The Electric sail concept is based on a distributed tether satellite system with tether lengths on the order of thousands-of meters. The system must deploy from stowed arrangement into a selected flight configuration in which thrust forces are transmitted through the tether to the satellite body. The system must be stable through deployment procedure and maintain stable, desired configuration during flight operations. Understanding the dynamic behavior of the satellite bodies and distributed, conductive tether are critical to long-range design and development of the Electric Sail concept. This paper's contribution is the presentation, development and validation of a mathematical model for simulating E-Sail deployment of a prototype system for testing on the MSFC Robotic Flat Floor Facility. A massed tether model is developed using the bead and string concept with equations of motion derived from Lagrange's Method. The model is validated using infrared motion capture data produced by controlled experiments of a representative tether portion outfitted with IR targets. Further, a prototype is presented which will be used to investigate an E-Sail deployment approach and associated control. The design of this 'system will allow for deployment on specially designed flat floor facilities at MSFC. The prototype will be used to: 1) gather data for validation of system dynamic model, 2) evaluate alternative deployment strategies, 3) evaluate tether reel-out and damping control strategies.

An Electric Sail (E-Sail) propulsion system consists of long, thin tethers - positively-charged wires extending radially and symmetrically outward from a spacecraft. Tethers must be biased using a high-voltage power supply to ensure that the solar wind produces thrust. While the E-Sail concept shows great promise for flying heliopause missions with higher characteristic acceleration than solar sails, there are significant technical challenges related to deploying and controlling multiple tethers. A typical full-scale design involves a hub and spoke arrangement of 10 to 100 tethers, each 20 km long. In the last 20 years, there have been multiple space mission failures due to tether deployment and control issues, and most configurations involved a single tether. This paper describes an effort to develop and test a simple yet robust single-tether deployment system for a two-6U CubeSat configuration. The project included the following: a) Tether dynamic modeling/simulation b) E-Sail single-tether prototype development and testing c) Space environmental effects testing to identify best materials for further development. These three areas of investigation were needed to provide technical rationale for an E-Sail flight demonstration mission that is expected to be proposed for the 2022 time frame. The project team used an “agile” engineering approach in which E-Sail single-tether prototype designs were iteratively developed and tested to solve problems and identify design improvements. The agile approach was ideal for this low Technology Readiness Level (TRL) project because tether deployer development involved many unknowns in prototype development that could only be discovered through iterative cycles of construction and testing. Extensive modeling and simulation were accomplished for three types of tether deployment: a) Stage 1: propulsive separation with one 6U fixed b) Stage 2: propulsive spin-up with one 6U fixed c) Stage 3: propulsive spin-up with both 6Us free Simulation results were valuable for understanding the propulsive and braking forces needed for controlled tether deployment. This paper describes the evolution, insights, and test/ performance data related to the resultant single-tether two-6U E-Sail test article which has been demonstrated in a test laboratory. The development effort suggests near-term work needed to achieve a useful flight demonstration, and provides ideas for how multiple-tether deployment systems might evolve going forward. A planned next-generation E-Sail prototype will include autonomous propulsive tether deployment while monitoring tether tension, location on the floor, distance between tether ends, acceleration, velocity, and propellant used.

In early 2013, Marshall Space Flight Center's upper management chartered a diverse team for a six-week “sprint” to speculate (in a disciplined manner) and paint (with broad brush strokes) a picture of how earth, space, and public/private entities might be operating and relating to each other... in the year 2100. Two 12-person groups of civil servants, one with members having 15 years or less of NASA experience and the other with more senior members, worked independently and then compared and integrated their conclusions. In 2014, the “Space 2100” team, with some new team members and different group boundaries, ran a longer sprint to a) develop more detailed estimates of the operations and economics of space activities in the vicinity of the Earth and Moon in the 2050 time frame, b) identify evolutionary paths, barriers, and opportunities, and c) suggest actions and philosophies to enable and invigorate progress towards the vision. This paper explores Space 2100's first two sprints and their projections of NASA's role in what will likely be a highly networked, international space industry and cis-lunar infrastructure.

In this paper, genetic algorithm based and gradient-based topology optimization is presented in application to a real hardware design problem. Preliminary design of a planetary lander mockup structure is accomplished using these methods that prove to provide major weight savings by addressing the structural efficiency during the design cycle. This paper presents two alternative formulations of the topology optimization problem. The first is the widely-used gradient-based implementation using commercially available algorithms. The second is formulated using genetic algorithms and internally developed capabilities. These two approaches are applied to a practical design problem for hardware that has been built, tested and proven to be functional. Both formulations converged on similar solutions and therefore were proven to be equally valid implementations of the process. This paper discusses both of these formulations at a high level.

