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At least 1,603 records · Page 89

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NASA NTRSDate not supplied

A Federated Learning Approach to Predicting Communication Demand in the National Airspace

Within the national airspace system (NAS), efficient use of spectrum remains a challenge; as UAS and UAM missions evolve, the amount of mission-critical aircraft communications are expected to significantly grow. To accommodate the increased demand, NASA Glenn Research Center is investigating artificial intelligence approaches that could dynamically allocate spectrum; however, these solutions are driven by communication and aviation data items, many of which are not directly available. One such cornerstone data item is communication demand, parameterizing the needs within a sector in terms that may directly inform spectrum allocation, such as channel access duration, bandwidth, and modulation type. This paper considers the complexity of predicting communication demand as a function of NAS behaviors. Unlike prior prediction work in communications, this research must consider how the NAS may be impacted by external factors - such as convective weather and closures - rather than estimating demand from time-series forecasting alone. Much of this research considers a federated learning design to predict communication events in terms of the type of event occurring (sector coordination, conflict resolution, etc). To do so, an investigation of products from Sherlock Data Warehouse is conducted, identifying the trends, sufficiency, and correlations of each product to identified events. Additionally, a preliminary discussion for inferring associations between these event types and their communication parameters (duration, bandwidth, modulation) is presented. By utilizing federated learning, imbalances in the types of events and data present throughout the NAS can inform local models without impairing global training. Furthermore, the two-stage approach proposed allows for robust and speculative communication modelling, as communication techniques continue to evolve. As a result, this model enables a generalized approach to understanding NAS communications which is able to inform long-term changes to aviation spectrum management.

Nathan Schimpf↗

NASA NTRSDate not supplied

Towards Accurate and Efficient Predictions of Martensitic Transition Temperatures for Shape Memory Alloys from First Principles

Recent rapid progresses in physics theory and computational power have made it possible to predict the martensitic transition temperatures (MTTs) in shape memory alloys (SMAs) from first principles [1-3]. In particular, rigorous while time-consuming thermodynamic integration has been employed to compute the anharmonic phonon free energies, which play a crucial role in determining martensitic phase transitions in SMAs. However, this approach has only been applied to simple binaries, and its accuracy is unsatisfying for certain SMAs such as the most commonly used NiTi. In this work, we report on several new developments to our method that bring first-principles theory and experiment much closer into agreement including the MTT of NiTi, and that improve the computational efficiency significantly. We have applied our refined approach to investigate the Ni0.5Ti0.5-xHfx and PdxNi0.5-xTi0.5 ternaries, and the predicted MTT for each composition is within 100K compared with experiment. We will address various techniques to overcome the difficulty encountered in studying ternaries. Our theoretical approach is expected to be a broadly applicable and predictive theory for designing complex SMAs with desirable properties.

Zhigang Wu↗

NASA NTRSDate not supplied

Boundary-Layer Transition Prediction Through Loose Coupling of OVERFLOW and LASTRAC

Transition prediction based on linear stability theory is expected to more accurately reflect the causality of transition onset than phenomenological transition models based on RANS-like transport equations. To help achieve the CFD vision 2030 aim of building a CFD tool chain with automated prediction of boundary layer transition, a technique to loosely tie the NASA OVERFLOW CFD solver with the LASTRAC stability analysis tool is described. The coupled solver is then used to compute transition over selected over a flat plate in a freestream with sufficiently low levels of turbulence, NLF(1)-0416 airfoil, the 6:1 prolate spheroid at an angle of attack, and a NASA juncture flow model with symmetric wing configuration. The findings show that the loosely coupled approach can reliably predict the transition location accurately in scenarios that are dominated by a single transition mechanism involving Tollmien-Schlichting instabilities, crossflow instabilities, or separation bubble-induced transition, or include a mixture of selected mechanisms. The toolset presents here appears to be robust to the prescription of the initial transition location, and it can lead to a converged solution in four or five rounds of the mean flow calculation and stability analysis, with minimal input from the user.

boundary layer transition↗

NASA NTRSDate not supplied

Predicting Char Yield of High-Temperature Resins

A simulation protocol is developed to predict the char yield of organic resins during high-temperature processing. Such in silico methods can help screen promising new formulations for advanced materials, but previously no chemistry-sensitive technique existed to predict the important experimental value of char yield. The method utilizes a reactive force field (ReaxFF) to model the chemical transformation of precursor monomers into carbonized structures during three processing stages: ramp-up to processing temperatures (~3000 K), pyrolysis, and quenching. Achieving good agreement with experimental char yields requires continuous removal of small byproduct molecules to mimic outgassing, and the application of high pressure to eliminate porosity and encourage graphitization. More than ten different resin chemistries are investigated, including arylacetylenes, cyanate esters, phthalonitriles, and polyimides, representing a diverse group of precursors with respect to initial cyclic content, heteroatoms and reactive groups. The protocol correctly predicts the relative char yield between the investigated chemistries and provides quantitative agreement with experimental values, especially for high char yield resins. The properties of the resins during processing are compared, including outgassing products, morphology of the final chemical configurations, cyclic content and mechanical properties.

