Results for “DECISION” — Page 16

NASA NTRSDate not supplied

Design, Formalization, and Verification of Decision Making for Intelligent Systems

The development of autonomous systems requires a rigorous process that can guarantee a system’s reliability in critical applications. At its core, an autonomous system bases its behavior on a well-defined decision making system. In this paper, we present a methodological basis for the design, formalization and formal verification of Decision Making systems for autonomous agents. The approach is generally applicable to operational objectives that can be functionally decomposed and subsequently represented as Hierarchical Finite State Machines. As a case study, we present the application of this method to implement a Decision Making model in Simulink. Furthermore, we present how we use NASA’s FRET tool to write requirements in structured natural language and generate formal specifications that can be automatically digested by NASA’s CoCoSim tool. Finally, we present how, by leveraging CoCoSim, we perform formal verification against the Simulink model and present analysis results.

Model-based development↗

NASA NTRSDate not supplied

Decision Aid for Conjunction Risk Mitigation by Differential Drag

In the previous five years, the rate of conjunctions that the NASA Conjunction Assessment Risk Analysis (CARA) team processed and analyzed has more than tripled. (NASA CARA, 2024) New missions in the early development phases are now required to plan for dealing with conjunctions under the present space environment, and also projecting forward into a future likely with even further increased utilization of the space environment. Some missions are investigating the possibility of using differential drag to remediate conjunctions without expending limited fuel or for missions without propulsive capabilities. The NASA CARA team studied the historical record of conjunctions to evaluate the circumstances under which differential drag may be successfully applied and have developed a series of tables to use as a decision aid for missions considering differential drag. CARA records all conjunctions of their protected payloads, with historical records starting in 2005 (with significant conjunction events starting to occur on or after 2013). From this record, approximately 7,300 had a probability of collision (Pc) greater than 1 in 10,000, the nominal requirement for a risk mitigation maneuver (RMM) to be made per NASA Procedural Requirements (NPR) 8079.1 (NASA, 2023), at 3 days prior to the time of closest approach, the time analyzed for differential drag efficacy. CARA’s maneuver trade space tool (MTS) was used to prop-agate the primary satellite forward from that decision point with varying degrees of increase to ballistic coefficient (BC), and then recalculate the Pc to evaluate whether or not the conjunction was mitigated (Pc < 3E-6). These results were then binned and sorted along several axes, including altitude, amount of delta-BC, and (pre-maneuver) rate of energy dissipation, to identify underlying patterns. The altitude plot is shown in Figure 1. We found that differential drag was most successful for satellites with perigees below 560 km, and which could adopt an average delta-BC of 2 or greater (that is, increasing their ballistic coefficient by a factor of 3). However, this is a difficult threshold for a mission to clear; very few spacecraft are capable of adopting a high-drag configuration for 72 hours continuously. Planet’s Dove spacecraft use differential drag to remediate conjunctions (Griffith, et al., 2021), and they have a maximum delta-BC factor of 9, but in practice (with mission and charging constraints) they achieve a time-averaged delta-BC that is closer to 2 (Foster, et al., 2017). In contrast, a NASA mission in development reached out to CARA to evaluate the utility of differential drag to remediate potential conjunctions for a spacecraft which is capable of a similar high delta-BC, but with operational constraints that limit their time-averaged delta-BC to less than 1. CARA has developed tables that can be used as decision aids to advise missions-in-development about the best way to utilize their differential drag capabilities. For missions below 560 km with the operational flexibility to devote multiple days to holding a high-drag configuration (or a sufficiently high drag ratio to compensate for limitations on that time), they are able to successfully remediate high-risk conjunctions. Conversely, missions that do not meet these ex-acting criteria – most missions – can instead be advised to use on-board propulsion systems to perform RMMs or to turn their minimum-area face towards the approach vector, thereby reducing Pc at the moment of conjunction. (NASA, 2023)

risk mitigation↗

NASA NTRS2024

A Computational Framework for Making Early Design Decisions in Deep Space Habitats

