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This paper presents an improved technique for predicting wet mass, dry mass, end-of-life power, and launch-configuration volume for Earth observation satellites based on inputs of mission type, pay...

In this paper, we demonstrate a self-supervised deep learning pipeline that can effectively learn to detect vehicles without using any pre-labeled training data. The pipeline uses a morphological vehicle detection algorithm to automatically generate training sets for a convolutional neural network (CNN). We tested this methodology on a mixed-use urban neighborhood in Riyadh, Saudi Arabia using 0.31-meter multispectral Worldview-3 satellite imagery with eight bands in the visible and near-infrared wavelengths. This method leverages the class imbalance inherent to many vehicle detection problems by generating a balanced training sample from a high-precision, low-recall morphological model to train a neural network to identify general vehicle characteristics. This approach is built on broadly applicable image processing methods and, with appropriate adjustments, might be adapted to high-resolution from various satellite or aerial sources.

With the development of greenhouse technologies using saltwater for evaporation and cooling, irrigation of arid regions may be possible with proper power sources. We present here space-based solar power (SBSP) as a way of expanding the irrigation network and demonstrating the relevant technologies. Once implemented, SBSP could be used for other terrestrial applications as well as space missions on the Moon or Mars.

The spacecraft early conceptual design effort often involves picking components from a catalog or database, combined with discretized decisions concerning operations, like the choice of ground station, type of orbit (LEO or MEO, syn-synchronous or polar), and, in the case of constellations, the number of planes and satellites per plane. One common strategy is to exhaustively enumerate all combinations, filter out unfeasible ones, and plot all resulting designs in the objective space where a Pareto frontier might be found between competing performance metrics. However, as the number of combinations grows exponentially in the number of options, this strategy quickly becomes limited. Based on the optimization field of mixed-integer nonlinear programming, a new method has shown promise in overcoming the challenges resulting from a large number of options. This paper enhances the previous method describing how to incorporate operational aspects of design: choice of ground station, the timing of communication downlinks, and modeling eclipse time windows. We apply the method to a simple Earth observation case study and evaluate the new optimization problems' computational performance as a function of time discretization level.

The Mars Oxygen ISRU Experiment (MOXIE) is an instrument traveling to Mars onboard NASA's Perseverance rover. It will demonstrate, for the first time ever, in-situ resource utilization (ISRU) on the surface of another celestial body. MOXIE will utilize the carbon dioxide atmosphere of Mars to create oxygen as a demonstration of a planned larger mission. The instrument itself will produce oxygen at roughly 0.5 percent of the scale that would be necessary to support a human mission to Mars. A scaled-up version of MOXIE would be sent to Mars twenty-six months ahead of the first human mission and would aim to produce approximately 3 kg/hr of oxygen while in operation. This production rate would fully fuel the oxidizer portion of a Mars Ascent Vehicle prior to the first crew landing on Mars, which would enable that crew to return to Earth. This is a key capability to reduce mission risk by providing a safe return option on Mars prior to the crew arriving. Additionally, the system could provide oxygen for life support systems and habitation pressure. The intent of this paper is to describe a model that has been created to optimize the design of this scaled ISRU plant. It takes lessons learned from the MOXIE project and combines them with parameters and constraints of a planned human mission to systematically identify optimal design solutions. The extensibility of MOXIE is formulated through a multiobjective optimization problem for early-stage conceptual design. The objective functions minimize the power and mass required to build this ISRU system by changing operating conditions and system architecture while satisfying a set of constraints. The subsystems modeled for this problem include carbon dioxide acquisition and compression (CAC) to compress the Mars atmosphere, solid oxide electrolysis (SOE) to produce oxygen from carbon dioxide, and liquefaction to prepare the oxygen for storage. Additionally, the power, electronics, and heat exchange systems are simulated to capture gas transfer and control mechanisms. The model is built in MATLAB and uses Simulink as a framework. Results from this multiobjective optimization study and an analysis on the scalability of the MOXIE instrument show that an ISRU system that produces 22,717 kg of oxygen over 14 months would have a mass of 7,512 kg and a power requirement of 19,526 W. These results provide NASA and other agencies with an optimized design of a scaled ISRU system and its potential to reduce cost and risk as they prepare for a human mission to Mars.

© 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.

