Search

© 2020 The MITRE Corporation. All Rights Reserved. The reuse of proven designs is a common practice in complex engineering systems. The contextual differences between missions, however, mean that design reuse is rarely a “copy and paste” effort – some amount of rework and adaptation is required. In the space industry, many projects adopt unstructured, non-systematic processes for assessing potential reuse scenarios. There is a need for a standardized and repeatable methodology for determining the technical and programmatic impacts of these scenarios before they are pursued. We claim that MBSE is an ideal environment within which to base such a methodology. We demonstrate development of and application to a sample problem of an MBSE Design Reuse Methodology. First, we build out the methodology logically and agnostic to MBSE implementation methods; then, we explore and select options for conducting the logical process within the MBSE environment. A Reuse Profile guides application of the methodology to the Mission of Interest – these specifications leverage the semantic richness of SysML to both describe the Mission of Interest and the candidate reuse missions/elements, as well as to coordinate the analytical efforts assessing technical feasibility and programmatic impacts. A sample problem – exploring design reuse for the robotic arm element of a robotic, multi-purpose lunar rover – is selected to demonstrate the capabilities of the tool while remaining relevant to the exploration objectives of the Artemis program. Results suggest that leveraging designs of past rover missions may yield a 15% improvement in systems engineering effort (measured in Person-Months). This work forms part of a larger effort towards building MBSE methods and theory to provide decision support leading to more strategic and sustainable reuse of successful designs.

With the emerging democratization of space, Earth Observation (EO) imagery is becoming increasingly important to a variety of industries. However, it remains difficult and expensive to build constellations that achieve continuous and high-quality global coverage. Reconfiguring a satellite constellation into different orbital planes to change its observational performance is traditionally a fuel intensive procedure. The concept of a reconfigurable constellation (ReCon) accounts for J2 perturbation effects when making fuel efficient maneuvers to shift a satellite’s ground track. ReCon reduces the cost of high revisit frequency, high-quality resolution, EO constellations compared to nonreconfigurable constellations by reducing the number of satellites required to achieve repeated observations of a given ground event on demand. This paper first explores the sensitivities of ReCon’s performance against uncertainties in reconfiguration demand, design costs, and imagery value. The sensitivity analysis reveals that in cases of extremely low demand, ReCon fails to provide a cost-effective solution in terms of events responded to per dollar spent. In cases of high demand ReCon fails to meet demand altogether. A Monte Carlo analysis over a range of demand scenarios shows using a staged deployment for ReCon offers a flexible, cost-effective solution to the uncertainties in the demand of EO imagery. Deferring launch costs to the future, through a staged deployment, not only provides flexibility in constellation design, but also allows the designer to capitalize on the continuation of lowering launch costs and increasing launch opportunities. Staging the deployment of constellations also allows for the satellites’ technology to evolve over time, facilitating the capture of higher value imagery and further enhancing the capabilities of ReCon. Implementing the option to deploy additional satellites in stages makes ReCon significantly better equipped to respond to the uncertainty in the demand of space assets.

© 2020 The MITRE Corporation. All Rights Reserved. Traditional Earth-observing satellite constellations must preselect orbits to match established mission objectives, inherently limiting the capability of the system to respond to dynamic events. A more flexible approach to constellation design which maneuvers satellites to new orbits throughout the mission lifetime can enable the tracking of mobile targets; such a concept will be explored and evaluated in the following paper. Currently, an existing approach for creating a reconfigurable constellation of satellites (ReCon) demonstrates some increased functionality. However, the repeating ground track (RGT) orbits used are only well-suited for static ground targets (i.e. particular latitude and longitude points) as they are defined by their repetitious passes over the particular points on the globe; inherently these orbits are not ideal for tracking mobile targets such as ships, oil spills, hurricanes, and other weather events. By addressing this gap and designing the constellation to follow these mobile targets, data return can be of higher quality and quantity leading toward improved predictions of future movements. Traditional non-maneuverable satellites may of course capture mobile feature data; however, the quality and quantity of data returned is driven by the satellites’ orbits which were not designed for the particular mobile target. A proposed concept of operations that models possible sequential satellite maneuvers as a graph has shown promise for tracking mobile targets in the case of a single satellite. In the following paper, this concept is further explored by the addition of an optimizer to this method, applied to both a single satellite and a constellation of maneuverable satellites, like ReCon. Results for a tropical storm case study indicate that with relatively low amounts of delta-V per satellite, many possible maneuver combinations are found which result in improved accesses with greater total access time and closer passes. Trade-offs are shown between total target access time, mean distance to targets upon access, and total delta-V used. The performance of increased constellation size is also explored. By having satellites dynamically respond to mobile targets such as hurricanes, satellite imagery and remote sensing can be of better quality and even contribute to better predictions for developing storms.