The dynamics of systems of systems often involve complex interactions among the individual systems, making the implications of design choices challenging to predict. Design features in such systems may trigger unexpected behaviors or result in large variations in safety, performance or resilience. To provide a means of simulating such systems for aiding in these decisions, we have developed a prototype tool, the control-oriented dynamic computational modeling tool (CDCM). The CDCM provides rapid simulation capabilities to perform trade studies in systems of systems. The general class of systems of systems that we aim to examine involve multiple hazards, damage, cascading consequences, repair and recovery. We especially focus on systems-of-systems that incorporate a health management system (HMS) that can monitor the state of the habitat and make decisions about actions to take. In this paper we describe the features of the CDCM, the architecture we devised for simulation of systems-of-systems, the unique functionalities of this tool, and we provide a demonstration of the capabilities by performing two illustrative examples. We articulate the use of this tool for making early design decisions and demonstrate its use for trade studies that consider a model of a deep space habitat. We also share some experiences and lessons that may be useful for others seeking to address similar problems.

Amir Behjat↗

NASA NTRSDate not supplied

Decision Aid for Conjunction Risk Mitigation by Differential Drag

In the previous five years, the rate of conjunctions that the NASA Conjunction Assessment Risk Analysis (CARA) team processed and analyzed has more than tripled. (NASA CARA, 2024) New missions in the early development phases are now required to plan for dealing with conjunctions under the present space environment, and also projecting forward into a future likely with even further increased utilization of the space environment. Some missions are investigating the possibility of using differential drag to remediate conjunctions without expending limited fuel or for missions without propulsive capabilities. The NASA CARA team studied the historical record of conjunctions to evaluate the circumstances under which differential drag may be successfully applied and have developed a series of tables to use as a decision aid for missions considering differential drag. Currently, if a CARA-protected mission with maneuvering capabilities is predicted to have a conjunction with probability of collision (Pc) greater than 7E-5 (the default value of the ‘yellow threshold’, which may have some other value agreed by CARA and the mission during the Orbital Collison Avoidance Planning (OCAP) process), CARA will use its Maneuver Trade Space (MTS) tool to evaluate and recommend options for the timing and magnitude of a risk mitigation maneuver (RMM), based on the mission’s capabilities. If the conjunction’s Pc is greater than 1E-4, the ‘red threshold’, then an RMM must be executed per NASA Procedural Requirements (NPR) 8079.1 (NASA, 2023), although missions may execute an RMM even if the Pc is lower. The magnitude of the maneuver is typically a few cm/s, and CARA estimates how many will be required for the mission’s nominal lifetime – typically a few per year – during the OCAP process, to inform the mission’s delta-V requirement. For traditional satellites, this is usually smaller than other requirements for orbit insertion, maintenance, and disposal, but for CubeSats or other small satellite missions, a propulsion system may not provide much more than a few cm/s of delta-V or may not fit at all within the available budget of money, time, size, weight, and/or power (SWaP). Conversely, CubeSats often have deployable solar panels, which offer the capacity to have much higher areas facing some directions than others. Such a mission can instead use ‘differential drag’ to remediate a conjunction -- in other words, change its drag area (usually increasing) to deviate from the predicted collision course. This is how Planet’s Dove spacecraft maintain their formations and remediate conjunction risks without having on-board propulsion (Foster, et al., 2017) (Griffith, et al., 2021). CARA has been developing improvements to MTS to support differential-drag for NASA's missions -- where it is effective. CARA records all conjunctions of their protected payloads, with historical records starting in 2005 (with significant conjunction events starting to occur on or after 2013). From this record, approximately 7,300 had a Pc greater than 1E-4 at 3 days prior to the time of closest approach, the time analyzed for differential drag efficacy. Of those, approximately 4,300 had fully-defined covariance matrices stored for both the primary and secondary objects; this set of conjunctions is the basis for the analysis of this work. CARA’s MTS tool was used to propagate the primary satellite forward from that decision point with varying degrees of increase to ballistic coefficient (BC). Because these conjunctions came from multiple missions, the nondimensional ‘delta-BC’ factor was used to quantify and normalize the increase in ballistic coefficient, defined as follows: Delta-BC = BC_new / BC_old - 1 Positive delta-BC factors represent an increase in drag compared to the nominal attitude, while negative delta-BC factors (to a minimum of -1) represent a decrease in drag. At the conclusion of the differential-drag ‘maneuver’, the Pc was recalculated to evaluate whether or not the conjunction was mitigated (Pc < 3E-6). These results were then binned and sorted along several axes, including altitude, amount of delta-BC, and (pre-maneuver) rate of energy dissipation (EDR), to identify underlying patterns. The altitude plot is shown in Figure 1. To validate this analysis, we consulted the record of a NASA mission which uses differential drag to maintain its orbit and remediate conjunction risk. CARA’s empirical record of the mission’s orbit history suggests it achieves a delta-BC of 2.2. Of the twenty-one RMM plans that were submitted by this mission, eighteen were matched with conjunctions in the historical record; of those, twelve were successfully remediated (final measured Pc < 3E-6), and six were not. This is consistent with the expected efficacy for missions orbiting at that altitude. We are presently simulating this mission’s RMMs with MTS; this work is ongoing, but so far, the MTS results are qualitatively in agreement with the empirical results -- correctly predicting that a maneuver would or would not remediate a conjunction, if not exactly matching the final post-remediation Pc value. We found that differential drag was most successful for satellites with perigees below 560 km, and which could adopt an average delta-BC of 2 or greater (that is, increasing their ballistic coefficient by a factor of 3). However, this is a difficult threshold for a mission to clear; very few spacecraft are capable of adopting a high-drag configuration for 72 hours continuously. Planet’s Dove spacecraft use differential drag to remediate conjunctions (Griffith, et al., 2021), and they have a maximum delta-BC factor of 9, but in practice (with mission and charging constraints) they achieve a time-averaged delta-BC that is closer to 2 (Foster, et al., 2017). A mission’s differential drag utility strongly depends on the operational constraints that has the capacity to limit the time-averaged delta-BC. A mission with a high maximum delta-BC of 5 or more can have an effective delta-BC of less than 1 due to operational constraints such as instrument and solar panel pointing, especially if this constraint results in holding an intermediate drag value for most of its orbit. CARA has developed tables that can be used as decision aids to advise missions-in-development about the best way to utilize their differential drag capabilities. For missions below 560 km with the operational flexibility to devote multiple days to holding a high-drag configuration (or a sufficiently high drag ratio to compensate for limitations on that time), they are -- more likely than not -- able to successfully remediate high-risk conjunctions. Conversely, missions that do not meet these exacting criteria -- most missions -- can instead be advised to use on-board propulsion systems to perform RMMs, or to turn their minimum-area face towards the approach vector, thereby reducing Pc at the moment of conjunction due to the decreased Hard-Body Radius (HBR), that is a strongly correlated variable in the Pc calculations. (NASA, 2023)

