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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 955 records · Page 53
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Time-Resolved Line Shapes of Single Quantum Emitters via Machine Learned Photon Correlations

Solid-state single-photon emitters (SPEs) are quantum light sources that combine atomlike optical properties with solid-state integration and fabrication capabilities. SPEs are hindered by spectral diffusion, where the emitter’s surrounding environment induces random energy fluctuations. Timescales of spectral diffusion span nanoseconds to minutes and require probing single emitters to remove ensemble averaging. Photon correlation Fourier spectroscopy (PCFS) can be used to measure time-resolved single emitter line shapes, but is hindered by poor signal-to-noise ratio in the measured correlation functions at early times due to low photon counts. Here, we develop a framework to simulate PCFS correlation functions directly from diffusing spectra that match well with experimental data for single colloidal quantum dots. We use these simulated datasets to train a deep ensemble autoencoder machine learning model that outputs accurate, noiseless, and probabilistic reconstructions of the noisy correlations. Using this model, we obtain reconstructed time-resolved single dot emission line shapes at timescales as low as 10 ns, which are otherwise completely obscured by noise. This enables PCFS to extract optical coherence times on the same timescales as Hong-Ou-Mandel two-photon interference, but with the advantage of providing spectral information in addition to estimates of photon indistinguishability. Further, our machine learning approach is broadly applicable to different photon correlation spectroscopy techniques and SPE systems, offering an enhanced tool for probing single emitter line shapes on previously inaccessible timescales.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

Topological Phenomena in Artificial Quantum Materials Revealed by Local Chern Markers

A striking example of frustration in physics is Hofstadter’s butterfly, a fractal structure that emerges from the competition between a crystal’s lattice periodicity and the magnetic length of an applied field. Current methods for predicting the topological invariants associated with Hofstadter’s butterfly are challenging or impossible to apply to a range of materials, including those that are disordered or lack a bulk spectral gap. Here, in this work, we demonstrate a framework for predicting a material’s local Chern markers using its position-space description and validate it against experimental observations of quantum transport in artificial graphene in a semiconductor heterostructure, inherently accounting for fabrication disorder strong enough to close the bulk spectral gap. By resolving local changes in the system’s topology, we reveal the topological origins of antidot-localized states that appear in artificial graphene in the presence of a magnetic field. Moreover, we show the breadth of this framework by simulating how Hofstadter’s butterfly emerges from an initially unpatterned 2D electron gas as the system’s potential strength is increased and predict that artificial graphene becomes a topological insulator at the critical magnetic field. Overall, we anticipate that a position-space approach to determine a material’s Chern invariant without requiring prior knowledge of its occupied states or bulk spectral gaps will enable a broad array of fundamental inquiries and provide a novel route to material discovery, especially in metallic, aperiodic, and disordered systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Convolutional Dictionary Regularizers for Tomographic Inversion

There has been a growing interest in the use of data-driven regularizers to solve inverse problems associated with computational imaging systems. The convolutional sparse representation model has recently gained attention, driven by the development of fast algorithms for solving the dictionary learning and sparse coding problems for sufficiently large images and data sets. Nevertheless, this model has seen very limited application to tomographic reconstruction problems. In this paper, we present a model-based tomographic reconstruction algorithm using a learnt convolutional dictionary as a regularizer. The key contribution is the use of a data-dependent weighting scheme for the l 1 regularization to construct an effective denoising method that is integrated into the inversion using the Plug-and-Play reconstruction framework. Using simulated data sets we demonstrate that our approach can improve performance over traditional regularizers based on a Markov random field model and a patch-based sparse representation model for sparse and limited-view tomographic data sets.

Singanallur Vaidyanathan, Venkatakrishnan↗
DOE OSTI2018

Closed-Form Solution for Synchrophasor Data Correction under GPS Spoofing Attack

Phasor Measurement Units (PMUs) are increasingly being deployed to augment monitoring, protection and control applications in the power grid. PMUs rely on the Global Positioning System (GPS) for generation of time-synchronized and accurate measurements. However, civilian GPS signals, being unencrypted, are susceptible to GPS spoofing attacks (GSA). Hence ensuring the integrity of GPS-timing dependent synchrophasor data has become critical. In this paper, a closed-form analytical solution for estimating the GSA phase shift has been proposed and integrated to the spoofing detection & correction framework. Extensive simulations have been performed in order to verify the accuracy of the solution in determining the location and the phase shift of the compromised PMU. Time domain dynamic simulations presented in this paper demonstrate the applicability of the proposed solution for near-real-time detection of GSA, and this will enable faster detection and correction of phase angles of compromised synchrophasor data.

