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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
NASA NTRS1985

Solution of partial differential equations on vector and parallel computers

The present status of numerical methods for partial differential equations on vector and parallel computers was reviewed. The relevant aspects of these computers are discussed and a brief review of their development is included, with particular attention paid to those characteristics that influence algorithm selection. Both direct and iterative methods are given for elliptic equations as well as explicit and implicit methods for initial boundary value problems. The intent is to point out attractive methods as well as areas where this class of computer architecture cannot be fully utilized because of either hardware restrictions or the lack of adequate algorithms. Application areas utilizing these computers are briefly discussed.

Ortega, J. M.↗
NASA NTRS1993

Spectrum formation in supernovae - Numerical techniques

The study combines several novel techniques for spectrum simulation in the Eddington computer program which solves the comoving frame equation of transfer coupled with the statistical and radiative equilibrium equations. One of these is a generalization of the accelerated lambda iteration (ALI) scheme to include an approximate frequency-derivative operator. This greatly enhances the convergence rate of ALI in optically thick, high-velocity shear flows. Another is a partial linearization technique which is capable of efficiently solving a very large number of rate equations on a moderately sized computer. An expansion opacity and emissivity approximation is derived which makes it possible to determine the effect on the transfer and statistical equilibrium of a very large number of lines not explicitly represented in the frequency grid and additionally to treat line-blanketing from species not explicitly included in the rate equations. The utility of these techniques is illustrated with models of two supernovae.

Eastman, Ronald G.↗
DOE OSTI2022

On the energy landscape of symmetric quantum signal processing

Symmetric quantum signal processing provides a parameterized representation of a real polynomial, which can be translated into an efficient quantum circuit for performing a wide range of computational tasks on quantum computers. For a given polynomial f , the parameters (called phase factors) can be obtained by solving an optimization problem. However, the cost function is non-convex, and has a very complex energy landscape with numerous global and local minima. It is therefore surprising that the solution can be robustly obtained in practice, starting from a fixed initial guess Φ 0 that contains no information of the input polynomial. To investigate this phenomenon, we first explicitly characterize all the global minima of the cost function. We then prove that one particular global minimum (called the maximal solution) belongs to a neighborhood of Φ 0 , on which the cost function is strongly convex under the condition ‖ f ‖ ∞ = O ( d − 1 ) with d = d e g ( f ) . Our result provides a partial explanation of the aforementioned success of optimization algorithms.

Wang, Jiasu↗
NASA NTRS2016

Prediction of Weather Impacts on Airport Arrival Meter Fix Capacity

This paper introduces a data driven model for predicting airport arrival capacity with a look-ahead time 2-8 hour forecast. The model is suitable for air traffic flow management by explicitly investigating the impact of convective weather on airport arrival meter fix throughput. Estimation of the arrival airport capacity under arrival meter fix flow constraints due to severe weather is an important part of Air Traffic Management (ATM). Airport arrival capacity can be reduced if one or more airport arrival meter fixes are partially or completely blocked by convective weather. When the predicted airport arrival demands exceed the predicted available airport's arrival capacity for a sustained period, Ground Delay Program (GDP) operations will be triggered by ATM system. Serious imbalances between demand and capacity occur most frequently when the airport capacity is severely degraded due to either bad airport terminal surface weather or inclement convective weather around airport arrival fixes. A model that predicts the weather-impacted airport arrival meter fix throughput may help ATM personnel to plan GDP operations more efficiently. This paper identifies the characteristics of air traffic flow across arrival meter fixes at Newark Liberty International Airport (EWR). The proposed approach, based on machine-learning methods, is developed to predict the weather impacted EWR arrival Meter Fix (MF) throughput. Sector forecast coverage is used to envision the weather impact on airport arrival MF flow, and the validation is accomplished by using Convective Weather Avoidance Model (CWAM) 0.5 to 2-hour and Collaborative Convective Forecast Product (CCFP) 4 to 8-hour look-ahead forecast data for the period of April-September in 2014. Furthermore, the regression tree ensemble learning of random forests approach for translating a sector forecast coverage model to an EWR arrival meter fix throughput model is examined. The results suggest that ATM decision makers in charge of MF flow control and GDP planning may benefit from adopting the airport arrival meter capacity prediction models to estimate the inclement weather impacts.