polymer simulations, molecular dynamics↗

NASA NTRSDate not supplied

Predicting Orion Launch Abort Acoustics

Computational fluid dynamics (CFD) scale-resolving simulations were performed to help assess the Orion launch abort system’s vibro-acoustic environment in collaboration with the Orion Loads and Dynamics team at the Johnson Space Center (JSC). The primary objectives are to establish best practices and to validate CFD predictions for surface fluctuating pressure spectra with ground, wind tunnel, and flight test data. Excellent agreement is obtained between CFD predictions by the Cartesian adaptive mesh refinement (AMR) module of the Launch, Ascent, and Vehicle Aerodynamics (LAVA) solver and measurements from a ground test representative of the start of a pad abort scenario, and with measurements from a wind tunnel test case corresponding to a transonic, high total angle of attack ascent abort scenario. A simplified (static and at rest) simulation of a pad abort flight test shows good agreement with the flight data, whereas a more costly simulation where the vehicle is accelerating relative to the ground from rest to Mach 0.4 shows consistent over-prediction of the spectra. Simulation results for a supersonic ascent abort flight test are in reasonable agreement with the measurements, but show larger excursions in regions where non-linear interactions between the plume turbulence, shocks, and turbulent boundary layer are strongest – likely due to CFD modeling assumptions, the effect of which are discussed. Overall, the CFD results demonstrate that scale-resolving simulation methodologies like the one presented can help characterize full-scale launch abort vehicle near-field noise and can complement wind tunnel, ground and flight tests by providing trends and reducing uncertainty for scenarios that cannot or are not planned to be tested.

Orion↗

NASA NTRSDate not supplied

Developing Deep Learning Models for System Remaining Useful Life Predictions: Application to Aircraft Engines

Prognostics and health management (PHM) is an important part of ensuring reliable operations of complex safety- critical systems. System-level remaining useful life (RUL) estimation is a much more complex problem than making estimations at the component level, and system-level RUL methodologies remain sparse in the literature. Model-based approaches have traditionally worked in the past for components such as capacitors, MOSFETs, batteries, or hard-drives (to name a few examples), but developing high fidelity dynamics models of cyber physical systems that can be used to study the effects of multiple degrading components in the system remains a challenging task. Some initial work on model-based System RUL predictions was demonstrated in Khorasgani, et al [1], but, to generalize the system-level prognostics problem, we have to resort to pure data driven and hybrid approaches. In this work, we propose an end-to-end data- driven framework for developing deep learning models to predict remaining useful life of cyber physical systems operating under unknown faulty conditions. The raw data is organized with a data schema that improves the model development process and down stream data analysis tasks. Due to the unknown faulty conditions, the raw sensor data is transformed into signals that expose the underlying degradation processes, which are then used for model development. Bayesian Optimization is used to tune the model parameters prior to training and validation. We show that this approach results in accurate predictions within 3 cycles to end of life (EOL). We demonstrate the effectiveness of our approach by applying it to the N-CMAPSS turbofan engine dataset recently released by NASA, which includes high fidelity degradation modeling, real world operating conditions, and a large set of fault operating modes.

Prognostics↗

NASA NTRSDate not supplied

Liquid Hydrogen Tank Chill and No-Vent Fill Prediction using Computational Fluid Dynamics

Cryogenic tank chill and fill is an important cryogenic fluid management (CFM) technology that supports and enables many of NASA’s long-duration space missions. For no-vent fill, the receiver tank pressure remains below the supply tank pressure during the entire duration of the fill so that the tank does not require venting. This is especially advantageous for tank fill operations in low gravity where the position of the liquid is not always known and venting the tank may cause loss of propellant by venting liquid. In lieu of expensive tests conducted on-orbit, accurate computational models capable of predicting receiver tank pressure during cryogenic propellant tank fill may be used to reduce system and propellant mass as well as mission risk. However, these numerical models must be validated or anchored to test data. This study presents a computational fluid dynamics (CFD) model with conjugate heat transfer that is used to predict a liquid hydrogen tank chill and no-vent fill ground test conducted at Lewis Research Center (now Glenn Research Center) in 1991. The specific test case chosen for model validation implemented an upward-facing jet near the bottom of a cylindrical 34 liter tank. CFD predictions are compared to experimental measurements of tank pressure, fill level, fluid temperatures, and wall temperatures. The CFD results show reasonable agreement to the test data but overpredict the pressure collapse near the end of the fill despite the liquid jet penetrating the liquid-vapor interface. Several sensitivity studies are considered due to notable uncertainties in the experiment.

