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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Bayesian calibration of strength model parameters from Taylor impact data

Materials strength plays a key role in determining the mechanical response of engineered structures. As such, accurate strength models are crucial in simulations involving complex loading conditions, particularly when deformation in the plastic regime is deemed important. In this work, a Gaussian process based surrogate for a finite element simulation of a Taylor impact test is developed and used for Bayesian calibration of the Preston–Tonks–Wallace strength model. Here, the surrogate model is shown to closely approximate the salient features of the Taylor cylinder deformation and is validated against simulation output before being used in the strength model calibration routine. The results show that Taylor impact test data can be used in the calibration of constitutive equations through the use of a combination of data science techniques, namely Gaussian processes and Bayesian inference.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Taylor-Expansion-Based Robust Power Flow in Unbalanced Distribution Systems: A Hybrid Data-Aided Method

Traditional power flow methods often adopt certain assumptions designed for passive balanced distribution systems, thus lacking practicality for unbalanced operation. moreover, their computation accuracy and efficiency are heavily subject to unknown errors and bad data in measurements or prediction data of distributed energy resources (ders). to address these issues, this paper proposes a hybrid data-aided robust power flow algorithm in unbalanced distribution systems, which combines taylor series expansion knowledge with a data-driven regression technique. the proposed method initiates a linearization power flow model to derive an explicitly analytical solution by modified taylor expansion. to mitigate the approximation loss that surges due to the der integration and bad data, we further develop a data-aided robust support vector regression approach to estimate the errors efficiently. comparative analysis in the 13-bus and 123-bus ieee unbalanced feeders shows that the proposed hybrid algorithm achieves superior computational efficiency, with guaranteed accuracy and robustness against outliers.

data-driven↗
DOE OSTI2020

Heat Loss Correction Factor for Fireball Yield Measurements

Fireball yield calculations are performed using Taylor’s radius equation. However, at early times, the x-ray diffusion process has a significant effect on the size of the fireball, and at late times the shock wave degenerates into an acoustic wave. Thus, earlier research determined that the range of validity for Taylor’s equation was in the scaled time regime of 0.004–0.008 s. So, when performing a yield calculation, the only values that are analyzed are those that lie within that range of validity. While Taylor’s equations offer an approximation of the scaled yield, they also rely on several assumptions. For example, the equations assume that the shock wave is adiabatic, when, in fact, the first light pulse on the nuclear test films, which occurs as the shock wave is forming, suggests that the shock wave is not adiabatic. Furthermore, the amount of heat loss from the shock wave that occurs prior to the scaled time regime of 0.004–0.008 s can change significantly as a function of air density. This may cause the shock wave radius to be smaller relative to the assumed adiabatic shock wave. Therefore, the objective of this project was to determine the correction factor for heat transfer, which should be applied in order to produce a more accurate approximation of the weapon’s yield.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2020

A Comparison of Interface Growth Models Applied to Rayleigh–Taylor and Richtmyer–Meshkov Instabilities

Sophisticated numerical models that contain fluid interfaces rely upon interface evolution models to approximate the transition to turbulence near interfaces, in the presence of Rayleigh–Taylor (RTI) or Richtmyer–Meshkov (RMI) instability. Semi-analytical models have been developed in recent decades to predict the interface growth from an initial state into the nonlinear regime. Two of these models are considered in this study. They are the Goncharov and the z-models. Both of these interface models have strengths and weaknesses, which are examined here. Both of them have been implemented in the xRAGE compressible flow solver, which models fluid interfaces. The flow solver provides the fluids acceleration as a body force to the interface model. The purpose of such interface model is to evolve the early times of interface position as a subgrid model within a compressible flow simulation in order to then initialize a turbulence model. In this work, the interface models are assessed and compared for their evolution of RTI and RMI. The z-model performed better than the Goncharov model for all cases that were explored.

