Power Measurements to Excavate Lunar Soil Simulant GRC-3B Using Arc Backhoe Trajectories
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A simple trajectory model has been developed and is presented. The particle trajectory path is estimated by computing the vertical position as a function of the horizontal position using a constant horizontal velocity and a vertical acceleration approximated as a power law. The vertical particle position is then found by solving the differential equation of motion using a double integral of vertical acceleration divided by the square of the horizontal velocity, integrated over the horizontal position. The input parameters are: x(sub 0) and y(sub 0), the initial particle starting point; the derivative of the trajectory at x(sub 0) and y(sub 0), s(sub 0) = s(x(sub 0))= dx(y)/dy conditional expectation y = y((sub 0); and b where bx(sub 0)/y(sub 0) is the final trajectory angle before gravity pulls the particle down. The final parameter v(sub 0) is an approximation to a constant horizontal velocity. This model is time independent, providing vertical position x as a function of horizontal distance y: x(y) = (x(sub 0) + s(sub 0) (y-y(sub 0))) + bx(sub 0) -(s(sub 0)y(sub 0) ((y - y(sub 0)/y(sub 0) - ln((y/y(sub 0)))-((g(y-y(sub 0)(exp 2))/ 2((v(sub 0)(exp 2). The first term on the right in the above equation is due to simple ballistics and a spherically expanding gas so that the trajectory is a straight line intersecting (0,0), which is the point at the center of the gas impingement on the surface. The second term on the right is due to vertical acceleration, which may be positive or negative. The last term on the right is the gravity term, which for a particle with velocities less than escape velocity will eventually bring the particle back to the ground. The parameters b, s(sub 0), and in some cases v(sub 0), are taken from an interpolation of similar parameters determined from a CFD simulation matrix, coupled with complete particle trajectory simulations.
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.
The Common Probe Study was funded by the NASA's Planetary Science Division in the Science Mission Directorate in 2018 to investigate the feasibility of a common aeroshell design for atmospheric probe missions at Venus, Jupiter, Saturn, Uranus, and Neptune. The study involved 4 NASA Centers: Ames Research Center, Goddard Space Flight Center, Langley Research Center, and the Jet Propulsion Laboratory. The common aeroshell design that was studied was a 400 kg, 1.5 m diameter, 45-degree sphere cone shape with a high density heatshield material (Heatshield for Extreme Entry Environments Technology, or HEEET) and a parachute system to extract the descent vehicle. This size of aeroshell could accommodate a descent vehicle of 0.75 m diameter, which could encompass both Tier 1 and Tier 2 science instruments at each of the 5 destinations. Study methodology: First, a notional payload of instruments for each destination was defined based on the top priority measurements indicated by the Planetary Science Decadal Survey. Steep and shallow entry flight path angles (EFPA) were defined for each planet based on qualification and operational g-load limits for current, state-of-the-art instruments. Interplanetary trajectories were then identified that bounded the EFPA range.Next, 3-DoF simulations for entry trajectories were run using the entry state vectors from the interplanetary trajectories. Conical ribbon parachutes were sized based on heatshield separation dynamics. Aero-heating correlations were used to generate stagnation point convective and radiative heat flux profiles. High fidelity thermal response models for various TPS materials were used to size stagnation point thicknesses, with margins based on previous studies. Backshell TPS masses were assumed based on scaled heat fluxes from the heatshield and also from previous mission concepts.Based on these analyses, we have found that the common design is applicable for atmospheric probe missions for 4 out of the 5 destinations. Because of the unique gravity well for Jupiter, the entry environments are more severe resulting in heat loads an order of magnitude higher than for the other destinations.The next step is to determine what follow-on activities NASA should engage in. A questionnaire for the atmospheric probe community has been developed, with a focus on what size of aeroshell should be further analyzed (smaller or same diameter), and what incentives would make using such an aeroshell, if assembled and available, desirable to mission proposers.Preliminary results from this questionnaire will be presented.
