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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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26 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Reduced-Order Modeling for Flutter/LCO Using Recurrent Artificial Neural Network

The present study demonstrates the efficacy of a recurrent artificial neural network to provide a high fidelity time-dependent nonlinear reduced-order model (ROM) for flutter/limit-cycle oscillation (LCO) modeling. An artificial neural network is a relatively straightforward nonlinear method for modeling an input-output relationship from a set of known data, for which we use the radial basis function (RBF) with its parameters determined through a training process. The resulting RBF neural network, however, is only static and is not yet adequate for an application to problems of dynamic nature. The recurrent neural network method [1] is applied to construct a reduced order model resulting from a series of high-fidelity time-dependent data of aero-elastic simulations. Once the RBF neural network ROM is constructed properly, an accurate approximate solution can be obtained at a fraction of the cost of a full-order computation. The method derived during the study has been validated for predicting nonlinear aerodynamic forces in transonic flow and is capable of accurate flutter/LCO simulations. The obtained results indicate that the present recurrent RBF neural network is accurate and efficient for nonlinear aero-elastic system analysis

Yao, Weigang↗
NASA NTRS2016

A Nonlinear Modal Aeroelastic Solver for FUN3D

A nonlinear structural solver has been implemented internally within the NASA FUN3D computational fluid dynamics code, allowing for some new aeroelastic capabilities. Using a modal representation of the structure, a set of differential or differential-algebraic equations are derived for general thin structures with geometric nonlinearities. ODEPACK and LAPACK routines are linked with FUN3D, and the nonlinear equations are solved at each CFD time step. The existing predictor-corrector method is retained, whereby the structural solution is updated after mesh deformation. The nonlinear solver is validated using a test case for a flexible aeroshell at transonic, supersonic, and hypersonic flow conditions. Agreement with linear theory is seen for the static aeroelastic solutions at relatively low dynamic pressures, but structural nonlinearities limit deformation amplitudes at high dynamic pressures. No flutter was found at any of the tested trajectory points, though LCO may be possible in the transonic regime.

Goldman, Benjamin D.↗
NASA NTRS2016

Status of the KTH-NASA Wind-Tunnel Test for Acquisition of Transonic Nonlinear Aeroelastic Data

This paper presents a status report on the collaboration between the Royal Institute of Technology (KTH) in Sweden and the NASA Langley Research Center regarding the design, fabrication, modeling, and testing of a full-span lighter configuration in the Transonic Dynamics Tunnel (TDT). The goal of the test is to acquire transonic limit-cycle- oscillation (LCO) data, including accelerations, strains, and unsteady pressures. Finite element models (FEMs) and aerodynamic models are presented and discussed along with results obtained to date.

Silva, Walter A.↗
NASA NTRSDate not supplied

TPSAS-NF1676L-31264-DND

Nonlinear unsteady aerodynamic models (TSD, Euler/Navier-Stokes (RANS), LES) are complex, expensive solutions. CFD used by industry mostly for steady/static loads cases. Limit Cycle Oscillation (LCO) is an equilibrium point of the nonlinear aeroelastic system induced by nonlinear unsteady aerodynamics (shock dynamics, separated flow) and/or nonlinear structural dynamics (geometric and material nonlinearities). While Euler solutions can generate LCOs, viscosity yields improved accuracy (shock strength, position). Most common aeroelastic CFD codes currently consist of nonlinear unsteady aerodynamics with linear structural dynamics. Van der Pol system is a simple example of LCOs.

Walter A Silva↗
NASA NTRSDate not supplied

Predicting Thermal Response in a Li-ion Cell on a UAV Fight Profile

As the energy storage devices continue to "pack" more energy in a small space, any damage, battery component failure, manufacturing defect, or electrically abusing the battery can lead to catastrophic thermal runaway events. A catastrophic thermal event in a cell leads to high temperature, in some instances spewing of battery materials due to gas development from side reactions initiated due to high internal temperatures. Also, a thermal runaway event can propagate from a single "failed" cell to the pack in a battery pack, leading to a more significant event. Mitigating a thermal runaway event is important in the commercial and automotive sectors. However, preventing such events in an electric aircraft (or air taxis) is paramount due to the lack of alternatives in the event of a failure. Battery prognostics algorithms allow the prediction of state-of-charge (SOC) and end-of-life (EOL) of a Li-ion battery in a UAV (unmanned air vehicle) [1]. For this presentation, we will extend this two-level battery predictive algorithm to predict SOC, EOL, and estimated maximum temperature during a simulated flight. The model is extended by integrating a lumped physics-driven thermal model for high current densities [2]. The parameters used to control SOC and EOL are maximum storable charge, time constant for Li-ion diffusivity in the carbon particles, and internal cell resistance. Cycling leads to an increase in the heat generated by an aged Li-ion cell with a LiyCoO2 (LCO) cathode and a LixC6 (MCMB) anode. The aging of a cell leads to increase in SEI layer thickness, the diffusion time for the lithium ions inside the electrodes, and the local reaction rates, in addition to the thermodynamic abuse caused by fixed cycling voltages controlled by a Battery Management System. As the battery ages, the cell resistance increases, while the onset temperature of the thermal runaway decreases (depends on the cell chemistry and cell abuse history). Any large deviation of the cell temperature from the estimated (expected) value can identify a faulty cell. Since SEI decomposition has the lowest onset temperature in the series of reactions leading to thermal runaway, the model considers the self-heating rate of the SEI decomposition as onset temperature (similar to Ref. [3]). The parameters in the Arrhenius equation for the SEI heating rate depend on the number of cycles, the cell's operating temperature, and the cell's abuse history [4,5]. Coupling the electrochemical, thermal, and aging model allow the prognostic algorithm to estimate a typical cell voltage and temperature as a function of age (cycling and calendar), whose departure from measured values from the BMS is used to identify a safety event. In addition, we will present the results from two simulated flight scenarios for a UAV: typical and extreme, since the power requirements vary significantly during take-off, landing, and changing altitudes, while the power requirements remain low during the cruise. For this presentation, the power requirement for a battery pack in a UAV is scaled to a single cell. This cell is cycled through a simulated profile, and the data is collected and used to predict a safety event.