conjunction assessment↗

NASA NTRSDate not supplied

Use of the SPoRT Stoplight Product to Support NWS Decision Support Services

The National Weather Service Forecast Offices (NWSFOs) use many weather tools and observational datasets to provide support for critical decision-making by core partners such as public safety officials, emergency managers, and first responders. These core partners who need weather decision support services (DSS) for outdoor events require up-to-the-minute weather information to ensure the safety and protection of attendees and workers. Storms and lightning, potentially deadly, pose a significant threat during outdoor events and are among the weather phenomena frequently cited as a DSS requirement. According to the National Lightning Safety Council, from 2014 up to August 2024, lightning resulted in 222 fatalities in the U.S. For outdoor events with hundreds to thousands of attendees, having the right tools to detect and monitor lightning activity is of utmost importance to protect lives. Common guidelines for lightning safety include moving inside a substantial structure at the first sight of threatening skies or the first sound of thunder, and waiting 30 minutes after the last lightning flash or thunder before returning outside. Using this guidance as a framework, scientists at the NASA Short-term Prediction Research and Transition (SPoRT) center have developed the Geostationary Lightning Mapper (GLM) Stoplight tool. This experimental tool uses the GLM Flash Extent Density imagery to display the location and recency of lightning flashes. To simplify interpretation, these lightning pixels are color-coded in 10-minute bins, ranging from red (lightning detected 0 to 10 minutes ago) to yellow (10 to 20 minutes ago) to green (20 to 30 minutes ago). The Stoplight tool also allows users to place markers at the location of outdoor events with range rings around the location to help in assessing the location and relative age of lightning flashes near and upstream of the event. The goal is to help NWS forecasters provide core partners with the necessary information to make the best decisions possible. While the Stoplight tool is experimental, forecasters at NWSFO Raleigh, NC, have periodically used the Stoplight guidance to evaluate its utility within NWS DSS. This presentation will discuss how the Stoplight tool was successfully used for DSS for four outdoor events in central NC in 2023 and 2024. Future improvements to this tool, including the addition of AI applications and the merging of ground-based lightning data with GLM data, will be reviewed.

