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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

Minimum Hamiltonian ascent trajectory evaluation (MASTRE) program (update to automatic flight trajectory design, performance prediction, and vehicle sizing for support of shuttle and shuttle derived vehicles) users manual

Documentation for the User Interface Program for the Minimum Hamiltonian Ascent Trajectory Evaluation (MASTRE) is provided. The User Interface Program is a separate software package designed to ease the user input requirements when using the MASTRE Trajectory Program. This document supplements documentation on the MASTRE Program that consists of the MASTRE Engineering Manual and the MASTRE Programmers Guide. The User Interface Program provides a series of menus and tables using the VAX Screen Management Guideline (SMG) software. These menus and tables allow the user to modify the MASTRE Program input without the need for learning the various program dependent mnemonics. In addition, the User Interface Program allows the user to modify and/or review additional input Namelist and data files, to build and review command files, to formulate and calculate mass properties related data, and to have a plotting capability.

Lyons, J. T.↗
DOE OSTI2025

Learning plasma dynamics and robust rampdown trajectories with predict-first experiments at TCV

The rampdown phase of a tokamak pulse is difficult to simulate and often exacerbates multiple plasma instabilities. To reduce the risk of disrupting operations, we leverage advances in Scientific Machine Learning (SciML) to combine physics with data-driven models, developing a neural state-space model (NSSM) that predicts plasma dynamics during Tokamak à Configuration Variable (TCV) rampdowns. The NSSM efficiently learns dynamics from a modest dataset of 311 pulses with only five pulses in a reactor-relevant high-performance regime. The NSSM is parallelized across uncertainties, and reinforcement learning (RL) is applied to design trajectories that avoid instability limits. High-performance experiments at TCV show statistically significant improvements in relevant metrics. A predict-first experiment, increasing plasma current by 20% from baseline, demonstrates the NSSM’s ability to make small extrapolations. The developed approach paves the way for designing tokamak controls with robustness to considerable uncertainty and demonstrates the relevance of SciML for fusion experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS2011

Trajectory-Based Complexity (TBX): A Modified Aircraft Count to Predict Sector Complexity During Trajectory-Based Operations

In this paper we introduce a new complexity metric to predict -in real-time- sector complexity for trajectory-based operations (TBO). TBO will be implemented in the Next Generation Air Transportation System (NextGen). Trajectory-Based Complexity (TBX) is a modified aircraft count that can easily be computed and communicated in a TBO environment based upon predictions of aircraft and weather trajectories. TBX is scaled to aircraft count and represents an alternate and additional means to manage air traffic demand and capacity with more consideration of dynamic factors such as weather, aircraft equipage or predicted separation violations, as well as static factors such as sector size. We have developed and evaluated TBX in the Airspace Operations Laboratory (AOL) at the NASA Ames Research Center during human-in-the-loop studies of trajectory-based concepts since 2009. In this paper we will describe the TBX computation in detail and present the underlying algorithm. Next, we will describe the specific TBX used in an experiment at NASA's AOL. We will evaluate the performance of this metric using data collected during a controller-inthe- loop study on trajectory-based operations at different equipage levels. In this study controllers were prompted at regular intervals to rate their current workload on a numeric scale. When comparing this real-time workload rating to the TBX values predicted for these time periods we demonstrate that TBX is a better predictor of workload than aircraft count. Furthermore we demonstrate that TBX is well suited to be used for complexity management in TBO and can easily be adjusted to future operational concepts.

trajectories↗
NASA NTRS2003

Simulating Fatigue Crack Growth in Spiral Bevel Pinion

This project investigates computational modeling of fatigue crack growth in spiral bevel gears. Current work is a continuation of the previous efforts made to use the Boundary Element Method (BEM) to simulate tooth-bending fatigue failure in spiral bevel gears. This report summarizes new results predicting crack trajectory and fatigue life for a spiral bevel pinion using the Finite Element Method (FEM). Predicting crack trajectories is important in determining the failure mode of a gear. Cracks propagating through the rim may result in catastrophic failure, whereas the gear may remain intact if one tooth fails and this may allow for early detection of failure. Being able to predict crack trajectories is insightful for the designer. However, predicting growth of three-dimensional arbitrary cracks is complicated due to the difficulty of creating three-dimensional models, the computing power required, and absence of closed- form solutions of the problem. Another focus of this project was performing three-dimensional contact analysis of a spiral bevel gear set incorporating cracks. These analyses were significant in determining the influence of change of tooth flexibility due to crack growth on the magnitude and location of contact loads. This is an important concern since change in contact loads might lead to differences in SIFs and therefore result in alteration of the crack trajectory. Contact analyses performed in this report showed the expected trend of decreasing tooth loads carried by the cracked tooth with increasing crack length. Decrease in tooth loads lead to differences between SIFs extracted from finite element contact analysis and finite element analysis with Hertz contact loads. This effect became more pronounced as the crack grew.

