Building a Trustworthy Product-level Shape-performance Integrated Digital Twin with Multi-fidelity Surrogate Model

2021 ◽  
pp. 1-28
Author(s):  
Shuo Wang ◽  
Xiaonan Lai ◽  
Xiwang He ◽  
Yiming Qiu ◽  
Xueguan Song
Keyword(s):  
Real Time ◽  
Sensor Data ◽  
Test Case ◽  
Digital Twin ◽  
Digital Twins ◽  
Training Samples ◽  
Practical Engineering ◽  
Simulation Results ◽  
Visualization Techniques ◽  
Quantities Of Interest

Abstract Digital twin has the potential for increasing production, achieving real-time monitor, and realizing predictive maintenance by establishing a real-time high-fidelity mapping between the physical entity and its digital model. However, the high accuracy and instantaneousness requirements of digital twins have hindered their applications in practical engineering. This paper presents a universal framework to fulfill the requirements and to build an accurate and trustworthy digital twin by integrating numerical simulations, sensor data, multi-fidelity surrogate (MFS) models, and visualization techniques. In practical engineering, the number of sensors available to measure quantities of interest is often limited, complementary simulations are necessary to compute these quantities. The simulation results are generally more comprehensive but not as accurate as the sensor data. Therefore, the proposed framework combines the benefits of both simulation results and sensor data by using an MFS model based on moving least squares, named MFS-MLS. The MFS-MLS was developed as an essential part to calibrate the continuous field of the simulation by limited sensor data to obtain accurate results for the digital twin. Then single-fidelity surrogate models are built on the whole domain using the calibrated results of the MFS-MLS as training samples and sensor data as inputs to predict and visualize the quantities of interest in real-time. In addition, the framework was validated by a truss test case, and the results demonstrate that the proposed framework has the potential to be an effective tool to build accurate and trustworthy digital twins.

scholarly journals Digital Twin for Monitoring of Industrial Multi-Effect Evaporation

Processes ◽  
2019 ◽  
Vol 7 (8) ◽  
pp. 537 ◽  
Author(s):  
Rafael M. Soares ◽  
Maurício M. Câmara ◽  
Thiago Feital ◽  
José Carlos Pinto
Keyword(s):  
Real Time ◽  
Fluid Dynamic ◽  
Successful Implementation ◽  
Processing Unit ◽  
Digital Twin ◽  
Dynamic Mass ◽  
Digital Twins ◽  
Process Operation ◽  
Energy Balances ◽  
Mass And Energy Balances

Digital twins are rigorous mathematical models that can be used to represent the operation of real systems. This connection allows for deeper understanding of the actual states of the analyzed system through estimation of variables that are difficult to measure otherwise. In this context, the present manuscript describes the successful implementation of a digital twin to represent a four-stage multi-effect evaporation train from an industrial sugar-cane processing unit. Particularly, the complex phenomenological effects, including the coupling between thermodynamic and fluid dynamic effects, and the low level of instrumentation in the plant constitute major challenges for adequate process operation. For this reason, dynamic mass and energy balances were developed, implemented and validated with actual industrial data, in order to provide process information for decision-making in real time. For example, the digital twin was able to indicate failure of process sensors and to provide estimates for the affected variables in real time, improving the robustness of the operation and constituting an important tool for process monitoring.

scholarly journals When Digital Twin Meets Network Softwarization in the Industrial IoT: Real-Time Requirements Case Study

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8194
Author(s):  
Mehdi Kherbache ◽  
Moufida Maimour ◽  
Eric Rondeau
Keyword(s):  
Real Time ◽  
Industrial Systems ◽  
Digital Twin ◽  
Digital Twins ◽  
Industrial Internet ◽  
Industrial Iot ◽  
Time Requirements ◽  
Loop Network ◽  
Sdn Controller

