Search

This article leverages a unique field experiment to characterize who in the crowd is willing and able to solve complex engineering design problems for the prospect of a prize. Contrary to popular skepticism, we find that the crowd is highly capable of not only providing quality point solutions but also contributing valuable engineering insight. However, although a diverse population of more than 9000 registrants showed initial interest in the competitions, of the 255 judgeable solutions received, the majority of good solutions came from solvers with prior within-discipline experience. Of those, more of the good solutions came from solvers who perceived the relevant problem as “at the boundary” of their expertise. This suggests that while complex engineering design problems do require some threshold of relevant training and skills, prize competitions still play an important role in attracting solvers who perceive themselves to be adjacent to the problem. Furthermore, the submissions revealed a role for solvers in the crowd in augmenting traditional concept generation and tradespace exploration processes, by leveraging the independence of solvers in the crowd. These findings contribute an empirical basis for resolving how and when the crowd can be used most effectively, as part of an innovation toolkit.

The purpose of this study is to define a holistic and effective architectural approach for implementing a safe and profitable pilot UAM airport shuttling system, which is extensible to the best available data at a given time. The proposition of this research is to leverage a system of autonomous UAVs capable of vertical takeoff and landing (VTOL) to transport passengers and cargo between airports and defined destinations in different urban cities in the United States: Atlanta, Los Angeles, and Dallas. The study comprises of defining a standard set of metrics to consider, developing a system model for defined case studies, and performing tradespace analyses to quantifiably evaluate alternative ways of achieving the most desirable architecture outcomes. This study models and evaluates 1,620 enumerable architectures and analyzes numerous tradespaces to examine the correlation and interrelationships between decision variables and performance metrics. Among the metrics, this analysis focuses on the interrelationships between Annual Profit, Mean Time Between Incident (MTBI), Upfront Cost, and Passengers Shuttled Per Day. The resulting analysis ranks architectures on the True and Fuzzy Pareto Front that is used to determine essential and quasi-necessary features in relation to these interrelationships. Based on the various stakeholders and their unique needs, the model recommends Los Angeles as the pilot city and for the system to leverage a FIFO queuing system, a smartphone interface, and a hybrid energy source that utilizes electric energy.

Purpose The US Government is challenged to maintain pace as the world’s de facto provider of space object cataloging data. Augmenting capabilities with nontraditional sensors present an expeditious and low-cost improvement. However, the large tradespace and unexplored system of systems performance requirements pose a challenge to successful capitalization. This paper aims to better define and assess the utility of augmentation via a multi-disiplinary study. Design/methodology/approach Hypothetical telescope architectures are modeled and simulated on two separate days, then evaluated against performance measures and constraints using multi-objective optimization in a heuristic algorithm. Decision analysis and Pareto optimality identifies a set of high-performing architectures while preserving decision-maker design flexibility. Findings Capacity, coverage and maximum time unobserved are recommended as key performance measures. A total of 187 out of 1017 architectures were identified as top performers. A total of 29% of the sensors considered are found in over 80% of the top architectures. Additional considerations further reduce the tradespace to 19 best choices which collect an average of 49–51 observations per space object with a 595–630 min average maximum time unobserved, providing redundant coverage of the Geosynchronous Orbit belt. This represents a three-fold increase in capacity and coverage and a 2 h (16%) decrease in the maximum time unobserved compared to the baseline government-only architecture as-modeled. Originality/value This study validates the utility of an augmented network concept using a physics-based model and modern analytical techniques. It objectively responds to policy mandating cataloging improvements without relying solely on expert-derived point solutions.

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.

