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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
NASA NTRS2008

Measurement and Prediction of the Thermomechanical Response of Shape Memory Alloy Hybrid Composite Beams

An experimental and numerical investigation into the static and dynamic responses of shape memory alloy hybrid composite (SMAHC) beams is performed to provide quantitative validation of a recently commercialized numerical analysis/design tool for SMAHC structures. The SMAHC beam specimens consist of a composite matrix with embedded pre-strained SMA actuators, which act against the mechanical boundaries of the structure when thermally activated to adaptively stiffen the structure. Numerical results are produced from the numerical model as implemented into the commercial finite element code ABAQUS. A rigorous experimental investigation is undertaken to acquire high fidelity measurements including infrared thermography and projection moire interferometry for full-field temperature and displacement measurements, respectively. High fidelity numerical results are also obtained from the numerical model and include measured parameters, such as geometric imperfection and thermal load. Excellent agreement is achieved between the predicted and measured results of the static and dynamic thermomechanical response, thereby providing quantitative validation of the numerical tool.

Davis, Brian↗
NASA NTRS1995

Angled injection: Hybrid fluid film bearings for cryogenic applications

A computational bulk-flow analysis for prediction of the force coefficients of hybrid fluid film bearings with angled orifice injection is presented. Past measurements on water-lubricated hybrid bearings with angle orifice injection have demonstrated improved rotordynamic performance with virtual elimination of cross-coupled stiffness coefficients and nul or negative whirl frequency ratios. A simple analysis reveals that the fluid momentum exchange at the orifice discharge produces a pressure rise in the recess which retards the shear flow induced by journal rotation, and consequently, reduces cross-coupling forces. The predictions from the model correlate well with experimental measurements from a radial and 45 deg angled orifice injection, five recess water hybrid bearings (C = 125 microns) operating at 10.2, 17.4, and 24.6 krpm and with nominal supply pressures equal to 4, 5.5, and 7 MPa. An application example for a liquid oxygen six recess/pad hybrid journal bearing shows the advantages of tangential orifice injection on the rotordynamic force coefficients and stability indicator for forward whirl motions and without performance degradation on direct stiffness and damping coefficients. The computer program generated, 'hydrojet,' extends and complements previously developed codes.

SanAndres, Luis↗
NASA NTRS2010

Concerning the Perturbations Generated by Flux Transfer Events

The interaction of the solar wind with the Earth's magnetosphere is often highly unsteady. Bursts of magnetic reconnection at multiple locations on the dayside equatorial magnetopause generate flux transfer events or FTEs: twisted ropes of interconnected magnetosheath and magnetospheric magnetic field lines. Once formed, the events move antisunward, displacing and perturbing the ambient media. This talk explores the perturbations predicted by both global numerical simulations and analytical models. Results from global hybrid code simulation confirm the predictions of analytical models indicating that the events generate standing forward slow mode waves as their speeds relative to the magnetosheath flow approach the Alfven velocity. Geometric considerations lead to the conclusion that events generated by component reconnection on the dayside magnetopause move poleward and exhibit strong signatures during intervals of southward IMF orientation, but move towards the flanks and exhibit weak signatures during intervals of northward IMF orientation. Changing event orientations and magnetosheath/magnetospheric magnetic field orientation can enhance the amplitudes of events reaching the flanks. Although the orientations of events on the flanks inferred from multispacecraft timing techniques are consistent with the predictions of the component reconnection model, occurrence patterns versus latitude and IMF orientation require an explanation in terms of both the component and antiparallel reconnection models.

Sibeck, David G.↗
DOE OSTI2020

Cumulene Wires Display Increasing Conductance with Increasing Length

One-dimensional sp-hybridized carbon wires, including cumulenes and polyynes, can be regarded as finite versions of carbynes. They are likely to be good candidates for molecular-scale conducting wires as they are predicted to have a high-conductance. In this study, we first characterize the single-molecule conductance of a series of cumulenes and polyynes with a backbone ranging in length from 4 to 8 carbon atoms, including [7]cumulene, the longest cumulenic carbon wire studied to date for molecular electronics. We observe different length dependence of conductance when comparing these two forms of carbon wires. Polyynes exhibit conductance decays with increasing molecular length, while cumulenes show a conductance increase with increasing molecular length. Their distinct conducting behaviors are attributed to their different bond length alternation, which is supported by theoretical calculations. Furthermore, this study confirms the long-standing theoretical predictions on sp-hybridized carbon wires and demonstrates that cumulenes can form highly conducting molecular wires.

