Optimization of Space Environments and Materials Related Resources
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Burns, H. DeWitt↗
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Optimizing Medical Kits for Spaceflight
The Integrated Medical Model (IMM) is a probabilistic model that estimates medical event occurrences and mission outcomes for different mission profiles. IMM simulation outcomes describing the impact of medical events on the mission may be used to optimize the allocation of resources in medical kits. Efficient allocation of medical resources, subject to certain mass and volume constraints, is crucial to ensuring the best outcomes of in-flight medical events. We implement a new approach to this medical kit optimization problem. METHODS We frame medical kit optimization as a modified knapsack problem and implement an algorithm utilizing a dynamic programming technique. Using this algorithm, optimized medical kits were generated for 3 different mission scenarios with the goal of minimizing the probability of evacuation and maximizing the Crew Health Index (CHI) for each mission subject to mass and volume constraints. Simulation outcomes using these kits were also compared to outcomes using kits optimized..RESULTS The optimized medical kits generated by the algorithm described here resulted in predicted mission outcomes more closely approached the unlimited-resource scenario for Crew Health Index (CHI) than the implementation in under all optimization priorities. Furthermore, the approach described here improves upon in reducing evacuation when the optimization priority is minimizing the probability of evacuation. CONCLUSIONS This algorithm provides an efficient, effective means to objectively allocate medical resources for spaceflight missions using the Integrated Medical Model.
Optimization↗
Hodograph analysis in aircraft trajectory optimization
An account is given of key geometrical concepts involved in the use of a hodograph as an optimal control theory resource which furnishes a framework for geometrical interpretation of the minimum principle. Attention is given to the effects of different convexity properties on the hodograph, which bear on the existence of solutions and such types of controls as chattering controls, 'bang-bang' control, and/or singular control. Illustrative aircraft trajectory optimization problems are examined in view of this use of the hodograph.
Cliff, Eugene M.↗
Learning to Control Advanced Life Support Systems
Advanced life support systems have many interacting processes and limited resources. Controlling and optimizing advanced life support systems presents unique challenges. In particular, advanced life support systems are nonlinear coupled dynamical systems and it is difficult for humans to take all interactions into account to design an effective control strategy. In this project. we developed several reinforcement learning controllers that actively explore the space of possible control strategies, guided by rewards from a user specified long term objective function. We evaluated these controllers using a discrete event simulation of an advanced life support system. This simulation, called BioSim, designed by Nasa scientists David Kortenkamp and Scott Bell has multiple, interacting life support modules including crew, food production, air revitalization, water recovery, solid waste incineration and power. They are implemented in a consumer/producer relationship in which certain modules produce resources that are consumed by other modules. Stores hold resources between modules. Control of this simulation is via adjusting flows of resources between modules and into/out of stores. We developed adaptive algorithms that control the flow of resources in BioSim. Our learning algorithms discovered several ingenious strategies for maximizing mission length by controlling the air and water recycling systems as well as crop planting schedules. By exploiting non-linearities in the overall system dynamics, the learned controllers easily out- performed controllers written by human experts. In sum, we accomplished three goals. We (1) developed foundations for learning models of coupled dynamical systems by active exploration of the state space, (2) developed and tested algorithms that learn to efficiently control air and water recycling processes as well as crop scheduling in Biosim, and (3) developed an understanding of the role machine learning in designing control systems for advanced life support.