Wang, Yao X.↗
DOE OSTI2023

QuadConv: Quadrature-based convolutions with applications to non-uniform PDE data compression

We present a new convolution layer for deep learning architectures which we call QuadConv — an approximation to continuous convolution via quadrature. Our operator is developed explicitly for use on non-uniform, mesh-based data, and accomplishes this by learning a continuous kernel that can be sampled at arbitrary locations. Moreover, the construction of our operator admits an efficient implementation which we detail and construct. As an experimental validation of our operator, we consider the task of compressing partial differential equation (PDE) simulation data from fixed meshes. Here, we show that QuadConv can match the performance of standard discrete convolutions on uniform grid data by comparing a QuadConv autoencoder (QCAE) to a standard convolutional autoencoder (CAE). Further, we show that the QCAE can maintain this accuracy even on non-uniform data. In both cases, QuadConv also outperforms alternative unstructured convolution methods such as graph convolution.

Compression↗
DOE OSTI2023

Mechanistic Insights into Aldehyde Production from Electrochemical CO 2 Reduction on CuAg Alloy via Operando X-ray Measurements

CO 2 electrolysis converts the greenhouse gas CO 2 into valuable fuels and chemicals, such as carbon monoxide, ethylene, ethanol, etc. Currently, Cu is the only known monometallic catalyst capable of producing multicarbon products from electrochemical CO 2 reduction reaction (eCO2RR), while the poor selectivity limits its further use. It has been found that introducing Ag atoms into the Cu lattice can modulate product preference. However, the synergistic effects between Cu and Ag, and thus, the catalytic performance, are strongly influenced by catalyst morphology, electrolyzer configuration, reaction conditions, etc. Operando measurements can provide explicit information on the catalyst dynamic variation during the reaction, but their operation and analysis are challenging. Herein, we prepared CuAg multiphase alloy catalysts by magnetron sputtering, which allowed for investigating the intrinsic interaction between Cu and Ag. eCO2RR performance exhibited an improved selectivity toward carbonyls at the expense of hydrogen and hydrocarbons. The partially alloyed Cu and Ag phases were confirmed by operando X-ray diffraction. By means of combining operando X-ray measurements and density functional theory (DFT) calculations, the preferred carbonyl production is attributed to the reduced electron density and compressive strain of Cu due to Ag incorporation, which leads to a deeper d-band center and therefore weakened intermediate adsorption and oxophilicity. In conclusion, this work provides evidence of the intrinsic structural and electronic interaction between Cu and Ag during eCO2RR. The obtained information will facilitate the design of bi/multi-phase metallic or alloy electrocatalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1991

Performance of hashed cache data migration schemes on multicomputers

After conducting an examination of several data-migration mechanisms which permit an explicit and controlled mapping of data to memory, a set of schemes for storage and retrieval of off-processor array elements is experimentally evaluated and modeled. All schemes considered have their basis in the use of hash tables for efficient access of nonlocal data. The techniques in question are those of hashed cache, partial enumeration, and full enumeration; in these, nonlocal data are stored in hash tables, so that the operative difference lies in the amount of memory used by each scheme and in the retrieval mechanism used for nonlocal data.

Hiranandani, Seema↗
DOE OSTI2025

Generalized entropy of gravitational fluctuations

The corrections to holographic entanglement entropy from bulk quantum fields in a classical gravitational background are now well understood. They lead, in particular, to unitary Page curves for evaporating black holes. However, the correct treatment of quantum fluctuations of the metric, including graviton excitations, is a longstanding problem. We provide a gauge-invariant prescription for the generalized entropy of gravitons in anti-de Sitter space in terms of areas and bulk entanglement entropy, generalizing the quantum extremal surface prescription to accommodate fluctuations in the semiclassical spacetime geometry. This task requires a careful treatment of the area operator on the graviton Hilbert space and the definition of a “quantum extremal gauge” in which the extremal surface is unperturbed. It also requires us to determine the correct vacuum modular Hamiltonian for the graviton field, which we fix by requiring that it doesn’t contain a boundary term in extremal gauge. We check our prescription with an explicit computation of the vacuum-subtracted generalized entropy of states containing a graviton in an AdS-Rindler background. Our results exactly match vacuum-subtracted von Neumann entropies for stress-tensor excited states in holographic conformal field theory with d > 2 dimensions. We also use covariant phase space techniques to give a partial proof of our prescription when the entanglement wedge for the background spacetime has a bifurcate Killing horizon. Along the way, we identify a class of perturbative graviton states that have parametrically larger generalized entropy, in the small G N expansion, than any low-energy excitations of an ordinary quantum field.

