those changes, plus all relevant data from each operation. Smart factories, such as GE’s Brilliant Factory and Siemens’ competing Industrie 4.0, need both types of digital twins—product and process—to work. Digital product models contain each component that goes into a product, from screws and welds to plastic shapes and machined metals. Digital twins also support greater automation. As artificial intelligence (AI) systems learn more about specific machines, they will use their digital twins to help engineers run plants more efficiently. AI can analyze it to see if a screw is loose or a bearing is starting to fail. The better the AI knows the machine, the more accurately it can predict when that failure is likely to happen.

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

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.

scholarly journals Digital Technologies in the Smart Production Management System

2021 ◽  
Vol 93 ◽  
pp. 01024
Author(s):  
Evgeniy Starikov ◽  
Marina Evseeva ◽  
Irina Tkachenko
Keyword(s):  
Management System ◽  
Production Management ◽  
Manufacturing Sector ◽  
Digital Technologies ◽  
Digital Twin ◽  
Twin Model ◽  
Digital Twins ◽  
Industrial Internet ◽  
Production Management System ◽  
Twin Models

The article analyzes the possibility of using such digital technologies as the Industrial Internet of Things (IIoT), Big Data and the creation of models of digital twins in the formation of intelligent management systems for "smart" production within the framework of the concept of digital transformation of the manufacturing sector Industry 4.0. The essence and features of these technologies, problematic aspects of their implementation in real production enterprises are considered. The concept of the functional structure of the digital production management system of a "smart" enterprise based on the digital twin model is proposed. The conclusion is made about the integrating role of technologies for the development and application of digital twin models in the construction of intelligent control systems for "smart" production.

scholarly journals Process Modeling and Simulation of Tableting—An Agent-Based Simulation Methodology for Direct Compression

Pharmaceutics ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 996
Author(s):  
Niels Lasse Martin ◽  
Ann Kathrin Schomberg ◽  
Jan Henrik Finke ◽  
Tim Gyung-min Abraham ◽  
Arno Kwade ◽  
...  
Keyword(s):  
Raw Materials ◽  
Pilot Scale ◽  
Physical Models ◽  
Agent Based Simulation ◽  
Validation Data ◽  
Agent Based ◽  
Industrial Processing ◽  
Digital Twins ◽  
Product And Process ◽  
The Impact

In pharmaceutical manufacturing, the utmost aim is reliably producing high quality products. Simulation approaches allow virtual experiments of processes in the planning phase and the implementation of digital twins in operation. The industrial processing of active pharmaceutical ingredients (APIs) into tablets requires the combination of discrete and continuous sub-processes with complex interdependencies regarding the material structures and characteristics. The API and excipients are mixed, granulated if required, and subsequently tableted. Thereby, the structure as well as the properties of the intermediate and final product are influenced by the raw materials, the parametrized processes and environmental conditions, which are subject to certain fluctuations. In this study, for the first time, an agent-based simulation model is presented, which enables the prediction, tracking, and tracing of resulting structures and properties of the intermediates of an industrial tableting process. Therefore, the methodology for the identification and development of product and process agents in an agent-based simulation is shown. Implemented physical models describe the impact of process parameters on material structures. The tablet production with a pilot scale rotary press is experimentally characterized to provide calibration and validation data. Finally, the simulation results, predicting the final structures, are compared to the experimental data.

Digital Twin for Smart Manufacturing: The Practitioner’s Perspective

2020 ◽  
Author(s):  
Maja Bärring ◽  
Björn Johansson ◽  
Goudong Shao
Keyword(s):  
Conceptual Development ◽  
Production Systems ◽  
Manufacturing Sector ◽  
Smart Manufacturing ◽  
Digital Twin ◽  
Adoption And Implementation ◽  
New Information ◽  
Digital Twins ◽  
Discrete Manufacturing ◽  
Design Production

Abstract The manufacturing sector is experiencing a technological paradigm shift, where new information technology (IT) concepts can help digitize product design, production systems, and manufacturing processes. One of such concepts is Digital Twin and researchers have made some advancement on both its conceptual development and technological implementations. However, in practice, there are many different definitions of the digital-twin concept. These different definitions have created a lot of confusion for practitioners, especially small- and medium-sized enterprises (SMEs). Therefore, the adoption and implementation of the digital-twin concept in manufacturing have been difficult and slow. In this paper, we report our findings from a survey of companies (both large and small) regarding their understanding and acceptance of the digital-twin concept. Five supply-chain companies from discrete manufacturing and one trade organization representing suppliers in the automotive business were interviewed. Their operations have been studied to understand their current digital maturity levels and articulate their needs for digital solutions to stay competitive. This paper presents the results of the research including the viewpoints of these companies in terms of opportunities and challenges for implementing digital twins.

