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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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Models of man making predictions of future states of discrete linear dynamic systems are considered. The task is forced-pace, but the pace is slow enough to eliminate the effects of reaction time and neuromuscular lag. The best of the several models considered includes the constraints of limited memory and observation noise.
The alternating direction implicit (ADI) method is adopted, modified, and applied to the Reynolds equation for thin, gas fluid films. An efficient code is developed to predict both the steady-state and dynamic performance of an aerodynamic journal bearing. An alternative approach is shown for hybrid journal gas bearings by using Liebmann's iterative solution (LIS) for elliptic partial differential equations. The results are compared with known design criteria from experimental data. The developed methods show good accuracy and very short computer running time in comparison with methods based on an inverting of a matrix. The computer codes need a small amount of memory and can be run on either personal computers or on mainframe systems.
Considerable advances have been made in battery safety models, but achieving predictive accuracy across a wide range of conditions continues to be challenging. Interactions between dynamically evolving mechanical, electrical, and thermal state variables make model prediction difficult during mechanical abuse scenarios. In this study, we develop a physics-based modeling approach that allows for choosing between different mechanical and electrochemical models depending on the required level of analysis. We demonstrate the use of this approach to connect cell-level abuse response to electrode-level and particle-level transport phenomena. A pseudo-two-dimensional model and simplified single-particle models are calibrated to electrical-thermal cycling data and applied to mechanically induced short-circuit scenarios to understand how the choice of electrochemical model affects the model prediction under abuse scenarios. These models are implemented using user-defined subroutines on ls-dyna finite element software and can be coupled with existing automotive crash safety models.
A computer program was developed and written in FORTRAN 5 which predicts the transient and steady state performance and heat transfer characteristics of a pulsing GO2/GH2 rocket engine. This program predicts the dynamic flow and ignition characteristics which, when combined in a quasi-steady state manner with the combustion and mixing analysis program, will provide the thrust and specific impulse of the engine as a function of time. The program also predicts the transient and steady state heat transfer characteristics of the engine using various cooling concepts. The computer program, test case, and documentation are presented. The program is applicable to any system capable of utilizing the FORTRAN 4 or FORTRAN 5 language.
Untethered soft robots that locomote using electrothermally-responsive materials like shape memory alloy (SMA) face challenging design constraints for sensing actuator states. At the same time, modeling of actuator behaviors faces steep challenges, even with available sensor data, due to complex electrical-thermal-mechanical interactions and hysteresis. This article proposes a framework for in-situ sensing and dynamics modeling of actuator states, particularly temperature of SMA wires, which is used to predict robot motions. A planar soft limb is developed, actuated by a pair of SMA coils, that includes compact and robust sensors for temperature and angular deflection. Data from these sensors are used to train a neural network-based on the long short-term memory (LSTM) architecture to model both unidirectional (single SMA) and bidirectional (both SMAs) motion. Predictions from the model demonstrate that data from the temperature sensor, combined with control inputs, allow for dynamics predictions over extraordinarily long open-loop timescales (10 min) with little drift. Prediction errors are on the order of the soft deflection sensor’s accuracy. This architecture allows for compact designs of electrothermally-actuated soft robots that include sensing sufficient for motion predictions, helping to bring these robots into practical application.
Self-consistent modeling of turbulence-driven transport is critical for optimizing confinement in magnetically confined fusion plasmas, such as tokamaks and stellarators. In particular, capturing the long-term co-evolution of turbulence, flow, and background plasma profiles remains computationally challenging. Direct numerical simulation of these multiscale, highly nonlinear processes is often demanding and impractical for real-time control or design optimization. To address this bottleneck, we investigate transformer-based neural operator partial differential equation surrogates for emulating the dynamics of drift-wave turbulence bifurcation mediated by zonal flows, using the modified Hasegawa–Wakatani (MHW) model as a prototypical system. We find that the finetuned neural operator model has excellent performance in capturing the multi-spatiotemporal-scales of MHW turbulence bifurcation and is robust to testing on rare and out-of-distribution dynamics. Specifically, we demonstrate that a single unified model accurately predicts both quasi-steady-state turbulence and a wide range of dynamical transition processes, such as nonlinear saturation, spontaneous suppression of turbulence, and the emergence of macroscopic zonal flows, over time horizons vastly exceeding the local turbulence correlation time. This computationally efficient approach establishes a strong foundation for fast, AI-based modeling of complex, multiscale phenomena in magnetized fusion plasmas.
