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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 361 records · Page 20
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NASA NTRSDate not supplied

Aerial Vehicle Routing and Scheduling for UAS Traffic Management: A Monte Carlo Tree Search Approach

Numerous unmanned aircraft systems operating at low altitudes to deliver goods and services may one day become ubiquitous in our cities. In the Unmanned Aircraft Systems (UAS) Traffic Management (UTM) framework, such a concept is envisioned, where aerial vehicles operate beyond visual line of sight (BVLOS) within specifically reserved and time stamped “corridors” in the airspace. For example, these corridors or operational intent volumes can connect an aerial vehicle’s origin site to its destination site for package delivery operations. There may also be more than one corridor available for an aerial vehicle to choose from and often different corridors may intersect with one another. Thus, it is imperative to ensure flight trajectories belonging to different aerial vehicles are not in conflict. Per the UTM CONOPs, we assume that a vehicle almost always stays inside its corridor or operational volume. This work provides a framework for strategic deconfliction of UTM or package delivery drones, where we schedule the departure time of all vehicles subject to various temporal constraints (including the corridor deconfliction at the intersections). We present the “multi-route weighted package delivery problem” which serves as an exemplifying model for strategic deconfliction in UTM. In the multi-route weighted package delivery problem, a graph network is given which consists of a set of depots (source) and drop-off (destination) nodes, with multiple routes (defined as a sequence of waypoints) connecting the depots to drop-off nodes. In addition, routes are weighted by the associated ground risk and total travel distance for package delivery. The goal is for a known set of aerial vehicles to depart from the depots, choose a route and take off time, while avoiding conflicts with other aerial vehicles, and minimizing both risk and distance traveled. We provide a mixed integer linear programming (MILP) formulation of the problem, as well as a heuristic solution based on Monte Carlo Tree Search (MCTS) – a method used in game theory and artificial intelligence – to overcome limitations inherent to optimal solvers. Computational results show the advantages of using MCTS over the MILP formulation; the former can provide a sub-optimal solution quickly, and may sometimes even reach an optimal solution, whereas the latter may not even produce a solution in reasonable time. Furthermore, results from both the MILP formulation and MCTS methods were validated using a preliminary agent-based simulator implementing the UTM concept of operations. Thus, the MCTS method can be seen as a scalable solution to the complex multi-route weighted package delivery problem and may possibly be extended to similar complex optimization problems.

Kenny Chour↗
NASA NTRSDate not supplied

L2-Charged Particle Environment (L2-CPE)Low Energy Radiation Fluence Model

The L2 Charged Particle Environment (L2-CPE) model provides estimates of number flux and fluence for the low energy electron, proton, and alpha particle populations in the near Earth solar wind and the Earth’s distant magnetosheath and magnetotail. The model is an engineering tool for specifying radiation environments over an energy range from a few eV to a few MeV of importance to surface dose and radiation damage to thin space exposed materials and is intended for use in space system design applications. Mission fluences are obtained by simulating a spacecraft flight trajectory through time-dependent bow shock and magnetopause boundaries with dimensions and orientations driven by solar wind parameters. Monte Carlo sampling of flux environments within individual plasma regimes and/or fluence accumulated along a fight trajectory through multiple regimes is used to determine statistical variations (means and extremes) of the differential number flux and fluence environments for each of the three charged particle species. Model output is differential (in energy) number fluence and flux for the three particle species in units of particles/cm2-keV and particles/cm2-sec-keV, respectively. Users can select the energy range of interest but the current version of the code (L2-CPE Version 1.4.2d) is limited to an energy range of 1 eV to 10 MeV. Flux is integrated over angle to give flux and fluence to surfaces in the ±XGSE, ±YGSE, and/or ±ZGSE directions. Figure 1 shows the opening screen from the L2-CPE graphical user interface (GUI) with options for flux, fluence, plotting model output. The flux scene generate is not currently implemented in the GUI.

