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Shaun Deacon

Publications and source records attributed to Shaun Deacon.

5 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

NASA’s Surface Deformation and Change Mission Study

The National Academies of Science, Engineering and Medicine 2017 Decadal Survey of Earth Science and Applications identified geodetic measurements of surface deformation and related change as one of the top five “observables” to be prioritized in NASA’s future program. In response, NASA commissioned a multi-center Surface Deformation and Change(SDC) team to perform a five year study of mission architectures that would support SDC observables and provide the most value to the diverse science and applications communities it serves. The study is being conducted in phases, in which the science and applications capabilities identified in the Decadal Survey are refined, candidate architectures and associated technologies to support these needs are identified, architectures are assessed against a science value framework specific to SDC, and recommendations to NASA are made. Ultimately, NASA will decide which amongst these recommendations will proceed to mission formulation. As synthetic aperture radar (SAR) was identified as the prime sensor technology to satisfy SDC observational needs, a key component of the SDC study is to assess the current state of the art in SAR sensor and supporting technology. The number of SAR systems, both civil and commercial, is growing rapidly, requiring that mission architectures not only consider technology, but availability of data from other missions, possible partnerships or collaborations, and even data purchase. The mechanism for assessment involves development of an end-to-end science performance evaluation tool for multi-satellite con-stellations, which feeds into a science value framework that con-siders science performance, technological programmatic risks, and cost. This paper will present an overview of the ongoing study including the candidate architectures and the technology road map needed to achieve the objectives of the mission.

Stephen Horst↗
NASA NTRS2021

Generation-based Evolutionary Tool for the Optimization of Constellations (GenETOC)

With the rapid growth in the capabilities of smaller satellites, satellite architectures that replace a single, extremely capable spacecraft with multiple, cheaper ones are gaining in popularity. Unfortunately, the orbit design process for constellations can be significantly more involved, especiallywhen the relative placement of the individual spacecraft within the constellation is not constrained by mission and/or science objectives. Optimizing a satellite constellation in the presence of multiple, competing objectives is a highly complex problem to which many traditional mathematical optimization methods cannot be applied and few tools exist to help mission designers search for promising candidate mission designs. The Generation-based Evolutionary Tool for the Optimization of Constellations (GenETOC) has been created to search for near-optimal constellation design options. GenETOC combines a modified version of the Non-dominated Sorting Genetic Algorithm II (NSGA II) with STK Components libraries (a 3rdparty .NET package created by Analytical Graphics Inc.) to create a framework that enables a mission designer to generate a simulation that models the design problem and obtain a family of potential, near-optimal solutions that can be investigated more in detail.

mission design↗
NASA NTRS2021

Generation-based Evolutionary Tool for the Optimization of Constellations (GenETOC)

With the rapid growth in the capabilities of smaller satellites, satellite architectures that replace a single, extremely capable spacecraft with multiple, cheaper ones are gaining in popularity. Unfortunately, the orbit design process for constellations can be significantly more involved, especiallywhen the relative placement of the individual spacecraft within the constellation is not constrained by mission and/or science objectives. Optimizing a satellite constellation in the presence of multiple, competing objectives is a highly complex problem to which many traditional mathematical optimization methods cannot be applied and few tools exist to help mission designers search for promising candidate mission designs. The Generation-based Evolutionary Tool for the Optimization of Constellations (GenETOC) has been created to search for near-optimal constellation design options. GenETOC combines a modified version of the Non-dominated Sorting Genetic Algorithm II (NSGA II) with STK Components libraries (a 3rdparty .NET package created by Analytical Graphics Inc.) to create a framework that enables a mission designer to generate a simulation that models the design problem and obtain a family of potential, near-optimal solutions that can be investigated more in detail. GenETOC was developed in C# using the .NET framework with Windows Presentation Foundation (WPF) serving as the framework from which to create the graphical user interface (GUI). GenETOC user inputs can be categorized into three major data components: definition of the problem (areas of interest, satellite decision parameters, and sensor configurations), definition of performance objectives, and specification of the genetic algorithm (GA) parameters. In the problem definition component, the user is prompted to define the areas of interest against which the performance metrics will be computed, define the sensor parameters and attach them to specific spacecraft, select which satellite orbital parameters will be added to the decision space of the GA, and specify the range of desired values for each optimization parameter. For performance objectives, the user is presented with a list of available coverage and revisit performance based calculation options from which two metrics are chosen to serve as the objective functions that the GA will use to evaluate solutions during the optimization process. Finally, the definition of the GA parameters provides user control over the number of generations (number of optimization iterations), the population size (number of candidate constellations created in each generation), and the adaptive mutation and crossover threshold values (control parameters for how frequently each process occurs during the optimization). GenETOC has been extensively tested to verify the individual components of the optimization process. The GA has been tested against a suite of GA test problems to confirm convergence to the known two and three-dimensional Pareto fronts. The coverage and revisit performance metrics obtained in GenETOC are compared with STK desktop scenarios, confirming the constellations are being appropriately modeled within GenETOC simulations. A walkthrough of a simple, example problem is provided to illustrate the workings of GenETOC and to demonstrate the output available to the mission designer.

mission design↗