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All sourcesNASA NTRSDOE OSTI
NASA NTRS2024

Decision Space Modeling: Trade Space Ontology

As the National Aeronautics and Space Administration (NASA) works to develop a crewed Moon to Mars Architecture, it is dealing with a large decision space consisting of the overlay of human exploration architectures for both the Moon and for Mars. Efforts are underway to enable reasoning, analysis, and deliberation on this decision space. A critical first step is to develop a model of the decision space, which will then allow for various methods and techniques to be applied in support of the larger architecture decision-making process. The Trade Space Ontology consists of a set of terminologies and relations (an ontology) and a MagicDraw resource that enables documentation of decisions and alternatives. It also provides a means by which decisions and alternatives can be traced to other Systems Engineering artifacts. For documenting alternatives, the Trade Space Ontology adapts the Morphological Matrix methodology to The Systems Modeling Language (SysML) through a profile; custom diagrams are also implemented to simplify the profile's use. With the profile and custom diagrams, system architects can specify options for architecture attributes, as well as compatibility between them, in a compact visual format. While the approach shares similarities to a trade tree, the emphasis at this stage is less on enumerating specific combinations of options and instead on specifying the options and their compatibility. Enumeration of alternatives is performed by an external analysis that operates on an output file from a model constructed using the Trade Space Ontology. For decisions, the Trade Space Ontology provides a way to model generic precedence relationships as well as documenting inputs and outputs. These may include what alternatives, criteria, and rationale are understood to be relevant for each decision. Importantly, the decision-making side of the Trade Space Ontology is defined at a more general level, such that it can be adapted to the specific terms in use by projects and programs at NASA. However, this adaptability also means that less capability is provided ``out-of-the-box'' from installation. Currently the resource includes plugin functionality to enumerate paths through generic precedence relationships between decisions and to export these paths to a spreadsheet. Custom dependency stereotypes are included in the profile to indicate the cross-cutting relationships between the trade space and the architecture decisions, providing a means to map which parts of the trade space enumerate alternatives for a decision, and to identify how the output of a decision may modify the trade space through pruning or down-selection. While the motivating use case for this resource is in human exploration architectures, the broad applicability of the Morphological Matrix methodology indicates that the Trade Space Ontology should also be useful for other activities and tasks at the agency.

Trade Tree↗
NASA NTRS2011

Space Missions Trade Space Generation and Assessment Using JPL Rapid Mission Architecture (RMA) Team Approach

The JPL Rapid Mission Architecture (RMA) capability is a novel collaborative team-based approach to generate new mission architectures, explore broad trade space options, and conduct architecture-level analyses. RMA studies address feasibility and identify best candidates to proceed to further detailed design studies. Development of RMA first began at JPL in 2007 and has evolved to address the need for rapid, effective early mission architectural development and trade space exploration as a precursor to traditional point design evaluations. The RMA approach integrates a small team of architecture-level experts (typically 6-10 people) to generate and explore a wide-ranging trade space of mission architectures driven by the mission science (or technology) objectives. Group brainstorming and trade space analyses are conducted at a higher level of assessment across multiple mission architectures and systems to enable rapid assessment of a set of diverse, innovative concepts. This paper describes the overall JPL RMA team, process, and high-level approach. Some illustrative results from previous JPL RMA studies are discussed.

real time systems↗
NASA NTRSDate not supplied

Characterization of in-Space Propulsion Trade Space to Support Initial Human Mars Segment

