Space Mission Engineering: The New SMAD is an entirely new approach to creating a text and a practical engineering reference for space mission design. Just as space technology has advanced, the way we learn and work has changed dramatically in recent years. The SME combines the best features of a traditional unified text and reference covering the entire field, an electronic version that does many calculations for you, and the web that allows regular updates and connections to the vast literature base available online. Among the many features of this new approach are: This is a completely rewritten, updated, and expanded follow-on to the 3rd edition of Space Mission Analysis and Design, covering a great many topics not previously covered, such as CubeSats, Inflatable Structures, Space Economics, End-of-Mission options, Space System Risk Analysis, and new, much more precise formulas for ground station and target coverage. Downloadable electronic spreadsheets for most of the numerical tables and plots in the booklet. You, for example, calculate all of the critical parameters for orbits about the Sun, Moon, Earth, and any of the other planets, or even new planets, moons, or stars of your choosing. An annotated bibliography and updated references on the web as new connections become available show you where to get nearly all of the concerns with direct links to those available at no cost and where on the web to buy copyrighted books and professional papers not available for free. All of the cross-referencing, careful definitions, and thoroughly explained equations are the key ingredients of any high-quality engineering text or reference, along with the wisdom and experience gained at substantial cost by some of the most experienced and knowledgeable space system engineers in the world.
Leverage the power and flexibility of FreeFlyer Astrodynamics Software in your next mission. FreeFlyer provides complete astrodynamics functionality for missions of any size, any scale, any orbit regime, ITAR-free. With heritage on over 225 missions, customizable interfaces, and easy integration into modern ground systems architectures, FreeFlyer supports the full lifecycle of your mission.
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FreeFlyer is a commercial off-the-shelf (COTS) software application for space mission design, analysis, and operations. FreeFlyer stands out as the most powerful tool of its kind by providing users with a robust scripting language for solving all types of astrodynamics problems. FreeFlyer has been independently verified and validated for flight-tested, proven accuracy, and is used for spacecraft analysis and operations by NASA, NOAA, USAF, NRO, and commercial satellite providers.
The FreeFlyer astrodynamics software comes in two license tiers, Engineer and Mission. FreeFlyer Engineer is an ideal choice for mission design and analysis, including constellation design, sensor coverages, maneuver planning, Monte Carlo analyses, and more. FreeFlyer Mission adds the FreeFlyer Orbit Determination suite as well as the FreeFlyer Runtime API for integration into external applications and operational satellite ground systems.
Aerospace Toolbox provides standards-based tools and functions for analyzing the motion, mission, and environment of aerospace vehicles. It includes aerospace math operations, coordinate system and spatial transformations, and validated environment models for interpreting flight data. The toolbox also includes 2D and 3D visualization tools and standard cockpit instruments for observing vehicle motion.
Aerospace Toolbox lets you design and analyze scenarios consisting of satellites and ground stations. You can propagate satellite trajectories from orbital elements or two-line element sets, load in satellite and constellation ephemerides, perform mission analysis tasks such as line-of-sight access, and visualize the scenario as a ground track or globe.
In this paper, a generic full-system estimation software tool is introduced and applied to a data set of actual flight missions to derive a heuristic for system composition for mass and power ratios of considered sub-systems. The capability of evolutionary algorithms to analyse and effectively design spacecraft (sub-)systems is shown. After deriving top-level estimates for each spacecraft sub-system based on heuristic heritage data, a detailed component-based system analysis follows. Various degrees of freedom exist for a hardware-based sub-system design; these are to be resolved via an evolutionary algorithm to determine an optimal system configuration. A propulsion system implementation for a small satellite test case will serve as a reference example of the implemented algorithm application. The propulsion system includes thruster, power processing unit, tank, propellant and general power supply system masses and power consumptions. Relevant performance parameters such as desired thrust, effective exhaust velocity, utilised propellant, and the propulsion type are considered as degrees of freedom. An evolutionary algorithm is applied to the propulsion system scaling model to demonstrate that such evolutionary algorithms are capable of bypassing complex multidimensional design optimisation problems. An evolutionary algorithm is an algorithm that uses a heuristic to change input parameters and a defined selection criterion (e.g., mass fraction of the system) on an optimisation function to refine solutions successively. With sufficient generations and, thereby, iterations of design points, local optima are determined. Using mitigation methods and a sufficient number of seed points, a global optimal system configurations can be found.
