You are here

Integrated Campaign and System Modeling

Description:

Scope Title:

Scope1: Campaign and System Modeling and Simulation

Scope Description:

As described above, this year NASA is focused on interoperability and its impact on general M&S challenges and solutions.  Specific areas of interest are listed below. Proposers are encouraged to address more than one of these areas with an approach that emphasizes integration with others on the list:


1.    Define, design, develop, and execute future NASA campaigns (collections of missions) and missions (human, robotic, mixed) by developing and utilizing advanced methods and tools that empower more comprehensive, broader, and deeper system and subsystem insights (typically via analysis using models), while enabling these insights to be achieved earlier in the lifecycle where the potential influence on the outcome is greater. 
2.    Enable disciplined system analysis for the design of future missions or campaigns, including modeling of decision support for those missions and integrated models of technical and programmatic aspects of future missions.
3.    Evaluate technology alternatives and impacts, science valuation methods, and programmatic and/or architectural trades.
4.    Conceptual phase models and tools that allow design teams to easily develop, populate, and visualize very broad, multidimensional trade spaces; also, methods for characterizing and selecting optimum candidates from those trade spaces, particularly at the architectural level. There is specific interest in models and tools that facilitate comprehensive comparison of variants of systems and subsystems.
5.    Capabilities for rapid-generation models of function or behavior of complex systems at either the system or the subsystem level. Such models should be capable of eliciting robust estimates of system performance, given appropriate environments and activity timelines, and should be tailored:

  • To support emerging usage of autonomy, both in mission operations and flight software as well as in growing usage of autocoding. 
  • To operate within highly distributed collaborative design environments, where models and/or infrastructure that support/encourage designers are geographically separated (including open innovation environments). This includes considerations associated with near-real-time (concurrent) collaboration processes and associated model integration and configuration management practices.
  • To be capable of execution at variable levels of fidelity/uncertainty. Ideally, models should have the ability to quickly adjust fidelity to match the requirements of the simulation (e.g., from broad and shallow to in depth and back again).

6.    Target models (e.g., phenomenological or geophysical models) that represent planetary surfaces, interiors, atmospheres, etc., and associated tools and methods that allow for integration into system design/process models for simulation of instrument responses. These models may be algorithmic or numeric, but should be useful to designers wishing to optimize remote-sensing systems for those planets.

 

 

Define, design, develop, and execute future NASA campaigns (collections of missions) and missions (human, robotic, mixed) by developing and utilizing advanced methods and tools that empower more comprehensive, broader, and deeper system and subsystem insights (typically via analysis using models), while enabling these insights to be achieved earlier in the lifecycle where the potential influence on the outcome is greater:

  1. Enable disciplined system analysis for the design of future missions or campaigns, including modeling of decision support for those missions and integrated models of technical and programmatic aspects of future missions.
  2. Evaluate technology alternatives and impacts, science valuation methods, and programmatic and/or architectural trades.
  3. Conceptual phase models and tools that allow design teams to easily develop, populate, and visualize very broad, multidimensional trade spaces; also, methods for characterizing and selecting optimum candidates from those trade spaces, particularly at the architectural level. There is specific interest in models and tools that facilitate comprehensive comparison of variants of systems and subsystems.
  4. Capabilities for rapid-generation models of function or behavior of complex systems at either the system or the subsystem level. Such models should be capable of eliciting robust estimates of system performance, given appropriate environments and activity timelines, and should be tailored:
  5. To support emerging usage of autonomy, both in mission operations and flight software as well as in growing usage of autocoding. 
  6. To operate within highly distributed collaborative design environments, where models and/or infrastructure that support/encourage designers are geographically separated (including open innovation environments). This includes considerations associated with near-real-time (concurrent) collaboration processes and associated model integration and configuration management practices.
  7. To be capable of execution at variable levels of fidelity/uncertainty. Ideally, models should have the ability to quickly adjust fidelity to match the requirements of the simulation (e.g., from broad and shallow to in depth and back again).
  8. Target models (e.g., phenomenological or geophysical models) that represent planetary surfaces, interiors, atmospheres, etc., and associated tools and methods that allow for integration into system design/process models for simulation of instrument responses. These models may be algorithmic or numeric, but should be useful to designers wishing to optimize remote-sensing systems for those planets.

