A Department of Defense mandate could have ramifications that extend digital engineering adoption far beyond the defense community.
If you were to build the perfect use case for the potential of digital engineering, you would be hard-pressed to choose a better sector than the defense industry. Modern defense systems are incredibly complex, with different teams often using different software to design and optimize components, products, and systems that must interoperate flawlessly in mission-critical applications to help ensure the security of entire nations. The Pentagon has long recognized the importance of digital engineering to help tame that complexity by bringing people, processes, and technology together — and now digital engineering has a mandate.
In 2018, Undersecretary of Defense for Research and Engineering Michael D. Griffin, as part of the official Department of Defense’s (DoD’s) “Digital Engineering Strategy” document, emphasized the importance of restoring readiness through the adoption of new practices in development, acquisition, and sustainment of defense systems. Specifically, Griffin identified sustained, predictable investment to restore readiness and modernize the industry as crucial to mitigating the impact of any conflicts in the future. The first goal in the DoD Engineering Strategy was to “formalize the development, integration, and use of models to inform enterprise and program decision making,” which aligns with many private industry practices for model-based systems engineering (MBSE).
The Office of the Undersecretary of Defense for Research and Engineering (OUSDR&E) has published DoD instruction (DoDI) 5000.97 “Digital Engineering,” which mandates digital engineering implementation.
That “Digital Engineering Strategy” document presented a vision and goals, but on December 21, 2023, the Office of the Under Secretary of Defense for Research and Engineering (OUSDR&E) published DoD instruction (DoDI) 5000.97 “Digital Engineering,” which mandates implementation policy. By making digital engineering mandatory and outlining how it will be implemented, OUSDR&E has essentially challenged the defense community to adopt a new level of innovation. Because that community is so broad and varied, DoDI 5000.97 could result in a blueprint for multiple industries — including those not tied to defense — to successfully implement technologies and processes related to digital engineering.
OUSDR&E shared this digital engineering framework as part of its summary of DoDI 5000.97.
Open Ecosystems Are Key to Digital Engineering
Central to DoDI 5000.97 is the use of model-based engineering simulation early in the development process and throughout the product life cycle. It enables better-informed decisions via analysis of high-fidelity, virtualized models of products in their environments, which supports the development of complex systems. Implementing digital engineering brings those insights to key decision-makers across an organization via a digital thread that connects authoritative data and digital models with simulation process and data management (SPDM) solutions.
Such benefits are possible because digital engineering extends beyond simulation, enabling transformation by integrating multiphysics simulation with workflows. Creating bidirectional connections between engineering analyses and system requirements helps ensure functionality, cost, and performance are on track.
That’s why an open ecosystem is key to a digital engineering strategy. According to an OUSDR&E DoDI 5000.97 summary document, a digital engineering ecosystem is “the infrastructure and architecture (hardware, software, networks, tools, workforce) necessary to support digital approaches for all phases of the system development life cycle.” Organizations already have established workflows. Connecting them enables engineers to use the tools they want to use — both now and years from now — by fitting technologies into gaps in a scalable, interoperable way. An open ecosystem that can quickly conform to the latest technological needs, market trends, and compliance mandates is the only answer to the flexibility required in today's and tomorrow's fast-changing business environment.
DoDI 5000.97 Defined
DoDI 5000.97 is a directive to incorporate digital engineering (DE) into all defense programs with few exceptions. It represents a new approach to system development and sustainment in the defense industry in the use of system and data models to implement DE across the entire DoD acquisition life cycle.
This policy change represents a significant shift from the defense department’s primary means of communicating system information, as it requires a move from a document-based approach to one driven by digital models and managing the data they provide. It also emphasizes creating a digital engineering ecosystem to establish a single authoritative source of truth for the data and models intended to support programs across an organization.
To this end, digital engineering capability is defined as that which provides for the development, verification, validation, use, curation, configuration management, and maintenance of technically accurate digital systems and models of systems, subsystems, and their components. This activity includes development, security, and operations (DevSec Ops) testing, process, and software. All must operate within a secure ecosystem in accordance with certain policies, standards, and best practices. That requires programs to establish an authoritative source of truth (ASoT) for data and models.
“Building a digital engineering environment with an open ecosystem eliminates or substantially reduces reinvention of effective legacy tools and processes,” wrote retired U.S. Air Force Brig. Gen. Steve Bleymaier, who is the chief technology officer for aerospace and defense at Ansys, and Kevin Flood, president of Ansys Government Initiatives, in an article they authored for Defense News. “It also enables organizations to adapt workflows in a way that keeps their human expertise fully engaged while making the most of the new technologies.”
