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At least 829 records · Page 46

Flight Mechanics Modeling and Simulation of the Earth Entry System

Introduction: The Mars Sample Return (MSR) Campaign being planned by NASA and ESA has the ambitious goal to return Mars samples back to Earth. This international collaboration had developed a concept of operations that included a ESA-designed Earth Return Orbiter (ERO) and NASA-designed Capture, Containment, and Return System (CCRS). The Earth Entry System (EES), consisting of a protective aeroshell that houses the samples as well as sample containment vessels, would conduct entry, descent, and landing (EDL) on a direct Earth trajectory. The EES would enter on a spin-stabilized ballistic trajectory with the goal to passively achieve aerodynamic stability throughout all regions of flight. The EDL sequence would end with the EES impacting the soft playa soil of the Utah Test and Training Range (UTTR). As of the submission of this abstract, the MSR campaign is undergoing a re-architecture leading to a pause in EES development. However, the novel approaches developed in flight mechanics modeling and simulation can significantly benefit the greater IPPW community in the development of Earth return vehicles. This paper will present the latest state of EES flight mechanics modeling and simulation. The paper will highlight the simulation architecture developed and key lessons learned from understanding of EDL trajectory sensitivities. Modeling and Simulation: Figure 1 provides a high-level concept of operations for the approach, entry, descent, and landing (AEDL) phase of the CCRS-portion of MSR. The objective of EES flight mechanics is to model and simulate the EES trajectory from ERO separation to ground impact at UTTR. A variety of flight mechanics simulation models were utilized to model both exo-atmopsheric and atmospheric portions of flight. 42, a 6-DOF simulation developed at Goddard Space Flight Center, is utilized for propagating the attitude of EES during exo-atmospheric flight. 42 allows for a variety of spin eject mechanism scenarios to be simulated for analysis. 10 minutes prior to entry, the 42 states are handed off to the EDL sims. The prime EDL sim utilized by EES is the Program to Optimize Simulated Trajectories II (POST2), a 6-DOF sim developed at Langley Research Center, and the independent verification and validation EDL sim utilized is DSENDS, a 6-DOF sim developed at Jet Propulsion Laboratory. Figure 2 provides a visualization of the flight mechanics simulation model flow through various points in the AEDL phase. Due to the existence of a variety of sim models, the EES flight mechanics team developed processes for data hand-off. These processes included the development of a centralized coordinate frame document, utilization of a single, centralized simulation input document for all sims to reference, and hand-off files containing both the technical data to be ingested by other flight mechanics sims as well as annotations of modeling assumptions utilized to generate the data. Figure~\ref{fig:post2simarchitecture} provides an overview of the POST2 sim architecture wherein POST2 ingests numerous subsystem models and input files. The dispersed state file generated by MONTE provides the position/velocity state of the trajectory while the 42 Handoff file provides the attitude. The aerodynamics database, delivered by the EES aeroscience team, is utilized to simulate the aerodynamic forces and moments experienced during EDL. A custom atmosphere model, developed by EES atmosphere team, is utilized to simulate the anticipated atmosphere environment around the region of Earth through which the EES trajectory flys. These inputs and subsystem models can be varied depending on the AEDL flight mechanics scenario being simulated. Monte Carlo simulations are utilized to generate statistical AEDL performance metrics in the form of scorecards and violin plots. Furthermore, outputs from the POST2 simulation are utilized for follow-on analyses including aerothermal and landing performance. \section{Flight Mechanics Lessons Learned} Though the EES flight mechanics team uncovered a variety of lessons learned through the analysis conducted to support CCRS through preliminary design review, this paper will highlight the most important lessons. A key AEDL performance goal is to ensure the landing footprint of EES remains on the UTTR south range. A common modeling strategy used in EDL analysis is One-Variable-At-a-Time (OVAT). OVAT analysis provides insight into the key drivers that affect AEDL performance metrics. Figure 3 shows the landing ellipses for single dispersion sources as compared to the baseline aggregate of all dispersions. The figure shows that atmosphere winds alone dominate the size of the footprint ellipse (note: EES does not use a parachute unlike previous Earth-return missions and is in wind-driven free fall for ~5min). The significance of the wind led the EES flight mechanics team to pursue the development of a Custom Atmosphere Model [4], in lieu of EarthGRAM [1], built on actual radiosonde wind measurements around the UTTR-region. This decision was driven by the realism in the generated footprint ellipses and lessons-learned from Stardust [5]. These findings will be invaluable for future Earth-return missions in providing an early understanding of the key drivers affecting footprint size and modeling considerations for which to account. Another lesson learned is tied to the AEDL performance goal of achieving passive stability throughout all regions of flight. It is well understood that blunt-body aeroshells are less stable as they transition from supersonic to subsonic. Eliminating a backshell does help improvestability; however, other phenomena such as roll-induced instability during terminal descent can still arise. The EES flight mechanics team developed stability metrics as tools to better understand the causes of and better predict the onset of dynamic instability. These tools were built upon analytical models developed by Jaffe [3] and Murphy [2]. The tools were shown to both be very accurate in correlation with actual unstable cases and useful in developing stability margin policies based on the vehicle design and simulation considerations (e.g. sphere-cone angle change, mass change, wind turbulence). These tools allowed for the current EES design to demonstrate the ability to achieve passive stability and can be an invaluable tool for consideration in the design of parachute-less Earth-return vehicles.