On-orbit assembly missions typically involve humans-in-the-loop and use large custom-built robotic arms designed to service existing modules. A proposed concept of on-orbit robotic assembly of modularized CubeSat components within a spacecraft locker eliminates the need for humans-in-the loop. The spacecraft locker supports use cases such as rapidly placing failed nodes within a constellation of satellites and providing sensing and propulsion capabilities in Low Earth Orbit. Despite the recent proliferation of small satellites, there are few planned demonstrations of on-orbit assembly and few demonstrations of on-orbit servicing. Key gaps challenges of in-space assembly of small satellites are (1) the lack of standardization of electromechanical CubeSat components for compatibility with commercial robotic assembly hardware, and (2) testing and modifying commercial robotic assembly hardware. In this work, we focus on testing and modifying: we develop an optimization process for a robotic assembly model to integrate small satellites in space. Our process focus is on the optimization of the on-orbit assembly time of small satellites. We use Commercial-Off-The-Shelf (COTS) robot arms to snap together components in a spacecraft, while minimizing humans-in-the-loop. Assembly time is the selected performance metric as it is critical to the assertion that building small satellites on-orbit results in reduced budget and satellite development time on Earth. We minimize on-orbit small satellite assembly time by optimizing assembly time with the Genetic Algorithm, which use dexterous robotic arms to assemble components, without any negative effects on the attitude and control system. We implement a robot arm assembly model in Python, using Inverse Kinematics. We use a Genetic Algorithm-based optimization scheme, with time as the objective function, and three constraints: robot assembly volume, power consumption, and peak power. Design variables such as joint damping, motor force (torque), position gain and velocity gain are used to model grasping a component and moving the component to the satellite assembly area of the spacecraft. The robot arms are required to be within a tolerance defined based on the 300 mm x 300 mm x 500 mm assembly area. In simulation, we observe that using a given baseline servo motor (7 V) at high proportional gains results in optimal assembly time of approximately 10-20 seconds per component assembly, compared to roughly double this time per component for a 1U CubeSat weighing 2 kg. However, we expect this improvement to result in 25% higher power consumption. Using a high gain value with a lower voltage (5 V) motor results in oscillations and additional time required to dampen out to within the given tolerance, and results in increased assembly time. The benchmarked small satellite assembly time with a human-in-the-loop requires 50 weeks to 90 months of component assembly and integration time on Earth. We anticipate that on-orbit assembly capability optimized for a 1 U functional CubeSat with 30 W of total power, would reduce the assembly time by an order of magnitude. With robotic arm models, for a 1 U CubeSat assembly, we show up to 42% saving benefit in robotic assembly time.

Due to recently growing adoption, residential photovoltaic (PV) installations are becoming a key contribution to renewable energy production. In order to efficiently track the inherently decentralized deployment of these PV systems, this study leverages widely available high-resolution satellite imagery, along with the demonstrated ability of convolutional neural networks (CNNs) in image classification tasks. In particular, this effort presents development of a custom-trained CNN that operates on images of residential areas received as part of a larger image processing pipeline. This custom-trained CNN is shown to achieve comparable accuracy to the state-of-the-art achieved via transfer learning, but with reduced computational burden and required image resolution. Using imagery from two cities in California, this approach achieves PV classification with a precision of 91.9% and recall of 92.4%. This accuracy is sufficient to inform actionable insights from satellite imagery regarding the political, social, and economic factors affecting PV deployment.

Autonomous Underwater Vehicle (AUV) missions are limited in range and duration by the vehicle’s battery capacity, and sensor payloads are limited by the processing power onboard which is also restricted by the vehicle’s battery capacity. Furthermore, the power consumption of a vehicle’s acoustic system limits the possibility of substantial data transmission, requiring the AUV be retrieved to download most data. The Platform for Expanding AUV exploRation to Longer ranges (PEARL), described in this paper, aims to extend the range and endurance of AUVs while reducing data latency and operating costs. PEARL is an integrated autonomous floating servicing station that utilizes renewable energy to simultaneously provide AUV battery recharging and data uplink via new generation high-bandwidth low-Earth orbit satellite constellations. This paper details the design and testing of two potential AUV docking modules of the PEARL system. The modules are uniquely located near the ocean surface, an energetic environment that presents a particular set of challenges for AUV docking. The results will be used to inform the design of a prototype system to be tested in an ocean setting.