Expected TRL or TRL Range at completion of the Project: 3 to 6

Primary Technology Taxonomy:

  • Level 1 11 Software, Modeling, Simulation, and Information Processing
  • Level 2 11.X Other Software, Modeling, Simulation, and Information Processing

Desired Deliverables of Phase I and Phase II:

  • Prototype
  • Software
  • Research
  • Analysis

Desired Deliverables Description:

Phase I will result in a final report that describes the methodology and a clear proof of concept, and/or a prototype, clearly demonstrating the relevance of the technology for NASA use, and provides insight into the next phase of maturation.

 

At the completion of Phase II, NASA requires a working prototype suitable for demonstrations with real data to make a compelling case for NASA usage. Use and development of the model—including any and all work performed to verify and validate it—shall be documented.  Also at the end of Phase II, there will be a clear indication of the path to commercialization.

State of the Art and Critical Gaps:

There are currently a variety of models, methods, and tools in use across the Agency and with our industry partners. These are often custom, phase-dependent, and poorly interfaced to other tools. The disparity between the creativity in the early phases and the detail-oriented focus in later phases has created phase transition boundaries, where missions not only change teams, but tools and methods as well. We aim to improve this.

 

As NASA continues its move into greater use of models for formulation and development of NASA projects and programs, there are recurring challenges to address. This subtopic focuses on encouraging solutions to these cross-cutting modeling challenges.

These cross-cutting challenges include greater modeling breadth (e.g., cost/schedule), depth (scalability), variable fidelity (precision/accuracy vs. computation time), trade space exploration (how to evaluate large numbers of options), and processes that link them together. The focus is not on specific tools, but demonstrations of capability and methodologies for achieving the above.

Relevance / Science Traceability:

As science missions continue to explore, they are growing in scope and complexity and will increasingly rely on modeling, simulation, and virtual qualification.  The payoffs from more sophisticated integration and usage of M&S are enormous: greater scope and depth of trade space exploration, reduction in development times and iterations due to increased connectedness, and earlier verification and validation (V&V) to name a few.  However, any goal worth achieving has its challenges, and this one is no different.  Increased complexity can be exacerbated by lack of interoperability; by inconsistent management of data and workflows; and by inconsistencies in fidelity, assumptions and scopes.  There are challenges both with deploying M&S as V&V surrogates and also in V&V of the M&S itself.

There are several large complex campaigns underway including Artemis and Mars Sample Return.  These campaigns consist of multiple spacecraft and complex interoperations and span almost 2 decades.  This complexity is exacerbated by the distribution of roles and functions across multiple organizations both within and outside the U.S.  The ability to share, collaborate, and manage data at a wide variety of levels, layers. and disciplines will be key to success.

Several concept/feasibility studies for potential large (flagship) astrophysics missions are in progress: Large UV/Optical/IR Surveyor (LUVOIR), Origins Space Telescope (OST), Habitable Exoplanet Observatory (HabEx), and Lynx. Following the 2020 Astrophysics decadal rankings, one of these will likely proceed to early Phase A, where the infusion of new and advanced systems modeling tools and methods would be a potential game changer in terms of rapidly navigating architecture trades, requirements development and flow down, and design optimization.   In addition, every planetary mission requires significant M&S across a variety of possible trade spaces. They are also supported by the general and specific aspects of this subtopic.

References:

Scope Title:

Scope 2: Digital Engineering Applications

Scope Description:

The explosion of MBx (model-based anything) has led to a proliferation of models, modeling processes, pedigree of models and associated data, and the integration/aggregation thereof. The model results are often combined with no clear understanding of their fidelity/credibility. Whereas some NASA personnel are looking for greater accuracy and "authoritative source of truth," others are looking for the generation and exploration of massive trade spaces. Both greater precision and greater robustness will require addressing a number of cross-cutting challenges.  This explosion of interoperability, via Digital Transformations, or Model-Based Anything, has led us to create this second focus area.

