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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 397 records · Page 22

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NASA NTRS2021

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↗

NASA NTRSDate not supplied

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↗

NASA NTRSDate not supplied

3D Material Response of the MSL Heatshield Using NuSil-Coated PICA

The Mars Science Laboratory (MSL) Entry, Descent and Landing Instrumentation (MEDLI) collected in-flight data largely used by the ablation community to verify and validate physics-based models for the response of the Phenolic Impregnated Carbon Ablator (PICA) material [1-4]. MEDLI data were recently used to guide the development of NASA’s high-fidelity material response models for PICA implemented in the Porous material Analysis Toolbox based on OpenFOAM (PATO) software [5-6]. A follow-up instrumentation suite, MEDLI2, was used for the Mars 2020 mission [7] after the large scientific impact of MEDLI. Recent analyses performed as part of MEDLI2 development drew the attention to significant effects of a protective coating to the aerothermal response of PICA. NuSil, a silicone-based overcoat sprayed onto the MSL heatshield, including the MEDLI plugs, as contamination control, is currently neglected in PICA ablation models. To mitigate the spread of phenolic dust from PICA, NuSil was applied to the entire MSL and Mars 2020 heatshields, including the MEDLI and MEDLI2 plugs. Ground testing of PICA-NuSil (PICA-N) models exhibited surface temperature jumps due to oxide scale formation and subsequent NuSil burn- off. It is therefore critical to include a model for the aerothermal response of the coating in ongoing code development and validation efforts. Figure 1 illustrates the PICA-N model implemented in PATO with a test case that shows a temperature jump and a change of recession rate at the wall after 20 seconds of simulation when the NuSil coating was fully removed.

Aerospace↗

NASA NTRSDate not supplied

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↗

NASA NTRSDate not supplied

Thermal Testing and Analysis Techniques for Wires and Wire Bundles

Determining wire and wire bundle amperage capacity (i.e., “ampacity”) currently relies on the use of standards to derate wire ampacity when in a bundle configuration. The feasibility of developing physics-based and regression thermal models of single wires and wire bundles to determine ampacity using a customized test apparatus was investigated during a pathfinder study. A wire bundle test apparatus was developed for measuring the temperatures of single wires and wire bundles and collected wire conductor temperature data under high vacuum and atmospheric conditions and varying environmental temperatures and currents for the two most common wire types used on spacecraft.

Thermal↗

NASA NTRS2022

A Deep Learning Approach to Fast Radiative Transfer

Due to the sheer volume of data, leveraging satellite instrument observations effectively in a data assimilation context for numerical weather prediction or for remote sensing requires a radiative transfer model as an observation operator that is both fast and accurate at the same time. Physics-based line-by-line radiative transfer (RT) models fulfil the requirement for accuracy, but are too slow and too costly in computational terms for operational applications. Therefore, fast methods were developed to be able to perform fast RT calculations using techniques such as spectral sampling or pre-computed look-up tables. The operational fast models currently calculate the absorption and scattering coefficients from the pre-computed regression coefficients and atmospheric state and cloud profiles. As a novel solution to this problem, this work investigates a deep learning approach to replace the regression coefficients in the fast RT models. A selection of hidden-layer neural network configurations is trained against atmospheric transmittance profile data computed by an accurate line-by-line model and their performance is evaluated and their advantages and disadvantages are discussed.

Machine learning↗

NASA NTRSDate not supplied

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↗

NASA NTRS2021

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↗

NASA NTRSDate not supplied

Modeling Stellar Jitter and its Effects on the Stellar Spectrum for More Precise Radial Velocity Measurements

Detection of Earth-size exoplanets is a technological and data analysis challenge. Future progress in Earth-mass exoplanet detection is expected from the development of extreme precision radial velocity (EPRV) measurements. The required precision of these measurements necessitates developing a new physics-based data analysis methodology to discriminate planetary signals from host-star-related effects, taking stellar variability and instrumental uncertainties into account. In this work, we investigate and quantify stellar disturbances of the planet-hosting solar-type star HD121504 from 3D radiative modeling obtained with the StellarBox code. The model was used to determine the statistical properties of the turbulent plasma and to obtain synthetic spectroscopic observations for several Fe I lines at different locations on the stellar disk to mimic high-resolution spectroscopic observations. Presented are the initial results of our effort to characterize stellar jitter properties as well as future objectives.

