Matthew Phillip Ferringer

Satellite constellation design tradeoffs using multiple-objective evolutionary computation

Multiple-objective evolutionary computation provides the satellite constellation designer with an essential optimization tool due to the discontinuous, temporal, and/or nonlinear characteristics of the metrics that architectures are evaluated against. In this work, the nondominated sorting genetic algorithm 2 (NSGA-2) is used to generate sets of constellation designs (Pareto fronts) that show the tradeoff for two pairs of conflicting metrics. The first pair replicates a previously published sparse-coverage tradeoff to establish a baseline for tool development, whereas the second characterizes the conflict between temporal (revisit time) and spatial (image quality) resolution. A thorough parameter analysis is performed on the NSGA-2 for the constellation design problem so that the utility of the approach may be assessed and general guidelines for use established. The approximated Pareto fronts generated for each tradeoff are discussed, and the trends exhibited by the nondominated designs are revealed. Nomenclature a = semimajor axis, km e = eccentricity F = focal length, m i = inclination, deg K = units conversion constant M = mean anomaly, deg P = pixel size, μm e = elevation, deg ρ = range, km � = right ascension of the ascending node, deg ω = argument of perigee, deg

Many-objective reconfiguration of operational satellite constellations with the Large-Cluster Epsilon Non-dominated Sorting Genetic Algorithm-II

A general framework for the reconfiguration of satellite constellations is developed for the operational scenario when a loss of capacity has occurred and the future configuration must be constructed from the remaining assets. A multi-objective evolutionary algorithm, ε-NSGA-2, adapted for use on large heterogeneous clusters, facilitated the exploration of a six-dimensional fitness landscape for several loss scenarios involving the Global Positioning System Constellation. An a posteriori decision support process was used to characterize and evaluate non-traditional but innovative constellation designs identified. The framework has enhanced design discovery and innovation for extremely complex space domain problems that have traditionally been considered computationally intractable.

Parallel Evolutionary Multi-Objective Optimization on Large, Heterogeneous Clusters: An Applications Perspective

Real-world operational use of parallel multi-objective evolutionary algorithms requires successful searches in constrained wall-clock periods, limited trial-and-error algorithmic analysis, and scalable use of heterogeneous computing hardware. This study provides a cross-disciplinary collaborative effort to assess and adapt parallel multi-objective evolutionary algorithms for operational use in satellite constellation design using large dedicated clusters with heterogeneous processor speeds/architectures. A statistical, metric-based evaluation framework is used to demonstrate how time-continuation, asynchronous evolution, dynamic population sizing, and epsilon dominance archiving can be used to enhance both simple master–slave parallelization strategies and more complex multiple-population schemes. Results for a benchmark constellation design coverage problem show that simple master– slave schemes that exploit time-continuation are often sufficient and potentially superior to complex multiple-population schemes.

Pareto-hypervolumes for the Reconfiguration of Satellite Constellations

A satellite constellation is designed to perform its mission with a nominal number of spacecraft. When a reduction in capacity is experienced, for whatever reason, the remaining constellation may be able to restore performance through reconfiguration. In this work we present a general framework that exploits recent efforts in parallel multiobjective evolutionary computation, to reconfigure satellite constellations that have suffered the loss of one or more of their vehicles. The framework is illustrated through several loss scenarios for the Global Positioning System constellation. Pareto-hypervolumes are constructed which are the set of solutions that approximate the optimum tradeoff between minimizing cost and risk while maximizing performance. The decision making processes using the high-dimensional data sets is illustrated. The results demonstrate a pragmatic approach to optimization wherein the insights gained from a multi-objective view of the design space tradeoffs allow for informed decision making. Nomenclature

Internationally coordinated multi-mission planning is now critical to sustain the space-based rainfall observations needed for managing floods globally

