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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 1,009 records · Page 56
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Recurrent convolutional neural networks for modeling nonadiabatic dynamics of quantum-classical systems

Recurrent neural networks (RNNs) have recently been extensively applied to model the time evolution in fluid dynamics, weather predictions, and even chaotic systems due to their ability to capture temporal dependencies and sequential patterns in data. Here we present an RNN model based on convolutional neural networks for modeling the nonlinear nonadiabatic dynamics of hybrid quantum-classical systems. The dynamical evolution of the hybrid systems is governed by equations of motion for classical degrees of freedom and von Neumann equation for electrons. The Physics-Aware Recurrent Convolution (PARC) neural network structure incorporates a differentiator-integrator architecture that inductively models the spatiotemporal dynamics of generic physical systems. Here, we apply our RNN approach to learn the space-time evolution of a one-dimensional semiclassical Holstein model after an interaction quench. For shallow quenches (small changes in electron-lattice coupling), the deterministic dynamics can be accurately captured using a single-CNN-based recurrent network. In contrast, deep quenches induce chaotic evolution, making long-term trajectory prediction significantly more challenging. Nonetheless, we demonstrate that the PARC-CNN architecture can effectively learn the statistical climate of the Holstein model under deep-quench conditions.

Holstein model↗
NASA NTRS1991

Diurnal forcing of planetary atmospheres

The utility of the Mars Planetary Boundary Layer Model (MPBL) for calculations in support of the Mars 94 balloon mission was substantially enhanced by the introduction of a balloon equation of motion into the model. Both vertical and horizontal excursions of the balloon are calculated along with its volume, temperature, and pressure. The simulations reproduce the expected 5-min vertical oscillations of a constant density balloon at altitude on Mars. The results of these calculations are presented for the nominal target location of the balloon. A nonlinear balanced model was developed for the Martian atmosphere. It was used to initialize a primitive equation model for the simulations of the Earth's atmosphere at the time of the El Chichon eruption in 1982. It is also used as an assimilation model to update the temperature and wind fields at frequent intervals.

Houben, Howard C.↗
NASA NTRS1991

Thermal barrier coating life prediction model development, phase 2

The objective of this program was to generate a life prediction model for electron-beam-physical vapor deposited (EB-PVD) zirconia thermal barrier coating (TBC) on gas turbine engine components. Specific activities involved in development of the EB-PVD life prediction model included measurement of EB-PVD ceramic physical and mechanical properties and adherence strength, measurement of the thermally grown oxide (TGO) growth kinetics, generation of quantitative cyclic thermal spallation life data, and development of a spallation life prediction model. Life data useful for model development was obtained by exposing instrumented, EB-PVD ceramic coated cylindrical specimens in a jet fueled burner rig. Monotonic compression and tensile mechanical tests and physical property tests were conducted to obtain the EB-PVD ceramic behavior required for burner rig specimen analysis. As part of that effort, a nonlinear constitutive model was developed for the EB-PVD ceramic. Spallation failure of the EB-PVD TBC system consistently occurred at the TGO-metal interface. Calculated out-of-plane stresses were a small fraction of that required to statically fail the TGO. Thus, EB-PVD spallation was attributed to the interfacial cracking caused by in-plane TGO strains. Since TGO mechanical properties were not measured in this program, calculation of the burner rig specimen TGO in-plane strains was performed by using alumina properties. A life model based on maximum in-plane TGO tensile mechanical strain and TGO thickness correlated the burner rig specimen EB-PVD ceramic spallation lives within a factor of about plus or minus 2X.

Meier, Susan Manning↗
NASA NTRS1992

The nonlinear forced response characteristics of contained fluids in microgravity

An experimental study of the change in the lateral slosh behavior of contained fluids between earth and space is presented. The experimental apparatus used to determine the slosh characteristics is described and a nonlinear analytic model of a coupled fluid/spacecraft system is outlined. The forced response characteristics of silicon oil and distilled water in cylinder tanks with either a flat or spherical bottom are reported and discussed. A comparison of the measured earth and space results identifies and highlights the effects of gravity on the linear and nonlinear slosh behavior of these fluids.