Gail Hartfield↗

NASA NTRS2025

Informatics and Decision Support Technology Roadmap

The purpose is to identify shortfalls/gaps, shortfall/gap closure strategies, and candidate investments to mature Informatics and Decision Support technologies in support of M2M Program risk mitigation activities, enable and enhance crew insight to system performance, decision making, and computer-human interaction. In this first publication, scope of the Informatics and Decision Support Roadmap is limited to Deep Space Crew Displays and Audio Systems. Future publications will expand scope to other capabilities.

Geraldo Cisneros↗

DOE OSTI2025

Bridging Equipment Reliability Data and Risk Informed Decisions in a Plant Operation Context

Industry equipment reliability and asset management programs are essential elements that help ensure the safe and economical operation of nuclear power plants. The effectiveness of these programs is addressed in several industry-developed and regulatory programs. The Risk-Informed Asset Management (RIAM) project is tasked to develop tools in support of the equipment reliability and asset management programs at nuclear power plants. These tools are designed to create a direct bridge between component health/lifecycle data and decision making (e.g., maintenance scheduling and project prioritization). The goal of this article is to provide a guide for specific use cases that the RIAM project is targeting. We have grouped uses cases into three main areas. The first area focuses on the analysis of equipment reliability data with a particular emphasis on condition-based data, such as test/surveillance reports and component monitoring data. The second area focuses on the integration of equipment reliability into system/plant reliability models to determine system/plant health and identify the components that are critical to maintain an operational system. Lastly, the third area manages plant resources, such as maintenance activities and replacement scheduling using optimization methods. Here the primary focus is on supporting typical system engineer decisions regarding maintenance activity scheduling and component aging management. This is performed in a risk-informed context where the term “risk” is broadly constructed to include both plant reliability and economics. This framework combines data analytics tools to analyze equipment reliability data with risk-informed methods designed to support system engineer decisions (e.g., maintenance and replacement schedules, optimal maintenance posture) in a customizable workflow.

97 - MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Hyperplane decision trees as piecewise linear surrogate models for chemical process design

Recent trends in chemical engineering research point towards an increasing reliance on data-driven modeling approaches. Neural networks, for instance, have proven to be accurate when data is plentiful and high-dimensional, but in many cases, they require computationally-intensive training procedures. Here, in this work, we describe hyperplane decision trees (HT) as a highly expressive and low-compute machine learning model architecture. These models are locally linear and have linear decision boundaries, resulting in a piecewise linear model of the data. This property allows them to be converted into mixed-integer linear constraints which can be globally optimized. Our open-source PyTorch implementation of this method is a fast, flexible, and accessible way to build accurate piecewise linear models of data.

Decision trees↗

DOE OSTI2025

Ecological connectivity and in-kind mitigation in a regulatory decision framework: A case study with an amphibian habitat specialist

Ecological connectivity is critical to the survival and long-term viability of populations but is often overlooked in regulatory frameworks. We integrated landscape-level processes into a mitigation strategy for impacts to aquatic resources on the U.S. Department of Energy (DOE) Oak Ridge Reservation (ORR) in eastern Tennessee. Wetlands on the ORR, which contain significant breeding populations of the imperiled four-toed salamander (Hemidactylium scutatum) and tubercled rein orchid (Platanthera flava var. herbiola), will be impacted by construction of an environmental waste disposal facility under the Comprehensive Environmental Response, Compensation, and Liability Act of 1980 (CERCLA). Here, we used a modified Kepner-Tregoe decision analysis to select general mitigation options that balanced regulatory requirements and interest group perspectives. We emphasized habitat connectivity through models that prioritized an area's importance to natural area connectivity (centrality) and maintenance of population structure for an affected habitat specialist (four-toed salamanders). We also emphasized in-kind mitigation through the preservation and enhancement of ecologically similar resources and the translocation and establishment of a new subpopulation of four-toed salamanders elsewhere on the ORR. We ultimately released over 500 juvenile salamanders that originated from the impacted site into the chosen mitigation wetlands. By doing so under the constraints of a time-sensitive CERCLA remediation effort and exceeding its substantive requirements, this work underscores feasibility. Ecological connectivity and the conservation of species that are not afforded explicit regulatory processes can be effectively and efficiently integrated into environmental decision-making and land use planning.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Navigating Uncertainty: Challenges in Visualizing Ensemble Data and Surrogate Models for Decision Systems