The National Aeronautics and Space Administration’s Exploration Systems Development Mission Directorate has been developing architecture concepts for human missions to Mars in alignment with the agency’s Moon to Mars blueprint objectives. The focus of the Architecture Development Office has been to decompose the blueprint objectives into the characteristics and needs for both lunar and Mars missions and develop functional needs to enable an integrated end-to-end mission architecture. One of the key components of a human Mars mission is the in-space transportation system that delivers crew and cargo to Mars vicinity and return crew safely back to Earth. The Mars Architecture Team has been evaluating four in-space transportation options to satisfy this functional need: 1) Hybrid Nuclear Electric/ Chemical Propulsion, 2) Nuclear Thermal Propulsion, 3) Hybrid Solar Electric/ Chemical Propulsion, and 4) All-Chemical propulsion. To answer the question “which transportation option is best?” decision makers will need to understand the performance characteristics—and limitations—for each architecture in the context of their preferred metric. Each option could be considered “better” than the others, depending on whether the primary selection criteria is fast, cheap, maximum cross-government applicability, maximizes commercial capabilities, and/or any other desired metrics. The Mars Architecture Team has completed considerable analysis on the Hybrid Nuclear Electric/Chemical Propulsion concept over the past three years. The Space Nuclear Propulsion program has also conducted significant work on the Nuclear Thermal Propulsion concept. The Hybrid Solar Electric/Chemical Propulsion concept, derived from technology planned for the Gateway program, was originally configurated for minimum vehicle stack mass (and, hence, minimum Earth-launched mass/cost), leveraging a highly efficient propulsion system on a minimum energy Conjunction Class trajectory. Historically, All-Chemical concepts were considered impractical due to the enormous propellant mass required, even for minimum energy Conjunction Class/long stay mission opportunities. However, considering the emerging commercial heavy-lift capabilities, the concept must be reevaluated across a range of concepts that leverage reusable launch vehicle capabilities to reduce the cost of launching large quantities of transportation elements needed for missions of various duration. This paper aims to provide an overview of the in-space transportation assessment work that is on-going within NASA.

Mars↗
NASA NTRSDate not supplied

Characterization of in-Space Propulsion Trade Space to Support Initial Human Mars Segment

The National Aeronautics and Space Administration’s Exploration Systems Development Mission Directorate has been developing architecture concepts for human missions to Mars in alignment with the agency’s Moon to Mars blueprint objectives. The focus of the Architecture Development Office has been to decompose the blueprint objectives into the characteristics and needs for both lunar and Mars missions and develop functional needs to enable an integrated end-to-end mission architecture. One of the key components of a human Mars mission is the in-space transportation system that delivers crew and cargo to Mars vicinity and return crew safely back to Earth. The Mars Architecture Team has been evaluating four in-space transportation options to satisfy this functional need: 1) Hybrid Nuclear Electric/ Chemical Propulsion, 2) Nuclear Thermal Propulsion, 3) Hybrid Solar Electric/ Chemical Propulsion, and 4) All-Chemical propulsion. To answer the question “which transportation option is best?” decision makers will need to understand the performance characteristics—and limitations—for each architecture in the context of their preferred metric. Each option could be considered “better” than the others, depending on whether the primary selection criteria is fast, cheap, maximum cross-government applicability, maximizes commercial capabilities, and/or any other desired metrics. The Mars Architecture Team has completed considerable analysis on the Hybrid Nuclear Electric/Chemical Propulsion concept over the past three years. The Space Nuclear Propulsion program has also conducted significant work on the Nuclear Thermal Propulsion concept. The Hybrid Solar Electric/Chemical Propulsion concept, derived from technology planned for the Gateway program, was originally configurated for minimum vehicle stack mass (and, hence, minimum Earth-launched mass/cost), leveraging a highly efficient propulsion system on a minimum energy Conjunction Class trajectory. Historically, All-Chemical concepts were considered impractical due to the enormous propellant mass required, even for minimum energy Conjunction Class/long stay mission opportunities. However, considering the emerging commercial heavy-lift capabilities, the concept must be reevaluated across a range of concepts that leverage reusable launch vehicle capabilities to reduce the cost of launching large quantities of transportation elements needed for missions of various duration. This paper aims to provide an overview of the in-space transportation assessment work that is on-going within NASA.

