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The successful navigation of the Comet Nucleus Tour spacecraft was performed at JPL and was conducted with the use of the new noncoherent transceiver technique developed by the Applied Physics Laboratory. Discussions include the conditioning performed on the 2-way noncoherent Doppler data, the orbit determination process, and the post SRM trajectory reconstruction.
The NASA James Webb Space Telescope (JWST) mission successfully launched on December 25, 2021, at 12:20 UTC. This paper details several novel challenges encountered in the orbit determination (OD) of JWST during the launch and early orbit. The first OD solution used only 6.5 hours of tracking data, much less than the 24 hours of data usually required for libration orbiters on an outbound trajectory. In addition, the observatory area exposed to solar radiation pressure changed through a series of sunshield deployments while concurrent momentum unloads and attitude telemetry outages occurred. This paper covers how the Flight Dynamics Team (FDT) prepared for these challenges, how these challenges were handled on-orbit, and the performance of the resulting OD solutions.
The NASA James Webb Space Telescope (JWST) mission successfully launched on December 25, 2021, at 12:20 UTC. This paper details several novel challenges encountered in the orbit determination (OD) of JWST during the launch and early orbit. The first OD solution used only 6.5 hours of tracking data, much less than the 24 hours of data usually required for libration orbiters on an outbound trajectory. In addition, the observatory area exposed to solar radiation pressure changed through a series of sunshield deployments while concurrent momentum unloads and attitude telemetry outages occurred. This paper covers how the Flight Dynamics Team (FDT) prepared for these challenges, how these challenges were handled on-orbit, and the performance of the resulting OD solutions.
Geomagnetic disturbance forecasting is based on the identification of solar wind structures and accurate determination of their magnetic field orientation. For nowcasting activities, this is currently a tedious and manual process. Focusing on the main driver of geomagnetic disturbances, the twisted internal magnetic field of interplanetary coronal mass ejections (ICMEs), we explore a convolutional neural network’s (CNN) ability to predict the embedded magnetic flux rope’s orientation once it has been identified from in situ solar wind observations. Our work uses CNNs trained with magnetic field vectors from analytical flux rope data. The simulated flux ropes span many possible spacecraft trajectories and flux rope orientations. We train CNNs first with full duration flux ropes and then again with partial duration flux ropes. The former provides us with a baseline of how well CNNs can predict flux rope orientation while the latter provides insights into real-time forecasting by exploring how accuracy is affected by percentage of flux rope observed. The process of casting the physics problem as a machine learning problem is discussed as well as the impacts of different factors on prediction accuracy such as flux rope fluctuations and different neural network topologies. Finally, results from evaluating the trained network against observed ICMEs from Wind during 1995–2015 are presented.
The breakneck expansion of multi-payload launches in low Earth orbit (LEO) has seen significant escalation over the last three years in the Space Domain Awareness (SDA) enterprise. The uptick in satellite deployment frequency, gradation of Mega-constellation deployments utilizing electric propulsion, and surge in metric observation density from the Space Surveillance Network (SSN) have imposed major system enhancements to ensure spaceflight safety. Beginning with the launch phase, new tactics, software, and procedures have been developed over the last few years to optimize the tasking of the SSN and ensure custody of all recently launched satellites that are added to the space catalog. Ensuring appropriate tasking levels were pivotal during the satellite separation phase, which necessitated updates to the mission systems for improved delineation between clustered satellites in a short window of time. The improved incorporation of utilizing owner operator provided ephemeris coupled with critical code improvements have now revolutionized how we maintain custody of earth orbiting objects. The exponential population growth of low-Earth orbiting satellites have also increased the quantity of Conjunction Data Messages sent out to partners such as the Trajectory Operations Group (TOPO) in Johnson Space Center. We will discuss the need for continued collaboration between the National Aeronautics and Space Administration (NASA) and other Mega-Constellation Satellite Operators to safeguard Human Space Flight operations. Lastly, we will discuss the increase in reentry reporting for satellites falling back to Earth’s atmosphere, and the challenges associated with these events.
