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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 109 records · Page 6

Thermodynamically informed priors for uncertainty propagation in first-principles statistical mechanics

Here, this work demonstrates how first-principles statistical mechanics approaches within a Bayesian framework can quantify and propagate uncertainties to downstream thermodynamic calculations. To address the issue of Bayesian prior selection, knowledge of 0 K ground states in the material system of interest is incorporated into the prior. The effectiveness of this framework is shown by creating a phase diagram for the fcc zirconium nitride system, including confidence intervals on order-disorder transition temperatures.

Bayesian methods↗

Understanding and Estimating Error Propagation in Neural Networks for Scientific Data Analysis

Neural networks are increasingly integrated into scientific discovery, where input data reduction and model quantization play a key role in accelerating inference. However, understanding and mitigating the impact of these techniques on output error is critical for ensuring reliable results, particularly in tasks demanding high numerical precision. This paper introduces a comprehensive framework for optimizing neural network inference in scientific computing by combining data reduction and weight quantization while maintaining error-controlled outcomes. We develop theoretical analyses to bound error propagation under these reductions and propose a framework that balances computational performance with error constraints. Evaluation on real-world learning-based combustion simulations and satellite image classification demonstrates that our derived error bounds accurately predict observed errors while enabling significant computational speedup under our framework. This work highlights the potential for further leveraging advancements in modern lossy compression algorithms and hardware accelerators that support lower-precision formats.

He, Weiming [New Jersey Institute of Technology]↗

SymProp: Scaling Sparse Symmetric Tucker Decomposition via Symmetry Propagation

Sparse symmetric tensors are an important class of tensors, and their decompositions serve as powerful tools for revealing low-rank structures. This paper introduces SymProp, a novel approach for scaling sparse symmetric Tucker decomposition by propagating symmetry through intermediate computations. SymProp optimizes two key computational kernels: Sparse Symmetric Tensor Times Same Matrix chain (S3 TTMc) for Higher-Order Orthogonal Iteration (HOOI) and Sparse Symmetric Tensor Times Same Matrix chain Times Core (S3 TTMcTC) for Higher-Order QR Iteration (HOQRI). Our method employs a metaprogramming-based index iteration approach to efficiently handle the upper triangular parts of intermediate dense symmetric tensors. SymProp achieves up to 50.9× speedup over SPLATT and up to 360.8× over Compressed Sparse Symmetric (CSS) format on the S3 TTMc operation. Moreover, our S3 TTMc and S3 TTMcTC implementations support tensor orders four levels higher than state-of-the-art methods. Our HOQRI demonstrates superior scalability and up to a 33.6× speedup over optimized HOOI. By enabling more scalable Tucker decompositions for higher orders, decomposition ranks, and dimension sizes, SymProp opens new possibilities for analyzing complex hypergraph structures in fields such as network science, data mining, and machine learning.

Li, Zecheng [North Carolina State University]↗

Crack Propagation Modeling of a High-Speed Outer Rotor Motor

This study aims to investigate the propagation of initial cracks in electric motors and their rate of spread through the rotor structure under various operating conditions. Traction motors are subjected to high centrifugal load owing to their speed and rapid acceleration and deceleration. In the manufacturing process, some initial imperfections or cracks in the rotor components are likely to occur. Under the influence of loading, these cracks will grow, and factors such as loading frequency, material strength, and location of material voids will affect this threshold speed. In the current study, a fracture mechanics–based approach has been used to investigate the crack growth rate for a 100 kW outer rotor motor with a rated speed of 6,000 rpm and a peak speed of 20,000 rpm for an integrated drive application.