Mars↗
NASA NTRSDate not supplied

Subsonic Single Aft Engine (SUSAN) Transport Aircraft Concept and Trade Space Exploration

A trade space exploration of a new NASA regional transport aircraft concept called the SUbsonic Single Aft eNgine (SUSAN) Electrofan is presented. The SUSAN concept uses a 20MW Electrified Aircraft Propulsion (EAP) system to enable advance Propulsion Airframe Integration (PAI) in transport category aircraft. Alternative fuels will be used to reduce the amount of emissions per energy used. By combining these features there is the potential to reduce aircraft emissions by 50% per passenger/mile while retaining the size, speed, and range of large regional jets. SUSAN is has a 750 mile economic mission, a 2500 mile design range and a maximum capacity of 180 passengers. The SUSAN configuration utilizes a single aft mounted engine and distributed electric wing-mounted thrusters on a tube and wing arrangement with a T-tail empennage. The SUSAN Electrofan employs a hybrid powertrain to enable: single turbofan operation on a large transport category aircraft; increased aerodynamic and propulsive efficiency through placement of electric engines; optimized turbofan sizing and efficiency through control and electric boosting, reduced control surface sizing through thrust augmentation. A single use battery is employed as the power source in case of turbofan failure. The design study also considers the constraints of operating within the current airport, airspace and economic constraints. This paper presents the status of the trade space exploration; however the concept definition is not finished. Forward work includes optimizing the overall aircraft configuration and including certain hard to model features like boundary layer ingestion or natural laminar flow across all appliable subsystems. Additional work forward work is a more extensive analysis of the configuration using alternative fuels.

Ralph H. Jansen↗
NASA NTRS2013

Trade Space Specification Tool (TSST) for Rapid Mission Architecture (Version 1.2)

Trade Space Specification Tool (TSST) is designed to capture quickly ideas in the early spacecraft and mission architecture design and categorize them into trade space dimensions and options for later analysis. It is implemented as an Eclipse RCP Application, which can be run as a standalone program. Users rapidly create concept items with single clicks on a graphical canvas, and can organize and create linkages between the ideas using drag-and-drop actions within the same graphical view. Various views such as a trade view, rules view, and architecture view are provided to help users to visualize the trade space. This software can identify, explore, and assess aspects of the mission trade space, as well as capture and organize linkages/dependencies between trade space components. The tool supports a user-in-the-loop preliminary logical examination and filtering of trade space options to help identify which paths in the trade space are feasible (and preferred) and what analyses need to be done later with executable models. This tool provides multiple user views of the trade space to guide the analyst/team to facilitate interpretation and communication of the trade space components and linkages, identify gaps in combining and selecting trade space options, and guide user decision-making for which combinations of architectural options should be pursued for further evaluation. This software provides an environment to capture mission trade space elements rapidly and assist users for their architecture analysis. This is primarily focused on mission and spacecraft architecture design, rather than general-purpose design application. In addition, it provides more flexibility to create concepts and organize the ideas. The software is developed as an Eclipse plug-in and potentially can be integrated with other Eclipse-based tools.

Wang, Yeou-Fang↗
NASA NTRS2020

Efficient Trade Space Exploration

Two of the principal challenges in efficient trade space exploration are (1) quickly evaluating options, and (2) quickly obtaining stakeholder understanding of the results of the evaluation. This presentation describes the processes & methodology that have led to a factor of nine improvement in the efficiency of trade space exploration of space systems in Team-X at the Jet Propulsion Laboratory. The principal method that has enabled this increase in efficiency is the separation of the exploration figures of merit into two distinct types, which are then addressed in an efficient order. The figures of merit in a trade space exploration of N subsystems either scale with the number of interactions between the subsystems and the external constraints, O(N), or with the number of interactions between subsystems, O(N2-N).

Nash, Alfred↗
NASA NTRS2010

Trade Spaces in Crewed Spacecraft Atmosphere Revitalization System Development

Developing the technological response to realizing an efficient atmosphere revitalization system for future crewed spacecraft and space habitats requires identifying and describing functional trade spaces. Mission concepts and requirements dictate the necessary functions; however, the combination and sequence of those functions possess significant flexibility. Us-ing a closed loop environmental control and life support (ECLS) system architecture as a starting basis, a functional unit operations approach is developed to identify trade spaces. Generalized technological responses to each trade space are discussed. Key performance parameters that apply to functional areas are described.