When developing a space system, many properties of the design space are initially unknown and are discovered during the development process. Therefore, the problem exhibits deep uncertainty. Deep uncertainty refers to the condition where the full range of outcomes of a decision is not knowable. A key strategy to mitigate deep uncertainty is to update decisions when new information is learned. In this paper, the spacecraft development problem is modeled as a dynamic, chance-constrained, stochastic optimization problem. The Model-based Adaptive Design under Uncertainty (MADU) framework is presented, in which conflict-directed search is combined with reuse of information to solve the development problem efficiently in the presence of deep uncertainty. The framework is built within a Model-based Systems Engineering (MBSE) paradigm in which a SysML model contains the design, the design space, and information learned during search. The development problem is composed of a series of optimizations, each different than the previous. Changes between optimizations can be the addition or removal of a design variable, expansion or contraction of the domain of a design variable, addition or removal of constraints, or changes to the objective function. These changes are processed to determine which search decisions can be preserved from the previous optimization. The framework is illustrated on a case study drawn from the thermal design of the REgolith X-ray Imaging Spectrometer (REXIS) instrument. This case study demonstrates the advantages of the MADU framework with the solution found 30% faster than an algorithm that doesn't reuse information. With this framework, designers can more efficiently explore the design space and perform updates to a design when new information is learned. Future work includes extending the framework to multiple objective functions and continuous design variables.

Future human deep space exploration will be faced with the challenges of long mission endurance, as well as demand for robust surface infrastructure, increased solar power generation, and reliable high data rate communications, just to name a few. In-space manufacturing (ISM) can potentially address these challenges, and thus revolutionize human spaceflight, by opening up design freedom previously limited by launch vehicle constraints. For example, ISM can drastically reduce spares logistics mass with on-demand manufactured spare parts, utilize in-situ resources for surface infrastructure, and manufacture high-gain antenna reflectors and solar array structures that are larger than existing deployables. This paper aims to understand how ongoing ISM technology development efforts can benefit human spaceflight by assessing the utility of different technology development paths. This analysis involves identifying the needs of a mission scenario, limitations on allowable manufacturing processes, the theoretical benefit from ISM for that case (including minimum capability thresholds and points of diminishing return), and the tracing of achieved benefit back to technology development parameters. For the case of on-demand spares, benefit is measured in terms of the spares logistics mass savings relative to the conventional carry-along strategy for a desired probability of sufficient spares over the mission duration. Increasing benefit is gained as more spares become manufacturable by ISM using manufacturing processes with increasing capability, such as larger build volumes, wider range of materials, and improved resolution. This same process is applicable for the other case studies of surface infrastructure and large external structures. This work can thus inform the way in which ISM technologies are pursued with the specific purpose of yielding the maximum benefit for future human space exploration.

This study aims to develop a network model driven platform that supports decision-makers to make well-informed decisions for the efficient water supplies, taking Saudi Arabia as a case study. The water/energy network analysis should be able to identify optimal locations for sustainable desalination infrastructure investments, accounting for the existing assets and the current investment plans. The geographical aspect of individual resource production and distribution can be quantitatively handled by a graph-theoretic approach. This study employs a new multicommodity network flow model called the INFINIT (interdependent network flows with induced internal transformation) model, which enables to address water-energy nexus issues and to optimize the flow of multiple resources as well as placement of new water/energy facilities at the individual facility level. The INFINIT model in this study formulates and solves mixed-integer linear programming (MILP) problems to minimize the designated multi-objective functions of the total cost and CO2 emission. As a result of optimization, the Pareto-optimal solutions with different network flow topology and the downselected potential locations for new facilities are obtained. To effectively visualize alternative design and policy scenarios, two ways of visualization of the results are developed: a MATLAB-based graphical user interface and tabletop 3D map projection.

Decision-makers often seek to design policies that support sustainable development. Prospective evaluations of how effectively such policies are likely to meet sustainability goals have nonetheless remained challenging. Evaluating policies against sustainability goals can be facilitated through the inclusive wealth framework, which characterizes development in terms of the value to society of its underlying capital assets, and defines development to be potentially sustainable if that value does not decline over time. The inclusive wealth approach has been developed at a theoretical level and applied to retrospective evaluations. Here, we apply inclusive wealth theory to prospective policy evaluation coupled with dynamic simulation modeling, using a case of electricity infrastructure policies in oil-exporting countries. To demonstrate the prospective evaluation, we analyze investment policies in non-fossil electricity capacity in terms of their forecast impact on several dimensions of inclusive wealth. Illustrative results show that investing in non-fossil capacity in Saudi Arabia and Kuwait can increase components of the countries' inclusive wealth, though the impacts depend on future uncertainties. In contrast, comparable components of the UAE's net inclusive wealth decline under similar investment policies. Finally, including human capital improvements in estimates of inclusive wealth substantially increases its value, though the amount varies across the countries.