Joseph I Minow↗
NASA NTRSDate not supplied

Off-Nominal Event Analysis in Autonomous Flights Based on Explainable Artificial Intelligence

A key objective in the Urban Air Mobility program at NASA is to intelligently perform an autonomous flight in a complex urban environment under all weather conditions with guaranteed levels of safety. To accomplish this, the mission manager (central decision-making module) of the vehicle needs to make informed decisions between various Courses of Action (CoA) based on its' interpretation of the inputs it receives. If an off-nominal event is detected either based on the amalgamation of sensor data or the use of machine learning models, the mission manager may greatly benefit from identification of the input features that most likely contributed to that specific event. Such an understanding is usually not possible to obtain from the classical machine learning models (deep learning) due to the inherent black box like structure. However, this understanding is achieved using eXplainable Artificial Intelligence (XAI) models that provide a human interpretable rationale for the predictions made. This work presents a game theory inspired XAI model for the off-nominal assessment of autonomous flights. The proposed approach based on Shapley values is model agnostic, provides local as well as global explanation and satisfies the four axioms (efficiency, symmetry, dummy, additivity) to achieve fair contribution. The versatility of the approach is first demonstrated on a simulated dataset in which the significance of each input to flight phase prediction is clearly identified. Subsequently, data from simulated flight trajectories are fed into the model which reveal the input features that most likely contributed to a rotor failure event thereby empowering the mission manager to take the appropriate CoA.

autonomy↗
NASA NTRSDate not supplied

Off-Nominal Event Analysis in Autonomous Flights Based on Explainable Artificial Intelligence

A key objective in the Urban Air Mobility program at NASA is to intelligently perform an autonomous flight in a complex urban environment under all weather conditions with guaranteed levels of safety. To accomplish this, the mission manager (central decision-making module) of the vehicle needs to make informed decisions between various Courses of Action (CoA) based on its' interpretation of the inputs it receives. If an off-nominal event is detected either based on the amalgamation of sensor data or the use of machine learning models, the mission manager may greatly benefit from identification of the input features that most likely contributed to that specific event. Such an understanding is usually not possible to obtain from the classical machine learning models (deep learning) due to the inherent black box like structure. However, this understanding is achieved using eXplainable Artificial Intelligence (XAI) models that provide a human interpretable rationale for the predictions made. This work presents a game theory inspired XAI model for the off-nominal assessment of autonomous flights. The proposed approach based on Shapley values is model agnostic, provides local as well as global explanation and satisfies the four axioms (efficiency, symmetry, dummy, additivity) to achieve fair contribution. The versatility of the approach is first demonstrated on a simulated dataset in which the significance of each input to flight phase prediction is clearly identified. Subsequently, data from simulated flight trajectories are fed into the model which reveal the input features that most likely contributed to a rotor failure event thereby empowering the mission manager to take the appropriate CoA.

autonomy↗
NASA NTRS2024

Wind Tunnel Test of A Scale Model of A Venus Probe to Determine Aeroacoustics Environment

The Zephyr probe for NASA’s DAVINCI project is being designed to fly to Venus and collect measurements to characterize the Venusian atmosphere while it descends towards the surface. Despite a slow descent speed as the probe approaches the surface, the very dense atmosphere means the vibro-acoustic environment that the probe must withstand could be very harsh. A wind tunnel test campaign was conducted to characterize the surface-pressure fluctuations (acoustics) on the external surface of the probe during the descent. The goal was to provide forcing functions for vibro-acoustic analysis of all instruments inside the probe. A 25% scale-model of the Zephyr probe was manufactured, instrumented with 18 microphones, and tested in two wind tunnels located in the Fluid Mechanics Lab at NASA Ames Research Center at velocities ranging from 13 m/s to 48 m/s. Trip strips were added to the model to ensure a turbulent boundary layer on the model. Microphones were located appropriately to provide spectra of pressure fluctuations in regions with different flow characteristics as well as for calculating various two-point statistics. The acoustic spectra measured in the wind tunnel tests were scaled up to flight-scale using the estimated flight trajectory information. Besides fluctuating surface-pressure measurements, hot-film velocity measurements as well as smoke-laser flow visualization were conducted to better understand the flow around the probe. Lastly, several different geometry configurations were tested to see whether any reduction in acoustic levels could be achieved, but none of the configurations that were tested offered significant improvements. The flow visualization showed 3 primary flow regimes on the model: an attached turbulent boundary layer on the front of the model, a region of separated flow upstream of the flared lip, and a large, separated wake at the rear of the model. The acoustic spectra generally show three different shapes corresponding to these flow regions, with the highest levels seen at the rear of the model, and directly in front of the drag plate. The scaled overall levels of pressure fluctuations were found to be high: in the range of 130dB to 155dB.