Vanschoor, M. C.↗
NASA NTRS2003

Analytical Modeling of the High Strain Rate Deformation of Polymer Matrix Composites

The results presented here are part of an ongoing research program to develop strain rate dependent deformation and failure models for the analysis of polymer matrix composites subject to high strain rate impact loads. State variable constitutive equations originally developed for metals have been modified in order to model the nonlinear, strain rate dependent deformation of polymeric matrix materials. To account for the effects of hydrostatic stresses, which are significant in polymers, the classical 5 plasticity theory definitions of effective stress and effective plastic strain are modified by applying variations of the Drucker-Prager yield criterion. To verify the revised formulation, the shear and tensile deformation of a representative toughened epoxy is analyzed across a wide range of strain rates (from quasi-static to high strain rates) and the results are compared to experimentally obtained values. For the analyzed polymers, both the tensile and shear stress-strain curves computed using the analytical model correlate well with values obtained through experimental tests. The polymer constitutive equations are implemented within a strength of materials based micromechanics method to predict the nonlinear, strain rate dependent deformation of polymer matrix composites. In the micromechanics, the unit cell is divided up into a number of independently analyzed slices, and laminate theory is then applied to obtain the effective deformation of the unit cell. The composite mechanics are verified by analyzing the deformation of a representative polymer matrix composite (composed using the representative polymer analyzed for the correlation of the polymer constitutive equations) for several fiber orientation angles across a variety of strain rates. The computed values compare favorably to experimentally obtained results.

Goldberg, Robert K.↗
DOE OSTI2022

A hybrid meshfree discretization to improve the numerical performance of peridynamic models

Efficient and accurate calculation of spatial integrals is of major interest in the numerical implementation of peridynamics (PD). The standard way to perform this calculation is a particle-based approach that discretizes the strong form of the PD governing equation. This approach has rapidly been adopted by the PD community since it offers some advantages. Additionally, it is computationally cheaper than other available schemes, can conveniently handle material separation, and effectively deals with nonlinear PD models. Nevertheless, PD models are still computationally very expensive compared with those based on the classical continuum mechanics theory, particularly for large-scale problems in three dimensions. This results from the nonlocal nature of the PD theory which leads to interactions of each node of a discretized body with multiple surrounding nodes. Here, we propose a new approach to significantly boost the numerical efficiency of PD models. We propose a discretization scheme that employs a simple collocation procedure and is truly meshfree; i.e., it does not depend on any background integration cells. In contrast to the standard scheme, the proposed scheme requires a much smaller set of neighboring nodes (keeping the same physical length scale) to achieve a specific accuracy and is thus computationally more efficient. Our new scheme is applicable to the case of linear PD models and within neighborhoods where the solution can be approximated by smooth basis functions. Therefore, to fully exploit the advantages of both the standard and the proposed schemes, a hybrid discretization is presented that combines both approaches within an adaptive framework. The high performance of the developed framework is illustrated by several numerical examples, including brittle fracture and corrosion problems in two and three dimensions.

42 ENGINEERING↗
DOE OSTI2024

Alternating Direction Decomposition with Strong Bounding and Convexification (ADDSBC) for Solving Security Constrained AC Unit Commitment Problems