The Inclusive Wealth Index has been proposed as a suitable measurement of sustainable growth and development of nations. This index consists of natural, produced, and human capital. A systematic and verifiable assessment of natural capital across countries has proven to be challenging. This paper proposes that natural capital be evaluated in a consistent manner through the use of earth observation satellites. 96 and 110 instruments were identified to observe forest and agricultural resources, respectively, thus a strong capability exists for these natural capital categories. However, current remote sensing capabilities can only go so far. Only 20 instruments were identified that can potentially be used for fish stock assessment. Although 72 instruments were identified to have mineral observation capabilities, data post processing is needed to accurately assess mineral stocks. 35 instruments were identified to assist in fossil fuel observation, but given that supply data exists for fossil fuels, this capability gap is not considered critical. By examining these capability gaps, scientists can direct their attention to payloads that will enable remote sensing technologies to assist in the measurement of the Inclusive Wealth Index.

We confirm the applicability of using small satellite formation flight for multi-angular earth observation to retrieve global, narrow band, narrow field-of-view albedo. The value of formation flight is assessed using a coupled systems engineering and science evaluation model, driven by Model Based Systems Engineering and Observing System Simulation Experiments. Albedo errors are calculated against bi-directional reflectance data obtained from NASA airborne campaigns made by the Cloud Absorption Radiometer for the seven major surface types, binned using MODIS’ land cover map – water, forest, cropland, grassland, snow, desert and cities. A full tradespace of architectures with three to eight satellites, maintainable orbits and imaging modes (collective payload pointing strategies) are assessed. For an arbitrary 4-sat formation, changing the reference, nadir-pointing satellite dynamically reduces the average albedo error to 0.003, from 0.006 found in the static reference case. Tracking pre-selected waypoints with all the satellites reduces the average error further to 0.001, allows better polar imaging and continued operations even with a broken formation. An albedo error of 0.001 translates to 1.36 W/m 2 or 0.4% in Earth’s outgoing radiation error. Estimation errors are found to be independent of the satellites’ altitude and inclination, if the nadir-looking is changed dynamically. The formation satellites are restricted to differ in only right ascension of planes and mean anomalies within slotted bounds. Three satellites in some specific formations show average albedo errors of less than 2% with respect to airborne, ground data and seven satellites in any slotted formation outperform the monolithic error of 3.6%. In fact, the maximum possible albedo error, purely based on angular sampling, of 12% for monoliths is outperformed by a five-satellite formation in any slotted arrangement and an eight satellite formation can bring that error down four fold to 3%. More than 70% ground spot overlap between the satellites is possible with 0.5° of pointing accuracy, 2 Km of GPS accuracy and commands uplinked once a day. The formations can be maintained at less than 1 m/s of monthly Δ V per satellite.

This paper develops a network optimization formulation for dynamic campaign-level space mission planning. Although many past space missions have been designed mainly from a mission-level perspective, a campaign-level perspective will be important for future space exploration. In order to find the optimal campaign-level space transportation architecture, a mixed-integer linear programming (MILP) formulation with a generalized multi-commodity flow and a time-expanded network is developed. Particularly, a new heuristics-based method, a partially static time-expanded network, is developed to provide a solution quickly. The developed method is applied to a case study containing human exploration of a near-Earth object (NEO) and Mars, related to the concept of the Flexible Path. The numerical results show that using the specific combinations of propulsion technologies, in-situ resource utilization (ISRU), and other space infrastructure elements can reduce the initial mass in low-Earth orbit (IMLEO) significantly. In addition, the case study results also show that we can achieve large IMLEO reduction by designing NEO and Mars missions together as a campaign compared with designing them separately owing to their common space infrastructure pre-deployment. This research will be an important step toward efficient and flexible campaign-level space mission planning.

