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At least 1,117 records · Page 62
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Scale resolving simulations of the NASA Juncture Flow Model using the LAVA solver

Two distinct simulation methodologies: Delayed Detached Eddy Simulation (DDES) and stress based Wall-modelled Large Eddy Simulation (WMLES) are evaluated using structured overset curvilinear grids for the NASA juncture flow model. While both methodologies are shown to mitigate the primary shortcomings of steady state Reynolds Averaged Navier Stokes(RANS) simulations, several unresolved aspects are identified. Strong sensitivity to RANS-typegrid refinement is observed in the DDES with a substantial deterioration of the solution quality with increasing spatial resolution associated with deficiencies in the shielding function. Mean profiles for attached boundary layers on the fuselage show spurious inflections suggesting modelled stress depletion on finer grids. Lower numerical dissipation in terms of spatial discretization and time step size is seen to improve the solution quality, and the advantage of using RC and QCR2000 corrections in the underlying RANS closure is demonstrated for DDES on RANS-type meshes. Equilibrium Wall-Modelled LES used grids that resolved the tripping dots over the fuselage nose and the wing leading edge, consistent with the experiment setup. These simulations resulted in a cost-competitive approach compared to DDES on RANS-type grids. Although the agreement between WMLES predictions of first and second order single point statistics with experimental measurements is promising, a fundamental shortcoming is noted in terms of an overshoot in streamwise momentum in corner regions of the wing-fuselage juncture. This excess momentum subsequently delays the onset of separation,thereby resulting in an underprediction in length of the separation bubble. Some quantitative sensitivity to numerical discretization is observed; lowering of numerical dissipation shows better agreement with the experiment.

T^3↗
NASA NTRSDate not supplied

A Heat and Mass Transfer Model of the Orion European Service Module Propulsion Sub-System

A tool for simulating the thermodynamics of the fluid components of the Orion European Service Module propulsion subsystem has been developed. The 1-dimensional,time-stepping method tracks energy and mass movement through the propellant and pressurant tanks during burn and coast phases of a simulated mission. The code is fast and flexible, allowing easy modifications and incorporation into other computational tools. The results from the simulation have been used to support mission planning, development of flight rules and to guide experimental testing of components and systems.

Spacecraft propulsion↗
NASA NTRSDate not supplied

AN INTRODUCTION TO THE GEONEX LEVEL-1G PRODUCTS: TOP-OF-ATMOSPHERE REFLECTANCE AND BRIGHTNESS TEMPERATURE

This paper introduces the GeoNEX (Geostationary-NASA Earth eXchange) Level-1G products of top-of-atmosphere (TOA) reflectance and brightness temperature. The products use data streams from the latest geostationary (GEO) sensors including the GOES-16/17 ABI and the Himawari-8/9 AHI. The GeoNEX processing pipeline starts by converting digital numbers to physical quantities with the latest radiometric calibration information. It integrates algorithms to automatically detect and remove residual geolocation errors, to estimate the pixel-wise data-acquisition time, and to accurately calculate the solar illumination angles for each pixel in the domain at every time step. The outputs are reprojected to a globally tiled common grid in geographic coordinates designed to facilitate inter-comparisons and/or synergies between the GeoNEX products and existing Earth observation datasets from polar-orbiting satellites. Therefore, the GeoNEX L1G products provide accurate and consistent TOA reflectance and brightness temperature datasets for scientific analyses and downstream product development.

Geostationary satellite, GOES-16, Himawari-8, NASA↗
NASA NTRSDate not supplied

Adaptive Stress Testing: Using Reinforcement Learning to Find Failures in Safety-Critical Systems

Emerging applications in artificial intelligence, such as driverless cars and autonomous aircraft promise to be more efficient, cheaper to operate, and always available. However, ensuring the safety of these systems remains a major challenge to their certification and adoption. These autonomous systems are expected to routinely make safety-critical decisions where failures can have serious consequences including loss of life and property. Testing and validation techniques aim to identify and diagnose potential failures before the system is deployed. However, finding failure scenarios in autonomous systems can be very challenging due to high-dimensional and continuous state spaces, interaction with large environments over many time steps, and the rarity of failures. This talk presents Adaptive Stress Testing (AST), a simulation-based testing framework for finding the most likely path to a failure event of a safety-critical system. The key idea of AST is that stress testing can be formulated as a Partially Observable Markov Decision Process (POMDP), which enables reinforcement learning techniques to be used for finding failure events. Reinforcement learning algorithms can efficiently explore the search space and have been shown to scale to very large systems. We present applications of AST to find failures in various safety-critical systems including the aircraft collision avoidance systems, autonomous cars, and small unmanned aerial vehicles.

