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In this tutorial we discuss the quantum alternating operator ansatz (QAOA), which is a variational algorithm that can be used for approximate optimization of combinatorial problems with soft and hard constraints.We go through the design of the quantum circuit and its actual implementation in real hardware, discussing compilation issues such as gate synthesis and scheduling of all the required gates and qubit-swapping overhead.
Traditional probabilistic risk assessment approaches often require failure scenarios to be explicitly defined through event sequences that are then quantified as part of the integrated analysis. This approach becomes difficult when failure propagation paths change as a function of the system operation. Additionally, if the propagation paths represent interactions among even a modest number of components, the scenario count becomes combinatorially intractable. This paper presents an alternate approach for quantifying the probability of failure propagation in such a case. Rather than explicitly defining scenario sequences, simple physical models are created for each of the components. In this way, only the physical states and rules of component interactions must be defined, rather than event sequences for each individual scenario. Initiating failures are introduced into the system, either randomly or as defined by relative likelihood, and the failures cascade through the system via the interaction rules. This process is repeated using Monte Carlo methods and, as a result, the most probable scenarios “self-evolve” in terms of both sequence path and frequency. This approach was applied to failures occurring in the engine compartment of a space launch vehicle with four liquid rocket engines and four high-pressure helium tanks. Each engine was modeled with key components, such as turbomachinery, combustion chamber, propellant lines, and additional support systems. Three test cases were conducted with different high-energy engine failures. End results of interest included an additional engine-out failure and tank burst, which represent the loss-of-mission (LOM) and loss-of-crew (LOC) failure environments, respectively. Observations show that almost every scenario outcome is unique and that many scenarios involve complex chain reactions that are difficult to predict. This validates the usefulness of the modeling approach in assessing the overall risks to the crew during a launch vehicle abort.
Challenging computational problems arising in the practical world are frequently tackled by heuristic algorithms. Small universal quantum computers will emerge in the next year or two, enabling a substantial broadening of the types of quantum heuristics that can be investigated beyond quantum annealing. The immediate question is What experiments should we prioritize that will give us insight into quantum heuristics? One leading candidate is the quantum approximate optimization algorithm (QAOA) metaheuristic. Here, we provide a framework for designing QAOA circuits for a variety of combinatorial optimization problems with both hard constraints that must be met and soft constraints whose violation we wish to minimize. We work through a number of examples, and discuss design principles and implementation considerations.
A rapid, grid-based, target-search algorithm is presented to find candidate se-quences of small-body encounters for mission design. The algorithm is especially relevant for cases with large combinatorial spaces. In this paper, the al-gorithm is used to identify candidate flyby sequences of multiple Kuiper-Belt Ob-jects (KBOs). Before reaching the first KBO in the sequence, the trajectories in this paper first use gravity assists at one or more of the giant planets to pump-uptheir orbital energy—reducing launch C3. The target-search algorithm consists offour sequential steps: (1) parameter definition, (2) fine-tuned Lambert-based gridsearch of ballistic trajectories visiting one KBO, (3) rapid, ∆V-based proximitysearch for additional KBOs using the state transition matrices (STMs), and (4) tra-jectory optimization of the most promising KBO sequences using the EvolutionaryMission Trajectory Generator (EMTG). The paper also defines an empirical-basedprocess to characterize the maximum step size for the target arrival dates in theLambert grid search. Lastly, a candidate mission to two KBOs is presented. Theresults indicate that the ∆V computed from the STM propagations is not repre-sentative of the final ∆V computed in EMTG; however, it does serve as a useful‘reachability’ metric to identify nearby KBOs.
A constellation of radio telescope spacecraft can leverage interferometry to accurately image distant objects throughout the universe, but mission design must balance among many interrelated constraints. In particular, the number of craft and the selection of time-varying orbital parameters play a pivotal role in determining what interferometric baselines are feasible with respect to different targets, and thus drives the breadth and quality of data available to the constellation. The large combinatorial orbit configuration space and competing concerns present a challenging problem that is not well addressed by traditional mission design processes. This paper describes application of automated optimization methods to help direct mission design effort to the most promising dynamic constellation geometries: those that achieve broad interferometric coverage but remain cost-effective and resilient to failures. Several automatic heuristic-driven optimization algorithms representing complementary search strategies were created to explore among concrete constellation configuration plans. Evaluation of each candidate constellation plan was accelerated by efficiently combining precomputed caches of orbital and interferometric data. Results indicate that leveraging automated optimization for constellation mission design is both practical and illuminating: generated solutions provided both evidence for existing design intuitions as well as fresh insights into novel configurations.