In this study, a water electro-mobility concept has been analyzed in the Amazon using the case study of the native communities in the Yarinacocha district in Peru. Multi-Attribute Tradespace Exploration (MATE) methodology is applied to identify the various stakeholders' objectives, generate the designs, and evaluate them. This methodology captured the intention of assessing not only technical aspects but also environmental and economic sustainability. Six monohull designs for each of the three navigation routes identified, plus an aluminum catamaran, resulted from the combination of the three materials (wooden, fiber reinforced plastic, and aluminum) of canoe-type monohulls with two types of electric propulsion systems: outboard pod propeller and long-tail shaft coupled with permanent magnet brushless DC motors. It was found that the Amazon canoe hulls studied require propulsion units of 3 kW to navigate in calm waters and 10 kW in rivers. The silicon-based solar photovoltaic systems are equipped with lithium iron phosphate storage batteries. Each design is valued according to the project's stakeholders' preferences, guiding the decision-maker to focus on those that offer higher value at a lower expense. Most solar boats on the two long routes had higher internal rates of return than the current design. The best options generate rates of 29.62% versus 11% and 10.79% versus 5.26%. It could be demonstrated that electric propulsion is competitive to fossil fuel-based propulsion for this case study.

Design and tradespace analysis of increasingly complex systems often require proprietary models from multiple industrial sources. These multi-partner collaborations require assurances that each company’s intellectual property will remain protected from unauthorized access or disclosure to competitors. In this paper, we present the security risks and considerations for these collaborations, and propose a cloud-based framework to address these concerns. The Engineering Resilient Systems (ERS) Cloud Computing Architecture (ECCA) system enables secure collaboration in the Department of Defense community, providing an infrastructure restricting access to connectable containerized assets: models and simulations, each of which remain isolated and maintained on the owner’s own cloud. ECCA further prevents unauthorized access to sensitive assets and infection by malicious media with an access control system supported by secure gRPC messaging and access agreements. We demonstrate the capability to connect disparate models for a simplified combat simulation and then how the new multi-model scenario enables additional capabilities in tradespace analysis.

According to Buede and Miller, not applying a holistic simulation and modeling approach to early design evaluation practices and trade studies results in greater developmental costs, time to market, and unforeseeable system-level design consequences. Completed research is presented for an innovative methodology that enhances the capacity of conventional design evaluations by integrating new types of simulations consistently into the existing Multi-attribute Tradespace Exploration (MATE) design evaluation architecture. This enhancement is particularly important for any envisioned software-intensive system that introduces greater system complexity. According to Ross et al., MATE was originally developed to guide engineering design and initial product selection by exploring candidate designs to deliver utility-based solutions. Unfortunately, the MATE methodology does not support software design evaluation and integration into complex systems. The results presented validate MATE improvements that are scalable and extensible to any software-based system of varying complexity and application. The subject research provides proof of this concept by focusing on sensor systems as an initial practical demonstration due to their rapidly expanding commercial use and Internet of Things (IoT) applications. Project engineers and especially software developers in any product field will benefit from this design method by leveraging its holistic evaluation of software and hardware system design performance for more efficient product design identification and implementation.

Users are often the last barrier in the detection and correction of abnormal behavior in autonomous systems, so understanding what can be expected from users in various contexts is crucial to the performance of such systems. This letter presents the first study characterizing a user ability to timely report and correct autonomous system failures in the context of small Unmanned Aerial Vehicles (sUAVs). The study aims to explore the complex tradespace which designers will encounter when developing effective transitions of control in sUAVs. We have analyzed these tradeoffs in terms of accuracy and response time while manipulating several key contextual elements including subtlety of failure, transitions of control, introduction of noise in autonomous paths, and amount of user training. Results indicate that: 1) increased accuracy is achieved under longer deadlines without large delays in responses, 2) increased noise, user training, and user responsibility for correction lead to increased reporting times, but only increased user responsibility increased accuracy, 3) users, particularly those with additional training, wanted to remain engaged in failure correction even when such interactions were not requested, 4) asking users to fix failures that they did not report resulted in increased response times and reduced accuracy.