autonomous vehicles↗
NASA NTRSDate not supplied

Scalability of Cohesive Fatigue Analyses Using Explicit Solvers

A cohesive fatigue law has been integrated into a constitutive material model compatible with an explicit finite element solver. The cohesive fatigue model response is based on engineering approximations of the endurance limit and the Goodman diagram. This approach can predict stress-life diagrams for crack initiation, the Paris law regime, and transient effects of crack initiation and stable tearing. Simplified cyclic loading is utilized so that the applied load(or displacement) corresponds to the peak load of a fatigue cycle. Loads are held constant during fatigue while damage develops with increasing solution increments. An automatically-calculated ratio of fatigue cycles per solution increment controls the rate of damage growth, ensuring that damage growth is modeled with a sufficient minimum number of increments and damage growth advances to a minimum desired extent within the explicit analysis step time. The compatibility with an explicit finite element solver enables the analysis of structures that are computationally intractable for implicit finite element solvers. Scalability studies are conducted for geometrically nonlinear problems involving fiber-reinforced composite structures that exhibit fatigue damage growth of interacting matrix cracks and delaminations

Frank A Leone↗
NASA NTRS2020

Multibody Simulation of REMORA CubeSat Docking to and Pushing a Spent Rocket Booster

This paper details the multibody simulation of three phases of critical importance to the feasibility of the REMORA CubeSat space debris mitigation concept: the final approach of the CubeSat to a spent rocket booster; the grappling of the spent rocket booster using a robotic arm; and the pushing of the spent rocket booster to divert its course from another on-orbit asset. The extension of a robotic mobility and manipulation modeling toolkit (M3TK) from multibody dynamics simulation of manipulators and ground vehicles to simulation of orbital robotics is outlined. This includes the identification of the appropriate parameters required to concisely and generically describe thruster loads, thruster mixing, spacecraft control, and spacecraft navigation for the purpose of on-orbit robotics simulation. A high-level spacecraft navigator commands maneuvers to target spacecraft states. A PID spacecraft controller takes the target states and calculates desired forces and torques. A thruster mixer solves a quadratic program to determine the optimal thruster firing times for the propulsion system. Pulse width modulated actuation of eight canted cold gas thrusters is used in the simulated approach to a rocket nozzle from a distance of 200 m. A five degree of freedom robotic arm is controlled to position a pair of pincers to grasp the rocket nozzle. Contact dynamics are used to accurately simulate the grasping of the rocket nozzle by the pincers. A similar, but separate simulation is performed to assess the ability of the REMORA CubeSat to push the large spent rocket booster. This diversion maneuver makes use of an additional, larger thruster, and pushes the rocket booster in excess of 400 m. Appropriate motor control gains on the robotic arm are found to be higher during the pushing phase than those which are appropriate during free motion; this increase promotes rigidity of the arm and allows it to properly direct the pushing force. Challenges encountered in time step selection for numerical stability of the simulation are also discussed.

Mukherjee, Rudranarayan↗
NASA NTRSDate not supplied

Dust Particle Aeroheating Calculations for Mars Entry Hypersonic Flows

The purpose of the current research effort is to predict particle trajectories in a hypersonic flowfield while accounting for all relevant particle-fluid and particle-particle interactions. The resulting particle solver is titled DUst Simulation & Tracking (DUST) and works in conjunction with the US3D CFD flow solver. The key elements underpinning the current work are an efficient mesh-localiztion algorithm, time-driven hard-sphere model, point-to-point MPI framework, coarse-graining using computational parcels, and high-order Adams-Bashforth time stepping. The coupled US3D-DUST framework will be applied to conduct a multi-physics examination of dust-laden flows around the the Schiarparelli capsule.