At present 4 of 10 dedicated rainfall observing satellite systems have exceeded their design life, some by more than a decade. Here, we show operational implications for flood management of a ?collapse? of space-based rainfall observing infrastructure as well as the high-value opportunities for a globally coordinated portfolio of satellite missions and data services. Results show that the current portfolio of rainfall missions fails to meet operational data needs for flood management, even when assuming a perfectly coordinated data product from all current rainfall-focused missions (i.e., the full portfolio). In the full portfolio, satellite-based rainfall data deficits vary across the globe and may preclude climate adaptation in locations vulnerable to increasing flood risks. Moreover, removing satellites that are currently beyond their design life (i.e., the reduced portfolio) dramatically increases data deficits globally and could cause entire high intensity flood events to be unobserved. Recovery from the reduced portfolio is possible with internationally coordinated replenishment of as few as 2 of the 4 satellite systems beyond their design life, yielding rainfall data coverages that outperform the current full portfolio (i.e., an optimized portfolio of eight satellites can outperform ten satellites). This work demonstrates the potential for internationally coordinated satellite replenishment and data services to substantially enhance the cost-effectiveness, sustainability and operational value of space-based rainfall observations in managing evolving flood risks.

Budget-constrained portfolio trades using multiobjective optimization

A decision support process using many-objective optimization, high-performance computing, and advanced visualization is applied to gain comprehensive insight into multistakeholder portfolio budgeting trades. The key tradeoffs among nondominated portfolio budget solutions are systematically identified and examined with respect to different stakeholder viewpoints. The approach is illustrated using a portfolio of 14 U.S. Air Force satellite development programs using budget data taken from the 2010 Future Year Development Plan [http://www.saffm.hq.af.mil/budget/, last accessed April 20, 2011]. Practical lessons learned in applying the approach are discussed. ©2012 Wiley Periodicals, Inc. Syst Eng 15

A Framework for the Discovery of Passive-Control, Minimum Energy Satellite Constellations

The hypothesis that high fidelity astrodynamics simulations that account for orbital perturbations can be exploited to discover passive control and/or minimum energy satellite constellations to dramatically reduce propellant demands and improve coverage performance, is explored. A new framework that couples many-objective evolutionary algorithms, high fidelity astrodynamics simulation, massive parallel computing, and visual analytics, is introduced and demonstrated to revisit Draim’s original 4-satellite global coverage problem. Draim’s original constellation design has a 60% coverage deterioration due to orbital perturbations over a 10 year period. A significant finding of this study is that this deterioration can be effectively eliminated through the discovery of constellation configurations that are capable of using perturbing accelerations instead of propellant to restore global coverage performance over decadal time scales. Further, a generalized stationkeeping model is contributed that when incorporated into the framework allows for a complete characterization of the performance tradeoffs that emerge when using passive control to seek minimum energy designs. With the investment of over 21 million hours of compute time, the results are drawn from one of the largest computational experiments to-date for the design of satellite constellations. Overall this study contributes a new paradigm for the way we conceive, design, and populate satellite constellations.

Constellation Design with Parallel Multi-Objective Evolutionary Computation

‡Multi-objective evolutionary algorithms (MOEA) have been shown to be effective optimization tools to search the complex trade-off space s of satellite constellation design. Often the metrics that make up the design trade-off req uire lengthy function evaluation time, resulting in a decreased utility of serial MOEA. In this research the authors implement two parallel processing MOEA paradigms, the master-slave and island models, on a heterogeneous system of processors and operating syst ems. The efficiency and effectiveness of each approach is studied in the conte xt of a regional coverage design problem. The island scheme outperforms the master-slave model with respect to efficiency. A study of the search dynamics for each paradigm demonstrates that both reliably meet the goals of multi-objective optimization (progressing towar ds the Pareto-optimal front while maintaining a diverse set of solutions). A key conclu sion of this research is that both paradigms provide excellent approximations of the true Pareto frontier using a single seed, and when combined across multiple trial runs find nearly the entire set of Pareto-optimal solutions.

Model Diagnostic Analysis of Global Coverage Satellites

We investigate the ability of a reduction in the complexity of a model to enable a more rapid optimization of that model, with minimal reduction in accuracy, if critical parameters and features are identified and contained within the reduced model. We present a Model Diagnostics analysis of the Draim global coverage constellations and the underlying coverage model used to analyze their performance using Sobol’s method of sensitivity indices. We propose complexity reduction based on the diagnostic analysis. Two separate optimizations are run using a Multi-Objective Evolutionary Algorithm optimization tool. One optimization uses the original problem formulation while the other uses a reduced-complexity formulation. We demonstrate that subtle behavior in the interaction of all parameters (not only the critical ones) dramatically affects performance of the optimization.