42 ENGINEERING↗
DOE OSTI2020

Efficient cosmological analysis of the SDSS/BOSS data from the Effective Field Theory of Large-Scale Structure

The precision of the cosmological data allows us to accurately approximate the predictions for cosmological observables by Taylor expanding up to a low order the dependence on the cosmological parameters around a reference cosmology. By applying this observation to the redshift-space one-loop galaxy power spectrum of the Effective Field Theory of Large-Scale Structure, we analyze the BOSS DR12 data by scanning over all the parameters of ΛCDM cosmology with massive neutrinos. Furthermore, we impose several sets of priors, the widest of which is just a Big Bang Nucleosynthesis prior on the current fractional energy density of baryons, Ω b h 2 , and a bound on the sum of neutrino masses to be less than 0.9 eV. In this case we measure the primordial amplitude of the power spectrum, A s , the abundance of matter, Ω m , the Hubble parameter, H 0 , and the tilt of the primordial power spectrum, n s , to about 19%, 5.7%, 2.2% and 7.3% respectively, obtaining ln (10 10 A s )=2.91± 0.19, Ω m =0.314± 0.018, H 0 =68.7± 1.5 km/(s Mpc) and n s =0.979± 0.071 at 68% confidence level.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Uncertainty Quantification Enabled by Automatic Differentiation for Hydrodynamic Simulation of Shock‐to‐Detonation Transition in High Explosives

Quantifying the effects of uncertainty in a reactive burn model on the run-to-detonation time in high explosives (HEs) provides a robust methodology for assessing the probability of an HE failing the IHE qualification standard. Moreover, uncertainty quantification helps evaluate whether the model calibration accurately represents data outside the calibration set. This study uses a specialized hydrodynamic simulation code for modeling detonation to determine the run-to-detonation time of the HE PBX 9502 for various impact velocities. To quickly approximate uncertainties in the model, a surrogate was constructed using a Taylor series expansion centered at the mean of the input parameters. To obtain the sensitivities required for constructing the Taylor series, HYP-percomplex Automatic Differentiation (HYPAD) was implemented. HYPAD is a methodology for infusing existing codes with automatic differentiation capabilities by augmenting variables with one or more imaginary units to compute step-size independent partial derivatives. These derivatives are accurate to machine precision with respect to the implemented numerical algorithm, meaning their accuracy reflects that of the underlying method (e.g., integration or discretization schemes). Using reduced order modeling techniques, the mean and standard deviation of the run-to-detonation time of a shock within PBX 9502 were computed for a number of initial impact velocities. A weighted least squares regression was then performed to obtain a best fit curve and prediction interval for the computed statistics. Historical data points from explosively driven wedge tests were utilized to validate the prediction interval, ensuring its reliability in predicting future outcomes. With this prediction interval and a known safety constraint curve, the most probable point of failure and the probability of failure for the HE PBX 9502 were determined.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

TANTE: Time-adaptive operator learning via neural Taylor expansion

Operator learning for time-dependent partial differential equations (PDEs) has seen rapid progress in recent years, enabling efficient approximation of complex spatiotemporal dynamics. However, most existing methods rely on fixed time step sizes during rollout, which limits their ability to adapt to varying temporal complexity and often leads to error accumulation. In this work, we propose the Time-Adaptive Transformer with Neural Taylor Expansion (TANTE), a novel operator-learning framework that produces continuous-time predictions with adaptive step sizes. TANTE predicts future states by performing a Taylor expansion at the current state, where neural networks learn both the higher-order temporal derivatives and the local radius of convergence. This allows the model to dynamically adjust its rollout based on the local behavior of the solution, thereby reducing cumulative error and improving computational efficiency. We demonstrate the effectiveness of TANTE across a wide range of PDE benchmarks, achieving superior accuracy and adaptability compared to fixed-step baselines, delivering accuracy gains of 60-80 % and speed-ups of 30-40 % at inference time.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Downscaled hyper-resolution (400 m) gridded datasets of daily precipitation and temperature (2008–2019) for the East–Taylor subbasin (western United States)

Abstract. High-resolution gridded datasets of meteorological variables are needed in order to resolve fine-scale hydrological gradients in complex mountainous terrain. Across the United States, the highest available spatial resolution of gridded datasets of daily meteorological records is approximately 800 m. This work presents gridded datasets of daily precipitation and mean temperature for the East–Taylor subbasin (in the western United States) covering a 12-year period (2008–2019) at a high spatial resolution (400 m). The datasets are generated using a downscaling framework that uses data-driven models to learn relationships between climate variables and topography. We observe that downscaled datasets of precipitation and mean temperature exhibit smoother spatial gradients (while preserving the spatial variability) when compared to their coarser counterparts. Additionally, we also observe that when downscaled datasets are upscaled to the original resolution (800 m), the mean residual error is almost zero, ensuring no bias when compared with the original data. Furthermore, the downscaled datasets are observed to be linearly related to elevation, which is consistent with the methodology underlying the original 800 m product. Finally, we validate the spatial patterns exhibited by downscaled datasets via an example use case that models lidar-derived estimates of snowpack. The presented dataset constitutes a valuable resource to resolve fine-scale hydrological gradients in the mountainous terrain of the East–Taylor subbasin, which is an important study area in the context of water security for the southwestern United States and Mexico. The dataset is publicly available at https://doi.org/10.15485/1822259 (Mital et al., 2021).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Simplifying activations with linear approximations in neural networks