While increasingly popular, small unmanned aerial vehicles, aka drones, are often flown illegally over outdoor public gatherings or public facilities like prisons, threatening the safety of those nearby. There is a clear need to address interloping drones in public spaces from a sociotechnical perspective, including understanding the tradespace of variables. Through surveys, interviews, technology and infrastructure design, and experimentation, we developed a tradespace model of those variables that managers and designers of high-risk settings like public spaces and prisons need to consider in their development or renovation. These include cost considerations, both capital and infrastructure, as well as technology design elements of range and false alarm rates potentially exacerbated by convolutional neural networks (aka, deep learning). Results also highlight that environmental design elements can provide a possible low-tech solution in the design of obstructions that either eliminate or complicate a drone pilot’s line of sight. This effort demonstrates that managers and designers of high-risk settings like public spaces and prisons will have to balance sometimes competing objectives to obtain the best possible outcomes for public safety.

It is critical to improve the resilience of military installations and their complex infrastructure systems to strengthen response to the uncertainty and threat driven by the increasing fre...

The stated objective of Human Systems Integration (HSI) in the DOD is optimization, specifically to maximize total system performance and minimize cost. Limited published precedents describe quantitative optimization across the HSI domain trade-spaces. The Model-Based HSI (MBHSI) process utilizes General Systems Performance Theory to relate HSI domain resource inputs to total system performance output. MBHSI also recharacterizes each domain in terms of constructs that are amenable to optimization via mathematical programs (MP). However, MBHSI does not provide an archetypal optimization model or method. This work pursues quantitative HSI optimization models and a method for creating those models in order to facilitate real-world tradespace decisions between the personnel, training, and human factors engineering domains. This work presents an MP formulation and solves it using an MBHSI data set and notional cost data. Sensitivity analyses further elucidate the trade space, and follow-on analyses demonstrate solution set changes based on varying resources, constraints, and cost parameters. Results indicate that MBHSI enables MP formulation and data set optimization. Decision makers are perpetually making trade-off decisions between HSI domains, many times without quantitative knowledge of the trade-space. MBHSI-derived MPs may enable responsive and evidence-based system change decisions in order to quantitatively achieve the stated goal of HSI— optimization. Outstanding Thesis Lieutenant Colonel, United States Air Force Approved for public release. Distribution is unlimited.

This effort characterizes proper sampling of laser speckle in wave-optics simulations, with an emphasis on active imagers in outdoor environments. Modeling of performance degradations induced by speckle is critical in the design of such devices. We expose tradeoffs between sampling conditions in multiple planes of interest, namely the object, pupil and focal planes of an imaging system. The goal of our analysis is to develop an optimized numerical tradespace that models the underlying physics of speckle and turbulence with high fidelity. We begin by showing that speckle statistics are relatively straightforward to produce in the case of vacuum propagation. Then by propagating through different strengths of turbulence, we demonstrate how sampling requirements can become much more difficult to satisfy. We pay particular attention to the problem of sufficiently sampling a target object without subjecting it to anisoplanatism. As a way of overcoming such challenges, we propose and test an optimization routine that defines acceptable simulation parameters based on user-defined physical parameters. Successful implementation of this approach streamlines the design process for applications that involve active target tracking and coherent imaging through turbulence.

Simulation of distributed space missions (DSMs) for the purpose of phase-A mission design studies and general tradespace analysis is computationally challenging owing to the necessity of evaluating thousands of architecture options. Machine learning and evolutionary optimization methods have enabled intelligent search of the architectural tradespace of DSMs, including spacecraft and instrument design specifications. A critical computational bottleneck in evaluating architectures is the ability to rapidly simulate orbits of many DSMs with varying parameters for global earth observation and compare their coverage-related performance. When design variables include heterogeneous payload types and characteristics, orbital characteristics, areas of interest, and user constraints, the parameter space may be in thousands. In this article, we describe the difficulty of coverage calculations for narrow field of view (FOV) and conical FOV sensors, and propose a novel algorithm, called quick search and correction (QSC), to overcome it. We also propose new temporal evaluation metrics to characterize the coverage performance of DSMs, as well as a uniform random sampling technique for fast evaluation of overall performance of DSMs. Performance of the proposed methods and metrics are verified on an example Landsat-derived DSM, showing ∼100x improvement in computational speed due to the QSC algorithm and ∼10–250x due to the sampling technique.

