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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 1,081 records · Page 60
All sourcesNASA NTRSDOE OSTI
NASA NTRS1996

Turbulence Modeling and Computation of Turbine Aerodynamics and Heat Transfer

The objective of the present research is to develop improved turbulence models for the computation of complex flows through turbomachinery passages, including the effects of streamline curvature, heat transfer and secondary flows. Advanced turbulence models are crucial for accurate prediction of rocket engine flows, due to existance of very large extra strain rates, such as strong streamline curvature. Numerical simulation of the turbulent flows in strongly curved ducts, including two 180-deg ducts, one 90-deg duct and a strongly concave curved turbulent boundary layer have been carried out with Reynolds stress models (RSM) and algebraic Reynolds stress models (ARSM). An improved near-wall pressure-strain correlation has been developed for capturing the anisotropy of turbulence in the concave region. A comparative study of two modes of transition in gas turbine, the by-pass transition and the separation-induced transition, has been carried out with several representative low-Reynolds number (LRN) k-epsilon models. Effects of blade surface pressure gradient, freestream turbulence and Reynolds number on the blade boundary layer development, and particularly the inception of transition are examined in detail. The present study indicates that the turbine blade transition, in the presence of high freestream turbulence, is predicted well with LRN k-epsilon models employed. The three-dimensional Navier-Stokes procedure developed by the present authors has been used to compute the three-dimensional viscous flow through the turbine nozzle passage of a single stage turbine. A low Reynolds number k-epsilon model and a zonal k-epsilon/ARSM (algebraic Reynolds stress model) are utilized for turbulence closure. An assessment of the performance of the turbulence models has been carried out. The two models are found to provide similar predictions for the mean flow parameters, although slight improvement in the prediction of some secondary flow quantities has been obtained by the ARSM model. It's found that the wake profiles inside the endwall boundary layers are predicted better than those near the mid-span.

Lakshminarayana, B.↗
DOE OSTI2021

Lowering tomography costs in quantum simulation with a symmetry projected operator basis

Measurement in quantum simulations provides a means for extracting meaningful information from a complex quantum state, and for quantum computing, reducing the complexity of measurement will be vital for near-term applications. For most quantum simulations, the targeted state will obey several symmetries inherent to the system Hamiltonian. Here, we obtain an alternative symmetry projected basis of measurement that reduces the number of measurements needed by a constant factor. Our scheme can be implemented at no additional cost on a quantum computer, can be implemented under various measurement or tomography schemes, and is reasonably resilient under noise.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

Optimizing the performance of reactive molecular dynamics simulations for many-core architectures

Reactive molecular dynamics simulations are computationally demanding. Reaching spatial and temporal scales where interesting scientific phenomena can be observed requires efficient and scalable implementations on modern hardware. In this article, we focus on optimizing the performance of the widely used LAMMPS/ReaxC package for many-core architectures. As hybrid parallelism allows better leverage of the increasing on-node parallelism, we adopt thread parallelism in the construction of bonded and nonbonded lists and in the computation of complex ReaxFF interactions. To mitigate the I/O overheads due to large volumes of trajectory data produced and to save users the burden of post-processing, we also develop a novel in situ tool for molecular species analysis. We analyze the performance of the resulting ReaxC-OMP package on two different architectures: (i) Mira, an IBM Blue Gene/Q system and (ii) Cori-II, a Cray XC-40 sytem with Knights Landing processors. For Pentaerythritol tetranitrate (PETN) systems of sizes ranging from 32 thousand to 16.6 million particles, we observe speedups in the range of 1.5-4.5x. We observe sustained performance improvements for up to 262,144 cores (1,048,576 processes) of Mira and a weak scaling efficiency of 91.5% in large simulations containing 16.6 million particles. Finally, the in situ molecular species analysis tool incurs only insignificant overheads across various system sizes and runs configurations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Dynamical Complexity of Non-Gaussian Many-Body Systems with Dissipation

We characterize the dynamical state of many-body bosonic and fermionic many-body models with intersite Gaussian couplings, on-site non-Gaussian interactions, and local dissipation comprising incoherent particle loss, particle gain, and dephasing. We first establish that, for fermionic systems, if the dephasing noise is larger than the non-Gaussian interactions, irrespective of the Gaussian coupling strength, the system state is a convex combination of Gaussian states at all times. Furthermore, for bosonic systems, we show that if the particle loss and particle gain rates are larger than the Gaussian intersite couplings, the system remains in a separable state at all times. Building on this characterization, we establish that at noise rates above a threshold, there exists a classical algorithm that can efficiently sample from the system state of both the fermionic and bosonic models. Finally, we show that, unlike fermionic systems, bosonic systems can evolve into states that are not convex Gaussian even when the dissipation is much higher than the on-site non-Gaussianity. Similarly, unlike bosonic systems, fermionic systems can generate entanglement even with noise rates much larger than the intersite couplings.

