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We present features for a second-generation thermomechanically compensated sapphire resonator. The new design shares the short thermal time constants characteristic of previously developed 10K and 77K CSO resonators. This, together with a thermal ballast methodology, allows effective compensation of temperature fluctuations over a wide range of time scales.
For planning of Mars surface missions, to be operated on a sol-by-sol basis by a team on Earth (where a "sol" is a Martian day), activities are described in terms of "sol types" that are strung together to build a surface mission scenario. Some sol types require ground decisions based on a previous sol's results to feed into the activity planning ("ground in the loop"), while others do not. Due to the differences in duration between Earth days and Mars sols, for a given Mars local solar time, the corresponding Earth time "walks" relative to the corresponding times on the prior sol/day. In particular, even if a communication window has a fixed Mars local solar time, the Earth time for that window will be approximately 40 minutes later each succeeding day. Further complexity is added for non-Mars synchronous communication relay assets, and when there are multiple control centers in different Earth time zones. The solution is the development of "ops efficiency factors" that reflect the efficiency of a given operations configuration (how many and location of control centers, types of communication windows, synchronous or non-synchronous nature of relay assets, sol types, more-or-less sustainable operations schedule choices) against a theoretical "optimal" operations configuration for the mission being studied. These factors are then incorporated into scenario models in order to determine the surface duration (and therefore minimum spacecraft surface lifetime) required to fulfill scenario objectives. The resulting model is used to perform "what-if" analyses for variations in scenario objectives. The ops efficiency factor is the ratio of the figure of merit for a given operations factor to the figure of merit for the theoretical optimal configuration. The current implementation is a pair of models in Excel. The first represents a ground operations schedule for 500 sols in each operations configuration for the mission being studied (500 sols was chosen as being a long enough time to capture variations in relay asset interactions, Earth/Mars time phasing, and seasonal variations in holidays). This model is used to estimate the ops efficiency factor for each operations configuration. The second model in a separate Excel spreadsheet is a scenario model, which uses the sol types to rack up the total number of "scenario sols" for that scenario (in other words, the ideal number of sols it would take to perform the scenario objectives). Then, the number of sols requiring ground in the loop is calculated based on the soil types contained in the given scenario. Next, the scenario contains a description of what sequence of operations configurations is used, for how many days each, and this is used with the corresponding ops efficiency factors for each configuration to calculate the "ops duration" corresponding to that scenario. Finally, a margin is applied to determine the minimum surface lifetime required for that scenario. Typically, this level of analysis has not been performed until much later in the mission, and has not been able to influence mission design. Further, the notion of moving to sustainable operations during Prime Mission - and the effect that that move would have on surface mission productivity and mission objective choices - has not been encountered until the most recent rover missions (MSL and Mars 2018).
Research at the Environmental Research Lab in support of Biosphere 2 was both basic and applied in nature. One aspect of the applied research involved the use of biological reactors for the scrubbing of trace atmospheric organic contaminants. The research involved a quantitative study of the efficiency of operation of Soil Bed Reactors (SBR) and the optimal operating conditions for contaminant removal. The basic configuration of a SBR is that air is moved through a living soil that supports a population of plants. Upon exposure to the soil, contaminants are either passively adsorbed onto the surface of soil particles, chemically transformed in the soil to usable compounds that are taken up by the plants or microbes as a metabolic energy source and converted to CO2 and water.
As part of the NASA Next Space Technologies for Exploration Partnerships (NextSTEP) Trash Compaction and Processing System (TCPS) program, the Generation 2 (Gen 2) Heat Melt Compactor (HMC) tests a variety of trash models (nominal, high liquid, and high cloth) in order to evaluate the technical risk associated with processing spacecraft trash. The trash components used in the trash models not only impacts the final solid trash disk quality, but also the water and gas effluent compositions. In order to better design an auxiliary system to treat the effluent streams, both the input trash components and the effluent must be characterized. Evolved gas analysis (EGA) methods are used to characterize the input trash components. The EGA results will be used to determine various HMC operational parameters, as well as to determine the optimal operation for the TCPS system.
The IMMs ability to assess mission outcome risk levels relative to available resources provides a unique capability to provide guidance on optimal operational medical kit and vehicle resources. Post-processing optimization allows IMM to optimize essential resources to improve a specific model outcome such as maximization of the Crew Health Index (CHI), or minimization of the probability of evacuation (EVAC) or the loss of crew life (LOCL). Mass and or volume constrain the optimized resource set. The IMMs probabilistic simulation uses input data on one hundred medical conditions to simulate medical events that may occur in spaceflight, the resources required to treat those events, and the resulting impact to the mission based on specific crew and mission characteristics. Because IMM version 4.0 provides for partial treatment for medical events, IMM Optimization 4.0 scores resources at the individual resource unit increment level as opposed to the full condition-specific treatment set level, as done in version 3.0. This allows the inclusion of as many resources as possible in the event that an entire set of resources called out for treatment cannot satisfy the constraints. IMM Optimization version 4.0 adds capabilities that increase efficiency by creating multiple resource sets based on differing constraints and priorities, CHI, EVAC, or LOCL. It also provides sets of resources that improve mission-related IMM v4.0 outputs with improved performance compared to the prior optimization. The new optimization represents much improved fidelity that will improve the utility of the IMM 4.0 for decision support.