Inflatable tubular structures that have annular cross sections rigidized with foams, and the means of erecting such structures in the field, are undergoing development. Although the development effort has focused on lightweight structural booms to be transported in compact form and deployed in outer space, the principles of design and fabrication are also potentially applicable to terrestrial structures, including components of ultralightweight aircraft, lightweight storage buildings and shelters, lightweight insulation, and sales displays. The use of foams to deploy and harden inflatable structures was first proposed as early as the 1960s, and has been investigated in recent years by NASA, the U.S. Air Force Research Laboratory, industry, and academia. In cases of deployable booms, most of the investigation in recent years has focused on solid cross sections, because they can be constructed relatively easily. However, solid-section foam-filled booms can be much too heavy for some applications. In contrast, booms with annular cross sections according to the present innovation can be tailored to obtain desired combinations of stiffness and weight through choice of diameters, wall thicknesses, and foam densities. By far the most compelling advantage afforded by this innovation is the possibility of drastically reducing weights while retaining or increasing the stiffnesses, relative to comparable booms that have solid foamfilled cross sections. A typical boom according to this innovation includes inner and outer polyimide film sleeves to contain foam that is injected between them during deployment.

Evolutionary computational (EC) techniques such as genetic algorithms (GA) have been identified as promising methods to explore the design space of mechanical and electrical systems at the earliest stages of design. In this paper the authors summarize their research in the use of evolutionary computation to develop preliminary designs for various space systems. An evolutionary computational solver developed over the course of the research, X-TOOLSS (Exploration Toolset for the Optimization of Launch and Space Systems) is discussed. With the success of early, low-fidelity example problems, an outline of work involving more computationally complex models is discussed.

Typically, structural topology optimization problems undergo relaxation of certain design parameters to allow the existence of intermediate variable optimum topologies. Relaxation permits the use of a variety of gradient-based search techniques and has been shown to guarantee the existence of optimal solutions and eliminate mesh dependencies. This Technical Publication (TP) will demonstrate the application of relaxation to a control point discretization of the design workspace for the structural topology optimization process. The control point parameterization with subdivision has been offered as an alternative to the traditional method of discretized finite element design domain. The principle of relaxation demonstrates the increased utility of the control point parameterization. One of the significant results of the relaxation process offered in this TP is that direct manufacturability of the optimized design will be maintained without the need for designer intervention or translation. In addition, it will be shown that relaxation of certain parameters may extend the range of problems that can be addressed; e.g., in permitting limited out-of-plane motion to be included in a path generation problem.

Long-duration surface missions to the Moon and Mars will require habitats for the astronauts. The materials chosen for the habitat walls play a direct role in the protection against the harsh environments found on the surface. Choosing the best materials, their configuration, and the amount required is extremely difficult due to the immense size of the design region. Advanced optimization techniques are necessary for habitat wall design. Standard optimization techniques are not suitable for problems with such large search spaces; therefore, a habitat design optimization tool utilizing genetic algorithms has been developed. Genetic algorithms use a survival of the fittest philosophy, where the most fit individuals are more likely to survive and reproduce. This habitat design optimization tool is a multi-objective formulation of structural analysis, heat loss, radiation protection, and meteoroid protection. This paper presents the research and development of this tool.

Future space exploration missions will require the development of more advanced in-space radiators. These radiators should be highly efficient and lightweight, deployable heat rejection systems. Typical radiators for in-space heat mitigation commonly comprise a substantial portion of the total vehicle mass. A small mass savings of even 5-10% can greatly improve vehicle performance. The objective of this paper is to present the development of detailed tools for the analysis and design of in-space radiators using evolutionary computation techniques. The optimality criterion is defined as a two-dimensional radiator with a shape demonstrating the smallest mass for the greatest overall heat transfer, thus the end result is a set of highly functional radiator designs. This cross-disciplinary work combines topology optimization and thermal analysis design by means of a genetic algorithm The proposed design tool consists of the following steps; design parameterization based on the exterior boundary of the radiator, objective function definition (mass minimization and heat loss maximization), objective function evaluation via finite element analysis (thermal radiation analysis) and optimization based on evolutionary algorithms. The radiator design problem is defined as follows: the input force is a driving temperature and the output reaction is heat loss. Appropriate modeling of the space environment is added to capture its effect on the radiator. The design parameters chosen for this radiator shape optimization problem fall into two classes, variable height along the width of the radiator and a spline curve defining the -material boundary of the radiator. The implementation of multiple design parameter schemes allows the user to have more confidence in the radiator optimization tool upon demonstration of convergence between the two design parameter schemes. This tool easily allows the user to manipulate the driving temperature regions thus permitting detailed design of in-space radiators for unique situations. Preliminary results indicate an optimized shape following that of the temperature distribution regions in the cooler portions of the radiator. The results closely follow the expected radiator shape.

