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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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DOE OSTI2022

Data-driven model for divertor plasma detachment prediction

We present a fast and accurate data-driven surrogate model for divertor plasma detachment prediction leveraging the latent feature space concept in machine learning research. Our approach involves constructing and training two neural networks: an autoencoder that finds a proper latent space representation (LSR) of plasma state by compressing the multi-modal diagnostic measurements and a forward model using multi-layer perception (MLP) that projects a set of plasma control parameters to its corresponding LSR. By combining the forward model and the decoder network from autoencoder, this new data-driven surrogate model is able to predict a consistent set of diagnostic measurements based on a few plasma control parameters. In order to ensure that the crucial detachment physics is correctly captured, highly efficient 1D UEDGE model is used to generate training and validation data in this study. The benchmark between the data-driven surrogate model and UEDGE simulations shows that our surrogate model is capable of providing accurate detachment prediction (usually within a few per cent relative error margin) but with at least four orders of magnitude speed-up, indicating that performance-wise, it has the potential to facilitate integrated tokamak design and plasma control. Comparing with the widely used two-point model and/or two-point model formatting, the new data-driven model features additional detachment front prediction and can be easily extended to incorporate richer physics. This study demonstrates that the complicated divertor and scrape-off-layer plasma state has a low-dimensional representation in latent space. Understanding plasma dynamics in latent space and utilising this knowledge could open a new path for plasma control in magnetic fusion energy research.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

LANL studies of ESD spark energy discharge through September 2019

Two concentric-sphere apparatuses were used to measure the energies deposited by electric sparks through air to calibrated loads. Both sets of spheres had clean, uninsulated metal surfaces. The 127-mm diameter pair of spheres had a gap of 10 mm and a capacitance of 52 pF. The maximum energy stored on these spheres was 12 mJ and the maximum energy delivered to the 50-mΩ load was 11 µJ, with or without inductance. This load energy amounted to 0.09% of the available input energy; the remainder of the input energy was expended in forming the spark. The second set of spheres was 330 mm in diameter with a gap of 14 mm and a capacitance of 240 pF. The maximum energy stored on these spheres was 71 mJ and the maximum energy delivered to the 50-mΩ load was 150 µJ without added inductance. This load energy was 0.27% of the input energy; again, the remainder of the input energy was expended in forming the spark. The inclusion of 250 nH and 500 nH inductances in the discharge path of the smaller spheres caused the current discharges to resonate, but had little effect on the maximum energies deposited in the loads. At the time of writing, inductances had not yet been added to the larger spheres. The magnitudes of the observed spark discharges were stochastic, so more than 1200 tests were required to develop the statistical bounds of the spark behavior, i.e., to estimate the worst case (highest likely) energy depositions in the loads. The action integrals of the current discharges $∫ i^2dt$ were found to be approximately independent of the load resistances for loads of less than ~1 Ω. Hence the energy deposition scaled with resistance as 207 µJ/Ω and 2.93 mJ/Ω for the two apparatuses.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2021

Decarbonization During Predevelopment of Modular Building Solutions

Off-site construction methods offer the opportunity to compress costs of net-zero energy (NZE) housing using the advantages of mass production. There has been limited investigation on trade-offs between site-built and industrialized construction from the perspective of reducing the incremental cost of NZE strategies and reducing greenhouse gas emissions during embodied and operational stages. Blokable, LLC, a vertically integrated modular builder, wanted to know how the learning curves of mass production would help them decarbonize their existing modular housing prototype at a relative cost advantage. The method developed for this question looked at the life cycle assessment of an individual apartment and used learning-curve efficiencies to approximate the relative advantage of construction-at-scale. Greenhouse gas emissions were quantified by a learning-affected, whole-life carbon emissions model and demonstrated a path to a 60% reduction of whole-life CO2-equivalent in the 2030 production year. The resultant roadmap considers a best-first approach to decarbonizing a modular building product line, and the method can be replicated for other modular builders.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Evaluation of AI-enabled Digital Documented Safety Analysis