The Industrial Internet of Things (IIoT) is known to be a complex system because of its severe constraints as it controls critical applications. It is difficult to manage such networks and keep control of all the variables impacting their operation during their whole lifecycle. Meanwhile, Digital Twinning technology has been increasingly used to optimize the performances of industrial systems and has been ranked as one of the top ten most promising technological trends in the next decade. Many Digital Twins of industrial systems exist nowadays but only few are destined to networks. In this paper, we propose a holistic digital twinning architecture for the IIoT where the network is integrated along with the other industrial components of the system. To do so, the concept of Network Digital Twin is introduced. The main motivation is to permit a closed-loop network management across the whole network lifecycle, from the design to the service phase. Our architecture leverages the Software Defined Networking (SDN) paradigm as an expression of network softwarization. Mainly, the SDN controller allows for setting up the connection between each Digital Twin of the industrial system and its physical counterpart. We validate the feasibility of the proposed architecture in the process of choosing the most suitable communication mechanism that satisfies the real-time requirements of a Flexible Production System.

scholarly journals A Generic Asset Model for Implementing Product Digital Twins in Smart Remanufacturing

2021 ◽  
Author(s):  
Mairi Kerin ◽  
Duc Truong Pham ◽  
Jun Huang ◽  
Jeremy Hadall
Keyword(s):  
Real Time ◽  
Manufacturing Industry ◽  
Integrated System ◽  
Embedded Sensors ◽  
Functional Requirements ◽  
Digital Twin ◽  
Digital Twins ◽  
The Impact ◽  
The Internet Of Things

Abstract A digital twin is a “live” virtual replica of a sensorised component, product, process, human, or system. It accurately copies the entity being modelled by capturing information in real time or near real time from the entity through embedded sensors and the Internet-of-Things. Many applications of digital twins in manufacturing industry have been investigated. This article focuses on the development of product digital twins to reduce the impact of quantity, quality, and demand uncertainties in remanufacturing. Starting from issues specific to remanufacturing, the article derives the functional requirements for a product digital twin for remanufacturing and proposes a UML model of a generic asset to be remanufactured. The model has been demonstrated in a case study which highlights the need to translate existing knowledge and data into an integrated system to realise a product digital twin, capable of supporting remanufacturing process planning.

scholarly journals Visualization and Analysis of Lighting Design Alternatives in Simulation Software

2017 ◽  
Vol 869 ◽  
pp. 212-225 ◽  
Author(s):  
Diana Fernandez-Prieto ◽  
Hans Hagen
Keyword(s):  
Design Process ◽  
Lighting Design ◽  
Simulation Software ◽  
Test Case ◽  
Interactive Exploration ◽  
Software Packages ◽  
Design Alternatives ◽  
Lighting Simulation ◽  
Simulation Results ◽  
Visualization Techniques

For decades, multiple lighting simulation software packages and plugins for commercial software have been developed in an effort to ease the usage and integration of simulation into the lighting design process. In this effort, one of the main challenges is to provide lighting designers with an easy and comprehensive access to simulation results. Visualization is used as a means to achieve this goal. In this paper, we explore two of the most used free lighting simulation packages towards the identification of visualization techniques that facilitate the access to the simulation results as well as the identification of opportunities for the enhancement of simulation-assisted lighting design processes. A test case of a metal workshop illustrates the output produced by both software packages. Based on this exploration, we identified an open gap regarding three main aspects: interactive exploration of simulation results, visualization of compliance with lighting standards, and visual comparison of lighting solutions. We provide a discussion on how approaches from other domains can be applied to close this gap.

Digital Twins for Cities: A State of the Art Review

2020 ◽  
Vol 46 (4) ◽  
pp. 547-573
Author(s):  
Bernd Ketzler ◽  
Vasilis Naserentin ◽  
Fabio Latino ◽  
Christopher Zangelidis ◽  
Liane Thuvander ◽  
...  
Keyword(s):  
Grey Literature ◽  
Physical Models ◽  
Global Perspective ◽  
Sensor Data ◽  
Dominant Term ◽  
Civic Society ◽  
Digital Twin ◽  
3D City Model ◽  
Digital Twins ◽  
City Model