Computational complexity↗
DOE OSTI2021

A Simple Criterion for Feasibility of Heat Integration between Distillation Streams Based on Relative Volatilities

In a multicomponent distillation configuration, there are numerous sources and sinks of heat, and a potential way to reduce the heat duty requirement is to perform heat integration. Unfortunately, an algorithmic search of the optimal heat integration opportunities is intractable when the required temperatures of intermediate streams are computed via complex models. Instead, in this work, we introduce pressure-scaled pseudo relative volatility, a new metric to compare stream temperatures. Here, we justify the use of pseudo relative volatility by proving that this variable is a monotonically increasing function of the liquid fraction in a saturated mixture stream. Using this metric, we derive a shortcut criterion to check the feasibility of various heat integration opportunities, such as thermal coupling via heat transfer (TCH). The advantage of this approach is that it circumvents the need for explicit temperatures and instead relies on composition, component relative volatilities, and pressure—quantities that are readily available in shortcut models for optimization of distillation configurations. Leveraging this fact, we propose a new optimization framework that identifies feasible TCHs that we consider within the formulation while minimizing the total heat duty of a distillation configuration. We demonstrate, on a few examples, that our formulation can identify heat duty efficient configurations, some of which are multieffect configurations. Using this methodology, we discover configurations that are not only simpler than the fully thermally coupled (FTC) configuration but also have a much lower heat duty.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Covalency of M–N Bonds in Isomorphous Lanthanide and Actinide 5-(2-Pyridyl)-1H-tetrazolate Complexes

Experimental and computational analyses of [M(pdtz) 3 (H 2 O) 3 ]·3.5H 2 O (M 3+ = Pu 3+ −Cm 3+ , La 3+ −Nd 3+ , and Sm 3+ −Ho 3+ , pdtz− = 5-(2-pyridyl)-1H-tetrazolate) were conducted to understand potential differences in bonding between lanthanide and actinide complexes with a N-donor ligand. Structural analyses show that the An−N bond distances in the Pu 3+ , Am 3+ , and Cm 3+ complexes are within error of one another. Whereas in the lanthanide series, there is a nearly linear decrease in the Ln−N bond lengths from La 3+ to Ho 3+ (excluding Pm 3+ ). The An−N bond lengths are ∼0.015 Å shorter than their similarly-sized lanthanide analogs, in agreement with computational results that suggest greater covalent character in these bonds versus those with lanthanides. QTAIM analysis indicates that the An−N orbital mixing remains essentially unchanged from Pu 3+ to Cm 3+ , consistent with the nearly identical An−N bond lengths. However, upon deconvolution of the NLMOs into orbital compositions, the metal orbital contributions to An−N bonding decreases slightly overall wherein the 6d involvement remains constant, 7s involvement slightly increases, and 5f participation decreases. The molecular orbital energy diagram indicates that energy degeneracy between the 5f metal and 2p ligand orbitals increases from Pu 3+ to Cm 3+ and counteracts the contraction of the 5f orbtials. Together with prior reports of decreasing energy degeneracy between 5f and 3p orbitals from Np 3+ to Cf 3+ , these observations provide guidance on understanding how chemical bonding evolves in the actinide series.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Factorizations of hyperpower family of iterative methods via least squares approach

A new method for finding proper factorizations of the hyperpower family of iterative methods for computing generalized inverses is proposed. The method is based on numerical optimization of an appropriate least squares problem. An iterative method in a non-normalized form for finding the generalized outer inverses with prescribed range and null space of any given complex matrix is proposed and considered. Computational complexity and convergence of the method are studied. Several numerical experiments are also presented.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Role of Interactions and Correlations on Collective Dynamics of Molecular Motors Along Parallel Filaments

Cytoskeletal motors known as motor proteins are molecules that drive cellular transport along several parallel cytoskeletal filaments and support many biological processes. Experimental evidences suggest that they interact with the nearest molecules of their filament while performing any mechanical work. These interactions modify the microscopic level properties of motor proteins. In this work, a new version of two-channel totally asymmetric simple exclusion process, that incorporates the intra-channel interactions in a thermodynamically consistent way, is proposed. As the existing approaches for multi-channel systems deviate from analyzing the combined effect of inter and intra-channel interactions, a new approach known as modified vertical cluster mean field is developed. The approach along with Monte Carlo simulations successfully encounters some correlations and computes the complex dynamic properties of the system. Role of symmetry of interactions and inter-channel coupling is observed on the phase diagrams, maximal particle current and its corresponding optimal interaction strength. Surprisingly, for all values of coupling rate and most of the interaction splittings, the optimal interaction strength corresponding to maximal current belongs to the case of weak repulsive interactions. Moreover, for weak interaction splittings and with an increase in the coupling rate, the optimal interaction strength tends towards the known experimental results. The effect of coupling as well as interaction energy is also measured for correlations. They are found to be short-range and weaker for repulsive and weak attractive interactions while they are long-range and stronger for large attractions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

