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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 379 records · Page 21

Medium-Sized Helicopter Noise Abatement Flight Test Data Report

Helicopter noise has consistently hindered operations within urban areas and surrounding communities. To combat this, an extensive flight test campaign, described herein, was conducted to gather acoustic data to support helicopter noise research (e.g., physics-based modeling, operational planning, etc.). Acoustic measurements were collected from four helicopters in the medium-sized weight class with maximum takeoff gross weights between 7,400 and 14,200 lb: a Bell 205 “Huey”, a Sikorsky S-76D, a Leonardo AW139, and a US Coast Guard MH-65 Dolphin (a variant of the commercial Airbus AS365 Dauphin). Each aircraft per- formed a subset of level flyovers, steady descents, turns with various initial conditions, and noise abatement approaches. A distributed ground microphone array spanning approximately 2,000 by 4,000 ft collected measurements of each flyover event. Additionally, a dedicated semicircular microphone array was used for gathering acoustic measurements of static conditions (e.g., hover). This report provides an overview of the flight test methodology, conditions tested, equipment used, and details of the accompanying distribution dataset. Figures of sample results generated for each test point are included to enable comparison and selection of the data at the discretion of the user. Specifically, source noise hemispheres of the steady flight conditions illustrate directivity of over- all levels. Noise contours over the array provide insight into the ground levels associated with approaches and turning events. These data figures are derived from the original acoustic pressure-time histories and vehicle tracking data that are provided in the distribution dataset.

Rotorcraft↗

Modeling Spectral Degradation of MODIS and VIIRS Solar Diffusers

Solar diffusers (SDs) onboard the Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra and Aqua spacecraft and the Visible Infrared Imaging Radiometer Suite (VIIRS) on SNPP and NOAA-20 spacecraft have been used as the primary onboard radiometric calibrator for reflective solar bands and their spectral reflectance is known to degrade on-orbit. All of the solar diffusers on these instrument show faster degradation of reflectance in the 0.4 to 0.6 µm channels than the longer wavelength channels. The spectral degradation of these SDs is due to the surface roughness growth under the exposure to solar UV and energetic particle radiation in space. In this paper, the origin of SD degradation is modeled by the physics-based Surface Roughness-induced Rayleigh Scattering (SRRS) model. The longterm growths of surface roughness of the SD on these instruments are characterized to show the consistency of SD roughness growth rates between Terra/Aqua MODIS and SNPP/NOAA-20 VIIRS. There was coincidental flattening or reverse in the growth trend of the surface roughness for the Terra/Aqua MODIS and SNPP VIIRS SDs, which can be due to the space weather variation. The applicability of the SRRS model is demonstrated with the long term spectral reflectance data from independent spaceborne SDs.

Calibration↗

Modeling the near-field ionospheric disturbances during earthquakes

We present the latest development of the Wave Perturbation-Global Ionosphere-Thermosphere Model (WP-GITM), a threedimensional physics-based numerical model for seismic/tsunami-ionosphere coupling via atmospheric acoustic-gravity waves. WP-GITM was previously applied to simulate the ionospheric perturbations resulted from the epicentral crustal movement by assuming spherical acoustic-gravity waves originated from a point source, which was specified by the seismic measurement at a single location nearby the epicenter. In this work, we extend WP-GITM to include the effects of Rayleigh surface waves and adapt WP-GITM to utilize seismic measurements from more than one location in the attempt to capture the radiation pattern of the seismic source. We apply the new WP-GITM to model the near-field co-seismic ionospheric perturbations during the 16 September 2015 Illapel earthquake. The comparison between the simulated ionospheric total electron content perturbations and the GPS observations shows promising results.

Ravanelli, Michela↗

Verification and Validation of Elastodynamic Simulation Software for Aerospace Research

Physics-based simulation of nondestructive evaluation (NDE) inspection can help to advance the inspectability and reliability of mechanical systems. However, NDE simulations applicable to non-idealized mechanical components often require large compute domains and long run times. This has prompted development of custom NDE simulation software tailored to high performance computing (HPC) hardware. Verification and validation (V&V) is an integral part of developing this software to ensure implementations are robust and applicable to inspection problems, producing tools and simulations suitable for computational NDE research. This presentation addresses factors common to V&V of several elastodynamic simulation codes applicable to ultrasonic NDE. Examples are drawn from in-house simulation software at NASA Langley Research Center, ranging from ensuring reliability in a 1D heterogeneous media wave equation solver to the V&V needs of 3D cluster-parallel elastodynamic software. Factors specific to a research environment are addressed, where individual simulation results can be as relevant as the software product itself. Distinct facets of V&V are discussed including testing to establish software reliability, employing systematic approaches for consistency with fundamental conservation laws, establishing the numerical stability of algorithms, and demonstrating concurrence with empirical data. This talk also addresses V&V practices for small groups of researchers. This includes establishing resources (e.g. time and personnel) for V&V during project planning to mitigate and control the risk of setbacks. Similarly, we identify ways for individual researchers to use V&V during simulation software development itself to both speed up the development process and reduce incurred technical debt.