Computational Fluid Dynamics↗

NASA NTRSDate not supplied

Integration of Fatigue R-Curve Effects into VCCT for Durability Predictions, Part 1: Buckled Composite Single-Stringer Stiffened Panels

Composite structures have become popular in modern aircraft because they help reduce weight and increase durability. In addition, hat-stiffened panels provide the stability that the airframe skin needs. However, they can be subject to delamination in the post-buckling regime. Progressive damage analysis (PDA) methods can help predict interlaminar and intralaminar failure events. Many aircraft structures are subject to cyclic loading in the post-buckling regime. Hence, fatigue life prediction becomes essential for design and sustainment purposes. Under the NASA Advanced Composites Project (ACP), composite panels stiffened with single hat-stringers were subject to a cyclic loading sequence from a pre-buckling state to a post-buckling state. This test campaign aimed to provide damage initiation and growth data for three initial damage scenarios: nominally pristine, initial Teflon inserts representing manufacturing defects, and impact-induced damage. This work uses the Abaqus FEA Virtual Crack Closure Technique (VCCT), capabilities enhanced through a novel empirical method integrating fatigue R-curve effects (different from R-ratio) into the Paris Law via user-defined subroutine to simulate the fatigue response of the panels with single hat-stringers. The analysis predictions were within 5% of the test results using this novel method.

single hat↗

NASA NTRS2022

Landslide Likelihood Prediction using Machine Learning Algorithms

The supply of electricity via power plants is criticalto the operation of many critical infrastructure systems in mod-ern society. Natural hazards can disrupt the power supply, causepower outages that can halt economic growth, and impede emer-gency response until power is restored. The proposed work aimsto predict the landslides likelihood in these critical infrastructurelocations in the Northeastern USA using integrated databases ofexplanatory variables and machine learning algorithms. First,data related to landslides are obtained and merged, includingtopographic, soil moisture, and precipitation-related data. Fiveregression algorithms, namely: Random Forest, Extreme Gradi-ent Boosting (XGBoost), K-Nearest Neighbor regression (KNN),Linear Support Vector Regressor (SVR), and Linear regression,are utilized to predict the landslide probability and evaluatedon the dataset. The accuracy of the models is assessed by usingstatistical metrics such as mean absolute error (MAE), meansquared error (MSE), and root mean squared error (RMSE).The study results show that Random Forest outperformed othermodels with the mutual information feature selection method.It achieved an MSE of 0.0011 with mutual information-basedfeature selection and an MSE of 0.00157 without feature selection.KNN regressor outperformed the other models with an MSEof 0.00139 with correlation-based information selection. Theproposed landslide identification model with Random Forestalgorithm shows outstanding robustness and great potential intackling the landslide likelihood prediction by employing MLalgorithms.

Vasundhara Acharya↗

NASA NTRSDate not supplied

Predicting Fiber Failure of Plain Weave Fabric with Recursive Multiscale Micromechanics

Recent advances in the development of machine learning (ML) algorithms have enabled the creation of predictive models that can improve decision making, decrease computational cost, and improve efficiency in a variety of fields. As an organization begins to develop and implement such models, the data used in the training, validation, and testing of machine learning models, the model parameters, and the use cases or limitations of the models must be properly stored to ensure models are both fully traceable and used correctly. In the context of predicting material behavior, advances in computationally intense, physics-based, modeling of material behavior at various length scales, and the emergence of Integrated Computational Materials Engineering (ICME) have driven the need for developing data-driven surrogate models of the physics-based simulation tools using machine learning (ML) techniques. Surrogate model development allows for accurate material behavior prediction at a fraction of the cost of its physics-based counterpart, allowing for multiscale simulations of real-world applications, further enabling the ability to design fit-for-purpose materials for a reasonable computational investment. However, training such models requires extensive data, and thus effective data management is necessary to reach the full potential that ML can offer to material design and ICME. This paper proposes a generalized, robust schema that allows organizations to store both real (experimental) and virtual (simulation) data used to train machine learning models and the defining model parameters and architectures. The developed schema allows for various types of data inputs and outputs, including single point values, time-series data, and images that can be used in for various types of machine learning models while following outlined best practices for effective data management. An effective schema for machine learning data and models can help prevent the recreation of virtual/real training data and surrogate models, can help reduce the time to create new models similar to existing ones by offering a starting point in the hyperparameter determination stages, minimize resources devoted to verification and validation (V&V) and certification of models, and ensure that data and surrogate models are not misused due to full traceability of both the data and ML model. It also allows organizations access to models that have already been developed, such that they can be used in the design of new materials, enabling the overall goals of ICME.

