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A renewed interest in lunar exploration with the focus on establishing a constant human presence on the Moon calls for developing new lunar human landing systems (HLS) which would deliver the crew from the prospective Lunar Gateway station to the surface of the Moon and back. Over the years, different human lunar lander architectures were proposed and multiple architecture studies were performed. However, those studies are relevant to the specific assumptions and lunar architectures proposed at the time of conducting the study. Since the current vision for lunar exploration includes new features, such as having the Lunar Gateway and switching to reusable systems, there is a need for a new HLS architecture study. Such studies are being performed by private companies; however, those are rarely publicly available. The goal of this paper is to address this gap and provide a publicly available architectural analysis within the current views on the future human lunar exploration. We assume the Lunar Gateway in an L2 near rectilinear halo orbit and a landing site at the lunar South Pole; the number of HLS crew of 4; the surface stay time of ~7 days, the payload mass delivered to the surface of 500 kg, and the payload mass returned from the surface of 250 kg. A set of parametric models including an HLS model and an HLS program cost model is developed for the analysis. 39 architectures with varying number of stages (1, 2, and 3 stages) and propellant combinations (LOX/LH2, LOX/CH4, and MMH/NTO) are explored. The Pareto analysis shows that there is a difference between typical performance trends for expendable and reusable architectures. For expendable architectures, the 2-stage option seems to be the most advantageous while, for reusable architectures, the 1- and 3-stage options are either comparable or win over the 2-stage option even for the number of system uses as low as 3. In terms of the propellant combinations, pure LOX/LH2 or combined LOX/LH2/LOX/CH4 architectures dominate the tradespace. Assuming that the inter-stage propellant compatibility is a preferred option for systems refueling from the Gateway, 1-stage and 3-stage all LOX/LH2 architectures are identified as the likeliest candidates to have lowest HLS-related production and launch costs. Further cost analysis of those two architectures shows that the 1-stage HLS wins over the 3-stage system in terms of the overall HLS program cost if a long-term exploration program (on the order of tens of missions) is assumed.

Conceptual State Analysis (CSA) is the process of defining, exploring, and reasoning about state attributes and state spaces in complex system architectures. Model-Based State Analysis enhances CSA with formal modeling and analysis methods. We propose a process for generating the state vector of a given system architecture, based on a graph representation of the architecture’s model in Object-Process Methodology. A robust graph data structure that represents the model is queried to produce the architecture’s state space. The process facilitates analysis, reasoning, and model revision based on improved understanding of state vectors and state spaces. State attribute refinements within the model allow for model validity, viability, and reusability as the architecture evolves, with multiple agents, attributes, and attribute state values. The CSA approach advocates and facilitates careful, dynamic, and interactive state space exploration in lieu of exhaustive upfront enumeration of state space permutations. The results can be fed into other analysis, simulation, or visualization tools. We demonstrate CSA on driver assistance technology.

Safety issues are one of the core attributes of the projects of the New Space economy such as SpaceShipTwo and New Shepard. While these projects are on their way to shifting the paradigms in space tourism and transportation, the question of tourist safety remains in the cornerstone for a successful operations. One of the important questions we encounter when we start the development of complex systems such as the suborbital transportation and space tourism systems is: what are the concepts available and how can these concepts be represented using strictly defined ontology and model semantics? And finally – how this model-based information would support the system architect in keeping track of safety aspects? In this work we present a model-based concept framework that aims to address this issue. First a concept framework methodology is presented, after which we demonstrate its applicability to suborbital human spaceflight missions–SpaceShipTwo and New Shepard. The analytical conceptual difference between these concepts is demonstrated. The proposed framework includes: the information about the stakeholders and their needs; the solution-neutral environment (the problem statement) in which we formulate the functional intent; the solution-specific environment (solution statement) in which we see the possible solutions; the integrated concept revealed by means of the decomposition of such solution into internal elements and functions; and the concept of operations. Each one of these entries of the concept framework has a counterpart represented in conceptual modeling languages, such as Object-Process Methodology (OPM) or the System Modeling Language (SysML). Such a model-based concept framework encodes the core information required to define a suborbital tourism concept and represent it in a digital environment. We believe this will become a powerful tool to support the makers of architectural decisions that lead to concept and eventually to architecture, and that provides a safe operations for suborbital tourist services.

In the early stages of maturity of a system built for a specific function, it is common for the solutions to lie in a broad architectural space, in which numerous concepts are being developed, built, and tested. As the product matures, certain concepts become more dominant. This pattern can currently be observed in the suborbital tourism industry, in which the obvious question is what system architecture will provide the best combination of cost and safety and in the long run become the dominant architecture. This paper addresses this question by defining a broad architectural space of thousands of possibilities and exploring it comprehensively. We identified 33 feasible architectures, 26 of which had not been proposed earlier. A genetic algorithm optimizes each architecture with respect to the launch mass (a proxy for cost) and operational safety. The launch mass has been calculated in a design analysis framework consisting of four modules: weight/size, propulsion, aerodynamics, and trajectory. A validation of this framework shows relative differences below 7%. A quantitative safety analysis is developed and validated with a survey of 11 leading experts. We identify six dominant architectures. These form a set from which optimal variants are likely to come.

This article provides an evaluation of the commercial viability of the suborbital tourism market while considering the large uncertainties in the current demand predictions. Since it is un...