36 MATERIALS SCIENCE↗
NASA NTRS1996

Structural Acoustic Response of Shape Memory Alloy Hybrid Composite Panels

A method has been developed to predict the structural acoustic response of shape memory alloy hybrid composite panels subjected to acoustic excitation. The panel is modeled by a finite element analysis and the radiated field is predicted using Rayleigh's integral. Transmission loss predictions for the case of an aluminum panel excited by a harmonic acoustic pressure are shown to compare very well with a classical analysis. Predictions of the normal velocity response and transmitted acoustic pressure for a clamped aluminum panel show excellent agreement with experimental measurements. Predicted transmission loss performance for a composite panel with and without shape memory alloy reinforcement are also presented. The preliminary results demonstrate that the transmission loss can be significantly increased with shape memory alloy reinforcement.

Turner, Travis L.↗
NASA NTRS2019

Qualification of a Hybrid Propulsion System for the Mars Ascent Vehicle

Technology for a hybrid based propulsion system is being developed to support a potential Mars Sample Return campaign. A Mars Ascent Vehicle (MAV) concept for launching samples off of Mars, and delivering them to orbit for further transport to Earth may utilize hybrid propulsion due to the predicted favorable low temperature characteristics and high performance of this option. However, the hybrid option is still undergoing technology development to demonstrate these capabilities. Once development of a capable hybrid propulsion system is proven, further work will be required. This will include environmental testing relative to the mission, and integration with the vehicle reaction control systems and payload. Qualification of such a system will be a significant effort. It will require specialized procurements for the propellants and environments involved, and further testing of the more specialized designs. This paper details an estimate of the tasks required to complete development efforts from Technical Readiness Level 5 (TRL5) through qualification. A success based program was formulated to reach the required performance metrics sufficient for a standard Preliminary Design Review (PDR). Using task level inputs from team members cost and schedule were conceived for continued progress to Critical Design Review (CDR), then through Qualification.

Prince, Andrew↗
DOE OSTI2021

Streamflow simulation in data-scarce basins using Bayesian and physics-informed machine learning models

Hydrologic predictions at rural watersheds are important but also challenging due to data shortage. Long short-term memory (LSTM) networks are a promising machine learning approach and have demonstrated good performance in streamflow predictions. However, due to its data-hungry nature, most LSTM applications focus on well-monitored catchments with abundant and high-quality observations. In this work, we investigate predictive capabilities of LSTM in poorly monitored watersheds with short observation records. To address three main challenges of LSTM applications in data-scarce locations, i.e., overfitting, uncertainty quantification (UQ), and out-of-distribution prediction, we evaluate different regularization techniques to prevent overfitting, apply a Bayesian LSTM for UQ, and introduce a physics-informed hybrid LSTM to enhance out-of-distribution prediction. Through case studies in two diverse sets of catchments with and without snow influence, we demonstrate that 1) when hydrologic variability in the prediction period is similar to the calibration period, LSTM models can reasonably predict daily streamflow with Nash–Sutcliffe efficiency above 0.8, even with only 2 years of calibration data; 2) when the hydrologic variability in the prediction and calibration periods is dramatically different, LSTM alone does not predict well, but the hybrid model can improve the out-of-distribution prediction with acceptable generalization accuracy; 3) L2 norm penalty and dropout can mitigate overfitting, and Bayesian and hybrid LSTM have no overfitting; and 4) Bayesian LSTM provides useful uncertainty information to improve prediction understanding and credibility. In conclusion, these insights have vital implications for streamflow simulation in watersheds where data quality and availability are a critical issue.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Modeling Shock-Separated Flow with the Active Model Split

The predictive accuracy of typical hybrid RANS/LES models is limited by several shortcomings, such as modeled-stress depletion and a dependence on scalar grid measures. The "active model-split" (AMS) hybrid RANS/LES model was developed by Haering, Oliver, and Moser to address these shortcomings. In this model, the mean and fluctuating portions of the stress are modeled separately; this allows for a consistent treatment of the mean flow even in the presence of resolved fluctuations. Part of this hybrid model is an active forcing, which generates turbulent fluctuations in regions that support LES-type fluctuations. In this presentation, some recent results are shown using the active model-split for shock-separated flows. The effects of the active forcing are examined, including the effects on anisotropy and acoustics. Superior predictive accuracy over RANS is shown for some quantities of interest, such as the shock location.