This paper presents a tradespace framework designed to analyze tradeoffs associated with different crewing configuration options. It was created in support of the United States Army’s Future Vertical Lift (FVL) program whose goal is to develop future Army rotorcraft that incorporate advanced automation and can be flexibly crewed depending on mission context. The tradespace framework provides an analytic tool that can help Army decision makers evaluate the tradeoffs associated with different crewing configurations and compare crewing options for different mission contexts. While the framework was developed to assess crew configurations for military rotorcraft, the use of tradeoffs as an organizing framework to analyze crewing alternatives can be applied to a variety of domains.

A growing interest in constellations of small satellites has recently emerged due to the increasing capability of these platforms and their reduced time and cost of development. However, in the absence of dedicated launch services for these systems, alternative methods for the deployment of these constellations must be considered which can take advantage of the availability of secondary-payload launch opportunities. Furthermore, a means of exploring the effects and tradeoffs in corresponding system architectures is required. This paper presents a methodology to integrate the deployment of constellations of small satellites into the wider design process for these systems. Using a method of design-space exploration, enhanced understanding of the tradespace is supported , whilst identification of system designs for development is enabled by the application of an optimisation process. To demonstrate the method, a simplified analysis framework and a multiobjective genetic algorithm are implemented for three mission case-studies with differing application. The first two cases, modelled on existing constellations, indicate the benefits of design-space exploration, and possible savings which could be made in cost, system mass, or deployment time. The third case, based on a proposed Earth observation nanosatellite constellation, focuses on deployment following launch using a secondary-payload opportunity and demonstrates the breadth of feasible solutions which may not be considered if only point-designs are generated by a priori analysis. These results indicate that the presented method can support the development of future constellations of small satellites by improving the knowledge of different deployment strategies available during the early design phases and through enhanced exploration and identification of promising design alternatives.

The Department of Defense Engineered Resilient Systems (ERS) goes beyond the concept of resilience as a response to a disruption. The ERS project focuses on improving design agility and cost-effectiveness leading to improvements in systems analysis, development, testing, manufacturing, and fielding of mission-effective and adaptable systems. Point-Based Design (PBD) seeks to select a point as a design baseline and proceed into development. Our research uses Set-Based Design (SBD) for early concept evaluation of engineered systems as an alternative to PBD. SBD seeks to eliminate less cost-effective sets (in the value vs. cost tradespace) and focus on one or more remaining sets during early design. Our research develops a technique called Convergent Set-Based Design (SBD), a procedure for mathematical elimination and improvement of sets of designs. We developed Convergent SBD for the ERS program. Our research includes the capability to measure and quantify mission resilience using probability trees for all system performance measures. Convergent SBD uses dominance identification equations to compare statistical means for value and cost. The demonstration illustrates the effect of adding mission resilience in the design tradespace. Convergent SBD provides a foundational mathematical technique for eliminating less promising design sets and identifying the most promising design sets.

Presented is a stochastic modeling method enabling rapid yet comprehensive space mission utility simulation. The method facilitates multivariate analysis with concurrent tradespace exploration, risk assessment, and holistic design while simultaneously exploring, assessing, and developing statistically validated concepts of prospective space missions. Modeling is achieved through the synergistic integration of statistical mechanics, blackbox, Bayesian, ansatz, and analytics techniques. The method is verified for its ability to accurately depict a human spaceflight mission and validated for its ability to perform mission utility analysis by backtesting the Apollo 11–17 missions to the Moon through Monte Carlo simulation.

