<p>The long-term sustainable human exploration of Mars is approached via the design and analysis of an orbital logistics architecture as part of a robust logistics infrastructure. In this investigation, we analyze the advantages of an orbital logistics node around Mars (which we call Mars Spacedock), which plays a crucial role to support the transport of vehicles and resupply of cargo to a base on the surface. The Mars Spacedock serves as one of the many logistics nodes at different locations between Earth and Mars that support the continuous movement of crew and cargo to and from Mars for the next several decades. The need of multiple nodes at strategic locations is supported by lessons learned from terrestrial analogs of complex missions such as military, Antarctic exploration, and the International Space Station. The Mars Spacedock is envisaged to have at least aggregation, refueling, resupply and refurbishing capabilities. The stationing orbit of the Spacedock is one of the primary design drivers in determining the associated propellant requirement and surface accessibility. The stationing orbit is selected from a range of Mars orbits such that it best accommodates (delta V  cost being a major determinant) arrival from a variety of interplanetary approaches, capture into Mars orbit, deorbit and entry into Mars atmosphere, surface accessibility, launch from surface to stationing orbit, and departure to Earth. A variety of mission types are evaluated over a 15-year cycle as follows: long-stay crewed missions, short-stay crewed missions, cargo transfer missions on low-thrust and ballistic trajectories. The perturbation of orbits due to aspherical gravity of Mars and timeline of missions are found to be crucial factors in selection of orbit. The Low Mars Orbits are found to be comparable to the Highly Elliptical Mars Orbits in total delta V requirement. The optimal stationing orbit is selected by minimizing a combination of mission propellant mass and transfer time for a given set of mission parameters. The sensitivity of the optimal solution to various mission parameters (landing site latitude, propellant, refueling capability in Mars orbit, deorbit method, mission type, and frequency of different mission types) is assessed. The analysis on orbit considerations aids mission designers in selecting suitable stationing orbit for a set of mission parameters and assessing the long term impacts of mission design choices on the logistics requirements. Finally, the viability of the Spacedock is analyzed in terms of landing site accessibility, station-keeping requirement, and initial mass in cislunar staging orbit. Here also Low Mars Orbits have accessibility over a wider range of landing sites compared to 1 sol orbit. The station-keeping requirement is found to be insignificant over the scale of the missions. The Spacedock refuel capability leads to lower mass in cislunar staging orbit, about 60 Mg lower per crewed MTV mission, and compensates for the higher capture and departure delta Vs.</p> <p>   </p> <p>   A logistics architecture stationed in a strategic orbit around Mars would enable long term sustainable operations for human exploration, reduce the logistics footprint of the exploration campaigns, and aid in transitioning to an eventual permanent presence on Mars. </p>

The past decades have seen the state of the art in aerospace system design progress from a scope of simple optimization to one including robustness, with the objective of permitting a single system to perform well even in off-nominal future environments. Integrating flexibility, or the capability to easily modify a system after it has been fielded in response to changing environments, into system design represents a further step forward. One challenge in accomplishing this rests in that the decision-maker must consider not only the present system design decision, but also sequential future design and operation decisions. Despite extensive interest in the topic, the state of the art in designing flexibility into aerospace systems, and particularly space systems, tends to be limited to analyses that are qualitative, deterministic, single-objective, and/or limited to consider a single future time period. To address these gaps, this thesis develops a stochastic, multi-objective, and multi-period framework for integrating flexibility into space system design decisions. Central to the framework are five steps. First, system configuration options are identified and costs of switching from one configuration to another are compiled into a cost transition matrix. Second, probabilities that demand on the system will transition from one mission to another are compiled into a mission demand Markov chain. Third, one performance matrix for each design objective is populated to describe how well the identified system configurations perform in each of the identified mission demand environments. The fourth step employs multi-period decision analysis techniques, including Markov decision processes (MDPs) from the field of operations research, to find efficient paths and policies a decision-maker may follow. The final step examines the implications of these paths and policies for the primary goal of informing initial system selection. Overall, this thesis unifies state-centric concepts of flexibility from economics and engineering literature with sequential decision-making techniques from operations research. The end objective of this thesis' framework and its supporting analytic and computational tools is to enable selection of the next-generation space systems today, tailored to decision-maker budget and performance preferences, that will be best able to adapt and perform in a future of changing environments and requirements. Following extensive theoretical development, the framework and its steps are applied to space system planning problems of (1) DARPA-motivated multiple- or distributed-payload satellite selection and (2) NASA human space exploration architecture selection.