Uncertainty visualization plays a critical role in transforming ensemble simulation data into actionable insights by effectively communicating various dimensions of uncertainty within a system. The emergence of artificial intelligence-driven surrogate models trained on multirun ensemble data offers a transformative opportunity to replace computationally intensive simulations with fast estimates, enabling users to explore data spaces with unprecedented depth and interactivity. However, integrating ensemble data and surrogate models into decision-making workflows and tools introduces novel challenges for uncertainty visualization. These include reconciling and clearly communicating the unique uncertainties associated with ensembles and their surrogate model estimates, and leveraging these approximations to inform actionable decisions. This work explores these challenges in the context of high-dimensional data visualization, bridging discrete datasets with their continuous representations and addressing the complexities of systems that support iterative navigation between input and output spaces. We evaluate the role of uncertainty visualization in fostering intuitive, actionable interactions and identify critical hurdles in advancing this frontier of computational simulation.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Decision-Dependent Uncertainty-Aware Distribution System Planning Under Wildfire Risk

The interaction between power systems and wildfires can be dangerous and costly. Distribution grids can be liable for the outbreak of wildfires during extreme weather. In wildfire-prone areas, investment planning should consider the impact of operational actions on wildfire-related uncertainties affecting line failure likelihood. Here, in this case, endogenous-based uncertainty modeling should comprise the backbone of the investment planning model viz-a-viz the inability of standard exogenous-based uncertainty modeling. Therefore, we propose a decision-dependent uncertainty (DDU) aware methodology to optimize investment portfolios for distribution systems, considering that high power-flow levels in high-threat areas can ignite wildfires and increase line failure probability. The methodology identifies the best combination of upgrades (new lines, hardening existing lines, and placing switching devices). Methodologically, we propose a two-stage distributionally robust planning optimization problem with DDU that considers the distribution system's multiperiod operation. The first stage determines optimal switching actions and line investments, and the second stage evaluates the worst-case expected operational cost under a DDU framework designed to account for the endogenous impact of power-flow levels and hardening investment decisions in the line failure probabilities. An iterative method is tailored to handle the problem and numerical experiments demonstrate a more prepared grid to deal with wildfire risk.

Power systems investment planning↗

DOE OSTI2024

Wildfire management decisions outweigh mechanical treatment as the keystone to forest landscape adaptation

Modern land management faces unprecedented uncertainty regarding future climates, novel disturbance regimes, and unanticipated ecological feedbacks. Mitigating this uncertainty requires a cohesive landscape management strategy that utilizes multiple methods to optimize benefits while hedging risks amidst uncertain futures. We used a process-based landscape simulation model (LANDIS-II) to forecast forest management, growth, climate effects, and future wildfire dynamics, and we distilled results using a decision support tool allowing us to examine tradeoffs between alternative management strategies. We developed plausible future management scenarios based on factorial combinations of restoration-oriented thinning prescriptions, prescribed fire, and wildland fire use. Results were assessed continuously for a 100-year simulation period, which provided a unique assessment of tradeoffs and benefits among seven primary topics representing social, ecological, and economic aspects of resilience. Projected climatic changes had a substantial impact on modeled wildfire activity. In the Wildfire Only scenario (no treatments, but including active wildfire and climate change), we observed an upwards inflection point in area burned around mid-century (2060) that had detrimental impacts on total landscape carbon storage. While simulated mechanical treatments (~ 3% area per year) reduced the incidence of high-severity fire, it did not eliminate this inflection completely. Scenarios involving wildland fire use resulted in greater reductions in high-severity fire and a more linear trend in cumulative area burned. Mechanical treatments were beneficial for subtopics under the economic topic given their positive financial return on investment, while wildland fire use scenarios were better for ecological subtopics, primarily due to a greater reduction in high-severity fire. Benefits among the social subtopics were mixed, reflecting the inevitability of tradeoffs in landscapes that we rely on for diverse and countervailing ecosystem services. This study provides evidence that optimal future scenarios will involve a mix of active and passive management strategies, allowing different management tactics to coexist within and among ownerships classes. Our results also emphasize the importance of wildfire management decisions as central to building more robust and resilient future landscapes.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Utah FORGE 6-3629: Application of Machine Learning, Geomechanics, and Seismology for Real-Time Decision Making Tools During Stimulation - 2024 Annual Workshop Presentation