Scheduling the coverage of a planet by scientific instruments on board spacecrafts under observational constraints is central in a significant number of current and future missions for the exploration of the solar system. In this paper, we describe the components and algorithms of a software used to study early-phase mission design or to schedule daily operations of currently in-flight spacecraft. The scheduling problem at hand is usually a large combinatorial problem. A discretization process is described and several ordering algorithms are designed and compared. Experiments show that high-quality schedules can be produced by approaches combining reasoning about rolling, coverage and priorities.
Initially and traditionally, space access and in-space propulsion utilized combustion and expulsion of chemical “fuels” carried on board, with performance governed by the rocket equation. These chemicals produced energy, and after combustion, constituted the propulsive mass/momentum. The best such chemicals in terms of Isp that are deemed safe engineering/mission wise are hydrogen and oxygen, producing some 450 seconds of Isp. There are more reactive chemicals, such as fluorine, which have higher Isp, but also have serious safety issues. Propulsion in atmospheres can ingest atmospheric constituents (aka “airbreathing propulsion”) which provide additional propulsive mass when heated and a component of the combustion/energy generation process. This utilization of non-stored/carried propulsive mass provides partial separation of propulsive mass and energy and produces higher Isp. The other approach to separating propulsive mass and energy is to, either on board or added from offboard, supply additional energy, such as from nuclear or solar sources. This is the approach for fission nuclear thermal and electric propulsion, which can provide an Isp in excess of 800 seconds of Isp. Other processes or in addition to thermal expansion, such as electro-magnetics, can be employed to increase exit velocity and Isp. Separating propulsive mass and energy for space faring is commonly referred to as a means to circumvent the rocket equation and is capable of producing major benefits for the development of commercial deep space and space faring in general including affordable fast transits to mitigate the human health impacts of galactic cosmic radiation (GCR) and microG. The key to higher than chemical Isp, beyond H2-O2, and beyond the radiated energy and systems limitations of solar for space faring, is a light weight, high energy density source, either on board or via utilization of energy beaming to the vehicle. Traditional energy sources include chemical, heat, electrics, mechanical, photons, and nuclear. Propulsive mass can be carried on board, sourced beyond the surface of Earth via in-situ resource utilization (ISRU), and includes harvesting from atmospheres. High Isp via electromagnetic related propulsive mass acceleration requires sufficient ionization and conductivity. The major metrics for space propulsion are costs, safety, Isp, weight, and thrust level, the latter dependent upon mission requirements. High thrust for human missions is needed to reduce time exposed to radiation and microG and high thrust is required for space access. Costs of space access are reducing via reusability, printing manufacture, and robotization of manufacturing and operation. Chemical rockets provide high thrust from the expansion of the heated mass constituents at high mass flow. The other high thrust propulsion approach is magnetohydrodynamics (MHD), which, in addition to high thrust, has a high Isp of over 2,000 seconds of Isp. The VASIMIR engine offers some 5,000 seconds of Isp at high thrust [ref. 3]. Electric propulsion cycles are capable of Isp much higher than that but at low thrust levels using available energy sources. With cost as a major metric, reusable rockets and improved manufacturing and operations are rapidly greatly lowering the costs of space access, which could provide/supply in space fuel depots and affordable chemical propulsion for the desired human fast transits (e.g., some 200-day round trips to Mars). The other option for fast transits is VASIMIR, given a nuclear on-board energy source that has the requisite many megawatts of power and an alpha, kgs of weight/kW of energy produced, on the order of one (i.e., a light weight, high energy density energy source). The purpose of this report is to examine the options beyond traditional rocket engines, where propulsive mass and energy are combined, specifically the separation of propulsive mass and energy. This report considers the spectrum of advanced energetics, sources of propulsive mass, conductivity enhancement approaches, energy beaming possibilities, and candidate propulsion cycles. Suggestions are made for various combinatorial, system level, beyond traditional combustion rocket, space propulsion approaches for human deep space missions given the changing conditions of increased knowledge of deep space resources for ISRU, revolutionary energetics, and technology advancements writ large.