Dusty Flows↗
NASA NTRSDate not supplied

Dust Particle Aeroheating Calculations for Mars Entry Hypersonic Flows

The purpose of the current research effort is to predict particle trajectories in a hypersonic flowfield while accounting for all relevant particle-fluid and particle-particle interactions. The resulting particle solver is titled DUst Simulation & Tracking (DUST) and works in conjunction with the US3D CFD flow solver. The key elements underpinning the current work are an efficient mesh-localiztion algorithm, time-driven hard-sphere model, point-to-point MPI framework, coarse-graining using computational parcels, and high-order Adams-Bashforth time stepping. The coupled US3D-DUST framework will be applied to conduct a multi-physics examination of dust-laden flows around the the Mars 2020 capsule.

Dusty Flows↗
NASA NTRS2020

Adaptive Stress Testing: Finding Likely Failure Events with Reinforcement Learning

Finding the most likely path to a set of failure states is important to the analysis of safety-critical systems that operate over a sequence of time steps, such as aircraft collision avoidance systems and autonomous cars. In many applications such as autonomous driving, failures cannot be completely eliminated due to the complex stochastic environment in which the system operates.As a result, safety validation is not only concerned about whether a failure can occur, but also discovering which failures are most likely to occur. This article presents adaptive stress testing (AST), a framework for finding the most likely path to a failure event in simulation. We consider a general black box setting for partially observable and continuous-valued systems operating in an environment with stochastic disturbances. We formulate the problem as a Markov decision process and use reinforcement learning to optimize it. The approach is simulation-based and does not require internal knowledge of the system, making it suitable for black-box testing of large systems. We present different formulations depending on whether the state is fully observable or partially observable. In the latter case, we present a modified Monte Carlo tree search algorithm that only requires access to the pseudorandom number generator of the simulator to overcome partial observability. We also present an extension of the framework, called differential adaptive stress testing (DAST), that can find failures that occur in one system but not in another. This type of differential analysis is useful in applications such as regression testing, where we are concerned with finding areas of relative weakness compared to a baseline. We demonstrate the effectiveness of the approach on an aircraft collision avoidance application, where a prototype aircraft collision avoidance system is stress tested to find the most likely scenarios of near mid-air collision.

Verification and Validation↗
NASA NTRSDate not supplied

Early Information Parameter-Set Analysis for Satellite Close Approaches using Machine Learning

Understanding orbital mechanics is essential in space flight and navigation applications, and leveraging modern force models for flight path projection remains an important aspect in space mission design and operation. However, force models do not capture all the dynamics or perturbations in the space environment and thus are subject to errors in predicting the state vectors. The further out the predicted miss distance between spacecraft is from the time of closest approach (TCA), the larger the propagated errors in the predicted miss distance at TCA is. The dependency on these force models for spacecraft flight state prediction calls for a more reliable method that can quantify, or even reduce, these propagated errors. With recent advances in the field artificial intelligence, specifically in machine and deep learning algorithms, a model that implements these approaches can improve on the modern force model approach. The goal for this work is to provide an early-information decision-making threshold, in order to prioritize risk assessment implementation, given the ongoing increase of space objects. In analyzing the relationship of several parameters from conjunction data messages(CDMs) and solar information, early information becomes viable in miss distance prediction with unsupervised learning techniques, which learn the parameters that are linked together with miss distance and probability of collision (Pc) variables. Another approach implemented for identifying relationships within CDMs is supervised learning, in which a shallow neural network binary classifier learns to distinguish events with Pc values¡108. These parameters detected in the unsupervised process are then applied to a regression neural network, which predicts the miss distance at TCA for a specific event within a given uncertainty bound. For the regression neural network, a Long Short Term Memory (LSTM) neural network is implemented, which yields memory about each time step in an event. Using an LSTM network, the model learns to predict miss distance within 0.2km of the value measured at TCA. Although there is a limited amount of "close miss" data to train a network, the network learns to associate parameters, like large energy dissipation rates with the secondary object, with an elevated Pc

Brianna I. Robertson↗
NASA NTRSDate not supplied

GeoNEX-SUBSETS: Geostationary Satellite Observations Over International Observing Network Sites

