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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Fully Homomorphic Encryption

This code implements a Fully Homomorphic Encryption (FHE) system, enabling secure computation on encrypted data without requiring decryption. It supports encryption, decryption, and homomorphic operations like matrix multiplication and addition. This code is adaptable for integrating FHE into linear-time invariant (LTI) systems, including digital control and filtering. With proper configuration from subject matter expertise, encrypted system parameters and signals can be manipulated to perform tasks like state updates, output calculations, and convolution in the encrypted domain. By preserving the structure of LTI systems while ensuring privacy, the framework facilitates secure applications in areas such as autonomous systems, signal processing, and industrial automation. The code initializes the encryption system using parameters provided in the env dictionary. These parameters include the ciphertext modulus, key dimension, plaintext fixed-point scaling factor, and noise bound. During initialization, a secret key is generated, which is essential for encrypting and decrypting data securely. The modular design allows users to tailor these parameters to specific use cases or security requirements. The code implements multiple cryptographic schemes. The learning with errors (LWE) encryption method encodes cleartext message to their plaintext fixed-point representation then encrypted into ciphertext space with additive noise. This noise ensures the security of the scheme, relying on the computational hardness of the LWE problem. The code also includes the Gentry-Sahai-Waters (GSW) scheme based off the LWE problem. Homomorphic matrix multiplication is performed between the LWE and GSW to encrypted data. This is achieved using a decomposition function on the LWE ciphertext during the multiplication operation. For higher-dimensional data, the code includes a method to encrypt entire matrices (GSWMat) using GSW encryption. These encrypted matrices can then be used for homomorphic matrix multiplications (MatMult). The decryption function uses the secret key to recover the original plaintext, removing the added noise and scaling that was originally applied during encryption.

Lois, Roberts [Idaho National Laboratory (INL), Id↗
DOE OSTI2024

Low loss fiber-coupled volumetric interconnects fabricated via direct laser writing

Photonic integrated circuits (PICs) are vital for high-speed data transmission. However, optical routing is limited in PICs composed of only one or a few stacked planes. Further, coupling losses must be low in deployed systems. Previously, we developed the subsurface controllable refractive index via beam exposure (SCRIBE) technique to write accurate 3D gradient refractive index (GRIN) profiles within a mesoporous silica scaffold. Here, we apply SCRIBE to fabricate low loss, broadband, polarization insensitive, fiber-coupled, single-mode volumetric interconnects that include waveguides traversing arbitrary 3D paths. By seamlessly integrating mode-matching subsurface lenses and GRIN waveguide tapers, calibrating for positional writing errors, implementing multipass exposure, automating alignment, and switching to antireflection coated fibers, we reduced the insertion loss for a fiber-PIC-fiber interconnect from 50 to 2.14 dB, or 1.47 dB, excluding the fiber array’s loss. Further, we establish an upper bound of 0.45 dB loss per coupler. We report quality factors of 27,000 and 77,000 and bending losses of 6 and 3 dB/cm for 15 and 30 µm radii microrings, respectively. We also demonstrate Bézier escalators, polarization-rotating and polarization-splitting interconnects, and a seven-channel 25 µm pitch volumetric interconnect. The SCRIBE platform presents a clear path toward realizing 3D PICs with unique functionality.

Optics↗
DOE OSTI2025

Three-Receiver Quantum Broadcast Channels: Classical Communication with Quantum Non-unique Decoding

In network communication, it is common in broadcasting scenarios for there to exist a hierarchy among receivers based on information they decode due, for example, to different physical conditions or premium subscriptions. This hierarchy may result in varied information quality, such as higher-quality video for certain receivers. This is modeled mathematically as a degraded message set, indicating a hierarchy between messages to be decoded by different receivers, where the default quality corresponds to a common message intended for all receivers, a higher quality is represented by a message for a smaller subset of receivers, and so forth. We extend these considerations to quantum communication, exploring three-receiver quantum broadcast channels with two- and three-degraded message sets. Our technical tool involves employing quantum non-unique decoding, a technique we develop by utilizing the simultaneous pinching method. Here, we construct one-shot codes for various scenarios and find achievable rate regions relying on various quantum Rényi mutual information error exponents. Our investigation includes a comprehensive study of pinching across tensor product spaces, presenting our findings as the asymptotic counterpart to our one-shot codes. By employing the non-unique decoding, we also establish a simpler proof to Marton’s inner bound for two-receiver quantum broadcast channels without the need for more involved techniques. Additionally, we derive no-go results and demonstrate their tightness in special cases.