Delay Tolerant Networking (DTN) is the standard approach to the networking of space systems with the goal of supporting the Solar System Internet (SSI). Current space networks have a small scale and often depend on rigorously scheduled (pre-determined) contact opportunities; this manual approach inhibits scalability. The goal of this paper is to recast these scheduling problems in order to apply the optimization machinery of tropical geometry. Contact opportunities in space are dependent on such factors as orbital mechanics and asset availability, which induce time-varying connectivity; indeed, end-to-end connectivity might never occur. Routing optimization within this structure is classically difficult and typically utilizes Dijkstra's algorithm as applied to contact graphs. Alternatively, we follow the successes of tropical geometry in train schedule optimization, job assignments, and even traditional networking, by extending this approach to this more general (i.e. disconnected) problem space. These successes imply tropical geometry provides a useful framework in the context of DTNs, starting with applications to queuing theory and long-haul links. Recently, tropical geometry has been applied to parametric path optimization on graphs with variable edge weights. In this work, we extend these advances to account for the problem of routing in a space network, and find that tropical geometry is well-suited to the challenges offered by this new setting, including contact schedules featuring probabilities. Our approach leverages the combinatorial nature of the problem to give feasible shortest path trees in the presence of variable channel conditions and latency, evolving topologies, and uncertainty inherent in space routing. We discuss our tropical approach to DTN for two Python implementations, a Verilog Tropical ALU implementation, tropical frameworks for other parametric graph problems, and solution stability. Lastly, a program for future work is included to illuminate the path ahead.
Runway configuration management (RCM) is a challenging task, and it affects the efficiency of the National Airspace System (NAS) and airport surface operations significantly. Each airport, depending on the geometry, capacity, local climate patterns, etc. has multiple configurations for the runway usage for arriving and departing flights. Many factors such as the incoming/outgoing traffic load, wind direction and speed, convective weather, cloud ceiling and other environmental factors might affect the choice of a runway configuration at any point in time. However, other factors such as safety measures and regulations, noise abatement, capacity of each configuration, and preference of the air traffic controllers (ATCs) can also play a significant role in selecting the configuration. A sub-optimal selection of the runway configuration, or delay in making configuration changes might result in significant increase in taxi times for aircraft on the surface of the airport, fuel and energy use of the aircraft, and maintenance costs. It can also lead to safety concerns, such as an aircraft performing one or more go-arounds before being able to land. All these factors make RCM an extremely important and challenging decision-making process for the ATCs. The current state of practice sets the runway configuration by the ATCs based on relevant information available at the time including weather, traffic, noise abatement, safety bounds, etc. This makes the decision-making process subjective based on the accuracy of the available information and the bias in human decision making. Unfortunately, this approach yields poor results (e.g., significant delays) if the predicted outcomes are uncertain and their relative impact is not well understood. This is especially evident when the uncertainty increases the size of possible predicted outcomes (combinatorial explosion in possible scenarios) that cannot be handled by human reasoning. On the other hand, an automated approach based on machine intelligence can make use of historical data and search through all (or significant amount of) possible scenarios under uncertainty and make well-informed decisions.
In all space exploratory activities involving humans there are associated risks to health and well-being. Exposure to ionizing radiation represents one of the more serious health concerns. Although medical issues can arise shortly following the rare acute exposures, the more likely radiation-associated injuries will manifest in a delayed fashion following sub-acute exposures or protracted exposures to relatively low radiation doses. The radiological conditions that present these health risks during extraterrestrial space travel are presented in this article, along with current physical and medical countermeasures for such exposure contingencies. The aim of this article is to discuss radiation medical countermeasures that may be considered for future space exploration and travel. Biomedical advances have occurred toward the control and minimization of acute, sub-acute, and fractionated radiation exposure injuries, whether they stem from intentional application of radio-therapeutic procedures or unintentional, accidental terrestrial-based exposures. Those advances, based largely on novel bioengineering, genetic, and combinatorial pharmaceutical strategies, are briefly reviewed here, along with the promising prospects of additional, new and improved medical countering systems/agents that will be forthcoming with additional research and development efforts.