Synchrophasor, PMUs, GPS, Spoofing, Data Correctio↗
DOE OSTI2022

A Risk-Driven Probabilistic Approach to Quantify Resilience in Power Distribution Systems

It is of growing concern to ensure resilience in power distribution systems to extreme weather events. However, there are no clear methodologies or metrics available for resilience assessment that allows system planners to assess the impact of appropriate planning measures and new operational procedures for resilience enhancement. In this paper, we propose a resilience metric using parameters that define system attributes and performance. To represent extreme events (tail probability), the conditional value-at-risk of each of the parameters are combined using Choquet Integral to evaluate the overall resilience. The effectiveness of the proposed resilience metric is studied within the simulation-based framework under extreme weather scenarios with the help of a modified IEEE 123-bus system. With the proposed framework, system operators will have additional flexibility to prioritize one investment over the others to enhance the resilience of the grid.

Poudyal, Abodh↗
DOE OSTI2015

Action Recommendation for Cyber Resilience

This paper presents an unifying graph-based model for representing the infrastructure, behavior and missions of an enterprise. We describe how the model can be used to achieve resiliency against a wide class of failures and attacks. We introduce an algorithm for recommending resilience establishing actions based on dynamic updates to the models. Without loss of generality, we show the effectiveness of the algorithm for preserving latency based quality of service (QoS). Our models and the recommendation algorithms are implemented in a software framework that we seek to release as an open source framework for simulating resilient cyber systems.

resilience, cyber security↗
DOE OSTI2017

URBANopt (Urban Renewable Building And Neighborhood Optimization) [SWR-16-33]

URBANopt is a user interface for creating and running district and city scale building energy simulations. The framework is built around the OpenStudio Urban Measures which are part of the OpenStudio project. Building footprints, building height, building type, and other data can be imported from public records or other sources. Footprints and locations for new buildings and district systems can also be specified. OpenStudio Measures are used to create starting point energy models and to model energy design features and efficiency measures for each building. URBANopt allows a user to pose several scenarios such as “what if 30% of the commercial retail buildings added roof top solar” or “what if all elementary schools converted to ground source heat pumps” and then visualize the impacts at a district or city scale. URBANopt is capable of modeling existing buildings, new construction, and district energy systems. URBANopt can be used to explore options for achieving Zero Energy across a collection of buildings (e.g., Zero Energy Districts).

Source record↗
DOE OSTI2022

ML-PSA

The computer code uses a parallel simulated annealing framework with embedded machine learning components to solve multi-constrained optimization problems. The software automatically balances the execution of low and high fidelity physics models within the optimization procedure. The low fidelity model is used to rapidly explore the design space while the high fidelity physics model is executed sparingly to account for complex design constraints that are not resolved by the quickly executing low fidelity model.

Gurecky, William↗
DOE OSTI2025

PUMA:POWDER UTILIZATION MODELING APPLICATION

SF-25-084 PUMA a high performance modeling framework to simulate powder processing. It provides a scalable tool for manufacturers to simulate powder pre- and post-processing. The tool can predict the distortion, residual stress, and (for reactive processes) reaction completion fraction of complex parts after curing/debinding, sintering, and infiltration processes. These predictions are key metrics industry uses to optimize these processes to produce dense, defect-free, stable components.