Salek, Farzin [Technical University of Munich (Ger↗
DOE OSTI2026

Connecting ground-state properties of 6 Li to each other and to scattering data

We examine the relationship between the asymptotic normalization coefficient (ANC) of 6 Li and other low-energy observables in the α–deuteron system. Our analysis uses a set of calculations carried out within the ab initio no core shell model with continuum (NCSMC) using a variety of inter-nucleon interactions and basis sizes, and yielding 6 Li deuteron separation energies between 1.3 and 1.8 MeV (Hebborn et al 2022 Phys. Rev. Lett. 129 042503). These NCSMC calculations show that the square of the ANC is strongly correlated with the separation energy over this range. In this work, we investigate the origin of this correlation using the phenomenological R-matrix, a single-channel potential and a perturbative approach. We show that this correlation occurs because the depth of the α–deuteron central potential changes by only a small relative amount as the separation energy varies. We then investigate if the ANC can be accurately extracted from α–deuteron phase shifts in an ideal case in which low-energy data are available and there are no experimental errors. We find that both R-matrix and Coulomb-modified effective-range theory (CM-ERE) yield extracted ANCs close to, although not exactly equal to, the NCSMC value, provided the extrapolation is constrained by the known position of the bound-state pole and at least three terms are included in the fit function. The R-matrix approach converges faster than the CM-ERE as the number of parameters increases and is also more robust against the inclusion of low-energy and high-energy phase shift data. Finally, our study also shows that a naive quantification of uncertainties by comparing different truncations used in both theories is not accurate, and suggests the accuracy of ANCs extracted from phase shift data needs further investigation.

R-matrix↗
DOE OSTI2023

Analyzing photovoltaic module mechanics using composite plate theories and finite element solutions

Deflection and stress calculated from an experimentally validated, high-fidelity finite element model (FEM) of a photovoltaic module experiencing mechanical load was compared to results from a simplified FEM treating the module laminate as a homogenized composite using a rule of mixtures approach, and further compared to analytical calculations treating the module as a Kirchoff-Love flat plate. The goal of this study was to determine the error incurred by analyzing module mechanics with varying levels of simplification, since resolving the aspect ratios of a module is computationally expensive. Homogenized FEMs were found to underpredict peak deflection under a 1.0 kPa load by between 13 and 19% for lower and upper bound application of the rule of mixtures. However, module shape was captured, implying that a useful replication of a resolved model could be achieved with a reduced, calibrated material stiffness. Homogenized stress results captured glass layer tensile stress components to within 46 to 52% at a sample location of interest, though agreement was poor through the remainder of the laminate due to the lack of material resolution. For plate theory, deflection was overpredicted by 45 to 67% for upper and lower bound homogenizations, and frame-adjacent module shapes were not adequately replicated. Stress results mirrored FEM trends but magnitudes were not well correlated to resolved model values. These results support the use of homogenized laminate models for module shape derivation, though resolved models remain necessary for stress analyses. The accuracy of plate theory was found to be inadequate for most applications.

14 SOLAR ENERGY↗
DOE OSTI2023

Pragmatic Uncertainty Quantification and Propagation in Inverse Estimation of Structural Dynamics Parameters given Material Property Uncertainties and Limited Sensor Data

In this report we demonstrate some relatively simple and inexpensive methods to effectively account for various sources of epistemic lack-of-knowledge type uncertainty in inverse problems. The demonstration problem involves inverse estimation of six parameters of a bolted joint that attaches a kettlebell shaped object to a thick plate. The parameters are efficiently inverted in a modal-based model calibration using gradient-based optimization. Two material properties of the kettlebell are treated as uncertain to within given epistemic uncertainty bounds. We apply and test interval and sparse-sample probabilistic approaches to account for uncertainty in the estimated parameters (and various scalar functionals of the parameters as generic quantities of interest, QOIs) due to uncertainties in the material properties. We also investigate the error effects of limited numbers of vibration sensors (accelerometers) on the kettlebell and plate, and therefore abbreviated excitation/response information in the parameter inversions. We propose and demonstrate a Leave-K-Sensors-Out “cross-prediction” UQ approach to estimate related uncertainties on the parameters and QOI functionals. We indicate how uncertainties from material properties and limited sensors are treated in a combined manner. The economical combined UQ approach involves just three to five samples (i.e. three to five inverse simulations), with no added complication or error/uncertainty from use of surrogate models for affordability. Finally, we describe a related economical UQ approach for handling potential parameter solution non-uniqueness and numerical optimization related precision uncertainties in the estimated parameter values. Indicated further research is identified.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Bayesian estimation of HIV acquisition dates for prevention trials