This paper defines Federations of Systems, introduces a tailored architecting framework, and demonstrates it in a satellite design case study. While certainly not being new concepts in the literature, federations are a class of Systems of Systems (SoS) that have not been extensively explored due to the scarcity of applied examples and still few practitioners facing their specific design challenges. This is expected to change with the advent of the Internet of Things, Machine to Machine applications, collaborative consumption, and Federated Satellite Systems. It has become henceforth necessary to develop approaches in design and architecting capable of addressing such interconnected sets of systems. Federations are SoS characterized by the absence of global goals and stakeholders, and cooperation enabled only on an opportunistic basis. Systems engage in federations when the perceived benefit exceeds the costs of the necessary interfaces. The proposed framework quantifies the upper limits to the net benefits of cooperation between systems through the concept of synergy. Using synergy as a heuristic to navigate federated design spaces, the framework predicts the appearance and evolution of federations. The demonstration of the framework in a six‐satellite federation shows that advantages of federating emerge with the collaboration between just two systems. Interface costs, architectural cost preferences, and network topology drive the achievable benefits. Specifically, when considering communication architectures under 80 MUSD for satellite systems, federating is advantageous when the interface lifecycle costs are below 15 MUSD. This demonstration case exemplifies how federating can assist systems in achieving design points that dominate the solutions of their local tradespace.

Requirement changes can result in substantial overdesign because of the way design margins are allocated at the beginning of the design process. We present a design optimization method for minimizing overdesign by making use of additive remanufacturing and recently defined constituents of design margins (buffer and excess). The method can be used to obtain a set of design decisions for different changing requirement scenarios. We demonstrate our method by means of a turbine rear structure design problem where changes in the temperature loads are met by depositing different types of stiffeners on the outer casing. The results of the case study are visualized in a tradespace, which allows for comparison between sets of optimal, flexible, and robust designs. Results show that the optimized set of design decisions balances flexibility and robustness in a cost-effective manner.

Neural network approaches have periodically been explored in the pursuit of high performing SAR ATR solutions. With deep neural networks (DNNs) now offering many state-of-the-art solutions to computer vision tasks, neural networks are once again being revisited for ATR processing. Here, we characterize and explore a suite of neural network architectural topologies. In doing so, we assess how different architectural approaches impact performance and consider the associated computational costs. This includes characterizing network depth, width, scale, connectivity patterns, as well as convolution layer optimizations. We have explored a suite of architectural topologies applied to both the canonical MSTAR dataset, as well as the more operationally realistic Synthetic and Measured Paired and Labeled Experiment (SAMPLE) dataset. The latter pairs high fidelity computational models of targets with actual measured SAR data. Effectively, this dataset offers the ability to train a DNN on simulated data and test the network performance on measured data. Not only does our in-depth architecture topology analysis offer insight into how different architectural approaches impact performance, but we also have trained DNNs attaining state-of-the-art performance on both datasets. Furthermore, beyond just accuracy, we also assess how efficiently an accelerator architecture executes these neural networks. Specifically, Using an analytical assessment tool, we forecast energy and latency for an edge TPU like architecture. Taken together, this tradespace exploration offers insight into the interplay of accuracy, energy, and latency for executing these networks.