The National Reactor Innovation Center (NRIC) is leading a transformative initiative to accelerate advanced reactor deployment by fundamentally reimagining how nuclear safety basis documentation is developed, reviewed, and maintained. Traditional Documented Safety Analysis (DSA) processes for DOE-authorized facilities rely on static, document-centric workflows that consume significant time and resources, exemplified by recent major licensing efforts requiring hundreds of thousands of staff hours and millions of pages of documentation review. These conventional approaches create barriers to the rapid, cost-effective deployment of advanced reactors that America's future energy needs demand. NRIC's DOE Authorization Digital Transformation Project addresses these challenges through an innovative framework that integrates artificial intelligence (AI), digital engineering, and systems-based data management into a cohesive digital ecosystem. This white paper presents NRIC's methodology for evaluating AI-enabled document generation capabilities within this broader digital infrastructure, using the Demonstration of Microreactor Experiments (DOME) facility as a pilot case study. The evaluation will assess an AI tool's ability to generate a Preliminary Documented Safety Analysis (PDSA) through progressive integration stages—from standalone document processing to full digital thread connectivity—while maintaining rigorous verification, validation, and regulatory acceptance standards. By establishing dynamic, traceable connections between design data and safety documentation, NRIC's approach has the potential to reduce both document development time and regulatory review cycles by as much as 50%, while simultaneously improving accuracy, consistency, and traceability. This initiative represents a critical step toward establishing reusable digital infrastructure that reactor developers can leverage to accelerate their path from concept to commercial operation, directly supporting NRIC's mission to demonstrate and deploy advanced nuclear energy technologies.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Adaptive Model-Free Vehicle Path-Tracking via Fast-Converging Prescribed-Time Newton-Based Extremum-Seeking Control

Model-free control (MFC) offers a simple and effective approach to automated vehicle path-tracking without requiring an explicit plant model for control law design. However, gain tuning in MFC is typically carried out through trial-and-error, which can be time-consuming and may lead to suboptimal performance. To address this limitation, extremum-seeking-based adaptive MFC has shown promise by enabling real-time adaptation of control gains, without relying on a predefined vehicle model. Nonetheless, existing ESC approaches often suffer from slow convergence. This paper integrates MFC, employing longitudinal and lateral ultra-local models of a rear-wheel-drive vehicle, with a novel prescribed-time (PT) Newton-based extremum-seeking control (ESC) strategy that ensures rapid convergence of control gains within the prescribed time. Unlike conventional gradient-based ESC methods, the PT Newton-based ESC leverages artificial delays and time-periodic gains, not only to guarantee convergence within the specified time, but also to compensate for feedback delays. Simulation results demonstrate that the proposed approach significantly improves gain adaptation speed and tracking accuracy. This work advances adaptive model-free vehicle control by offering a high-performance, delay-resilient alternative to existing ESCMFC frameworks.

Waleed khan, Muhammad [The University of Texas at ↗
DOE OSTI2018

Verification of an Algorithm for Processing Data from Multifrequency Lidar Sensing of Aerosols

We consider an algorithm for processing lidar signals, which for a lidar path with constant qualitative aerosol characteristics (particle size distribution in each aerosol mode, complex refractive index of the particles) lets us obtain a concentration profile for the fine aerosol component without using additional a priori information. The algorithm was verified based on lidar data from the Institute of Physics (National Academy of Sciences of Belarus). The vertical volume concentration profile for fine particles obtained by our algorithm is quite consistent with the results of its independent retrieval, based on integrated processing of lidar and sun photometer data.

42 ENGINEERING↗
DOE OSTI2022

Computational Design of Alloys for Energy Technologies

Advanced materials that maintain their mechanical performance under elevated temperatures, corrosive environments, and a range of static and evolving stresses are needed to improve the efficiency and reduce the environmental impact of a wide spectrum of energy technologies. For instance, cost-efficient alloys that can withstand high temperatures (e.g., 700 °C) have a critical role in improving the efficiency and economics of power generation to support decarbonization of the energy sector; such is true of both the nuclear and fossil energy sectors. Considering both the threats of the energy crisis, namely soaring costs of greenhouse gas emission-producing energy and climate change, it is essential to increase the pace of material discovery and enable rapid paths for material qualification to advance clean energy technologies. Conventionally, alloy development has followed a slow Edisonian process that uses repeated cycles of making, characterizing, and modifying to arrive at optimum composition and processing conditions to achieve the desired component performance. This optimization is followed by the necessary stepwise materials qualification. Furthermore, the increasing adoption of sound data management and physics-informed machine learning represents the next step in the acceleration of materials design and development. In the integrated computational materials engineering (ICME) approach, computational modeling and simulation data from different length and time scales can be combined with complex microstructural details from multimodal experimental characterization and selective property testing to close the design loop for rapid alloy development.

