Digital Twin: A Quick Overview

MARCH 2024 I Volume 45, Issue 1

Digital Twin: A Quick Overview

Dr. Bonny Banerjee

University of Memphis

Mr. Kishor Chakravarthy

Booz Allen Hamilton

Mr. Robert Riley

Air Force Research Laboratory

Mr. Erwin Sabile

ITEA 2022 National Symposium Chairperson

Mr. James Sabino

Raytheon Technologies

Dr. Tim Scully

Booz Allen Hamilton

Mr. John Silvas

Booz Allen Hamilton

Dr. Armond E. Sinclair

L3Harris Technologies

Dr. Policarpio Soberanis

Northrop Grumman

Dr. Himanshu Upadhyay

Florida International University

Dr. Jeremy Werner



The purpose of this brief article is to normalize the definition of “digital twin” within the Test and Evaluation (T&E) community, including government, industry, and academia. It presents the consensus of opinions across multiple organizations that participated in the 2022 International Test and Evaluation Association (ITEA) National Symposium hosted by the ITEA Hampton Roads Chapter, while building upon the digital twin discussion at that event with an emphasis on their use for T&E.

This topic is especially germane to the defense T&E community as comprehensive live testing of future warfighting capabilities will not be possible in many cases due to environmental, fiscal, safety, classification, or ethical constraints, and so T&E will become more dependent on digital representations to test the performance and interoperability of our systems.

Keywords: Digital model, digital twin, digital thread, test & evaluation, acquisition program lifecycle


In this overview, we will discuss the definition of “digital twin” and the relationship between “digital twin” and related terms often used in the same discussion. Many definitions of “digital twin” proposed throughout the engineering community are listed in Table 1 of the Appendix. The commonality they share are the following features, conveyed either implicitly or explicitly:

  • A digital twin is a digital model
  • A digital twin represents a specific real-world physical asset
  • A digital twin is kept up to date with changes to the real-world physical asset.

Combined, the digital twin framework includes a digital model (twin), its corresponding physical asset, and a data connection between the two as shown in Figure 1.

Figure 1: Physical and Digital Environment

While not always agreeing on the details, the community suggests that digital twins have three variable characteristics:

  • Fidelity of the digital model
  • Direction of information flow between the digital twin and corresponding physical asset
  • Timeliness of the data flowing between the digital twin and corresponding physical asset.

Thus, the most basic definition of a digital twin must reflect that it is a digital representation of a real-world physical asset with parameters that update with sufficient rapidity to enable practical applications as the physical asset changes.  The definition of model is a representation of a system of interest for a specific purpose.  It follows that the most basic definition of a digital twin is:

A digital representation of a specific real-world system of interest for a specific purpose

This definition contains all the common features listed above but also allows for additional features if the specific purpose requires them to add value. The clause “for a specific purpose” guides us by preventing “modeling for modeling’s sake” or in this case, “digital twinning for digital twinning’s sake.”

We extend the most basic definition to include the variable characteristics, resulting in ITEA’s definition of a digital twin for the T&E community:

Digital Presentation

We also point out that the boundary that defines the “system” changes based on the use case. A digital twin may include what some consider “environment” in other use cases. For example, in T&E, a digital twin may include a digital representation of some or all of a test range. The flexibility of a use case sensitive definition of “system” allows us to focus on what digital twins are intended to do: provide value by allowing some use cases to take advantage of the infinitely composable digital world rather than the more constrained physical world.

Digital twins are distinguished from digital threads. The Defense Acquisition University (DAU) defines a digital thread as:

An extensible, configurable, and component, enterprise-level analytical framework that seamlessly expedites the controlled interplay of authoritative technical data, software, information, and knowledge in the enterprise data-information-knowledge systems, based on the Digital System Model template, to inform decision makers throughout a system’s life cycle by providing the capability to access, integrate, and transform disparate data into actionable information.
Digital threads can and should be used to develop models and digital twins. The diagram below shows the relationship among a digital twin, its corresponding physical asset, and a digital thread.