Ural, Ani↗
NASA NTRS2004

Mars Exploration Rover Cruise Orbit Determination

The Mars Exploration Rover project consisted of two missions (MER-A: spirit rover and MER-B: opportunity rover) that launched spacecraft on June 10, 2003, and July 8, 2003, respectively. The spacecraft arrived at Mars approximately seven months later on January 4, 2004, and January 24, 2004. These spacecraft needed to be precisely navigated to a Mars atmospheric entry flight path angle of -11.5 deg +/-0.12 deg (3(sigma)) for MER-A and +/-0.14 deg (3(sigma)) for MER-B in order to satisfy the landing site delivery requirements. The orbit determination task of the navigation team needed to accurately determine the trajectory of the spacecraft, predict the trajectory to Mars atmospheric entry, and account for all possible errors sources so that the each spacecraft could be correctly targeted using five trajectory corrections along the way. This paper describes the orbit determination analysis which allowed MER-A to be targeted using only four trajectory correction maneuvers to an entry flight path angle of -11.49 deg +/-O.010 deg (3(sigma)) and MER-B to be targeted using only three trajectory correction maneuvers to an entry flight path angle of -11.47 +/-0.021 deg(3(sigma)).

navigation↗
NASA NTRSDate not supplied

Multi-Model Monte Carlo Estimators for Trajectory Simulation

Predicting landing radius and other quantities of interest (QoI) for entry, descent, andlanding (EDL) applications requires a viable uncertainty propagation method for quantifying the impact of uncertainties in aerodynamics, atmosphere, mass properties, etc. While standard Monte Carlo (MC) simulation is the de facto standard for producing robust and unbiasedstatistical estimators, it is often infeasible for expensive, high-fidelity models. Low-fidelity models are commonly constructed to replace the high-fidelity model in MC simulation for computational speedup, but at the expense of accuracy and unbiasedness. Emerging multi-model MC methods are bridging this gap by combining predictions from two or more modelsof varying fidelity and computational cost for efficient and unbiased uncertainty propagation.This works establishes a proof of concept for using multi-model MC to increase the speed and precision of trajectory simulation for EDL. It is shown that combining a high-fidelity EDL model with low-fidelity models (e.g., data-driven, reduced physics) in this manner has the potential to yield significant efficiency and accuracy gains for certain EDL QoIs versusa standard MC approach. Moreover, the unbiasedness of multi-model MC predictions ishighlighted by showing increased accuracy versus an approach that leverages a low-fidelity model alone.

James E Warner↗
NASA NTRS2008

Trajectory Model of Lunar Dust Particles

The goal of this work was to predict the trajectories of blowing lunar regolith (soil) particles when a spacecraft lands on or launches from the Moon. The blown regolith is known to travel at very high velocity and to damage any hardware located nearby on the Moon. It is important to understand the trajectories so we can develop technologies to mitigate the blast effects for the launch and landing zones at a lunar outpost. A mathematical model was implemented in software to predict the trajectory of a single spherical mass acted on by the gas jet from the nozzle of a lunar lander.

Source record↗
NASA NTRS2017

UAV Trajectory Modeling Using Neural Networks

Massive small unmanned aerial vehicles are envisioned to operate in the near future. While there are lots of research problems need to be addressed before dense operations can happen, trajectory modeling remains as one of the keys to understand and develop policies, regulations, and requirements for safe and efficient unmanned aerial vehicle operations. The fidelity requirement of a small unmanned vehicle trajectory model is high because these vehicles are sensitive to winds due to their small size and low operational altitude. Both vehicle control systems and dynamic models are needed for trajectory modeling, which makes the modeling a great challenge, especially considering the fact that manufactures are not willing to share their control systems. This work proposed to use a neural network approach for modelling small unmanned vehicle's trajectory without knowing its control system and bypassing exhaustive efforts for aerodynamic parameter identification. As a proof of concept, instead of collecting data from flight tests, this work used the trajectory data generated by a mathematical vehicle model for training and testing the neural network. The results showed great promise because the trained neural network can predict 4D trajectories accurately, and prediction errors were less than 2:0 meters in both temporal and spatial dimensions.

Neural Networks↗
NASA NTRS1979

Prediction of fragment velocities and trajectories

Analytical techniques are described which predict: (1) the velocities of two unequal fragments from bursting cylindrical pressure vessels; (2) the velocity and range of portions of vessels containing a fluid which, when the vessel ruptures, causes the fragment to accelerate as the fluid changes from the liquid to the gaseous phase; and (3) the ranges of fragments subjected to drag and lift forces during flight. Numerous computer runs were made with various initial conditions in an effort to generalize the results for maximum range in plots of dimensionless range versus dimensionless velocity.