Computational Design Of Materials↗
DOE OSTI2014

The rigorous stochastic matrix multiplication scheme for the calculations of reduced equilibrium density matrices of open multilevel quantum systems

Understanding the roles of the temporary and spatial structures of quantum functional noise in open multilevel quantum molecular systems attracts a lot of theoretical interests. I want to establish a rigorous and general framework for functional quantum noises from the constructive and computational perspectives, i.e., how to generate the random trajectories to reproduce the kernel and path ordering of the influence functional with effective Monte Carlo methods for arbitrary spectral densities. This construction approach aims to unify the existing stochastic models to rigorously describe the temporary and spatial structure of Gaussian quantum noises. In this paper, I review the Euclidean imaginary time influence functional and propose the stochastic matrix multiplication scheme to calculate reduced equilibrium density matrices (REDM). In addition, I review and discuss the Feynman-Vernon influence functional according to the Gaussian quadratic integral, particularly its imaginary part which is critical to the rigorous description of the quantum detailed balance. As a result, I establish the conditions under which the influence functional can be interpreted as the average of exponential functional operator over real-valued Gaussian processes for open multilevel quantum systems. I also show the difference between the local and nonlocal phonons within this framework. With the stochastic matrix multiplication scheme, I compare the normalized REDM with the Boltzmann equilibrium distribution for open multilevel quantum systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

Surface geometry of protoplanetary disks inferred from near-infrared imaging polarimetry

We present a new method of analysis for determining the surface geometry of five protoplanetary disks observed with near-infrared imaging polarimetry using Subaru-HiCIAO. Using as inputs the observed distribution of polarized intensity (PI), disk inclination, assumed properties for dust scattering, and other reasonable approximations, we calculate a differential equation to derive the surface geometry. This equation is numerically integrated along the distance from the star at a given position angle. We show that, using these approximations, the local maxima in the PI distribution of spiral arms (SAO 206462, MWC 758) and rings (2MASS J16042165-2130284, PDS 70) are associated with local concave-up structures on the disk surface. We also show that the observed presence of an inner gap in scattered light still allows the possibility of a disk surface that is parallel to the light path from the star, or a disk that is shadowed by structures in the inner radii. Our analysis for rings does not show the presence of a vertical inner wall as often assumed in studies of disks with an inner gap. Finally, we summarize the implications of spiral and ring structures as potential signatures of ongoing planet formation.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Corrosion in low dielectric constant Si-O based thin films: Buffer concentration effects

Organosilicate glass (OSG) is often used as an interlayer dielectric (ILD) in high performance integrated circuits. OSG is a brittle material and prone to stress-corrosion cracking reminiscent of that observed in bulk glasses. Of particular concern are chemical-mechanical planarization techniques and wet cleans involving solvents commonly encountered in microelectronics fabrication where the organosilicate film is exposed to aqueous environments. Previous work has focused on the effect of pH, surfactant, and peroxide concentration on the subcritical crack growth of these films. However, little or no attention has focused on the effect of the conjugate acid/base concentration in a buffer. Accordingly, this work examines the “strength” of the buffer solution in both acidic and basic environments. The concentration of the buffer components is varied keeping the ratio of acid/base and therefore pH constant. In addition, the pH was varied by altering the acid/base ratio to ascertain any additional effect of pH. Corrosion tests were conducted with double-cantilever beam fracture mechanics specimens and fracture paths were verified with ATR-FTIR. Shifts in the threshold fracture energy, the lowest energy required for bond rupture in the given environment, G{sub TH}, were found to shift to lower values as the concentration of the base in the buffer increased. This effect was found to be much larger than the effect of the hydroxide ion concentration in unbuffered solutions. The results are rationalized in terms of the salient chemical bond breaking process occurring at the crack tip and modeled in terms of the chemical potential of the reactive species.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Resolving Dynamics in the Ensemble: Finding Paths through Intermediate States and Disordered Protein Structures

Proteins have been found to inhabit a diverse set of three-dimensional structures. The dynamics that govern protein interconversion between structures happen over a wide range of time scales-picoseconds to seconds. Our understanding of protein functions and dynamics is largely reliant upon our ability to elucidate physically populated structures. From an experimental structural characterization perspective, we are often limited to measuring the ensemble-averaged structure both in the steady-state and time-resolved regimes. Generating kinetic models and understanding protein structure–function relationships require atomistic knowledge of the populated states in the ensemble. Here, we present ensemble refinement methodologies that integrate time-resolved experimental signals with molecular dynamics models. We first discuss integration of experimental structural restraints to molecular models in disordered protein systems that adhere to the principle of maximum entropy for creating a complete set of ensemble structures. We then propose strategies to find kinetic pathways between the refined structures, using time-resolved inputs to guide molecular dynamics trajectories and the use of inference to generate tailored stimuli to prepare a desired ensemble of protein states.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Synchronization in electric power networks with inherent heterogeneity up to 100% inverter-based renewable generation