Automated guidance and navigation systems are an integral part to successful space missions. Previous researchers created Python tools to receive and parse data from a JAVAD TR-G2 space-capable GPS receiver. I improved the tool by customizing the output for plotting and comparing several simulations. I analyzed position errors, data loss, and signal loss by comparing simulated receiver data from an IFEN GPS simulator to ‘truth data’ from a proposed trajectory. By adjusting the trajectory simulation’s gain, attitude, and start time, NASA can assess the best time to launch the SLS, where to position the antennas on the Block 1-B, and which filter to use. Some additional testing has begun with the Novatel SpaceQuestGPS receiver as well as a GNSS SDR receiver.
A numerical optimization technique is used to fully automate the trajectory design process for an symmetric configuration of the proposed Advanced Launch System (ALS). The objective of the ALS trajectory design process is the maximization of the vehicle mass when it reaches the desired orbit. The trajectories used were based on a simple shape that could be described by a small set of parameters. The use of a simple trajectory model can significantly reduce the computation time required for trajectory optimization. A predictive simulation was developed to determine the on-orbit mass given an initial vehicle state, wind information, and a set of trajectory parameters. This simulation utilizes an idealized control system to speed computation by increasing the integration time step. The conjugate gradient method is used for the numerical optimization of on-orbit mass. The method requires only the evaluation of the on-orbit mass function using the predictive simulation, and the gradient of the on-orbit mass function with respect to the trajectory parameters. The gradient is approximated with finite differencing. Prelaunch trajectory designs were carried out using the optimization procedure. The predictive simulation is used in flight to redesign the trajectory to account for trajectory deviations produced by off-nominal conditions, e.g., stronger than expected head winds.
Characterization of entry vehicle pitch damping coefficient curves is crucial to ensure appropriate re-entry and overall mission success. The pitch damping coefficient (C_(m_q )+C_(m_α ̇ )) is used to encapsulate the oscillatory growth or decay of a body during a trajectory. The inverse estimation technique utilizes an existing Free-Flight CFD (FF-CFD) dataset and wraps a reconstruction algorithm in an optimizer. The reconstruction integrates the planar equations of motion derived by Schoenenberger, Queen [1] using Python’s scipy.integrate.solve_ivp. The optimizer’s objective function is the normalized 𝐿2 residual of the angle of attack peaks between the reconstructed trajectory and the original data produced with FF-CFD. Inclusion of the peak times in this residual calculation allows for simultaneous optimization of the pitch moment coefficient, C_(m_α ). This residual equation is shown below in Eq. 1. The optimizer scipy.optimize.minimize was used with the gradient-based Powell method for the analysis presented, however the differential evolution method was investigated as means of comparison, and was found to produce marginally lower residual values with prohibitively longer run times. Further, the pitch damping curve is found by fitting a cubic interpolation function to a set of (α, (C_(m_q )+C_(m_α ̇ ))) control points, where the α points are held constant and the (C_(m_q )+C_(m_α ̇ )) values are the optimized parameters. The pitch moment curve uses a linear interpolation between the minimum and maximum α in the dataset. FF-CFD generated trajectories using the Dragonfly capsule geometry with the Genesis ballistic range model parameters were simulated and used for this analysis. These FF-CFD trajectories simulate planar motion, as restricted by the reconstructing the equations of motion, of three different cases: 1-DoF (free-to-pitch), 2-DoF (free-to-pitch and heave), and 3-DoF (free-to-pitch, heave, and decelerate). Pitch damping coefficient curves generated using this inverse estimation curve technique with FF-CFD 1-DoF Dragonfly data are found in Fig. 1. Preliminary results reconstructing ballistic range shots using these FF-CFD derived predictions of the pitch damping curve (Fig. 1) are shown in Fig. 2. It should be noted that the ballistic range shot used a Genesis model whereas the FF-CFD data used a Dragonfly geometry, however these geometries are similar.