Molecular dynamics simulations offer detailed insights into atomic motions but face timescale limitations. Enhanced sampling methods have addressed these challenges but even with machine learning, they often rely on pre-selected expert-based features. Here, in this work, we present a Graph Neural Network-State Predictive Information Bottleneck (GNN-SPIB) framework, which combines graph neural networks and the state predictive information bottleneck to automatically learn low-dimensional representations directly from atomic coordinates. Tested on three benchmark systems, our approach predicts essential structural, thermodynamic and kinetic information for slow processes, demonstrating robustness across diverse systems. The method shows promise for complex systems, enabling effective enhanced sampling without requiring pre-defined reaction coordinates or input features.
A procedure has been developed for predicting peak dynamic inlet distortion. This procedure combines Computational Fluid Dynamics (CFD) and distortion synthesis analysis to obtain a prediction of peak dynamic distortion intensity and the associated instantaneous total pressure pattern. A prediction of the steady state total pressure pattern at the Aerodynamic Interface Plane is first obtained using an appropriate CFD flow solver. A corresponding inlet turbulence pattern is obtained from the CFD solution via a correlation linking root mean square (RMS) inlet turbulence to a formulation of several CFD parameters representative of flow turbulence intensity. This correlation was derived using flight data obtained from the NASA High Alpha Research Vehicle flight test program and several CFD solutions at conditions matching the flight test data. A distortion synthesis analysis is then performed on the predicted steady state total pressure and RMS turbulence patterns to yield a predicted value of dynamic distortion intensity and the associated instantaneous total pressure pattern.
A method of estimating the undistorted stall line for an axial-flow compressor by using the digital computer is presented. The method involves linearization of nonlinear dynamic equations about an operating point on a speed line, and then application of the first method of Lyapunov to determine the stability of the nonlinear system from the stability of the linear system. The method is applied to a simulation of the J85 compressor, which utilizes stage stacking and lumped volume techniques for the interstage regions to simulate steady-state and dynamic compressor performance. The stability boundary predicted by the digital simulation compares quite well with the stall line predicted by a dynamic simulation of the J85 compressor programmed on the analog computer. Since previous studies have shown that the analog-predicted stall line agrees well with the stall line of the compressor, the digital method presented is also a good means of estimating the stall line.
We present an ab initio free energy model derived from a fully relativistic density functional theory (DFT) electronic structure with dynamic magnetism for δ -plutonium (face-centered cubic, fcc). The DFT model is extended with orbital-orbital interaction in a parameter free orbital polarization (OP) mechanism consistent with previous modeling of plutonium. Gibbs free energy is built from components associated with the temperature dependence of the electronic structure and the corresponding electronic entropy, lattice vibrations within an anharmonic lattice dynamics model, and dynamical fluctuations of the magnetization density, i.e. magnetic fluctuations. The fluctuation model consists of transverse and longitudinal modes driven by temperature induced excitations of the DFT + OP electronic structure. The ab initio model thus incorporates fluctuating states beyond the electronic ground state. Thanks to the dynamic magnetism, the theory predicts excellent thermodynamic properties and a Gibbs free energy in accord with CALPHAD and semi-empirical modeling developed from the thermodynamic observables. The magnetic fluctuations further explain anomalous behaviors of the thermal expansion in plutonium. Specifically, a thermal expansion for the δ -plutonium system turning from positive to negative at temperatures above room temperature, a tendency for gallium to reduce and remove the negative thermal expansion depending on composition, and a positive thermal expansion for the high temperature ϵ phase.