The synchronized operation of power generators is the foundation of electric power network stability and a key to the prevention of undesired power outages and blackouts. Here, we derive the conditions that guarantee synchronization in power networks with inherent generator heterogeneity when subjected to small perturbations, and perform a parametric sensitivity analysis to understand synchronization with varied types of generators. As inverter-based resources, which are the primary interfacing technology for many renewable sources of energy, have supplanted synchronous generators in ever growing numbers, the center of attention on associated integration challenges have resided primarily on the role of declining system inertia. Our results instead highlight the critical role of generator damping in achieving a stable state of synchronization. Additionally, we report the feasibility of operating interconnected electric grids with up to 100% power contribution from inverter-based renewable generation technologies. Our study has important implications as it sets the basis for the development of advanced control architectures and grid optimization methods that ensure synchronization and further pave the path towards the decarbonization of the electric power sector.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2015

Large-deviation joint statistics of the finite-time Lyapunov spectrum in isotropic turbulence

One of the hallmarks of turbulent flows is the chaotic behavior of fluid particle paths with exponentially growing separation among them while their distance does not exceed the viscous range. The maximal (positive) Lyapunov exponent represents the average strength of the exponential growth rate, while fluctuations in the rate of growth are characterized by the finite-time Lyapunov exponents (FTLEs). In the last decade or so, the notion of Lagrangian coherent structures (which are often computed using FTLEs) has gained attention as a tool for visualizing coherent trajectory patterns in a flow and distinguishing regions of the flow with different mixing properties. A quantitative statistical characterization of FTLEs can be accomplished using the statistical theory of large deviations, based on the so-called Cramér function. To obtain the Cramér function from data, we use both the method based on measuring moments and measuring histograms and introduce a finite-size correction to the histogram-based method. We generalize the existing univariate formalism to the joint distributions of the two FTLEs needed to fully specify the Lyapunov spectrum in 3D flows. The joint Cramér function of turbulence is measured from two direct numerical simulation datasets of isotropic turbulence. Results are compared with joint statistics of FTLEs computed using only the symmetric part of the velocity gradient tensor, as well as with joint statistics of instantaneous strain-rate eigenvalues. When using only the strain contribution of the velocity gradient, the maximal FTLE nearly doubles in magnitude, highlighting the role of rotation in de-correlating the fluid deformations along particle paths. We also extend the large-deviation theory to study the statistics of the ratio of FTLEs. The most likely ratio of the FTLEs λ{sub 1} : λ{sub 2} : λ{sub 3} is shown to be about 4:1:−5, compared to about 8:3:−11 when using only the strain-rate tensor for calculating fluid volume deformations. The results serve to characterize the fundamental statistical and geometric structure of turbulence at small scales including cumulative, time integrated effects. These are important for deformable particles such as droplets and polymers advected by turbulence.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

Magnetic Field Mapping of the CLAS12 Torus - A Comparative Study between the Engineering Model and Measurements at JLab

This study provides an overview of the magnetic field measurement and subsequent electromagnetic re-modeling of the CLAS12 torus during the commissioning of the magnet in the fall of 2016. The CLAS12 detector in Hall B is part of the 12 GeV Accelerator Upgrade project at Jefferson Lab. The torus magnet allows precise determination of particle momenta in the forward direction (~ 5 0 x 4 0 ) in the forward direction. The ability to do this requires we know the fB.dl of the torus to within an accuracy of 0.5 % or better. To achieve this, an accurate model of the field along the particle paths is required. The TOSCA code is used to generate a full 3D simulation of the magnetic envelope of the magnet as designed. Experimentally the actual magnetic field within the magnet was surveyed to confirm the model design and to measure the deviation from the ideal case. The magnetic field deviations are attributed to manufacturing variability and assembly tolerances. A final model was created with allowances for these deviations guided by the survey data to create a more precise field integral model which greatly improves momentum resolution capability allowing it to deliver the required specifications.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Tailored Fiber Placement for Complex Preforms