Rohan Deshmukh↗

Advanced Supersonic Parachute Inflation Research Experiment 2 (ASPIRE2) Flight Mechanics and Parachute Performance

The Advanced Supersonic Parachute Inflation Re-search Experiment-2 (ASPIRE2) program consists of a sounding rocket flight test to be launched at Wallops Flight Facility (WFF) in 2025. This effort seeks to qualify the supersonic parachute for the Sample Retrieval Lander (SRL) under the Mars Sample Return (MSR) program, building off the successes of its predecessor program, ASPIRE, which launched in both 2017 and 2018. The conops for the ASPIRE2 mission are shown in Figure 1 relative to its predecessor. While the ASPIRE pro-gram qualified a 21.5 m diameter disk-gap band parachute deployed at Mach 1.7, the SRL parachute will be increased to 24 m in diameter and target a Mach 2.1 parachute deployment condition, the largest diameter supersonic parachute deployed at the highest Mach number to date. These requirements are driven by the increased landing mass of the sample retrieval lander, which is roughly a 50% increase over that of the Mars2020 entry vehicle. These changes necessitate ASPIRE2 to certify the parachute performance under similar deployment conditions. This work will cover the 6-DoF para-chute model implementation and flight mechanics performance for the ASPIRE2 campaign and discuss how these results will impact the design of the test article. The ASPIRE2 trajectory is simulated using a multi-body flight dynamics tool, Program to Optimize Simulated Trajectories II (POST2). This work will summarize both the flight mechanics and parachute modeling (from payload separation until splashdown) and performance for this mission. Various design trades will be assessed such as para-chute tuning parameters as well as vehicle center of gravity location.

Evan Roelke↗

Mars Sample Return Earth Entry System Helicopter Drop Test Reconstruction

The Mars Sample Return campaign conducted four successful helicopter drop tests of the Earth Entry System at the Utah Test and Training Range on October, 2023. The tests acquired data for the 52.5 deg sphere cone geometry that can be used to develop models for the terminal descent aerodynamics and flight dynamics used to model ground impact conditions. The acquired test data included both sensor data outputs from an on-board inertial measurement unit, global positioning system, and video cameras as well as atmospheric measurements from weather balloons. This data was processed using a Kalman filter/smoother to reconstruct the capsule’s trajectory and aerodynamics. An equation-error method was used to reconcile the aerodynamics by solving for a set of dispersions in the aerodynamic database that form a best-fit to the reconstructed aerodynamics. The reconstructed trajectories were compared with flight simulations generated using Program to Optimize Simulated Trajectories II. The results indicate that the simulation underpredicted the vehicle oscillation amplitudes. The reconstruction exhibits a persistent low-amplitude oscillation that does not damp out as predicted.