This viewgraph presentation reviews the preliminary design of a ramjet for integration with a ground based launch assist. The reasons for the use of ground-based launch assist and the proposed mechanism for a system are reviewed. The use of a Optimal Trajectory by Implicit Simulation (OTIS), to model the flight and comparison with an actual rocket trajectory is given. The OTIS system is reviewed, The benefits of a launch assist system are analyzed concluding that a launch assist can provide supersonic speeds thus allowing ignition of ramjet without an onboard compressor. This means a further reduction in total launch weight. The Ramjet study is reviewed next. This included a review of the ONX simulations, the verification of the ONX results with the use of Holloman Sled experiment data as derived from the Feasibility of Ramjet Engine Test Capability on The Holloman AFB Sled Track. The conclusion was that the ONX system was not sufficient to meet the needs for the modeling required. The GECAT (Graphical Engine Cycle Analysis Tool) is examined. The results of the GECAT simulations was verified with data from Stataltex and D21 flights. The Next steps are: to create a GECAT Model of a launch assist ramjet, to adjust the geometry to produce the desired thrust, and to survey the ramjet's performance over a range of Mach numbers. The assumptions and requirements of a launch assist ramjet are given, and the acceptable flight regimes are reviewed.
Earth science (ES) digital twins will help us understand the complex interactions and interrelationships that make up our Earth system and the impacts of earth science phenomena on it. Our work addresses two underdeveloped areas in current ES digital twin work: improving the understanding and interaction with ES model outputs by using Virtual and Mixed Reality (XR) tools and improving the non-intuitive mapping of continuous ES natural phenomena to gridded reference frames in current numerical models. Traditionally, scientists working on ES view and analyze the results of calculated or measured observables with static 1-dimensional (1D), 2D or 3D plots displayed on flat computer screens or paper. Using such limited mediums, it can be very difficult to identify, track and understand the evolution of key features due to poor viewing angles and the nature of flat computer screens. In addition, numerical models, such as the NASA Goddard Earth Observing System (GEOS) ES model, are almost exclusively formulated, visualized and analyzed in an Eulerian reference frame with fixed grid points in space and time. However, ES phenomena such as convective clouds, hurricanes and wildfire smoke plumes are visualized and analyzed in a Lagrangian reference frame: therefore it is often difficult and unnatural to understand these phenomena in relation to each other, visualized either in an Eulerian or Lagrangian context. In 3D visualizations, data generally takes one of three forms: gridded (e.g., voxelized) data, where space is divided into regions; point clouds, where data is represented as a set of points; and meshes, where objects are rendered as surfaces composed of small polygons (usually triangles). A gridded, Eulerian reference frame has been the default representation for the 2D visual analysis of atmospheric data in part because the numerical methods used to generate atmospheric model data in the first place use a gridded approach, with equations defining the relationships between the physical variables in each of a grid's cells across successive timesteps. In our work, we are particularly interested in data from GEOS. Another reason why gridded representations tend to be used for visualizing data from such models is because trajectories are difficult to interpret from representations on 2D surfaces, due to line-of-sight ambiguity. Instead of a fixed grid from GEOS, we embed a trajectory model to simulate particles' movement throughout a GEOS run. We then ingest these particle trajectories as animated point clouds with a NASA open source XR toolkit, the Mixed Reality Exploration Toolkit (MRET), and merge GEOS data with ES phenomena data onto one combined visualization that the user can intuitively interact with. Efficient rendering of arbitrarily large point clouds is an ongoing challenge being addressed by the computer science community, with the GPU-based optimizations and efficient GPU memory utilization a common theme of recent advances, especially for XR, where sustained high frame rate is mandatory to save the user from suffering due to simulation sickness. In this work, we describe and evaluate our progress in choosing and implementing appropriate methods for rendering arbitrarily large point clouds within MRET for XR. While tracking the XR headset enables the immersion of a user within a 3D scene of a data visualization, tracking of XR handheld controllers or user’s hands enables us to implement intuitive user interactions with the visualized datasets. Conventional tools require a user working with an ES visualization to conduct many interactions to commit their intended selections or manipulations with a visualized dataset; for example to specify a set of points in 3D space. Doing so in a 2D flat screen interface has traditionally required specifying a set of points in three distinct 2D coordinate systems (XY, XZ, and YZ), which is cumbersome. In other scientific domains, it has been shown that specifying or selecting a location or volume in XR using handheld controllers or tracked hands allows for greater speed and accuracy. We anticipate the same will hold true for atmospheric data, and we will share initial results of measuring the utility of such an interface. Notably, as the data being visualized is generated by GEOS as a prediction based on initial conditions, an intended application of our tool is to serve as part of an iterative feedback loop. Through XR, a scientist will review and manipulate a GEOS model run, modifying the conditions as needed to do subsequent runs of GEOS. Thereby, XR-based improvements to speed and accuracy of 3D tagging of points minimizes the effort required by both the scientist and the computer cluster conducting the necessary calculations.
