Model-based anything (MBx) targets the use of models in any function that might include engineering, design, manufacturing, safety, testing and validation, operations, finance, human resources, facilities and infrastructure, and acquisition. Integration of models across multiple domains will enable the creation of model-based enterprise, which would facilitate high-complexity decision making by embodying agile processes to achieve efficiency, accuracy, confidence, and adaptability in support of NASA’s mission, programmatic development, digital transformation, and institutional activities. The model-based enterprise adopts modeling technologies to integrate and manage both technical and business processes related to various NASA missions. The MBx envisions a future where automated analyses and seamless information exchange among engineering, programmatic, and institutional domains enable informed decision making in real-time and engineering of novel systems. In this vision, complexity and capability will be an order of magnitude greater than current systems. Advances in artificial intelligence (AI), machine learning (ML), and deep learning along with MBx will be utilized to make engineering, program management, and institutional decisions. Transformation process efficiency gains can be realized as MBx reduces the effort, time, and cost to execute engineering, program management, and institutional processes.
Key technologies relevant to MBx include modeling and simulation, data analytics, process mining, AI and ML, digital twin, virtual reality (VR) and augmented reality (AR), metaverse, and digital thread. Integration of multiple technologies and interoperability of the tool set across multiple platforms and organizations are essential for the application of MBx to engineering and institutional functions. In addition, a robust MBx approach inherently depends on the ease of data transformation, which is significantly enhanced by the collaborative capabilities of the modeling tools used to create data and the standards used to exchange that data. There is a need for appropriate standards to ensure the seamless flow of data throughout the mission lifecycle and reusability of data.
This subtopic will focus on (1) development of the key technologies previously cited for engineering and business functions that can be integrated to create a model-based enterprise, (2) digital twins of engineering systems, facilities, and business functions, (3) application of VR/AR for engineering system development and operation of various physical assets on ground and in space, (4) application of metaverse for engineering development and operations, and (5) development of digital thread with seamless flow of data and models throughout the mission lifecycle.
Model-Based Enterprise, Digitally Interacting Comprehensive Frameworks and Models, and Automated Decision Making for Agency Operations
Scope Description:
Model-based enterprise targets the use of models in any function, from engineering to safety to finance to facilities and more (i.e., Model-Based "Anything" or MBx), to enable high-complexity decision making embodying agile processes to achieve efficiency, accuracy, confidence, and adaptability in support of NASA’s mission, programmatic development, and institutional activities.
Consider an example of how Model-Based Systems Engineering (MBSE) is increasing in importance to future projects and programs as demonstrated by the strategic thrust towards "Model-Based Anything" of the Digital Transformation Initiative. At the same time, the nature of work at NASA is increasingly distributed with a workforce that may continue partial telework even after pandemic-related restrictions are relaxed.
As previously indicated, the Agency will need to focus on efforts associated with the new changes in the "future of work" at NASA (Refs. 6 and 8). NASA will likely have fewer people working in buildings post-pandemic, and such buildings may be used differently than at present because many people will be working offsite and less frequently working in NASA facilities—except for special activities and needs. We will need to restructure our present older facilities for this type of change and/or plan to design differently for any new facilities, and we will need models for that.
NASA is seeking specific innovative, transformational, model-based solutions in the area of “Digital Twin” Institutional Management of Health/Automated Decision Support of Agency Facilities, which represents an opportunity to make revolutionary changes in how our Agency conducts business by investing in nascent technologies. The Agency’s newly minted Digital Transformation Office is interested in how to help reposition and accelerate the modernization of digital systems that support modernapproaches to managing the Agency's aging infrastructure. Recent initiatives in smart city technologies focus on condition-based/preventive maintenance, smart buildings, and smart lighting, which will address pressing Agency facility needs.
The STTR vehicle offers the small business community an opportunity to have a hand in this process towards repositioning and accelerating the modernization of digital systems supporting the Agency's aging infrastructure to:
- Save energy costs due to water and electricity usage that is poorly measured and managed.
- Enable the deployment of nascent technological trends in data-driven decision making and support tools based upon statistical methods to help streamline and improve the efficiency of facility operations and maintenance activities.
- Determine how well technologies using techniques from the previous bullet can be broadly deployed across NASA.
- Enabling new agency-centric insight and management capabilities (building upon center models) to meet evolving future of work and other challenges in a more proactive and seamless manner.
At the conclusion of a Phase II effort, we anticipate that offerors should deliver a means to develop a model that is capable of context switching among various categorical factors established according to various levels of granularity including, but not limited to, the following: independent facility needs, facility inventory lifecycle balancing needs, workforce needs, etc.
For example, such a model should use past years' data to predict the condition of certain facility systems, and which ones should be invested in first for repairs to improve the return on investment (ROI) or improve the overall condition and reliability of the facility. A deferred maintenance assessment is conducted at NASA every year or on a 2- to 3-year cycle, where the inventory of buildings at every center is considered, for 27 systems total. A comparison of the current condition of those systems to previous years for each of those building systems is conducted. At the moment, there is a (sometimes categorical, sometimes numerical) mission dependency index (MDI) that comprises six factors (ref. 7), which is used to decide the highest priority for investments.