Failure↗

NASA NTRSDate not supplied

Toward Monitoring and Predicting Cognitive Resilience

"Toward Monitoring and Predicting Cognitive Resilience" for IWS 2023 session DS04, Monitoring Cognitive Resilience. Definitions and characterizations of Cognitive Resilience are presented, along with other considerations for monitoring and predicting it in the context of space radiation and other stressors. This session will feature “lightning-style" talks focused on how HRP research could be used in conjunction with work from interagency partnerships to monitor or predict cognitive resilience for astronauts followed by facilitated panel discussion focused on key questions.

cognition↗

NASA NTRS2021

Self-Consistent Implementation of a Zero-Equation Transport Model Into a Predictive Model for a Hall Effect Thruster

The performance of an axisymmetric multi-fluid Hall thruster code that incorporates a self-consistent, data-driven closure model for the anomalous electron transport is investigated. Five different operating conditions of the H9 magnetically shielded Hall thruster are simulated with the Jet Propulsion Laboratory’s Hall2De. In order to capture the inherent uncertainty associated with the closure model, O(100) simulations are run for each condition, each of using a coefficient set sampled randomly from a probability distribution. The results of these simulations provide probabilistic predictions of thruster performance quantities including thrust, and discharge current, as well as several component efficiencies and centerline plasma properties. The model is found to yield converged solutions at all conditions, with large 10 kHzrange oscillations and performance trends with voltage and flow rate similar to experiment. The model under-predicts the thrust by 15-25% and over-predicts the discharge current by 20% on average compared to experiments at the same discharge voltage and mass flow rate. This performance discrepancy is due to lower beam utilization, mass utilization, and divergence efficiency than experiment, resulting from high Hall parameters in the acceleration region, which lead to a protracted ion acceleration region. The physical processes underlying this result are discussed in the context of future data-driven modeling efforts.

Jorns, Benjamin A.↗

NASA NTRSDate not supplied

Predicting Char Yield of High-Temperature Resins

A simulation technique has been developed for predicting the char yield of organic resins during high-temperature processing. In silico methods can aid in the screening of new advanced materials for a number of important properties, but no chemistry-sensitive protocol currently exists for predicting the important experimental value of char yield. The proposed method utilizes a reactive force field (ReaxFF) to model the chemical transformation of precursor monomers into carbonized structures during three processing stages: ramp-up to pyrolysis temperatures (~3000 K), pyrolysis, and quenching. Achieving good agreement with experimental char yields requires continuous removal of small by-product molecules to mimic outgassing, and the application of high pressure to encourage the formation of a dense, glassy network. Six different resin chemistries were investigated: an ethynyl, a phenylethynyl, a cyanate ester, a phthalonitrile, acrylonitrile, and adamantane. These candidates represent a diverse group of precursors with respect to initial cyclic content, presence of heteroatoms, and types of reactive groups. The protocol developed accurately predicts the relative char yield between the investigated chemistries and provides quantitative agreement with experimental values, especially for high char yield resins. Several simulated properties of the carbonized structures are compared with experimental results, including outgassing products, morphology of the final chemical configurations, cyclic content, and mechanical properties.

polymer simulations, molecular dynamics↗

NASA NTRSDate not supplied

Liquid Hydrogen Tank Chill and No-Vent Fill Prediction Using Computational Fluid Dynamics

Computational Fluid Dynamics↗

NASA NTRSDate not supplied

Liquid Hydrogen Tank Chill and No-Vent Fill Prediction using Computational Fluid Dynamics

Cryogenic tank chill and fill is an important cryogenic fluid management technology that supports and enables many of NASA’s long-duration space missions. For no-vent fill, the receiver tank pressure remains below the supply tank pressure during the entire duration of the fill so that the tank does not require venting. This is especially advantageous for tank fill operations in low gravity where the position of the liquid is not always known and venting the tank may cause loss of propellant by venting liquid. In lieu of expensive tests conducted on-orbit, accurate computational models capable of predicting receiver tank pressure during cryogenic propellant tank fill may be used to reduce system and propellant mass as well as mission risk. However, these numerical models must be validated or anchored to test data. This study presents computational fluid dynamics (CFD) models with conjugate heat transfer that are used to predict a liquid hydrogen tank chill and no-vent fill ground test conducted at Lewis Research Center (now Glenn Research Center) in 1991. The specific test case chosen for model validation implemented an upward-facing jet near the bottom of a cylindrical 34-liter tank. CFD predictions are compared to experimental measurements of tank pressure, fill level, and wall temperature. The CFD results show reasonable agreement to the test data but overpredict the pressure collapse near the end of the fill despite the liquid jet penetrating the liquid-vapor interface.