HU, TIANCHEN (GARY)↗
DOE OSTI2026

Dynamic Disruption Resilience in Intermodal Transport Networks: Integrating Flow Weighting and Centrality Measures

Resilient intermodal freight networks are vital for sustaining supply chains amid increasing threats from natural hazards and cyberattacks. Transportation resilience has been widely studied; understanding how random and targeted disruptions affect structural connectivity and functional performance remains a key challenge. To address this, this study evaluates the robustness of the US intermodal freight network, which consists of rail and water modes, using a simulation-based framework that integrates graph-theoretic metrics with flow-weighted centrality measures. Disruption scenarios are examined, including random failures as well as targeted node and edge removals based on static and dynamically updated degree and betweenness centrality. To reflect more realistic conditions, flow-weighted degree centralities (WDC) and partial node degradation are considered. Two resilience indicators are used: (1) the size of the giant connected component to measure structural connectivity; and (2) flow-weighted network efficiency (NE) to assess freight mobility under disruption. The results show that progressively degrading nodes ranked by WDC to 60% of their original functionality causes a sharper decline in normalized NE, for up to approximately 45 affected nodes, than complete failure (100% loss of functionality) applied to nodes targeted by weighted betweenness centrality or selected at random. This highlights how partial degradation of high-tonnage hubs can produce disproportionately large functional losses. The findings emphasize the need for resilience strategies that go beyond network topology to incorporate freight flow dynamics.

42 ENGINEERING↗
DOE OSTI2018

Emergence and selection of isoniazid and rifampin resistance in tuberculosis granulomas

Drug resistant tuberculosis is increasing world-wide. Resistance against isoniazid (INH), rifampicin (RIF), or both (multi-drug resistant TB, MDR-TB) is of particular concern, since INH and RIF form part of the standard regimen for TB disease. While it is known that suboptimal treatment can lead to resistance, it remains unclear how host immune responses and antibiotic dynamics within granulomas (sites of infection) affect emergence and selection of drug-resistant bacteria. We take a systems pharmacology approach to explore resistance dynamics within granulomas. We integrate spatio-temporal host immunity, INH and RIF dynamics, and bacterial dynamics (including fitness costs and compensatory mutations) in a computational framework. We simulate resistance emergence in the absence of treatment, as well as resistance selection during INH and/or RIF treatment. There are four main findings. First, in the absence of treatment, the percentage of granulomas containing resistant bacteria mirrors the non-monotonic bacterial dynamics within granulomas. Second, drug-resistant bacteria are less frequently found in non-replicating states in caseum, compared to drug-sensitive bacteria. Third, due to a steeper dose response curve and faster plasma clearance of INH compared to RIF, INH-resistant bacteria have a stronger influence on treatment outcomes than RIF-resistant bacteria. Finally, under combination therapy with INH and RIF, few MDR bacteria are able to significantly affect treatment outcomes. Overall, our approach allows drug-specific prediction of drug resistance emergence and selection in the complex granuloma context. Since our predictions are based on pre-clinical data, our approach can be implemented relatively early in the treatment development process, thereby enabling pro-active rather than reactive responses to emerging drug resistance for new drugs. Furthermore, this quantitative and drug-specific approach can help identify drug-specific properties that influence resistance and use this information to design treatment regimens that minimize resistance selection and expand the useful life-span of new antibiotics.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Community Based Data of Potentiometric Titration of Iron Oxides: Ferrihydrite (HFO), Goethite, Hematite, Magnetite

This data release includes experimental data of potentiometric titration for iron oxides. The data in the provided .csv files is not our own experimental data but have been compiled from the multiple literature sources. The master database is L-SCIE (LLNL Surface Complexation/Ion Exchange) database, and the provided .csv files are extracted data from L-SCIE. The .csv files were obtained by using the Lawrence Livermore National Laboratory Surface Complexation Database Converter (SCDC) code written in the R programming language (free licensing available at https://ipo.llnl.gov/technologies/software/llnl-surface-complexation-database-converter-scdc).The released data was used for developing a comprehensive community data-driven surface complexation modeling (SCM) framework for simulating potentiometric titration of mineral surfaces. Compiled community data for ferrihydrite, goethite, hematite, and magnetite are fit to produce representative protolysis constants that can reproduce potentiometric titration data collected from multiple literature sources.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Data Files for Runoff Evaluation in an Earth System Land Model for Permafrost Regions