Perry, Jay L.↗
NASA NTRS2017

End-to-End Trade-Space Analysis for Designing Constellation

Multipoint measurement missions can provide a significant advancement in science return and this science interest coupled with as many recent technological advances are driving a growing trend in exploring distributed architectures for future NASA missions. Distributed Spacecraft Missions (DSMs) leverage multiple spacecraft to achieve one or more common goals. In particular, a constellation is the most general form of DSM with two or more spacecraft placed into specific orbit(s) for the purpose of serving a common objective (e.g., CYGNSS). Because a DSM architectural trade-space includes both monolithic and distributed design variables, DSM optimization is a large and complex problem with multiple conflicting objectives. Over the last two years, our team has been developing a Trade-space Analysis Tool for Constellations (TAT-C), implemented in common programming languages for pre-Phase A constellation mission analysis. By evaluating alternative mission architectures, TAT-C seeks to minimize cost and maximize performance for pre-defined science goals. This presentation will describe the overall architecture of TAT-C including: a User Interface (UI) at several levels of details and user expertise; Trade-space Search Requests that are created from the Science requirements gathered by the UI and validated by a Knowledge Base; a Knowledge Base to compare the current requests to prior mission concepts to potentially prune the trade-space; a Trade-space Search Iterator which, with inputs from the Knowledge Base, and, in collaboration with the Orbit & Coverage, Reduction & Metrics, and Cost& Risk modules, generates multiple potential architectures and their associated characteristics. TAT-C leverages the use of the Goddard Mission Analysis Tool (GMAT) to compute coverage and ancillary data, modeling orbits to balance accuracy and performance. The current version includes uniform and non-uniform Walker constellations as well as Ad-Hoc and precessing constellations, and its cost model represents an aggregate model consisting of Cost Estimating Relationships (CERs) from widely accepted models. The current GUI automatically generates graphics representing metrics such as average revisit time or coverage as a function of cost. The end-to-end system will be demonstrated as part of the presentation.

Mission Desig↗
NASA NTRS2005

Model-Based Trade Space Exploration for Near-Earth Space Missions

We developed a capability for model-based trade space exploration to be used in the conceptual design of Earth-orbiting space missions. We have created a set of reusable software components to model various subsystems and aspects of space missions. Several example mission models were created to test the tools and process. This technique and toolset has demonstrated itself to be valuable for space mission architectural design.

space mission↗
NASA NTRS2016

Trade-Space Analysis Tool for Constellations (TAT-C)

Traditionally, space missions have relied on relatively large and monolithic satellites, but in the past few years, under a changing technological and economic environment, including instrument and spacecraft miniaturization, scalable launchers, secondary launches as well as hosted payloads, there is growing interest in implementing future NASA missions as Distributed Spacecraft Missions (DSM). The objective of our project is to provide a framework that facilitates DSM Pre-Phase A investigations and optimizes DSM designs with respect to a-priori Science goals. In this first version of our Trade-space Analysis Tool for Constellations (TAT-C), we are investigating questions such as: How many spacecraft should be included in the constellation? Which design has the best costrisk value? The main goals of TAT-C are to: Handle multiple spacecraft sharing a mission objective, from SmallSats up through flagships, Explore the variables trade space for pre-defined science, cost and risk goals, and pre-defined metrics Optimize cost and performance across multiple instruments and platforms vs. one at a time.This paper describes the overall architecture of TAT-C including: a User Interface (UI) interacting with multiple users - scientists, missions designers or program managers; an Executive Driver gathering requirements from UI, then formulating Trade-space Search Requests for the Trade-space Search Iterator first with inputs from the Knowledge Base, then, in collaboration with the Orbit Coverage, Reduction Metrics, and Cost Risk modules, generating multiple potential architectures and their associated characteristics. TAT-C leverages the use of the Goddard Mission Analysis Tool (GMAT) to compute coverage and ancillary data, streamlining the computations by modeling orbits in a way that balances accuracy and performance.TAT-C current version includes uniform Walker constellations as well as Ad-Hoc constellations, and its cost model represents an aggregate model consisting of Cost Estimating Relationships (CERs) from widely accepted models. The Knowledge Base supports both analysis and exploration, and the current GUI prototype automatically generates graphics representing metrics such as average revisit time or coverage as a function of cost.