planetary probe↗
NASA NTRS2024

Quantification of Uncertainty and Risk Sensitivity for Safety of Emerging Operations

The growing need to develop and deploy small unmanned aerial vehicles (sUAVs) for various applications in the airspace necessitates reliable tools to accurately predict the flight trajectories of the sUAVs. The knowledge of the predicted trajectories help decision makers anticipate potential conflict, assess the risk, and take appropriate risk mitigation actions. In addition, uncertainties in vehicle models, weather, and controller action further highlights the need for reliable prediction tools. In this project, the application of mixed sparse grid-based quadrature and generalized polynomial chaos(gPC) expansion method for uncertainty quantification and collision assessment in air traffic consisting of fixed-wing small unmanned aerial vehicles (sUAV) was studied. From the results obtained, it can be concluded that this provides a reliable framework to carry out quantitative conflict assessment in an unmanned air traffic, which when employed, can improve the functionalities of the unmanned traffic management system. It was observed that the results from the gPC expansion framework developed in the project can be utilized to conduct rapid probabilistic collision assessment for near real-time unmanned traffic management in the airspace. From the vehicle models, position updates, and wind-field data, a priori gPC based 3-σcon-fidence ellipses can provide estimates of potential conflict at some future instants. The computational costs scaled linearly when the uncertain inputs were fewer. Further, the largest allowable distribution of para-metric uncertainties that leads to the smallest risk of collision in traffic of small unmanned aerial vehicles could be calculated. The time of closest approach between two sUAVs can be established paving way for development of proactive mitigation strategies. The separation between the sUAVs was found to be most significantly affected by uncertainties in the maximum available thrusts, zero-lift drag coefficients, and wing planform areas of the sUAVs. The study of uncertain wind-fields indicated that a heterogeneous traffic mix resulted in an increased probability of conflict. Increased measurement update rate reduced the uncertain-ties in the trajectories of the vehicles, further reducing the probability of conflict but rapid updates of all vehicles in the airspace poses a stringent communication limitation. The gPC framework also provided the means to analyze vehicle impact (crash region) due to loss of control resulting from actuator failure in sUAS traffic, essentially to predict impact and crash zones for representative vehicles. The predicted regions when compared with non-participant density, provides a means to develop an early mitigation strategy, should the sUAV detect an imminent actuator failure.

Rajnish Bhusal↗
NASA NTRSDate not supplied

Evaluation of the Recent Improvements of the Nowcast of Aerospace Ionizing Radiation System (NAIRAS)

The Nowcast of Aerospace Ionizing Radiation System (NAIRAS) model is a real-time, global, physics-based model used to assess radiation exposure now running in real-time and in run on request (RoR) mode at NASA Goddard’s Community Coordinated Modeling Center. NAIRAS was recently updated to extend the galactic cosmic ray (GCR) model to include ultra-heavy nuclei (Z=29-92, A=64-238) for single event effects assessment from high linear energy transfer processes, to expand the geomagnetic cutoff rigidity model to use the either the TS05 (Tsyganenko and Sitnov, 2005), T89 (Tsyganenko, 1989), or the International Geomagnetic Reference Field model (IGRF) magnetic field models, and to improve the solar energetic particle (SEP) proton spectral fitting to better represent relativist protons during ground level enhancements. Here, we evaluate the recent NAIRAS improvements for a range of conditions. First, we demonstrate the effect of choice in magnetic field model to the NAIRAS computed dosimetric quantities for a United States domestic flight and a transatlantic flight during the May 11, 2024 SEP event. Second, the effect of ultra-heavy ions on SEP dose rate is examined for two different flight trajectories during the top SEP events. Lastly, the effect of ultra-heavy ions on NAIRAS computed GCR dose rates at solar minimum and maximum is demonstrated.