Chris D Karlgaard↗

Mars Sample Return Earth Entry System Helicopter Drop Test Reconstruction

The Mars Sample Return campaign conducted four successful helicopter drop tests of the Earth Entry System at the Utah Test and Training Range on October, 2023. The tests acquired data for the 52.5 deg sphere cone geometry that can be used to develop models for the terminal descent aerodynamics and flight dynamics used to model ground impact conditions. The acquired test data included both sensor data outputs from an on-board inertial measurement unit, global positioning system, and video cameras as well as atmospheric measurements from weather balloons. This data was processed using a Kalman filter/smoother to reconstruct the capsule’s trajectory and aerodynamics. An equation-error method was used to reconcile the aerodynamics by solving for a set of dispersions in the aerodynamic database that form a best-fit to the reconstructed aerodynamics. The reconstructed trajectories were compared with flight simulations generated using Program to Optimize Simulated Trajectories II. The results indicate that the simulation underpredicted the vehicle oscillation amplitudes. The reconstruction exhibits a persistent low-amplitude oscillation that does not damp out as predicted.

Chris D Karlgaard↗

Performance Analysis of Magnetohydrodynamic Drag Modulation for Actively Controlled Aerocapture at Neptune

Missions to the Ice Giants are a top priority for flagship missions this coming decade. However, a fully propulsive orbital insertion into these planets requires an immense amount of fuel, taking a significant portion of the spacecraft mass and restricting the scientific payload. To mitigate this, aerocapture has been heavily investigated. Although simulations have shown that aerodynamically controlled aerocapture can successfully insert into an orbit around both Ice Giants, the deep atmospheric pass required necessitates a complex, mass expensive, and sometimes prohibitive thermal protection system. Magnetohydrodynamic drag modulation serves as a potential alternative control method for aerocapture which could not only save propellant mass compared to fully propulsive orbital insertion, but also save thermal protection system mass compared to conventional aerocapture methods. Both aerodynamically controlled and magnetohydrodynamically controlled aerocapture methodologies were simulated in NASA Langley’s high-fidelity six degree-of-freedom flight dynamics code, the Program to Optimize Simulated Trajectories II. Each method was simulated to identical missions to Neptune using a numerical predictor-corrector algorithm to optimize the control towards the target orbit. The results concluded that magnetohydrodynamic drag modulation can successfully capture and performs on par with aerodynamic drag modulation while significantly reducing the heatflux and aeroshell complexity.

Danny N Nguyen↗

Onboard Navigation Error Analysis for Aerocapture at Uranus

Capturing into an orbit around Uranus using aerocapture allows one to design a mission with faster interplanetary trajectories and less propellant requirements. Such an aerocapture mission would rely on the onboard Guidance, Navigation, and Control (GNC) subsystems to successfully capture into an orbit around Uranus. Uncertainty in the state information and the noise in the sensor measurements induce navigation errors in the guidance and control subsystems, which can affect the overall performance of the aerocapture mission at Uranus. Understanding the effect of these navigation errors on mission performance is essential. To this end, this work considers different sensors with varying quality to understand their impact on the overall mission performance. In addition, this paper studies the impact of the uncertainty in the initial states used to initialize the onboard navigation filter and understands their effect on mission performance. This paper also shows the onboard navigation errors obtained from the Linear Covariance (LinCov) analysis and uses them for verification and validation (V&V) of the results from Program to Optimize and Simulate Trajectories-II (POST2).

Pardha Sai Chadalavada↗

Onboard Autonomous Trajectory Planning for Mars Power Descent

In recent years, there has been an increasing interest in space-qualified processors such as multi-core central processing units and graphics processing units that can withstand the adverse effects of space radiation. These processors can allow parallel programming to perform tasks that typically demand high computational power. One can study guidance schemes that can take advantage of these currently developing processors and provide more robust guidance. Software for Multi-model Autonomous Real-time Trajectories (SMART) guidance can identify robust trajectories by running an onboard Monte Carlo analysis. SMART guidance can take advantage of knowledge updates obtained from the onboard sensors, allowing it to consider the off-nominal cases that it would not typically encounter during the offline trajectory analysis. This work uses the SMART guidance for the powered divert at Mars simulation in Program to Optimize and Simulated Trajectories- II.