During the last decades, a variety of digital tools have been developed to support both the planning and management of cities, as well as the inclusion of civic society. Here, the concept of a Digital Twin – which is rapidly emerging throughout many disciplines due to advances in technology, computational capacities and availability of large amounts of data – plays an important role. In short, a digital twin is a living virtual model, a connected digital representation of a physical system and has been a central concept in the manufacturing industry for the past decades. In this article, we review the terminology of digital twins for cities and identify commonalities and relations to the more established term 3D city models. Our findings indicate an increasing use of the term digital twin in academic literature, both in general and in the context of cities and the built environment. We find that while there is as yet no consensus on the exact definition of what constitutes a digital twin, it is increasingly being used to describe something that is more than a 3D city model (including, e.g. semantic data, real-time sensor data, physical models, and simulations). At the same time, the term has not yet replaced the term 3D city model as the most dominant term in the 3D GIS domain. By looking at grey literature we discuss how digital twins for cities are implemented in practice and present examples of digital twins in a global perspective. Further, we discuss some of the application areas and potential challenges for future development and implementation of digital twins for cities. We conclude that there are significant opportunities for up-scaling digital twins, with the potential to bring benefits to the city and its citizens and clients.

Online (Remote) Teaching for Laboratory Based Courses Using “Digital Twins” of the Experiments

2021 ◽  
Author(s):  
Sabri Deniz ◽  
Ulf Christian Müller ◽  
Ivo Steiner ◽  
Thomas Sergi
Keyword(s):  
Applied Sciences ◽  
Fluid Mechanics ◽  
Laboratory Experiments ◽  
Web Applications ◽  
Energy Systems ◽  
Sensor Data ◽  
Test Rig ◽  
Digital Twin ◽  
Digital Twins ◽  
University Of Applied Sciences

Abstract The Covid-19 pandemic has changed the university education, with most teaching moved off campus and students learning online or remote at home, but a cornerstone of undergraduate engineering education has been a big challenge, namely the laboratory classes. As the engineering and education communities continue to adapt to the realities of a global pandemic, it is important to recognize the importance of the laboratory-based courses. In order to address to this situation, an ambitious approach is taken to recreate the laboratory experience entirely online with the help of the digital twins of the fluid mechanics, thermodynamics, and turbomachinery laboratory experiments. Laboratory based undergraduate courses such as EFPLAB1, EFPLAB2 (Energy; Fluid and Process Laboratory 1 & 2) and EFPENG (Energy; Fluid and Process Engineering) are important parts of the “mechanical engineering” and “energy systems engineering” curricula of the Lucerne University of Applied Sciences (HSLU) in Switzerland. Each course mentioned above include six different laboratory experiments about fluid mechanics, thermodynamics, turbomachinery, energy efficiency, and energy systems, including mass- and energy flow balances in energy systems. During the Covid-19 pandemic, it was necessary to adapt to the new environment of remote learning courses and modify the laboratory experiments so that they can be carried out online. The approach was developing digital twins of each laboratory experiment with web applications and providing an environment together with supporting videos and interactive problems so that the laboratory experiments can be carried out remotely. A digital twin is a digital representation of a physical system, e.g., the test rig. It may contain a collection of various digital models with related physical equations and solutions, information related to the operation of the test rig, including 2D or 3D models, process models, sensor data records, and documentation. Ideally, all quantities and attributes that could be measured or observed from the real experiment should be accessible from its digital twin. The digital twin not only reproduces the experimental setup in the laboratory but also helps to improve the knowledge related to the theory and concepts of the laboratory experiments. One major advantage of the digital twin is that the number and range of the parameters, which can be manipulated or varied, is larger in comparison to the actual testing in the laboratory. This paper explains the development of the digital twins (web applications) of the laboratory experiments and provides information about the selected experiments such as potential vortex, linear momentum equation, diffuser flow, radial compressor, fuel cell, and pump test rig with the measurement of pump characteristics. A remote or distance learning has many hurdles, one of the largest being how to teach hands-on laboratory courses outside of an actual laboratory. The experience at the Lucerne University of Applied Sciences showed that teaching online labs using the digital twins of the laboratory experiments can work and the students can take part in remote laboratories that meet the learning outcomes and objectives as well as engage in scientific inquiry from a distance.

scholarly journals Towards Integrated Digital Twins for Industrial Products: Case Study on an Overhead Crane