A hierarchical structured control model was used to describe pilot performance in a multivariable control problem. The model is based on the assumption that a well trained operator optimized a quadratic optimization criterion. The model considers some special characteristics of the human operator as time delay, neuromuscular time lag, observation and motor noise and the ability to extract the first derivative of a displaced value. Numerical results of a simple human engineering example using the BBN model demonstrate the influence of the variation of different parameters on covariance matrices of the state and observation vectors.
Slide 1] The Desert Research and Technology Studies (DRATS) include large scale field tests of manned lunar surface exploration systems; these tests are sponsored by the Director s Office of Integration (DOI) [sic, Directorate Integration Office (DIO)] within the Constellation Program and they include geological exploration objectives along well designed traverses. These traverses are designed by the Traverse Team, an ad hoc group of some 10 geologists form NASA and academia, as well as experts in mission operation who define the operational constraints applicable to specific simulation scenarios. [Slide 2] These DRATS/DOI tests focus on 1) the performance of major surface systems, such as rovers, mobile habitats, communication architecture, navigation tools, earth-moving equipment, unmanned reconnaissance robots etc. under realistic field conditions and 2) the development of operational concepts that integrate all of these systems into a single, optimized operation. The participation of science is currently concentrating on geological sciences, with the objective of developing suitable tools and documentation protocols to sample representative rocks for Earth return, and to generate some conceptual understanding of the ground support structure that will be needed for the real time science-support of a lunar surface crew. [Slide 3] Major surface systems exercised in the June 2008 analog tests at the Moses Lake site, WA. [Upper left] The Chariot Rover (developed at Johnson Space Center) is an unpressurized vehicle driven by fully suited crews. [Upper right] Mobile Habitat provided by the Jet Propulsion Laboratory. Chariot is the more nimble and mobile vehicle and the idea is to drive the habitat remotely to some rendezvous place where Chariot would catch up - after a lengthy traverse - at the end of the day. [Lower left] The K-10 remotely operated robot (provided by NASA Ames Research Center) conducting scientific/geologic reconnaissance of the prospective traverse region, locating specific sites for more detailed exploration by Chariot and its crew. [Lower right] This earth-moving equipment (provided by NASA KSC) can be attached to Chariot and is envisioned to, for example, level an outpost site or to mine lunar soi
Due to the electric propulsion's inherent propellant mass savings over chemical propulsion, electric propulsion orbit transfer vehicles (EPOTV's) are highly efficient mode of orbit transfer. When selecting an electric propulsion device (ion, MPD, or arcjet) and propellant for a particular mission, it is preferable to use quick, analytical system optimization methods instead of time intensive numerical integration methods. It is also of interest to determine each thruster's optimal operating characteristics for a specific mission. Analytical expressions are derived which determine the optimal specific impulse (Isp) for each type of electric thruster to maximize payload fraction for a desired thrusting time. These expressions take into account the variation of thruster efficiency with specific impulse. Verification of the method is made with representative electric propulsion values on a LEO-to-GEO mission. Application of the method to specific missions is discussed.
The national airspace has evolved over many years to accommodate increased traffic demand while simultaneously maintaining air travel as one of the safest forms of transportation. One of the reasons for this success is the ability of the air traffic control system and the operators to adapt and accommodate to situations that routinely disrupt normal operations. These situations may include: adverse weather, delays, early arrivals, equipment outages, and other factors that are outside the operators’ ability to control. These factors can lead to states where automation is unable to properly handle these issues, and therefore air traffic controllers and pilots have to intervene — ultimately increasing communication between operators resulting in higher workload. As controller workload increases to handle sub-optimal operating conditions, complexity increases. This is because, under these conditions humans are required to make tactical decisions in response to external factors. This results in a departure from the original strategic plan where operations would be more efficiently managed. Human operators manage airspace complexity under rigid regulations but in a constantly changing environment. The airspace is divided into sectors and the number of aircraft assigned to each controller is limited for safe handling. Some prior studies devised airspace complexity metrics in commercial aviation and related these metrics to controller workload. The upper bounds on the system load are pre-determined. Such bounds on complexity make for a safe system, but the system cannot scale and adapt to autonomous, dense, and heterogeneous traffic — including the many types of Unmanned Aerial Vehicles (UAVs) envisioned to be added to the operations. We hypothesize that, as traffic density and heterogeneity grow, and other key metrics change, there will be phase transitions at which the way traffic should be managed changes significantly. We offer a method for in-time detection of contributing factors that lead to phase transitions, characterized by increased complexity. To the best of our knowledge, there is no tool similar to ours that identifies such contributing factors or precursor patterns.