Figure 2

Figure 2: Digital Twin, Corresponding Physical Asset, and Digital Thread

The digital thread provides stakeholders with seamless access to tools and an authoritative source of truth for the technical information pertaining to the physical asset and its operational environment.

1.1 Digital Model vs. Digital Shadow vs. Digital Twin

A digital model is typically a manually updated system representation implemented at the type or class level whereas a digital twin is an automatically updated representation that is implemented at the specific unit level to dynamically respond to the changes in the physical world. Figure 3 below illustrates these manual and automatic data flows between physical and digital objects.
Figure 3: Physical and Digital Manual and Automatic Data Flows

Figure 3: Physical and Digital Manual and Automatic Data Flows

Digital shadows are another type of model, differing from digital twins only in that they have manual data flows to their physical counterpart.
A digital twin then is the most advanced model and, like a digital shadow, is a tailored digital representation of not just the class type of object it represents but rather the specific individual unit of that type (e.g., specific hull or tail number). The distinguishing factor between digital twins and digital shadows is that automated data flows are bi-directional in the former but not the latter. This allows for changes and updates to digital twins to be tested in digital environments and then pushed in real-time to their physical counterparts. Automatic data flows allow for more informed decisions, updates, and improvements of both the digital and physical objects, as well as autonomous scenario execution.

2 Lifecycle Phases of Digital Models and Twins

This section describes how digital models and twins can provide value across the entire acquisition program lifecycle of a system, including the phases of design and development, integration, T&E, and operations and sustainment.

Figure 4 illustrates the Digital Twin as a virtual representation of a connected physical asset and is borrowed from “Digital Twin: Definition & Value,” an Aerospace Industries Association (AIA) position paper.

Figure 4. AIAA Representation of a Digital Twin
Figure 4. AIAA Representation of a Digital Twin

2.1 Design and Development

During system design and development, basic technological components are integrated to establish that they will work together. The conceptual system model is translated into schematics and diagrams, including model-based systems engineering simulations that are consistent with the intended use of the model. Hardware and software specifics are defined. Information about how the model and simulations are organized, constructed, and executed are generated. The model development plan is published, outlining basic assumptions, capabilities, and limitations and risks associated with system development, testing, and operations and sustainment. Digital twins of early physical prototypes can be implemented and increased in fidelity as system development progresses; these twins can be exposed to operational contexts in virtual environments much earlier than physical prototypes can be in the physical environment, enabling earlier identification of deficiencies as well as their corrections. The required level of digital twin maturity and fidelity needed to support the system across its lifecycle is defined; in many cases only high-level system data is needed to address questions of interest and so it is important to remember that digital twins are not one-size-fits-all solutions. Indeed, digital twins should be built for specific applications and users.

2.2 Integration

During the integration phase, models are used to assess system, sub-system, and component-level elements for interface compatibility and data exchange interoperability. Models during this phase are valuable for physical and logical integration and test planning. Ultimately, the goal of integration is to ensure that the individual system elements function properly as a whole and that the system’s technical requirements and operational capabilities are satisfied. The diagram below shows the bottom-up branch of the Vee Model, including the tasks of assembly, integration, element-level verification during integration, and system-level validation; data flows between the physical and digital domains throughout this process.

Figure 5: Integrated Models Interactions During Integration Phase
Figure 5: Integrated Models Interactions During Integration Phase

2.3 Test and Evaluation (T&E)

Comprehensive live testing of future warfighting capabilities will not be possible in many cases due to environmental, fiscal, safety, classification, or ethical constraints, and so T&E will become more dependent on digital representations to test the performance and interoperability of our systems.
By taking advantage of the composability, editability, and parallelism of digital representations, digital twins can be used to augment T&E by replacing physical testing with virtual testing. In the future, we envision using digital twins alongside other digital models to not only provide a thorough understanding of the operational performance of single systems, but also to glean the high-level and synergistic mission-level effects characteristic of future joint warfighting operations, as well as the emergent behaviors they entail inside of “digital arenas”. This same method can be extended to support tactical decision-making during real-world combat operations in real-time.