2021 ◽  
Vol 11 (2) ◽  
pp. 683
Author(s):  
Juuso Autiosalo ◽  
Riku Ala-Laurinaho ◽  
Joel Mattila ◽  
Miika Valtonen ◽  
Valtteri Peltoranta ◽  
...  
Keyword(s):  
System Integration ◽  
Sensor Data ◽  
Overhead Crane ◽  
Application Development ◽  
Multiple Systems ◽  
Digital Twin ◽  
Industrial Companies ◽  
Digital Twins ◽  
Industrial Internet ◽  
Application Programming

Industrial Internet of Things practitioners are adopting the concept of digital twins at an accelerating pace. The features of digital twins range from simulation and analysis to real-time sensor data and system integration. Implementation examples of modeling-oriented twins are becoming commonplace in academic literature, but information management-focused twins that combine multiple systems are scarce. This study presents, analyzes, and draws recommendations from building a multi-component digital twin as an industry-university collaboration project and related smaller works. The objective of the studied project was to create a prototype implementation of an industrial digital twin for an overhead crane called “Ilmatar”, serving machine designers and maintainers in their daily tasks. Additionally, related cases focus on enhancing operation. This paper describes two tools, three frameworks, and eight proof-of-concept prototypes related to digital twin development. The experiences show that good-quality Application Programming Interfaces (APIs) are significant enablers for the development of digital twins. Hence, we recommend that traditional industrial companies start building their API portfolios. The experiences in digital twin application development led to the discovery of a novel API-based business network framework that helps organize digital twin data supply chains.

scholarly journals Machining Digital Twin using real-time model-based simulations and lookahead function for closed loop machining control

2021 ◽  
Author(s):  
Rob Ward ◽  
Chao Sun ◽  
Javier Dominguez-Caballero ◽  
Seun Ojo ◽  
Sabino Ayvar-Soberanis ◽  
...  
Keyword(s):  
Residual Stress ◽  
Machine Tool ◽  
Real Time ◽  
Closed Loop ◽  
Machining Process ◽  
Time Model ◽  
Stress Control ◽  
Digital Twin ◽  
Model Based ◽  
Digital Twins

AbstractThe future of machining lies in the fully autonomous machine tool. New technologies must be developed that predict, sense and action intelligent decisions autonomously. Digital twins are one component on this journey and are already having significant impact in the manufacturing industries. Despite this, the implementation of machining Digital Twins has been slow due to the computational burden of simulating cutting forces online resulting in no commercially available Digital Twin that can automatically control the machining process in real time. Addressing this problem, this research presents a machining Digital Twin capable of real-time adaptive control of intelligent machining operations. The computational bottleneck of calculating cutter workpiece engagements online has been overcome using a novel method which combines a priori calculation with real-time tool centre point position data. For the first time, a novel online machine-induced residual stress control system is presented which integrates real-time model-based simulations with online feedback for closed loop residual stress control. Autonomous Digital Twin technologies presented also include chatter prediction and control and adaptive feed rate control. The proposed machining Digital Twin system has been implemented on a large-scale CNC machine tool designed for high-speed machining of aerostructure parts. Validation case studies have been conducted and are presented for each of the machining Digital Twin applications.

scholarly journals Greenhouse industry 4.0 – digital twin technology for commercial greenhouses

2021 ◽  
Vol 4 (S2) ◽  
Author(s):  
Daniel Anthony Howard ◽  
Zheng Ma ◽  
Christian Veje ◽  
Anders Clausen ◽  
Jesper Mazanti Aaslyng ◽  
...  
Keyword(s):  
Big Data ◽  
Energy Consumption ◽  
Industry 4.0 ◽  
Influential Factors ◽  
Sensor Data ◽  
Greenhouse Production ◽  
Production Chain ◽  
Improve Energy Efficiency ◽  
Digital Twin ◽  
Digital Twins