This project is testing the hypothesis that Ionizing Radiation (IR), microgravity and social isolation combine synergistically to trigger an oxidative stress response that alters immune homeostasis, brain structure/function, and neurobehavioral/cognitive performance. Specific Aims for this project are to: (1) Determine dose-response curves for acute ‘Five-Ion GCR Simulation’ exposure for immune, brain and performance responses in crew age-matched adult male and female mice; (2) Determine effects of acute ‘Five-Ion GCR Simulation’ exposure singly and in combination with simulated microgravity and social isolation, on immune, brain and performance responses in crew age-matched male and female mice mimicking deep space missions; and (3) Determine efficacy of the dietary antioxidant, Nicotinamide Mononucleotide (NMN), a key intermediate in nicotinamide adenine dinucleotide (NAD+) biosynthesis. Here we report findings from our studies of mature (24-week-old) male and female mice exposed to simulated 5-Ion GCRsim (0, 5, 15, or 50cGy) at the NASA Space Radiation Laboratory (NSRL) followed by combinatorial exposures to 15cGy, simulated microgravity via head-down tilt (hindlimb unloading) and social isolation. Immune, brain and behavioral (sensorimotor, risk-taking & cognitive) measures were acquired at ‘Acute’ (IR+24hrs, IR+72hrs), ‘Intermediate’ (IR+14 days) and ‘Delayed’ (IR+28 to IR+124 days) to inform biological responses anticipated during a transit to Mars. This project addresses NASA’s efforts to characterize risks and identify appropriate countermeasures in both women and men in anticipation of future deep space missions. Ensuring crew health and performance during extended transits necessitates that sensorimotor and cognitive abilities remain strong to avoid potentially catastrophic health and safety outcomes. Supported by the NASA Human Research Program (HRP) Human Factors Behavioral Performance Element Grant 18 18FLAG 2 0028.
Combinatorial optimization is anticipated to be one of the primary use cases for quantum computation in the coming years. The Quantum Approximate Optimization Algorithm (QAOA) and Quantum Annealing (QA) have the potential to demonstrate significant run-time performance benefits over current state-of-the-art solutions. Using existing methods for characterizing classical optimization algorithms, we analyze solution quality obtained by solving Max-Cut problems using a quantum annealing device and gate-model quantum simulators and devices. This is used to guide the development of an advanced benchmarking framework for quantum computers designed to evaluate the trade-off between run-time execution performance and the solution quality for iterative hybrid quantum-classical applications. The framework generates performance profiles through effective visualizations that show performance progression as a function of time for various problem sizes and illustrates algorithm limitations uncovered by the benchmarking approach. The framework is an enhancement to the existing open-source QED-C Application-Oriented Benchmark suite and can connect to the open-source analysis libraries. The suite can be executed on various quantum simulators and quantum hardware systems.
In its 2022 commercial market outlook, Boeing forecasted an 80% increase in the global fleet through 2041 compared to 2019 pre-pandemic levels. This sharp rise in demand will drive pressure onto airframe manufacturers to ramp up production and find more efficient ways to design and manufacture airplanes. Complicating this challenge is the industry’s recent transformation from traditional metal-based airframes towards hybrid composite-metal aircraft. While composites have been used in aviation for decades, aircraft manufacturers are still struggling to design and manufacture quality parts at a high rate. Automated Fiber Placement (AFP) is one of the main manufacturing techniques used to produce large-scale composite parts. After a design has been created, a manufacturing strategy has to be developed based on the working material, part geometry, and machine capabilities. This process planning stage is essential to the AFP workflow and currently requires a high level of manual input from an experienced process planner. In an effort to automate and optimize this stage, the Computer Aided Process Planning (CAPP) module was developed. CAPP assists process planners in identifying optimal starting point location and layup strategy for each ply of a laminate. This Ply-Level Optimization (PLO) phase operates on the quantification of ply quality through predictable geometry-based defects such as gaps, overlaps, angle deviation, and steering. As you move from PLO to Laminate-Level Optimization (LLO) the design space grows exponentially, emphasizing the need for automated optimization. The work presented in this thesis expands CAPP’s functionality by comparing the planned fiber paths through the thickness of the laminate to mitigate stacked area defects and achieve an optimal laminate-level manufacturing strategy. Within CAPP, predicted gap and overlap defects are imported from Vericut Composites Programming (VCP) and then discretized to streamline the through-thickness comparison. Two objective functions are used to score different combinations of ply layup strategies based on defect stacking both globally and locally. Four combinatorial optimization algorithms were coupled with these objective functions to investigate the laminate-level manufacturing strategy design space and converge on the optimal plan. These algorithms were evaluated based on accuracy and efficiency through virtual testing on a complex tool surface. A separate LLO approach was developed to achieve near-optimal laminates in significantly less time. The end result is a software package which greatly reduces the required input from process planners, shortening the design-build cycle time and improving part quality.