The breakneck expansion of multi-payload launches in low Earth orbit (LEO) has seen significant escalation over the last three years in the Space Domain Awareness (SDA) enterprise. The uptick in satellite deployment frequency, gradation of mega-constellation deployments utilizing electric propulsion, and surge in metric observation density from the Space Surveillance Network (SSN) have driven major system enhancements to ensure spaceflight safety. Beginning with the launch phase, new tactics, software, and procedures have been developed over the last few years to optimize the tasking of the SSN and ensure custody of all recently launched satellites that are added to the space catalog. Ensuring appropriate tasking levels were pivotal during the satellite separation phase, which necessitated updates to the mission systems for improved delineation between clustered satellites in a short window of time. The improved incorporation of utilizing satellite owner/operator-provided ephemeris coupled with critical code improvements have revolutionized how we maintain custody of Earth orbiting objects. The exponential population growth of Low-Earth orbiting satellites have also increased the quantity of Conjunction Data Messages sent out to partners such as the Trajectory Operations Group (TOPO) in Johnson Space Center. We will discuss the need for continued collaboration between the National Aeronautics and Space Administration (NASA) and other mega-constellation satellite operators to safeguard human spaceflight operations. Lastly, we will discuss how mega-constellations are driving best practices for safe operations across the space community.
The Deep Atmosphere Venus Investigation of Noble gases, Chemistry, and Imaging(DAVINCI) mission is currently scheduled to launch in June 2029 to explore Venus via two flybys and a probe descent scheduled for June 2031. The goals of this mission is to study the origin, evolution, and current state of Venus, especially the deep Venusian atmosphere using an atmospheric probe named Zephyr. The Zephyr will take key measurements in the deep atmosphere of Venus and be the first probe to take high-resolution aerial photographs of a mountainous tesserae surface as it descends over the Alpha Regio highlands region, which has the oldest surfaces of Venus. The Zephyr’s descent trajectory, which determines the flight over the Alpha Regio, is crucial to meet the science objectives of the DAVINCI mission. To ensure the success of the DAVINCI mission, the Entry, Descent, and Landing (EDL) modeling has undergone several developments to track its trajectory, its uncertainties, and calculate metrics on the science data that can be transmitted. This paper will cover these developments and the current status of the EDL modeling.
The investigation summarized in this paper describes recent normal gravity experiments involving the behavior of compound drops in rotating flows and in particular, the subsequent migration of the less dense phase (air bubble) toward the rotation axis. The data are compared to two models. The first was developed to predict the trajectory of a fluid particle in an infinitely large drop in the presence of both gravitational and rotational fields at the limit of quasi-steady creeping flow. The second predicts the trajectory of a fluid particle in a compound drop in the presence of a rotational field and at the limit of creeping flow. Gravity has not yet been incorporated into this second model.
Ion mobility-mass spectrometry (IM-MS) can be a powerful tool for determining structural information about ions in the gas phase, from small covalent analytes to large, native-like or denatured proteins and complexes. For large biomolecular ions, which may have a wide variety of possible gas-phase conformations and multiple charge sites, quantitative, physically explicit modeling of collisional cross sections (CCSs) for comparison to IMS data can be challenging and time-consuming. We present a “trajectory method” (TM) based CCS calculator, named “Collidoscope,” which utilizes parallel processing and optimized trajectory sampling, and implements both He and N{sub 2} as collision gas options. Also included is a charge-placement algorithm for determining probable charge site configurations for protonated protein ions given an input geometry in pdb file format. Results from Collidoscope are compared with those from the current state-of-the-art CCS simulation suite, IMoS. Collidoscope CCSs are within 4% of IMoS values for ions with masses from ~18 Da to ~800 kDa. Collidoscope CCSs using X-ray crystal geometries are typically within a few percent of IM-MS experimental values for ions with mass up to ~3.5 kDa (melittin), and discrepancies for larger ions up to ~800 kDa (GroEL) are attributed in large part to changes in ion structure during and after the electrospray process. Due to its physically explicit modeling of scattering, computational efficiency, and accuracy, Collidoscope can be a valuable tool for IM-MS research, especially for large biomolecular ions. .
