MBRE: Model-Based Requirements Engineering

Model-Based Requirements Engineering (MBRE) emphasizes the use of detailed models to define, analyze, and manage system requirements. Instead of relying on text requirements, MBRE employs formal models as primary method of capturing, relaying, and optimizing requirements.

Requirements Engineering

Requirements engineers play a critical role in engineering complex systems. Their day-to-day responsibilities revolve around defining, documenting, and maintaining system requirements.

Ease of Management

“When requirements are written in the classical format, significant resources (including many brains) are required to develop and manage them, which can lead to identifying problems late in the development cycle,” Daniel R. Herber of Colorado State University’s Department of Systems Engineering said in a March 2030 presentation at the INCOSE Requirements Working Group Meeting.

Visualized Requirements

Models in requirements can be graphical, mathematical, or both. They provide a way to visually represent requirements, facilitating understanding and clear communication understanding and clear communication among engineers, management, and external stakeholders.

Requirements Traceability

In MBRE, requirements in models are linked to design components, test cases, verification criteria, and other elements to unlock traceability from requirements to implementation to testing.

Simulation and Analysis

The model-based approach to requirements enables strenuous analysis capabilities. Engineers can use modeling tools to simulate and validate requirements for feasibility and conflicts for feasibility and conflicts, spotting potential issues early in the development process.

Creating Models

Engineers use Systems Modeling Language (SysML) to create models for business requirements, user requirements, system requirements, and other requirement types. The models detail capabilities and behavior the system requires.

Complex Systems

Nataliya Shevchenko writes in “An Introduction to MBSE” for CMU SEI’s blog, “A model must have a structure. A well-structured model can make the model understandable, usable, and maintainable, which is particularly important for complex systems. The goal of a model is to show stakeholders that the presented design satisfies the system’s requirements.

Evolving Scope

MBRE gained traction as part of the broader MBSE shift. Rapidly advancing progress in Internet of Things (IoT), cyber-physical systems, and AI continues to enhance capabilities in building, documenting, managing, and optimizing complex systems at scale.

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Let's examine how we got here and where we may be heading.

Emerging from the aerospace and automotive industries, digital twin technology is now gaining popularity across sectors. The virtual replicas of real-world entities are used for comprehensive simulations, predictive maintenance, and virtual prototyping.

0:17 Alan Turing's Computing Machinery and Intelligence
Though it’s primarily focused on AI, Turing’s paper provides the theoretical and computational foundations necessary to build smart, data-driven virtual models of physical assets.

1:06 First Commercial Computer (UNIVAC I)
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Monte Carlo simulations go mainstream around 1952. The experimentation method was initially developed for the Manhattan Project efforts to create an atomic bomb during World War II.

2:10 Development of FORTRAN
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2:37 Launch of Sputnik and Advances in Aerospace Simulation
In 1957, the Soviet Union launches Sputnik, touching off the Space Race with the United States that accelerates simulation technology. The pressure pushes scientists to develop superior computer models to predict satellite paths and behavior in space.

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In the early 1960s, the aerospace industry begins using digital simulations to design and test aircraft. 

3:22 Introduction of CAD (Computer-Aided Design)
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3:33 1964 - Jay Forrester Introduces System Dynamics
In 1964, Jay Forrester introduces System Dynamics, a methodology for modeling and simulating complex systems. 

3:57 1970 - Apollo 13 Lunar Mission
In April 1970, the Apollo 13 mission to the Moon almost ends tragically. 

4:16 1982 - Release of Autodesk's AutoCAD
In the early 1980s, CAD software enters the mainstream. 

4:45 Advancements in Product Lifecycle Management (PLM) Systems
Throughout the 1990s, PLM platforms integrate various tools and processes, including CAD, to ensure consistency and accuracy of data and enhanced communication across departments.

5:21 Dr. Michael Grieves Coins the Term "Digital Twin"
In 2002, Michael Grieves introduces the concept of the digital twin at a Society of Manufacturing Engineers conference in Michigan.

5:47 NASA's Strategic Roadmap for Digital Twin Technology
In 2010, NASA develops a strategic roadmap for digital twin adoption for future missions.

6:09 Industry 4.0 Concept Introduced
The fourth industrial revolution begins in earnest in 2011 as the Industry 4.0 concept is introduced at Germany’s Hannover Messe. 

6:40 General Electric's Digital Twin for Industrial Internet
In 2017, General Electric introduces its digital twin technology for industrial applications.

7:02 Microsoft's Azure Digital Twins Platform
The 2018 launch of Microsoft’s Azure Digital Twins platform accelerates adoption with a comprehensive cloud-based service. 

7:25 COVID-19 Pandemic Accelerates Digital Twin Adoption
In 2020, the COVID-19 pandemic accelerates the adoption of advanced manufacturing technologies, including digital twins, as companies seek to mitigate the disruptions in their operations, supply chains, and workforces.

7:37 Siemens Xcelerator Platform
Siemens introduces its Xcelerator platform in 2021.

8:00 NVIDIA Omniverse Platform
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8:20 Manufacturers Embrace the Industrial Metaverse
Heading into the mid-2020s, manufacturers warm up to the industrial metaverse. 