The physical design and operation of electric aircraft like NASA Maxwell X-57 are significantly different than conventionally fueled aircraft. Operational optimization will require close coupling of aerodynamics, propulsion, and power. To address the uncertainty of electric aircraft operation, a system level Mission Planning Tool is developed to simulate all aircraft trajectory phases: taxi, motor run-up, takeoff, climb, cruise, and descent. The Mission Planning Tool captures performance parameters at each point of the trajectory including battery state of charge, the temperatures of components in the electrical system, and propulsion system thrust. This work describes the modeling of each mission phase, and compares the results of simulating a user-specified trajectory, and using a collocated optimal control approach to determine an optimal trajectory. The results show that optimization of the mission show a significant increase in the final battery state of charge over the user- specified simulation strategy. These results will inform the operation of the NASA Maxwell X-57 test flights that will take place this year.
Five different hybrid solar power systems using silicon solar cells to produce thermal and electric power are modeled and compared with a hybrid system using a GaAs cell. Among the indices determined are capital cost per unit electric power plus mechanical power, annual cost per unit electric energy, and annual cost per unit electric plus mechanical work. Current costs are taken to be $35,000/sq m for GaAs cells with an efficiency of 15% and $1000/sq m for Si cells with an efficiency of 10%. It is shown that hybrid systems can be competitive with existing methods of practical energy conversion. Limiting values for annual costs of Si and GaAs cells are calculated to be 10.3 cents/kWh and 6.8 cents/kWh, respectively. Results for both systems indicate that for a given flow rate there is an optimal operating condition for minimum cost photovoltaic output. For Si cell costs of $50/sq m optimal performance can be achieved at concentrations of about 10; for GaAs cells costing 1000/sq m, optimal performance can be obtained at concentrations of around 100. High concentration hybrid systems offer a distinct cost advantage over flat systems.
Research was conducted in development of operating procedures and standard methods to evaluate fiber reinforced composite materials, bonded or sprayed insulation, coatings, and laminated structures with MSFC electronic shearography systems. Optimal operating procedures were developed for the Pratt and Whitney Electronic Holography/Shearography Inspection System (EH/SIS) operating in shearography mode, as well as the Laser Technology, Inc. (LTI) SC-4000 and Ettemeyer SHS-94 ISTRA shearography systems. Operating practices for exciting the components being inspected were studied, including optimal methods for transient heating with heat lamps and other methods as appropriate to enhance inspection capability.
An interactive graphical method for planning fuel-efficient rendezvous trajectories in the multi-spacecraft environment of the space station is presented. The method allows the operator to compose a multi-burn transfer trajectory between arbitrary initial chaser and target trajectories. The available task time of the mission is limited and the maneuver is subject to various operational constraints, such as departure, arrival, plume impingement and spatial constraints. The maneuvers are described in terms of the relataive motion experienced in a Space-Station centered coordinate system. The optimization method is based on the primer vector and its extension to non-optimal trajectories. The visual feedback of trajectory shapes, operational constraints, and optimization functions, provided by user-transparaent and continuously active background computations, allows the operator to make fast, iterative design changes which rapidly converge to fuel-efficient solutions. The optimization functions are presented. A variety of simple design examples has been presented to demonstrate the usefulness of the method. In many cases the addition of a properly positioned intermediate waypoint resulted in fuel savings of up to 30%. Furthermore, due to the counter-intuitive character of the optimization functions, most fuel-optimal solutions could not have been found without the aid of the optimization tools. Operating the system was found to be very easy, and did not require any previous in-depth knowledge of orbital dynamics or trajectory. The planning tool is an example of operator assisted optimization of nonlinear cost-functions.