A key step in Neural Networks is activation. Among the different types of activation functions, sigmoid, tanh, and others involve the usage of exponents for calculation. From a hardware perspective, exponential implementation implies the usage of Taylor series or repeated methods involving many addition, multiplication, and division steps, and as a result are power-hungry and consume many clock cycles. We implement a piecewise linear approximation of the sigmoid function as a replacement for standard sigmoid activation libraries. This approach provides a practical alternative by leveraging piecewise segmentation, which simplifies hardware implementation and improves computational efficiency. In this paper, we detail piecewise functions that can be implemented using linear approximations and their implications for overall model accuracy and performance gain. Our results show that for the DenseNet, ResNet, and GoogLeNet architectures, the piecewise linear approximation of the sigmoid function provides faster execution times compared to the standard TensorFlow sigmoid implementation while maintaining comparable accuracy. Specifically, for MNIST with DenseNet, accuracy reaches 99.91% (Piecewise) vs. 99.97% (Base) with up to 1.31x speedup in execution time. For CIFAR-10 with DenseNet, accuracy improves to 98.97% (Piecewise) vs. 99.40% (Base) while achieving 1.24x faster execution. Similarly, for CIFAR-100 with DenseNet, the accuracy is 97.93% (Piecewise) vs. 98.39% (Base), with a 1.18x execution time reduction. These results confirm the proposed method’s capability to efficiently process large-scale datasets and computationally demanding tasks, offering a practical means to accelerate deep learning models, including LSTMs, without compromising accuracy.

Activation function↗
DOE OSTI2021

Connection Between Mass Flux Transport and Eddy Diffusivity in Convective Atmospheric Boundary Layers

Turbulence parameterizations for convective boundary layer in coarse-scale atmospheric models usually consider a combination of the eddy-diffusive transport and a non-local transport, typically in the form of a mass flux term, such as the widely adopted eddy-diffusivity mass-flux (EDMF) approach. These two types of turbulent transport are generally considered to be independent of each other. Using results from large-eddy simulations, here, we show that a Taylor series expansion of the updraft and downdraft mass-flux transport can be used to approximate the eddy-diffusivity transport in the atmospheric surface layer and the lower part of the mixed layer, connecting both eddy-diffusivity and mass-flux transport theories in convective conditions, which also quantifies departure from the Monin-Obukhov similarity (MOS) in the surface layer. This study provides a theoretical support for a unified EDMF parameterization applied to both the surface layer and mixed layer and highlights important correction required for surface models relying on MOS

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A New Analytical Expression for Estimating the Adiabatic Temperature Rise in Lithium-Ion Batteries During High-Power Pulses

A coupled, thermal-electrochemical model is used to explain why the Joule heating assumption (i.e., I 2 R) does not provide a good representation of the temperature rise during high power pulses in lithium-ion batteries, even in cases where the reversible heat generation can be neglected. The poor agreement occurs because the internal resistance changes during the pulse due to the opposing effects of mass transport in the electrolyte, which raises the resistance, and heat generation (temperature rise), which lowers the resistance. These insights are used to propose a new analytical expression for predicting the temperature rise during adiabatic pulses with limited experimental and physical data. The expression accounts for thermal effects using a Taylor series expansion of an Arrhenius-type equation. It accounts for transport effects using an approximate solution to the one-dimensional diffusion equation. The new expression is shown to accurately estimate the simulated, adiabatic temperature rise across a range of loadings (1 to 4 mAh cm -2 ) and C-rates (1C to 10C) for cells containing LiNi 0.5 Mn 0.3 Co 0.2 O 2 positive electrodes and graphite negative electrodes. In conclusion, it is also shown to accurately estimate the experimental, adiabatic temperature rise measured for cells with LiFePO 4 positive electrodes and mesocarbon negative electrodes with minimal changes to the fitted parameters.