Daniel B Phoenix↗
DOE OSTI2025

Back trajectories for TBS flights (c1-level)

The ARMTRAJ VAP provides four trajectory datasets initialized at ARM deployment coordinates and configured using ARM datasets. The four trajectory datasets support aerosol, cloud, and planetary boundary layer research. Trajectory calculations use the HYSPLIT model informed by the ERA5 reanalysis dataset at its highest spatial resolution (~31 km). For each sample in each of the four datasets, HYSPLIT will also be run at multiple starting locations surrounding ARM deployments, enabling an ensemble of runs from which the mean and variability (estimated uncertainty) of each sample's trajectory coordinates, thermodynamic properties, or other fields will be reported.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2012

Adaptive Trajectory Prediction Algorithm for Climbing Flights

Aircraft climb trajectories are difficult to predict, and large errors in these predictions reduce the potential operational benefits of some advanced features for NextGen. The algorithm described in this paper improves climb trajectory prediction accuracy by adjusting trajectory predictions based on observed track data. It utilizes rate-of-climb and airspeed measurements derived from position data to dynamically adjust the aircraft weight modeled for trajectory predictions. In simulations with weight uncertainty, the algorithm is able to adapt to within 3 percent of the actual gross weight within two minutes of the initial adaptation. The root-mean-square of altitude errors for five-minute predictions was reduced by 73 percent. Conflict detection performance also improved, with a 15 percent reduction in missed alerts and a 10 percent reduction in false alerts. In a simulation with climb speed capture intent and weight uncertainty, the algorithm improved climb trajectory prediction accuracy by up to 30 percent and conflict detection performance, reducing missed and false alerts by up to 10 percent.

Seperation Assurance↗
NASA NTRS1999

X-33 Ascent Flight Controller Design by Trajectory Linearization: A Singular Perturbational Approach

The flight control of X-33 poses a challenge to conventional gain-scheduled flight controllers due to its large attitude maneuvers from liftoff to orbit and reentry. In addition, a wide range of uncertainties in vehicle handling qualities and disturbances must be accommodated by the attitude control system. Nonlinear tracking and decoupling control by trajectory linearization can be viewed as the ideal gain-scheduling controller designed at every point on the flight trajectory. Therefore it provides robust stability and performance at all stages of flight without interpolation of controller gains, and eliminates costly controller redesigns due to minor airframe alteration or mission reconfiguration. A prototype trajectory linearization design for X-33 ascent flight controller was designed and tested with 3-DOF and 6-DOF simulations during the 10 weeks SFFP. It is noted that the 6-DOF results were obtained from the 3-DOF design with only a few hours of tuning, which demonstrates the inherent robustness of the design technique. It is this "plug-and-play" feature that is much needed by NASA for the development, test and routine operations of the RLVs. Plans for further research are also presented.

Zhu, J. Jim↗
NASA NTRS2000

X-33 Ascent Flight Controller Design by Trajectory Linearization: A Singular Perturbational Approach

The flight control of X-33 poses a challenge to conventional gain-scheduled flight controllers due to its large attitude maneuvers from liftoff to orbit and reentry. In addition, a wide range of uncertainties in vehicle handling qualities and disturbances must be accommodated by the attitude control system. Nonlinear tracking and decoupling control by trajectory linearization can be viewed as the ideal gain-scheduling controller designed at every point on the flight trajectory. Therefore it provides robust stability and performance at all stages of flight without interpolation of controller gains and eliminates costly controller redesigns due to minor airframe alteration or mission reconfiguration. In this paper, a prototype trajectory linearization design for an X-33 ascent flight controller is presented along with 3-DOF and 6-DOF simulation results. It is noted that the 6-DOF results were obtained from the 3-DOF design with only a few hours of tuning, which demonstrates the inherent robustness of the design technique. It is this "plug-and-play" feature that is much needed by NASA for the development, test and routine operations of the RLV'S. Plans for further research are also presented, and refined 6-DOF simulation results will be presented in the final version of the paper.