This project aims to develop efficient and robust computational methods for solving the security-constrained unit commitment and alternating current optimal power flow problem (SC-UC-ACOPF). The SC-UC-ACOPF problem is at the center of the short-term operation of the U.S. Power Grid. It is solved every week, every day, and every 10 minutes to plan for the optimal action of electricity generation and consumption by minimizing the generation cost and maintaining power system reliability against potential disruptions of equipment failures. In mathematical terms, SC-UC-ACOPF is a challenging large-scale mixed-integer nonlinear optimization model. This means that the decisions involve both discrete variables, e.g. the turning on and off of generators and switching of transmission lines and transformers, and continuous decisions, e.g. the amount of energy generated by each generator and the power flows in the power grid. The physics of the power flow is described by nonlinear equations involving real and reactive power and bus voltages. Another key feature is the large number of contingencies, i.e. the system needs to stay reliable in face of failure of any one equipment, such as transmission lines and generators. The U.S. power grids are extremely complicated and large scale with more than 5,000 generators, 50,000 buses, and 100,000 high-voltage transmission lines, making the SC-UC-ACOPF a very large-scale computation challenge. The research developed in this project aims to solve the SC-UC-ACOPF problems in the three timescales, i.e. weekly, daily, and every 10-min. The proposed computational methods are built on a principled algorithmic approach of decomposition and penalization. More specifically, the algorithm develops spatial and temporal decomposition by exploiting the strong temporal coupling and weak spatial coupling of the UC problem and the complementary feature, i.e. weak temporal coupling and strong spatial coupling of the ACOPF problem. The algorithm also leverages recent progresses in strong convex relaxation of ACOPF. A unique feature of the proposed approach is that it generates a valid, global upper bound on the optimal maximum profit. In this way, a global optimality gap is available to measure the quality of the solution. To further speed up computation, the research team has developed a plethora of effective heuristics to strengthen the iterative penalty-based decomposition framework. For instance, a heuristic is developed to construct inner approximations of the time coupling constraints within the time decoupled problems. Contingencies are pre-screened and low-rank matrix computation is exploited to find the almost unique solution to each contingency. A novel heuristic for line switching is proposed and tested with positive impacts on instances where line switching is beneficial. Taking a systematic approach and carefully handling every detail of the problem pays off. The TIM-GO’s performance throughout the trials and the final event was stellar. TIM-GO garnered the second highest total prize money and is ranked in the top three positions across all categories of comparison.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1982

Simplified aeroelastic modeling of horizontal axis wind turbines

Certain aspects of the aeroelastic modeling and behavior of the horizontal axis wind turbine (HAWT) are examined. Two simple three degree of freedom models are described in this report, and tools are developed which allow other simple models to be derived. The first simple model developed is an equivalent hinge model to study the flap-lag-torsion aeroelastic stability of an isolated rotor blade. The model includes nonlinear effects, preconing, and noncoincident elastic axis, center of gravity, and aerodynamic center. A stability study is presented which examines the influence of key parameters on aeroelastic stability. Next, two general tools are developed to study the aeroelastic stability and response of a teetering rotor coupled to a flexible tower. The first of these tools is an aeroelastic model of a two-bladed rotor on a general flexible support. The second general tool is a harmonic balance solution method for the resulting second order system with periodic coefficients. The second simple model developed is a rotor-tower model which serves to demonstrate the general tools. This model includes nacelle yawing, nacelle pitching, and rotor teetering. Transient response time histories are calculated and compared to a similar model in the literature. Agreement between the two is very good, especially considering how few harmonics are used. Finally, a stability study is presented which examines the effects of support stiffness and damping, inflow angle, and preconing.

Wendell, J. H.↗
DOE OSTI2020

Path Properties of Atmospheric Transitions: Illustration with a Low-Order Sudden Stratospheric Warming Model

Many rare weather events, including hurricanes, droughts, and floods, dramatically impact human life. To accurately forecast these events and characterize their climatology requires specialized mathematical techniques to fully leverage the limited data that are available. Here we describe transition path theory (TPT), a framework originally developed for molecular simulation, and argue that it is a useful paradigm for developing mechanistic understanding of rare climate events. TPT provides a method to calculate statistical properties of the paths into the event. As an initial demonstration of the utility of TPT, we analyze a low-order model of sudden stratospheric warming (SSW), a dramatic disturbance to the polar vortex that can induce extreme cold spells at the surface in the midlatitudes. SSW events pose a major challenge for seasonal weather prediction because of their rapid, complex onset and development. Climate models struggle to capture the long-term statistics of SSW, owing to their diversity and intermittent nature. We use a stochastically forced Holton–Mass-type model with two stable states, corresponding to radiative equilibrium and a vacillating SSW-like regime. In this stochastic bistable setting, from certain probabilistic forecasts TPT facilitates estimation of dominant transition pathways and return times of transitions. These “dynamical statistics” are obtained by solving partial differential equations in the model’s phase space. With future application to more complex models, TPT and its constituent quantities promise to improve the predictability of extreme weather events through both generation and principled evaluation of forecasts.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2002