Satellite remote sensing is crucial for monitoring the Earth’s surface and for simulating global carbon and water cycles. Subsets of satellite data from sensors such as MODIS over established observing networks have been valuable for researchers for comparing the ground observed phenomena with satellite observations. Here, we introduce new subsets of the GeoNEX geostationary satellite datasets over locations of several ground observation networks. The NASA Earth Exchange group has been generating geostationary satellite products with our international partners and universities to cover the entire globe. In comparison to polar-orbiting satellite sensors such as MODIS, the new generation geostationary satellites observe target areas at a higher frequency (5-10 minutes), which also significantly increased the data volume. To reduce the burden of downloading and extracting geostationary time series data and provide easy access to the community, we provide subsets of GeoNEX products through our data portal [www.data.nas.nasa.gov]. The ready-to-use subset follows the same format as the MODIS fixed sites subset tools for users who are familiar with MODIS subset data and software. The selected networks include Fluxnet, AERONET, and Phenocam over the conterminous US. We demonstrate the usage of the GeoNEX fixed-site subset data and showcase thir advantages with three example studies. The first example is simulating the diurnal cycle of plant ecophysiology at Fluxnet sites. The high frequency of the GeoNEX time series allows us to run ecological models at sub-hour time steps and directly compare the simulated carbon and water fluxes with half-hourly Fluxnet data. The second example uses the geostationary data to track phenological changes. It highlights the high-frequent observations of geostationary satellites in helping mask cloud covers and capture the quick responses of vegetation to environmental changes. As such, these examples demonstrate the value of ready-to-use GeoNEX subsets data in terrestrial ecophysiology research.

GeoNEX↗
NASA NTRSDate not supplied

Validation of Kestrel IDDES Simulations for SLS Transition Analysis

Complex computational simulations are needed to support the Space Launch System (SLS) program, and the fidelity of the computational results must be defended. More specifically, a number of databases are produced for the transition phase of flight, which occurs after the rocket clears the tower but before reaching transonic speeds. In an effort to reduce computational uncertainty, many of the computational parameters were altered to determine the sensitivity of the results to the value of the parameter and then updating the best practice procedures. The baseline routines were developed over many years through the maturation of the SLS program, and this paper delivers a detailed discussion of these baseline results. This section is followed by demonstration of perturbing some of the more significant parameters, including time step and turbulence model, and concluded with a summary of the herein determined best practices for such analysis.

CFD↗
NASA NTRSDate not supplied

High-Fidelity Simulations of a Quadrotor Vehicle for Urban Air Mobility

High-fidelity computational fluid dynamics simulations have been carried out to analyze NASA’s quadrotor air taxi concept for urban air mobility. High-order accurate schemes, dual-time stepping, and the delayed detached-eddy simulation model have been employed. The flow solver has been loosely coupled with the rotorcraft comprehensive analysis code. The vehicle simulated is a six-passenger quadrotor for air taxi operations. The quadrotor has been simulated in hover and cruise, with flight conditions representative of an air taxi mission. Initially, the quadcopter vehicle is analyzed using low-fidelity tools. Then, a complete analysis using high-fidelity simulations of the quadcopter is performed in conjunction with collective pitch control. Airloads, wake geometry, and performance are studied and compared. A comparison between isolated rotors and complete vehicle simulations is presented. NASA’s quadrotor is one of NASA’s concept vehicles designed in support of aircraft development for vertical take-off and landing air taxi operations.

ARMD↗
NASA NTRSDate not supplied

Estimation With Range Dependent Sensor Model

This paper focuses on the improvement of target tracking accuracy taking into account the sensor’s reading degradation as the relative range increases. The approach is based on the assumption that range measurement error depends on the actual range. Specifically, we model the measurement error as a proportional to the actual range term plus a zero-mean, Gaussian distributed, and uncorrelated process. We present two approaches to tracking a target with constant velocity model. The first approach uses Extended Kalman Filter (EKF) framework to estimate target's states and the unknown proportionality coefficient with linearization on the observation model on the predicted states at each time step. The second one uses a measurement conversion method to estimate the target's scaled by the unknown constant term states, which is shown to be unbiased. This conversion results in linear state and observation models, hence the standard Kalman filter can be applied. The actual target state is computed by application of back scaling with the estimate of unknown scale factor. We evaluate the approaches in desktop simulations.