NDE↗

Seawater Debye Model Function at L-Band and Its Impact on Salinity Retrieval From Aquarius Satellite Data

A model function of seawater, which specifies the dielectric constant of seawater as a function of salinity, temperature, and frequency, is important for the retrieval of sea surface salinity using satellite data. In 2017, a model function has been developed based on measurement data at 1.4134 GHz using a third-order polynomial expression in salinity ( S ) and temperature ( T ). Although the model showed improvements in salinity retrieval, it had an inconsistent behavior between partitioned salinities. To improve the stability of the model, new dielectric measurements of seawater have been made recently over a broad range of salinities and temperatures to expand the data set used for developing the model function. The structure of the model function has been changed from a polynomial expansion in S and T to a physics-based model consisting of a Debye molecular resonance term plus a conductivity term. Each unknown parameter is expressed in S and T based on the expanded measurement data set. Physical arguments have been used to limit the number of unknown coefficients in these expressions to improve the stability of the model function. The new model function has been employed in the retrieval algorithm of the Aquarius satellite mission to obtain a global salinity map. The retrieved salinity using a different model function is compared with in situ data collected by Argo floats to evaluate the impact and the performance of model functions. The results indicate that the new model function has significant improvements in salinity retrieval compared with other existing models.

Seawater↗

Physical Properties of the Solar Atmosphere Derived from Comparison of Spectro-Polarimetric SDO/HMI Observables with 3D Radiative MHD Simulations

In this study, we compare the SDO/HMI line-of-sight observables (magnetic field, velocity, continuum intensity, and line depth) with the related physical properties for several dynamo simulation runs performed using the "StellarBox" 3D Radiative MHD code. The modeling of the Fe I 6173 A Stokes profiles is performed using the SPINOR radiative transfer code in the LTE approximation. The reproduced SDO/HMI line-of-sight pipeline is applied to the modeled spectra, and the observables are synthesized with high (numerical) and SDO/HMI (instrumental) resolutions. Correlations between the observables and the physical properties at various heights in the atmosphere are studied for a set of view angles (0, 30, 45, 60, 70, and 80 degrees away from the solar disk center). It is found that the SDO/HMI magnetic field and velocity measurements are unambiguously correlated with physical parameters at certain heights of the solar atmosphere. These heights increase from about 100 km above the photosphere for the disk center case to 300-600 km above the photosphere for the 80-degree case. The heights are found to be slightly lower in regions where stronger magnetic fields are found. The comparison of the photospheric magnetic flux and integrated continuum intensity derived from the SDO/HMI observables and high-resolution observations and spectra is discussed. The results of our study improve physics-based interpretations of the SDO/HMI observables and provide a better understanding of the physical properties of the solar atmosphere.

SMD↗

A Recursive Multi-step Machine Learning Approach for Airport Configuration Prediction

Airport configuration selection is a complex decision-making process that involves several operational and human factors. In this paper we propose a novel recursive multi-step machine learning (ML) approach to predict airport configuration. The multi-step approach guarantees stability of the predicted configuration by taking as input the configuration predicted at the previous time step. The features of the proposed model include weather data, future arrival and departure counts and current configuration. Due to the importance of arrival and departure counts in predicting the airport configuration, arrival counts are calculated using landing time predictions selected from physics-based landing time predictions available in FAA System Wide Information Management data feeds for each flight. The selection rules were developed and refined to select the most accurate time for different phases of flight. The proposed model predicts the airport configurations up to 6 hours ahead. In this paper we show the predictive performance of the proposed model for six major US airports, including Charlotte Douglas International Airport (CLT), Dallas/Fort Worth International Airport (DFW), John F. Kennedy International Airport (JFK), Newark Liberty International Airport (EWR), LaGuardia Airport (LGA) and Dallas Love Field Airport (DAL). We trained and evaluated models on 2019 and 2020 data in order to study the effect of the pandemic and how changes in traffic patterns affected the performance of the proposed model. Results are compared with a baseline assuming no airport configuration changes. In our results for DFW, we obtained a prediction accuracy of 89.3% for 3 hours ahead prediction, and 82.8% for 6 hours ahead when applied on 2019 data.