In preparation for humanity's return to the Moon, it is important to advance technologies and capabilities that will allow for sustainability on the lunar surface, as well as eventual human missions to Mars. As charged in Space Policy Directive-1, NASA's Artemis program will advance and develop technologies on the lunar surface that can be leveraged towards a safe and successful human round-trip mission to Mars. NASA's Human Exploration and Operations Mission Directorate Capabilities Integration Team is advancing a continuous effort to identify and map gaps between exploration capabilities and anticipated human spaceflight architecture elements. As upcoming exploration missions approach, the architectural design tradespace increasingly narrows. This paper explores certain architectural and element design choices for upcoming missions to the Moon and Mars, comparing and contrasting capability gaps and classes of capability gaps that are architecture robust with those that are not. In doing so, we identify certain capabilities as relevant, and therefore robust, across varying architectural possibilities while identifying other capabilities as only applicable to specific architectural pathways. Capabilities that are applicable to multiple elements across the architecture have more architectural breadth because of their increased likelihood of remaining relevant even if changes are made to some elements they are mapped to. Correspondingly, capabilities that are applicable to specific elements across the architecture but are necessary under almost any eventuality have more architectural depth because of their increased likelihood of remaining relevant even if architecture changes are made. One of the key analyses in this paper is an exploration of architecture robustness as a function of capability area, as defined by the NASA Technology Taxonomy. We also identify current areas of interest that have historical continuity with previous architectural and capability designs for Moon and Mars exploration architectures.

Cost is one of the biggest obstacles to sending humans to Mars. However, spacecraft costs are typically estimated after the preliminary vehicle and mission concepts have been designed. By automating the cost estimation process, the effect of any change in vehicle or mission design on the mission cost can be determined more rapidly. This paper uses the tool SAPE-C (Systems Analysis for Planetary Entry, Descent, and Landing-Cost). SAPE-C integrates the cost modeling software SEER-H (System Estimation and Evaluation of Resources-Hardware) with a number of systems analysis tools and enables large design tradespace exploration early in the design process. SAPE-C is used to analyze several tradespaces of the Mid-Lift-to-Drag ratio rigid entry vehicle for human Mars missions. Key findings include quantifying the impacts of payload, vehicle length, and main engine specific impulse on the overall cost of the vehicle, and how the payload per lander and number of landers affects the cost of a campaign to Mars.

The sparse vector recovery problem can lead to a combinatorial search of prohibitive computations. Hence, reformulations amenable to convex optimization strategies have been considered. Alternatively, Bayesian inference approaches can curtail computations such as variational Bayesian methods (VBM). VBM, however, intentionally introduces a misspecified model to improve the efficiency of computational requirements. This talk will review the theory of misspecified parameter bounds and extensions to the Bayesian framework. Additionally, it will be shown that misspecified bounds can provide tight prediction of sparse Bayesian learning approaches, and thus can be used to tune the hyperparameters of VBM for improved performance. The VBM gains in computational efficiency, however, come at the cost of increased mean squared error (MSE) when compared to the perfectly specified model case. Examples will be shown that quantify this MSE increase and illustrate the apparent tradespace.

Essential for services such as communication, navigation, and weather prediction, satellite constellations must minimize loss of coverage while being subject to constraints on space traffic and the number of satellites available. Genetic algorithms (GAs) offer a versatile method of optimization with demonstrated success in applied problems. In the case of a computationally expensive problem, the necessity of repeated fitness evaluations prevents convergence of a GA in a feasible timeframe. A potential solution to this dilemma is to implement a computationally-efficient surrogate model to estimate the computationally-expensive objective function value. Recent advances in machine learning methods, in particular neural networks, make them a compelling candidate as a surrogate function. A GA incorporating an ensemble of neural networks as a surrogate function is applied to the problem of constellation design. We demonstrate that through the incorporation of an ensemble of neural networks, we can improve function evaluation and optimization runtime for the GA.

Signals of opportunity (SoOp) is a promising technique to measure key geophysical variables at high spatiotemporal scales using multiple microwave frequencies outside bands allocated for science. To better understand spaceborne SoOp observations, an end-to-end mission simulator has been developed. It determines observation geometries of multiple transmitters and receivers and generates SoOp observables for land remote sensing by integrating geophysical data and instrument properties. Using the simulator, the coverage and the instrument data of a hypothetical constellation of spaceborne SoOp receivers were evaluated over different orbital planes. This integrative tool facilitates the spaceborne SoOp mission design by exploring and optimizing the tradespace of a variety of design parameters.