AbstractThe project aims to create a Greenhouse Industry 4.0 Digital Twin software platform for combining the Industry 4.0 technologies (IoT, AI, Big Data, cloud computing, and Digital Twins) as integrated parts of the greenhouse production systems. The integration provides a new disruptive approach for vertical integration and optimization of the greenhouse production processes to improve energy efficiency, production throughput, and productivity without compromising product quality or sustainability. Applying the Industry 4.0 Digital Twin concept to the Danish horticulture greenhouse industry provides digital models for simulating and evaluating the physical greenhouse facility’s performance. A Digital Twin combines modeling, AI, and Big Data analytics with IoT and traditional sensor data from the production and cloud-based enterprise data to predict how the physical twin will perform under varying operational conditions. The Digital Twins support the co-optimization of the production schedule, energy consumption, and labor cost by considering influential factors, including production deadlines, quality grading, heating, artificial lighting, energy prices (gas and electricity), and weather forecasts. The ecosystem of digital twins extends the state-of-the-art by adopting a scalable distributed approach of “system of systems” that interconnects Digital Twins in a production facility. A collection of specialized Digital Twins are linked together to describe and simulate all aspects of the production chain, such as overall production capacity, energy consumption, delivery dates, and supply processes. The contribution of this project is to develop an ecosystem of digital twins that collectively capture the behavior of an industrial greenhouse facility. The ecosystem will enable the industrial greenhouse facilities to become increasingly active participants in the electricity grid.

Will This Be the Decade of Full Digital Twins for Well Construction?

2021 ◽  
Vol 73 (03) ◽  
pp. 34-37
Author(s):  
Judy Feder
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Real World ◽  
Data Analytics ◽  
Oil And Gas ◽  
Real Life ◽  
Physical World ◽  
Sensor Technology ◽  
Digital Twin ◽  
Digital Twins

The time needed to eliminate complications and accidents accounts for 20–25% of total well construction time, according to a 2020 SPE paper (SPE 200740). The same paper notes that digital twins have proven to be a key enabler in improving sustainability during well construction, shrinking the carbon footprint by reducing overall drilling time and encouraging and bringing confidence to contactless advisory and collaboration. The paper also points out the potential application of digital twins to activities such as geothermal drilling. Advanced data analytics and machine learning (ML) potentially can reduce engineering hours up to 70% during field development, according to Boston Consulting Group. Increased field automation, remote operations, sensor costs, digital twins, machine learning, and improved computational speed are responsible. It is no surprise, then, that digital twins are taking on a greater sense of urgency for operators, service companies, and drilling contractors working to improve asset and enterprise safety, productivity, and performance management. For 2021, digital twins appear among the oil and gas industry’s top 10 digital spending priorities. DNV GL said in its Technology Outlook 2030 that this could be the decade when cloud computing and advanced simulation see virtual system testing, virtual/augmented reality, and machine learning progressively merge into full digital twins that combine data analytics, real-time, and near-real-time data for installations, subsurface geology, and reservoirs to bring about significant advancements in upstream asset performance, safety, and profitability. The biggest challenges to these advancements, according to the firm, will be establishing confidence in the data and computational models that a digital twin uses and user organizations’ readiness to work with and evolve alongside the digital twin. JPT looked at publications from inside and outside the upstream industry and at several recent SPE papers to get a snapshot of where the industry stands regarding uptake of digital twins in well construction and how the technology is affecting operations and outcomes. Why Digital Twins Gartner Information defines a digital twin as a digital representation of a real-world entity or system. “The implementation of a digital twin,” Gartner writes, “is an encapsulated software object or model that mirrors a unique physical object, process, organization, person or other abstraction.” Data from multiple digital twins can be aggregated for a composite view across several real-world entities and their related processes. In upstream oil and gas, digital twins focus on the well—and, ultimately, the field—and its lifecycle. Unlike a digital simulation, which produces scenarios based on what could happen in the physical world but whose scenarios may not be actionable, a digital twin represents actual events from the physical world, making it possible to visualize and understand real-life scenarios to make better decisions. Digital well construction twins can pertain to single assets or processes and to the reservoir/subsurface or the surface. Ultimately, when process and asset sub-twins are connected, the result is an integrated digital twin of the entire asset or well. Massive sensor technology and the ability to store and handle huge amounts of data from the asset will enable the full digital twin to age throughout the life-cycle of the asset, along with the asset itself (Fig. 1).

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