Pardha Sai Chadalavada↗

Assessing Huygens Probe Entry, Descent, and Landing at Titan Simulation using Dragonfly Atmosphere Model

Dragonfly is a New Frontiers Program mission that will deliver a rotorcraft to Saturn's moon, Titan. This mission follows Huygens as the previous mission that successfully landed a vehicle on Titan. A flight mechanics simulation of Dragonfly's Entry, Descent, and Landing sequence has been developed using the Program to Optimize Simulated Trajectories II. The simulation incorporates several subsystem models, including aerodynamics, gravity, and mass properties, to fully capture the multi-body six degree of freedom dynamics. Among all the subsystem models that inform the Entry, Descent, and Landing dynamics, the atmosphere model of Titan is a critical component. The atmosphere model characterizes the density, temperature, pressure, and winds that the entry vehicle experiences during the descent. This impacts several aspects of the descent such as the peak heating, aerodynamics, parachute release conditions, the dynamics of the vehicle and parachutes, and the landing ellipse. In the course of developing Dragonfly, an updated model of the Titan atmosphere has been created corresponding to Dragonfly's arrival in the mid-2030s, approximately one Titan year after the Huygens mission successfully landed a probe on Titan. This work leverages previous work done to investigate Huygens EDL sequence to assess the atmosphere model developed for Dragonfly. This is done by utilizing the updated Titan atmosphere model, the Dragonfly atmosphere model, within the Huygens POST2-based flight simulation with the goal of characterizing the differences between the atmospheric models and assessing how the Dragonfly atmosphere model impacts Huygens entry dynamics.

Huygens↗

Dragonfly Mission Entry and Descent Modeling and Simulation Overview

Dragonfly is a New Frontiers Program mission, led by The Johns Hopkins Applied Physics Laboratory, that will deliver a rotorcraft lander to Saturn’s moon, Titan. The focus of this work is to analyze the trajectory of the entry vehicle from cruise stage separation until lander separation. This analysis is done by the NASA Langley Research Center Entry, Descent, and Landing team using the Program to Optimize Trajectories II. This paper provides an overview of the current design and the robustness of the overall entry sequence using a Monte Carlo uncertainty analysis. The work presented in this study includes the updated design, models, and analysis completed since the Dragonfly Entry, Descent, and Landing Mission Preliminary Design Review.

Simulation↗

Onboard Navigation Error Analysis for Aerocapture at Uranus

Capturing into an orbit around Uranus using aerocapture allows one to design a mission with faster interplanetary trajectories and less propellant requirements. Such an aerocapture mission would rely on the onboard Guidance, Navigation, and Control (GNC) subsystems to successfully capture into an orbit around Uranus. Uncertainty in the state information and the noise in the sensor measurements induce navigation errors in the guidance and control subsystems, which can affect the overall performance of the aerocapture mission at Uranus. Understanding the effect of these navigation errors on mission performance is essential. To this end, this work considers different sensors with varying quality to understand their impact on the overall mission performance. In addition, this paper studies the impact of the uncertainty in the initial states used to initialize the onboard navigation filter and understands their effect on mission performance. This paper also shows the onboard navigation errors obtained from the Linear Covariance (LinCov) analysis and uses them for verification and validation (V\&V) of the results from Program to Optimize and Simulate Trajectories-II (POST2).

Aerocapture↗

Assessing Huygens Probe Entry, Descent, and Landing at Titan Simulation using Dragonfly Atmosphere Model