Li-ion↗
NASA NTRS2022

Transit Timing Variations for AU Microscopii b and c

We explore the transit timing variations (TTVs) of the young (22 Myr) nearby AU Mic planetary system. For AU Mic b, we introduce three Spitzer (4.5 μm) transits, five TESS transits, 11 LCO transits, one PEST transit, one Brierfield transit, and two transit timing measurements from Rossiter–McLaughlin observations; for AU Mic c, we introduce three TESS transits. We present two independent TTV analyses. First, we use EXOFASTv2 to jointly model the Spitzer and ground-based transits and obtain the midpoint transit times. We then construct an O − C diagram and model the TTVs with Exo-Striker. Second, we reproduce our results with an independent photodynamical analysis. We recover a TTV mass for AU Mic c of -10.8+2.22.3 M⊕. We compare the TTV-derived constraints to a recent radial velocity (RV) mass determination. We also observe excess TTVs that do not appear to be consistent with the dynamical interactions of b and c alone or due to spots or flares. Thus, we present a hypothetical nontransiting “middle-d” candidate exoplanet that is consistent with the observed TTVs and candidate RV signal and would establish the AU Mic system as a compact resonant multiplanet chain in a 4:6:9 period commensurability. These results demonstrate that the AU Mic planetary system is dynamically interacting, producing detectable TTVs, and the implied orbital dynamics may inform the formation mechanisms for this young system. We recommend future RV and TTV observations of AU Mic b and c to further constrain the masses and confirm the existence of possible additional planet(s).

Justin M Wittrock↗
NASA NTRS2022

HD 28109 Hosts A Trio of Transiting Neptunian Planets Including A Near-Resonant Pair, Confirmed By ASTEP From Antarctica

We report on the discovery and characterization of three planets orbiting the F8 star HD 28109, which sits comfortably in TESS ’s continuous viewing zone. The two outer planets have periods of 56 . 0067 ±0 . 0003 d and 84 . 2597 + 0 . 0010 −0 . 0008 d, which implies a period ratio very close to that of the first-order 3:2 mean motion resonance, exciting transit timing variations (TTVs) of up to 60 min. These two planets were first identified by TESS , and we identified a third planet in the TESS photometry with a period of 22 . 8911 ±0 . 0004 d. We confirm the planetary nature of all three planetary candidates using ground-based photometry from Hazelwood , ASTEP , and LCO , including a full detection of the ∼9 h transit of HD 28109 c from Antarctica. The radii of the three planets are R b = 2 . 199 + 0 . 098 −0 . 10 R ⊕, R c = 4 . 23 ±0 . 11 R ⊕, and R d = 3 . 25 ±0 . 11 R ⊕; we characterize their masses using TTVs and precise radial velocities from ESPRESSO and HARPS, and find them to be M b = 18 . 5 + 9 . 1 −7 . 6 M ⊕, M c = 7 . 9 + 4 . 2 −3 . 0 M ⊕, and M d = 5 . 7 + 2 . 7 −2 . 1 M ⊕, making planet b a dense, massive planet while c and d are both underdense. We also demonstrate that the two outer planets are ripe for atmospheric characterization using transmission spectroscopy, especially given their position in the CVZ of James Webb Space Telescope . The data obtained to date are consistent with resonant (librating) and non-resonant (circulating) solutions; additional observations will show whether the pair is actually locked in resonance or just near-resonant.

Georgina Dransfield↗
NASA NTRSDate not supplied

Predicting Maximum Thermal Response in a Li-ion Cell as a Thermal Runaway Predictor for a UAV Fight

As the energy storage devices continue to "pack" more energy in a small space, any damage, battery component failure, manufacturing defect, or electrically abusing the battery can lead to catastrophic thermal runaway events. A catastrophic thermal event in a cell leads to high temperature, in some instances to spewing of battery materials due to gas development from side reactions initiated due to high internal temperatures. Moreover, a thermal runaway can propagate from a single "failed" cell to a whole battery pack, resulting in a more serious event. The commercial and automotive sectors need to mitigate thermal runaway events. An electric aircraft (or air taxi) has no alternative in the event of a failure, so preventing such events is paramount. Currently, battery prognostics algorithms only predict the state-of-charge (SOC) and end-of-life (EOL) of a Li-ion battery in a UAV (unmanned air vehicle), but do not predict the maximum temperature during a flight or the likelihood of thermal runaway. Our current work focuses on adding the thermal model (based on Birkl's OCV model, in Ref. [1]) to the existing hybrid electrochemical model (single-particle model with lumped parameters as in Ref. [2]) to determine end-of-discharge, end-of-life, and maximum temperature during discharge.

Thermal runaway↗