RANS↗
DOE OSTI2020

A Hybrid AI/ML and Computational Mechanics Based Approach for Time-Series State and Fatigue Life Estimation of Nuclear Reactor Components

Environmental fatigue modeling is a complex problem due to multiple failure modes and their intermixing. The failure modes are function of various underlying causes in addition to the corrosive effect of reactor coolant environment. Some of the major causes are time-dependence of material associated with cyclic loading, load sequence effect associated with random/variable amplitude loading, effect of strain amplitude and rates, effect of varying temperature (along both temporal and spatial directions) and the effect of mean strain and stress. The nonlinear intermixing of failure modes associated with above mentioned causing parameters makes the environmental fatigue modeling is a challenging task. Because of this challenge, fatigue is traditionally being modeled based on experimental data. However, test based empirical approach often requires hundreds of fatigue tests to model the above-mentioned intermixing failure causes even for a single material system. The problem is further exaggerated for reactor component made from multi-material systems such as made from both carbon and stainless-steel base metals and their similar and dissimilar metal welds. With the difficulty of conducting hundreds of fatigue tests to capture the above-mentioned intermixing failure causes, fatigue modeling approaches often depends on empirical models based on limited available test data such as available through ASME code and NUREG 6909. However, these limited test-data-based models may not be enough to accurately predict the life of reactor components. Accurate prediction of life of reactor component would become a necessity, particularly when the license of the reactors to be extended for long-term-operation (LTO) that is for well beyond its original design life of 40 years. The requirement of extending the license of reactor under LTO requires hundreds of fatigue tests to be conducted to understand the mechanism associated with the above-mentioned interdependent failure causes. However, conducting large number of fatigue tests is not a feasibility due to the cost involved. To address this issues Argonne National Laboratory (ANL) with the sponsorship of DOE Light Water Reactor Sustainability (LWRS) program trying to develop a hybrid predictive modeling approach. This is based on limited experiment-data, Artificial-intelligence (AI) – Machine-Learning (ML) - Deep-Learning (DL) based techniques and Multiphysics-computational-mechanics based modeling tools. The hybrid approach not-only can improve the accuracy of the existing stress analysis and fatigue modeling approach but also can reduce the over-dependency on test-based approach. Towards this goal following are some of the major contributions based on ANL’s FY-20 environmental fatigue modeling activities: 1) A cyclic plasticity material model database for 82/182 dissimilar metal weld, which can be readily shared with US nuclear industry and regulatory agency on request. 2) A well validated analytical modeling methodology to perform cycle-by-cycle stress prediction under both constant amplitude fatigue loading and variable amplitude fatigue loading (with load-sequence effect). 3) An AI/ML/DL based methodology to predict unmeasurable cyclic strain based on other available sensor signals. This type of approach can be used for estimating strain in real reactor components from other sensor readings. 4) An AI/ML based approach to improve the US capability on environmental fatigue testing. This is by improving ANL’s existing environmental fatigue testing capacity to conduct ASME required strain-controlled tests (by controlling strain amplitudes and its rate), while not measuring the strain (due to the difficulty of placing an extensometer in a narrow autoclave in a PWR-water-test system). 5) A simulation and experiment based probabilistic modeling methodology for time-series fatigue state and life estimation of reactor metal such as dissimilar metal weld.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Part-scale microstructure prediction for laser powder bed fusion Ti-6Al-4V using a hybrid mechanistic and machine learning model

Laser powder bed fusion (LPBF) Ti-6Al-4V is widely studied for use in structural applications in aerospace and medical industries, but mechanical anisotropy and microstructural inhomogeneity prohibits its wider adoption. Although successful microstructure prediction models have been developed, a remaining challenge is their limited integration across length/time scales and validation by experimental studies. Here, this work proposes a physics-augmented machine learning surrogate model to unite predictions of LPBF temperature, β phase morphology and texture, and α/α’ formation into a single framework that is calibrated and validated with experiments. First, a phase field (PF) model of the martensitic β→α’ transformation is developed and calibrated using data from in-situ synchrotron cyclic heating/cooling studies quantifying the variation of α phase fraction with time. In parallel, an established finite difference-Monte Carlo (FDMC) model predicts the part-scale temperature profile and β grain formation during solidification. A dataset is developed using LPBF cyclic temperature descriptors from the FDMC model as inputs and corresponding α/α’ phase fraction and width from the PF model as outputs. Five machine learning (ML) regression models are tested and optimized, having mean absolute error in testing ≤ 4 %, and the k-nearest neighbors (KNN) model is selected as the best performing. The KNN model is called at the nodal level during post-processing of the FDMC model to replace and downscale the response of the PF model. The combined agility and accuracy of the hybrid FDMC-ML model enables part-scale microstructure predictions that can be further used for property predictions to accelerate AM process optimization.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Fluid and hybrid simulations of the ionization instabilities in Hall thruster