On the validity of the double integrator approximation in deep space formation flying

Free-flying models are commonly used f o r path planning and open loop control design (i. e., guidance design) and translational feedback control design (i. e., control design) for deep space precision formation flying. The free flying model, essentially a double integrator, results from discarding small terms in the relative spacecraft equations of motion. While the magnitude of these discarded terms may be small, one must show that their dynamic effects are small as compared to the precision performance requirements. We do so by deriving a theoretical method for bounding the difference between the solution of a nonlinear truth model of the relative translational spacecraft dynamics and a Simplified linear time-invariant model. Presently, the method incorporates feedforward and static output feedback control. The method is applied to a Terrestrial Planet Finder- based example. Using only feedforward control (guidance) the free-flying model and a Hill- Clohessy- Wiltshire Equations-based model are shown to be accurate to 1 c m for up to 4 and 30 hours, respectively. Also shown is that the simplest free-flying model may not be sufficient for low-gain feedback control design-closed-loop tracking errors can be as large as 8 meters.

formation flying free flying model double integrat↗
NASA NTRS1985

Impact of airfoil profile on the supersonic aerodynamics of delta wings

A nonlinear flow model was employed to predict the flowfield, pressure and force data for delta wings at supersonic speeds. The goal of the study was to investigate the influence the airfoil profiles has on the wing aerodynamics. The analysis covers wing aspect ratios from 0.5-3.0 with leading edge sweep of 0.5-4.0 on diamond, circular arc and NACA modified 4-digit airfoils. Nonlinear aerodynamics are approximated with nonlinear zero-lift wave drag curves, yielding results significantly different from those obtained from linear calculations. The analytical technique, useful in preliminary design studies, indicates in all cases that 90 percent of wave drag is generated at the wing apex and trailing edge.

Wood, R. M.↗
DOE OSTI2024

Characterizing climate pathways using feature importance on echo state networks

The 2022 National Defense Strategy of the United States listed climate change as a serious threat to national security. Climate intervention methods, such as stratospheric aerosol injection, have been proposed as mitigation strategies, but the downstream effects of such actions on a complex climate system are not well understood. The development of algorithmic techniques for quantifying relationships between source and impact variables related to a climate event (i.e., a climate pathway) would help inform policy decisions. Data-driven deep learning models have become powerful tools for modeling highly nonlinear relationships and may provide a route to characterize climate variable relationships. In this paper, we explore the use of an echo state network (ESN) for characterizing climate pathways. ESNs are a computationally efficient neural network variation designed for temporal data, and recent work proposes ESNs as a useful tool for forecasting spatiotemporal climate data. However, ESNs are noninterpretable black-box models along with other neural networks. The lack of model transparency poses a hurdle for understanding variable relationships. We address this issue by developing feature importance methods for ESNs in the context of spatiotemporal data to quantify variable relationships captured by the model. We conduct a simulation study to assess and compare the feature importance techniques, and we demonstrate the approach on reanalysis climate data. In the climate application, we consider a time period that includes the 1991 volcanic eruption of Mount Pinatubo. This event was a significant stratospheric aerosol injection, which acts as a proxy for an anthropogenic stratospheric aerosol injection. Furthermore, we are able to use the proposed approach to characterize relationships between pathway variables associated with this event that agree with relationships previously identified by climate scientists.

black-box models↗
DOE OSTI2024

A Multiphysics Multiscale Simulation Platform for Damage, Environmental Degradation, and Life Prediction of CMCs in Extreme Environments