The Low-Earth Orbit Flight Test of an Inflatable Decelerator (LOFTID) successfully demonstrated the capability of an inflatable aeroshell. Prior to launch, flight mechanics analysis was conducted to better understand the predicted splashdown point of the re-entry vehicle and ejectable data recorder. Best estimated trajectory separation states were utilized to understand the LOFTID trajectory over the entire 36 minute launch window. Weather forecasting model were integrated into the simulation to improve trajectory prediction accuracy. Automation tools were developed to facilitate the rapid generation of trajectory predictions during operations. The resulting improvements to the flight mechanics modeling and simulation allowed for pre-launch splashdown point to be within 3 nautical miles of the actual splashdown point.
The Low-Earth Orbit Flight Test of an Inflatable Decelerator (LOFTID) successfully demonstrated the capability of an inflatable aeroshell. Prior to launch, flight mechanics analysis was conducted to better understand the predicted splashdown point of the re-entry vehicle and ejectable data recorder. Best estimated trajectory separation states were utilized to understand the LOFTID trajectory over the entire 36 minute launch window. Weather forecasting model were integrated into the simulation to improve trajectory prediction accuracy. Automation tools were developed to facilitate the rapid generation of trajectory predictions during operations. The resulting improvements to the flight mechanics modeling and simulation allowed for pre-launch splashdown point to be within 3 nautical miles of the actual splashdown point.
An optical navigation system is being flown as the backup system to the primary Deep Space Network telemetry for navigation and guidance purposes on Orion. This is required to ensure Orion can recover from a loss of communication, which would simultaneously cause a loss of DSN telemetry. Images taken of the Moon and Earth are used to give range and position information to the navigation computer for trajectory calculations and maneuver execution. To get telemetry data from these images, the size and location of the moon need to be calculated with high accuracy and precision. The reentry envelope for the Orion EM-1 mission requires the centroid and radius of the moon images to be determined within 1/3 of a pixel 3 sigma. In order to ensure this accuracy and precision can be attained, I was tasked with building precise dot grid images for camera calibration as well as building a hardware in the loop test stand for flight software and hardware proofing. To calibrate the Op-Nav camera a dot grid is imaged with the camera, the error between the image dot location and the actual dot location can be used to build a distortion map of the camera and lens system so that images can be fixed to display truth locations. To build the dot grid images I used the Electro Optics Lab optical bench Bright Object Simulator System, and gimbal. The gimbal was slewed to a series of elevations and azimuths. An image of the collimated single point light source was then taken at each position. After a series of 99 images were taken at different locations the single light spots were extracted from each image and added to a composite image containing all 99 points. During the development of these grids it was noticed that an intermittent error in the artificial "star" locations occurred. Prior to the summer this error was attributed to the gimbal having glitches in it's pointing direction and was going to be replaced, however after further examining the issue I determined it to be a software issue. I have since narrowed the likely source of the error down to a Software Development Kit released by the camera supplier PixeLink. I have since developed a workaround in order to build star grids for calibration until the software bug can be isolated and fixed. I was also tasked with building a Hardware in the Loop test stand in order to test the full Op-Nav system. A 4k screen displays simulated Lunar and Terrestrial images from a possible Orion trajectory. These images are then projected through a collimator and then captured with an Op-Nav camera controlled by an Intel NUC computer running flight software. The flight software then analyzes the images to determine attitude and position, this data is then reconstructed into a trajectory and matched to the simulated trajectory in order to determine the accuracy of the attitude and position estimates. In order for the system to work it needs to be precisely and accurately aligned. I developed an alignment procedure that allows the