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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A Digital Three Level Space Vector Modulator for High Frequency Vector Sequence Generation

This letter proposes a digital high-speed three-level space vector pulse width modulator (3L-SVPWM). A conventional 3L-SVPWM is typically computation-based, involving a sequential execution of sub-tasks on a digital signal processor (DSP) based controller. The resulting high computation time of 5.4 μs limits the implementation of additional control blocks for switching frequencies greater than 100 kHz. This is overcome by transforming sub-tasks into digital blocks with 1-0 decisions and simpler arithmetic operations. The sub-task blocks are executed concurrently on a programmable logic device (PLD). Hence, a fast 3L-SVPWM execution in 140 ns is achieved. The proposed digital 3L-SVPWM enables high switching frequency operation of wide bandgap (WBG) device-based 3 L inverters to generate high fundamental frequency waveforms. A finite state machine is an integral part of the proposed implementation with the ability to generate any vector sequence, maximizing the usage of redundant vector states in 3L-SVPWM. Here, the proposed digital 3L-SVPWM operation is demonstrated with a GaN-based 3 L active neutral point clamped (3L-ANPC) inverter. Experimental results are presented at 250 kHz switching frequency to generate vector sequences for center-aligned SVPWM (CA-SVPWM) and common mode voltage reduced SVPWM (CMVR-SVPWM). The results also showcase a high fundamental frequency generation capability of 10 kHz.

active neutral point clamped inverter↗
DOE OSTI2025

A resolution independent neural operator

The Deep operator network (DeepONet) is a powerful yet simple neural operator architecture that utilizes two deep neural networks to learn mappings between infinite-dimensional function spaces. This architecture is highly flexible, allowing the evaluation of the solution field at any location within the desired domain. However, it imposes a strict constraint on the input space, requiring all input functions to be discretized at the same locations; this limits its practical applications. Here, in this work, we introduce a general framework for operator learning from input–output data with arbitrary number and locations of sensors. This begins by introducing a resolution-independent DeepONet (RI-DeepONet), enabling it to handle input functions that are arbitrarily, but sufficiently finely, discretized. To this end, we propose two dictionary learning algorithms to adaptively learn a set of appropriate continuous basis functions, parameterized as implicit neural representations (INRs), from correlated signals defined on arbitrary point cloud data. These basis functions are then used to project arbitrary input function data as a point cloud onto an embedding space (i.e., a vector space of finite dimensions) with dimensionality equal to the dictionary size, which can be directly used by DeepONet without any architectural changes. In particular, we utilize sinusoidal representation networks (SIRENs) as trainable INR basis functions. The introduced dictionary learning algorithms are then used in a similar way to learn an appropriate dictionary of basis functions for the output function data, which defines a new neural operator architecture referred to as the R esolution I ndependent N eural O perator (RINO). In the RINO, the operator learning task simplifies to learning a mapping from the coefficients of input basis functions to the coefficients of output basis functions. We demonstrate the robustness and applicability of RINO in handling arbitrarily (but sufficiently richly) sampled input and output functions during both training and inference through several numerical examples.

Deep operator network (DeepONet)↗
DOE OSTI2025

Replacing non-biomedical concepts improves embedding of biomedical concepts

Embeddings are semantically meaningful representations of words in a vector space, commonly used to enhance downstream machine learning applications. Traditional biomedical embedding techniques often replace all synonymous words representing biological or medical concepts with a unique token, ensuring consistent representation and improving embedding quality. However, the potential impact of replacing non-biomedical concept synonyms has received less attention. Embedding approaches often employ concept replacement to replace concepts that span multiple words, such as non-small-cell lung carcinoma, with a single concept identifier (e.g., D002289). Also, all synonyms of each concept are merged into the same identifier. Here, we additionally leveraged WordNet to identify and replace sets of non-biomedical synonyms with their most common representatives. This combined approach aimed to reduce embedding noise from non-biomedical terms while preserving the integrity of biomedical concept representations. We applied this method to 1,055 biomedical concept sets representing molecular signatures or medical categories and assessed the mean pairwise distance of embeddings with and without non-biomedical synonym replacement. A smaller mean pairwise distance was interpreted as greater intra-cluster coherence and higher embedding quality. Embeddings were generated using the Word2Vec algorithm applied to a corpus of 10 million PubMed abstracts. Our results demonstrate that the addition of non-biomedical synonym replacement reduced the mean intra-cluster distance by an average of 8%, suggesting that this complementary approach enhances embedding quality. Future work will assess its applicability to other embedding techniques and downstream tasks. Python code implementing this method is provided under an open-source license.