Subramanian, Devika↗
IAF 15 Draft Paper
With the International Space Station Program transition from assembly to utilization, focus has been placed on the optimization of essential resources. This includes resources both resupplied from the ground and also resources produced by the ISS. In an effort to improve the use of two of these, the ISS Engineering teams, led by the ISS Program Systems Engineering and Integration Office, undertook an effort to modify the techniques use to perform several key on-orbit events. The primary purposes of this endeavor was to make the ISS more efficient in the use of the Russian-supplied fuel for the propulsive attitude control system and also to minimize the impacts to available ISS power due to the positioning of the ISS solar arrays. Because the ISS solar arrays are sensitive to several factors that are present when propulsive attitude control is used, they must be operated in a manner to protect them from damage. This results in periods of time where the arrays must be positioned, rather than autonomously tracking the sun, resulting in negative impacts to power generated by the solar arrays and consumed by both the ISS core systems and payload customers. A reduction in the number and extent of the events each year that require the ISS to use propulsive attitude control simultaneously accomplishes both these goals. Each instance where the ISS solar arrays normal sun tracking mode must be interrupted represent a need for some level of powerdown of equipment. As the magnitude of payload power requirements increases, and the efficiency of the ISS solar arrays decreases, these powerdowns caused by array positioning, will likely become more significant and could begin to negatively impact the payload operations. Through efforts such as this, the total number of events each year that require positioning of the arrays to unfavorable positions for power generation, in order to protect them against other constraints, are reduced. Optimization of propulsive events and transitioning some of them to non-propulsive CMG control significantly reduces propellant usage on the ISS leading to the reduction of the propellant delivery requirement. This results in move available upmass that can be used for delivering critical dry cargo, additional water, air, crew supplies and science experiments.
Menkin, Evgeny↗
A methodology to compare policies for exploring the solar system
Solar system unmanned exploration decision making, discussing resource utilization, sequence optimization, etc
Chamberlain, R. G.↗
SURE (Science User Resource Expert): A science planning and scheduling assistant for a resource based environment
SURE (Science User Resource Expert) is one of three components that compose the SURPASS (Science User Resource Planning and Scheduling System). This system is a planning and scheduling tool which supports distributed planning and scheduling, based on resource allocation and optimization. Currently SURE is being used within the SURPASS by the UARS (Upper Atmospheric Research Satellite) SOLSTICE instrument to build a daily science plan and activity schedule and in a prototyping effort with NASA GSFC to demonstrate distributed planning and scheduling for the SOLSTICE II instrument on the EOS platform. For the SOLSTICE application the SURE utilizes a rule-based system. Development of a rule-based program using Ada CLIPS as opposed to using conventional programming, allows for capture of the science planning and scheduling heuristics in rules and provides flexibility in inserting or removing rules as the scientific objectives and mission constraints change. The SURE system's role as a component in the SURPASS, the purpose of the SURE planning and scheduling tool, the SURE knowledge base, and the software architecture of the SURE component are described.
Thalman, Nancy E.↗
Cargo transfer vehicle RCS propellant contamination issues
The purpose of this report is to address Cargo Transfer Vehicle (CTV) RCS contamination issues and contribute to the resources necessary to optimize the vehicle and propulsion systems required in the CTV of the National Launch System (NLS) Heavy Lift Launch Vehicle (HLLV). This study reviews the thruster-induced contaminants; their transportation from the thrust chamber to the vehicle, payload, and SSF; and the mechanism by which damage is inflicted on their components. The effect of both monopropellant and bipropellant RCS rocket exhaust plumes on a spacecraft and related functional surfaces has been the subject of considerable study over the years. It is recognized that the RCS rocket produces contaminants which can significantly degrade the performance of optical windows, solar cells, thermal-protective coatings, and other external vehicle components. This is particularly true when the rocket is operating in the pulse mode. The exhaust plume impingement pressure and heat-transfer phenomena also complicate the environment to which the vehicle and its functional surfaces are exposed, but are not addressed in this study. Bipropellant contamination presented several modes of damage to incident surfaces, which can pose a long-term deleterious consequence to CTV payloads and the Space Station Freedom (SSF). Monopropellant contamination did not pose any significant long-term issues other than the possibility of aniline deposition. The use of either bipropellant and monopropellant propulsion systems can have a design impact on the CTV propulsion system with respect to maneuvering operations in the proximity of SSF.