Accurate timing estimates of when participants acquire HIV in HIV prevention trials are necessary for determining antibody levels at acquisition. The Antibody-Mediated Prevention (AMP) Studies showed that a passively administered broadly neutralizing antibody can prevent the acquisition of HIV from a neutralization-sensitive virus. We developed a pipeline for estimating the date of detectable HIV acquisition (DDA) in AMP Study participants using diagnostic and viral sequence data. Using a Bayesian strategy that combines three streams of data (REN [rev/vpu/env/Δnef] sequence, GP [gag/Δpol] sequence, and diagnostic) where their 95% credible intervals overlap based on pre-specified criteria and decision rules. We evaluated the performance of our AMP pipeline using PacBio viral sequence data from 41 participants across two prospective acute HIV acquisition cohort studies, FRESH and RV217, with twice-weekly sampling. These cohort studies enrolled young women in South Africa and men and women in Kenya and Thailand, respectively, with a high likelihood of HIV acquisition. In evaluating performance, “true DDA” was the center of bounds between last-negative and first-positive RNA diagnostic tests (median time 4 days, range 2–7 days); bias was the mean difference between estimated and true DDA. Using diagnostic data alone yielded timing estimates with a bias of 2.4 days and root mean square error (RMSE) of 7.9 days. These results were improved using sequence + diagnostic data (bias 1.5 days, RMSE 6.9 days), as well as by restricting sequence-based estimation to samples from ≤5 weeks post-DDA (bias 0.2 days, RMSE 7.8 days).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Robust Containment Queries over Collections of Rational Parametric Curves via Generalized Winding Numbers

Point containment queries for regions bound by watertight geometric surfaces, i.e., closed and without self-intersections, can be evaluated straightforwardly with a number of well-studied algorithms. When this assumption on domain geometry is not met, such methods are either unusable, or prone to misclassifications that can lead to cascading errors in downstream applications. More robust point classification schemes based on generalized winding numbers have been proposed, as they are indifferent to these imperfections. However, existing algorithms are limited to point clouds and collections of linear elements. We extend this methodology to encompass more general curved shapes with an algorithm that evaluates the winding number scalar field over unstructured collections of rational parametric curves. In particular, we evaluate the winding number for each curve independently, making the derived containment query robust to how the curves are arranged. We ensure geometric fidelity in our queries by treating each curve as equivalent to an adaptively constructed polyline that provably has the same generalized winding number at the point of interest. Our algorithm is numerically stable for points that are arbitrarily close to the model, and explicitly treats points that are coincident with curves. We demonstrate the improvements in computational performance granted by this method over conventional techniques as well as the robustness induced by its application.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Information transmission with continuous variable quantum erasure channels

Quantum capacity, as the key figure of merit for a given quantum channel, upper bounds the channel's ability in transmitting quantum information. Identifying different types of channels, evaluating the corresponding quantum capacity, and finding the capacity-approaching coding scheme are the major tasks in quantum communication theory. Quantum channel in discrete variables has been discussed enormously based on various error models, while error model in the continuous variable channel has been less studied due to the infinite dimensional problem. In this paper, we investigate a general continuous variable quantum erasure channel. By defining an effective subspace of the continuous variable system, we find a continuous variable random coding model. We then derive the quantum capacity of the continuous variable erasure channel in the framework of decoupling theory. The discussion in this paper fills the gap of a quantum erasure channel in continuous variable setting and sheds light on the understanding of other types of continuous variable quantum channels.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Assessing the stability of noisy quantum computation

Quantum computation has made considerable progress in the last decade with multiple emerging technologies providing proof-of-principle experimental demonstrations of such calculations. However, these experimental demonstrations of quantum computation face technical challenges due to the noise and errors that arise from imperfect implementation of the technology. Here, we frame the concepts of computational accuracy, result reproducibility, device reliability and program stability in the context of quantum computation. We provide intuitive definitions for these concepts in the context of quantum computation that lead to operationally meaningful bounds on program output. Our assessment highlights the continuing need for statistical analyses of quantum computing program to increase our confidence in the burgeoning field of quantum information science.