This project successfully developed a multiphysics, multiscale computational framework to enhance the design and development of CMCs, with a focus on modeling highly nonlinear, time-dependent damage mechanisms and material degradation under extreme conditions, such as those experienced in turbine service environments. The project made significant advances in improving our understanding of progressive damage, oxidative degradation, and time-dependent inelastic deformation in CMCs, with particular attention to the role of uncertainties in predictions. Key outcomes include the integration of advanced material characterization, uncertainty quantification, and multiphysics constitutive models to predict the behavior of CMCs over their service life. A novel multiscale methodology was employed, which integrated microscale constituent behaviors with structural-scale responses, enabling the manufacturing defects in the microstructure that are prone to damage nucleation. Through the development of DL algorithms, the project advanced the prediction of damage initiation and crack propagation, taking into account the defect morphology and statistical variations across multiple scales. The framework was rigorously validated using thermomechanical experiments, which tested CMCs under various mechanical loadings at elevated temperatures, further enhancing the model's predictive capability. Overall, the research outcomes have provided a more accurate, reliable method for predicting CMC component life, significantly advancing material design, and improving component reliability in extreme environments. This work has strong implications for the optimization of turbine components and other high-performance applications where CMCs are used.

03 NATURAL GAS↗
DOE OSTI2023

Deep-Learning-Based Koopman Modeling for Online Control Synthesis of Nonlinear Power System Transient Dynamics

Power system stability and control have become more challenging due to the increasing uncertainty associated with renewable generation. Here, the performance of conventional control is highly driven by the physics-based offline-developed dynamic models that can deviate from the actual system characteristics under different operating conditions and/or configurations. Data-driven approaches based on online measurements can be a better solution to addressing these issues by capturing real-time operation conditions. This article describes a novel fully data-driven probabilistic framework to derive a linear representation of postcontingency grid dynamics and online prescribe control based on the derived model to enhance transient stability. The complex nonlinear power system dynamics is approximated by a linear model by using multiple neural network modules that infer distributions of the observations and introducing a Koopman layer to sample possible Koopman linear models from the inferred distributions. The trained model features linearity that can be easily incorporated into the existing linear control design paradigm and ease the controller design process. The effectiveness of Koopman-based control designs is validated through comparative case studies, which demonstrate increased prediction accuracy and control performance when applied to a power system with heterogeneous generator dynamics.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Estimating Leaf Area Index in Row Crops Using Wheel-Based and Airborne Discrete Return Light Detection and Ranging Data

Leaf area index (LAI) is an important variable for characterizing plant canopy in crop models. It is traditionally defined as the total one-sided leaf area per unit ground area and is estimated by both direct and indirect methods. This paper explores the effectiveness of using light detection and ranging (LiDAR) data to estimate LAI for sorghum and maize with different treatments at multiple times during the growing season from both a wheeled vehicle and Unmanned Aerial Vehicles. Linear and nonlinear regression models are investigated for prediction utilizing statistical and plant structure-based features extracted from the LiDAR point cloud data with ground reference obtained from an in-field plant canopy analyzer (indirect method). Results based on the value of the coefficient of determination ( R 2 ) and root mean squared error for predictive models ranged from ∼0.4 in the early season to ∼0.6 for sorghum and ∼0.5 to 0.80 for maize from 40 Days after Sowing to harvest.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1974

Computational study of nonlinear plasma waves: 1: Simulation model and monochromatic wave propagation

An economical low noise plasma simulation model is applied to a series of problems associated with electrostatic wave propagation in a one-dimensional, collisionless, Maxwellian plasma, in the absence of magnetic field. The model is described and tested, first in the absence of an applied signal, and then with a small amplitude perturbation, to establish the low noise features and to verify the theoretical linear dispersion relation at wave energy levels as low as 0.000,001 of the plasma thermal energy. The method is then used to study propagation of an essentially monochromatic plane wave. Results on amplitude oscillation and nonlinear frequency shift are compared with available theories. The additional phenomena of sideband instability and satellite growth, stimulated by large amplitude wave propagation and the resulting particle trapping, are described.

Matda, Y.↗