Ballard, Richard O.↗
Ionic signaling in plant gravity and touch responses
Plant roots are optimized to exploit resources from the soil and as each root explores this environment it will encounter a range of biotic and abiotic stimuli to which it must respond. Therefore, each root must possess a sensory array capable of monitoring and integrating these diverse stimuli to direct the appropriate growth response. Touch and gravity represent two of the biophysical stimuli that plants must integrate. As sensing both of these signals requires mechano-transduction of biophysical forces to biochemical signaling events, it is likely that they share signal transduction elements. These common signaling components may allow for cross-talk and so integration of thigmotropic and gravitropic responses. Indeed, signal transduction events in both plant touch and gravity sensing are thought to include Ca(2+)- and pH-dependent events. Additionally, it seems clear that the systems responsible for root touch and gravity response interact to generate an integrated growth response. Thus, primary and lateral roots of Arabidopsis respond to mechanical stimuli by eliciting tropic growth that is likely part of a growth strategy employed by the root to circumvent obstacles in the soil. Also, the mechano-signaling induced by encountering an obstacle apparently down-regulates the graviperception machinery to allow this kind of avoidance response. The challenge for future research will be to define how the cellular signaling events in the root cap facilitate this signal integration and growth regulation. In addition, whether other stimuli are likewise integrated with the graviresponse via signal transduction system cross-talk is an important question that remains to be answered.
Review↗
Incorporation of a Chemical Equilibrium Equation of State into LOCI-Chem
Renewed interest in development of advanced high-speed transport, reentry vehicles and propulsion systems has led to a resurgence of research into high speed aerodynamics. As this flow regime is typically dominated by hot reacting gaseous flow, efficient models for the characteristic chemical activity are necessary for accurate and cost effective analysis and design of aerodynamic vehicles that transit this regime. The LOCI-Chem code recently developed by Ed Luke at Mississippi State University for NASA/MSFC and used by NASA/MSFC and SSC represents an important step in providing an accurate, efficient computational tool for the simulation of reacting flows through the use of finite-rate kinetics [3]. Finite rate chemistry however, requires the solution of an additional N-1 species mass conservation equations with source terms involving reaction kinetics that are not fully understood. In the equilibrium limit, where the reaction rates approach infinity, these equations become very stiff. Through the use of the assumption of local chemical equilibrium the set of governing equations is reduced back to the usual gas dynamic equations, and thus requires less computation, while still allowing for the inclusion of reacting flow phenomenology. The incorporation of a chemical equilibrium equation of state module into the LOCI-Chem code was the primary objective of the current research. The major goals of the project were: (1) the development of a chemical equilibrium composition solver, and (2) the incorporation of chemical equilibrium solver into LOCI-Chem. Due to time and resource constraints, code optimization was not considered unless it was important to the proper functioning of the code.
Cox, Carey F.↗
The Development of Jomo Kenyatta International Airport as a Regional Aviation Hub
Air transportation plays an important role in the social and economic development of the global system and the countries that seek to participate in it. As Africa seeks to take its place in the global economy, it is increasingly looking to aviation as the primary means of connecting its people and goods with the world. It has been suggested that Africa as a continent needs to move toward a system of hubs to optimize its scarce resources. Jomo Kenyatta International Airport in Nairobi, Kenya, is one of the airports in the eastern region of Africa that is seeking to fill this role. This paper discusses the prospects for success and the challenges that it will need to overcome, including projections through 2020 for the growth in passenger and cargo traffic.
Irandu, Evaristus M.↗
Self-Adjusting Hash Tables for Embedded Flight Applications
A common practice in computer science to associate a value with a key is to use a class of algorithms called a hash-table. These algorithms enable rapid storage and retrieval of values based upon a key. This approach assumes that many keys will need to be stored immediately. A new set of hash-table algorithms optimally uses system resources to ideally represent keys and values in memory such that the information can be stored and retrieved with a minimal amount of time and space. These hash-tables support the efficient addition of new entries. Also, for large data sets, the look-up time for large data-set searches is independent of the number of items stored, i.e., O(1), provided that the chance of collision is low.
James, Mark↗
What Can We Learn from One Billion Ground System Log Messages?