25 ENERGY STORAGE↗
DOE OSTI2025

Development, Verification, and Validation of an OpenFOAM-Based Solver for Modeling Inertial Fusion Energy Chambers

Our work seeks to introduce a computational tool tailored to the physics of inertial fusion energy chambers, in particular, those concepts based on thick liquid walls. In this approach, the structural materials are protected by several neutron mean-free-paths of renewable liquid and thus will be able to survive much longer than un-shielded walls, with virtually all structures lasting for the life of the plant and enabling the use of commercially available and qualified materials. The OpenFOAM-based solver named rhoCentralFoam has been used as a starting point. rhoCentralFoam belongs to the standard OpenFOAM solver toolset. It is a high-speed, explicit compressible flow solver with shock-capturing capability. While the main features have been retained, the solver had to be restructured to make use of tabular data for equations of states, a necessary addition to model the complex thermo-physical properties of ionized gasses. This entailed the need to change the independent state variables used by the solver, resulting in a new thermodynamic library and slightly different solution algorithm. Moreover, a radiation heat transfer model based on the P-1 approximation was added to the solver. The solver is verified against an analytical solution from the Sedov-Taylor-Neumann test problem to showcase the ability of the hydrodynamic solvers to handle strong shocks, whereas the P-1 model was verified using a simple one-dimensional problem with an analytical solution. Additionally, a validation case involving shock-wave propagation through jet array is presented, and the results are compared with experimental data from the open literature. Lastly, in order to showcase the utility of the solver for practical cases, we applied the refined solver to two representative scenarios: gas venting within the HYLIFE-II chamber and the compression of the gas following the partial ablation of the liquid wall.

Chamber dynamics↗
DOE OSTI2023

Measuring scale-dependent shape anisotropy by coarse-graining: Application to inhomogeneous Rayleigh-Taylor turbulence

Here, we generalize the “filtering spectrum” to probe scales along different directions by spatial coarse-graining. This multidimensional filtering spectrum quantifies the spectral content of flows that are not necessarily homogeneous. From multidimensional spectral information, we propose a simple metric for shape anisotropy at various scales. The method is applied to simulations of two-dimensional (2D) and 3D Rayleigh-Taylor (RT) turbulence, which is inhomogeneous and anisotropic. We show that 3D RT has clear shape anisotropy at large scales with approximately 4:3 vertical to horizontal aspect ratio but tends toward isotropy at small scales as expected. In sharp contrast, we find that RT in 2D simulations, which are still the main modeling framework for many applications, is isotropic at large scales and its shape anisotropy increases at smaller scales where structures tend to be horizontally elongated. While this may be surprising, it is consistent with recent results that large-scale isotropy in 2D RT is due to the generation of a large-scale overturning circulation via an upscale cascade, while small-scale anisotropy is due to the stable stratification resultant from such overturning and the inefficient mixing in 2D.

42 ENGINEERING↗
DOE OSTI2020

A passive model for the evolution of subgrid-scale instabilities in turbulent flow regimes

In this work, a zero-dimensional model for the development of turbulent mixing layers driven by Rayleigh–Taylor (RT) or Richtmyer–Meshkov (RM) instabilities is presented, for application in Reynolds-Averaged Navier–Stokes (RANS) simulations. The model approximates the behavior of the turbulent mixing layer at a passively advected tracer particle that sits on the material interface of interest in the simulation, and is used to provide initial conditions for the RANS model once the mixing layer width has developed to a size that may be resolved on the computational mesh. The model relies on approximations to the Besnard–Harlow–Rauenzahn (BHR) RANS closure equations and is thus consistent the turbulent development of the mixing layer, unlike previous models of this type which have focused on the early-time laminar development of RT or RM instabilities. These instabilities will often grow through an initially laminar state before developing into turbulence, and criteria for switching from an existing laminar model to the proposed turbulent model are considered. The proposed model reproduces the mixing layer development of RT and RM instabilities seen in mixed width resolving BHR RANS simulations to a reasonable degree of accuracy, and relaxes the need for the RANS simulation to resolve the mixed width as soon as the mixing layer becomes turbulent, which may be impractical in cases where the mixing layer grows through a wide range of scales.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Effects of Strain and Strain Rate on Dynamic Grain Growth and Subgrain Evolution During Plastic Deformation of an Interstitial-Free Steel at 850 ° C