This paper presents an innovative Reduced-order model (ROM) for merging experimental and simulation data using data assimilation (DA) to estimate the "True" state of a fluid dynamics system, leading to more accurate predictions. Our methodology introduces a novel approach by implementing the ensemble Kalman filter (EnKF) within a reduced-dimensional framework, grounded in a robust theoretical foundation and applied to fluid dynamics. To address the substantial computational demands of DA, the proposed ROM employs low-resolution (LR) techniques to drastically reduce computational costs. This innovative approach involves downsampling datasets for DA computations, followed by an advanced reconstruction technique based on low-cost singular value decomposition (lcSVD). The lcSVD method, a key innovation in this paper, has never been applied to DA before and offers a highly efficient way to enhance resolution with minimal computational resources. Our results demonstrate significant reductions in both computation time and RAM usage through these LR techniques without compromising the accuracy of the estimations. For instance, in a turbulent test case, for a data compression rate of 15.9, the LR approach can achieve a speed-up of 13.7 and a RAM compression of 90.9% while maintaining a low relative root mean square error (RRMSE) of 2.6%, compared to 0.8% in the high-resolution (HR) reference. Furthermore, we highlight the effectiveness of the EnKF in estimating and predicting the state of fluid flow systems based on limited observations and given low-fidelity numerical data. This paper highlights the potential of the proposed DA method in fluid dynamics applications, particularly for improving computational efficiency in CFD and related fields. Its ability to balance accuracy with low computational and memory costs makes it especially suitable for large-scale and real-time applications, such as environmental monitoring or engineering design. This method will be incorporated into ModelFLOWs-app.
Flow field characteristics predicted from a computational model show that the dynamical state of the flow, for practical crystal growth conditions of mercurous chloride, can range from steady to unsteady. Evidence that the flow field can be strongly dominated by convection for ground-based conditions is provided by the prediction of asymmetric velocity profiles bv the model which show reasonable agreement with laser Doppler velocimetry experiments in both magnitude and planform. Unsteady flow is shown to be correlated with a degradation of crystal quality as quantified by light scattering pattern measurements, A microgravity experiment is designed to show that an experiment performed with parameters which yield an unsteady flow becomes steady (diffusive-advective) in a microgravity environment of 10(exp -3) g(sub 0) as predicted by the model, and hence yields crystals with optimal quality.
A semiclassical collision model is applied to the study of energy transfer rates between a vibrationally excited diatomic molecule and a structureless atom. The molecule is modeled as an anharmonic oscillator with a multitude of dynamically coupled vibrational states. Three main aspects in the prediction of vibrational energy transfer rates are considered. The applicability of the semiclassical model to an anharmonic oscillator is first evaluated for collinear encounters. Second, the collinear semiclassical model is applied to obtain numerical predictions of the vibrational energy transfer rate dependence on the initial vibrational state quantum number. Thermally averaged vibration-translation rate coefficients are predicted and compared with CO-He experimental values for both ground and excited initial states. The numerical model is also used as a basis for evaluating several less complete but analytic models. Third, the role of rational motion in the dynamics of vibrational energy transfer is examined. A three-dimensional semiclassical collision model is constructed with coupled rotational motion included. Energy transfer within the molecule is shown to be dominated by vibration-rotation transitions with small changes in angular momentum. The rates of vibrational energy transfer in molecules with rational frequencies that are very small in comparison to their vibrational frequency are shown to be adequately treated by the preceding collinear models.
Abstract Nonequilibrium dynamics governed by electron–phonon ( e -ph) interactions plays a key role in electronic devices and spectroscopies and is central to understanding electronic excitations in materials. The real-time Boltzmann transport equation (rt-BTE) with collision processes computed from first principles can describe the coupled dynamics of electrons and atomic vibrations (phonons). Yet, a bottleneck of these simulations is the calculation of e –ph scattering integrals on dense momentum grids at each time step. Here we show a data-driven approach based on dynamic mode decomposition (DMD) that can accelerate the time propagation of the rt-BTE and identify dominant electronic processes. We apply this approach to two case studies, high-field charge transport and ultrafast excited electron relaxation. In both cases, simulating only a short time window of ~10% of the dynamics suffices to predict the dynamics from initial excitation to steady state using DMD extrapolation. Analysis of the momentum-space modes extracted from DMD sheds light on the microscopic mechanisms governing electron relaxation to a steady state or equilibrium. The combination of accuracy and efficiency makes our DMD-based method a valuable tool for investigating ultrafast dynamics in a wide range of materials.