Shortage of log-based data in a ground system they have traditionally been the under achievers in a satellite ground system. This is due to several factors: Once log messages scroll out of view on the TTC event console window they are soon forgotten. Application and system log files are scattered across directories within a system, across a multitude of servers, and across one or more databases making access cumbersome. Typical tools to perform log file content searching are generally crude and typically only employed as part of trouble-shooting exercises.As we move towards satellite constellations and fleets and add even more status information, the number of messages keeps growing. One mission now estimates that they could generate 3,000,000 messages per day 1 billion per year - for the life of their mission. What to do with those 1 billion messages? That is the challenge. With the recent technological advances in the management of large data sets, text-based processing, and data analytics, there are now capabilities that we can provide to the ground system engineers and satellite operators to address what we postulate are missed opportunities. Advanced real-time log analysis can allow us to be less reactionary in favor of being more proactive. Analytics goals include the ability to: Identify root cause of unexpected events, failures or error conditions enabled by correlating disparate data. Detect security breaches attempts before they are successful. Help admins ensure IT resources continue running optimally. Identify trends and patterns that may indicate impending failures or error conditions for valuable assets before they happen. Compare satellites in a fleet or constellation in terms of number of alarms, number of command sent to them, etc.. Answer questions like "Are the operations support needs increasing over the past year?" or "Have we seen this combination of alarm conditions before?" But really, once the tools are readily available the users will start realizing what can be done with their new powers. In this presentation we will show the results of analyzing millions of actual mission operations log messages, how the results can be displayed to the user, and how new products now available as open source can be applied to the challenges of large scale time-tagged text-based mission operations messages. Flight operations team members believe that this is a powerful new option for how they assess overall system and space asset health. Technical descriptions of the design, tools, and storage will be provided. One billion messages? Bring'em on!
Orsborne, Sharon↗
Quantifying the Sensitivity of Condition Incidence Parameters in the Evidence Library
One approach to quantifying spaceflight risk at NASA makes use event driven probabilistic techniques. The Medical Extensible Dynamic Probabilistic Risk Assessment Tool (MEDPRAT) is such a tool that estimates medical risk metrics via simulation and enables optimization of medical resources subject to mission constraints [1]. Previous analyses have informed medical set composition, exercise countermeasures, and water intake, where each analysis quantifies the risk associated with proposed variations in system design. As future mission profiles extend beyond Low-Earth Orbit (LEO) and lengthen in duration, understanding these risks and contributing factors is critical. MEDPRAT employs Monte Carlo sampling techniques to simulate missions and track the occurrence of medical events. These events follow fault-tree-like progressions through levels of severity and mitigation via medical treatment to many possible outcomes and these are reported throughout the mission. Making this possible, are the medical databases that contain evidence gathered by the Human Research Program (HRP). Quantifying the impact of uncertainty or variability in the input data is an important step in evaluating the credibility of modeling and simulation results. In this work, we investigate the sensitivity of medical risk metrics with respect to the condition incidence parameters within the Evidence Library (EL) [2] as the medical database input for MEDPRAT. The medical conditions, contained in the EL, are equipped with incidence rates that describe the likelihood that the condition will occur. These incidence rates reflect historical spaceflight data or when appropriate, terrestrial data. In this presentation, we will explore how uncertainty in these rates propagate to the medical risk described by MEDPRAT. These results identify the conditions and parameters with the largest contribution to medical risks.
Ian Lim↗
Tracing structural optima as a function of available resources by a homotopy method
Optimization problems are typically solved by starting with an initial estimate and proceeding iteratively to improve it until the optimum is found. The design points along the path from the initial estimate to the optimum are usually of no value. The present work proposes a strategy for tracing a path of optimum solutions parameterized by the amount of available resources. The paper specifically treats the optimum design of a structure to maximize its buckling load. Equations for the optimum path are obtained using Lagrange multipliers, and solved by a homotopy method. The solution path has several transitions from unimodal to bimodal solutions. The Lagrange multipliers and second-order optimality conditions are used to detect branching points and to switch to the optimum solution path. The procedure is applied to the design of a foundation which supports a column for maximum buckling load. Using the total available foundation stiffness as a homotopy parameter, a set of optimum foundation designs is obtained.
Haftka, Raphael T.↗
Planning with Continuous Resources in Stochastic Domains
We consider the problem of optimal planning in stochastic domains with metric resource constraints. Our goal is to generate a policy whose expected sum of rewards is maximized for a given initial state. We consider a general formulation motivated by our application domain--planetary exploration--in which the choice of an action at each step may depend on the current resource levels. We adapt the forward search algorithm AO* to handle our continuous state space efficiently.