machine learning↗

A Recursive Multi-step Machine Learning Approach for Airport Configuration Prediction

Airport configuration selection is a complex decision-making process that involves several operational and human factors. In this paper we propose a novel recursive multi-step machine learning (ML) approach to predict airport configuration. The multi-step approach guarantees stability of the predicted configuration by taking as input the configuration predicted at the previous time step. The features of the proposed model include weather data, future arrival and departure counts and current configuration. Due to the importance of arrival and departure counts in predicting the airport configuration, arrival counts are calculated using landing time predictions selected from physics-based landing time predictions available in FAA System Wide Information Management data feeds for each flight. The selection rules were developed and refined to select the most accurate time for different phases of flight. The proposed model predicts the airport configurations up to 6 hours ahead. In this paper we show the predictive performance of the proposed model for six major US airports, including Charlotte Douglas International Airport (CLT), Dallas/Fort Worth International Airport (DFW), John F. Kennedy International Airport (JFK), Newark Liberty International Airport (EWR), LaGuardia Airport (LGA) and Dallas Love Field Airport (DAL). We trained and evaluated models on 2019 and 2020 data in order to study the effect of the pandemic and how changes in traffic patterns affected the performance of the proposed model. Results are compared with a baseline assuming no airport configuration changes. In our results for DFW, we obtained a prediction accuracy of 89.3% for 3 hours ahead prediction, and 82.8% for 6 hours ahead when applied on 2019 data.

machine learning↗

Methods for Modeling Particle Impingement in Solid Rocket Motor Nozzles

This paper describes several approaches available to analysts to capture the mechanical erosion effects of alumina droplet impingement in solid rocket motor nozzles. Supersonic alumina impingement is of great interest to solid rocket motor analysts, as it can be one of the main contributors to overall nozzle erosion, particularly in nozzles with highly contoured exit cones. An overview of the basic concepts of mechanical impingement is provided, along with discussion on some of the various methods available, ranging from the purely empirical to more physics-based techniques, and their respective advantages and disadvantages.

William Allen Till↗

Li-ion Battery Aging with Hybrid Physics-Informed Neural Networks and Fleet-wide Data

In this work, we propose a hybrid model for Li-ion battery discharge and aging prediction that leverages fleet-wide data to predict future capacity drops.The model is built upon an hybrid approach merging physics-based and empirical equations, as well as neural network models in a recurrent neural network cell. The hybrid physics-informed neural network can predict voltage discharge cycles given the loading profile, and estimate the used capacity of the battery under random-loading conditions by tracking aging parameters connected to the residual capacity of the battery. By merging information on the battery aging parameters with existing fleet-wide aging data, the model can predict the future residual capacity of the battery that is being monitored, and therefore enable predictions of voltage discharge curves far ahead in the battery life cycle. We validated the approach using the NASA Prognostics Data Repository Battery data-set, which contains experimental data on Li-ion batteries discharged at random loading conditions in a controlled environment. The approach also allows the identification of discrepancies between the battery aging trend and the trend observed at the fleet level, so that batteries behaving differently from the rest of the fleet can be subject to closer monitoring and further testing to refine predictions.

PINN↗

Landing and Takeoff (LTO) noise aspects of supersonic Flight

Landing and Takeoff (LTO) noise of proposed commercial supersonic vehicles will be compatible with the fleet currently flying today. The aero technology to accomplish this exists, but the lack of noise data for these technologies means there is significant uncertainty about exactly how loud the aircraft will be. This uncertainty is creating a stumbling block in efforts to establish noise regulations for the new supersonic market. The presentation shows NASA’s efforts to quantify this uncertainty and to reduce it using physics-based simulations over the next three years. Note: No voice included or available for this presentation.