1/N expansion↗
DOE OSTI2025

The role of intermediate ΔΔ states in nucleon–nucleon scattering in the large-Nc and unitary limits, and ΔΔ and ΩΩ scattering

We explore potential explanations for why using large-Nc (Nc is the number of colors) scaling to determine the relative size of few-nucleon low-energy operators agrees with experiment even when dynamical Δ’s are not explicitly included. Given that the large-Nc analysis is predicated on the nucleons and Δ’s being degenerate, this is a curious result. We show that for purely S-wave interactions the relationships dictated by large-Nc scaling are unaffected whether the Δ is included or not. In the case of higher partial waves that do not mix with S-waves, the impact of the Δ is perturbative, which makes the agreement with naive (Δ-less) large-Nc ordering unsurprising. For higher partial waves that mix with S-waves, the nucleon and Δ would need to decouple to get agreement with naive large-Nc ordering. We find all NN, ΔN, and ΔΔ low energy coefficients for leading-order baryon–baryon scattering in Δ-full pionless effective field theory in terms of the two independent parameters dictated by the SU(2F) spin-flavor symmetry that arises in the Nc → ∞ limit. Because of recent lattice quantum chromodynamics results and experimental interest, we extend our analysis to the three-flavor case to study ΩΩ scattering. We show that in the unitary limit (where scattering lengths become infinite) one of the two SU(2F) parameters is driven to zero, resulting in enhanced symmetries, which agree with those found in spin-1/2 entanglement studies.

Richardson, Thomas R↗
NASA NTRS1988

The equation of state for stellar envelopes. II - Algorithm and selected results

A free-energy-minimization method for computing the dissociation and ionization equilibrium of a multicomponent gas is discussed. The adopted free energy includes terms representing the translational free energy of atoms, ions, and molecules; the internal free energy of particles with excited states; the free energy of a partially degenerate electron gas; and the configurational free energy from shielded Coulomb interactions among charged particles. Internal partition functions are truncated using an occupation probability formalism that accounts for perturbations of bound states by both neutral and charged perturbers. The entire theory is analytical and differentiable to all orders, so it is possible to write explicit analytical formulas for all derivatives required in a Newton-Raphson iteration; these are presented to facilitate future work. Some representative results for both Saha and free-energy-minimization equilibria are presented for a hydrogen-helium plasma with N(He)/N(H) = 0.10. These illustrate nicely the phenomena of pressure dissociation and ionization, and also demonstrate vividly the importance of choosing a reliable cutoff procedure for internal partition functions.

Mihalas, Dimitri↗
DOE OSTI2022

Physics constrained learning for data-driven inverse modeling from sparse observations

Deep neural networks (DNN) have been used to model nonlinear relations between physical quantities. Those DNNs are embedded in physical systems described by partial differential equations (PDE) and trained by minimizing a loss function that measures the discrepancy between predictions and observations in some chosen norm. This loss function often includes the PDE constraints as a penalty term when only sparse observations are available. As a result, the PDE is only satisfied approximately by the solution. However, the penalty term typically slows down the convergence of the optimizer for stiff problems. We present a new approach that trains the embedded DNNs while numerically satisfying the PDE constraints. We develop an algorithm that enables differentiating both explicit and implicit numerical solvers in reverse-mode automatic differentiation. This allows the gradients of the DNNs and the PDE solvers to be computed in a unified framework. We demonstrate that our approach enjoys faster convergence and better stability in relatively stiff problems compared to the penalty method. Furthermore, our approach allows for the potential to solve and accelerate a wide range of data-driven inverse modeling, where the physical constraints are described by PDEs and need to be satisfied accurately.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Dynamic Linking of Upstream Energy and Freight Demands for Bio and Fossil Energy Pathways in the Global Change Analysis Model

Comprehensive study of the environmental impacts associated with demand for an energy resource or carrier in any one sector requires a full consideration of the direct and indirect impacts on the rest of the regional and global energy system. It is important to consider the energy that is consumed in producing primary energy resources and energy consumed in transporting these energy resources internationally and domestically in order to produce a complete picture of these impacts. Biofuels are especially complex since they have feedbacks not just to the energy system but also to regional and global crop markets. Different modeling and analysis strategies have been applied to consider these impacts, which occur “upstream” of final energy consumption, with some success. Traditional life cycle models allow for rich technological detail in linking upstream impacts of energy demand with downstream final consumption; however, they cannot by themselves represent dynamic economic feedbacks across multiple sectors and regions over time. Computable general equilibrium (CGE) modeling does account for economic feedback among all sectors and regions; however, CGE modeling does not lend itself to physical representation of technological detail. Partial equilibrium (PE) modeling is a heterogeneous category describing economic models that focus on a subset of the economy, and they differ in coverage and ease in incorporating more sectors and economic links. In this study, we present a strategy for dynamically including the direct and indirect impacts of energy demand with physical and technology detail by explicitly adding these upstream energy and transportation links to GCAM, a PE model of global energy, land use, and emissions. We incorporate the following inter-sectoral linkages: energy inputs to crop production, energy inputs to fossil resource production, and freight transport requirements of energy and agricultural commodities. We assess the implications of explicitly including these links by measuring the global impacts of increased corn ethanol demand in the United States with and without these links included. Although the net global impact of the upstream links on energy and emissions are relatively modest in the scenarios we studied, the inclusion of these links illustrates interesting trade-offs in energy and transportation demand among fossil fuel and agriculture sectors. These sectoral interactions suggest that this level of modeling detail could be important in evaluating future analytical questions.

