Spacecraft thermal design is an inverse problem that requires one to determine the choice of surface properties that yield a desired temperature distribution within a satellite. The current techniques for spacecraft thermal design are very much in the frame of trial and error. The goal of this work is to move away from that procedure, and have the thermal design solely dependent on heat transfer parameters. It will be shown that the only relevant parameters to attain this are ones which pertain to radiation. In particular, these parameters are absorptivity and emissivity. We intend to utilize an optimal/analytical approach, and obtain a solution via optimization. As mentioned in the motivation, having a purely passive thermal system will greatly reduce costs, and our optimization solution will enable that. This topic involves heat transfer (conduction and radiation), spacecraft thermal network models, numerical optimization, and materials selection.

Spacecraft thermal design is an inverse problem that requires one to determine the choice of surface properties that yield a desired temperature distribution within a satellite. The current techniques for spacecraft thermal design are very much in the frame of trial and error. The goal of this work is to move away from that procedure, and have the thermal design solely dependent on heat transfer parameters. It will be shown that the only relevant parameters to attain this are ones which pertain to radiation. In particular, these parameters are absorptivity and emissivity. We intend to utilize an optimal/analytical approach, and obtain a solution via optimization. As mentioned in the motivation, having a purely passive thermal system will greatly reduce costs, and our optimization solution will enable that. This topic involves heat transfer (conduction and radiation), spacecraft thermal network models, numerical optimization, and materials selection.

A deep space concept vehicle created from a core stage barrel section of the Space Launch System rocket has been designed by the National Aeronautics and Space Administration for use in future manned Mars missions. The spacecraft, known as the Space Launch System-Derived Habitat, features a dedicated space for exercise equipment. A human factors assessment was performed to determine whether or not the exercise area has adequate volume for multiple microgravity exercise machines to be used by multiple crew members simultaneously. It was determined that in its current design the exercise area does not have adequate volume to house the machines required for bone and muscle maintenance as required for long-duration spaceflight missions. It was recommended that the volume either be vastly expanded or dissolved entirely in favor of multiple, smaller exercise volumes that could each house one machine.

Project Upsilon is a proposed NASA Flagship Class, Uranus Orbiter and Probe mission concept to investigate Uranus' planetary magnetic field and atmosphere. Three spacecraft - the Upsilon-0 Propulsion Module, the Upsilon-1 Science Orbiter, and the Upsilon-2 Atmosphere Probe - shall be implemented to meet needs, goals, and objectives as stated by the NASA Solar System Planetary Science Decadal Survey 2013-2022. Upsilon-0 shall be expended in order to complete orbital capture about Uranus. Upsilon-1 shall study Uranus' planetary magnetic field, obtaining real-time measurements for nominally 20 months within the first two years of arrival; and for as long as possible after the first two years, as part of an extended science mission. Upsilon-2 shall be descended into Uranus' cloud tops to obtain physical data and imagery well into the atmosphere's depths. Chemical propulsion is employed in place of solar-electric propulsion, with regard to the interplanetary system-level trade tree. The interplanetary trajectory requires a single un-powered flyby of Jupiter, selected among several flyby node configurations. The science orbit produces nearly repeating latitude-longitude tracks over a rotating Uranus. The statistical estimation method combines an orbit determination model with respect to Uranus' flattening, and a simple magnetic dipole model for field line modeling. A 7-year period is allotted for the technology research and development, and the testing and verification stages of the project life cycle; the interplanetary journey to Uranus requires 21 years; and the nominal in-situ operation lifetime is 2 years. The Project Upsilon spacecraft launch in 2021 to "revolutionize our understanding of ice giant properties and processes, yielding significant insight into their evolutionary history"; contributing to the Planetary Science Decadal Survey's, and NASA's, key planetary science and deep space exploration visions. / text

Conceptual design optimization (CDO) is a technique proposed for the structured evaluation of different design concepts. Design grammars provide a flexible modular modelling architecture. The model is generated by a compiler based on predefined components and rules. The rules describe the composition of the model; thus, different models can be optimized by the CDO in one run. This allows considering a mission design including the mission analysis and the system design. The combination of a CDO approach with a model based on design grammars is shown for the concept study of a near-Earth asteroid mission. The mission objective is to investigate two asteroids of different kinds. The CDO reveals that a mission concept using two identical spacecrafts flying to one target each is better than a mission concept with one spacecraft flying to two asteroids consecutively.

In this report we present a graphical tool, Behavioral Analysis of Spacecraft Systems (BASS), that can be used by spacecraft designers to perform system-level behavioral analysis of small satellites. The domain-specific spacecraft meta-model is created in the visual modeling tool Generic Modeling Environment (GME) such that spacecraft designs created using the meta-model appear familiar to the spacecraft designers. Users can model scenarios that are to be verified for the design in BASS. The graphical models are assigned formal semantics facilitating the creation of formally verifiable spacecraft models. The C++ application that translates the modeling objects to equivalent mathematical representation of interest is called BASS Interpreter and is bound to the meta-model. BASS Interpreter that generates Communicating Sequential Processes (CSP) semantics for the visual spacecraft models is supported in the current work. The model-checker for CSP called Failures Divergences and Refinement (FDR) is run to explore the state-space of the spacecraft process model to comment on the design. We demonstrate the feasibilty and advantage of incorporating BASS into initial design phases of small satellite development by successfully verifying the design of Tomographic Remote Observer of Ionospheric Disturbances (TOROID).