Mausam, Mausau↗
Computer-Simulation Surrogates for Optimization: Application to Trapezoidal Ducts and Axisymmetric Bodies
Engineering design and optimization efforts using computational systems rapidly become resource intensive. The goal of the surrogate-based approach is to perform a complete optimization with limited resources. In this paper we present a Bayesian-validated approach that informs the designer as to how well the surrogate performs; in particular, our surrogate framework provides precise (albeit probabilistic) bounds on the errors incurred in the surrogate-for-simulation substitution. The theory and algorithms of our computer{simulation surrogate framework are first described. The utility of the framework is then demonstrated through two illustrative examples: maximization of the flowrate of fully developed ow in trapezoidal ducts; and design of an axisymmetric body that achieves a target Stokes drag.
Otto, John C.↗
Steps Toward Optimal Competitive Scheduling
This paper is concerned with the problem of allocating a unit capacity resource to multiple users within a pre-defined time period. The resource is indivisible, so that at most one user can use it at each time instance. However, different users may use it at different times. The users have independent, se@sh preferences for when and for how long they are allocated this resource. Thus, they value different resource access durations differently, and they value different time slots differently. We seek an optimal allocation schedule for this resource. This problem arises in many institutional settings where, e.g., different departments, agencies, or personal, compete for a single resource. We are particularly motivated by the problem of scheduling NASA's Deep Space Satellite Network (DSN) among different users within NASA. Access to DSN is needed for transmitting data from various space missions to Earth. Each mission has different needs for DSN time, depending on satellite and planetary orbits. Typically, the DSN is over-subscribed, in that not all missions will be allocated as much time as they want. This leads to various inefficiencies - missions spend much time and resource lobbying for their time, often exaggerating their needs. NASA, on the other hand, would like to make optimal use of this resource, ensuring that the good for NASA is maximized. This raises the thorny problem of how to measure the utility to NASA of each allocation. In the typical case, it is difficult for the central agency, NASA in our case, to assess the value of each interval to each user - this is really only known to the users who understand their needs. Thus, our problem is more precisely formulated as follows: find an allocation schedule for the resource that maximizes the sum of users preferences, when the preference values are private information of the users. We bypass this problem by making the assumptions that one can assign money to customers. This assumption is reasonable; a committee is usually in charge of deciding the priority of each mission competing for access to the DSN within a time period while scheduling. Instead, we can assume that the committee assigns a budget to each mission.This paper is concerned with the problem of allocating a unit capacity resource to multiple users within a pre-defined time period. The resource is indivisible, so that at most one user can use it at each time instance. However, different users may use it at different times. The users have independent, se@sh preferences for when and for how long they are allocated this resource. Thus, they value different resource access durations differently, and they value different time slots differently. We seek an optimal allocation schedule for this resource. This problem arises in many institutional settings where, e.g., different departments, agencies, or personal, compete for a single resource. We are particularly motivated by the problem of scheduling NASA's Deep Space Satellite Network (DSN) among different users within NASA. Access to DSN is needed for transmitting data from various space missions to Earth. Each mission has different needs for DSN time, depending on satellite and planetary orbits. Typically, the DSN is over-subscribed, in that not all missions will be allocated as much time as they want. This leads to various inefficiencies - missions spend much time and resource lobbying for their time, often exaggerating their needs. NASA, on the other hand, would like to make optimal use of this resource, ensuring that the good for NASA is maximized. This raises the thorny problem of how to measure the utility to NASA of each allocation. In the typical case, it is difficult for the central agency, NASA in our case, to assess the value of each interval to each user - this is really only known to the users who understand their needs. Thus, our problem is more precisely formulated as follows: find an allocation schedule for the resource that maximizes the sum ofsers preferences, when the preference values are private information of the users. We bypass this problem by making the assumptions that one can assign money to customers. This assumption is reasonable; a committee is usually in charge of deciding the priority of each mission competing for access to the DSN within a time period while scheduling. Instead, we can assume that the committee assigns a budget to each mission.
Frank, Jeremy↗