Science Data Processing↗
NASA NTRS2016

Exploring the Science Trade Space with the JPL Innovation Foundry A-Team

The JPL Innovation Foundry has established a new approach for exploring, developing, and evaluating early concepts with a group called the Architecture Team (A-Team). The A-Team combines innovative collaborative methods and facilitated sessions with subject matter experts and analysis tools to help mature mission concepts. Science, implementation, and programmatic elements are all considered during an ATeam study. In these studies, Concept Maturity Levels (CML) are used to group methods. These levels include idea generation and capture (CML 1), initial feasibility assessment (CML 2), and trade space exploration (CML 3). Methods used for exploring the science objectives, feasibility, and scope will be described including use of a new technique for understanding the most compelling science, called a Science Return Diagram (SRD). In the process of developing the SRD, gradients in the science trade space are uncovered along with their implications for implementation and mission architecture. Special attention is paid towards developing complete investigations, establishing a series of logical claims that lead to the natural selection of a measurement approach. Over 20 science-focused A-Team studies have used these techniques to help science teams refine their mission objectives, make implementation decisions and reveal the mission concept’s most compelling science. This paper will describe the A-Team process for exploring the mission concept's science trade space and the Science Return Diagram technique.In June of 2011 a new collaborative engineering approach forearly concept formulation began in the JPL InnovationFoundry [1], six months later becoming the “A-Team” [2].Responding to a need for exploring mission architecturelevel trades [3], the A-Team precedes Team X [4,5] in asequence of concurrent engineering teams at JPL that can beused to mature a concept from a “cocktail napkin” level ideato a complete mission point design. The A-Team efficientlyexplores the science, implementation, and programmatictrade space in early concept formulation. Small, facilitatedgroups of experts generate innovative ideas, quantitativelyassess feasibility, and discover key sensitivities in the tradespace through collaborative analysis and use of advancedmethods and tools. The A-Team process builds off theexperience within JPL and other recent approaches to earlyconcept formulation [6] including best practices of the JPLInnovation Foundry, Project Systems Engineering &Formulation Section, Team Eureka and the Rapid MissionArchitecture Team[7].The A-Team is a focal point for innovative formulationapproaches and people within JPL. It relies on a largebackground of study resources, creative thinkers and “greybeard” scrutinizers, advanced tools, and subject matterexperts with both breadth and depth in experience andexpertise that are all available at JPL. The A-Team isdesigned to be a rapid and efficient process takingapproximately 6 weeks (the entire process can be as short asjust a few days or as long as up to three months) and costingthe equivalent of a work-month of a full-time employee orless. Studies begin with detailed planning and client reviewfollowed by study sessions, analysis work, and reporting.The staffing on each study is customized to the study goalsand objectives, and it is addressed early in the A-Teamprocess. Sessions are generally half-day or whole-day eventsand conducted over a series of days with focused agendas thatare moderated by a trained facilitator. Preliminary results andknowledge capture are available within hours of each session,and a final report is generally available two weeks later.One of the biggest challenges facing early conceptdevelopment is understanding the gradient in science returnversus various available mission scenarios and payload options. Often times, major areas of scientific inquiry havealready been prioritized by science groups, including throughthe National Research Council’s Decadal Studies inAstronomy, Planetary, and Earth Science. Yet science teamscontinue to struggle, especially in competitive missionsolicitations, to capture the right amount of scope that’sachievable within the cost constraints of the opportunity.Often the desire to completely and comprehensively study ascience area in just one mission (after all, true missionopportunities are rare) drives teams to take on too much,providing requirements that are unachievable within theresources of the opportunity without inducing unacceptableimplementation risk. Alternatively, science teams can seekto reduce risk by using an established instrument, but havenot thought through the traceability and key aspects of thescience question to justify its use. Both scenarios lead to badassumptions at the beginning of the concept development thatcan then ripple through implementation option choices,potentially preventing what would have been a good scienceinvestigation from being selected.The purpose of this paper is first to provide some additionalbackground and summary of the A-Team process, tools,people, and facilities. We then focus on the A-Teammethodology for overcoming the barriers of defining thescience scope well at the early concept development stage.This includes understanding the science story andtraceability, and then examining the gradient in science returnversus key characteristics of observables, developing theright payload and mission requirement specification throughexamining the science and implementation trade space.