screen, collimator and camera to be squared, centered and collinear with each other within a micron spatially and 5 arcseconds in rotation. I also designed a rigid mount for the screen that was machined on site in Building 10 by another intern. While I was working in the EOL we received a $500k Orion startracker for alignment procedure testing. Due to my prior experience in electronics development, as an ancillary duty, I was tasked with building the cables required to operate and power the startracker. If any errors are made building these cables the startracker would be destroyed, I was honored that the director of the lab entrusted such a critical component with me. This internship has cemented my view on public space exploration. I always preferred public sector to privatization because, as a scientist, the most interesting aspects of space for me are not necessarily the most profitable. I was concerned that the public sector was faltering however, and that in order to improve human space exploration I would be forced into private sector. I now know that, at least at JSC, human spaceflight is still progressing, and exciting work is still being done. I am now actively seeking employment at JSC after I complete my Ph.D and have met with my branch chiefs and mentor to discuss transitioning to a grad Co-op position.
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Symmetric and asymmetric rotational discontinuities are studied by means of a one-dimensional computer simulation and by single-particle trajectory calculations. The numerical simulations show the symmetric rotation to be stable for both ion and electron senses of rotation with a thickness of the order of a few ion gyroradii when the rotation angle of the tangential field is 180 deg or less. Larger rotation angles tend to be unstable. In an expansive discontinuity, when the magnetic field on the downstream side of the discontinuity is larger, an expanding transition layer separating the highfield from a low-field region develops on the downstream side, and a symmetric rotational discontinuity forms at the upstream edge. The implication of these results for magnetopause structure and energy flow through the magnetopause is described.
In early 2004, JPL successfully landed two Rovers, named Spirit and Opportunity, on the surface of Mars after traveling > 300 million miles over a 6-7 month period. In order to operate for extended duration (>9 months), both Rovers are equipped with rechargeable Lithium-ion batteries, which have enabled operation for over 854 and 834 Sols of operation, respectively, to date. Given that the batteries were required to support the mission for 90 Sols of operation by design, it is significant that the batteries have demonstrated over a nine fold increase in life over mission objectives. In addition to supporting the surface operations in conjunction with a triple-junction deployable solar arrays, the batteries were designed to aid in the launch and the EDL pyros, and allow for anomalies during cruise. In summary, the requirements of the Lithium-ion battery include the ability to provide power at least 90 sols on the surface of Mars, operate over a wide temperature range (-20 C to +30 C), withstand long storage periods (e.g., cruise period), operate in an inverted orientation, and support high current pulses (e.g., firing pyro events). In order to determine the viability of meeting these requirements, ground testing was performed on a Rover Battery Assembly Unit (RBAU), consisting of two 8-cell 10 Ah lithium-ion batteries connected in parallel. The RBAU upon which the performance testing was performed is nearly identical to the batteries incorporated into the two Rovers currently on Mars. The testing includes, (a) performing initial characterization tests (discharge capacity at different temperatures), (b) simulating the launch conditions, (c) simulating the cruise phase conditions (including trajectory correction maneuvers), (d) simulating the entry, decent, and landing (EDL) pulse load profile (required to support the pyros) (e) simulating the Mars surface operation mission simulation conditions, as well as, (f) assessing capacity loss and impedance characteristics as a function of temperature and life. This paper provides further detail to previously reported results1 of the RBAU testing program, especially with regard to the life characteristics. To date, the lithium-ion batteries (fabricated by Lithion/Yardney, Inc.) have been demonstrated to far exceed the requirements defined by the mission, both on Mars and on the ground, and are projected to support an extended mission (> 4 years).