Structural optimization is a field of research that has experienced noteworthy growth for many years. Researchers in this area have developed optimization tools to successfully design and model structures, typically minimizing mass while maintaining certain deflection and stress constraints. Numerous optimization studies have been performed to minimize mass, deflection, and stress on a benchmark cantilever truss problem. Predominantly, traditional optimization theory is applied to this problem. The cross-sectional area of each member is optimized to minimize the aforementioned objectives. This paper will present a structural optimization technique that has been previously applied to compliant mechanism design. This technique demonstrates a method that combines topology optimization, geometric refinement, finite element analysis, and two forms of evolutionary computation—genetic algorithms and differential evolution—to successfully optimize a benchmark structural optimization problem. A nontraditional solution to the benchmark problem is presented in this paper, specifically, a geometrically refined topological solution. The design process begins with an alternate control mesh formulation, multilevel geometric smoothing operation, and an elastostatic structural analysis. The design process is wrapped in an evolutionary computing optimization tool set.

According to the Life Safety Codereg, the geometry of a building, the location of exits, and the number of exits dictate the means of egress for all people occupying a building. In this paper we show how evolutionary computations in the form of Genetic Algorithms and Estimation of Distribution Algorithms are used to evolve the placement of exits in order to optimize overall evacuation time. In particular, a generational GA, a steady-state GA, and an elitist EDA are used to evolve the placement of exits for two practical design problems. The algorithms are evaluated in terms of success rate, number of function evaluations, and best fitness. For both problems, the steady-state GA outperformed the other algorithms in all evaluation categories.

The nuclear electric vehicle optimization toolset (NEVOT) optimizes the design of all major nuclear electric propulsion (NEP) vehicle subsystems for a defined mission within constraints and optimization parameters chosen by a user. The tool currently uses a number of evolutionary computations (ECs) for designing NEP vehicles. Since evaluating candidate vehicle designs is computationally expensive, it is important that a set of robust control parameters be discovered. In order to accomplish this, a meta-genetic algorithm (meta-GA) was developed to discover control parameters for generational, steady-state, and steady-generational GAs as well as for particle swarm optimizers (PSOs) with ring, star, and random topologies. Our results show that the high-performance GAs are more efficient than the high-performance PSOs on a NASA asteroid mission problem.

Nuclear electric propulsion (NEP) vehicles will be needed for future manned missions to Mars and beyond. Candidate designs must be identified for further detailed design from a large array of possibilities. Genetic algorithms have proven their utility in conceptual design studies by effectively searching a large design space to pinpoint unique optimal designs. This research combined analysis codes for NEP subsystems with a genetic algorithm. The use of penalty functions with scaling ratios was investigated to increase computational efficiency. Also, the selection of design variables for optimization was considered to reduce computation time without losing beneficial design search space. Finally, trend analysis of a reference mission to the asteroids yielded a group of candidate designs for further analysis.

Nuclear electric propulsion (NEP) vehicles will be needed for future manned missions to Mars and beyond. Candidate vehicles must be identified through trade studies for further detailed design from a large array of possibilities. Genetic algorithms have proven their utility in conceptual design studies by effectively searching a large design space to pinpoint unique optimal designs. This research combines analysis codes for NEP subsystems with genetic algorithm-based optimization. Trade studies for a NEP reference mission to the asteroids were conducted to identify important trends, and to determine the effects of various technologies and subsystems on vehicle performance. It was found that the electric thruster type and thruster performance have a major impact on the achievable system performance, and that significant effort in thruster research and development is merited.

The purpose of this paper is to utilize the genetic algorithm (GA) optimization method for structural design of a nuclear propulsion vehicle. Genetic algorithms provide a guided, random search technique that mirrors biological adaptation. To verify the GA capabilities, other traditional optimization methods were used to generate results for comparison to the GA results, first for simple two-dimensional structures, and then for full-scale three-dimensional truss designs.

The Nuclear Electric Vehicle Optimization Toolset (NEVOT) optimizes the design of all major nuclear electric propulsion (NEP) vehicle subsystems for a defined mission within constraints and optimization parameters chosen by a user.The tool uses a genetic algorithm (GA) search technique to combine subsystem designs and evaluate t h e fitness of the integrated design to fulfill a mission.The fitness of an individual is used within the GA to determine its probability of survival through successive generations in which the designs with low fitness are eliminated and replaced with combinations or mutations of designs with higher fitness.The program can find optimal solutions for different sets of fitness metrics without modification and can create and evaluate vehicle designs that might never be considered through traditional design techniques.It is anticipated that the flexible optimization methodology will expand present knowledge of the design trade-offs inherent in designing nuclear powered space vehicles and lead to improved NEP designs.