Kulesz, J. J.↗
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↗
DOE OSTI2024

Evaluation of the EarthSHAB Stratospheric Solar Hot Air Balloon Flight Prediction Model Using Balloon Trajectory Data

Abstract The heliotrope is a solar balloon design which is constructed out of painter’s plastic, and the exterior is coated in charcoal powder. Darkening the plastic gives the balloon a high solar absorptance, which allows it to ascend into the lower stratosphere and float for hours at a time. The balloons have previously been used to lift scientific instruments into the stratosphere to study chemical explosions, earthquakes, and stratospheric aerosols. They have also been proposed as a platform for planetary exploration. Flight predictions are crucial to preflight planning to reduce safety risks and meet flight objectives. However, there exists a wide range of possible flight paths due to varying environmental conditions and solar balloon configurations. EarthSHAB is one such software that was designed to support flight planning using the weather forecasts and balloon properties to predict the flight path of a solar balloon. We compare EarthSHAB-simulated flight paths to a set of observed flight paths for the 3.5-m diameter heliotrope design called the “Cloudskimmer.” Using the criteria that the modeled paths must fall within 5% of the observations to be considered successful, we found that EarthSHAB successfully predicted the Cloudskimmer ascent rate and average float altitude 10% and 90% of the time, respectively. We also found that the average difference in the observed and predicted landing locations was 97 km and landing times were 54 ± 38 min. Significant deviations between the observed and predicted ascent rates and excursions at float were found to be associated with heavy payloads and convective cloud development, respectively. Significance Statement Solar balloons are used to lift scientific instruments into the lower stratosphere for hours at a time to study chemical explosions, earthquakes, stratospheric aerosols, and more. The flight paths of solar balloons can be difficult to predict due to variability in their design and surrounding environment. We evaluate the accuracy of EarthSHAB, a software that predicts the altitude profile and horizontal trajectory of a solar balloon using inputs such as the weather, balloon size, and balloon mass. Our results suggest that for the balloon design used in this study, EarthSHAB is best suited for modeling the behavior of balloons with lightweight payloads that do not fly within or directly above clouds.

Lien, Jessica M. [Sandia National Laboratories, Al↗
NASA NTRS1991

Bubble trajectories in rotating drops

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.

Brone, D.↗
NASA NTRSDate not supplied

VFR Trajectory Forecasting using Deep Generative Model for Autonomous Airspace Operations

To enable the airspace integration of autonomous operations, such as uncrewed aircraft conducting cargo deliveries, there is a need to forecast the positions of the surrounding traffic with which they may interact. This paper focuses on forecasting Visual Flight Rules traffic, a significant source of uncertainty and risk in the airspace, especially around small regional airports, due to the unplanned and often untracked nature of such flights. A deep generative model is developed, trained on historical traffic data at example towered and non-towered airports, and used to predict flight trajectories. Experimental results are presented comparing the performance of variational autoencoder and classical machine learning forecasting when applied to both the towered and non-towered airports over varying time horizons. The results show the advantages of the variational autoencoder in producing accurate probabilistic forecasts over varying time horizons.

uncrewed aircraft↗
NASA NTRSDate not supplied

VFR Trajectory Forecasting using Deep Generative Model for Autonomous Airspace Operations

To enable the airspace integration of autonomous operations, such as uncrewed aircraft conducting cargo deliveries, there is a need to forecast the positions of the surrounding traffic with which they may interact. This paper focuses on forecasting Visual Flight Rules traffic, a significant source of uncertainty and risk in the airspace, especially around small regional airports, due to the unplanned and often untracked nature of such flights. A deep generative model is developed, trained on historical traffic data at example towered and non-towered airports, and used to predict flight trajectories. Experimental results are presented comparing the performance of variational autoencoder and classical machine learning forecasting when applied to both the towered and non-towered airports over varying time horizons. The results show the advantages of the variational autoencoder in producing accurate probabilistic forecasts over varying time horizons.

uncrewed aircraft↗
NASA NTRS1978

Wind Field and Trajectory Models for Tornado-Propelled Objects

A mathematical model to predict the trajectory of tornado born objects postulated to be in the vicinity of nuclear power plants is developed. An improved tornado wind field model satisfied the no slip ground boundary condition of fluid mechanics and includes the functional dependence of eddy viscosity with altitude. Subscale wind tunnel data are obtained for all of the missiles currently specified for nuclear plant design. Confirmatory full-scale data are obtained for a 12 inch pipe and automobile. The original six degree of freedom trajectory model is modified to include the improved wind field and increased capability as to body shapes and inertial characteristics that can be handled. The improved trajectory model is used to calculate maximum credible speeds, which for all of the heavy missiles are considerably less than those currently specified for design. Equivalent coefficients for use in three degree of freedom models are developed and the sensitivity of range and speed to various trajectory parameters for the 12 inch diameter pipe are examined.

Redmann, G. H.↗
NASA NTRS2011

Ares I-X Best Estimated Trajectory and Comparison with Pre-Flight Predictions

The Ares I-X trajectory reconstruction produced best estimated trajectories of the flight test vehicle ascent through stage separation, and of the first and upper stage entries after separation. The trajectory reconstruction process combines on-board, ground-based, and atmospheric measurements to produce the trajectory estimates. The Ares I-X vehicle had a number of on-board and ground based sensors that were available, including inertial measurement units, radar, air- data, and weather balloons. However, due to problems with calibrations and/or data, not all of the sensor data were used. The trajectory estimate was generated using an Iterative Extended Kalman Filter algorithm, which is an industry standard processing algorithm for filtering and estimation applications. This paper describes the methodology and results of the trajectory reconstruction process, including flight data preprocessing and input uncertainties, trajectory estimation algorithms, output transformations, and comparisons with preflight predictions.

Karlgaard, Christopher D.↗