algorithms↗
DOE OSTI2025

First Order Transport of a Cold Uniform Ellipsoid of Charge

A single particle has a 6-element phase space vector $s = (x, γv)$ where $γv =\frac{p}{m}$ s proportional to the relativistic momentum. The ellipsoid is represented by a 21-element vector $(s, C, V)$ where $s$ is the phase space location of the centroid. $C$ is the 3 × 3 covariance matrix defined by $C_{ij} = ⟨δx_iδx_j⟩$, where $δx$ is the distance of a particle from the centroid $x$. $C$ is symmetric so only counts as 6 elements of the overall vector. $V$ is a general 3 × 3 matrix that determines the velocity distribution in the bunch by $δ(γv) = Vδx$. In total this makes 6 + 6 + 9 = 21 elements.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

A Type II Hamiltonian Variational Principle and Adjoint Systems for Lie Groups

We present a novel Type II variational principle on the cotangent bundle of a Lie group which enforces Type II boundary conditions, i.e., fixed initial position and final momentum. In general, such Type II variational principles are only globally defined on vector spaces or locally defined on general manifolds; however, by left translation, we are able to define this variational principle globally on cotangent bundles of Lie groups. Type II boundary conditions are particularly important for adjoint sensitivity analysis, which is our motivating application. As such, we additionally discuss adjoint systems on Lie groups, their properties, and how they can be used to solve optimization problems subject to dynamics on Lie groups.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Leveraging operator learning to accelerate convergence of the preconditioned conjugate gradient method

We propose a new deflation strategy to accelerate the convergence of the preconditioned conjugate gradient (PCG) method for solving parametric large-scale linear systems of equations. Unlike traditional deflation techniques that rely on eigenvector approximations or recycled Krylov subspaces, we generate the deflation subspaces using operator learning, specifically the Deep Operator Network (DeepONet). To this aim, we introduce two complementary approaches for assembling the deflation operators. The first approach approximates near-null space vectors of the discrete PDE operator using the basis functions learned by the DeepONet. The second approach directly leverages solutions predicted by the DeepONet. To further enhance convergence, we also propose several strategies for prescribing the sparsity pattern of the deflation operator. Here, a comprehensive set of numerical experiments encompassing steady-state, time-dependent, scalar, and vector-valued problems posed on both structured and unstructured geometries is presented and demonstrates the effectiveness of the proposed DeepONet-based deflated PCG method, as well as its generalization across a wide range of model parameters and problem resolutions.

Deflation↗
DOE OSTI2025

Deep Koopman operators for causal discovery

Causal discovery aims to identify cause-effect mechanisms for better scientific understanding, explainable decision-making, and more accurate modeling. Standard statistical frameworks, such as Granger causality, lack the ability to quantify causal relationships in nonlinear dynamics due to the presence of complex feedback mechanisms, timescale mixing, and nonstationarity. Thus, applying these methods to study causal dynamics in real-world systems, such as the Earth, is a major challenge. Addressing this shortcoming, we leverage deep learning and a Koopman operator-theoretic formalism to present a class of causal discovery algorithms. Kausal uses deep Koopman operator methods to approximate nonlinear dynamics in a linearized vector space in which traditional causal inference methods such as Granger causality can be more easily applied. Our idealized experiments demonstrate Kausal’s superior ability in discovering and characterizing causal signals compared to existing deep learning and non-deep learning state-of-the-art approaches. Finally, the successful identification of major El Niño and La Niña events in observations showcases Kausal’s skill to handle real-world applications.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Evaluating the gradients of localized diabatic state energies and couplings at minimum cost

We calculate the gradients of Boys diabatic state energies and couplings when the electronic vector space is generated by configuration interaction singles. Our approach follows the Lagrangian approach of Paz and Glover (rather than direct differentiation of the adiabatic-to-diabatic approaches that have been published previously). The result is that we achieve a dramatic increase in savings over previous approaches, and the present approach should be immediately useful to scientists focused on electronic relaxation, especially chemists studying electron transfer who wish to go beyond the Condon approximation. Here, a future extension to time-dependent density functional theory in the Tamm–Dancoff approximation is clear.