8:35 2030s - Digital Twins Become More Intelligent and Autonomous

9:11 2040s - Synthetic Holos Replace Digital Twins


#digitaltwin #digitaltransformation #industry40 #singularity #artificialintelligence #ai #machinelearning #robotics #humanoid #humanoidrobot #humanoidrobots #digitalthread #plm #digitalengineering #cad #3d #bigdata #blockchain #iiot #4ir #manufacturing #digitaltwins #futuretechnology #futuretech #smartcity #iot #internetofthings #innovation #quantumcomputing #digitalimmortality #transhumanism #simulation

Digital twins are everywhere.

The virtual replicas of physical entities are revolutionizing industries from manufacturing to healthcare to urban planning with their advanced simulation capabilities.

Let's examine how we got here and where we may be heading.

Emerging from the aerospace and automotive industries, digital twin technology is now gaining popularity across sectors. The virtual replicas of real-world entities are used for comprehensive simulations, predictive maintenance, and virtual prototyping.

0:17 Alan Turing's Computing Machinery and Intelligence
Though it’s primarily focused on AI, Turing’s paper provides the theoretical and computational foundations necessary to build smart, data-driven virtual models of physical assets.

1:06 First Commercial Computer (UNIVAC I)
The UNIVAC, the first commercially produced computer in the United States, is released in 1951. First deployed at the US Census Bureau, the UNIVAC I offers a glimpse into the potential of computing to handle vast amounts of data quickly and accurately to solve complex problems.

1:59 Monte Carlo Simulations
Monte Carlo simulations go mainstream around 1952. The experimentation method was initially developed for the Manhattan Project efforts to create an atomic bomb during World War II.

2:10 Development of FORTRAN
In the mid-50s, IBM’s FORTRAN delivers the computational power necessary for early forms of digital modeling and simulations. Its ability to handle large-scale computations and numerical analysis advances technology required for future digital twinning.

2:37 Launch of Sputnik and Advances in Aerospace Simulation
In 1957, the Soviet Union launches Sputnik, touching off the Space Race with the United States that accelerates simulation technology. The pressure pushes scientists to develop superior computer models to predict satellite paths and behavior in space.

3:09 Digital Simulation in Aerospace
In the early 1960s, the aerospace industry begins using digital simulations to design and test aircraft.

3:22 Introduction of CAD (Computer-Aided Design)
Ivan Sutherland develops Sketchpad for computer-aided design. It revolutionizes the way engineers and designers work by enabling precise digital drawings and models.

3:33 1964 - Jay Forrester Introduces System Dynamics
In 1964, Jay Forrester introduces System Dynamics, a methodology for modeling and simulating complex systems.

3:57 1970 - Apollo 13 Lunar Mission
In April 1970, the Apollo 13 mission to the Moon almost ends tragically.

4:16 1982 - Release of Autodesk's AutoCAD
In the early 1980s, CAD software enters the mainstream.

4:45 Advancements in Product Lifecycle Management (PLM) Systems
Throughout the 1990s, PLM platforms integrate various tools and processes, including CAD, to ensure consistency and accuracy of data and enhanced communication across departments.

5:21 Dr. Michael Grieves Coins the Term "Digital Twin"
In 2002, Michael Grieves introduces the concept of the digital twin at a Society of Manufacturing Engineers conference in Michigan.

5:47 NASA's Strategic Roadmap for Digital Twin Technology
In 2010, NASA develops a strategic roadmap for digital twin adoption for future missions.

6:09 Industry 4.0 Concept Introduced
The fourth industrial revolution begins in earnest in 2011 as the Industry 4.0 concept is introduced at Germany’s Hannover Messe.

6:40 General Electric's Digital Twin for Industrial Internet
In 2017, General Electric introduces its digital twin technology for industrial applications.

7:02 Microsoft's Azure Digital Twins Platform
The 2018 launch of Microsoft’s Azure Digital Twins platform accelerates adoption with a comprehensive cloud-based service.

7:25 COVID-19 Pandemic Accelerates Digital Twin Adoption
In 2020, the COVID-19 pandemic accelerates the adoption of advanced manufacturing technologies, including digital twins, as companies seek to mitigate the disruptions in their operations, supply chains, and workforces.

7:37 Siemens Xcelerator Platform
Siemens introduces its Xcelerator platform in 2021.

8:00 NVIDIA Omniverse Platform
NVIDIA’s Omniverse platform, introduced in 2023, integrates AI, simulation, and photorealistic visualization technologies

8:20 Manufacturers Embrace the Industrial Metaverse
Heading into the mid-2020s, manufacturers warm up to the industrial metaverse.

8:35 2030s - Digital Twins Become More Intelligent and Autonomous

9:11 2040s - Synthetic Holos Replace Digital Twins


#digitaltwin #digitaltransformation #industry40 #singularity #artificialintelligence #ai #machinelearning #robotics #humanoid #humanoidrobot #humanoidrobots #digitalthread #plm #digitalengineering #cad #3d #bigdata #blockchain #iiot #4ir #manufacturing #digitaltwins #futuretechnology #futuretech #smartcity #iot #internetofthings #innovation #quantumcomputing #digitalimmortality #transhumanism #simulation

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YouTube Video UExZUkdCOF9hWE80bk5tUTZpWFNfY05naTZ3cmQzWmFSYi4wN0FBRUVFNEVBMTZBQ0Mx

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