G protein-coupled receptors (GPCRs) play a key role in many cellular signaling mechanisms, and must select among multiple coupling possibilities in a ligand-specific manner in order to carry out a myriad of functions in diverse cellular contexts. Much has been learned about the molecular mechanisms of ligand-GPCR complexes from Molecular Dynamics (MD) simulations. However, to explore ligand-specific differences in the response of a GPCR to diverse ligands, as is required to understand ligand bias and functional selectivity, necessitates creating very large amounts of data from the needed large-scale simulations. This becomes a Big Data problem for the high dimensionality analysis of the accumulated trajectories. Here we describe a new machine learning (ML) approach to the problem that is based on transforming the analysis of GPCR function-related, ligand-specific differences encoded in the MD simulation trajectories into a representation recognizable by state-of-the-art deep learning object recognition technology. We illustrate this method by applying it to recognize the pharmacological classification of ligands bound to the 5-HT 2A and D2 subtypes of class-A GPCRs from the serotonin and dopamine families. The ML-based approach is shown to perform the classification task with high accuracy, and we identify the molecular determinants of the classifications in the context of GPCR structure and function. This study builds a framework for the efficient computational analysis of MD Big Data collected for the purpose of understanding ligand-specific GPCR activity.
Raw temperature replica-exchange molecular dyanmics trajectories of 23 different SARS CoV-2 Systems including S Protein ACE2-receptor binding-domain, MPro, PLPro, NSP3 ADRP (X-domain/macrodomain/phosphatase), NSP15 (endoribonuclease), NSP9, NSP10, NSP16, and N-protein N-terminus. Systems were prepared with charmm-gui and simulated using the GROMACS simulation software suite. Non-demuxed, i.e. discontinuous/constant temperature window, trajectories are provided in compressed GROMACS xtc and full trr formats. This data supplements the data releases DOI: 10.13139/OLCF/160650 'SARS-CoV2 Protein-Ligand Simulation Dataset: Layer 1 (Simulation Initial Conditions and Parameters)' and DOI: 10.13139/OLCF/1657844 'SARS-CoV2 Protein-Ligand Simulation Dataset: Layer 2 (Extracted Protein Coordinate Trajectories)'
Estimating subsurface density is important for imaging various geologic structures such as volcanic edifices, reservoirs, and aquifers. Muon tomography has recently been used to complement traditional gravity measurements as a powerful method for probing shallow subsurface density structure beneath volcanoes. Gravity and muon data have markedly different spatial sensitivities and, as a result, the combination is useful for imaging structures on spatial scales that are larger than the area encompassed by crossing muon trajectories. Here we explore and test a joint inversion of gravity and muon data in a study area where there is an independently-characterised target anomaly: a regionally extensive, high-density layer beneath Los Alamos, New Mexico, USA. We resolve the nearly flat-lying structure using a unique experimental set-up wherein surface and subsurface gravity and muon measurements are obtained above and below the target volume. Our results show that with minimal geologic (prior) constraints, the joint inversion correctly recovers salient features of the expected density structure. The results of our study illustrate the potential of combining surface and subsurface (e.g., borehole) gravity and muon measurements to invert for shallow geologic structures.
Fully automatic, simultaneous control of polymer composition and molecular weight trajectories during free radical copolymer synthesis was achieved by coupling the continuous ACOMP data stream into a feedback controller which operated pumps for reactor feed of comonomers. The controller operates without recourse to a detailed kinetic model or reactivity ratios. Rather, it uses the fact that instantaneous copolymer chain composition is related to the instantaneous rate of consumption of comonomers, and the rate for each of these is described by rates α 1 and α 2 for comonomers 1 and 2, respectively. The instantaneous weight average molecular weight M w,inst is proportional to total comonomer concentration via a constant p. Hence, target trajectories for composition and M w can be independently prescribed and followed. Finally, since α 1 , α 2 and p are directly measurable from the ACOMP data stream, their values can be constantly updated by the automatic controller during the reaction process to insure the target trajectories are followed.
Trajectory, load, aerodynamic heating, materials, structural, and thermal analyses for hypersonic cruise vehicle wings
Two aspects of the orbit determination of the Tracking and Data Relay Satellite-East (TDRS-E) are considered. The TDRS-E solar reflectivity parameter, as it affects the accuracy of the determination and propagation of the TDRS-E geosynchronous orbit, varies seasonally due to the changing declination of the sun. Orbit solutions are carried out using varying tracking data arc lengths to ascertain the minimum arc length needed for an accurate determination of the TDRS-E orbital elements and the solar reflectivity parameter.