James Bridges↗

Mineral Dust Cycle in the Multiscale Online Nonhydrostatic AtmospheRe CHemistry Model (Monarch) Version 2.0

We present the dust module in the Multiscale Online Non-hydrostatic AtmospheRe CHemistry model (MONARCH) version 2.0, a chemical weather prediction system that can be used for regional and global modeling at a range of resolutions. The representations of dust processes in MONARCH were upgraded with a focus on dust emission (emission parameterizations, entrainment thresholds, considerations of soil moisture and surface cover), lower boundary conditions (roughness, potential dust sources), and dust–radiation interactions. MONARCH now allows modeling of global and regional mineral dust cycles using fundamentally different paradigms, ranging from strongly simplified to physics-based parameterizations. We present a detailed description of these updates along with four global benchmark simulations, which use conceptually different dust emission parameterizations, and we evaluate the simulations against observations of dust optical depth. We determine key dust parameters, such as global annual emission/deposition flux, dust loading, dust optical depth, mass-extinction efficiency, single-scattering albedo, and direct radiative effects. For dust-particle diameters up to 20 µm, the total annual dust emission and deposition fluxes obtained with our four experiments range between about 3500 and 6000 Tg, which largely depend upon differences in the emitted size distribution. Considering ellipsoidal particle shapes and dust refractive indices that account for size-resolved mineralogy, we estimate the global total (longwave and shortwave) dust direct radiative effect (DRE) at the surface to range between about −0.90 and −0.63 W m−2 and at the top of the atmosphere between −0.20 and −0.28 W m−2. Our evaluation demonstrates that MONARCH is able to reproduce key features of the spatiotemporal variability of the global dust cycle with important and insightful differences between the different configurations.

Dust cycle↗

Coupling of the FUN3D Unstructured Flow Solver and the LASTRAC Stability Code to Model Transition

We develop an iterative automated method to predict transition locations in boundary-layer flows by using the FUN3D solver to perform flow simulations and the LASTRAC code for linear stability computations. The coupling of FUN3D and LASTRAC allows for a robust physics-based approach to model boundary-layer transition by analyzing the growth of different instability waves and then using that information to iteratively update the resulting transition location. There is no user involvement during the iterative computations. We apply this automated method to subsonic flow over a flat plate with a sharp leading edge. The final solution has regions of laminar and turbulent flow with a transition onset location that agrees with experiments and stability-based correlations. This iterative automated method is also applied to an NLF(1)-0416 airfoil for conditions with and without a separation bubble. Along with predicting transition locations, we compare the streamwise distributions of surface-pressure and skin-friction coefficients to a transport-equation-based model. We consider a 6:1 prolate spheroid at three angles of attack, namely, five, ten, and fifteen degrees, where mixed-mode transition occurs due to both Tollmien-Schlichting and crossflow instabilities. The skin-friction contours and transition fronts at every angle of attack from our iterative automated method show good agreement with past experimental and computational results in the literature for the 6:1 prolate spheroid.

Transition↗

Local Ultrasonic Resonance Spectroscopy of Lithium Metal Batteries for Aerospace Applications

As next-generation aircraft and vehicles continue to develop, so do their associated energy demands. Lithium metal batteries are a leading candidate to fulfill this energy requirement, but these batteries are prone to internal dendrite defects that can lead to catastrophic thermal runaway events. Current battery management systems are capable of mitigating such risks, but are unable to detect such defects until thermal runaway has already begun. Various nondestructive evaluation (NDE) techniques, particularly ultrasonic NDE, can directly monitor internal battery parameters giving them the potential to detect critical defects prior to catastrophic failure. However, most of the current battery NDE research has focused on improved battery state-of-charge (SOC) and stateof- health (SOH) monitoring with little emphasis on critical defect detection. Thus, a measurement technique sensitive to subtle battery defects is needed. In addition, the complex mechanics of ultrasound in porous, thin, multilayered batteries prompt the use of physics-based simulation to guide inspections. In this work, an ultrasonic NDE technique has been developed utilizing frequency domain analysis of local battery resonances to detect the presence of battery defects. This technique is a practical extension of local ultrasonic resonance spectroscopy (LURS) – which previously required non-contact laser ultrasonics – to measurements with piezoelectric contact and immersion scan transducers. To extend the technique to work with piezoelectric transducers, ultrasonic battery measurements were compared to a sans-battery calibration measurement. Then, a linear systems deconvolution was used to eliminate the transfer functions of extraneous factors such as the transducer and electronics, leaving only the frequency-dependent battery reflection coefficient. The LURS technique was first validated on stainless steel and aluminum plates, producing reflection coefficients in line with analytical and numerical finite element modeling (FEM) results. Functioning Li-metal pouch cells were then seeded with lithium chip defects prior to LURS measurements. The presence of these defects is shown to cause a measurable shift in the battery’s through-thickness local resonances. 2D, frequency-domain poroelastic models of ultrasonic propagation in a single-cell lithium metal pouch battery were created and corroborated these findings. Thus, this work has both extended and proven the feasibility of the LURS technique in the detection of local battery defects.