The scope of the ESDC tool is the complete (preliminary) design of a spacecraft. Several relevant tools and platforms with roughly similar scope exist and are briefly introduced here. Platforms and tools vary significantly in terms of design scope and depth, specific mission focus, availability and costs.
The well-established General Mission Analysis Tool (GMAT) [66] is an open-source tool [67] for trajectory optimisation. Thus, a predefined spacecraft has to be applied as input for consideration in further mission optimisation. A commercial pendant to GMAT is the System Tool Kit (STK) by AGI-ANSYS [68], with similar scope [69] and requiring a preset spacecraft definition. It comes with better usability but considerable license fees.
NASA currently explores a much broader scope for generic spacecraft design with the Trade-space Analysis Tool for Constellations (TAT-C) development [72,73,74]. The tool scope considers single or multiple small satellites up to flagship sizes, where multiple mission objectives have to be met, and overall performance and cost optimisation is performed. TAT-C aims for an open-access solution [75, 76] without dependencies to commercial license restrictions such as STK [68]. Spacecraft cost estimation is achieved using cost estimating relationships from accepted public sources [73]. Automation for closing design feedback loops is the scope of future work in TAT-C [74]. Recent extensions of TAT-C considered improved value functions for Earth Observation satellites [77].
Similar to Aerospace corporations activities via CEF operations, a system engineering tool has been developed [78]. This tool allows to design small satellites for scientific missions. It was later extended to a model-based design tool [79].
ESA activities in support of its CDF [59] include the development of robust and automated space system design methods [81], where uncertainty analysis of given parameters (i.e., thruster specifications) is considered to achieve a reliable worst-case analysis of a given spacecraft in its early design phase.
The ESDC algorithm generates initially a first guess of data points (i.e., system configurations) based on (incomplete) input data. The calculated system configuration is then rated concerning the given requirements. Suitable configurations/data points are selected for the next iterative generation. The new generation is iteratively mutated by varying permitted degrees of freedom in the configuration and re-rating the newly generated solutions for further selection. This process of mutation and selection is repeated until convergence is achieved, i.e., no incremental improvements are found. An illustration of the evolutionary process is given in Fig. 3. This method can successively optimise a system to find multiple optimal points in the multidimensional space of possible solutions.
In the second stage, the initial system guesses are considered as a baseline for a refined system design. Here, actual system dependencies are applied, shown in the ontology utilised for the ESDC given in Fig. 16. Additional dimensions are introduced here in the form of data and heat budgets to be considered, resulting in additional Knapsack capacity dimensions \(W_i\) for the entire system. Adaption of these capacities is performed during iterations and additionally allows for localized optimisation of each system. Existing degrees of freedom, e.g., the considered technology and corresponding operating parameters, are solved with an evolutionary algorithm. Where applicable, scaling laws from a component database are derived. The defined mission requirements are now considered for the individual system design. The result is now refined to obtain self-consistent Knapsack capacities (i.e., sub-system definitions and requirements) for an optimal spacecraft configuration.
The previously introduced fit algorithm is now applied to generate the initialisation of a suitable spacecraft. For this individual system, masses and power consumptions of space missions with flight heritage are utilised [105].
First, the most straightforward case with a known total spacecraft mass \(m_\text tot\) is considered. A safety mass margin of \(m_\text Margin = 30\%\) is deducted, and the remaining mass is distributed according to the calculated fraction for similar missions. If some of the sub-system masses \(m_\text i\) are known, these masses will be considered here. In the case of mass overuse, the margin mass will be automatically reduced to achieve self-consistency. If the available margin mass becomes negative, the design is unachievable. 2ff7e9595c
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