Machine learning-based digital twins reduce seasonal remapping in aeroderivative gas turbines

2021 ◽  
pp. 1-7
Author(s):  
Nick Petro ◽  
Felipe Lopez
Keyword(s):  
Machine Learning ◽  
Ambient Temperature ◽  
Gas Turbines ◽  
Operating Conditions ◽  
Pilot Site ◽  
Digital Twin ◽  
Digital Twins ◽  
Machine Learning Model ◽  
Unacceptable Behavior ◽  
External Conditions

Abstract Aeroderivative gas turbines have their combustion set points adjusted periodically in a process known as remapping. Even turbines that perform well after remapping may produce unacceptable behavior when external conditions change. This article introduces a digital twin that uses real-time measurements of combustor acoustics and emissions in a machine learning model that tracks recent operating conditions. The digital twin is leveraged by an optimizer that select adjustments that allow the unit to maintain combustor dynamics and emissions in compliance without seasonal remapping. Results from a pilot site demonstrate that the proposed approach can allow a GE LM6000PD unit to operate for ten months without seasonal remapping while adjusting to changes in ambient temperature (4 - 38 °C) and to different fuel compositions.

Digital Twins: Review and Challenges

2021 ◽  
Vol 21 (3) ◽  
Author(s):  
Maria G. Juarez ◽  
Vicente J. Botti ◽  
Adriana S. Giret
Keyword(s):  
Computer Science ◽  
Industry 4.0 ◽  
Multi Agent System ◽  
Agent System ◽  
Digital Twin ◽  
Digital Twins ◽  
Science Approach ◽  
Multi Agent ◽  
Current Operating

Abstract With the arises of Industry 4.0, numerous concepts have emerged; one of the main concepts is the digital twin (DT). DT is being widely used nowadays, however, as there are several uses in the existing literature; the understanding of the concept and its functioning can be diffuse. The main goal of this paper is to provide a review of the existing literature to clarify the concept, operation, and main characteristics of DT, to introduce the most current operating, communication, and usage trends related to this technology, and to present the performance of the synergy between DT and multi-agent system (MAS) technologies through a computer science approach.

The Digital Twin of a System: A Structure for Networks of Digital Twins

2021 ◽  
Author(s):  
Leif- Thore Reiche ◽  
Claas Steffen Gundlach ◽  
Gian Frederik Mewes ◽  
Alexander Fay
Keyword(s):  
Digital Twin ◽  
System A ◽  
Digital Twins

Engine Fleet-Management: The Use of Digital Twins From a MRO Perspective

2017 ◽  
Author(s):  
Joern Kraft ◽  
Stefan Kuntzagk
Keyword(s):  
Multilevel Models ◽  
Operating Cost ◽  
Maintenance Cost ◽  
Fleet Management ◽  
Component Level ◽  
The Real ◽  
Digital Twin ◽  
Digital Twins ◽  
The Individual ◽  
Life Consumption

Engine operating cost is a major contributor to the direct operating cost of aircraft. Therefore, the minimization of engine operating cost per flight-hour is a key aspect for airlines to operate successfully under challenging market conditions. The interaction between maintenance cost, operating cost, asset value, lease and replacement cost describes the area of conflict in which engine fleets can be optimized. State-of-the-art fleet management is based on advanced diagnostic and prognostic methods on engine and component level to provide optimized long-term removal and work-scoping forecasts on fleet level based on the individual operation. The key element of these methods is a digital twin of the active engines consisting of multilevel models of the engine and its components. This digital twin can be used to support deterioration and failure analysis, predict life consumption of critical parts and relate the specific operation of a customer to the real and expected condition of the engines on-wing and at induction to the shop. The fleet management data is constantly updated based on operational data sent from the engines as well as line maintenance and shop data. The approach is illustrated along the real application on the CFM56-5C, a mature commercial two-spool high bypass engine installed on the Airbus A340-300. It can be shown, that the new methodology results in major improvements on the considered fleets.

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