Computational Fluid Dynamics↗

NASA NTRSDate not supplied

Predicting Char Yield of High-Temperature Resins

A simulation protocol is developed to predict the char yield of organic resins during high-temperature processing. Simulation protocol in silico methods can help screen promising new formulations for advanced materials, but previously no chemistry-sensitive technique existed to predict the important experimental value of char yield. The method utilizes a reactive force field (ReaxFF) to model the chemical transformation of precursor monomers into carbonized structures during three processing stages: ramp-up to processing temperatures (~3000 K), pyrolysis, and quenching. Achieving good agreement with experimental char yields requires continuous removal of small byproduct molecules to mimic outgassing, and the application of high pressure to eliminate porosity and encourage graphitization. More than ten different resin chemistries are investigated, including arylacetylenes, cyanate esters, phthalonitriles, and polyimides, representing a diverse group of precursors with respect to initial cyclic content, heteroatoms and reactive groups. The protocol correctly predicts the relative char yield between the investigated chemistries and provides quantitative agreement with experimental values, especially for high char yield resins. The properties of the resins during processing are compared, including outgassing products, morphology of the final chemical configurations, cyclic content and mechanical properties.

polymer simulations, molecular dynamics↗

NASA NTRSDate not supplied

Enhancements to Linear Stability-Based, CFD-integrated Transition Prediction for High-Speed Flows

Combining linear stability calculations with computational fluid dynamics (CFD) simulations has great potential for the automated modeling of high-speed flows, especially when adequate information about the configuration and the disturbance environment is available. However, a significant impediment to the applicability of this technique is the lack of an efficient method to calculate the crucial amplification ratio corresponding to the onset of transition in hypersonic flows. This ratio, also known as the "transition N-factor," is dependent upon the freestream disturbance environment as well as the surface properties of the test article. In response to the need for an engineering solution to predict the transition N-factor within conventional hypersonic wind tunnels, this paper presents a data-driven correlation that expands the existing correlations from straight circular cones with a narrow range of half angles to a broader array of axisymmetric configurations. Furthermore, when tested against a chosen dataset that was not used in its calibration, the suggested correlation shows good predictive accuracy with an RMS error of only 6.9%. Although similar accuracy may also be achieved via existing correlations based on similar datasets, predictions based on the proposed correlation have the advantage of not requiring an extensive amount of configuration-specific data. Practical applications often have access to the input parameters needed for this correlation, such as the freestream disturbance intensity, Mach number, and body-based slenderness Reynolds number. Additionally, this correlation outperforms the traditional assumption of a constant N-factor, particularly for configurations with blunted nose geometries. The development of this correlation is grounded in an extensive dataset encompassing conical models with body half-angles varying between 5 degrees and 16 degrees, Mach numbers ranging from 5 to 14, and nosetip-based Reynolds numbers approaching the transition reversal limit for blunt-nosed cones.

CFD↗

NASA NTRSDate not supplied

Prediction of Broadband Blade-Wake Back-Scatter Noise from a Hovering Ideally Twisted Rotor using OVERFLOW2-ANOPP2

This work illustrates the use of a hybrid RANS/LES framework for the prediction of broadband noise via integral formulations from an ideally twisted hovering rotor. Four simulations were conducted, which investigated the effect of different turbulence modeling and spatiotemporal resolutions. The aerodynamic performance and aeroacoustics predicted by these four computational cases were compared against data acquired in the Small Hover Anechoic Chamber at the NASA Langley Research Center and against previous simulations conducted using the lattice-Boltzmann solver, PowerFLOW. The simulation case with the finest spatial resolution had the most favorable aerodynamic performance and broadband noise comparison with measured data; however, the simulation case utilizing the SST-DDES turbulence model predicted the most accurate tonal noise signature. Aerodynamic flowfield visualizations identified near-wake trailing edge vortex shedding, perpendicular blade-vortex interactions, and tip vortex formation, which are known to cause broadband noise. The fluid dynamics associated with a new broadband noise source, blade-wake back-scatter, were also identified. Lastly, a broadband noise source visualization technique was utilized to confirm the presence of blade-wake back-scatter as a dominant broadband noise source.

blade-wake back-scatter↗