Ultrasound↗

Current Status and Perspectives to Detect Natural Hazards Using GNSS-Based Ionospheric Measurements

We discuss current status and new directions in detection of natural hazards generated ionospheric signatures using GNSS-based ionospheric measurements. By studying the propagation properties of ionospheric perturbations generated by natural hazards, and applying sophisticated physics-based modeling, we are on track to develop new techniques for detection of natural hazards that can greatly reduce damage to human lives and property. It is also expected that real-time ionospheric monitoring of TEC perturbations might become an integral part of existing natural hazards warning systems.

Komjathy, Attila↗

Simulation-Based Analysis and Prediction of Thrust Vector Servoelastic Coupling

A method of analysis and prediction of servoelastic coupling in launch vehicles is presented, surveying the discovery and subsequent resolution of a predicted servoelastic resonance phenomenon affecting the NASA Space Launch System launch vehicle at specific flight conditions. A physics-based linearized multibody mechanization of the governing equations is combined with first principles analysis to demonstrate that antisymmetric bending of the solid rocket motors leads to a reduction of equivalent viscous modal damping through coupling with the thrust vector control actuators. The sensitivity to parameters and the effects of the resonance phenomenon on flight control performance and stability are confirmed through extensive simulation verification in the time and frequency domain. A novel enhancement in model fidelity that accounts for Coriolis effects of fluid flow on bending within the solid rocket motor case and nozzle is shown to add sufficient damping to reduce the risk of adverse control-structure interaction.

Jeb S Orr↗

Plume Impingement Software Module for Real-Time Proximity Operations

Successfully executing proximity operations in space, such as docking or in-orbit servicing, requires sophisticated spacecraft design that accounts for induced environments. As a chaser vehicle’s attitude control thrusters fire, they create rarefied plumes that can impact the target vehicle, with the potential to overload components, exceed thermal limits, and spin the target vehicle out of control. High-fidelity simulations of the thruster plume impingement environment require the direct simulation Monte Carlo (DSMC) method, but DSMC is too computationally expensive to simulate proximity operations that involve thousands of thruster firings. For this analysis to be tractable, engineering models of the plume flowfield and impingement events are used to simulate these trajectories [1]. Currently, on-orbit plume impingement environments are modeled through an inefficient open-loop analysis cycle where the vehicle’s flight controller and plume impingement teams iterate on the trajectories until they pass the target vehicle’s plume requirements. As complex on-orbit missions evolve and become more frequent, lengthy design cycles will become operational bottlenecks. To address this gap, this work develops an advanced plume impingement module capable of operating at real-time scale that can be integrated with existing mission planning tools and onboard flight systems. The plume module leverages state-of-the-art plume simulation techniques [2] to deliver fast, physics-based impingement predictions in a software architecture that can be tailored to diverse proximity operations scenarios. A prototype of this plume impingement module is built to demonstrate the feasibility of real-time performance. This prototype completes plume impingement calculations in microseconds per target geometry mesh point. The software serves as a foundational capability for plume-aware trajectory design, operational risk assessment, and future autonomous decision-making systems.

Plume Impingement↗

Integrated Process-Structure-Property Simulations for Additive Manufacturing Using the Open-Source Materialite Package

The microstructure and properties of additively manufactured (AM) metals are strongly dependent on process conditions. Therefore, process-structure-property (PSP) simulations are a useful tool for exploring process parameter space, studying process variations, and quantifying uncertainty in material properties. However, integrating process-structure and structure-property simulations often involves connecting multiple software packages. Each package may use unique data structures and require substantial domain knowledge. This presentation demonstrates PSP simulation capabilities of Materialite, an open-source package developed at NASA Langley Research Center. Materialite simplifies model linkages by using a common data structure and model interface, enabling straightforward simulation across a PSP model chain. Physics-based models, including kinetic Monte Carlo and crystal plasticity, are implemented within the package. The model interface is also intended to simplify implementation of new models and enable integration with external simulation tools. Example use cases include uncertainty quantification with PSP models and GPU-accelerated powder bed fusion AM process models.

additive manufacturing↗