To achieve this, however, digital twins must pass verification, validation, and accreditation (VV&A) prior to their use in T&E, following all relevant DoD policy on the use of M&S and its VV&A. In doing so, quantitative estimates of model error or uncertainty are determined with an emphasis on enabling rigorous interpolation and extrapolation beyond the parameter space or operational envelope that the digital twins have been directly validated on.
Program managers should plan early for the investment required to develop and validate digital twins while carefully considering the expected limitations of live testing that digital twins will be able to mitigate.

2.4 Operations and Sustainment

As described in the previous section, digital twins and other models can be injected into virtual environments to create mirrors of real-world combat operations, enabling intelligent data-driven tactical decision-making in real-time.

During sustainment, decisions regarding the models’ maintenance, disposal, disposition, etc. are made. These decisions include how much a system costs to maintain and how much its capability depreciates over time relative to the current technological landscape. One valuable application of digital twins during this phase is using them to predict when system maintenance tasks need to be performed; data from the physical system can be analyzed alongside the system’s maintenance log to predict when maintenance tasks are required, helping to prevent downtime and reduce costs.


Digital twins are revolutionizing how industries approach product design and development, T&E, and operations and sustainment by bridging the gap between the physical and digital worlds. They are enabling organizations to make more informed decisions, reduce risks, and maximize the efficiency of their processes and products across their lifecycles while achieving deeper understanding of systems’ performance. As programs mature across their lifecycles, so too do their digital twins—beginning as primarily simple, static digital models that evolve over time into twins that can exchange operational data with their physical counterparts in real-time.

This overview has provided the T&E community with a baseline definition and understanding of digital twins as well as related technologies such as digital models and digital shadows. We recommend continuing collaboration among government, industry, and academia to provide further guidance on the value of digital twins across program lifecycles. T&E Lessons learned, best practices, and standards must be developed and shared across the T&E and acquisition communities to advance the application of digital twins. As we look for ways to accelerate the delivery of capability to our warfighters while also achieving deeper understanding of systems’ operational performance, now is the time to start developing and using digital twins in T&E as well as the other phases of acquisition.


Thank you ITEA Hampton Roads Chapter for hosting the workshop and the many companies and Universities who participated.


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Author Biographies

Dr. Bonny Banerjee received M.S. in Electrical Engineering and Ph.D. in Computer Science from the Ohio State University. He is an Associate Professor at the Institute for Intelligent Systems, and Department of Electrical and Computer Engineering, University of Memphis. Just after Ph.D., he spent 3.5 years leading the research at a startup, which resulted in 7 patents, substantial investor funding, and the launch of a commercial product. The IP was acquired by the leading company in the field. He has published over 70 peer-reviewed articles in artificial intelligence. His research has been funded by NSF, DHS, Army, and City of Memphis.

Mr. Kishor Chakravarthy works as a Chief Engineer in Aerospace business in the Global Defense Sector at Booz Allen Hamilton, where his primary focus is on providing digital engineering and computational intelligence based engineering solutions to customers. He has close to three decades of experience in various industry verticals and holds a graduate degree in systems engineering from George Washington University.

Dr. William Fisher received a Ph.D. in Applied Physics from the University of Michigan. He is a Principal Systems Security Engineer with The MITRE Corporation primarily supporting OUSD R&E DTE&A in efforts to advance model-based engineering in T&E. He has been a system architect and lead systems engineer on large scale cyber-physical systems as well as solving hard problems for over 20 years in superconductivity, materials processing, optics, algorithm development, cyber security, and more in defense and academia.

Mr. Robert “Rob” Riley is the Digital Engineering Lead for the Experimental Demonstrations Branch organized within the Air Force Research Laboratory’s Aerospace Systems Directorate. Over the last 20 years, he has served in various military aerospace research & development, test and evaluation, systems engineering, and acquisition program management roles. He has led multiple test activities including flight test projects for Air Force weapon data links, tactical network management systems, and small unmanned aerial systems. He holds dual B.S. degrees in Electrical Engineering and Physics from Tuskegee University, a M.S. degree in Electrical Engineering from North Carolina A&T State University, and a M.Eng. degree in Systems Engineering from Cornell University.