In recent years, the Mars One program has gained significant publicity for its plans to colonize the red planet. Beginning in 2025, the program plans to land four people on Mars every 26 months via a series of one-way missions, using exclusively existing technology. This one-way approach has frequently been cited as a key enabler of accelerating the first crewed landing on Mars. While the Mars One program has received considerable attention, little has been published in the technical literature regarding the formulation of its mission architecture. In light of this, we perform an independent analysis of the technical feasibility of the Mars One mission plan, focusing on the architecture of the life support and in-situ resource utilization (ISRU) systems, and their impact on sparing and space logistics. To perform this analysis, we adopt an iterative analysis approach in which we model and simulate the mission architecture, assess its feasibility, implement any applicable modifications while attempting to remain within the constraints set forth by Mars One, and then resimulate and reanalyze the revised version of the mission architecture. Where required information regarding the Mars One mission architecture is not available, we assume numerical values derived from standard spaceflight design handbooks and documents. Through four iterations of this process, our analysis finds that the Mars One mission plan, as publicly described, is not feasible. This conclusion is obtained from analyses based on mission assumptions derived from and constrained by statements made by Mars One, and is the result of the following findings: (1) several technologies including ISRU, life support, and entry, descent, and landing (EDL) are not currently “existing, validated and available” as claimed by Mars One; (2) the crop growth area described by Mars One is insufficient to feed their crew; (3) increasing the crop growth area to provide sufficient food for the crew leads to atmospheric imbalances that requires a prohibitively large ISRU atmospheric processor or a notably different system architecture to manage; and (4) at least 13 Falcon Heavy launches are needed to deliver a portion of the required equipment to the Martian surface, a value that is at least double that planned by Mars One for the same mission phase. Most importantly, we find that the one-way nature of the Mars One mission, coupled with its plans to increase its crew population every 26 months, causes the operating costs of the program to grow continually over time. This is due to the fact that maintaining a growing colony on the Martian surface incurs increasing equipment and spare parts resupply requirements and hence launch costs over time. Based on published launch vehicle and lander estimates, our analysis finds that by the launch of the fifth crew, the cost associated with launching a portion of all required equipment and spares is approximately equal to half of the total NASA FY2015 budget – and this cost will grow when other critical systems outside the scope of this analysis are included. To mitigate these costs and bring the plan closer towards feasibility, we recommend a number of mission architecture modifications and technology development efforts be implemented before the initiation of any Mars settlement campaign. These include the further development of EDL, life support, and ISRU technologies, as well as additive manufacturing technology that utilizes ISRU-derived Martian feedstock as a potential means to address the growing cost of resupply., United States. National Aeronautics and Space Administration (NASA Grant NNX13AL76H), United States. National Aeronautics and Space Administration (NASA Grant NNX14AM42H), Josephine De Karman Fellowship Trust

Difficulties, weaknesses, and deficiencies in projects in accordance with various key subjects are listed in Table 12.1. It reflects the experiences of the authors and their project partners in countless client projects. It can be comforting to learn that nobody is left alone with these and similar difficulties, but is in good company, even with well-respected firms.

In his comprehensive, empirical analysis, W. Keplinger formulated the characteristics of a successful project. His results are summarized below

The following case study is based on a project that one of the authors carried out using the principles presented here – not strictly and formally, but hopefully recognizable in the logic.

Below we show, in an encyclopedic format, common methods and tools (M&Ts) that, on the one hand, support the work of systems design (architectural design and conceptual design) and, on the other, the work of project management.

In Earth Science and Telecommunications from space we are now transitioning from phase 1 (single monolithic satellites in GEO or LEO) to phase 2 which consists of distributed ensembles of LEO and GEO satellites. This however is not the end game. The third phase will be the manufacturing and assembly of satellites directly in space, allowing significantly larger apertures and orders of magnitude improvement in spatial resolution. The key challenges and opportunities of this paradigm shift are summarized and quantified.

Corporate research and technology (R&T) management needs reliable information for strategic planning. A commonly used tool are technology roadmaps, since they describe vision and corporate strategy for technology evolution. The process of creating a technology roadmap involves experts from engineering, product development and all support functions evaluating and comparing opportunities for incremental as well as disruptive innovation. This work describes an approach, where experts build and run models in a concurrent design environment allowing the evaluation of potential product architectures according to a defined set of figures of merit and at different time horizons. This mapping of the landscape can then inform the planning of technology investment and development. The process is illustrated using the example of a solar-electric aircraft technology roadmap.

Design of complex systems requires collaborative teams to overcome limitations of individuals; however, teamwork contributes new sources of complexity related to information exchange among members. This paper formulates a human subjects experiment to quantify the relative contribution of technical and social sources of complexity to design effort using a surrogate task based on a parameter design problem. Ten groups of 3 subjects each perform 42 design tasks with variable problem size and coupling (technical complexity) and team size (social complexity) to measure completion time (design effort). Results of a two-level regression model replicate past work to show completion time grows geometrically with problem size for highly coupled tasks. New findings show the effect of team size is independent from problem size for both coupled and uncoupled tasks considered in this study. Collaboration contributes a large fraction of total effort, and it increases with team size: about 50–60 % of time and 70–80 % of cost for pairs and 60–80 % of time and 90 % of cost for triads. Conclusions identify a role for improved design methods and tools to anticipate and overcome the high cost of collaboration., American Society for Engineering Education. National Defense Science and Engineering Graduate Fellowship