DownScaleBench for developing and applying a deep learning based urban climate downscaling- first results for high-resolution urban precipitation climatology over Austin, Texas

Abstract Cities need climate information to develop resilient infrastructure and for adaptation decisions. The information desired is at the order of magnitudes finer scales relative to what is typically available from climate analysis and future projections. Urban downscaling refers to developing such climate information at the city (order of 1 – 10 km) and neighborhood (order of 0.1 – 1 km) resolutions from coarser climate products. Developing these higher resolution (finer grid spacing) data needed for assessments typically covering multiyear climatology of past data and future projections is complex and computationally expensive for traditional physics-based dynamical models. In this study, we develop and adopt a novel approach for urban downscaling by generating a general-purpose operator using deep learning. This ‘DownScaleBench’ tool can aid the process of downscaling to any location. The DownScaleBench has been generalized for both in situ (ground- based) and satellite or reanalysis gridded data. The algorithm employs an iterative super-resolution convolutional neural network (Iterative SRCNN) over the city. We apply this for the development of a high-resolution gridded precipitation product (300 m) from a relatively coarse (10 km) satellite-based product (JAXA GsMAP). The high-resolution gridded precipitation datasets is compared against insitu observations for past heavy rain events over Austin, Texas, and shows marked improvement relative to the coarser datasets relative to cubic interpolation as a baseline. The creation of this Downscaling Bench has implications for generating high-resolution gridded urban meteorological datasets and aiding the planning process for climate-ready cities.

Singh, Manmeet (ORCID:0000000233747149)↗
DOE OSTI2017

Average subentropy, coherence and entanglement of random mixed quantum states

Compact expressions for the average subentropy and coherence are obtained for random mixed states that are generated via various probability measures. Surprisingly, our results show that the average subentropy of random mixed states approaches the maximum value of the subentropy which is attained for the maximally mixed state as we increase the dimension. In the special case of the random mixed states sampled from the induced measure via partial tracing of random bipartite pure states, we establish the typicality of the relative entropy of coherence for random mixed states invoking the concentration of measure phenomenon. Our results also indicate that mixed quantum states are less useful compared to pure quantum states in higher dimension when we extract quantum coherence as a resource. This is because of the fact that average coherence of random mixed states is bounded uniformly, however, the average coherence of random pure states increases with the increasing dimension. As an important application, we establish the typicality of relative entropy of entanglement and distillable entanglement for a specific class of random bipartite mixed states. In particular, most of the random states in this specific class have relative entropy of entanglement and distillable entanglement equal to some fixed number (to within an arbitrary small error), thereby hugely reducing the complexity of computation of these entanglement measures for this specific class of mixed states.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

The F-t-Pj-RG method: An adjacent-rolling-windows based steady-state detection technique for application to kinetic Monte Carlo simulations

A window-based steady-state detection algorithm has been developed for application to kinetic Monte Carlo simulation data. The algorithm, termed F-t-Pj-RG sequentially applies an F-test, a t-test, and a projection test on adjacent windows of the data while rolling (or shifting) and growing the windows when any of the tests fail. In aggregate, the algorithm is able to (a) automatically reject the warm-up period as not being at steady-state, as well as (b) determine an appropriate window size for converged statistics when sampling the data, which is necessary for detection of steady-state, and (c) detect steady-state within a particular tolerance. The last step, the projection test, is actually an oscillating-slope projection test, and is performed on j sequential data windows (i.e., more than two adjacent windows). It requires more than simply being within the user defined tolerance: the oscillating-slope projection test includes a condition that the slope must oscillate around zero when 2, which is an additional indication of steady-state. When all three tests are passed, the F-t-Pj test is passed, indicating that the prerequisites of steady-state detection have been met and also that conditions consistent with the definition of steady-state have been realized. This algorithm is applied to a variety of data sets that correspond to the diverse type of data trends that can be produced by kinetic Monte Carlo simulations. The algorithm is shown to be robust in its ability to handle differing functional forms, and is able to detect steady-state with low computational cost. Finally, the low computational cost of this method and its robustness towards varied data trends make it suitable for on-the-fly use in kinetic Monte Carlo simulations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Harnessing distributed GPU computing for generalizable graph convolutional networks in power grid reliability assessments