Target tracking↗
NASA NTRSDate not supplied

Computational Analysis of a Quiet Single-Main Rotor Helicopter for Air Taxi Operations

A computational study has been done on NASA’s quiet single-main rotor helicopter concept for urban air mobility using high-fidelity computational fluid dynamics, rotorcraft comprehensive analysis tools, and computational aeroacoustics. High-order accurate schemes, dual-time stepping, and the delayed detached-eddy simulation model have been employed. A loose-coupling approach between the flow solver and the rotorcraft comprehensive code is implemented to include vehicle trim and blade motions. Acoustic simulations based on the Ffowcs-Williams and Hawkings equations have been performed to compute the rotor noise. Different blade geometries and tip speeds are analyzed, and performance and acoustics results are compared. The vehicle has been simulated in hover and cruise, with flight conditions representative of an air taxi mission. The quiet single-main rotor helicopter is one of the conceptual designs intended to focus and guide NASA’s research activities in support of aircraft development for vertical take-off and landing air taxi operations.

ARMD↗
NASA NTRSDate not supplied

A Hybrid RANS-LES Perspective for the High Lift Common Research Model Using LAVA

An assessment of a Hybrid RANS/LES (HRLES) approach for𝐶𝐿,max prediction is presented for the NASA High-Lift Common Research Model (CRM-HL). Both the free air and the wind tunnel configuration of the CRM-HL are investigated and the results are compared to the QinetiQ wind tunnel experiments and to two other numerical approaches: Reynolds Averaged Navier-Stokes (RANS) and Wall-Modeled Large Eddy Simulations (WMLES). For the free-air configuration, HRLES was shown to address some of the known shortcomings in RANS methods and prevent inboard and outboard flow separation particularly in the region of𝐶𝐿,max and post-stall. To achieve these improvements over RANS, LES-appropriate grids and numerical discretizations are required. HRLES predicts a weak pitch break at the highest angle-of-attack due to onset of wing-root corner flow separation whereas the free-air corrected experiment values indicate an occurrence of a much stronger pitch break. The improvements of HRLES over a URANS approach has been objectively shown by computing a set of solutions with the same grid, same numerics and time-step size and comparing the solutions. It was also found that when applying HRLES to a RANS best practice grid and numerics that the HRLES method significantly under performed RANS. For the in tunnel configuration, HRLES showed good agreement with the loads, surface pressure and oil-flow photographs obtained in the experiment. HRLES was able to improve upon the RANS simulations, which showed a sharp loss of lift at the two highest angles-of-attack due to large scale inboard and outboard separation on the wing, by correctly predicting the corner flow separation and showing remarkably close agreement in the flow topologies with the experiment.

TTT↗
NASA NTRSDate not supplied

Wall-Modeled Large Eddy Simulation Method for Unstructured-Grid Navier-Stokes Solvers

This paper reports on the implementation and assessment of a Wall-Modeled Large-Eddy Simulation (WMLES) methodology in an unstructured-grid, node-centered flow solver, FUN3D that is developed and supported at the NASA Langley Research Center. Finite-volume (FV) and finite-element (FE) discretization schemes considered in the study provide formal second-order spatial accuracy. Large-Eddy Simulations (LES) resolve large-scale turbulent-flow features and filter out small-scale effects using the Vreman subgrid-scale model. At solid-wall boundaries, a shear-stress model is employed to provide a proper boundary-flux closure. The nonlinear equations are integrated in time using either an optimized backward difference formula or an implicit multistage Runge-Kutta temporal scheme. The implicit equations at each time step are solved by strong nonlinear iteration schemes. WMLES demonstrations are shown for two high-lift configurations, namely, the McDonnell Douglas 30P30N multielement airfoil and a NASA High-Lift Common Research Model. Results show that the WMLES approaches implemented in the FV and FE discretization methods produce consistent solutions and are capable of capturing key aerodynamic characteristics and flow structures for high-lift configurations at a wide range of angles of attack including maximum-lift conditions. In the 30P30N example, correct trends in the variations of integrated aerodynamic forces and moments, surface pressure distributions, and boundary-layer profiles are captured as the Reynolds number is increased.

CFD; turbulence modeling; High-Lift flow simulatio↗