This study examines three possible approaches to improving the speed in generating wind-optimal routes for air traffic at the national or global level. They are: (a) using the resources of a supercomputer, (b) running the computations on multiple commercially available computers and (c) implementing those same algorithms into NASAs Future ATM Concepts Evaluation Tool (FACET) and compares those to a standard implementation run on a single CPU. Wind-optimal aircraft trajectories are computed using global air traffic schedules. The run time and wait time on the supercomputer for trajectory optimization using various numbers of CPUs ranging from 80 to 10,240 units are compared with the total computational time for running the same computation on a single desktop computer and on multiple commercially available computers for potential computational enhancement through parallel processing on the computer clusters. This study also re-implements the trajectory optimization algorithm for further reduction of computational time through algorithm modifications and integrates that with FACET to facilitate the use of the new features which calculate time-optimal routes between worldwide airport pairs in a wind field for use with existing FACET applications. The implementations of trajectory optimization algorithms use MATLAB, Python, and Java programming languages. The performance evaluations are done by comparing their computational efficiencies and based on the potential application of optimized trajectories. The paper shows that in the absence of special privileges on a supercomputer, a cluster of commercially available computers provides a feasible approach for national and global air traffic system studies.
This study examines three possible approaches to improving the speed in generating wind-optimal routes for air traffic at the national or global level. They are: (a) using the resources of a supercomputer, (b) running the computations on multiple commercially available computers and (c) implementing those same algorithms into NASA’s Future ATM Concepts Evaluation Tool (FACET) and compares those to a standard implementation run on a single CPU. Wind-optimal aircraft trajectories are computed using global air traffic schedules. The run time and wait time on the supercomputer for trajectory optimization using various numbers of CPUs ranging from 80 to 10,240 units are compared with the total computational time for running the same computation on a single desktop computer and on multiple commercially available computers for potential computational enhancement through parallel processing on the computer clusters. This study also re-implements the trajectory optimization algorithm for further reduction of computational time through algorithm modifications and integrates that with FACET to facilitate the use of the new features which calculate time-optimal routes between worldwide airport pairs in a wind field for use with existing FACET applications. The implementations of trajectory optimization algorithms use MATLAB, Python, and Java programming languages. The performance evaluations are done by comparing their computational efficiencies and based on the potential application of optimized trajectories. The paper shows that in the absence of special privileges on a supercomputer, a cluster of commercially available computers provides a good option for computing wind-optimal trajectories for national and global air traffic system studies.
Time-accurate FUN3D simulations are utilized to estimate buffet-induced unsteady forces experienced by the Space Launch System during the Artemis I (AR01) flight. A set of FUN3D simulations was developed that employed time-accurate mesh translations to simulate the changing velocity and attitude based on the AR01 best estimated trajectory. In these simulations, referred to as accelerating-flow simulations, the freestream Mach number increased from 0.80 to 1.92. Additional time-accurate simulations were obtained at constant freestream Mach number equal to 0.95, 1.18, 1.70, thereby simulating stationary conditions experienced by the flow in a wind-tunnel. On the grounds of favorable comparisons between simulated and flight-measured environments, the FUN3D-based surface pressures were utilized to develop a buffet forcing function (BFF) database. This BFF database was analyzed to characterize the spatial distribution and frequency content of the buffet forces during transonic and supersonic portions of the AR01 flight. The region of interest is located downstream of the forward attachment (FA) hardware between the core stage and the solid rocket boosters where vortex shedding off the FA protuberance produces significant unsteadiness. The analysis reveals that, at transonic and supersonic conditions, buffet forces that are based on constant freestream Mach number data are a good approximation of those based on accelerating-flow simulations.