As next-generation space exploration missions necessitate increasingly autonomous systems, there is a critical need to better detect and anticipate crewmember interactions with these systems. The success of present and future autonomous technology in exploration spaceflight is ultimately dependent upon safe and efficient interaction with the human operator. Optimal interaction is particularly important for surface missions during highly coordinated extravehicular activity (EVA), which consists of high physical and cognitive demands with limited ground support. Crew functional state may be affected by a number of variables including workload, stress, and motivation. Real-time assessments of crew state that do not require a crewmember’s time and attention to complete will be especially important to assess operational performance and behavioral health during flight. In response to the need for objective, passive assessment of crew state, the aim of this work is to develop an accurate and precise prediction model of human functional state for surface EVA using multi-modal psychophysiological sensing. The psychophysiological monitoring approach relies on extracting a set of features from physiological signals and using these features to classify an operator’s cognitive state. This work aims to compile a non-invasive sensor suite to collect physiological data in real-time. Training data during cognitive and more complex functional tasks will be used to develop a classifier to discriminate high and low cognitive workload crew states. The classifier will then be tested in an operationally relevant EVA simulation to predict cognitive workload over time. Once a crew state is determined, further research into specific countermeasures, such as decision support systems, would be necessary to optimize the automation and improve crew state and operational performance.
Solar Cruiser is a solar sailing mission, riding as a secondary payload to the Interstellar Mapping and Acceleration Probe (IMAP) mission, slated to launch in February of 2025. The Solar Cruiser vehicle will generate thrust via a complex, low-thrust solar sail. The extreme low-thrust nature of the solar sail will leave Solar Cruiser highly sensitive to external environmental effects (such as solar radiation pressure and high-order gravitational perturbations) throughout the entirety of flight. Because of this, preliminary operational optimization routines must be intricately tied to high-order predictive propagation models to ensure the greatest possible confidence in mission success. The Solar Cruiser Mission Design and Navigation (MDNav) team has designed a software tool suite, employing the latest in software containerization technology, to accomplish this task; allowing for seamless development across several users and operating systems. Combining JPL’s Monte toolkit with high-performance optimizers written by the University of Alabama, the proposed architecture allows for instant verification of optimized trajectories within the same development environment that the optimization takes place, removing the need for mission designers and navigators to switch between tools. The MDNav suite image deployment process is separated from the development and operational scripts to be used in flight, which allows for maintaining a low-footprint version control profile without unnecessary file bloating. This paper discusses the historical differences between previous iterations of the Solar Cruiser MDNav tool suite and the current iteration, planned operational interfaces of the tool with other software and subsystems, and the planned path forward in maintaining containerization services for the software throughout the lifetime of Solar Cruiser.
Solar Cruiser is a solar sailing mission, riding as a secondary payload to the Interstellar Mapping and Acceleration Probe (IMAP) mission, expected to launch in February of 2025. The Solar Cruiser vehicle will generate thrust via a complex, low-thrust solar sail. The extreme low-thrust nature of the solar sail will leave Solar Cruiser highly sensitive to external environmental effects (such as solar radiation pressure and high-order gravitational perturbations) throughout the entirety of flight. Because of this, preliminary & operational optimization routines must be intricately tied to high-order predictive propagation models to ensure the greatest possible confidence in mission success. The Solar Cruiser Mission Design and Navigation (MDNav) team has designed a software tool suite, employing the latest in software containerization technology, to accomplish this task, allowing for seamless development across several users and operating systems. Combining JPL’s Monte toolkit with high-performance optimizers written by researchers at the University of Alabama, the proposed architecture allows for instant verification of optimized trajectories within the same development environment that the optimization takes place, removing the need for mission designers and navigators to switch between tools. The MDNav software suite itself is separated from the development and operational scripts to be used in flight, which allows for maintaining a low-footprint version control profile – thus avoiding unnecessary file bloating. This paper discusses the historical differences between previous iterations of the Solar Cruiser MDNav tool suite and the current iteration, planned operational interfaces of the tool with other software and subsystems, and the planned path forward in maintaining containerization services for the software throughout the lifetime of Solar Cruiser.
A new derivation of an algorithm which fuses the outputs of two Kalman filters is presented within the context of previous research in this field. Unlike other works, this derivation clearly shows the combination of estimates to be optimal, minimizing the trace of the fused covariance matrix. The algorithm assumes that the filters use identical models, and are stable and operating optimally with respect to their own local measurements. Evidence is presented which indicates that the error ellipsoid derived from the covariance of the optimally fused estimate is contained within the intersections of the error ellipsoids of the two filters being fused. Modifications which reduce the algorithm's data transmission requirements are also presented, including a scalar gain approximation, a cross-covariance update formula which employs only the two contributing filters' autocovariances, and a form of the algorithm which can be used to reinitialize the two Kalman filters. A sufficient condition for using the optimally fused estimates to periodically reinitialize the Kalman filters in this fashion is presented and proved as a theorem. When these results are applied to an optimal spacecraft rendezvous problem, simulated performance results indicate that the use of optimally fused data leads to significantly improved robustness to initial target vehicle state errors. The following applications of estimate fusion methods to spacecraft rendezvous are also described: state vector differencing, and redundancy management.