Chemical physics↗
DOE OSTI2025

CSGL: chemical synthesis graph learning for molecule representation

Abstract Motivation Molecule representation learning (MRL) translates molecules into a real vector space, serving as input to downstream tasks in biology, chemistry, and computer science. This article introduces a chemical synthesis graph learning (CSGL) framework, which enhances MRL by considering both the atomic structures of molecules and their roles in chemical reactions through a hierarchical graph representation. Specifically, molecules are first modeled based on their molecular graphs, which capture atomic-level structural information. They are then further refined using a chemical synthesis graph, where nodes represent reactant and product molecule sets, and edges encode chemical transformations between reactants and products (e.g. changes in molecular structures). CSGL optimizes molecular embeddings of reactant and product nodes in a fashion that ensures the embeddings conform to a chemical balance constraint. Results Experimental results show that our method CSGL achieves strong performance on a variety of tasks, including product prediction, reaction classification, and molecular property prediction. Availability and implementation https://github.com/li-2023/CSGL.

Biochemistry & Molecular Biology↗
DOE OSTI2024

Detecting damage in composites using volume decomposition analysis of tomographic data

Detection of damage in a single tow ceramic matrix composite specimen has been achieved using orthogonal decomposition of volumetric tomographic datasets collected at four tensile loads. This decomposition approach has been applied at two different length scales: (i) individual fibres and (ii) bulk volumes containing fibres and matrix material. Volumes were first decomposed to feature vectors, orders of magnitude smaller than the original volume they describe, and then comparisons between datasets at different load levels were made in feature vector space. The results show quantitative measurements of damage location, damage morphology and the relative growth of this damage with increased load when compared with a dataset with less or no damage. No prior knowledge of the dataset or training of algorithms is required for damage to be detected, it is only necessary that at least two datasets are available for comparison, e.g. from in situ or repeated scanning measurements. Results are generated on significantly shorter timescales when compared with previous automated approaches to tomography data processing. This approach has the potential to be applied to damage detection in a range of materials through comparisons of volumetric datasets from a range of measurement or computational techniques.

Middleton, Ceri A.↗
DOE OSTI2025

Optimizing stochastic algorithms for hadron correlation function computations in lattice QCD using a localized distillation basis

Distillation is a quark-smearing method for the construction of a broad class of hadron operators useful in lattice QCD computations and defined via a projection operator into a vector space of smooth gauge-covariant fields. A new orthonormal basis for this space is constructed which builds in locality. This basis is useful for the construction of stochastic methods to estimate the correlation functions computed in Monte Carlo calculations relevant for hadronic physics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Close-coupling approach to electron scattering with multielectron targets

Momentum-space close coupling calculations of electron scattering require removal of spurious and unphysical solutions. Here, we demonstrate here that removal of these solutions involve regulator operators that enforce Pauli exclusion selection rules in addition to removing spurious solutions. The form of a regular operator for e-H scattering has already been established, but a general extension to the multielectron case has been elusive. Here we present a general method for scattering on multielectron targets, atoms, or molecules, ensuring that the scattering solutions obey Pauli-exclusion selection rules. The regulator operator is obtained by finding the null space vectors of the 𝑁+1 electrons of the projectile and target atom scattering system. We demonstrate that this general procedure reduces to the e-H result and provide examples for He- and Li-like targets as well as guidance for implementation.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

Inverse Segmented Motor Drive Using Dual ANPC Inverters for Common-Mode Voltage and Neutral-Point Current Cancellation