Dasgupta, Samudra↗
DOE OSTI2023

Muon capture on deuteron using local chiral potentials

The muon capture reaction μ – + d → n + n + ν μ in the doublet hyperfine state is studied using nuclear potentials and consistent currents derived in the chiral effective field theory, which are local and expressed in coordinate space (the so-called Norfolk models). Only the largest contribution due to the 1 S 0 nn scattering state is considered. Particular attention is given to the estimate of theoretical uncertainty, for which four sources have been identified: 1) the model dependence, 2) the chiral-order convergence for the weak nuclear current, 3) the uncertainty in the single-nucleon axial form factor, and 4) the numerical technique adopted to solve the bound and scattering A = 2 systems. This last source of uncertainty has turned out to be essentially negligible. For the 1 S 0 doublet muon capture rate Γ D ( 1 S 0 ), we obtain Γ D ( 1 S 0 ) = 255.8(0.6) (4.4)(2.9) s –1 , where the three errors come from the first three sources of uncertainty. The value for Γ D ( 1 S 0 ) obtained within this local chiral framework is compared with previous calculations and found in very good agreement.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Analysis of Radiological Release From Fueled Irradiation Experiments During Manual Handling (Slides)

Irradiation experiments are manually handled at the Advanced Test Reactor (ATR) by qualified operators using long handled tools. Standard handling evolutions include insertion and removal from the reactor vessel, transfer to/from a storage location or cask in the ATR canal, and handling/reconfigurations at a canal working tray. Such routine handling has the potential to result in mechanical damage to the experiment boundary which is credited to retain fission products. Since damage can occur due to operator error, this is an anticipated occurrence. This work determines the radiological consequences to receptors inside the ATR facility, as well as public receptors. Given the wide variety of fuel types tested at the ATR, a generic approach to the analysis is taken. The radiological inventory is determined to bound a variety of fuel types (e.g., ceramic and molten fuel matrices) and fission powers on the U-235 enrichment spectrum. The source term analysis considers different release fractions to bound different fuel types and burnups. The postulated handling events occur underwater; thus, retention of the released isotopic content is considered within the canal water column. Retention of radionuclides in the water column is determined using a modified approach of United States (US) Nuclear Regulatory Commission (NRC) Regulatory Guide (RG) 1.183. Radiological dose to the facility receptor is determined using a compartment release model. Dose to the public receptor is determined using atmospheric dispersion models using site specific atmospheric conditions with the use of the Radiological Safety Analysis Computer (RSAC) program version 7.2.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗
DOE OSTI2023

Improved quantum algorithms for linear and nonlinear differential equations

We present substantially generalized and improved quantum algorithms over prior work for inhomogeneous linear and nonlinear ordinary differential equations (ODE). Specifically, we show how the norm of the matrix exponential characterizes the run time of quantum algorithms for linear ODEs opening the door to an application to a wider class of linear and nonlinear ODEs. In [1], a quantum algorithm for a certain class of linear ODEs is given, where the matrix involved needs to be diagonalizable. The quantum algorithm for linear ODEs presented here extends to many classes of non-diagonalizable matrices including singular matrices. The algorithm here is also exponentially faster than the bounds derived in [1] for certain classes of diagonalizable matrices. Our linear ODE algorithm is then applied to nonlinear differential equations using Carleman linearization (an approach taken recently by us in [2]). The improvement over that result is two-fold. First, we obtain an exponentially better dependence on error. This kind of logarithmic dependence on error has also been achieved by [3], but only for homogeneous nonlinear equations. Second, the present algorithm can handle any sparse matrix (that models dissipation) if it has a negative log-norm (including non-diagonalizable matrices), whereas [2] and [3] additionally require normality.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Evidence of scaling advantage for the quantum approximate optimization algorithm on a classically intractable problem

The quantum approximate optimization algorithm (QAOA) is a leading candidate algorithm for solving optimization problems on quantum computers. However, the potential of QAOA to tackle classically intractable problems remains unclear. Here, we perform an extensive numerical investigation of QAOA on the low autocorrelation binary sequences (LABS) problem, which is classically intractable even for moderately sized instances. We perform noiseless simulations with up to 40 qubits and observe that the runtime of QAOA with fixed parameters scales better than branch-and-bound solvers, which are the state-of-the-art exact solvers for LABS. The combination of QAOA with quantum minimum finding gives the best empirical scaling of any algorithm for the LABS problem. We demonstrate experimental progress in executing QAOA for the LABS problem using an algorithm-specific error detection scheme on Quantinuum trapped-ion processors. Our results provide evidence for the utility of QAOA as an algorithmic component that enables quantum speedups.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Qubit coupled cluster singles and doubles variational quantum eigensolver ansatz for electronic structure calculations

Variational quantum eigensolver (VQE) for electronic structure calculations is believed to be one major potential application of near term quantum computing. Among all proposed VQE algorithms, the unitary coupled cluster singles and doubles excitations (UCCSD) VQE ansatz has achieved high accuracy and received a lot of research interest. However, the UCCSD VQE based on fermionic excitations needs extra terms for the parity when using Jordan–Wigner transformation. Here we introduce a new VQE ansatz based on the particle preserving exchange gate to achieve qubit excitations. The proposed VQE ansatz has gate complexity up-bounded to O ( n 4 ) for all-to-all connectivity where n is the number of qubits of the Hamiltonian. Numerical results of simple molecular systems such as BeH 2 , H 2 O, N 2 , H 4 and H 6 using the proposed VQE ansatz gives very accurate results within errors about 10 -3 Hartree.