A key component of the small unmanned aircraft systems traffic management ecosystem is the design of scalable algorithms for strategic deconfliction of drones prior to takeoff. In this work, we focus on efficient flow management of drones on a network of intersecting edges subject to two kinds of spacing constraints: 1) between any two adjacent vehicles on an edge and 2) between any two vehicles on two different edges arriving one after the other at an intersection. The spacing is designed to enable non-intersection of operational volumes corresponding to two different vehicles thereby properly separating the vehicles inside each volume. For simplicity, we assume a constant ground speed for the drones and fixed dimensions for the operational volume blocks. The deconfliction is managed by adjusting the takeoff time of the drones, thereby regulating their arrival time at various crossing waypoints in the network. This framework allows us to study the maximum flow (throughput) of vehicles on a network of edges connecting depots to drop off sites subject to the temporal spacing constraints. The departure scheduling of individual drones results in a combinatorial optimization problem. To alleviate this, we solve a max-flow formulation and use queueing theory to simplify the analysis and provide upper bounds to the underlying optimization problem for individual drone departure scheduling. Our results indicate that throughput drops rapidly after the density of drones in the network passes the max-flow limits.
As NASA plans for exploration beyond Low-Earth Orbit, it is imperative to understand how deep spaceflight stressors impact human physiology. Deep space exposes crew members to multiple NASA-defined risks, including altered gravity, ionizing radiation, and social isolation. While the effects of individual stressors have been studied, there are still gaps in our understanding of their combined effects. In this study, we utilized the simulated five-ion galactic cosmic radiation (GCR) and isolation to assess the combinatorial effects of space stressors on the central nervous system (CNS) in mice. Our study captures sexually dimorphic responses by quantifying CNS outcomes in crewage-matched male and female mice. We assessed CNS responses in mice exposed to four days of isolation, followed by three different dosage of acute radiation exposure (5 cGy, 15 cGy, and 50 cGy 5-ion GCRsim). Comprehensive hippocampal cytokine biomarker analyses were conducted in both males and females at two weeks post-irradiation (intermediate cohort) and 124 days post-irradiation (delayed cohort). Proteomics on hippocampal lysates were conducted in the delayed cohort. Our results provide evidence for sex-specific differentially regulated hippocampal cytokines and proteomic profiles. Immunohistochemical analysis of the mice brain from intermediate and delayed cohorts is currently underway for multiple protein markers, including microglial activation, astrocytes, and dopaminergic neuronal population, among others. This project closely aligns with NASA’s efforts to characterize sex-specific risks associated with deep space exploration. We anticipate the results from this project will aid in our understanding of spaceflight stressors to ensure crew health and performance.
The interactions that occur in human-teaming are inspiring novel aerospace designs aimed at improving how humans and machines, or multiple machines, work together. Unfortunately, current Systems Engineering processes are ill-equipped to handle these complex relationships and are unable to design and assure the safety for these systems. To close part of this gap, this paper introduces a novel system-theoretic analytical process to identify unsafe collaborative control actions. It is part of a broader set of techniques that extend the state-of-the-art in hazard analysis, System Theoretic Process Analysis (STPA), to systematically address collaboration. The method rigorously expresses the different ways multiple commands may be unsafe together. Using Systems Theory, it employs abstraction to manage the combinatorial complexity in enumerating control contributions from multiple collaborating components. An algorithm integrates these concepts into an end-to-end process and is supported by automation to enumerate, refine, prune, and prioritize unsafe combinations of control actions. The output of the method feeds the specification of system requirements to implement safety-guided design starting early in concept development. The process is demonstrated on a manned-unmanned aircraft teaming case study and finds new causal factors that were not previously found in a past hazard analysis of the same system.