Barua, Himel↗

Performance of an optical TPC Geant4 simulation with opticks GPU-accelerated photon propagation

We investigate the performance of Opticks, a NVIDIA OptiX API 7.5 GPU-accelerated photon propagation tool compared with a single-threaded Geant4 simulation. We compare the simulations using an improved model of the NEXT-CRAB-0 gaseous time projection chamber. Performance results suggest that Opticks improves simulation speeds by between 58.47 ± 0.02 and 181.39 ± 0.28 times relative to a CPU-only Geant4 simulation and these results vary between different types of GPU and CPU. A detailed comparison shows that the number of detected photons, along with their times and wavelengths, are in good agreement between Opticks and Geant4.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Supplemental material for paper "Radiosonde-to-Space (R2S) atmospheric specifications: Bridging observations and models for infrasound propagation"

This document describes the supplemental materials for the paper: TITLE: "Radiosonde-to-Space (R2S) atmospheric specifications: Bridging observations and models for infrasound propagation" DOI: xxxxxxxxxxx JOURNAL: TBD Written by Loring Schaible, September 17, 2025 This folder contains eight subfolders, each for a specific date and UTC time (in format YYYY-MM-DD_HH). All eight of these dates are exemplified and described in the manuscript. Within each folder is six documents. As an example, consider the folder "2022-12-31_00". The six files are comprised of: - Two text files (extension .txt) that describe the atmospheric specifications of Albuquerque, NM. One is the G2S model, the other is the R2S model. "G2S_2022-12-31_00.txt" "R2S_2022-12-31_00.txt" - Two text files (extension .dat) that contain the ground arrivals predicted by infraGA using the parameters described in the manuscript. One each for the two atmospheric specification models above, G2S and R2S. "G2S_2022-12-31_00.arrivals.dat" "R2S_2022-12-31_00.arrivals.dat" - Two image files (extension .png) that illustrate the predicted arrivals contained in the two files above. One image shows all arrivals as either black (G2S) or red (R2S). The other image shows the same but with arrivals common to both models in gray. "2022-12-31_00_Arrivals.png" "2022-12-31_00_DifArrivals.png"

Schaible, Loring Pratt [Sandia National Laboratori↗

Efficient general method for numerically modeling laser pulse propagation, overlap, and lifetime effects in amplifiers

An efficient numerical time-dependent general method is developed to address incoherent pulse overlap and lifetime effects in laser amplifiers. The alternating propagation-population laser energetics method (APPLE) has been validated against a semi-discrete coupled rate equation numerical method (SDRE) and analytic formalisms in bounding cases. APPLE is based on decoupled rates applied to a time-dependent framework where both space-time-dependent populations and pulse energetics are consistently updated in each time step. A significant advantage of APPLE lies in its conceptual simplicity, ease of implementation, and relatively small computational cost. SDRE tracks the populations through coupled rates and uses the method of lines to discretize the hyperbolic partial differential transport equations allowing for use of ordinary differential equation solvers. With reasonably sized mesh, we report both energetic and power pulse shape relative differences on the order of one percent between the models over a large range of initial conditions.

47 OTHER INSTRUMENTATION↗

Universal structure of propagation-invariant optical pulses

Space–time structuring of light—where spatial and temporal degrees of freedom are deliberately coupled and controlled—is an emerging area of optics that enables novel configurations of electromagnetic fields. Of particular importance for applications are optical pulses whose peak intensity travels at an arbitrary, tunable velocity while maintaining its spatiotemporal profile. Space–time wave packets (STWPs) and the ideal flying focus (FF) are two prominent realizations of these pulses. Here, we show that these realizations share an identical spatiotemporal field structure and that this structure represents a universal solution for constant-velocity, propagation-invariant pulses.

Almeida, R. (ORCID:0000000288435855)↗

Towards robust laser beam propagation in atmospheric turbulence

High-fidelity optical propagation through the atmosphere is essential for free-space optical technologies, including laser-based remote sensing and optical communication. However, atmospheric turbulence severely distorts beams and compromises system performance. In this work, we employ hypergeometric-Gaussian (HyGG) vortex beams as probes to characterize and mitigate atmospheric turbulence. Using over 250,000 experimental and simulated frames, we show that refining the power spectrum density (PSD) can reduce numerical prediction errors by up to 79.8%. Concurrently, experimental observations supported by numerical simulations demonstrate that HyGG beams exhibit superior turbulence resilience across multiple metrics compared to conventional Gaussian beams, particularly in their ability to withstand over 5 times stronger turbulence while maintaining similar intensity fluctuations. These dual investigations, on both turbulence mitigation and robust beam solutions, converge to form a unified strategy for enhancing free-space optical system performance. Collectively, our findings provide new insights into light–turbulence interactions and highlight the practical utility of vortex beams under atmospheric conditions.