Space-based radar sensors have transformed Earth observation since their first use by Seasat in 1978. Radar instruments are less affected by daylight or weather conditions than optical counterparts, suitable for continually monitoring the global biosphere. The current trends in synthetic aperture radar (SAR) platform design are distinct from traditional approaches in that miniaturized satellites carrying SAR are launched in multiples to form a SAR constellation. A systems engineering perspective is presented in this paper to track the transitioning of space-based SAR platforms from large satellites to small satellites. Technological advances therein are analyzed in terms of subsystem components, standalone satellites, and satellite constellations. The availability of commercial satellite constellations, ground stations, and launch services together enable real-time SAR observations with unprecedented details, which will help reveal the global biomass and their changes owing to anthropogenic drivers. The possible roles of small satellites in global biospheric monitoring and the subsequent research areas are also discussed.

The COVID‐19 pandemic has caught many nations by surprise and has already caused millions of infections and hundreds of thousands of deaths worldwide. It has also exposed a deep crisis in modeling and exposed a lack of systems thinking by focusing mainly on only the short term and thinking of this event as only a health crisis. In this paper, authors from several of the key countries involved in COVID‐19 propose a holistic systems model that views the problem from a perspective of human society including the natural environment, human population, health system, and economic system. We model the crisis theoretically as a feedback control problem with delay, and partial controllability and observability. Using a quantitative model of the human population allows us to test different assumptions such as detection threshold, delay to take action, fraction of the population infected, effectiveness and length of confinement strategies, and impact of earlier lifting of social distancing restrictions. Each conceptual scenario is subject to 1000+ Monte‐Carlo simulations and yields both expected and surprising results. For example, we demonstrate through computational experiments that maintaining strict confinement policies for longer than 60 days may indeed be able to suppress lethality below 1% and yield the best health outcomes, but cause economic damages due to lost work that could turn out to be counterproductive in the long term. We conclude by proposing a hierarchical Computerized, Command, Control, and Communications (C4) information system and enterprise architecture for COVID‐19 with real‐time measurements and control actions taken at each level.

© 2020 IEEE. Space systems development choices made decades ago, such as decisions for the Space Shuttle and the International Space Station (ISS), still influence and constrain human spaceflight technology development in the present day. Similarly, upcoming decisions in NASA's Artemis program and other Moon / Mars programs will likely influence the development of human spaceflight capabilities for subsequent decades. Given this, we argue that space systems architects require a new approach suitable for tradespace exploration across decades, and we have developed a new metric and tools to illustrate this approach. Typical tradespace exploration approaches trade off metrics for cost and benefit. The main families of metrics are monetary, utility and physical. Unfortunately, dollar, mass or utility metrics are fraught with weak assumptions when applied to decades-long timelines and to space settlements that will benefit from reusable rockets and in-situ resource utilization. Past work by the authors had proposed a new physics-based cost metric suitable for multi-decade architectures termed Lifetime Embodied Energy. Here, we propose a new benefit metric which may be of interest to diverse stakeholders and suitable for the comparison of alternative long-lived architectures: the Sustainable Long-term Growth Rate (SLGR). The SLGR has two components, a sustainability test to be defined by the architect and an average growth rate for key internal and external Figures of Merit (FOM). We show the application of the SLGR metric to a (future) long-term Mars base campaign using only Apollo-derived notional elements without ISRU, and to a case of a human settlement on Mars using future in-situ resource utilization technologies. The key FOM in both scenarios are habitable volume, stock of life support equipment and stock of consumables, as well as their derivative, which is carrying capacity of humans. We use a System Dynamics model to simulate the long-term growth, decline and stability behaviors of these FOM variables under each design option. For this model, we defined the SLGR's sustainability test as avoidance of collapse, meaning that the key FOM's per capita must remain above predefined dynamic thresholds at all times within the multi-decade model horizon. Architectures which collapse are deemed potentially fragile and discarded. For the remaining, potentially robust architectures, the growth rate of each of the key FOM across the multi-decade model horizon is its SLGR. We conclude with an assessment of SLGR compared to other metrics, a discussion of potential pros and cons, and a framing of the SLGR methodology in terms of pathfinding as opposed to traditional tradespace exploration.