Mr. Erwin Sabile works as a Chief Engineer in Navy and Marine Corps business in the Global Defense Sector at Booz Allen Hamilton. His primary focus is T&E and Mission Based Test Design. With over 20 years of PM and T&E experience, he supported various Operational Test Agencies and served as PMO T&E Lead within PEO C4I, IWS, PEO SUB, Chem Bio Defense Programs, DHS CBP, and others. He led multiple DT, IT, and OT&E Events in support of key Acquisition Milestone Decisions. Mr. Sabile is also a Navy Reservist supporting U.S. Navy 7th Fleet (Yokosuka, Japan) and serves as the Assessment Warfare Director. He holds a B.S. degree in Civil Engineering from Old Dominion University and a M.A. in Defense and Strategic Studies with the Navy War College.

Mr. James Sabino is currently a Principal Systems Engineer at Raytheon Technologies, where he has been for over 22 years. He is an expert in Model-based Systems Engineering (MBSE). He earned a B.S. in Electrical and Computer Engineering from Northeastern University (graduated magna cum laude from the College of Engineering), and an M.S. in Systems Engineering from Johns Hopkins University.

Dr. Tim Scully is a Chief Engineer and Fellow for T&E at Booz Allen Hamilton. Presently he leads Test and Evaluation activities in multiple domains (Air, Land, Sea, Space, and Cyberspace) most recently focusing on test strategies for Space sencing and AI/ML systems. Additionally, he oversees the Test and Evaluation Enterprise within Booz Allen and is the lead instructor / curriculum manager for Booz Allen’s Test and Evaluation Bootcamp. Dr. Scully earned Bachelors and Masters degrees in Electrical Engineering and a Doctorate in Computer Science.

Mr. John Silvas joined Booz Allen in June 1998 and is the Global Defense Vice President of Digital Engineering based in El Segundo, CA. John oversees the Firm’s DE operation while also serving as the Chief Engineer on the USSF SSC Ground SE&I Support (GSIS) contract. Prior to joining Booz Allen, John served in the USAF on active duty during the first Gulf War and then the NSA as a Reservist while attending Virginia Tech where he received a B.S. in Industrial Design in May of 1998. He received his INCOSE Expert Systems Engineering Professional (ESEP) certification in Feb 2018.

Dr. Armond E. Sinclair graduated with a Ph.D. in Manufacturing & Technology Management from The University of Toledo where his research focuses on Concurrent Engineering for Major Defense Acquisition Programs. He is also a graduate of the Wharton Business College’s CTO program. He has over 27 years of experience in Aerospace, Engineering & Technology. Currently, he is serving as Director of Systems Engineering at L3Harris Technologies’ Corporate Headquarters Office, where he leads critical initiatives including the enterprise microelectronics strategy and Digital Engineering Strategy. He has provided technical support and program oversight for platforms such as F-35, MQ-4C Triton, B-2, F-18, and other advanced restricted programs.

Dr. Policarpio Soberanis attended Loyola Marymount University earning a B.S. in Mathematics. He then pursued graduate school at the University of Iowa earning an M.S. in Mathematics, followed by a Ph.D. in Operations Research. His career spans multiple companies in the defense industry. Currently, he is an Engineering Manager overseeing the development of digital transformation capabilities that pull test and evaluation to the left and move the company towards the digital solutions that the customers are looking for. This includes the development of digital twin capabilities, model validation criteria, and processes for adoption through the Northrop Grumman Digital Transformation Office.

Dr. Himanshu Upadhyay is serving Florida International University’s Applied Research Center for the past 23 years, leading the Artificial Intelligence/Cybersecurity/Enterprise System research group & AI Center of Excellence. He is currently working as an Associate Professor in Electrical & Computer Engineering teaching Artificial Intelligence and Cybersecurity courses. His current research focuses on Artificial Intelligence, Machine Learning, Deep Learning, Generative AI, Cyber Security, Big Data, Cyber Analytics, Cyber Forensics, Malware Analysis and Blockchain. He is performing research & mentoring DOE Fellows, Cyber Fellows, AI Fellows, undergraduate and graduate students supporting multiple AI & Cybersecurity research projects from various federal agencies.