Low-frequency axial oscillations in the range of 5–50 kHz stand out as a pervasive feature observed in many types of Hall thrusters. While it is widely recognized that the ionization effects play the central role in this mode, as manifested via the large-scale oscillations of neutral and plasma density, the exact mechanism(s) of the instabilities remain unclear. To gain further insight into the physics of the breathing mode and evaluate the role of kinetic effects, a one-dimensional time-dependent full nonlinear low-frequency model describing neutral atoms, ions, and electrons is developed in full fluid formulation and compared to the hybrid model in which the ions and neutrals are kinetic. Both models are quasi-neutral and share the same electron fluid equations that include the electron diffusion, mobility across the magnetic field, and the electron energy evolution. The ionization models are also similar in both approaches. Further, the predictions of fluid and hybrid simulations are compared for different test cases. Two main regimes are identified in both models: one with pure low-frequency behavior and the other one, where the low-frequency oscillations coexist with high-frequency oscillations in the range of 100–200 kHz, with the characteristic time scale of the ion channel fly-by time, 100–200 kHz. The other test case demonstrates the effect of a finite temperature of injected neutral atoms, which has a substantial suppression effect on the oscillation amplitude.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1987

Stability and rheology of dispersions of silicon nitride and silicon carbide

The relationship between the surface and colloid chemistry of commercial ultra-fine silicon carbide and silicon nitride powders was examined by a variety of standard characterization techniques and by methodologies especially developed for ceramic dispersions. These include electrokinetic measurement, surface titration, and surface spectroscopies. The effects of powder pretreatment and modification strategies, which can be utilized to augment control of processing characteristics, were monitored with these technologies. Both silicon carbide and nitride were found to exhibit silica-like surface chemistries, but silicon nitride powders possess an additional amine surface functionality. Colloidal characteristics of the various nitride powders in aqueous suspension is believed to be highly dependent on the relative amounts of the two types of surface groups, which in turn is determined by the powder synthesis route. The differences in the apparent colloidal characteristics for silicon nitride powders cannot be attributed to the specific absorption of ammonium ions. Development of a model for the prediction of double-layer characteristics of materials with a hybrid site interface facilitated understanding and prediction of the behavior of both surface charge and surface potential for these materials. The utility of the model in application to silicon nitride powders was demonstrated.

Feke, Donald L.↗
DOE OSTI2023

Accurate Prediction of HSE06 Band Structures for a Diverse Set of Materials Using Δ-Learning

Here we used machine learning (ML) to accurately predict eigenvalues of the hybrid HSE06 functional using eigenvalues computed by the less computationally expensive PBE functional and associated electronic features based on the k-point resolved atomic band character. The ML model was trained by using eigenvalues from only one k-point for each of the 168 compounds in the training set. The HSE06 eigenvalues across all k-points were then predicted for a separate set of 169 compounds with a mean absolute error (MAE) of 0.13 eV, representing a significant improvement over the error of PBE-computed eigenvalues relative to that of HSE06 (MAE = 0.96 eV). These accurately predicted eigenvalues result in remarkably accurate predictions for the band structures, projected density of states, and band gaps, even though the model was not explicitly trained on these other properties. Finally, we demonstrate that our ML model has a similar accuracy for both ternary and quaternary compounds well outside the initial training set and on systems with 112 and 160 atoms, demonstrating its potential to rapidly predict HSE06-quality electronic structures of complex materials that are practically unfeasible for HSE06.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1983

Hybrid perturbation/Bubnov-Galerkin technique for nonlinear thermal analysis

A two step hybrid analysis technique to predict the nonlinear steady state temperature distribution in structures and solids is presented. The technique is based on the regular perturbation expansion and the classical Bubnov-Galerkin approximation. The functions are obtained by using the regular perturbation method. These functions are selected as coordinate functions and the classical Bubnov-Galerkin technique is used to compute their amplitudes. The potential of the proposed hybrid technique for the solution of nonlinear thermal problems is discussed. The effectiveness of this technique is demonstrated by the effects of conduction, convection, and radiation modes of heat transfer. It is indicated that the hybrid technique overcomes the two major drawbacks of the classical techniques: (1) the requirement of using a small parameter in the regular perturbation method; and (2) the arbitrariness in the choice of the coordinate functions in the Bubnov-Galerkin technique. The proposed technique extends the range of applicability of the regular perturbation method and enhances the effectiveness of the Bubnov-Galerkin technique.

Noor, A. K.↗
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↗