Simple logistics strategies such as “carry-along” and Earth-based “resupply” were sufficient for past human space programs. Next-generation space logistics paradigms are expected to be more complex, involving multiple exploration destinations and in situ resource utilization. Optional in situ resource utilization brings additional complexity to the interplanetary supply chain network design problem. This paper presents an interdependent network flow modeling method for determining optimal logistics strategies for space exploration and its application to the human exploration of Mars. It is found that a strategy using lunar resources in the cislunar network may improve overall launch mass to low Earth orbit for recurring missions to Mars compared to NASA’s Mars Design Reference Architecture 5.0, even when including the mass of the in situ resource utilization infrastructures that need to be predeployed. Other findings suggest that chemical propulsion using liquid oxygen/liquid hydrogen, lunar in situ resource utilization water production, and the use of aerocapture significantly contribute to reducing launch mass from Earth. A sensitivity analysis of in situ resource utilization reveals that, under the given assumptions, local lunar resources become attractive at productivity levels above 1.8 kg / year / kg in the context of future human exploration of Mars., Jet Propulsion Laboratory (U.S.). Strategic University Research Partnerships Program

Distributed Space Missions (DSMs) are gaining momentum in their application to earth science missions owing to their unique ability to increase observation sampling in angular, spatial, spectral and temporal dimensions simultaneously. DSM architectures have a large number of design variables and since they are expected to increase mission flexibility, scalability, evolvability and robustness, their design is a complex problem with many variables and objectives affecting performance. There are few open-access tools available to explore the tradespace of variables which allow performance assessment and are easy to plug into science goals, and therefore select the most optimal design. This paper presents a software framework developed on the MATLAB engine interfacing with STK, for DSM orbit design and selection. The associated tool is capable of generating thousands of homogeneous constellation or formation flight architectures based on pre-defined design variable ranges and sizing those architectures in terms of pre-defined performance metrics. The metrics can be input into observing system simulation experiments, as available from the science teams, allowing dynamic coupling of science and engineering designs. Design variables include constellation type, formation flight type, instrument view, altitude and inclination of chief orbits, differential orbital elements, leader satellites, latitudes or regions of interest, planes and satellite numbers. Intermediate performance metrics include angular coverage, number of accesses, revisit coverage, access deterioration over time at every point of the Earth's grid. The orbit design process can be streamlined and variables more bounded along the way, owing to the availability of low fidelity and low complexity models such as corrected HCW equations up to high precision STK models with J2 and drag. The tool can thus help any scientist or program manager select pre-Phase A, Pareto optimal DSM designs for a variety of science goals without having to delve into the details of the engineering design process. This paper uses cases measurements for multi-angular earth observation to demonstrate the applicability of the tool.

A two-sided market involves two different user groups whose interactions are enabled over a platform that provides a distinct set of values to either side. In such market systems, one side’s participation depends on the value created by presence of the other side over the platform. Two-sided market platforms must acquire enough users on both sides in appropriate proportions to generate value to either side of the user market. In this paper, we present a simplified, generic mathematical model for two-sided markets with an intervening platform that enables interaction between the two different sets of users with distinct value propositions. The proposed model captures both the same side as well as cross-side effects (i.e., network externalities) and can capture any behavioral asymmetry between the different sides of the two-sided market system. The cross-side effects are captured using the notion of affinity curves while same side effects are captured using four rate parameters. We demonstrate the methodology on canonical affinity curves and comment on the attainment of stability at the equilibrium points of two-sided market systems. Subsequently a stochastic choice-based model of consumers and developers is described to simulate a two-sided market from grounds-up and the observed affinity curves are documented. Finally we discuss how the two-sided market model links with and impacts the engineering characteristics of the platform.Copyright © 2015 by ASME