Global Navigation Satellite Systems (GNSS) provide ubiquitous, continuous and reliable positioning, navigation and timing information around the world. However, GNSS space segment design decisions have been based on the precursor Global Positioning System (GPS), which was designed in the 70s. This paper revisits those early design decisions in view of major technological advancements and new GNSS environmental threats. The rich tradespace between User Navigation Error (UNE) performance and constellation deployment costs is explored and conclusions are complemented by sensitivity analysis and association rule mining. This study finds that constellations at an orbit altitude of ~2 Earth radii can outperform existing GNSS in terms of cost, robustness and UNE. These insights should be taken into account when designing future generations of GNSS., 22 pages, submitted to IEEE Transactions on Aerospace and Electronic Systems, second round of review Corrected figure 10

In early system design, Model-Based Engineering techniques with an integrated framework provide the means to better identify and explore the tradespace. Several researchers have proposed frameworks to explore the tradespace for single and multiple attribute exploration. This paper 1) describes one approach used in the literature, the Integrated Trade-off Analysis Framework, 2) proposes a validation technique to assess the adequacy of a tradespace exploration technique, and 3) applies the validation technique on an unmanned aerial vehicle (UAV) case study. The research hypothesis was that optimization could be used to validate the tradespace exploration technique by determining if the exploration method found the efficient frontier. However, the integrated framework with set-based design found better solutions than the optimization technique. The UAV case study contained 189 design points that dominated the points found in the validation process. Additionally, a better tradespace was produced when more design points were explored by using the Integrated Trade-off Analysis Framework.

Data-driven design approaches such as Multi-Attribute Tradespace Exploration and Set-Based Design are increasing in popularity due to their ability to capture broader decision spaces than traditional point-based design. These methods share many of the same features and have complementary goals. Artificial intelligence offers a way to process the large amounts of data created by these methods in a fast and objective manner, supporting the insights of subject matter experts. This paper discusses the intersection of these three research areas and demonstrates an approach for combining these techniques to rapidly identify the most value-driving decisions available to the design team.

Space suits are crucial to human spaceflight, but can restrict motion, require additional energy, and increase injury risk. Previous planetary suits were largely based on flexible components, which generate additional forces on the occupant as they resist volumetric changes from flexing components. The NASA Mark III suit addresses this problem using a Hip Brief Assembly (HBA), composed of rigid, constant-volume sections connected by bearings. However, due to the rigid components and fixed degrees of freedom (DoFs), the HBA and other hard-component joint assemblies (HCJAs) have stricter bounds on motion. For example, previous analysis shows that the hip multi-DoF range of motion (ROM) for an HBA occupant is not well-aligned with the nominal hip ROM during gait (gait NHROM). In this paper, a set of methods for describing HCJA geometry and the effect on occupant ROM is presented. A generalized model builds on Denavit-Hartenberg parameterization to describe HCJA structure, rotation, and surface shape. Also included is a computational approach to estimate the multi-DoF ROM for joints of an HCJA occupant, and compare and score the suit-restricted ROMs against nominal, unencumbered ROMs. These models are utilized to analyze HBA geometry and improve alignment between in-suit occupant ROM and gait NHROM. A set of design constraints based on feasible geometries and parameter bounds were devised and used to limit a tradespace analysis of alternate geometries in the HBA model. The geometries were evaluated and given a ROM score based on occupant access to gait NHROM. Over 1.3 billion alternate geometries were tested, with 100 million used to sample the tradespace. Of these designs, 52,276 met or bested the nominal geometry. The top-scoring geometry showed a more than sixfold improvement on access to gait NHROM, as well as a more natural neutral leg position, a significant increase in adduction range, and improved kneeling ability. The geometry also performed well across different anthropometries. The tradespace data set was used to analyze trends in HBA geometry, suggesting the most dominant factors behind a high ROM score is the cant angle in the Proximal and Distal sections.