Zhang, Boyu↗

Conditional Pseudo-Reversible Normalizing Flow for Surrogate Modeling in Quantifying Uncertainty Propagation

We introduce a conditional pseudo-reversible normalizing flow (PR-NF) that directly learns conditional probability distributions from noisy physical models to efficiently quantify both forward and inverse uncertainty propagation. Traditional surrogate modeling approaches approximate only the deterministic component of physical models, requiring separate noise characterization and computationally expensive sampling methods for inverse problems. Here, in this work, we develop the conditional PR-NF model to directly learn and efficiently generate samples from the conditional probability density functions (PDFs). The training process utilizes dataset consisting of input-output pairs without requiring prior knowledge about the noise and the function. Once trained, our model efficiently generates samples from conditional PDFs for any input within the training domain. Moreover, the pseudo-reversibility feature allows for the use of fully connected neural network architectures, which simplifies the implementation and enables theoretical analysis. We provide a rigorous convergence analysis of the conditional PR-NF model, showing its ability to converge to the target conditional PDF using the Kullback−Leibler divergence. To demonstrate the effectiveness of our method, we apply it to several benchmark tests and a real-world geologic carbon storage problem.

97 MATHEMATICS AND COMPUTING↗

Sigma: Uncertainty Propagation for C++

Sigma is a header-only C++-17 library for uncertainty propagation, inspired by uncertainties for Python and Measurements.jl for Julia. The library tracks the functional correlation between dependent and independent variables, ensuring that the uncertainty of the independent variables is properly considered in the calculation of the dependent variables’ uncertainties. It is intended as a near drop-in replacement for the standard floating point types (aside from uncertainty specification), and aims to be easily interoperable with the existing standard types.

97 MATHEMATICS AND COMPUTING↗

Heart Shape to Fracture Distance: Characterizing Hydraulic Fracture Propagation before Hits

Estimating the distance from the hydraulic fracture tip to the monitor well can be useful for fracture characterization, well spacing optimization, and preventing parent-child well interference. A heart-shaped signal is referred to as the extensional precursor of a fracture hit recorded by crosswell strain measurements and can serve as a vital tool for such estimation. This study incorporates the 3D displacement discontinuity method (DDM) to understand the impact of fracture geometry and monitor well offset on the heart-shaped signal’s characteristics. Results from numerical simulation and analytical solutions reveal a strong linear correlation between the spatial extent of the heart-shaped signal and the fracture tip distance. This relationship was further developed to predict tip distance using field data from the Hydraulic Fracture Test Site 2 (HFTS2). A reasonable approximation result from field data further validates the methodology. In addition, it is worth noting that the estimation accuracy depends on the ratio between fracture dimension and tip distance. The findings of this study offer a novel approach for real-time monitoring and characterizing hydraulic fracture propagation, which can be further used for well spacing optimization in unconventional and enhanced geothermal system reservoir development, as well as caprock integrity monitoring for carbon sequestration projects.

58 GEOSCIENCES↗

Characterizing Hydraulic Fracture Propagation Before Fracture Hits

Estimating the distance from hydraulic fracture tip to monitor well can be very useful for fracture characterization, well spacing optimization, and preventing parent-child well interference. Heart-shape signal is referred to as the extensional precursor of fracture hit recorded by cross-well strain measurements and can be served as a vital tool to make such estimation. This study incorporates the 3D Displacement Discontinuity Method to understand the impact of fracture geometry and monitor well offset on the heart-shape signal’s characteristics. Results from numerical simulation and analytical solutions reveal a strong linear correlation between the spatial extent of heart shape signal and fracture tip distance. This relationship was further developed to predict tip distance using field data from the Hydraulic Fracture Test Site 2. A reasonable approximation result from field data further validates the methodology. In addition, it is worth noting that the estimation accuracy depends on the ratio between fracture dimension and tip distance. The findings of this study offer a novel approach for real-time monitoring and characterizing hydraulic fracture propagation, which can be further used for well spacing optimization in unconventional and Enhanced Geothermal System reservoir development, as well as cap rock integrity monitoring for carbon sequestration projects.