Engineering simulation software and connecting technologies advance digital engineering throughout the product life cycle across multiple industries.
Tools, Training, and Support To Prioritize Industry Success
Important to this shift is the willingness to prioritize quantitative analysis over intuition and experience, exclusively, in adopting digital engineering. It's a significant departure from traditional environments in which roles have been more rigidly separated and defined in an organizational structure that tends to be risk averse and more hierarchical in nature.
“Ansys prioritizes the development of an array of user-intuitive data management and process integration tools able to support a digital thread concept that provides traceability and consistency across an entire system life cycle,” says Armond Sinclair, senior technical account manager at Ansys. “All integrate seamlessly with model-based engineering environments, speeding the transition from document-centric to model-centric engineering practices.”
The Ansys product collection includes several tools that support digital engineering implementation:
- Ansys Minerva enterprise-level SPDM software secures critical simulation data as a single source of truth.
- Ansys ModelCenter software empowers engineers to create and automate multi-tool workflows, optimize product designs, and enable MBSE by connecting requirements to engineering analysis.
- Ansys optiSLang process integration and design optimization software enables simulation toolchain automation via algorithms that efficiently and automatically search for the most robust design configuration, speeding decision-making.
- The Ansys System Architecture Modeler tool provides a comprehensive framework to visualize, design, and manage complex systems to help ensure efficient stakeholder communication, align technical solutions with business goals, identify potential risks early, and enhance decision-making by enabling MBSE.
- Ansys Granta MI Enterprise materials management software enables you to store, control, and analyze materials.
- The Ansys Twin Builder simulation-based digital twin platform enables engineers to create simulation-based digital twins to enable system design and optimization, and predictive maintenance.
Simulation-based digital twins enable system design and optimization, as well as predictive maintenance.
Ansys solutions are also accelerated for scalability thanks to the Ansys SimAI cloud-enabled generative AI platform and AI integrations, as well as both on-premises and cloud-based high-performance computing to facilitate multidisciplinary collaboration across different engineering specialties and geographical locations. Those scalability and collaboration benefits extend to multidomain operational environments with digital mission engineering solutions that enable engineers, operators, and analysts to connect modeling and simulation efforts across all phases of the engineering product life cycle. Additionally, Ansys has implemented robust security measures that meet Cybersecurity Maturity Model Certification (CMMC) Program standards in its tools for security-focused development, particularly in cloud-based solutions to help ensure defense sector data protection requirements.
“By its very nature, the open Ansys ecosystem facilitates the conjoining of a diverse number of software tools and platforms required to implement DoDI 5000.97 with speed and certainty,” says Sinclair. “Their seamless integration ensures the interoperability between different modeling tools, the development of standardized data exchange formats for system models, and the creation of unified interfaces for modeling, validation, and visualization.”
Learn how to transform complex systems with digital engineering.
Tools and Processes To Enable Digital Engineering
- An authoritative source of truth (ASOT), which is defined as the single, definitive source of a given piece of information that all consumers of that information should reference to mitigate the risk of relying on outdated, inconsistent, or inaccurate information, according to the U.S. Space Force
- Artificial intelligence, which can be used to support many goals of digital engineering by automating optimization and enabling scalability
- Digital threads to connect authoritative data and digital models, providing actionable information to decision makers throughout a system’s life cycle, according to the Under Secretary of Defense for Research and Engineering (OUSDR&E)
- Digital twins, which are virtual representations of real-world entities and processes synchronized at a specified frequency and fidelity
- Model-based systems engineering, which is a methodology that focuses on creating and exploiting digital system and engineering domain models as the primary means of exchange of information, feedback, and requirements, as opposed to document-centric systems engineering
- Process integration and design optimization (PIDO) software, which enables the “tool-chaining” of multiple steps or subprocesses involving multiple software tools into a convenient package to maximize the desired output while minimizing costs
- Simulation process and data management (SPDM), which streamlines workflows, enables collaboration, manages data, and leads to a digital thread by connecting simulation activities with other product life cycle processes
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A digital engineering ecosystem is “the infrastructure and architecture (hardware, software, networks, tools, workforce) necessary to support digital approaches for all phases of the system development life cycle.”
— OUSDR&E DoDI 5000.97 summary document