Frequent and significant cost and schedule overruns in large aerospace and defense projects are hypothesized to be attributed to limitations on designers’ perception of complex systems. New design methods and tools to improve perception could reduce design effort. This paper extends an existing model of system project management to incorporate new methods for collaborative modeling and rapid sensitivity analysis using web- and browser-based technologies. A JavaScript-based API and model implementation in the system dynamics formalism replicate previous model results. Performance benchmarks demonstrate model execution in around 100 milliseconds on consumer hardware. Data storage and remote model execution services compose and query model results across executions. Browser-based user interfaces and visualizations allow users to interact with model components and provide batch model execution, tradespace exploration, sensitivity analysis, and time series comparison.

Planning a safe and productive human space exploration mission involves a dual approach addressing both the health of the vehicle and the crew. The goal of this study was to develop a quantitative model of astronaut health during long-duration space flight and a medical supply demand model in support of such missions. The model provides two outputs, Alphah and Mass of Medical Consumables (MMC), for each set of input parameters. Alphah is an estimate of total crew health and is displayed as a percentage. MMC is a measure of medical consumables expended during the mission and is displayed in units of kilograms. We have demonstrated that Alphah is a function of three scaling parameters, the type of mission, duration of mission, and gender mix of the crew. The type of mission and gender of crew are linked to radiation fatality data published by NASA. Mission duration is incorporated into the model with predicted incidence of illness and injury data published on US Navy submarine crews. MMC increases non-linearly with the number of crew, the duration of the mission and the distance of the mission away from Earth. This article describes the relationships between these parameters and discusses implications for future crewed space missions.

While there is growing interest in implementing future NASA Earth Science missions as Distributed Spacecraft Missions (DSMs), there are currently very few tools available to help in the design of DSMs. The objective of our project is to provide a framework that facilitates DSM Pre-Phase A investigations and optimizes DSM designs with respect to a-priori Science goals. Our Trade-space Analysis Tool for Constellations (TAT-C) enables the investigation of questions such as: “Which type of constellations should be chosen? How many spacecraft should be included in the constellation? Which design has the best cost/risk value?”. This paper provides a description of the TAT-C tool and its components.

This research develops a dynamic logistics network formulation for lifecycle optimization of mission sequences as a system-level integrated method to find an optimal combination of technologies to be used at each stage of the campaign. This formulation can find the optimal transportation architecture considering its technology trades over time. The proposed methodologies are inspired by the ground logistics analysis techniques based on linear programming network optimization. Particularly, the time-expanded network and its extension are developed for dynamic space logistics network optimization trading the quality of the solution with the computational load. In this paper, the methodologies are applied to a human Mars exploration architecture design problem. The results reveal multiple dynamic system-level trades over time and give recommendation of the optimal strategy for the human Mars exploration architecture. The considered trades include those between In-Situ Resource Utilization (ISRU) and propulsion technologies as well as the orbit and depot location selections over time. This research serves as a precursor for eventual permanent settlement and colonization of other planets by humans and us becoming a multi-planet species.

A modern and efficient information and communication technology (ICT) infrastructure is essential for managing the challenges in the complex urban systems development. The ICT infrastructure is a complex system consisting of many subsystems and interconnections, which makes the process of planning, designing, and maintaining a comprehensive ICT infrastructure expensive and difficult. Most approaches used for the ICT infrastructure modeling focus typically on a single ICT system, for example, a wireless network. This paper presents a systems modeling approach based on integrating different subsystems and their characteristics into a single model, applying system decomposition, establishing the logical relations between system components, and defining relevant key performance indicators. It is shown that this systems modeling approach facilitates holistic planning, design, and evaluation of the complex ICT infrastructure for a sustainable city. This is demonstrated in the form of a two-scenario Masdar city case study. The case study exhibits the practicality of the derived ICT model and the feasibility of the results.