By the end of Phase II, offerors should have developed a model capable of identifying which of these 27 systems to invest in to increase the overall MDI. For example, given a specific building and the relative condition of its 27 systems, the model should make a recommendation on which systems to focus on for the highest ROI and fastest payback, as not all systems will feasibly be invested in for concurrent improvements.
The model should also be capable of the following:
- Identifying an optimal sequence of investments for which systems and which projects should be undertaken first.
- Be scalable and be capable of prioritizing project(s) by looking at 27 systems to identify the best investments based on a large number of buildings (e.g., 100 or more).
- Capable of identifying macro-level systemic issues throughout the entire facility inventory from independent predictions made at the local level.
Several years worth of data (potentially up to 10 years) can be supplied to support the development of these enhanced features of such a model as well.
However, it should be noted that it is easier to provide data for specific facility-level improvements rather than for facility inventory optimization due to the diverse and nontraditional set of facility functions that NASA as an Agency is challenged with due to unique mission needs and requirements. Data to support this type of macro-level analysis is not readily available, e.g., on the quality of the spaces.
However, at the local level, there are a limited number of high-performance modern facilities in the Agency that may offer very granular levels of detail to inform the development of a model that could effectively be used to address post-pandemic facility layout optimization needs, e.g., due to social distancing requirements, etc.
Expected TRL or TRL Range at completion of the Project: 4 to 6
Primary Technology Taxonomy:
- Level 1: TX 11 Software, Modeling, Simulation, and Information Processing
- Level 2: TX 11.X Other Software, Modeling, Simulation, and Information Processing
Desired Deliverables of Phase I and Phase II:
- Research
- Analysis
- Prototype
- Hardware
- Software
Desired Deliverables Description:
Phase I Deliverables—Reports identifying use cases, proposed tool views/capabilities, identification of NASA or industry leveraging and/or integration opportunities, test data from proof-of-concept studies, and designs for Phase II.
Phase II Deliverables—Delivery of models/tools/platform prototypes that demonstrate capabilities or performance over the range of NASA target areas identified in use cases. Working integrated software framework capable of direct compatibility with existing programmatic tools.
State of the Art and Critical Gaps:
Outside of NASA, industry is rapidly advancing Model-Based Systems Engineering (MBSE) tools and scaling them to larger, more complex development activities. Industry sees scaling as a natural extension of their ongoing digitization efforts. These scaling and extension efforts will result in reusable, validated libraries containing models, model fragments, patterns, contextualized data, etc. They will enable the ability to build upon, transform, and synthesize new concepts and missions, which has great attraction to both industry and government alike. Real-time collaboration and refinement of these validated libraries into either “single source” or “authoritative sources” of truth provide further appeal as usable knowledge can be pulled together much more quickly from a far wider breadth of available knowledge than was ever available before.
One example of industry applying MB/MBe/MBSE is through Digital Thread™, a communication framework that helps facilitate an integrated view and connected data flow of the product's data throughout its lifecycle. In other words, it helps deliver the right information at the right time and at the right place. Creating an “identical” copy (sometimes referred to as a "digital twin") is another use, a digital replica of potential and actual physical assets, processes, people, places, systems, and devices that can be used for various purposes. These twins are used to conduct virtual cost/technical trade studies, virtual testing, virtual qualification, etc., that are made possible through an integrated model-based network. Given the rise of MBSE in industry, NASA will need to keep pace in order to continue to communicate with industry, manage and monitor supply chain activities, and continue to provide leadership in spaceflight development.
Within NASA, our organization is faced with increasingly complex problems that require better and timelier integration and synthesis of both models and larger sets of data, not only in the systems engineering or MBSE realm, but in the broader MB Institution, MB Mission Management, and MB Enterprise Architecture. NASA is challenged to sift through and pull out the particular pieces of information needed for specific functions, as well as to ensure requirements are traced into designs, tested, and delivered; thus, confirming that the Agency gets what it has paid for. On a broader cross-agency scale, we need to ensure that needed information is available to support critical decisions in a timely and cost-effective manner. All of these challenges are addressed through the benefits of model-based approaches. Practices such as reusability, common sources of data, and validated libraries of authoritative information become the norm, not the exception, using an integrated, model-based environment. This model-based environment will contribute to a diverse, distributed business model encompassing multicenter and government-industry partnerships as the normal way of doing business.
Relevance / Science Traceability:
MBx solutions can benefit all NASA Mission Directorates and functional organizations. NASA activities could be a dramatically more efficient and lower risk through MBx support of more automated creation, execution, and completion verification of important agreements, such as international, supply chain, or data use.