Tailored Fiber Placement (TFP) offers a novel approach to optimize fiber architecture for the fabrication of complex, structural parts not traditionally suitable for advanced composites. This technology not only offers new routes for weight reduction via metal substitution, it also offers cost reduction through minimization of material scrap and reduced labor. This reduction in component weight leads to increased fuel efficiency, and reduced production energy consumption, thereby, helping to achieve the stated IACMI technical goals. This technology leverages centuries of manufacturing development in support of the textile and embroidery industry. One major drawback to this technology is the lack of commercial or non- proprietary structural performance data and robust analytical tools used to optimize fiber architecture and predict performance. This project was structured to utilize common sub-element features to validate analytical performance tools, generate performance data, and gather cost and performance data on components of interest. This project was designed to give industry sponsors the confidence and ability to take full advantage of TFP to fabricate primary, highly loaded structure and integrate features such as metallic fasteners. The project focused principally on the use of high strength carbon fiber, such as T700, and the use of aerospace epoxy resin matrix to primarily support development of new composite applications in vehicle, aerospace, and industrial markets. This project applied previously developed analytical tools to predict the performance of TFP produced parts. This work focused on developing the pipeline to characterize material in order to accurately predict component performance when modifying the TFP print paths and stitch density. This focused on experimental characterization via standardized ASTM testing, alongside experimental testing of more representative service components by testing curved beam strength, beam shear performance, a large scale TFP lug, and ultimately designing a fully TFP clip bracket that reduced weight and cost compared to a traditional metallic component. The new knowledge gained from this program included: 1) development and demonstration of novel analytical tools applied to analysis of TFP preforms; 2) development and demonstration of a building block approach using coupons and sub-elements to optimize the design of a more complex component; 3) demonstration that optimized fiber orientation using TFP can exceed performance of conventional textile composite materials and can open new applications currently limited to metallic components; 4) Demonstration of performance and cost benefits of the TFP process as compared to metallic and conventional textile composites. Recommendations for follow-on work include development of design allowables to assess the impact of high temperature/moisture exposure or saturation during loading, tracking the impact of stitching needle wear on the performance of parts and ability to stitch thicker preforms, using TFP preforms as local reinforcement at areas of bearing or complex loading, and topology optimization of components by tow steering. The expertise developed during the course of this project can be leveraged to provide commercial engineering design and fabrication services using TFP. UDRI is in the process of formalizing their partnership with Spintech, who will serve as the commercialization partner for this technology and provide molding services and deliver finished components to the end user. UDRI will continue to produce the preforms until the economics allow Spintech to procure its own TFP equipment or lease UDRI equipment, at which point UDRI will step away from manufacture and serve as the engineering and design lead on product development.

36 MATERIALS SCIENCE↗
DOE OSTI2022

An ontology-based fault generation and fault propagation analysis approach for safety-critical computer systems at the design stage

Abstract Fault propagation analysis is a process used to determine the consequences of faults residing in a computer system. A typical computer system consists of diverse components (e.g., electronic and software components), thus, the faults contained in these components tend to possess diverse characteristics. How to describe and model such diverse faults, and further determine fault propagation through different components are challenging problems to be addressed in the fault propagation analysis. This paper proposes an ontology-based approach, which is an integrated method allowing for the generation, injection, and propagation through inference of diverse faults at an early stage of the design of a computer system. The results generated by the proposed framework can verify system robustness and identify safety and reliability risks with limited design level information. In this paper, we propose an ontological framework and its application to analyze an example safety-critical computer system. The analysis result shows that the proposed framework is capable of inferring fault propagation paths through software and hardware components and is effective in predicting the impact of faults.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Knowledge-guided machine learning can improve carbon cycle quantification in agroecosystems

Abstract Accurate and cost-effective quantification of the carbon cycle for agroecosystems at decision-relevant scales is critical to mitigating climate change and ensuring sustainable food production. However, conventional process-based or data-driven modeling approaches alone have large prediction uncertainties due to the complex biogeochemical processes to model and the lack of observations to constrain many key state and flux variables. Here we propose a Knowledge-Guided Machine Learning (KGML) framework that addresses the above challenges by integrating knowledge embedded in a process-based model, high-resolution remote sensing observations, and machine learning (ML) techniques. Using the U.S. Corn Belt as a testbed, we demonstrate that KGML can outperform conventional process-based and black-box ML models in quantifying carbon cycle dynamics. Our high-resolution approach quantitatively reveals 86% more spatial detail of soil organic carbon changes than conventional coarse-resolution approaches. Moreover, we outline a protocol for improving KGML via various paths, which can be generalized to develop hybrid models to better predict complex earth system dynamics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2011

Goldstone bosons and a dynamical Higgs field

Higgs inflation uses the gauge variant Higgs field as the inflaton. During inflation the Higgs field is displaced from its minimum, which results in associated Goldstone bosons that are apparently massive. Working in a minimally coupled U(1) toy model, we use the closed-time-path formalism to show that these Goldstone bosons do contribute to the one-loop effective action. Therefore the computation in unitary gauge gives incorrect results. Our expression for the effective action is gauge invariant upon using the background equations of motion.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