Water supply infrastructure planning in groundwater-dependent regions is often challenged by uncertainty in future groundwater resource availability. Many major aquifer systems face long-term water table decline due to unsustainable withdrawals. However, many regions, especially those in the developing world, have a scarcity of groundwater data. This creates large uncertainties in groundwater resource predictions and decisions about whether to develop alternative supply sources. Developing infrastructure too soon can lead to unnecessary and expensive irreversible investments, but waiting too long can threaten water supply reliability. This study develops an adaptive infrastructure planning framework that applies Bayesian learning on groundwater observations to assess opportunities to learn about groundwater availability in the future and adapt infrastructure plans. This approach allows planners in data scarce regions to assess under what conditions a flexible infrastructure planning approach, in which initial plans are made but infrastructure development is deferred, can mitigate the risk of overbuilding infrastructure while maintaining water supply reliability in the face of uncertainty. This framework connects engineering options analysis from infrastructure planning to groundwater resources modeling. We demonstrate a proof-of-concept on a desalination planning case for the city of Riyadh, Saudi Arabia, where poor characterization of a fossil aquifer creates uncertainty in how long current groundwater resources can reliably supply demand. We find that a flexible planning approach reduces the risk of over-building infrastructure compared to a traditional static planning approach by 40% with minimal reliability risk (

This paper develops a problem formulation that can be applied to the integrated space-station system design. The integrated space-station optimization formulation combines both a space habitat and its supporting logistics design into one single optimization problem. In this way, a space-based habitat and its logistics resupply system can be designed concurrently, and effective trades can be conducted between them. Numerical results show the benefits of the proposed problem formulation, comparing it with conventional design strategies. Data from the 2012 International Space Station reference configuration are used to develop four cases, each emphasizing a different mix of scientific experiments in orbit.

The water end-use segment (WES), consisting of activities that utilize water in homes and buildings, has been identified as a major component of energy use in the urban water supply system. In this paper, an analytical framework is presented which can be used at the planning stages of new urban developments to assess future building-level water demands and associated energy requirements. The framework is applied to Masdar City, a new urban area in the United Arab Emirates, which has been targeted in its design to be a future zero-carbon and zero-waste city. Results show that the energy intensity (in electric kWh) in WES for Masdar City may range from 2.6 to 4 kWh=m3. The dominant use of energy in this segment is attributed to water heating requirements, and the total energy use for obtaining hot water is estimated to range from approximately 20 to 50 million kWh annually. It is found that the residential sector in the city can have the greatest impact in affecting energy requirements associated with water use. For every unit reduction (in L=person=day) of indoor residential water use, it is estimated that up to 225 MWh may be saved annually.

PurposeThis research was performed with the aim of determining if the emptying of latrines in a flood‐prone urban slum area would be a sustainable and profitable business for private‐sector service providers.Design/methodology/approachMonte‐Carlo analysis was used to evaluate the economic sustainability of a proposed public‐private waste transportation service. A GIS‐assisted route analysis was also performed, with participation by private and public sector stakeholders.FindingsThe analysis also showed that if a low‐cost subscription service is implemented in the area, the commercial service providers will operate at a loss in all cases, unless changes are made in the truck fuel efficiency, the operating hours of discharge sites, and the transportation network.Research limitations/implicationsThe research was based on service provider operations costs extrapolated from previous studies and updated through informal interviews. A thorough and transparent review of cost accounting procedures is necessary to validate the results.Practical implicationsThis study identified challenges and potential solutions which must be addressed by practitioners in order to ensure success of a subscription‐based service.Originality/valueThis study adds to the existing literature by demonstrating the applicability of an analytic modeling technique based on Monte‐Carlo simulation and provides an example of how academic research can be tightly coupled with practitioner needs in order to have a direct impact on operational humanitarian projects.

Many of the world’s largest and most profitable offshore oil and gas basins are reaching maturity and are declining in profitability. Most new discoveries tend to be smaller and present as fragmented and geographically dispersed reservoirs with substantial uncertainty concerning geology and marginal exploration costs. Many of these discoveries only make sense when considered as a set. This situation is compounded by large oil and gas price volatility and a very large combinatorial size of the design and operational decision space. Subsea tiebacks that connect new fields to existing production facilities are a means of extending the life and profitability of offshore facilities. The challenges of subsea tiebacks are both technical – they require connecting fields and facilities over large distances (>10 km) in deep water (>500 m) – and conceptual. It is not immediately clear where we should place production facilities, how we should size them, and in what sequence and when we should place tiebacks to optimize value. This article presents a methodology that evaluates three kinds of flexibility as a means to mitigate uncertainty in subsea tiebacks: the ability to tie back new fields, the ability to expand the capacity of a central processing facility, and the dynamic allocation of processing capacity to the connected fields. The methodology uses an integrated mid-fidelity system model in conjunction with Monte Carlo simulation to identify potential platform design capacities and tieback phasing strategies under reservoir, facilities, and market uncertainties. We demonstrate the methodology on an offshore multiple-oilfield development patterned after a real case off the West Coast of Africa. The results show that because of the concurrent presence of reservoir, facilities, and market uncertainties, implementing flexibility significantly increases, by as much as 76%, the expected net present value of the project compared to a traditional point-optimal inflexible design.