Zhu, J. Jim↗
NASA NTRSDate not supplied

Adaptive Stress Testing of Trajectory Predictions in Flight Management Systems

To find failure events and their likelihoods in flight-critical systems, we investigate the use of an advanced black-box stress testing approach called adaptive stress testing. We analyze a trajectory predictor from a developmental commercial flight management system which takes as input a collection of lateral waypoints and en-route environmental conditions. Our aim is to search for failure events relating to inconsistencies in the predicted lateral trajectories. The intention of this work is to find likely failures and report them back to the developers so they can address and potentially resolve shortcomings of the system before deployment. To improve search performance, this work extends the adaptive stress testing formulation to be applied more generally to sequential decision-making problems with episodic reward by collecting the state transitions during the search and evaluating at the end of the simulated rollout. We use a modified Monte Carlo tree search algorithm with progressive widening as our adversarial reinforcement learner. The performance is compared to direct Monte Carlo simulations and to the cross-entropy method as an alternative importance sampling baseline. The goal is to find potential problems otherwise not found by traditional requirements-based testing. Results indicate that our adaptive stress testing approach finds more failures and finds failures with higher likelihood relative to the baseline approaches.

adaptive stress testing↗
NASA NTRS1985

Challenger STS-17 (41-G) post-flight best estimate trajectory products: Development and summary results

Results from the STS-17 (41-G) post-flight products are presented. Operational Instrumentation recorder gaps, coupled with the limited tracking coverage available for this high inclination entry profile, necessitated selection of an anchor epoch for reconstruction corresponding to an unusually low altitude of h approx. 297 kft. The final inertial trajectory obtained, BT17N26/UN=169750N, is discussed in Section I, i.e., relative to the problems encountered with the OI and ACIP recorded data on this Challenger flight. Atmospheric selection, again in view of the ground track displacement from the remote meteorological sites, constituted a major problem area as discussed in Section II. The LAIRS file provided by Langley was adopted, with NOAA data utilized over the lowermost approx. 7 kft. As discussed in Section II, the Extended BET, ST17BET/UN=274885C, suggests a limited upper altitude (H approx. 230 kft) for which meaningful flight extraction can be expected. This is further demonstrated, though not considered a limitation, in Section III wherein summary results from the AEROBET (NJ0333 with NJ0346 as duplicate) are presented. GTFILEs were generated only for the selected IMU (IMU2) and the Rate Gyro Assembly/Accelerometer Assembly data due to the loss of ACIP data. Appendices attached present inputs for the generation of the post-flight products (Appendix A), final residual plots (Appendix B), a two second spaced listing of the relevant parameters from the Extended BET (Appendix C), and an archival section (Appendix D) devoting input (source) and output files and/or physical reels.

Kelly, G. M.↗
NASA NTRSDate not supplied

Progress on Inverse Estimation Technique of Non-Linear Pitch Damping Coefficient Curves Using Free-Flight CFD Generated Trajectories

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.

entry↗
NASA NTRS1989

Optimal trajectories for the aeroassisted flight experiment

The determination of optimal trajectories for the aeroassisted flight experiment (AFE) is discussed. The intent of this experiment is to simulate a GEO-to-LEO transfer, where GEO denotes a geosynchronous earth orbit and LEO denotes a low earth orbit. The trajectories of an AFE spacecraft are analyzed in a 3D-space, employing the full system of 6 ordinary differential equations (ODEs) describing the atmospheric pass. The atmospheric entry conditions are given, and the atmospheric exit conditions are adjusted. Two possible transfers are considered: (1) indirect ascent to a 178 NM perigee via a 197 NM apogee; and (2) direct ascent to a 178 NM apogee.

Miele, A.↗
NASA NTRS1975

A general algorithm for relating ground trajectory distance, elapsed flight time, and aircraft airspeed and its application to 4-D guidance

A general solution using an elliptic integral approximation which relates flight time, aircraft airspeed, and ground distance on straight-line and circular-arc trajectory segments is developed. The solution procedure is applicable to both constant and accelerating aircraft flight. In addition, wind shear including both magnitude and heading change is incorporated in the solution. The solution equations are used in a four-dimensional (4-D) control algorithm where both flight time and final airspeed are specified. The results show that the algorithm converges rapidly and accurately.

Foudriat, E. C.↗