Integration of Digital Twin With Augmented and Virtual Reality in Metaverse
Scope Description:
Digital twins is a critical emerging technology that consists of a physical asset, a virtual counterpart, and the data exchanged between the two. Enabled by models and simulations, advanced computing, and cyber and immersive technologies, digital twins tackle the challenge of integration between the physical and digital world, facilitating rapid analysis and real-time decision making. Digital twins transform the traditional design-build-test waterfall approach to a model-analyze-build-test spiral approach. This provides the capability to experiment, validate, and optimize solutions in the virtual space before building and testing, potentially jeopardizing the real-world asset. After a higher confidence design is built, measured test results can be used to update the model to forecast performance and evaluate risk of unforeseen operational scenarios. In the early stages of product/mission development, multiphysics models, simulations, and analytics (to include artificial intelligence (AI) and machine learning (ML)) can be used to conduct tradeoff analyses under various mission operating conditions and what-if scenarios in the virtual world. Insights can be obtained on manufacturability, cost, schedule, and performance by experimenting with a wide range of scenarios and evaluating optimized solutions and/or mitigation strategies. This results in significant reduction in time taken for development of design and new product/mission concepts. Digital twins can provide real-time monitoring, diagnostics, and corrective action for the operating assets. For operational assets like aircraft, spacecraft, habitats, power systems on lunar surface, planetary rovers, or large test facilities, digital twins fed by real-time sensor data on as-experienced environmental conditions can transform assumptions that drive the current scheduled and preventive maintenance practices to enable a more efficient predictive maintenance based on the actual condition of the operating asset.
The application of augmented and virtual reality (AR/VR) is undergoing significant growth for many engineering applications that include design and virtual testing of new products/concepts, manufacturing, and operations. The use of AR/VR allows designers, engineers, and end users to be immersed in a simulated environment (virtual reality) and in an environment where actual environments and objects are superimposed (augmented reality), or a hybrid between the two (mixed reality). By experiencing a new product in an immersive environment, designers and engineers can collaborate to accelerate the iterative product development process and reduce development costs. They can conduct research, design, modeling, prototyping, and user testing to validate ideas virtually in ways that would be too costly, impractical, or impossible to recreate in the real world. Besides design and development of new products, AR/VR technologies are also used for training.
Integration of digital twins with AR/VR offers many benefits. Utilizing a virtual or augmented experience for digital twins allows stakeholders to digest, understand and visualize real-world depictions, and the ability to move and interact in these spaces. Digital twins integrated with AR/VR would provide virtual, behaviorally accurate representation of product designs and operating assets. By experiencing a component, subsystem, or system in an immersive environment along with the simulation tools associated with digital twins, engineers and designers can bring a product to life without physically constructing a single thing. An integrated digital twin-AR/VR system would allow training of operators for large facilities and manufacturing operations in a virtual dynamic environment, where they could practice responding to live operational conditions without risk to the asset or down time.
While the computational tools for digital twins, real-time sensors, and AR/VR technologies have been developed in parallel and independent paths, the possibility of the combined use of these tools has grown. Typically, the simulation software used for digital twins lacks the AR/VR functionalities and lacks a mechanism to ingest live sensor data. It is timely to develop the connectivity between the digital twin, sensor data, and VR/AR software to take advantage of their strengths. It is envisioned the metaverse, which is rapidly evolving, will be the platform for integration of digital twins and live data with AR/VR. The metaverse will help recreate the existence of the real world digitally. For the integrated operational digital twin-AR/VR concept to be a reality, the necessary computational tools and architectures need to be developed to integrate digital twins and data streams with immersive technologies.
Expected TRL or TRL Range at completion of the Project: 3 to 6
Primary Technology Taxonomy:
- Level 1: TX 11 Software, Modeling, Simulation, and Information Processing
- Level 2: TX 11.X Other Software, Modeling, Simulation, and Information Processing
Desired Deliverables of Phase I and Phase II:
- Research
- Analysis
- Prototype
- Hardware
- Software
Desired Deliverables Description:
Phase I Deliverables—
- Methodology and approach for integrating digital twin in metaverse to design and develop advanced aerospace concepts and design
- Methodology and approach for integrating digital twin with metaverse for a large test facility (like wind tunnel) that will enhance facility operations and collaborative testing among geographically dispersed partners.
Phase II Deliverables—
- Delivery of models/tools/platform prototypes that demonstrate capabilities or performance over the range of NASA target areas identified in use cases. Working integrated software framework capable of direct compatibility with existing programmatic tools.
- All other requirements remain unchanged.
State of the Art and Critical Gaps:
One of the targets for NASA’s Digital Transformation (DT) strategic initiative is to transform engineering. Building blocks toward operational digital twins are currently being developed within NASA’s DT effort, with a goal of reducing time to develop new systems, significantly reducing time for anomaly detection in operating systems and enabling predictive maintenance of NASA facilities and infrastructure. Integration of operational digital twins with AR/VR technologies in metaverse will accelerate development of new aerospace systems and will offer engineers a better platform to share, interact, and collaborate with multiple partners. In addition, the integration of operational digital twins and AR/VR in the metaverse will enable training of new operators and engineers in large and complex test facilities.
Relevance / Science Traceability:
Covers Aeronautics Research Mission Directorate (ARMD) priorities, such as zero-emission aircraft and green aviation as potential targets, along with OSI (Office of Strategic Infrastructure) priorities such as large test facilities and laboratories across the Agency, under the stewardship of the SETMO (Space Environments Testing Management Office). This would help with upskilling and training the current and future cohort of facility technicians and collaboration with external partners.