This article proposes an inverse segmented motor drive (SgMD) utilizing dual active neutral point clamped (ANPC) inverters. In the proposed configuration, the neutral point current and common-mode (CM) voltage is topologically canceled, achieving zero total neutral point current and CM voltage under ideal conditions. Also, the zero total neutral point current minimizes the neutral point voltage imbalance in ANPC inverters. The mechanisms behind neutral point current and CM voltage cancellation in the proposed inverse SgMD are first introduced. The modifications to the motor windings for implementing the inverse SgMD are explained, showing that a standard motor can be readily adapted for the proposed configuration. A space vector modulation (SVM) scheme tailored for the proposed topology is presented, along with a carrier-based implementation. Simulation results validate that the proposed topology can achieve zero total CM voltage and neutral point current. It is also shown that the proposed inverse SgMD can reduce neutral point voltage fluctuation by about 90% and RMS current stress in the dc-link capacitors by about 43% compared to the conventional SgMD.

Lee, Sangwhee [ORNL] (ORCID:0000000335490057)↗
DOE OSTI2025

Geometric Interpretation of the Cluster Location Problem Part I: Theory

We present a new framing of the seismic location problem using principles drawn from differential geometry. Our interpretation relies upon the common assumption that travel times observed across a network are continuous, differentiable functions of source location. In consequence, travel‐time functions constitute a differentiable map between the source region and a Riemannian manifold. The manifold is said to be the image of the source region embedded in a generally high‐dimension travel‐time vector space. A cluster of events in the source region has an image of discrete points on the manifold, that, except in the simplest cases, cannot be viewed directly. However, it is possible to project the image of a cluster into a tangent space of the manifold for direct visualization. The projection operator can be computed directly from the data without a velocity model, but produces a distorted rendering of the cluster geometry. With a model we can predict the distortions and correct them to estimate cluster geometry. We develop these points with the simplest possible example, one for which direct visualization of the manifold is possible, using the example as an introduction to the relevant concepts from differential geometry in a familiar setting. The tangent space, a local linearization of the manifold, plays a key role. We develop a metric to estimate the limits of linearization, that is, to determine when the curvature of the manifold invalidates the linear assumption. We also examine the interplay of model error, inadequate network geometry, and pick error. We then generalize our results from the simple case to the general case of 3D source regions observed by general networks. Although we do suggest a new “project and correct” method for location, we do not develop it into a practical algorithm. In conclusion, our intention rather is to highlight new analytical methods grounded in differential geometry.

East Pacific Ocean Islands↗
DOE OSTI2025

Analysis of Bis(trifluoromethylsulfonyl)imide Interactions with Metal Cations Through a Chemical Informatics Approach

Nominally weakly coordinating anions are useful for modulating the solubility and chemical properties of metal complexes, but identification and analysis of the systematics of the interactions of anions with cationic metal complexes has not received the attention it deserves. Here, a chemical informatics approach is demonstrated for identifying and quantitatively analyzing the ways that the bis(trifluoromethylsulfonyl)imide anion (TFSI) can interact with metal-containing species. An open access computer program (PyCIFTer) was developed to facilitate large-scale structural analysis of TFSI-containing species by utilization of experimental atomic coordinate data from single-crystal X-ray diffraction (XRD) studies obtained from the Cambridge Structural Database (CSD). PyCIFTer establishes a three-dimensional vector space from the raw atomic coordinates, generating acyclic, undirected graphs that are used to rapidly analyze the structural properties (bond lengths and angles) of TFSI in individual structures in sequential/batch fashion. The structures are sorted by PyCIFTer into groups based on pre-set and chemically sensible criteria, affording a comprehensive and systematic view of TFSI structural chemistry. This approach avoids tedious one-at-a-time interrogation of structures, a prospect unreasonable in this case, and many others of contemporary chemical relevance; there were over 1500 structures in the CSD containing TFSI as of November 2024. The results demonstrate that TFSI only rarely binds to cations in the solid state, favoring the formation of species in which TFSI is found in cations’ outer coordination spheres. The prospect of applying PyCIFTer to other moieties is also discussed. PyCIFTer is also schematically compared to the commercial CSD Python application programming interface (API). Taken together, this work demonstrates the usefulness of modular workflows for sequential/batch analysis of structural data from XRD, an approach that appears poised to accelerate the translation of legacy structural results into new chemical insights and hypotheses.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Accelerating high-order continuum kinetic plasma simulations using multiple GPUs