SMD↗

NASA NTRS2021

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↗

NASA NTRSDate not supplied

Development of a Trajectory-Centric CFD-RBD Framework for Advanced Multidisciplinary/Multiphysics Simulation

The desire to model increasingly complex unsteady flow phenomena drives coupling of physics-based disciplinary analysis tools, such as coupled aerodynamics-rigid body dynamics simulations. This paper documents the creation of a framework linking the six-degree-of-freedom trajectory propagator POST2 with NASA’s FUN3D computational fluid dynamics flow solver. Cross-code verification between the framework and a CFD-centric 6DOF code is performed using the Army-Navy Finner projectile experiencing unsteady accelerating flow. Free-flight simulations of an entry vehicle ballistic range test are validated against physical and computational experiments.

Zachary J Ernst↗

NASA NTRS2021

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↗

NASA NTRS2020

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↗

NASA NTRS2022

AST Critical Propulsion and Noise Reduction Technologies for Future Commercial Subsonic Engines Aeroacoustic Prediction Codes - Supplement: Code Descriptions and Users Guides

This report describes work performed on Contract NAS3–27720 AoI 13 as part of the NASA Advanced Subsonic Transport (AST) Noise Reduction Technology effort. Computer codes were developed to provide quantitative prediction, design, and analysis capability for several aircraft engine noise sources. The objective was to provide improved, physics-based tools for the exploration of noise-reduction concepts and the understanding of experimental results. Methods and codes focused on fan broadband and “buzz saw” noise and on low-emissions combustor noise and complement work done by other contractors under the NASA AST program to develop methods and codes for fan harmonic tone noise and jet noise. The methods and codes developed and reported herein employ a wide range of approaches, from the strictly empirical to the completely computational, with some being semiempirical, analytical, and/or analytical/computational. Emphasis was on capturing the essential physics while still considering method or code utility as a practical design and analysis tool for everyday engineering use. Codes and prediction models were developed for: (1) an improved empirical correlation model for fan rotor exit flow mean and turbulence properties, for use in predicting broadband noise generated by rotor exit flow turbulence interaction with downstream stator vanes; (2) fan broadband noise models for rotor and stator/turbulence interaction sources including 3D effects, noncompact-source effects, directivity modeling, and extensions to the rotor supersonic tip-speed regime; (3) fan multiple-pure-tone in-duct sound pressure prediction methodology based on CFD analysis; and (4) low-emissions combustor prediction methodology and computer code based on CFD and actuator disk theory. In addition, the relative importance of dipole and quadrupole source mechanisms was studied using direct CFD source computation for a simple cascade/gust interaction problem, and an empirical combustor-noise correlation model was developed from engine acoustic test results. This work provided several insights on potential approaches to reducing aircraft engine noise. Code development is described in Volume I, and those insights are discussed. Volume II documents the computer codes developed and gives instructions on how to use them.

Philip Gliebe↗

NASA NTRSDate not supplied

Quantifying Uncertainty of Landing and Takeoff Noise for Commercial Supersonic Aircraft

Of the many challenges faced by manufacturers attempting to offer supersonic travel to the public, the uncertainty in predicting the noise of these aircraft in airport operations has an immediate impact. No noise regulation exists in FAA or ICAO for certifying such aircraft, as these organization require solid data, usually from existing aircraft. Manufacturers are taking large risks to design a vehicle not knowing whether it will be allowed to fly. A partial solution to this conundrum is to use physics-based simulations to provide the “data” used to calibrate system-level prediction methods, carefully documenting the uncertainty of the method for application to supersonic aircraft. A close look at the accuracy of empirical prediction methods points to areas where improvements need to be made if noise studies of supersonic aircraft are to be useful. As NASA embarks on a focused research program to improve predictions of noise from the noise-dominant propulsion noise of commercial supersonic aircraft, this paper documents the work done to baseline the uncertainties found in today’s noise prediction methods. A relatively simplistic method was developed, summarizing the error of the empirical methods on a component basis and following their impact on the total aircraft during landing and takeoff operations using Monte Carlo analysis. By this method it is found that current empirical noise prediction methods have an uncertainty of 1.5 EPNdB cumulative for propulsion noise of a representative conventional subsonic passenger aircraft. When applied to likely near-term supersonic commercial aircraft, the uncertainty is 7.6 EPNdB cumulative, a difference that must be reduced if the prediction methods are to guide decision makers.

Airport noise↗

NASA NTRS2020

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↗

NASA NTRS2023

New Transient Finite Energy Shock Prediction Methodology

Shock prediction is one of the top loads and dynamics discipline technical challenges identified within NASA and industry programs and projects. The physics-based Transient Finite Energy (TFE) shock prediction methodology has been developed and compared favorably against test results. TFE can provide another approach to develop predictions of shock response spectra (SRS) for use in the analysis of structural margins.

NESC Technical Bulletin No. 23-03↗

NASA NTRSDate not supplied

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↗