The idea of providing Internet access from space has made a strong comeback in recent years. After a relatively quiet period following the setbacks suffered by the projects proposed in the ’90s, a new wave of proposals for large constellations of low Earth orbit (LEO) satellites to provide global broadband access emerged between 2014 and 2016. Compared to their predecessors, the main differences of these systems are: increased performance that results from the use of digital communication payloads, advanced modulation schemes, multi-beam antennas, and more sophisticated frequency reuse schemes, as well as the cost reductions from advanced manufacturing processes (such as assembly line, highly automated, and continuous testing) and reduced launch costs. This paper compares three such large LEO satellite constellations, namely SpaceX's 4425 satellites Ku-Ka-band system, OneWeb's 720 satellites Ku-Ka-band system, and Telesat's 117 satellites Ka-band system. First, we present the system architecture of each of the constellations (as described in their respective FCC filings as of September 2018), highlighting the similarities and differences amongst the three systems. Following that, we develop a statistical method to estimate the total system throughput (sellable capacity), considering both the orbital dynamics of the space-segment and the variability in performance induced by atmospheric conditions both for the user and feeder links. Given that the location and number of ground stations play a major role in determining the total system throughput, and since the characteristics of the ground segment are not described in the FCC applications, we then run an optimization procedure to minimize the total number of stations required to support the system throughput. Finally, we conclude by identifying some of the major technical challenges that the three systems will have to overcome before becoming operational.

The new era of satellite communications is approaching as the next generation of constellations is set to launch during the next few years. Digital payloads, steerable beams, and larger constellations will allow operators to serve better and more demand than ever before. However, better service comes at the cost of operational complexity. Manual distribution of satellite resources becomes unfeasible and automatic tools have to be developed to deal with the additional variables.From all the different sub-problems that constitute the resource allocation problem in satellite communications, this paper focuses on analyzing the beam placement problem in high-dimensional scenarios using graph theory, and, driven by the shown NP-hardness, solving it using a genetic algorithm (GA). To optimize the problem from a system perspective, we develop novel problem-specific metrics (number of beams and number of frequency slots per beam) that intend to reflect a trade-off in the general resource allocation problem.The results show a fast convergence towards the Pareto-Front that allows us to solve a new-era constellation with thousands of users in around 50 generations. The full system-level analysis proves that the problem-specific metrics developed in this work represent a system-level trade-off independently from allocation algorithms chosen for the other satellite resources. On top of that, when we compare our approach with a previously published heuristic, we show that we reduce both power usage, by between 20% and 40%, and unmet demand, by between 50% and 100%.

The study and benchmarking of Deep Reinforcement Learning (DRL) models has become a trend in many industries, including aerospace engineering and communications. Recent studies in these fields propose these kinds of models to address certain complex real-time decision-making problems in which classic approaches do not meet time requirements or fail to obtain optimal solutions. While the good performance of DRL models has been proved for specific use cases or scenarios, most studies do not discuss the compromises and generalizability of such models during real operations. In this paper we explore the tradeoffs of different elements of DRL models and how they might impact the final performance. To that end, we choose the Frequency Plan Design (FPD) problem in the context of multibeam satellite constellations as our use case and propose a DRL model to address it. We identify six different core elements that have a major effect in its performance: the policy, the policy optimizer, the state, action, and reward representations, and the training environment. We analyze different alternatives for each of these elements and characterize their effect. We also use multiple environments to account for different scenarios in which we vary the dimensionality or make the environment non-stationary. Our findings show that DRL is a potential method to address the FPD problem in real operations, especially because of its speed in decision-making. However, no single DRL model is able to outperform the rest in all scenarios, and the best approach for each of the six core elements depends on the features of the operation environment. While we agree on the potential of DRL to solve future complex problems in the aerospace industry, we also reflect on the importance of designing appropriate models and training procedures, understanding the applicability of such models, and reporting the main performance tradeoffs.

We introduce the Concept-Model-Graph-View-Concept (CMGVC) transformation cycle. The CMGVC cycle facilitates coherent architecture analysis, reasoning, insight, and decision-making based on conceptual models that are transformed into a common, robust graph data structure (GDS). The GDS is then transformed into multiple views on the model, which inform stakeholders in various ways. This GDS-based approach decouples the view from the model and constitutes a powerful enhancement of model-based systems engineering (MBSE). CMGVC applies the rigorous foundations of Category Theory, a mathematical framework of representations and transformations. The CMGVC architecture is superior to direct transformations and language-coupled common representations. We demonstrate the CMGVC cycle to transform a conceptual system architecture model built with the Object Process Modeling Language (OPM) into dual graphs and a decision support matrix (DSM) that stimulates system architecture insight.

© 2020 John Wiley & Sons, Ltd. LEO satellites are growing rapidly in number, but also in configurability of the payload, which presents new challenges of how to allocate the satellite's resources. We present an algorithm to solve two subproblems of the resource allocation problem for multi-beam satellites in large LEO constellations. Our approach produces a static beam placement and a static frequency plan, taking into account the user demands and interference conditions, respectively. We provide an easy to implement and fast approximation for the beam placement problem, which is converted into an Edge Clique Cover problem using graph theory. The frequency assignment problem is translated into a constraint satisfaction problem, which is later solved with a first-fit heuristic. We apply our algorithm to a scenario based on SpaceX's Starlink constellation, and when compared to two different baselines, we show that our method requires 40% fewer beams while being able to provide service to 2% more users.

Although joint programs are typically established to save the government money, recent studies suggest that instead of reducing program cost, jointness may actually induce cost growth. Motivated by three case studies that explored the cost of acquiring systems jointly, this paper presents a model that explains why joint programs often experience large cost growth and how jointness itself may induce it. Specifically, our proposed Agency Action Model suggests that on joint programs, the collaborating agencies' institutional interest in retaining or regaining their autonomy induces cost growth. After explaining the basic components of the model, we demonstrate its ability to explain the cost growth observed in our case studies. Finally, we use the model and our case study data to generate recommendations for managing joint programs in the future.