Kinetic plasma simulations solve the Vlasov-Poisson or Vlasov-Maxwell equations to evolve scalar-variable distribution functions in position-velocity phase space and vector-variable electromagnetic fields in configuration space. The immense computational cost of evolving high-dimensional variables, and their large number of degrees of freedom, often limits the utility of continuum kinetic simulations and presents a challenge when it comes to accurately simulating real-world physical phenomena. To address this challenge, we present techniques that accelerate and minimize the computational work required for a scalable Vlasov-Poisson solver. We show theoretical hardware compute and communication bounds for solving a fourth-order finite-volume Vlasov-Poisson system. These bounds are then used to inform and evaluate the design of performance portable algorithms for a multiple graphics processing unit (GPU) accelerated version of the Vlasov-Poisson solver VCK-CPU [1]. We demonstrate that the multi-GPU Vlasov solver implementation, VCK-GPU, simultaneously minimizes required inter-process data transfer while also being bounded by the machine network performance limits. This results in an overall strong scaling speedup per timestep of up to 40x in three-dimensional phase space (one position, two velocity coordinates) and 54x in four dimensional phase space (two position, two velocity coordinates) and a 341x increase in simulation throughput of the GPU accelerated code over the existing CPU code. The GPU code is also able to weak scale up to 256 compute nodes and 1024 GPUs. In conclusion, we demonstrate that the improved compute performance enables exploring configurations which were previously computationally infeasible, including resolving fine-scale distribution function filamentation and multi-species dynamics with realistic electron-proton mass ratios.

Continuum kinetics↗
DOE OSTI2025

Accelerating eigenvalue computation for nuclear structure calculations via perturbative corrections

Subspace projection methods utilizing perturbative corrections have been proposed for computing the lowest few eigenvalues and corresponding eigenvectors of large Hamiltonian matrices. In this paper, we build upon these methods and introduce the term Subspace Projection with Perturbative Corrections (SPPC) method to refer to this approach. We tailor the SPPC for nuclear many-body Hamiltonians represented in a truncated configuration interaction subspace, i.e., the no-core shell model (NCSM). We use the hierarchical structure of the NCSM Hamiltonian to partition the Hamiltonian as the sum of two matrices. The first matrix corresponds to the Hamiltonian represented in a small configuration space, whereas the second is viewed as the perturbation to the first matrix. Eigenvalues and eigenvectors of the first matrix can be computed efficiently. Because of the split, perturbative corrections to the eigenvectors of the first matrix can be obtained efficiently from the solutions of a sequence of linear systems of equations defined in the small configuration space. These correction vectors can be combined with the approximate eigenvectors of the first matrix to construct a subspace from which more accurate approximations of the desired eigenpairs can be obtained. We show by numerical examples that the SPPC method can be more efficient than conventional iterative methods for solving large-scale eigenvalue problems such as the Lanczos, block Lanczos and the locally optimal block preconditioned conjugate gradient (LOBPCG) method. The method can also be combined with other methods to avoid convergence stagnation.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Counting topological interface modes using simplicial characteristic classes

A computational approach for predicting the number of topological interface modes (TIMs) in hermitian systems using the spectral flow—monopole correspondence is presented. The number of TIMs is determined by calculating the Chern number of a complex line bundle of local polarisation vectors over a phase space sphere surrounding a Weyl point. The Chern number is computed by constructing the simplicial first Chern class of a discrete vector bundle on a simplicial mesh. This approach is gauge invariant, derivative free, structure preserving, and robust to noise. The algorithm is shown to reproduce the expected number of TIMs for the case of equatorial fluid waves and the topological Langmuir cyclotron wave. The possibility of using this algorithm to analyse experimental measurements of bulk wave polarisations and predict the associated number of TIMs is explored in a synthetic example.

discrete vector bundles↗