Spacecraft design is inherently difficult due to the nonlinearity of the systems involved as well as the expense of testing hardware in a realistic environment. The number and cost of flight tests can be reduced by performing extensive simulation and analysis work to understand vehicle operating limits and identify circumstances that lead to mission failure. A Monte Carlo simulation approach that varies a wide range of physical parameters is typically used to generate thousands of test cases. Currently, the data analysis process for a fully integrated spacecraft is mostly performed manually on a case-by-case basis, often requiring several analysts to write additional scripts in order to sort through the large data sets. There is no single method that can be used to identify these complex variable interactions in a reliable and timely manner as well as be applied to a wide range of flight dynamics problems. This dissertation investigates the feasibility of a unified, general approach to the process of analyzing flight dynamics Monte Carlo data. The main contribution of this work is the development of a systematic approach to finding and ranking the most influential variables and combinations of variables for a given system failure. Specifically, a practical and interactive analysis tool that uses tractable pattern recognition methods to automate the analysis process has been developed. The analysis tool has two main parts: the analysis of individual influential variables and the analysis of influential combinations of variables. This dissertation describes in detail the two main algorithms used: kernel density estimation and nearest neighbors. Both are non-parametric density estimation methods that are used to analyze hundreds of variables and combinations thereof to provide an analyst with insightful information about the potential cause for a specific system failure. Examples of dynamical systems analysis tasks using the tool are provided.

<p dir="ltr">Design under uncertainty (DUU) for spacecraft is crucial in ensuring mission success, especially given the criticality of their failure. To obtain a more realistic understanding of space systems, it is beneficial to holistically couple the modeling of the spacecraft and its trajectory as a multidisciplinary analysis (MDA). In this work, a MDA model is developed for an Earth-Mars mission by employing the general mission analysis tool (GMAT) to model the mission trajectory and rocket propulsion analysis (RPA) to design the engines. By utilizing this direct MDA model, the deterministic optimization (DO) of the system is performed first and yields a design that completed the mission in 307 days while requiring 475 kg of fuel. The direct MDA model is also integrated into a Monte Carlo simulation (MCS) to investigate the uncertainty quantification (UQ) of the spacecraft and trajectory system. When considering the combined uncertainty in the launch date for a 20-day window and the specific impulses, the time of flight ranges from 275 to 330 days and the total fuel consumption ranges from 475 to 950 kg. The spacecraft velocity exhibits deviations ranging from 2 to 4 km/s at any given instance in the Earth inertial frame. The amount of fuel consumed during the TCM ranges from 1 to 250 kg, while during the MOI, the amount of fuel consumed ranges from 350 to 810 kg. The usage of the direct MDA model for optimization and uncertainty quantification of the system can be computationally prohibitive for DUU. To address this challenge, the effectiveness of utilizing surrogate-based approaches for performing UQ is demonstrated, resulting in significantly lower computational costs. Gaussian processes (GP) models trained on data from the MDA model were implemented into the UQ framework and their results were compared to those of the direct MDA method. When considering the combined uncertainty from both sources, the surrogate-based method had a mean error of 1.67% and required only 29% of the computational time. When compared to the direct MDA, the time of flight range matched well. While the TCM and MOI fuel consumption ranges were smaller by 5 kg. These GP models were integrated into the DUU framework to perform reliability-based design optimization (RBDO) feasibly for the spacecraft and trajectory system. For the combined uncertainty, the DO design yielded a poor reliability of 54%, underscoring the necessity for performing RBDO. The DUU framework obtained a design with a significantly improved reliability of 99%, which required an additional 39.19 kg of fuel and also resulted in a reduced time of flight by 0.55 days.</p>

Weight growth has been a significant factor in nearly every space and launch vehicle development program. In order to account for weight growth, program managers allocate a design margin. However, methods of estimating design margin are not well suited for the task of assigning a design margin for a novel concept. In order to address this problem, a hybrid method of estimating margin is developed. This hybrid method utilizes range estimating, a well-developed method for conducting a bottom-up weight analysis, and a new forecasting technique known as executable morphological analysis. Executable morphological analysis extends morphological analysis in order to extract quantitative information from the morphological field. Specifically, the morphological field is extended by adding attributes (probability and mass impact) to each condition. This extended morphological field is populated with alternate baseline options with corresponding probabilities of occurrence and impact. The overall impact of alternate baseline options can then be estimated by running a Monte Carlo analysis over the extended morphological field. This methodology was applied to two sample problems. First, the historical design changes of the Space Shuttle Orbiter were evaluated utilizing original mass estimates. Additionally, the FAST reference flight system F served as the basis for a complete sample problem; both range estimating and executable morphological analysis were performed utilizing the work breakdown structure created during the conceptual design of this vehicle.