<div>Adsorbent technology is widely used in many industrial applications including waste heat recovery, water purification, and atmospheric revitalization in confined habitations. Astronauts depend on adsorbent-based systems to remove metabolic carbon dioxide (CO₂) from the cabin atmosphere; as NASA prepares for the journey to Mars, engineers are redesigning the adsorbent-based system for reduced weight and optimal efficiency. These efforts hinge upon the development of accurate, predictive models, as simulations are increasingly relied upon to save cost and time over the traditional design-build-test approach. Engineers rely on simplified models to reduce computational cost and enable parametric optimizations. Amongst these simplified models is the axially dispersed plug-flow model for predicting the adsorbate concentration during flow through an adsorbent bed. This model is ubiquitously used in designing fixed-bed adsorption systems. The current work aims to improve the accuracy of the axially dispersed plug-flow model because of its wide-spread use. This dissertation identifies the critical model inputs that drive the overall uncertainty in important output quantities then systematically improves the measurement and prediction of these input parameters. Limitations of the axially dispersed plug-flow model are also discussed, and recommendations made for identifying failure of the plug-flow assumption.</div><div><br></div><div>An uncertainty and sensitivity analysis of an axially disperse plug-flow model is first presented. Upper and lower uncertainty bounds for each of the model inputs are found by comparing empirical correlations against experimental data from the literature. Model uncertainty is then investigated by independently varying each model input between its individual upper and lower uncertainty bounds then observing the relative change in predicted effluent concentration and temperature (<i>e.g.</i>, breakthrough time, bed capacity, and effluent temperature). This analysis showed that the LDF mass transfer coefficient is the largest source of uncertainty. Furthermore, the uncertainty analysis reveals that ignoring the effect of wall-channeling on apparent axial dispersion can cause significant error in the predicted breakthrough times of small-diameter beds.</div><div><br></div><div>In addition to LDF mass transfer coefficient and axial-dispersion, equilibrium isotherms are known to be strong lever arms and a potentially dominant source of model error. As such, detailed analysis of the equilibrium adsorption isotherms for zeolite 13X was conducted to improve the fidelity of CO₂ and H₂O on equilibrium isotherms compared to extant data. These two adsorbent/adsorbate pairs are of great interest as NASA plans to use zeolite 13X in the next generation atmospheric revitalization system. Equilibrium isotherms describe a sorbent’s maximum capacity at a given temperature and adsorbate (<i>e.g.</i>, CO₂ or H₂O) partial pressure. New isotherm data from NASA Ames Research Center and NASA Marshall Space Flight Center for CO₂ and H₂O adsorption on zeolite 13X are presented. These measurements were carefully collected to eliminate sources of bias in previous data from the literature, where incomplete activation resulted in a reduced capacity. Several models are fit to the new equilibrium isotherm data and recommendations of the best model fit are made. The best-fit isotherm models from this analysis are used in all subsequent modeling efforts discussed in this dissertation.</div><div><br></div><div>The last two chapters examine the limitations of the axially disperse plug-flow model for predicting breakthrough in confined geometries. When a bed of pellets is confined in a rigid container, packing heterogeneities near the wall lead to faster flow around the periphery of the bed (<i>i.e.</i>, wall channeling). Wall-channeling effects have long been considered negligible for beds which hold more than 20 pellets across; however, the present work shows that neglecting wall-channeling effects on dispersion can yield significant errors in model predictions. There is a fundamental gap in understanding the mechanisms which control wall-channeling driven dispersion. Furthermore, there is currently no way to predict wall channeling effects a priori or even to identify what systems will be impacted by it. This dissertation aims to fill this gap using both experimental measurements and simulations to identify mechanisms which cause the plug-flow assumption to fail.</div><div><br></div><div>First, experimental evidence of wall-channeling in beds, even at large bed-to-pellet diameter ratios (<i>d</i>_bed/<i>d</i>_p=48) is presented. These experiments are then used to validate a method for accurately extracting mass transfer coefficients from data affected by significant wall channeling. The relative magnitudes of wall-channeling effects are shown to be a function of the adsorption/adsorbate pair and geometric confinement (<i>i.e.</i>, bed size). Ultimately, the axially disperse plug-flow model fails to capture the physics of breakthrough when nonplug-flow conditions prevail in the bed.</div><div><br></div><div>The final chapter of this dissertation develops a two-dimensional (2-D) adsorption model to examine the interplay of wall-channeling and adsorption kinetics and the adsorbent equilibrium capacity on breakthrough in confined geometries. The 2-D model incorporates the effect of radial variations in porosity on the velocity profile and is shown to accurately capture the effect of wall-channeling on adsorption behavior. The 2-D model is validated against experimental data, and then used to investigate whether capacity or adsorption kinetics cause certain adsorbates to exhibit more significant radial variations in concentration compared than others. This work explains channeling effects can vary for different adsorbate and/or adsorbent pairs—even under otherwise identical conditions—and highlights the importance of considering adsorption kinetics in addition to the traditional <i>d</i>_bed/<i>d</i>_p criteria.</div><div><br></div><div>This dissertation investigates key gaps in our understanding of fixed-bed adsorption. It will deliver insight into how these missing pieces impact the accuracy of predictive models and provide a means for reconciling these errors. The culmination of this work will be an accurate, predictive model that assists in the simulation-based design of the next-generation atmospheric revitalization system for humans’ journey to Mars.</div>