We investigate potential benefits of employing the Design Structure Matrix (DSM) in the context of Model–Based Systems Engineering (MBSE) for the purposes of analyzing and improving the design of a product–project ensemble. Focusing on process DSM, we present an algorithm for a bidirectional transformation framework between a product–project system model and its corresponding Model–Based DSM (MDSM). Using Object–Process Methodology (OPM) as the underlying modeling language, we examine and characterize useful and insightful relationships between the system model and its MDSM. An unmanned aerial vehicle case study demonstrates the semantics of and analogy between various types of relationships as they are reflected in both the OPM system model and the MDSM derived from it. We conclude with further research direction on showing how clustering of DSM processes can be reflected back as an improvement of the OPM model. © 2013 Wiley Periodicals, Inc. Syst Eng 16

As the supply of desalinated water becomes significant in many countries, the reliable long-term operation of desalination infrastructure becomes paramount. As it is not realistic to build desalination systems with components that never fail, instead the system should be designed with more resilience. To answer the question how resilient the system should be, we present in this paper a quantitative approach to measure system resilience using semi-Markov models. This approach allows to probabilistically represent the resilience of a desalination system, considering the functional or failed states of its components, as well as the probability of failure and repair rates. As the desalination plants are connected with the end-user through water transportation and distribution networks, this approach also enables an evaluation of various network configurations and resilience strategies. A case study addressing a segment of the water system in Saudi Arabia is given with the results, benefits, and limitations of the technique discussed.

Maintenance logistics support is a significant challenge for extended human operations in space, especially for missions beyond Low Earth Orbit (LEO). For missions to Mars (such as NASA's Evolvable Mars Campaign (EMC)), where timely resupply or abort in the event of emergency will not be possible, maintenance logistics mass is directly linked to the Probability of Loss of Crew (P(LoC)), and the cost of driving down risk is an exponential increase in mass requirements. The logistics support strategies that have maintained human operations in LEO will not be effective for these deep space missions. In-Space Manufacturing (ISM) is a promising technological solution that could reduce logistics requirements, mitigate risks, and augment operational capabilities, enabling Earth- independent human spaceflight. This paper reviews maintenance logistics challenges for spaceflight operations in LEO and beyond, and presents a summary of selected results from a systems analysis of potential ISM applications for the ISS and EMC. A quantitative modeling framework and sample assessment of maintenance logistics and risk reduction potential of this new technology is also presented and discussed.

Long-endurance human spaceflight - such as missions to Mars or its moons - will present a never-before-seen maintenance logistics challenge. Crews will be in space for longer and be farther way from Earth than ever before. Resupply and abort options will be heavily constrained, and will have timescales much longer than current and past experience. Spare parts and/or redundant systems will have to be included to reduce risk. However, the high cost of transportation means that this risk reduction must be achieved while also minimizing mass. The concept of increasing system and component reliability is commonly discussed as a means to reduce risk and mass by reducing the probability that components will fail during a mission. While increased reliability can reduce maintenance logistics mass requirements, the rate of mass reduction decreases over time. In addition, reliability growth requires increased test time and cost. This paper assesses trends in test time requirements, cost, and maintenance logistics mass savings as a function of increase in Mean Time Between Failures (MTBF) for some or all of the components in a system, based on a review of reliability growth models in literature and a quantitative case study. In general, reliability growth results in superlinear growth in test time requirements, exponential growth in cost, and sublinear benefits in terms of maintenance logistics mass saved. In the Mars transit case study examined here, doubling the reliability of all components results in a 24% reduction in corrective maintenance mass requirements. However, if only some components experience improved reliability the benefits are reduced; if only the ten largest contributors to corrective maintenance requirements experience doubled reliability, the decrease in mass is reduced to 9%. These trends indicate that it is unlikely that reliability growth alone will be a cost-effective approach to maintenance logistics mass reduction and risk mitigation for long-endurance missions. This paper discusses these trends as well as other options to reduce logistics mass such as direct reduction of part mass, commonality, or In-Space Manufacturing (ISM). Overall, it is likely that some combination of all available options - including reliability growth - will be required to reduce mass and mitigate risk for future deep space missions., United States. National Aeronautics and Space Administration. Space Technology Research Fellowship (NNX14AM42H)