 

NASA seeks innovative methods and tools addressing the following needs: Define, design, develop, and execute future project and programs by developing and utilizing advanced methods and tools that fully integrate all of the digital engineering and science activities across the entirety of the project/program lifecycle and allow for interagency/NASA-industry collaboration and datacentric information exchange. Ideally, the proposed solutions should leverage standard industry tools where at all possible, allow for easier integration of disparate tools and data, and be compatible with current NASA science and systems engineering processes.

 

There is specific interest in the integration of tools and data for rapid generation of function or behavior of complex systems, at either the system or the subsystem level across all lifecycle phases from a datacentric approach and an integrated design/science environment between NASA and its various partners:

  • To support emerging collaboration between NASA and domestic industry and international program partners, understanding standard approaches to integrating toolchains and data models, while protecting International Traffic in Arms Regulations (ITAR) and/or proprietary information.
  • To support integration of existing toolchains and workflows.
  • To be capable of using/developing standardized ontology/ies to enable modern information exchange, integration, and contract data deliverables to ensure all parties receive the information needed in the format expected and most useful, all the while minimizing integration of the productions of multiple suppliers.
  • To be capable of standardizing model complexity to optimize complexity vs. managing, sustaining, and model proliferation.
  • The ability to provide a standard approach for the validation of models, for customizing these validations, and for profiling this pedigree along not only with the model itself, but also with the data generated/provided by said models.

 

Note that this topic area focus is focused on Digital Transformation and is a special case of the broader topic.

Expected TRL or TRL Range at completion of the Project: 3 to 6

Primary Technology Taxonomy:

  • Level 1 11 Software, Modeling, Simulation, and Information Processing
  • Level 2 11.2 Modeling

Desired Deliverables of Phase I and Phase II:

  • Research
  • Analysis
  • Prototype
  • Software

Desired Deliverables Description:

We seek innovative solutions that address NASA needs to integrate engineering and science activities across the program/project lifecycle. The solution can investigate processes, data products, translation between the lifecycle gates. The goal is to support streamlining of engineering or science business processes, achieving high-value collaboration and interaction, and accelerate risk-informed and evidence-based decision making. The Phase I products and deliverables should identify a Phase II plan that will provide a more NASA-focused/relevant collection of products and deliverables that support integrating complex and disparate data into cohesive patterns.

State of the Art and Critical Gaps:

The current, relevant shortfalls in the state of the art in this area includes:

  1. Each discipline tends to have their own tools and toolchains.
  2. Tools and models are emerging, but they may not be consistent with each other.  These inconsistencies also occur at the workflow/process level and lower at the data exchange level. 
  3. A lack of a common architectures and approaches for validating data source(s) that fit within the NASA workflow.  These separate, but connected Authoritative Sources of Truth are often a source of conflict during the project life cycle.
  4. Vendors may provide portions of the toolchain and are often incompatible with each other.  This often forces a variety of inefficiencies on NASA, including: (1) requiring manual data entry, or worse, data checking; (2)  choosing the "least worst" monolithic solutions; (3) making it difficult for NASA to implement cultural changes; (4) making it difficult for NASA to avoid duplicative efforts, or worse, contradictory efforts; and (5) making it difficult for NASA to leverage/utilize merging technology breakthroughs.

Relevance / Science Traceability:

NASA's Robotic and Human Exploration efforts are complex, challenging endeavors.  Requirements for any/all of these programs and projects trace back to science; either science we are doing now or science that will be enabled.  Traceability between and among requirements is key, and in particular, the traceability from any given requirement to the science source(s) and reference(s) that it traces to.  This traceability will lead to interoperability and NASA's endgame goal: to be able to integrate seamlessly between engineering, science missions, and operations with an deeply integrated approach to tooling and data exchange across NASA and all of its partners.

References:

 

US Flag An Official Website of the United States Government