The NASA Johnson Space Center built a 4200 sq ft parafoil for the U.S. Army Natick Soldier Center to demonstrate autonomous flight using a guided parafoil system to deliver 10,000 lbs of useable payload. The parafoil's design was based upon that developed during the X-38 program. The drop test payload consisted of a standard 20-foot Type V airdrop platform, a standard 12-foot weight tub, a 60 ft drogue parachute, a 4200 ft2 parafoil, an instrumentation system, and a Guidance, Navigation, and Control (GN&C) system. Instrumentation installed on the load was used to gather data to validate simulation models and preflight loads predictions and to perform post flight trajectory and performance reconstructions. The GN&C system, developed during NASA's X-38 program, consisted of a flight computer, modems for uplink commands and downlink data, a compass, laser altimeter, and two winches. The winches were used to steer the parafoil and to perform the dynamic flare maneuver for a soft landing. The laser was used to initiate the flare. The GN&C software was originally provided to NASA by the European Space Agency. NASA incorporated further software refinements based upon the X-38 flight test results. Three full-scale drop tests were conducted, with the third being performed during the Precision Airdrop Technology Conference and Demonstration (PATCAD) Conference at the U.S. Army Yuma Proving Ground (YPG) in November of 2003. For the PATCAD demonstration, the parafoil and GN&C software and hardware performed well, concluding with a good flare and the smallest miss distance ever experienced in NASA's parafoil drop test program. This paper describes the 4200 sq ft parafoil system, simulation results, and the results of the drop tests.
Heat transfer data measured along the leeward centerline and on the side fuselage of the Space Shuttle orbiter during STS-2 and STS-3 are compared with predictions of empirical heating techniques derived from wind tunnel tests. Steps required to extrapolate an existing leeward centerline theory to flight conditions are described. Generally favorable comparisons from Mach 24 down to approximately Mach 7 for both flights are presented. The side fuselage impingement heating method is currently under development, but some preliminary results are available. The method is briefly described and compared with wind tunnel and flight measurements. Side heating predictions are given for an STS-3 trajectory point near Mach 10 showing good agreement with flight data. There is evidence of embedded vortices emanating from the side fuselage impingement line which significantly enhance local heating rates at both wind tunnel and flight conditions.
An oceanic data assimilation system which allows to utilize the forthcoming Tropical Rainfall Measuring Mission (TRMM) data has been developed and applied to the Pacific Ocean to produce the velocity field. The assimilated data will be indispensable to examine the effects of rainfall and its variability on the structure and circulation of the tropical oceans and to assess the impact of global warming due to the increase of carbon dioxide on the ocean circulation system and the marine pollution caused by oil spill and ocean damping of radionuclide. The data will also provide the verification for the oceanic and ocean-atmosphere coupled General Circulation Models (GCM's). The system consists of oceanic GCM, analysis scheme and data. In the system the flow field has been determined to be physically consistent with the observed density field and the sea surface winds derived from the Special Sensor Microwave Imagery (SSM/I) data which drive the ocean current. The time integration has been performed for five years until the flow field near the surface attained the steady state starting from the rest ocean with observed temperature and salinity fields, and the SSM/I surface wind velocity. The resultant flow field showed high producibility of the system. Especially the flow near the ocean surface agreed well with available observed data. The system, for the first time, succeeded to produce the eastward subtropical current which has been discovered in the joint investigation on Kuroshio current (CSK) in the 1960s. To verify the quality of the flow field a trajectory analysis has been carried out and compared with the Algos buoy data. BRIEF DESCRIPTION OF THE DATA ASSIMILATION SYSTEM ## Oceanic GCM and analysis scheme--The basic equations are much the same as used for the GCM's, except for the Newtonian damping terms introduced into the prediction equations for the potential temperature and salinity to maintain these fields as observed. The C grid of 2'lat. by 2'long. in horizontal and the 11 vertical levels are applied to the entire Pacific Ocean. At the east and west ocean boundaries the periodic boundary conditions are applied creating fictitious ocean there. The SMAC Method is used to increase the accuracy of mass conservation. * Data--The JODC temperature and salinity data obtained from 1906 to 1988 are used in the system between Long.100'E. and 60'W. The surface wind data are derived from the SSM/I data by Dr-R. Atlas of NASA/GSFC. The data set contains every 6 hours data from July 1987 to June 1989 on the grid of 2'lat. by 2.5'long. The averaged for the whole period and then interpolated into the 2'lat. by 2'long. grid data are used to force the system. The sea bottom topography data was based on the General Bathymetric Chart of the Ocean (GEBCO) supplied by the Canadian Hydrographic Service under contract with the International Hydrographic Organization and International Oceanographic Commission of UNESCO.