Sampedro Martinez de Estivariz, Jon↗
NASA NTRS1984

The uniform asymptotic swallowtail approximation - Practical methods for oscillating integrals with four coalescing saddle points

Methods that can be used in the numerical implementation of the uniform swallowtail approximation are described. An explicit expression for that approximation is presented to the lowest order, showing that there are three problems which must be overcome in practice before the approximation can be applied to any given problem. It is shown that a recently developed quadrature method can be used for the accurate numerical evaluation of the swallowtail canonical integral and its partial derivatives. Isometric plots of these are presented to illustrate some of their properties. The problem of obtaining the arguments of the swallowtail integral from an analytical function of its argument is considered, describing two methods of solving this problem. The asymptotic evaluation of the butterfly canonical integral is addressed.

Connor, J. N. L.↗
DOE OSTI2021

Ab Initio Study of Atomic and Electronic Structure of Promising Ba4XMn3O12 (X = Nb, Ce, Pr) Oxides for Solar Thermochemical Hydrogen Production

The two-step metal oxide water-splitting cycle is one of the most viable approach for Solar Thermochemical Hydrogen (STCH) production. Challenges exist in finding suitable oxides that can satisfy thermodynamics of the STCH redox cycle under viable range of temperatures and partial pressures. Recently, BXM family members, i.e., Ba4NbMn3O12 (BNM), Ba4CeMn3O12 (BCM), Ba4PrMn3O12 (BPM) are discovered to exhibit promising STCH performance. However, the magnetic degrees of freedom of their experimental crystal structures is not characterized, and only hypothetical models exist for their electronic structure. We performed Monte-Carlo sampling for the magnetic spin arrangement of Mn atoms to provide explicit atomic structure models for their ground state 12R polytype and determined the most stable spin configuration for each member after structure relaxation using density functional theory (DFT) calculations. We also elucidate the most stable charge configuration (Mn3+/Mn4+ arrangement) for BNM among the existing hypothesis in literature. We investigate these strongly correlated oxides using different levels of theory (DFT+U, Hybrid-DFT) to develop better understanding of their electronic structure. These calculations allowed us to develop a model based on molecular orbital interactions that help explain the observed changes in their experimental x-ray spectroscopy measurements. Thereby, help link differences in electronic structure of BXM family members to their varying water-splitting behavior. These structure models will further facilitate future in-depth defect studies to model STCH redox cycle of these oxides.

atomic and magnetic structure↗
DOE OSTI2025

Analytic reconstruction with massive particles: one-loop amplitudes for $0\to \overline{q} qt\overline{t}H$

We present an analytic reconstruction of one-loop amplitudes for the process $0\to \overline{q} qt\overline{t}H$. Our calculation is a novel use of analytic reconstruction, retaining explicit covariance in the massive spin states through the massive spinor-helicity formalism. The analytic reconstruction relies on embedding the massive five-point kinematics in a fully massless eight-point phase space while still building a minimal ansatz directly in the five-point phase space. In order to obtain compact analytic expressions it is necessary to identify suitable partial fraction decompositions and extract common numerator factors, which we achieve through careful inspection of limits in which pairs of denominators vanish. We find that the resulting amplitudes are more numerically efficient than ones computed using automatic methods but that the gains are not as significant as in the massless case, at least at present. The method opens the door to applications at two-loop order, where numerical efficiency and improvements in the reconstruction methodology are more crucial, especially with regards to the number of free parameters in the ansatz.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Non-intrusive data-driven model reduction for differential–algebraic equations derived from lifting transformations