Unmanned Aerial Vehicles (UAVs) are increasingly used in a wide range of applications such as cinematography, package delivery, and surveying. As a result, regulators have become interested in developing UAV Traffic Management (UTM) systems to coordinate UAV traffic. One possible framework for UTM is a combinatorial auction. Under this framework, airspace is modeled as a grid of space-time cells. UAV operators bid on sets of cells which collectively form flight paths for their UAVs. An ideal airspace auction should: be fair, be incentive-aligned, be scalable, allocate airspace economically-efficiently, enable price discovery, and reduce the work required to participate where possible. In this paper, we propose the first auction mechanism for airspace allocation that meets the criteria above. Our mechanism: (a) is provably economically-efficient, fair and incentive-aligned, (b) shares pricing information with bidders and (c) has features which reduce the burden of participating. We evaluate our mechanism on scenarios based on a Japan Aerospace Exploration Agency (JAXA) case study and find that it can scale to 26,000 bids.
Introduction: Historically, the focus of planetary protection at NASA has been on unmanned, robotic missions. Such missions have paved the way for understanding how to implement planetary protection in a feasible and cost-sensitive way. However, with the introduction of crewed missions to Mars in the not-sodistant future, there is a need to better define and understand how to implement planetary protection under new circumstances, as well as understand the risk of contaminating Mars. One unavoidable fact is that microbes will go where humans go. Therefore, it is critical to understand how these microbes may (and will) impact our ability to conduct meaningful, reliable astrobiological science. Microorganisms have spent millions of years evolving to survive in extreme environments here on Earth. Already there are indications that microbes aboard the International Space Station evolve and adapt to life in low earth orbit. The microbes that are eventually taken to Mars with humans will also adapt, potentially causing harmful effects to crew and/or the planetary or astrobiological science conducted. Therefore, it is of critical interest that we evaluate and characterize the potential risks of microbial evolution on Mars. It is expected that microbes carried by humans will begin to evolve to new environments even before landing on Mars, during the several month cruise phase. Once landed, microbes will encounter different stressors within the crew habitats on Mars. During extravehicular activities, venting, or other release events, microbes will find their way out onto the Martian surface. The induced environments around crewed systems will create potentially-favorable conditions for microbes to continue evolving on Mars. Eventually, microbes may find their way beyond the close confines of the crewed area and continue evolving so as to fill new or distant niches on the Martian surface. It is challenging to replicate Martian environments here on Earth, making it nearly impossible to predict the evolutionary changes that microbes would undergo on Mars. But this work is critical. Serial passaging experiments performed by Richard Lenski on E. coli show the dramatic changes microbes can undergo even within a laboratory setting. Furthermore, experiments performed by Michael Baym also demonstrate the power of single mutations in microbial development of antibiotic resistance [3]. Long duration experiments should be performed on a suite of microbes exposed to environments likely to be experienced on the Martian surface. While simulating space environments can be challenging, facilities exist that can achieve individual and combinatorial environmental conditions to simulate space and planetary conditions. Such chambers should be employed for microbial studies. Currently, at the Marshall Space Flight Center, we have used various stressors like drying, vacuum, proton radiation, and ultraviolet light both separately and in combination, to evaluate the survival of cleanroom microbes. Shockingly, several non-spore forming isolates have demonstrated the ability to survive many extreme conditions (manuscript in preparation). These short duration exposures must be augmented with larger and more gradual studies to replicate what microbes might experience in the transition from cruise, to surface habitats, to induced surface environments, and finally true Martian environments. While no Earth-based experiment can perfectly replicate the Martian environment, nor could we test every possible microbe in simulation experimental regimes, efforts should be made to examine the evolutionary potential of the “usual suspects” seen on the ISS or in other crewed environments to begin to fill this important knowledge gap.