Dragonfly is a New Frontiers Program mission that will deliver a rotorcraft to Saturn's moon, Titan. This mission follows Huygens as the previous mission that successfully landed a vehicle on Titan. A flight mechanics simulation of Dragonfly's Entry, Descent, and Landing sequence has been developed using the Program to Optimize Simulated Trajectories II. The simulation incorporates several subsystem models, including aerodynamics, gravity, and mass properties, to fully capture the multi-body six degree of freedom dynamics. Among all the subsystem models that inform the Entry, Descent, and Landing dynamics, the atmosphere model of Titan is a critical component. The atmosphere model characterizes the density, temperature, pressure, and winds that the entry vehicle experiences during the descent. This impacts several aspects of the descent such as the peak heating, aerodynamics, parachute release conditions, the dynamics of the vehicle and parachutes, and the landing ellipse. In the course of developing Dragonfly, an updated model of the Titan atmosphere has been created corresponding to Dragonfly's arrival in the mid-2030s, approximately one Titan year after the Huygens mission successfully landed a probe on Titan. This work leverages previous work done to investigate Huygens EDL sequence to assess the atmosphere model developed for Dragonfly. This is done by utilizing the updated Titan atmosphere model, the Dragonfly atmosphere model, within the Huygens POST2-based flight simulation with the goal of characterizing the differences between the atmospheric models and assessing how the Dragonfly atmosphere model impacts Huygens entry dynamics.

Entry Descent Landing↗

Dragonfly Mission Entry and Descent Modeling and Simulation Overview

Dragonfly is a New Frontiers Program mission, led by The Johns Hopkins Applied Physics Laboratory, that will deliver a rotorcraft lander to Saturn’s moon, Titan. The focus of this work is to analyze the trajectory of the entry vehicle from cruise stage separation until lander separation. This analysis is done by the NASA Langley Research Center Entry, Descent, and Landing team using the Program to Optimize Trajectories II. This paper provides an overview of the current design and the robustness of the overall entry sequence using a Monte Carlo uncertainty analysis. The work presented in this study includes the updated design, models, and analysis completed since the Dragonfly Entry, Descent, and Landing Mission Preliminary Design Review.

Simulation↗

Onboard Autonomous Trajectory Planning for Mars Power Descent

In recent years, there has been an increasing interest in space-qualified processors such as multi-core central processing units and graphics processing units that can withstand the adverse effects of space radiation. These processors can allow parallel programming to perform tasks that typically demand high computational power. One can study guidance schemes that can take advantage of these currently developing processors and provide more robust guidance. Software for Multi-model Autonomous Real-time Trajectories (SMART) guidance can identify robust trajectories by running an onboard Monte Carlo analysis. SMART guidance can take advantage of knowledge updates obtained from the onboard sensors, allowing it to consider the off-nominal cases that it would not typically encounter during the offline trajectory analysis. This work uses the SMART guidance for the powered divert at Mars simulation in Program to Optimize and Simulated Trajectories- II.

Autonomous Planning↗

Further developments in generating type-safe messaging

At ICALEPCS 09, we introduced a source code generator that allows processes to communicate safely using data types native to each host language. In this paper, we discuss further development that has occurred since the conference in Kobe, Japan, including the addition of three more client languages, an optimization in network packet size and the addition of a new protocol data type. The protocol compiler is continuing to prove itself as an easy and robust way to get applications written in different languages hosted on different computer architectures to communicate. We have two active Erlang projects that are using the protocol compiler to access ACNET data at high data rates. We also used the protocol compiler output to deliver ACNET data to an iPhone/iPad application. Since it takes an average of two weeks to support a new language, we're willing to expand the protocol compiler to support new languages that our community uses.

43 PARTICLE ACCELERATORS↗

PyRAT - python radiography analysis tool (u)

PyRAT is a radiography analysis tool used to reconstruction images of unknown 1-0 objects. The tool is written in Python and developed for use on LINUX and Windows platforms. The tool is capable of performing nonlinear inversions of the images with minimal manual interaction in the optimization process. The tool utilizes the NOMAD mixed variable optimization tool to perform the optimization.

97 MATHEMATICS AND COMPUTING↗

Apparatus and method for optimal phase balancing using dynamic programming with spatial consideration

Provided are an apparatus and method for load-balancing of a three-phase electric power distribution system having a multi-phase feeder, including obtaining topology information of the feeder identifying supply points for customer loads and feeder sections between the supply points, obtaining customer information that includes peak customer load at each of the points between each of the feeder sections, performing a phase balancing analysis, and recommending phase assignment at the customer load supply points.

24 POWER TRANSMISSION AND DISTRIBUTION↗