Ziemer, John K.↗
NASA NTRS2016

TAT-C: A Trade-Space Analysis Tool for Constellations

Under a changing technological and economic environment, there is growing interest in implementing future NASA Earth Science missions as Distributed Spacecraft Missions (DSM). The objective of our project is to provide a framework that facilitates DSM Pre-Phase A investigations and optimizes DSM designs with respect to a-priori Science goals. In this first version of our Trade-space Analysis Tool for Constellations (TAT-C), we are investigating questions such as: Which type of constellations should be chosen? How many spacecraft should be included in the constellation? Which design has the best costrisk value? This paper describes the overall architecture of TAT-C including: a User Interface (UI) interacting with multiple users - scientists, missions designers or program managers; an Executive Driver gathering requirements from UI and formulating Trade-space Search Requests for the Trade-space Search Iterator, which in collaboration with the Orbit Coverage, Reduction Metrics, and Cost Risk modules generates multiple potential architectures and their associated characteristics. UI will include Graphical, Command Line and Application Programmer Interfaces to respond to the demands of various levels of users expertise. Science inputs are grouped into various mission concepts, satellite specifications, and payload specifications, while science outputs are grouped into several types of metrics - spatial, temporal, angular and radiometric. Orbit Coverage leverages the use of the Goddard Mission Analysis Tool (GMAT) to compute coverage and ancillary data that are passed to Reduction Metrics. Then, for each architecture design, Cost Risk will provide estimates of the cost and life cycle cost as well as technical and cost risk of the proposed mission. Additionally, the Knowledge Base module is a centralized store of structured data readable by humans and machines. It will support both TAT-C analysis when composing new mission concepts from existing model inputs, and TAT-C exploration when discovering new mission concepts by querying previous results.

Science Data Processing↗
NASA NTRS2009

Proof of Concept Study of Trade Space Configuration Tool for Spacecraft Design

Spacecraft design is a very difficult and time consuming process because requirements and criteria are often changed or modified as the design is refined. Accounting for these adjustments in the design constraints plays a significant role in furthering the overall progress. There are numerous aspects and variables that hold significant influence on various characteristics of the design. This can be especially frustrating when attempting to conduct rapid trade space analysis on system configurations. Currently, the data and designs considered for trade space evaluations can only be displayed by using the traditional interfaces of Excel spreadsheets or CAD (Computer Aided Design) models. While helpful, these methods of analyzing the data from a systems engineering approach can be rather complicated and overwhelming. As a result, a proof of concept was conducted on a dynamic data visualization software called Thinkmap SDK (Software Developer Kit) to allow for better organization and understanding of the relationships between the various aspects that make up an entire design. The Orion Crew Module Aft Bay Subsystem was used as the test case for this study because the design and layout of many of the subsystem components will be significant in ensuring the overall center of gravity of the capsule is correct. A simplified model of this subsystem was created and programmed using Thinkmap SDK to create a preliminary prototype application of a Trade Space Configuration Tool. The completed application ensures that the core requirements for the Tool can be met. Further development is strongly suggested to produce a full prototype application to allow final evaluations and recommendations of the software capabilities.