This is a presentation on the Cutting Edge Application of Machine Learning, Geomechanics, and Seismology for Real-Time Decision Making Tools During Stimulation by the University of Utah, presented by No'am Zach Dvory. This video slide presentation, by the University of Utah, discussed the technical objectives of developing a real-time decision-making platform to enhance seismic monitoring and risk management during stimulation activities. This presentation was featured in the Utah FORGE R&D Annual Workshop on August 15, 2024.

15 GEOTHERMAL ENERGY↗

DOE OSTI2025

Biomass for Carbon Removal and Storage (BiCRS) Counterfactual Decision Tree

Counterfactual is the term used to describe a "business-as-usual" scenario which used as a baseline to compare against a new project, allowing the calculation of net impacts for a life cycle analysis (LCA). The choice of counterfactual is critical for determining the results from LCA and must be carefully justified to ensure a fair and accurate comparison. Using forest residues as an example, this decision tree illustrates decision points to be considered for sustainable biomass sourcing and provides a framework for estimating the carbon emissions or storage under the "business-as-usual” scenarios for biomass otherwise destined for use in Biomass for Carbon Removal and Storage (BiCRS) projects.

09 BIOMASS FUELS↗

NASA NTRS2004

A decision support system for technology R&D planning: connecting the dots from information to innovation

This paper describes an information technology innovation developed to assist decision makers faced with complex R&D tasks. The decision support system (DSS) was developed and applied to the analysis of a 10-year, 700 million dollar technology program for the exploration of Mars. The technologies were to enable a 4.8 billion dollar portfolio of exploration flight missions to Mars.

DSS↗

NASA NTRS2004

Got risk? risk-centric perspective for spacecraft technology decision-making

A risk-based decision-making methodology conceived and developed at JPL and NASA has been used to aid in decision making for spacecraft technology assessment, adoption, development and operation. It takes a risk-centric perspective, through which risks are used as a reasoning step to interpose between mission objectives and risk mitigation measures.

risk↗

NASA NTRS2006

Architecting Space Exploration Campaigns: A Decision-Analytic Approach

This paper shows the benefits of Decision Analysis techniques for campaign design and evaluation. Important concepts of decision analysis are reviewed through the lens of designing a campaign to find exploitable equatorial water on Mars. The method developed herein is general to any search campaign. The paper concludes with a discussion of the challenges and opportunities in applying similar techniques to other types of campaigns.

campaigns↗

NASA NTRS2006

Intertwining Risk Insights and Design Decisions

The state of systems engineering is such that a form of early and continued use of risk assessments is conducted (as evidenced by NASA's adoption and use of the 'Continuous Risk Management' paradigm developed by SEI). ... However, these practices fall short of theideal: (1) Integration between risk assessment techniques and other systems engineering tools is weak. (2) Risk assessment techniques and the insights they yield are only informally coupled to design decisions. (3) Individual riskassessment techniques lack the mix of breadth, fidelity and agility required to span the gamut of the design space. In this paper we present an approach that addresses these shortcomings. The hallmark of our approach is a simple representation comprising objectives (what the system is to do), risks (whose occurrence would detract from attainment of objectives) and activities (a.k.a. 'mitigations') that, if performed, will decrease those risks. These are linked to indicate by how much a risk would detract from attainment of an objective, and by how much an activity would reduce a risk. The simplicity of our representational framework gives it the breadth to encompass the gamut of the design space concerns, the agility to be utilized in even the earliest phases of designs, and the capability to connect to system engineering models and higher-fidelity risk tools. It is through this integration that we address the shortcomings listed above, and so achieve the intertwining between risk insights and design decisions needed to guide systems engineering towards superior final designs while avoiding costly rework to achieve them. The paper will use an example, constructed to be representative of space mission design, to illustrate our approach.

systems engineering↗