There is currently a trend towards developing and commissioning satellite constellation missions, which has necessitated tradespace studies to design high-performance, low cost and low risk constellations. An open-source software tool called Tradespace Analysis Tool for Constellations (TAT-C) has been developed at the NASA Goddard Space Flight Center, which aims to facilitate pre-phase A mission studies by generating and optimizing the tradespace of the constellations, involving a multitude of possibly coupled parameters such as orbits, satellites, launchers, ground-stations, and instruments. The performance attributes investigated by TAT-C are instrument data metrics, coverage metrics, cost and risk. While previous research has explored optimization of the constellation satellite orbits using metrics associated with coverage (such as maximizing access duration, maximizing the number of revisits over a region), there is relatively less work on exploring the tradespace of instrument parameters and associated data metrics. Previous research has also used relatively rudimentary data-metrics such as imaged pixel resolutions, range, and angles at which observations are made. This paper describes the instrument data-metrics evaluator of TAT-C which generates more sophisticated data metrics characteristic of the instrument type. The basic concept and the architecture of the evaluator have been developed to accommodate instruments without assuming specifics about the underlying technology. In this paper, we describe the modeling of the two most common types of Earth Observing instruments, namely passive optical sensors and synthetic aperture radars (SARs). The models allow for evaluation of commonly used data-metrics such as the signal to noise ratio, noise equivalent delta temperature, ground-pixel resolutions, dynamic range, noise-equivalent sigma0, etc. The challenges in making the models generic enough for wide usage, while being able to appropriately mimic characteristics of complex real-world instruments, are described using examples such as the Landsat-8 Thermal Infrared Sensor and Operational Land Imager. Lastly, we present results from the instrument data metrics evaluator for three important use cases of mission design with passive-optical sensors and SARs. The first use case explores the tradespace of Sun-Synchronous Orbits from a perspective of data-metrics as opposed to commonly considered coverage metrics. The second use case explores the tradespace of SAR parameters and highlights quantitative trade-offs between instrument parameters influencing the performance, size, and complexity. Such tradespace analysis allows the user to appreciate and consider a fundamental constraint on the SAR antenna size depending on radar frequency. In the last use case, we explore the tradespace of pushbroom vs whiskbroom scanners, and evaluate the conditions under which their performance match.

© 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.

Methodologies are required to model, evaluate, and analyze the resilience of system architectures at the conceptual design level to support needs evaluations, policy development, or study of social decisions. The primary objective of this paper was to develop a generalized resilience analysis model for the characterization of multistate engineered systems subject to disruptions to support system design selection during trade studies. Each design variant within the tradespace is modeled using continuous time Markov chains that simulate the transient response of each design variant subject to a user-defined disruption. The operating condition of the multistate system is characterized by a finite number of machine states. Each design variant's utility response is generated as a function of the system's machine state response to the disruption. Resilience capacities and capabilities are generated to populate trade studies. A case study consisting of an unmanned surface vessel provides a system theoretic approach to illustrate the concepts presented within this paper and demonstrates the ability of this methodology to aid decision maker's selection of resilient system architectures. Use of an analytical framework serves as a unifying foundation for incorporating resilience behaviors of multistate engineered systems into early stage design considerations.

Recently, tradespace analysis and exploration has emerged as an important focus area within the Department of Defense Engineered Resilient System initiative, which draws upon engineering concepts, science, and design tools to produce trusted and effective solutions for a wide range of operational contexts. Most of the previous research on tradespace analysis, including those developed for rotorcraft, emphasize performance. However, non-functional requirements such as reliability, availability, and maintainability have received minimal consideration, despite their direct influence on program level concerns such as operation and support as well as affordability. This paper proposes a strategy to incorporate reliability engineering into tradespace analysis. We also develop a subsystem-level reliability investment model that is illustrated through a simplified example. Our results suggest that reliability investment could achieve significant savings over a systems lifecycle, thereby enabling improved fleet availability and a larger fleet size.