The designs of three existing testers (Hybrid Bearing Tester, OTV Bearing Tester, and Long Life Bearing Tester) owned by NASA were reviewed for their capability to serve as a multi-purpose cryogenic fluid film bearing tester. The primary tester function is the validation of analytical predictions for fluid film bearing steady state and dynamic performance. Evaluation criteria were established for test bearing configurations, test fluids, instrumentation, and test objectives. Each tester was evaluated with respect to these criteria. A determination was made of design improvements which would allow the testers to meet the stated criteria. The cost and time required to make the design changes were estimated. A recommendation based on the results of this study was made to proceed with the Hybrid Bearing Tester.
The United States urgently needs to tackle the climate crisis while enhancing energy security and resiliency. The complexity of the U.S. energy system, with its interconnected elements, makes predicting future states challenging, especially with the introduction of novel energy systems like wind, solar, clean hydrogen, and advanced nuclear technologies. Modern systems engineering methods and tools can provide deeper insights into these dynamics and future behaviors. This research aims to develop a comprehensive model that captures the main elements and behaviors of new energy technologies within the existing energy system. We hypothesized that the market uptake of novel energy systems is influenced by multiple diverse factors, such as technological learning, availability of resources, and economic incentives; examined the history of electricity generation using land-based wind technologies; and developed a system dynamics model to investigate the relationships between capacity growth and influencing factors, both internal and external. The developed model yielded outcomes that confirmed the hypothesized dynamics of wind energy system diffusion through a quantitative comparison of installed capacity and highlighted the significant influence of resource availability, federal incentives (production tax credits), and technological learning on capacity growth and cost reduction. This research aims to support informed decision-making for investments in novel energy systems and aid in developing effective policies for technology deployment.
In this study, we present a physics-constrained deep learning method to discover and visualize from data the invariant nonlinear normal modes (NNMs) which contain the spatiotemporal dynamics of the fluid flow potentially containing strong nonlinearity. Specifically, we develop a NNM-physics-constrained convolutional autoencoder (NNM-CNN-AE) integrated with a multi-temporal-step dynamics prediction block to learn the nonlinear modal transformation, the NNMs containing the spatiotemporal dynamics of the flow, and reduced-order reconstruction and long-time future-state prediction of the flow fields, simultaneously. In test cases, we apply the developed method to analyze different flow regimes past a cylinder, including laminar flows with low Reynolds number in transient and steady states (RD = 100) and high Reynolds number flow (RD = 1000), respectively. The results indicate that the identified NNMs are able to reveal the nonlinear spatiotemporal dynamics of these flows, and the NNMs-based reduced-order modeling consistently achieves better accuracy with orders of magnitudes smaller errors in construction and prediction of the nonlinear velocity and vorticity fields, compared to the linear proper orthogonal decomposition (POD) method and the Koopman-constrained-CNN-AE using the same number or dimension of modes. We perform an analysis of the modal energy distribution of NNMs and find that compared to POD modes, the few fundamental NNMs capture a very high level of total energy of the flow, which is advantageous for reduced-order modeling and representation of the complex flows. Finally, we discuss the potentials and limitations of the presented method.
Significant advancements in computational fluid dynamics (CFD) and their coupling with computational structural dynamics (CSD, or comprehensive codes) for rotorcraft applications have been achieved recently. Despite this, CSD codes with their engineering level of modeling the rotor blade dynamics, the unsteady sectional aerodynamics and the vortical wake are still the workhorse for the majority of applications. This is especially true when a large number of parameter variations is to be performed and their impact on performance, structural loads, vibration and noise is to be judged in an approximate yet reliable and as accurate as possible manner. In this article, the capabilities of such codes are evaluated using the HART II International Workshop database, focusing on a typical descent operating condition which includes strong blade-vortex interactions. A companion article addresses the CFD/CSD coupled approach. Three cases are of interest: the baseline case and two cases with 3/rev higher harmonic blade root pitch control (HHC) with different control phases employed. One setting is for minimum blade-vortex interaction noise radiation and the other one for minimum vibration generation. The challenge is to correctly predict the wake physics-especially for the cases with HHC-and all the dynamics, aerodynamics, modifications of the wake structure and the aero-acoustics coming with it. It is observed that the comprehensive codes used today have a surprisingly good predictive capability when they appropriately account for all of the physics involved. The minimum requirements to obtain these results are outlined.