Although machine learning (ML) has emerged as a powerful tool for rapidly assessing grid contingencies, prior studies have largely considered a static grid topology in their analyses. This limits their application, since they need to be re-trained for every new topology. Here, this paper explores the development of generalizable graph convolutional network (GCN) models by pre-training them across a range of grid topologies and contingency types. We found that a GCN model with auto-regressive moving average (ARMA) layers with a line graph representation of the grid offered the best predictive performance in predicting voltage magnitudes (VM) and voltage angles (VA). We introduced the concept of phantom nodes to consider disparate grid topologies with a varying number of nodes and lines. For pre-training the GCN ARMA model across a variety of topologies, distributed graphics processing unit (GPU) computing afforded us significant training scalability. The predictive performance of this model on grid topologies that were part of the training data is substantially better than the direct current (DC) approximation. Although direct application of the pre-trained model to topologies that are not part of the grid is not particularly satisfactory, fine-tuning with small amounts of data from a specific topology of interest significantly improves predictive performance. In general, this paper highlights the feasibility of training large-scale GNN models to assess the reliability of power grids by considering a wide variety of grid topologies and contingency types. With the advent of foundational models in ML and the exponential increase in GPU computing clusters, generalizable ML models will significantly enhance how utilities manage power systems and make decisions in real-time or near-real-time.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

Thermodynamic, Spectroscopic, and Computational Studies of f -Element Complexation by N -Hydroxyethyl-diethylenetriamine- N,N ', N ", N "-tetraacetic Acid

Potentiometric and spectroscopic techniques were combined with DFT calculations to probe the coordination environment and determine thermodynamic features of trivalent f-element complexation by N-hydroxyethyl-diethylenetriamine-N,N',N",N"-tetraacetic acid, HEDTTA. Ligand protonation constants and lanthanide stability constants were determined using potentiometry. Five protonation constants were accessible in I = 2.0 M (H + /Na + )ClO 4 . UV–vis spectroscopy was used to determine stability constants for Nd 3+ and Am 3+ complexation with HEDTTA. Luminescence spectroscopy indicates two water molecules in the inner coordination sphere of the Eu/HEDTTA complex, suggesting HEDTTA is heptadentate. Luminescence data was supported by DFT calculations, which demonstrate that substitution of the acetate pendant arm by a N-hydroxyethyl group weakens the metal–nitrogen bond. This bond elongation is reflected in HEDTTA’s ability to differentiate trivalent actinides from trivalent lanthanides. The trans-lanthanide Ln/HEDTTA complex stability trend is analogous to Ln/DTPA complexation; however, the loss of one chelate ring resulting from structural substitution weakens the complexation by ~3 orders of magnitude. Successful separation of trivalent americium from trivalent lanthanides was demonstrated when HEDTTA was utilized as aqueous holdback complexant in a liquid–liquid system. Time-dependent extraction studies for HEDTTA were compared to diethylenetriamine-N,N,N',N",N"-pentaacetic acid (DTPA) and N-hydroxyethyl-ethylenediamine-N,N',N'-triacetic acid (HEDTA). The results presented here indicate substantially enhanced phase-transfer kinetic rates for mixtures containing HEDTTA.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2020

Biased Spin-State Energetics of Fe(II) Molecular Complexes within Density-Functional Theory and the Linear-Response Hubbard U Correction

The spin-state energetics of six Fe(II) molecular complexes are computed using the linear-response Hubbard U approach within DFT. The adiabatic energy differences, ΔE H-L , between the high-spin (S = 2) and the low-spin (S = 0) states are computed and compared with accurate-coupled cluster-corrected CASPT2 results. We show that DFT+U fails in correctly capturing the ground state for strong-field ligands yielding ΔE H-L that are almost constant throughout the molecular series. This bias toward high spin together with the metal/ligand charge transfer upon U correction are here quantified and explained using molecular orbital diagrams involving both σ- and π-bonding interactions. Furthermore, with increasing ligand-field strengths this bias also increases owing to the stronger molecular character of the metal/ligand Kohn–Sham orbitals thus resulting in large deviations from the reference larger than 4 eV. Smaller values of U can be employed to mitigate this effect and recover the right energetics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Combining Pseudopotential and All Electron Density Functional Theory for the Efficient Calculation of Core Spectra Using a Multiresolution Approach

Broadly speaking, the calculation of core spectra such as electron energy loss spectra (EELS) at the level of density functional theory (DFT) usually relies on one of two approaches: conceptually more complex but computationally efficient projector augmented wave based approaches or more straightforward but computationally more intensive all electron (AE) based approaches. In this work we present an alternative method, which aims to find a middle ground between the two. Specifically, we have implemented an approach in the multiwavelet MADNESS molecular DFT code that permits a combination of atoms treated at the AE and pseudopotential (PSP) level. Atoms for which one wishes to calculate the core edges are thus treated at an AE level, while the remainder can be treated at the PSP level. This is made possible thanks to the multiresolution approach of MADNESS, which permits accurate and efficient calculations at both the AE and PSP level. Through examples of a small molecule and a carbon nanotube, this study demonstrates the potential applications of our approach.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