Modeling of hydrological runoff is essential for accurately capturing spatiotemporal feedbacks within the land–atmosphere system, particularly in sensitive regions such as permafrost landscapes. However, substantial uncertainties persist in the terrestrial runoff parameterization schemes used in Earth system and land surface models. This is particularly true in permafrost regions, where landscape heterogeneity is high and reliable observational data are scarce.This data set includes all files that were produced and applied in the paper Runoff Evaluation in an Earth System Land Model for Permafrost Regions [Xiang et al. in review]. The paper is in review as of July 1 2025 in Geoscientific Model Development (GMD). In this study, we evaluate the performance of runoff parameterization schemes in the Energy Exascale Earth System Model (E3SM) land model (ELM). Our proposed framework leverages simulation results from the Advanced Terrestrial Simulator (ATS), which is a physics-rich integrated surface/subsurface hydrologic model that has been successfully evaluated previously in Arctic tundra regions. We used ATS to simulate runoff from 22 representative hillslopes in the Sagavanirktok River basin, located on the North Slope of Alaska, then compared the output with ELM’s parameterized representation of total runoff. This dataset contains 2 figure image files (*.png, *jpg) that describe the study site and methods, as well as folders (Figure*.zip) that contain the associated data files (*.csv, *.dat) and python code notebooks (*.ipynb) for figures 3-7 in the paper. Jupyter notebook (*.ipynb) files that produce the figure files using the associated data files will run within a python environment configured with Jupyter Lab or Notebook packages.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Data-Driven Energy Resilience Assessment and Enhancement in Urban Communities: A Case Study in Detroit

This paper presents a data-driven framework for assessing and enhancing energy resilience in urban communities. The resilience assessment is based on two datasets: 1) annual aggregated power outage data and 2) 15-minute interval outage data. High-impact, low-probability (HILP) events are identified within these datasets to evaluate community resilience under extreme conditions. To enhance resilience, an optimization framework utilizing mixed integer linear programming is developed to determine the optimal sizing and placement of solar photovoltaic (PV) systems and battery energy storage systems (BESS). This method offers a cost-effective and practical solution for improving energy resilience in vulnerable communities. Furthermore, a case study of the City of Detroit in Michigan demonstrates the effectiveness of the framework through simulation and validation.

Energy resilience assessment↗
DOE OSTI2015

Anderson Acceleration for Fixed-Point Iterations

The purpose of this grant was to support research on acceleration methods for fixed-point iterations, with applications to computational frameworks and simulation problems that are of interest to DOE.

43 PARTICLE ACCELERATORS↗
DOE OSTI2018

Light-Duty Vehicle Attribute Projections (Years 2015-2030)

This report describes the approach and results associated with projecting vehicle attributes for future light-duty vehicle classes and powertrains, from year 2015 to 2030. The focus is the California light-duty vehicle market and the projected attributes include fuel economy, acceleration, driving range (total and all-electric), and manufacturer suggested retail price from year 2015 to year 2030. Attributes are weighted by California vehicle sales, which are projected using a historically validated consumer choice model - the Automotive Deployment Option Projection Tool (ADOPT) - integrated with a modeling framework that simulates vehicle fuel economy, cost, and acceleration performance while optimizing vehicle components - the Future Automotive Systems Technology Simulator model (FASTSim). Both models were developed at the National Renewable Energy Laboratory and have been adapted to represent the future California light-duty vehicle market for the present report. The analysis includes several scenarios pertaining to electricity and, essentially, electric vehicle demand in California, as established by the Energy Commission. Results suggest that implementation of certain policies, such as the Corporate Average Fuel Economy standards, impacts vehicle attribute projections. The results also show that standards and policy targets are not exclusively met by changes in vehicle attributes, but also through shifts in market demand and sales for certain vehicle powertrains.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2016

Integrable Optics Design Principles for Beam Halo Suppression in Accelerator Rings at the Intensity Frontier

The project was dedicated to quantifying the possibilities and limitations of the integrable optics, a fundamentally new approach to high-intensity accelerator ring design, using the parallel Synergia framework for simulations. The following topics were studied: a) the effects of space charge forces, field errors, magnet misalignments, and other sources of parametric resonance; b) the effect of controlled nonlinearities, such as sextupoles for chromaticity control; c) the effect of longitudinal dynamics driven by finite bunch length and rf cavities; and d) reexamination of relevant lattices in present accelerators to explore potential benefits of these new design principles.

43 PARTICLE ACCELERATORS↗