In this paper we present a non-intrusive data-driven approach for model reduction of nonlinear systems. The approach considers the particular case of nonlinear partial differential equations (PDEs) that form systems of partial differential–algebraic equations (PDAEs) when lifted to polynomial form. Such systems arise, for example, when the governing equations include Arrhenius reaction terms (e.g., in reacting flow models) and thermodynamic terms (e.g., the Helmholtz free energy terms in a phase-field solidification model). Using the known structured form of the lifted algebraic equations, the approach computes the reduced operators for the algebraic equations explicitly, using straightforward linear algebra operations on the basis matrices. The reduced operators for the differential equations are inferred from lifted snapshot data using operator inference, which solves a linear least squares regression problem. The approach is illustrated for the nonlinear model of solidification of a pure material. The lifting transformations reformulate the solidification PDEs as a system of PDAEs that have cubic structure. The operators of the lifted system for this solidification example have affine dependence on key process parameters, permitting us to learn a parametric reduced model with operator inference. Numerical experiments show the effectiveness of the resulting reduced models in capturing key aspects of the solidification dynamics.

42 ENGINEERING↗
DOE OSTI2025

A higher-order finite-element implementation of the nonlinear Fokker–Planck collision operator for charged particle collisions in a low density plasma

Collisions between particles in a low density plasma are described by the Fokker–Planck collision operator. In applications, this nonlinear integro-differential operator is often approximated by linearised or ad-hoc model operators due to computational cost and complexity. In this work, we present an implementation of the nonlinear Fokker–Planck collision operator written in terms of Rosenbluth potentials in the Rosenbluth–MacDonald–Judd (RMJ) form. The Rosenbluth potentials may be obtained either by direct integration or by solving partial differential equations (PDEs) similar to Poisson's equation: we optimise for performance and scalability by using sparse matrices to solve the relevant PDEs. We represent the distribution function using a tensor-product continuous-Galerkin finite-element representation and we derive and describe the implementation of the weak form of the collision operator. We present tests demonstrating a successful implementation using an explicit time integrator and we comment on the speed and accuracy of the operator. Finally, we speculate on the potential for applications in the current and next generation of kinetic plasma models.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Forest aboveground biomass estimation through integration of sentinel-2 and PALSAR-2 time series: assessing models trained on GEDI and field inventory benchmarks

Accurate and spatially explicit forest Aboveground Biomass (AGB) mapping through remote sensing is critical for quantifying terrestrial carbon stocks and informing effective forest management strategies. However, AGB estimation in dense forests with complex terrain remains challenging due to satellite sensor signal saturation problem (saturation issue occurs in high biomass forests), structural complexity, and limited ground truth for calibration. This study presents a novel framework that integrates multi-temporal Sentinel-2 optical imagery, ALOS PALSAR-2 Synthetic Aperture Radar (SAR) data, and topographic variables with explainable Machine Learning to map AGB across mountainous forests within subtropical and temperate oceanic climate zones of Mexico. We evaluate the effects of temporal granularity and sensor synergy by comparing multiple temporal inputs and sensor configurations (Sentinel-2, PALSAR-2, and their fusion), and assess model performance using two reference datasets: NASA GEDI LiDAR-derived biomass and Mexico’s National Forest and Soil Inventory (INFyS). Our results showed that models trained on INFyS consistently outperformed those trained on GEDI, highlighting limitations in GEDI’s reliability in biomass estimates within this study region. Furthermore, the integration of Sentinel-2 and PALSAR-2 provided improved predictions compared to single-sensor models, particularly when combined with temporally explicit yearly statistics. The best-performing model, which was trained on INFyS data, and considered both Sentinel-2 and PALSAR-2 yearly statistics, as well as topographic variables, achieved an R2 of 0.64, RMSE of 51.10 Mg/ha, and relative RMSE (rRMSE) of 58.69%. Explainable ML analysis identified Sentinel-2 spectral indices and topographic features as key predictors, while PALSAR-2 metrics provided complementary information, partially mitigating saturation effects in high-biomass areas. Specifically, integrating both sensors substantially improved AGB estimation in high biomass forest (≥200 Mg/ha), yielding 98% gains over optical-only model, with resulting estimates exceeding GEDI L4B by 29% and ESA-CCI-BIOMASS by 174%. Terrain-stratified analysis indicated close agreement with GEDI in low-slope areas, with increasing divergence as slope steepness increased, while estimates remained consistently higher than ESA-CCI-BIOMASS across all slope classes. The proposed approach advances multi-sensor fusion and temporal feature engineering for AGB mapping using open-access satellite datasets, providing a scalable and reproducible framework for annual biomass monitoring in topographically complex mountainous forests. The resulting 25 m resolution biomass product has the potential to provide spatially detailed information for forest monitoring and may support applications in carbon accounting and forest management.

54 ENVIRONMENTAL SCIENCES↗