Here, the effects of strain and strain rate on dynamic grain growth (DGG) and subgrain evolution are reported for an interstitial-free steel deformed at 850 ° C. Microstructures produced during tension tests at true-strain rates of 10 -4 and to 10 -3 s -1 true strains ranging from 0.02 to 0.2 were preserved following deformation. These were characterized using electron backscatter diffraction (EBSD), including the application of spherical harmonic transform indexing to produce high-angular-resolution EBSD (HR-EBSD) data. HR-EBSD data resolved the small misorientation angles of subgrain boundaries while imaging much larger data fields than possible with previously available techniques. The resulting data confirmed that steady-state flow stress is inversely proportional to the average subgrain size and that subgrain boundary misorientation angle increases with strain. The following new observations are reported. The rate of DGG increased with respect to time but decreased with respect to strain as strain rate increased. This behavior is rationalized through a simple model using separate rate parameters for the effects of time and strain. Subgrain size was not constant during steady-state deformation, but decreased slowly with increasing strain. Subgrain size distributions and subgrain boundary misorientation angle distributions were measured, and both remained approximately log-normal during steady-state deformation. Subgrain evolution demonstrated no dependence on parent grain size, crystallographic orientation, or Taylor factor. These new data suggest that steady-state flow stress is more likely controlled by the dislocation density internal to subgrains than by the spacing between subgrain boundaries.

dynamic grain growth↗
DOE OSTI2024

Adaptive Power Flow Approximations With Second-Order Sensitivity Insights

The power flow equations are fundamental to power system planning, analysis, and control. However, the inherent non-linearity and non-convexity of these equations present formidable obstacles in problem-solving processes. To mitigate these challenges, recent research has proposed adaptive power flow linearizations that aim to achieve accuracy over wide operating ranges. The accuracy of these approximations inherently depends on the curvature of the power flow equations within these ranges, which necessitates considering second-order sensitivities. In this paper, we leverage second-order sensitivities to both analyze and improve power flow approximations. We evaluate the curvature across broad operational ranges and subsequently utilize this information to inform the computation of various sample-based power flow approximation techniques. Additionally, we leverage second-order sensitivities to guide the development of rational approximations that yield linear constraints in optimization problems. In conclusion, this approach is extended to enhance accuracy beyond the limitations of linear functions across varied operational scenarios.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Computing dissipation for molecular-level turbulence simulations

A major difficulty in the analysis of molecular-level simulations is that macroscopic flow quantities are inherently noisy due to molecular fluctuations. An important example for turbulent flows is the kinetic energy dissipation rate. Traditionally, this quantity is calculated from gradients of the macroscopic velocity field, which exacerbates the noise problem. The inability to accurately compute the dissipation rate makes meaningful comparison of molecular-level and continuum simulation results a serious challenge. Herein, we extend previously developed coarse-graining theories to derive an exact molecular-level expression for the dissipation rate, which would circumvent the need to compute gradients of noisy fields. Although the exact expression cannot feasibly be implemented in Sandia’s direct simulation Monte Carlo (DSMC) code SPARTA, we utilize an approximate “hybrid” approach and compare it to the conventional gradient-based approach for planar Couette flow and the two-dimensional Taylor-Green vortex, demonstrating that the hybrid approach is significantly more accurate. Finally, we explore the possibility of adopting a Lagrangian approach to calculate the energy dissipation rate.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2022

Thermodynamically consistent versions of approximations used in modelling moist air

Abstract Some existing approaches to modelling the thermodynamics of moist air make approximations that break thermodynamic consistency , such that the resulting thermodynamics does not obey the first and second laws or has other inconsistencies. Recently, an approach to avoid such inconsistency has been suggested: the use of thermodynamic potentials in terms of their natural variables , from which all thermodynamic quantities and relationships (equations of state) are derived. In this article, we develop this approach for unapproximated moist‐air thermodynamics and two widely used approximations: the constant‐ approximation and the dry heat capacities approximation. The (consistent) constant‐ approximation is particularly attractive because it leads to, with the appropriate choice of thermodynamic variable, adiabatic dynamics that depend only on total mass and are independent of the breakdown between water forms. Additionally, a wide variety of material from different sources in the literature on thermodynamics in atmospheric modelling is brought together. It is hoped that this article provides a comprehensive reference for the use of thermodynamic potentials in atmospheric modelling, especially for the three systems considered here.

54 ENVIRONMENTAL SCIENCES↗