Jin, Ge↗

STAT7 v1.2 User Guide: The STAT7 Code for Statistical Propagation of Uncertainties in Steady-State Thermal Hydraulics Analysis of Plate-Fueled Reactors

The STAT7 software was developed to perform steady-state, single-phase thermal hydraulics analysis of plate-fueled reactors based on statistical propagation of uncertainties. Application of the software is for non-power research and test reactors, including conversion to low-enriched uranium fuel of U.S. High-Performance Research Reactors such as Massachusetts Institute of Technology Research Reactor. Since it can be necessary to repeat analysis during fuel reloading, STAT7 accommodates flexibility in analyzing many realistic aspects of reactor fuel management. STAT7 uses a Monte Carlo approach to model uncertainty in common fuel fabrication parameters and other key reactor operating parameters required for thermal hydraulics analyses of research and test reactors. These safety calculations are ultimately intended to protect against high fuel plate temperatures due to critical heat flux, or onset of flow instability. STAT7 supports water properties based on the IAPWS-IF97 functions (The International Association for the Properties of Water and Steam Industrial Formulation 1997 for the Thermodynamic Properties of Water and Steam) in addition to the fit functions. STAT7 predicts axial profiles of fuel, cladding, and coolant temperature along a lateral stripe that runs the full length of the fuel plate from the bottom to the top. STAT7 can simultaneously analyze all of the axial nodes of all of the fuel plates and all of the coolant channels for one latera stripe of a fuel element. Power splits are calculated for each axial node of each plate to determine how much of the power goes out each face of the plate. By running STAT7 multiple times, full core analysis can be performed by analyzing the margin to onset of nucleate boiling and onset of flow instability for each axial node of each stripe of each plate of each fuel element in the core.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Hierarchically Informed Engineering Models for Predictive Modeling of Turbulent Premixed Flame Propagation in Pre- chamber Turbulent Jet Ignition

The goal of the project is to improve the predictive accuracy and efficiency of turbulent combustion sub-models for pre-chamber turbulent jet ignition (TJI). This goal is achieved through the development of a hierarchically informed engineering model for turbulent combustion in TJI. The model development starts with the highest level of model description of turbulent combustion with direct numerical simulation (DNS) from which fundamental characteristics and scaling properties of turbulent premixed flame propagation under TJI relevant conditions are obtained.

42 ENGINEERING↗

Addressing tokens dynamic generation, propagation, storage and renewal to secure the GlideinWMS pilot based jobs and system

GlideinWMS has been one of the first middleware in the WLCG community to transition from X.509 to support also tokens. The first step was to get from the prototype in 2019 to using tokens in production in 2022. This paper will present the challenges introduced by the wider adoption of tokens and the evolution plans for securing the pilot infrastructure of GlideinWMS and supporting the new requirements. In the last couple of years, the GlideinWMS team supported the migration to tokens of experiments and resources. Inadequate support in the current infrastructure, more stringent requirements, and the higher spatial and temporal granularity forced GlideinWMS to revisit once more how credentials are generated, used, and propagated. The new credential modules have been designed to be used in multiple systems (GWMS, HC) and use a model where credentials have type, purpose, and different flows. Credentials are dynamically generated in order to customize the duration and limit the scope to the targeted resource. This allows to enforce the least privilege principle. Finally, we also considered adding credential storage, renewal, and invalidation mechanisms within the GlideinWMS infrastructure to serve better the experiments’ needs.

Mambelli, Marco↗

Reduced Order Model for Guided Wave Propagation on Gas Pipelines to Enable Real-Time Simulation

Reduced order model for simulation of Guided wave propagation is presented here. The utilization of reduced order models ensures efficient data generation for a variety of parameters where it takes huge computational effort to simulate, crucial for timely monitoring and decision-making. Autoencoder based reduced order models are proposed here, which are trained on simulated data from open-source finite element framework, Firedrake.

Bukka, Sandeep Reddy↗