Dr. David “Fuzzy” Wells is Principal Cyber Simulationist for The MITRE Corporation supporting the National Cyber Range Complex. He is the former INDOPACOM Cyber War Innovation Center Director where he built the first combatant command venue for cyber testing, training, and experimentation; managed the Command’s joint cyber innovation & experimentation portfolio; and executed cyber range testing & training events for service, joint, and coalition partners. He was the first Air Force officer to obtain a Ph.D. in Modeling, Virtual Environments, and Simulation from the Naval Postgraduate School and M.S. in Modeling & Simulation from the Air Force Institute of Technology.

Dr. Jeremy Werner was appointed DOT&E’s Chief Scientist in December 2021 after joining as an Action Officer for Naval Warfare in August 2021. Before then, Jeremy was at Johns Hopkins University Applied Physics Laboratory, where he founded a data science-oriented military operations research team that transformed the analytics of an ongoing military mission. He previously served as a Research Staff Member at the Institute for Defense Analyses where he supported DOT&E in the rigorous assessment of a variety of systems/platforms. Dr. Werner earned a Ph.D. in physics from Princeton University where he was an integral contributor to the Compact Muon Solenoid collaboration in the experimental discovery of the Higgs boson at the Large Hadron Collider at CERN, the European Organization for Nuclear Research in Geneva, Switzerland. He earned a bachelor’s degree in physics from the University of California, Los Angeles where he received the E. Lee Kinsey Prize (most outstanding graduating senior in physics).


Author Title Definition
AIAA Digital Engineering Integration Committee (2020) Digital Twin: Definition & Value A Digital Twin is a set of virtual information constructs that mimics the structure, context and behavior of an individual or unique physical asset, that is dynamically updated with data from its physical twin throughout its life cycle, and that ultimately informs decisions that realize value.
Amazon (2024) What is Digital Twin Technology A digital twin is a virtual model of a physical object. It spans the object’s lifecycle and uses real-time data sent from sensors on the object to simulate the behavior and monitor operations.
Barricelli B.R., Casiraghi E. & Fogli D. (2019) A Survey on digital twin: Definitions, characteristics, applications, and design implications. The main characteristics that DTs are supposed to possess: Both the physical and the digital twins must be equipped with networking devices to guarantee a seamless connection and a continuous data exchange either through direct physical communications or through indirect cloud-based connections.
U.S. DoD (MIL-HDBK-539) (2022) N/A A virtual replica of a physical entity that is synchronized across time. Digital twins exist to replicate configuration, performance, or history of a system. Two primary sub-categories of digital twin are digital instance and digital prototype.
Digital Twin Consortium (2024) The Definition Of A Digital Twin A digital twin is a virtual representation of real-world entities and processes, synchronized at a specified frequency and fidelity.
Hu W., Zhang T., Deng X., Liu Z. & Tan J. (2021) Digital twin: A state-of-the-art review of its enabling technologies, applications and challenges. A general definition of DT may refer to the digital replica of physical assets, processes, people, places and systems, which provides both the elements and the dynamics of how the complex system operates and evolves throughout its lifecycle.
IBM (2024) What is a digital twin? A digital twin is a virtual representation of an object or system that spans its lifecycle, is updated from real-time data, and uses simulation, machine learning and reasoning to help decision making.
Jones D., Snider C., Nassehi A., Yon J. & Hicks B. (2020)



Characterizing the Digital Twin: A systematic literature review. A complete virtual description of a physical product that is accurate to both micro and macro level.
Nividia (2024) What is a Digital Twin? A digital twin is a virtual representation — a true-to-reality simulation of physics and materials — of a real-world physical asset or system, which is continuously updated.
Unity Technologies (2024) What is a Digital Twin? A digital twin is a dynamic virtual copy of a physical asset, process, system or environment that looks like and behaves identically to its real-world counterpart.
Wikipedia (2024) Digital Twin A digital twin is a digital model of an intended or actual real-world physical product, system, or process (a physical twin) that serves as the effectively indistinguishable digital counterpart of it for practical purposes, such as simulation, integration, testing, monitoring, and maintenance.
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