Physics↗
DOE OSTI2026

Automated Classification of Vehicle Movements at Signalized Intersections Using Vehicle Trajectories

Accurate vehicle movement classification through signalized intersections is of paramount importance to the analysis of intersection performance and the optimization of traffic control strategies. Conventional techniques for tracking vehicle turning movements depend on infrastructure-based strategies like human counts, loop detectors, and video analytics, all of which are costly, prone to errors, and spatially constrained. High-frequency trajectory data can be utilized to determine vehicle movement patterns in a scalable and infrastructure-independent method due to the adoption of connected vehicles (CVs). In recent years, several studies have utilized connected vehicle data to generate performance measures. Most of the trajectory-based performance measures approaches, however, require map matching-i.e., extracting geospatial references from maps to identify the movements that individual vehicles make at a signalized intersection. These approaches are often time-consuming and hinder scalability since geographic features need to be provided for an analysis to be conducted. Map matching methods are prone to errors as different map versions change these geographic features. This research presents a novel automatic classification pipeline that uses CV trajectory data to classify vehicle movements at signalized crossings, specifically pass-through left-turn and right-turn maneuvers. The process starts by filtering trips that cross a spatial bounding box that has been defined at the target intersection. Approach and departure headings for each trajectory crossing the boundary are computed and are clustered together to identify dominant movements. The proposed algorithm is used to classify the movement of vehicles at 10 intersections in the state of California, and the results indicate that the algorithm can classify movements at these intersections with varying traffic volumes and road network configurations, all in a map-less framework with no need for conflation of vehicle trajectories to a digital base map.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Newton versus the machine: solving the chaotic three-body problem using deep neural networks

ABSTRACT Since its formulation by Sir Isaac Newton, the problem of solving the equations of motion for three bodies under their own gravitational force has remained practically unsolved. Currently, the solution for a given initialization can only be found by performing laborious iterative calculations that have unpredictable and potentially infinite computational cost, due to the system’s chaotic nature. We show that an ensemble of converged solutions for the planar chaotic three-body problem obtained using an arbitrarily precise numerical integrator can be used to train a deep artificial neural network (ANN) that, over a bounded time interval, provides accurate solutions at a fixed computational cost and up to 100 million times faster than the numerical integrator. In addition, we demonstrate the importance of training an ANN using converged solutions from an arbitrary precise integrator, relative to solutions computed by a conventional fixed precision integrator, which can introduce errors in the training data, due to numerical round-off and time discretization, that are learned by the ANN. Our results provide evidence that, for computationally challenging regions of phase space, a trained ANN can replace existing numerical solvers, enabling fast and scalable simulations of many-body systems to shed light on outstanding phenomena such as the formation of black hole binary systems or the origin of the core collapse in dense star clusters.

Breen, Philip G.↗
DOE OSTI2026

Quantitative 14 N NMR with Monte Carlo Uncertainty Analysis of Nitrate/Nitrite in Alkaline Nuclear Waste

While monitoring of nitrate and nitrite concentrations is important for managing corrosion in nuclear waste systems, existing analytical methods are hindered by turbidity, spectral interference, and delays from sample handling. Here, we demonstrate quantitative 14 N nuclear magnetic resonance (qNMR) spectroscopy as a direct, matrix-tolerant approach for nitrate and nitrite detection at natural abundance. Monte Carlo resampling was integrated into the workflow to quantify random error, establish precision–time tradeoffs, and separate noise-limited uncertainty from systematic bias arising from shimming, transmitter offset, or excitation pulse conditions. Quantification of nitrate and nitrite were validated in controlled alkaline matrix challenges and in 18-component Hanford-type simulants. These results establish 14 N qNMR as a practical, uncertainty-bounded tool for monitoring redox-active nitrogen species in chemically complex environments and provide a generalizable framework for quantitative analysis of quadrupolar nuclei.

Graham, Trent R. [Pacific Northwest National Labor↗