The steady increase in oil prices and awareness regarding environmental risks due to carbon dioxide emissions are promoting the current interest in electric vehicles. However, the current relatively low driving range (autonomy) of these vehicles, especially compared with the autonomy of existing internal combustion vehicles, remains an obstacle to their development. In order to reassure a driver of an electric vehicle and allow him to reach his destinations beyond the battery capacity, we describe a system which generates an energy plan for the driver. We present in this paper the electric vehicle ecosystem and we focus on the contribution of using the generalized multi-commodity network flow (GMCNF) model as a vehicle routing model that considers energy consumption and charging time in order to ensure the usage of an electric vehicle beyond its embedded autonomy by selecting the best routes to reach the destination with minimal time and/or cost. We also present some perspectives related to the utilization of autonomous electric vehicles and wireless charging systems. We conclude with some open research questions.

The future of space exploration will not be limited to sortie-style missions to single destinations. Even in present exploration taking place at the International Space Station in low-Earth orbit, logistics is complicated by flights arriving from five launch sites on Earth. The future challenges of space logistics given complex campaigns of interconnected missions in deep space will require innovative tools to aid planning and conceptual design. This paper presents a modeling framework to evaluate the propulsive and logistics feasibility of space exploration from the macro-logistics perspective, which covers the delivery of elements and resources to support demands generated during exploration. The modeling framework is implemented in a versatile and unifying software tool, SpaceNet, for general space exploration scenario analysis. Four space exploration scenarios are presented as application cases to highlight the applicability of the framework across vastly different scenarios. The first case investigates the resupply of the International Space Station between 2010 and 2015 using 77 missions combining NASA, European Space Agency, Japanese Space Agency, Russian Space Agency, and commercial space transportation. The second case models a lunar outpost build-up consisting of 17 flights to achieve continuous human presence over eight years. The third case models and evaluates a conceptual sortie-style mission to a near-Earth object, 1999 AO10. Finally, the fourth case models a flexible path type human exploration in the vicinity of Mars using a combination of human and tele-operated exploration. Taken together these cases demonstrate the challenges and logistical requirements of future human space exploration campaigns during the period from 2010-2050 and illustrate the ability of SpaceNet to model and simulate the feasibility of meeting these requirements., United States. Dept. of Defense, United States. Air Force Office of Scientific Research, American Society for Engineering Education. National Defense Science and Engineering Graduate Fellowship, 32 CFR 168a

This paper develops a qualitative/quantitative network approach, namely a “Stakeholder Value Network”, to understand the impacts of both direct and indirect relationships between stakeholders on the success of large engineering projects. Specifically, this paper explores the feasibility and benefit of applying the Dependency Structure Matrix (DSM) as the modelling platform for Stakeholder Value Networks. Further, an efficient algorithm is designed for computing indirect stakeholder influence and implemented in a case study for a multinational energy project. The results derived from this analysis are able to answer three fundamental questions for stakeholder management: What are the critical paths/themes for a project to engage other stakeholders? Who are the most important stakeholders for a project? How can the complexity of a large relationship network be reasonably managed?

In order to better understand the technological difficulties involved in designing and building a sparse aperture array, the challenge of building a white light Golay-3 telescope was undertaken. The MIT Adaptive Reconnaissance Golay-3 Optical Satellite (ARGOS) project exploits wide-angle Fizeau interferometer technology with an emphasis on modularity in the optics and spacecraft subsystems. Unique design procedures encompassing the nature of coherent wavefront sensing, control and combining as well as various system engineering aspects to achieve cost effectiveness, are developed. To demonstrate a complete spacecraft in a 1-g environment, the ARGOS system is mounted on a frictionless air-bearing, and has the ability to track fast orbiting satellites like the ISS or the planets. Wavefront sensing techniques are explored to mitigate initial misalignment and to feed back real-time aberrations into the optical control loop. This paper presents the results and the lessons learned from the conceive, design and implementation phases of ARGOS. A preliminary assess-ment shows that the beam combining problem is the most challenging aspect of sparse optical arrays. The need for optical control is paramount due to tight beam combining tolerances. The wavefront sensing/control requirements appear to be a major technology and cost driver.