Glidden, Geoffrey L.↗
NASA NTRS2004

Model-Based Engineering Design for Trade Space Exploration throughout the Design Cycle

This paper presents ongoing work to standardize model-based system engineering as a complement to point design development in the conceptual design phase of deep space missions. It summarizes two first steps towards practical application of this capability within the framework of concurrent engineering design teams and their customers. The first step is standard generation of system sensitivities models as the output of concurrent engineering design sessions, representing the local trade space around a point design. A review of the chosen model development process, and the results of three case study examples, demonstrate that a simple update to the concurrent engineering design process can easily capture sensitivities to key requirements. It can serve as a valuable tool to analyze design drivers and uncover breakpoints in the design. The second step is development of rough-order- of-magnitude, broad-range-of-validity design models for rapid exploration of the trade space, before selection of a point design. At least one case study demonstrated the feasibility to generate such models in a concurrent engineering session. The experiment indicated that such a capability could yield valid system-level conclusions for a trade space composed of understood elements. Ongoing efforts are assessing the practicality of developing end-to-end system-level design models for use before even convening the first concurrent engineering session, starting with modeling an end-to-end Mars architecture.

models↗
NASA NTRS2017

Exploring the Science Trade Space with the JPL Innovation Foundry A-Team

The Jet Propulsion Laboratory Innovation Foundry has established a new approach for exploring, developing, and evaluating early concepts with a group called the Architecture Team. The Architecture Team combines innovative collaborative methods and facilitated sessions with subject matter experts and analysis tools to help mature mission concepts. Science, implementation, and programmatic elements are all considered during an A-Team study. In these studies, Concept Maturity Levels are used to group methods. These levels include idea generation and capture (Concept Maturity Level 1), initial feasibility assessment (Concept Maturity Level 2), and trade space exploration (Concept Maturity Level 3). Methods used for exploring the science objectives, feasibility, and scope will be described including the use of a new technique for understanding the most compelling science, called a Science Return Diagram. In the process of developing the Science Return Diagram, gradients in the science trade space are uncovered along with their implications for implementation and mission architecture. Special attention is paid toward developing complete investigations, establishing a series of logical claims that lead to the natural selection of a measurement approach. Over 20 science-focused A-Team studies have used these techniques to help science teams refine their mission objectives, make implementation decisions, and reveal the mission concept's most compelling science. This article will describe the A-Team process for exploring the mission concept's science trade space and the Science Return Diagram technique.

science↗
NASA NTRS2017

Knowledge Base for Distributed Spacecraft Mission Design Using the Trade-Space Analysis Tool for Constellations (TAT-C)

Opportunities for multi-point measurements, greater revisit frequency, failure robustness, and improved cost effectiveness motivate consideration of Distributed Spacecraft Missions (DSMs) for future Earth science missions. However, careful analysis is required to assess the distributed sensing capabilities of a constellation compared to more mature monolithic spacecraft while also considering other important dimensions such as cost and risk. The large combinatorial DSM design space limits existing mission analysis tools and exploration methods which emphasize monolithic design variables. The Trade-space Analysis Tool for Constellations (TAT-C) under development at Goddard seeks to enumerate and evaluate alternative mission architectures to minimize cost and maximize scientific return for pre-defined goals during pre-phase A analysis.Similar to other model-centric engineering efforts, efficient data management is a significant challenge for DSM mission analysis. In TAT-C, a Knowledge Base (KB) is envisioned as a cumulative central repository of information and meta-information about DSMs. Initial KB concepts store related data for reuse within or across mission analyses; however, over time, the KB is envisioned to be an important layer to coordinate actions of both human analysts and automated design agents to search a large design space for desirable mission alternatives. Preliminary KB research builds on a modern web technology stack to provide the following functionality: 1) storage of trade-space search requests which set requirements and constraints for DSM concepts, 2) storage of analysis results which quantify performance metrics for evaluated DSM concepts, 3) a RESTful application programming interface (API) for scripted access to data from TAT-C modules, 4) web-based graphical user interface (GUI) for manual access to underlying data, and 5) access control and management restrictions relevant to data protection and security. These efforts have culminated in a prototype KB used by the research team during TAT-C development to assess opportunities for future work.

Pattern Recognition↗